{
 "title": "AI Fault Line: annotated papers",
 "source": "https://aifaultline.com",
 "generated_at": "2026-11-01T11:07:42.370Z",
 "count": 313,
 "notes": "Published records only. Summaries, explainers and researcher bios are AI-written (see each record's labels); positions are documented positions with cited evidence. Keep source links when reusing, and give position counts with their denominator. Methods: https://aifaultline.com/about/",
 "records": [
  {
   "abstract": "Artificial intelligence (AI) holds enormous promise. Properly deployed, with suitable guardrails against its risks, AI is likely to raise living standards by enhancing productivity growth and to lead to new scientific discoveries, the development of new cures for all kinds of disease, and the creation of new products and services. Yet even as China accelerates efforts to develop and diffuse AI throughout its economy, public attitudes in the United States are markedly more wary. In 2025, nearly equal shares of Republicans (50%) and Democrats (51%) said that the growing use of AI in daily life made them more concerned than excited. 1 This apprehension is reflected in broad opposition to local AI data center construction, which 71% of Americans opposed in a 2026 Gallup poll, including majorities of Republicans, independents, and Democrats, and in concern that AI can enable misinformation, fraud, and other misuse and may disrupt jobs and careers. 2 Since that poll, concerns about AI’s potential existential risks have intensified, following public warnings from current and former employees of leading AI companies that current AI systems have insufficient oversight. 3 I focus here, however, on just the job-displacement fears, which are likely to persist because AI use nonetheless will continue to grow even if data center construction and AI model development in the U.S. are slowed or halted. Even recent warnings that AI could take over the internet seem unlikely, by themselves, to halt AI adoption. AI is also being deployed to strengthen cyber defenses, and surveys document its growing use by businesses and individuals. 4 So the job threat will remain. In July 2026, over 1,000 economists signed an online petition organized by Stanford’s Erik Brynjolfsson urging action to deal with the threat of future labor market disruption posed by AI. 5 Even if those worries eventually prove unfounded as they so far appear to be—and even if over the longer run our economy and workers w",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-30",
   "doi": null,
   "explainer": null,
   "id": "title:cd6c24c4dbb10c57",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/a-detailed-affordable-plan-for-transition-insurance-in-the-ai-era/",
   "summary": "This policy paper sets out a detailed plan for transition insurance for workers whose jobs are disrupted by AI, motivated by growing US public concern about AI. It notes that about half of both Republicans and Democrats said in 2025 that AI made them more concerned than excited.",
   "theme": "Labor & Work",
   "title": "A detailed, affordable plan for transition insurance in the AI era",
   "url": "https://wemustactnow.onrender.com/research/p/title-cd6c24c4dbb10c57/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "As TAI advances faster than economic institutions can adapt, fundamental questions about economic growth, science and innovation, income distribution, decision making, safety, and more must be addressed.",
   "authors": [
    "Erik Brynjolfsson",
    "Anton Korinek",
    "Ajay Agrawal"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-21",
   "doi": "10.1145/3769433",
   "explainer": {
    "approach": "A research agenda article that sets out open questions rather than reporting new data.",
    "findings": "It names economic growth, science and innovation, income distribution, decision making and safety among the fundamental questions that must be addressed.",
    "limitations": "The abstract describes an agenda only; it reports no estimates or empirical results.",
    "method_type": "commentary",
    "question": "Which economic questions about transformative AI most need research as the technology advances faster than institutions adapt?"
   },
   "id": "doi:10.1145/3769433",
   "institutions": [
    "Stanford University",
    "University of Virginia",
    "University of Toronto"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1145/3769433",
   "summary": "Brynjolfsson, Korinek and Agrawal set out a research agenda for the economics of transformative AI. They argue that because AI is advancing faster than economic institutions can adapt, open questions about growth, science and innovation, income distribution, decision making and safety need to be addressed.",
   "theme": "Growth & Macro",
   "title": "A Research Agenda for the Economics of Transformative AI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1145-3769433/",
   "venue": "Communications of the ACM",
   "year": 2026
  },
  {
   "abstract": "There is little consensus on the effect of AI adoption on employment. Economy-wide measures of pay and headcount have moved little even as AI adoption has spread rapidly, yet narrower measures document declining hiring for workers at the start of their careers or in AI-exposed occupations . In this post, we revisit this topic for U.S. small businesses. We use data from the 2025 Small Business Credit Survey (SBCS) to examine the twelve-month-forward employment and revenue expectations of AI users. We find that firms currently using AI are significantly more likely than non-users to expect increased employment and revenues over the next year, even after accounting for firm and owner characteristics and location. Revenue expectations are most optimistic for AI users that report facing operational challenges in utilizing technology.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-08",
   "doi": null,
   "explainer": null,
   "id": "title:3b53fbc4d191cd73",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://libertystreeteconomics.newyorkfed.org/2026/10/ai-adoption-and-employment-expectations-evidence-from-a-survey-of-small-business-owners/",
   "summary": "Using the 2025 Small Business Credit Survey, New York Fed economists find that US small businesses using AI are significantly more likely than non-users to expect higher employment and revenue over the next year, after controlling for firm and owner characteristics and location.",
   "theme": "Labor & Work",
   "title": "AI Adoption and Employment Expectations: Evidence from a Survey of Small Business Owners",
   "url": "https://wemustactnow.onrender.com/research/p/title-3b53fbc4d191cd73/",
   "venue": "New York Fed Liberty Street Economics (institute research)",
   "year": 2026
  },
  {
   "abstract": "The adoption of artificial intelligence (AI) by large enterprises is an important potential source of aggregate productivity improvement and labor market impact. We study AI adoption of S&P 500 firms over the period 2016 to 2025, estimating adoption at the enterprise level. While generative AI tools are useful for personal and professional applications, our focus is on the deep integration of AI in the business processes of large enterprises which are bellwethers for firm adoption more broadly. We develop a novel measure to assess deep AI adoption (and distinguish it from AI hype) that is based on SEC 10-K filings, where laws and regulations ``prohibit companies from making materially false or misleading statements.\" In 2025, 11% of S&P 500 enterprises had AI deeply integrated into their business processes, and a further 10% were using AI in the production of goods and delivery of services. AI adoption has more than quadrupled from 5% in 2022 with slowly accelerating adoption among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption. Firm profitability shows a \"J-curve\" as firms move from no adoption to deep adoption, but we observe no differences in capex or productivity. Among technology firms, but not others, AI adoption is higher for firms with more employees and higher values of Tobin's q.",
   "authors": [
    "Yang Yu",
    "Martin Fleming",
    "Lucy Hampton",
    "Christophe Combemale",
    "Neil Thompson"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-07-09",
   "doi": null,
   "explainer": {
    "approach": "The authors measure enterprise-level AI adoption from SEC 10-K filings, where misleading statements are legally prohibited, distinguishing deep integration of AI in business processes from mere mentions, and relate adoption to firm outcomes.",
    "findings": "In 2025, 11% of S&P 500 firms had AI deeply integrated into business processes and a further 10% used AI in producing goods or services, up from 5% in 2022. Technology firms account for two-thirds of deep adoption. Profitability follows a J-curve with adoption, but the authors see no differences in capex or productivity.",
    "limitations": "Adoption is inferred from what firms disclose in annual filings, and the sample covers only the largest US listed firms, so results may not carry over to smaller firms or other countries.",
    "method_type": "observational",
    "question": "How far have S&P 500 firms deeply integrated AI into their business processes between 2016 and 2025, and how does adoption relate to profitability, investment and productivity?"
   },
   "id": "title:f19be997e9cdf786",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2607.08920v1",
   "summary": "Using SEC 10-K filings — where misleading statements are legally prohibited — as a hype-resistant measure, the authors find 11% of S&P 500 firms had AI deeply integrated into business processes in 2025 (plus 10% using it in production/delivery), up more than fourfold from 5% in 2022, with tech firms accounting for two-thirds of deep adoption. Profitability follows a J-curve across adoption stages, but capex and productivity show no differences, feeding directly into the Solow-paradox debate: even among bellwether firms, deep AI integration has yet to show up in measured productivity.",
   "theme": "Productivity & Firms",
   "title": "AI Adoption in S&P 500 Firms",
   "url": "https://wemustactnow.onrender.com/research/p/title-f19be997e9cdf786/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "The purpose of this project is to understand how workers respond to the introduction of generative artificial intelligence (AI). We run a survey motivated by foundational concepts from labor economics and aim to show that the standard frameworks used by labor economists work well in explaining heterogeneous worker responses to AI.",
   "authors": [
    "Kiara (Ji Hyun) Kim",
    "Gregory Sun",
    "Nathan Mester"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-16",
   "doi": null,
   "explainer": null,
   "id": "title:05e0616e5de7ec8e",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://osf.io/qjym2",
   "summary": null,
   "theme": "Labor & Work",
   "title": "AI and the Labor Market: A Worker's Eye View",
   "url": "https://wemustactnow.onrender.com/research/p/title-05e0616e5de7ec8e/",
   "venue": "OSF Preprints (OSF Preprints)",
   "year": 2026
  },
  {
   "abstract": "We provide evidence from a 2025 survey of individuals in Spain on workplace use of artificial intelligence (AI) and other modern digital technologies. AI use is already economically meaningful but far from pervasive, with about 16% of workers reporting AI use. Its adoption is highly uneven across workers and jobs, with strong gradients by education, age, earnings, and firm size and scope, as well as substantial within-occupation heterogeneity. AI use is strongly correlated with the use of other modern technologies, which accounts for a larger share of variation than any worker or job characteristic considered separately, including occupational exposure measures. The purposes of AI use also vary markedly across demographic groups and jobs. Users report predominantly positive productivity effects in abstract cognitive tasks, together with limited but systematic time reallocation across tasks.",
   "authors": [
    "Vahagn Jerbashian",
    "Montserrat Vilalta-Bufí"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.65864/3i4nlxdho1",
   "explainer": null,
   "id": "doi:10.65864/3i4nlxdho1",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.65864/3i4nlxdho1",
   "summary": null,
   "theme": "Labor & Work",
   "title": "AI at Work: Use, Complementary Technologies, and Task Reallocation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-65864-3i4nlxdho1/",
   "venue": "CESifo",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) coding assistants are transforming software development by helping programmers generate code, identify errors, debug programs, and improve development efficiency. Tools powered by large language models can provide real-time code suggestions, explain complex programming concepts, and automate repetitive coding tasks. However, their actual impact on software development productivity, code quality, and debugging efficiency requires careful evaluation. This research aims to examine the influence of AI coding assistants on software development productivity by considering factors such as programming task completion time, code correctness, maintainability, debugging effort, and developer experience. It explores the potential benefits and limitations of AI-assisted programming, including inaccurate code suggestions, security vulnerabilities, overreliance on generated code, and the need for human verification. The study will review existing research on AI-assisted software development and propose a comparative evaluation framework for traditional programming and AI-assisted programming. Relevant performance metrics and experimental procedures will be identified to assess productivity and code quality objectively. Where empirical data are available, the study will analyze the results to determine the conditions under which AI coding assistants may provide meaningful benefits. The findings are intended to support students, software developers, educators, and software engineering teams in understanding the effective and responsible use of AI coding assistants. The research will also identify limitations and opportunities for future investigation in AI-powered software development.",
   "authors": [
    "DEEPAK N"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.5281/zenodo.23686381",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23686381",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23686381",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "AI Coding Assistant",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23686381/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "A set of exposure scores calculated in 2023 has become a central empirical input to the future of work debate. Produced by Eloundou et al. (2023) and referred to here as the GPTs are GPTs scores, they define exposure as the share of occupational tasks a large language model can assist with. This work is a genuine methodological contribution, but as the scores travel from the time and place they were produced, the limitations the authors named do not always travel with them. Two gaps have widened as a result. The first is structural, between what static exposure scores measure and what policy questions actually require. Taking the diffusion of these scores as a case study, we show how their temporal, geographic, and ontological limitations compound in policy-facing analyses, and we survey five families of research responding to these limits: dynamic and benchmark-based measures, ensemble methods, task-framework extensions, worker-centered metrics, and adoption and usage data. The second gap is the one we argue needs more attention: the coordination between researchers and policymakers. The policy-relevant work which ask who is harmed, who benefits, how, and when, continues to reference the static GPTs are GPTs scores without engagement with the methodological updates that would let these questions be answered more reliably. We then ask what additional steps towards navigating uncertainty remain: ex-post frameworks and the deliberate, political work of reimagining what futures are worthy of building towards are. Closing the research-policy gap is a shared task: policymakers must widen their evidence base, engage workers as epistemic partners, and shift from prediction to preparedness; researchers must build data infrastructure, adopt participatory methods, and write with policymakers in mind. Better measurement matters, but it will not close the second gap alone.",
   "authors": [
    "Campbell Lund",
    "Thomas Euyang",
    "Zanele Munyikwa",
    "Marzieh Fadaee"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-22",
   "doi": null,
   "explainer": null,
   "id": "title:6712129d6040b300",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.23633v1",
   "summary": null,
   "theme": "Labor & Work",
   "title": "AI Exposure Scores: what they measure, what they miss, and what comes next",
   "url": "https://wemustactnow.onrender.com/research/p/title-6712129d6040b300/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Enterprise AI adoption is moving beyond experimentation, but the organizations seeing the strongest returns are doing more than adding new tools. They are redesigning workflows, establishing clear ownership, and aligning employees around how AI should be used. The 2026 study surveyed approximately 1,400 employees at large U.S. companies. Only 28% of managers and senior leaders say AI is fully embedded in their core business processes, and just 20% of departments meet the report's definition of successful workflow transformation. Only 7% of organizations meet all three criteria. Only 13% of departments that measure efficiency alone report strongly positive ROI, compared with 35% of departments that track at least one growth-related measure. The researchers also cautioned that productivity gains do not automatically become innovation.",
   "authors": [
    "Nyrie Bouloutian"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-10-29",
   "doi": null,
   "explainer": null,
   "id": "title:06336f6db8e7e77b",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://ai.wharton.upenn.edu/updates/ai-horizons-how-the-organizations-seeing-the-most-ai-value-are-redesigning-work/",
   "summary": "A write-up of Wharton's 2026 survey of about 1,400 employees at large US companies, which finds that only 7% of organisations combine strongly positive AI returns, significant workflow change and internal alignment. Departments that track growth measures report strongly positive returns more often (35%) than those tracking efficiency alone (13%), a correlation the authors say is not causal.",
   "theme": "Productivity & Firms",
   "title": "AI Horizons: How the Organizations Seeing the Most AI Value Are Redesigning Work",
   "url": "https://wemustactnow.onrender.com/research/p/title-06336f6db8e7e77b/",
   "venue": "Wharton Human-AI Research",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) integration in India’s financial sector offers transformative potential but poses challenges like algorithmic bias, data privacy risks, and regulatory fragmentation. This study employed both one-on-one interviews and surveys with various stakeholders in the financial services sector to analyse India’s AI governance framework through expert interviews and a comparative policy analysis of global models (the EU’s risk-based AI Act and the US sector-specific guidelines). Findings reveal gaps in accountability, transparency, and enforcement mechanisms, particularly for high-risk applications like credit scoring. This study proposes a hybrid regulatory model that combines binding rules for high-risk AI systems (e.g., fraud detection) with co-regulation for low-risk tools, emphasising scientific risk assessment, consumer grievance mechanisms, and iterative policymaking. While leveraging India’s existing financial laws (e.g., Reserve Bank of India guidelines), we recommend AI-specific updates to address explainability, bias audits, and systemic risk monitoring. However, this study is limited by its reliance on publicly available regulatory documents and expert interviews, and by its focus on the Indian context, which may overlook cross-border AI governance challenges. Stakeholder collaboration and phased implementation are critical to balancing innovation with ethical safeguards in India’s evolving digital economy.",
   "authors": [
    "Mohammed Afzal",
    "Maryam Meraj",
    "Mohd Shamim Ansari"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-19",
   "doi": "10.21098/jcli.v5i2.419",
   "explainer": null,
   "id": "doi:10.21098/jcli.v5i2.419",
   "institutions": [
    "Aligarh Muslim University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.21098/jcli.v5i2.419",
   "summary": "Using stakeholder interviews, surveys and a comparison with the EU AI Act and US sector rules, this study finds gaps in accountability, transparency and enforcement in India's governance of AI in finance, especially for credit scoring. It proposes a hybrid model: binding rules for high-risk systems and co-regulation for low-risk tools.",
   "theme": "Policy & Governance",
   "title": "AI in India’s Financial Sector: Navigating the Regulatory Landscape",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-21098-jcli-v5i2-419/",
   "venue": "Journal of Central Banking Law and Institutions",
   "year": 2026
  },
  {
   "abstract": "Whether artificial intelligence policy can expand labor demand, and how the additional demand is distributed across different types of jobs, is an important question in evaluating the economic consequences of technological development. This paper matches city-level annual online job posting data and city statistical yearbook data with policy information on the National New-Generation Artificial Intelligence Pilot Zones, and employs a difference-in-differences model to examine the labor market effects of pilot zone construction. The results show that the policy significantly increases overall local hiring demand. The growth in recruitment covers workers at different levels of education, skills, and experience, and is reflected in high-skill, low-technology, technology- and knowledge-intensive, and labor-intensive positions. At the same time, the posted wage gap between high-skill and low-skill positions widens. These findings indicate that the construction of AI pilot zones may bring about both a multi-tiered expansion of job demand and a divergence in skill-based wages, providing empirical evidence for coordinating technological development, worker skill upgrading, and employment policy.",
   "authors": [
    "Jiayi Wei"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.54254/2754-1169/2026.38359",
   "explainer": null,
   "id": "doi:10.54254/2754-1169/2026.38359",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.54254/2754-1169/2026.38359",
   "summary": null,
   "theme": "Labor & Work",
   "title": "AI Pilot Zones and Labor Market Demand: Evidence from China",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54254-2754-1169-2026-38359/",
   "venue": "Advances in Economics Management and Political Sciences",
   "year": 2026
  },
  {
   "abstract": "Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the \\emph{AI Premium}. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the AI factor -- high AI beta firms -- earn higher subsequent returns, and the AI premium is large and heterogeneous. A value-weighted long-short strategy earns 64.1 basis points per week, and the premium is large for loadings on the intensive, frontier-oriented margin of AI consumption -- closed-source models, paying and seasoned users, and long prompts -- but not on casual or open-weight use. Third, the premium reaches beyond technology firms into consumer-facing and capital-heavy parts of the economy, but is absent in emerging markets, including China. Fourth, the AI exposure is more positive in nonroutine interactive work and more negative in analytical, scientific, and operations-control skills -- an occupation one standard deviation higher in interaction-and-communication content has 0.36-standard-deviation higher market-implied AI exposure. Additionally, we provide early evidence of the rise of the agentic economy.",
   "authors": [
    "Nicola Borri",
    "Yukun Liu",
    "Aleh Tsyvinski"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-29",
   "doi": null,
   "explainer": null,
   "id": "title:90a68c62c2cace4a",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.30583v2",
   "summary": "Using 380 trillion tokens of AI consumption across more than 400 language models from OpenRouter data, the authors build a high-frequency AI factor and estimate firms' stock-return exposure to it. High-exposure firms earn a sizable return premium (64.1 basis points per week long-short), concentrated in frontier, paid and closed-model usage, and occupations heavy in interaction and communication show more positive AI exposure.",
   "theme": "Labor & Work",
   "title": "AI Premium",
   "url": "https://wemustactnow.onrender.com/research/p/title-90a68c62c2cace4a/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is a nascent technology that could become a general-purpose technology and can have significant impacts on economic development, productivity growth, labour markets and good governance. The study will delve into the potential impact of Artificial Intelligence on the economy and job landscape of India as part of the vision of Viksit Bharat by 2047. The study uses qualitative review-based research methodology that is based on analyzing evidence from the academic literature, government documents, as well as reports produced by national and international institutions. AI has the potential to augment productivity, innovate solutions, modernize industries, contribute to digital governance and create job opportunities, as revealed by this study. At the same time, the implementation of AI can be challenging due to the changes in labor markets, lack of skills, digital divide, fragility of the informal sector, data privacy and security concerns, algorithmic biases, and institutional preparedness. In this study, it is argued that AI will not necessarily take the place of jobs, but rather change the employment type if suitable reskilling measures are taken. It identifies the following policy priorities – human capital, digital infrastructure, responsible AI governance, research and innovation, and social protection.",
   "authors": [
    "Tirthraj Singh"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-22",
   "doi": "10.55829/m84wav48",
   "explainer": null,
   "id": "doi:10.55829/m84wav48",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.55829/m84wav48",
   "summary": "A qualitative review of AI's potential effects on India's economy and jobs under the Viksit Bharat 2047 vision, drawing on academic, government and institutional reports. It argues AI is more likely to change the type of employment than replace jobs if reskilling follows, and names human capital, digital infrastructure, responsible governance and social protection as policy priorities.",
   "theme": "Labor & Work",
   "title": "AI-Driven Economic Transformation and Employment Dynamics in India 2047: Opportunities, Challenges, and Policy Pathways toward Viksit Bharat",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-55829-m84wav48/",
   "venue": "International Journal of Management Public Policy and Research",
   "year": 2026
  },
  {
   "abstract": "We examine how different generations of artificial intelligence (AI) substitute for human knowledge worker tasks, leveraging two sequential natural experiments in the translation industry. We study the introduction of Google Neural Machine Translation (GNMT), a predictive, task-specific AI, as well as ChatGPT, a broad, general-purpose generative AI, in a major online labor market. Distinguishing between analytical tasks (regular translations) and intuitive tasks (transcreations, which require creative and cultural adaptation), we estimate difference-in-differences models on transaction-level data from 2016 to 2017 and 2022 to 2023. We find that GNMT reduced the demand for analytical tasks by economically meaningful magnitudes, while having no detectable effect on intuitive tasks. By contrast, ChatGPT produced substantially larger and more uniform reductions for both analytical and intuitive tasks, indicating a marked expansion in the scope and magnitude of AI substitution. By comparing these two technological shocks within the same labor market, we show that conclusions about which types of human work are resilient to automation do not straightforwardly generalize from predictive to generative AI. This paper was accepted by Joshua Gans, business strategy. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06740 .",
   "authors": [
    "Erdem Dogukan Yilmaz",
    "Ivana Naumovska",
    "Vikas A. Aggarwal"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-14",
   "doi": "10.1287/mnsc.2024.06740",
   "explainer": {
    "approach": "Two natural experiments in a large online labor market for translation: the launch of Google Neural Machine Translation and of ChatGPT. Difference-in-differences models on transaction-level data from 2016-2017 and 2022-2023 compare regular translations with transcreations, which need creative and cultural adaptation.",
    "findings": "Google's task-specific translation AI reduced demand for regular translation tasks by economically meaningful amounts but had no detectable effect on transcreation. ChatGPT produced larger and more uniform reductions in demand for both kinds of task.",
    "limitations": "Evidence comes from one occupation on one online platform, so effects in other markets or in conventional employment may differ.",
    "method_type": "observational",
    "question": "Do different generations of AI replace different kinds of human knowledge work?"
   },
   "id": "doi:10.1287/mnsc.2024.06740",
   "institutions": [
    "INSEAD"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1287/mnsc.2024.06740",
   "summary": "Using difference-in-differences on transaction data from an online translation labour market, the study compares two AI shocks. Google's neural machine translation cut demand for routine translation but not for creative transcreation, while ChatGPT produced larger and more uniform demand reductions across both task types.",
   "theme": "Labor & Work",
   "title": "AI-Driven Human Task Substitution: Evidence from Google Translate and ChatGPT",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1287-mnsc-2024-06740/",
   "venue": "Management Science",
   "year": 2026
  },
  {
   "abstract": "Protocol of a rapid systematic review that synthesises causal evidence on how AI-related technology shocks (industrial robots, software automation, machine learning and generative AI) affect inclusive labour market resilience: resistance, recovery, re-orientation and renewal of employment, hours, hiring, wages and reallocation, and the distribution of these effects across age, skill, gender and region. Studies are classified by identification tier and synthesised with evidence synthesis for constructing directed acyclic graphs (ESC-DAG) against a prior DAG. Screening and extraction are LLM-assisted (Claude, Anthropic) under a pre-specified validation rule, following the Cochrane rapid review guidance and the 2025 position statement on AI use in evidence synthesis.",
   "authors": [
    "М. В. Козлова",
    "Ye. V. Varavin"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-11",
   "doi": "10.17605/osf.io/gs8c5",
   "explainer": null,
   "id": "doi:10.17605/osf.io/gs8c5",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.17605/osf.io/gs8c5",
   "summary": "A protocol for a rapid systematic review of causal evidence on how robots, software automation, machine learning and generative AI affect employment, hours, hiring and wages, and how those effects are distributed by age, skill, gender and region. Studies will be graded by identification strength, with LLM-assisted screening under a pre-set validation rule.",
   "theme": "Labor & Work",
   "title": "AI-induced labour market shocks and inclusive labour market resilience: an LLM-assisted rapid causal evidence synthesis using directed acyclic graphs (ESC-DAGs)",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17605-osf-io-gs8c5/",
   "venue": "Open Science Framework",
   "year": 2026
  },
  {
   "abstract": "This chapter examines how artificial intelligence (AI) may affect productivity and long-term economic growth. It begins by clarifying what economists mean by productivity, total factor productivity (TFP), and growth accounting, then explains why AI can raise task-level efficiency without immediately producing a visible aggregate productivity boom. The chapter develops the productivity paradox, the role of intangible complementary investment, the AI productivity J-curve, and the distinction between genuine productivity-enhancing innovation and so-so automation. It then analyzes the main channels through which AI may increase growth: cheaper prediction, process innovation, better allocation, scientific discovery, organizational redesign, and new products. The chapter also considers the limits of growth optimism by contrasting singularity-style scenarios with Baumol-style sectoral bottlenecks. The central claim is that AI’s growth impact will depend less on raw model capability than on diffusion, reorganization, competition, institutions, and the distribution of complementary capital.",
   "authors": [
    "Ceyhun Elgin"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-20",
   "doi": "10.4324/9781042011162-3",
   "explainer": null,
   "id": "doi:10.4324/9781042011162-3",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.4324/9781042011162-3",
   "summary": "A book chapter on AI, productivity and long-run growth that explains why task-level efficiency gains may not show up quickly in aggregate statistics, covering the productivity paradox, intangible investment and the J-curve. It contrasts singularity-style scenarios with Baumol-style bottlenecks and argues AI's growth impact depends more on diffusion, reorganisation, competition and institutions than on model capability.",
   "theme": "Growth & Macro",
   "title": "AI, productivity, and economic growth",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4324-9781042011162-3/",
   "venue": "Routledge eBooks",
   "year": 2026
  },
  {
   "abstract": "Using panel data of Chinese A-share listed media firms over 2013–2023, this study examines whether and how Artificial Intelligence Generated Content (AIGC) affects total factor productivity (TFP) in emerging markets. Employing instrumental variable and difference-in-differences identification strategies, we document that AIGC adoption significantly enhances media firms’ TFP. Technological innovation and resource allocation efficiency constitute dual mediating channels, with innovation quantitatively dominant. The productivity dividend is markedly stronger for small-scale, non-state-owned firms located in digitally advanced regions. AIGC drives both technological progress and technical efficiency, and generates positive industry spillovers along media value chains, informing digital transformation policies for emerging economies.",
   "authors": [
    "Runhai Yuan",
    "Haiyu Wang",
    "Yuanchao Zhang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-21",
   "doi": "10.1080/00036846.2026.2752123",
   "explainer": null,
   "id": "doi:10.1080/00036846.2026.2752123",
   "institutions": [
    "University of Manchester",
    "Beijing Institute of Graphic Communication",
    "Lanzhou University of Finance and Economics"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.1080/00036846.2026.2752123",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "AIGC and total factor productivity in emerging-market media firms: mechanisms, heterogeneity, and industrial spillovers",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1080-00036846-2026-2752123/",
   "venue": "Applied Economics",
   "year": 2026
  },
  {
   "abstract": "Introduction Human uplift studies like the one we did in 2025 are becoming more expensive as working without AI becomes increasingly costly. In this post, I investigate whether coding agent transcripts could serve as a cheaper alternative for estimating uplift. I prototyped this using 5305 Claude Code transcripts generated in January 2026 by 7 METR technical staff 1 . I used an LLM judge to estimate how long each task would have taken an experienced software engineer without AI tools, then compared that to the time people actually spent on these tasks to calculate a time savings factor . Takeaways This method estimates a time savings factor of ~1.5x to ~13x on Claude Code-assisted tasks for 7 METR technical staff in January 2026 – though this result comes with substantial caveats. I believe the true productivity multiplier is substantially lower, and the time savings factor is a soft upper bound for the true uplift that the individuals experienced. Increased agent concurrency may contribute to a higher time savings factor on the Claude Code-assisted task distributions. Limitations The time savings factor on the coding agent-assisted task distributions does not equal the productivity multiplier. People likely do not create 10x as much value with AI, even if we observe a 10x time savings factor on tasks that people do with AI. I believe the time savings factor overestimates AI-enabled productivity gains for reasons including: Task Substitution. With AI assistance, people sometimes complete tasks they wouldn’t do otherwise, such as making nice-to-have tools. These tasks tend to deliver lower counterfactual value, even if the observed time savings factor on these tasks might be high. Task Selection Effects. People don’t spend their entire workday using AI—they use it only on tasks where they expect it to be helpful. Since coding transcripts exclude tasks people do without AI, the time savings factor likely overestimates total AI-enabled uplift. Pre-existing Worker Speci",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-02-17",
   "doi": null,
   "explainer": null,
   "id": "title:2b2d14aec5cbd0dd",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/notes/2026-02-17-exploratory-transcript-analysis-for-estimating-time-savings-from-coding-agents/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Analyzing coding agent transcripts to upper bound productivity gains from AI agents",
   "url": "https://wemustactnow.onrender.com/research/p/title-2b2d14aec5cbd0dd/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "One year ago, most Claude usage took the form of a conversation between a user and an assistant. With the rapid growth of Claude Code and Cowork, Claude sessions now increasingly consist of long-running agentic tasks. Chat transcripts no longer fully capture how people are using AI, and our methods for studying Claude’s economic impacts have had to adapt. To keep pace, we made several changes to our data pipeline for the Economic Index. In this version, we: We describe additional methodological changes in the Appendix . Together, these changes provide a clearer picture of how AI mirrors and diffuses into economic life. In addition, we’ve previously lacked visibility into Claude’s impact outside of user sessions. How do people perceive AI to be changing their work, or the opportunities available to them? Does their usage of AI shape their expectations? In an ideal world, what would they want from AI? We report initial findings from the Anthropic Economic Index Survey , launched in April 2026. Our new privacy-preserving telemetry, which continuously samples a slice of conversations every day, allows us to study daily and hourly patterns in usage, in contrast to the seven-day samples each previous Economic Index report drew on. These analyses capture ebbs and flows in work patterns around the world. 2 We find that Claude usage mirrors the workweek, with personal prompts spiking on the weekend. The hourly data captures within-day patterns—people most often ask for sleep advice around 5 a.m. and for recipes around 6 p.m. We also see usage reflecting key dates. For instance, tax-related requests surged just before the US filing deadline on April 15. The share of chat and Cowork 3 conversations categorized as personal use spikes from around 35% on weekdays to just under 50% on weekends during the sample period (Figure 1.1). Outside the workweek, users’ conversations shift from business correspondence, marketing copy, and slide decks to emotional support, medical questions,",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-06-26",
   "doi": null,
   "explainer": null,
   "id": "title:a1d3e56d850f5ce4",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/economic-index-june-2026-report",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Anthropic Economic Index report: Cadences",
   "url": "https://wemustactnow.onrender.com/research/p/title-a1d3e56d850f5ce4/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "This latest report studies Claude usage in February 2026, building on the economic primitives framework introduced in our previous report (which used data from November 2025). Our sample covers February 5 to February 12, three months following the release of Claude Opus 4.5 and coincident with the release of Claude Opus 4.6. We first document how usage has changed relative to our previous reports: the rate of augmentation, collaborative interaction where the AI complements the user’s abilities, increased slightly in both Claude.ai and API traffic. In Claude.ai, usage diversified, with the top 10 tasks accounting for a smaller share of usage last month than in November 2025. As a result of this diversification, the average conversation in Claude.ai had a slightly lower-wage task than in previous reports. We then focus on an important determinant of Claude’s impact on the labor market and the broader economy: learning curves in Claude adoption. We present evidence that high-tenure users have developed habits and strategies that allow them to better harness Claude’s capabilities. Indeed, we document that more experienced users not only attempt higher-value tasks, but are also more likely to elicit successful responses in their conversations. In the first chapter, we revisit findings from our previous Economic Index report , published in January 2026. We find that: A central finding in the Economic Index is that early adoption of Claude is very uneven: Claude is used more intensely in high-income countries, within the US in places with more knowledge workers, and for a relatively small set of specialized tasks and occupations. An important question is how inequality of adoption might determine where and to whom the benefits of AI will accrue. If, for example, effective AI use requires complementary skills and expertise—which we argued in our previous report —and if such skills can be acquired through use and experimentation, then the benefits from early adoption may be ",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-03-24",
   "doi": null,
   "explainer": null,
   "id": "title:75ca55dafb5b0a21",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/economic-index-march-2026-report",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Anthropic Economic Index report: Learning curves",
   "url": "https://wemustactnow.onrender.com/research/p/title-75ca55dafb5b0a21/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "So I can see a world where A.I. brings the developed world G.D.P. growth to something like 10, 15 percent. Five, 10, 15",
   "authors": [
    "Dario Amodei"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-02-12",
   "doi": null,
   "explainer": null,
   "id": "title:69054a4dfd389d99",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.nytimes.com/2026/02/12/opinion/artificial-intelligence-anthropic-amodei.html",
   "summary": null,
   "theme": "Other",
   "title": "Anthropic's Chief on A.I.: 'We Don't Know if the Models Are Conscious'",
   "url": "https://wemustactnow.onrender.com/research/p/title-69054a4dfd389d99/",
   "venue": "The New York Times (interview)",
   "year": 2026
  },
  {
   "abstract": "This study investigates the relationship between Artificial Intelligence (AI) adoption and employee productivity within Seplat Energy, an indigenous Nigerian energy company, focusing on its Eket operational base. Despite rising AI investment across Nigeria's oil and gas sector, independent empirical evidence on whether such investment translates into measurable employee productivity gains remains scarce. Drawing on the Technology Acceptance Model, Task-Technology Fit theory, and Diffusion of Innovation theory, the study examined three dimensions of AI adoption, automation of routine tasks, AI-assisted decision support, and employee perception/readiness for AI as predictors of employee productivity. A descriptive survey design was adopted; from a population of 190 staff, a sample of 128 was drawn using Taro Yamane's formula and stratified random sampling. A validated structured questionnaire (Cronbach's alpha 0.79-0.87) was administered, yielding 120 usable responses (93.8% response rate). Data were analyzed using descriptive statistics and the Chi-square test of independence at p < 0.05. Automation of routine tasks (χ² = 10.594, p = 0.001) and AI assisted decision support (χ² = 5.672, p = 0.017) were significantly associated with employee productivity, while employee perception/readiness was not (χ² = 1.558, p = 0.212). The findings suggest that task-embedded dimensions of AI adoption exert a more direct influence on productivity than general employee attitudes, with implications for how Nigerian energy companies sequence training and change-management investment. Note: given constraints on direct access to live organizational survey deployment, the dataset analyzed is a realistic, internally consistent illustrative dataset constructed to demonstrate the full analytical procedure; it should be replaced with field data prior to any policy application.",
   "authors": [
    "Asian Uko",
    "Nsikak Edet",
    "Irene Ene"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-09",
   "doi": "10.67487/ijprss.v2i3.244",
   "explainer": null,
   "id": "doi:10.67487/ijprss.v2i3.244",
   "institutions": [
    "University of Uyo",
    "Ministry of Energy"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.67487/ijprss.v2i3.244",
   "summary": "A survey of 120 staff at one Nigerian energy company's operational base finds that routine-task automation and AI-assisted decision support are significantly associated with self-reported employee productivity, while employee readiness for AI is not. The analysis uses chi-square tests of association rather than causal methods.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence Adoption and Employee Productivity in Nigerian Organizations: Evidence from Seplat Energy, Eket",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-67487-ijprss-v2i3-244/",
   "venue": "INTERNATIONAL JOURNAL OF PUBLIC RELATIONS AND SOCIAL SCIENCES",
   "year": 2026
  },
  {
   "abstract": "This study examined the effect of artificial intelligence (AI) adoption on employee productivity in public institutions and higher education organisations in Nigeria, disaggregating AI adoption into AI-assisted decision making, AI-based automation, and generative AI utilisation, and testing organisational readiness—comprising management support, digital infrastructure, employee AI knowledge and skills, and institutional policy—as a mediating mechanism linking adoption to productivity outcomes. A descriptive cross-sectional survey design was employed, targeting lecturers, academic administrators, registry staff, ICT personnel, departmental administrators, and management staff drawn from federal and state universities, polytechnics, colleges of education, and government agencies. Data were collected using a structured, validated five-point Likert-scale questionnaire adapted from established scales, and analysed using hierarchical multiple regression together with bootstrapped mediation analysis (Hayes' PROCESS macro, Model 4) at the 0.05 significance level, preceded by reliability, multicollinearity, and normality diagnostics. Findings revealed that AI-assisted decision making (β = 0.472, p < 0.001), AI-based automation (β = 0.357, p < 0.001), and generative AI utilisation (β = 0.167, p < 0.001) each exerted a positive and significant effect on employee productivity. Furthermore, organisational readiness significantly mediated the relationships between AI-based automation and employee productivity (indirect effect = 0.261, 95% CI: 0.189–0.331) and between generative AI utilisation and employee productivity (indirect effect = 0.221, 95% CI: 0.099–0.362). However, organisational readiness did not significantly mediate the relationship between AI-assisted decision making and employee productivity (indirect effect = -0.038, 95% CI: -0.121–0.041). The study extends the limited Nigeria-centric evidence base on AI adoption within public-sector and higher-education settings a",
   "authors": [
    "Mohammed Munirat Nma",
    "Adebayo Helen Oluwatoyin",
    "Obichie Elvis Ugo",
    "Ahmed Hassan Ahmed",
    "Chida Ezekiel"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-21",
   "doi": "10.22004/ag.econ.414534",
   "explainer": null,
   "id": "doi:10.22004/ag.econ.414534",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "http://ageconsearch.umn.edu/record/414534",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "ARTIFICIAL INTELLIGENCE ADOPTION AND EMPLOYEE PRODUCTIVITY IN PUBLIC INSTITUTIONS AND HIGHER EDUCATION ORGANISATIONS IN NIGERIA: THE MEDIATING ROLE OF ORGANISATIONAL READINESS",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-22004-ag-econ-414534/",
   "venue": "AgEcon Search (University of Minnesota, USA)",
   "year": 2026
  },
  {
   "abstract": "Micro, Small, and Medium Enterprises (MSMEs) are widely recognized as the backbone of India's economy due to their substantial contribution to employment generation, industrial production, exports, and regional development. In recent years, rapid advancements in digital technologies have fundamentally transformed the operational landscape of MSMEs. Technologies such as Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, Big Data Analytics, Blockchain, Internet of Things (IoT), and Digital Payment Systems have enabled enterprises to improve efficiency, reduce operational costs, enhance customer engagement, and strengthen competitiveness. The integration of computer science innovations into business operations has provided MSMEs with opportunities to overcome traditional limitations related to finance, market access, productivity, and information asymmetry.",
   "authors": [
    "Dr.Haritha.K.B, G.K.Karthika, .Priyadarshini.M, Saranya.N,  Rifa Mariam.N"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.5281/zenodo.23757218",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23757218",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23757218",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and Digital Technologies as Drivers of MSME Growth: The Mediating Role of Digital Transformation Capability in India's MSME Sector",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23757218/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Abstract This study explores the relationship between artificial intelligence and economic development through a systematic review of 194 peer-reviewed articles published between 2011 and 2025. The analysis synthesizes evidence across multiple dimensions identified in the literature, including labour markets, economic growth, income distribution, productivity, trade, innovation, environmental sustainability, and poverty. The study also examines how these dimensions are interconnected within the broader framework of sustainable development. The findings suggest that AI functions as a general-purpose technology capable of enhancing productivity, fostering innovation, and supporting economic growth. However, its effects are often uneven and highly context-dependent, giving rise to potential trade-offs such as job displacement, widening inequality, and environmental challenges. Furthermore, the review reveals that the existing literature remains fragmented, with limited integration across these development dimensions. By providing a comprehensive and structured synthesis, this study highlights the interconnected pathways through which AI influences economic development and identifies important gaps that warrant further research.",
   "authors": [
    "Sin‐Yu Ho",
    "Fiyinfoluwa Giwa"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-03",
   "doi": "10.1007/s13132-026-03385-w",
   "explainer": null,
   "id": "doi:10.1007/s13132-026-03385-w",
   "institutions": [
    "University of South Africa"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1007/s13132-026-03385-w",
   "summary": "A systematic review of 194 peer-reviewed articles from 2011-2025 on AI and economic development, covering labor markets, growth, income distribution, productivity, trade, innovation and poverty. It finds AI can raise productivity and growth but with uneven, context-dependent effects including job displacement and wider inequality, and notes the literature is fragmented.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence and Economic Development: A Systematic Review of Patterns and Pathways",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1007-s13132-026-03385-w/",
   "venue": "Journal of the Knowledge Economy",
   "year": 2026
  },
  {
   "abstract": "We ask what artificial intelligence (AI) is actually doing to macroeconomic growth in the Hashemite Kingdom of Jordan, and by what route. Three channels are considered: efficiency gains, wider financial inclusion, and lower transaction costs. The paper also treats the “Commercial Banking Applications” platform, hereafter CBAs, as a mediating channel, based on the argument that innovation only reaches the real economy when some concrete piece of infrastructure carries it there. The design is mixed and descriptive–analytical. On the quantitative side, a questionnaire was administered to 403 respondents drawn from employees and experts across Jordan’s banking and financial environment. On the qualitative side, we conducted semi-structured interviews with a purposive sample of 12 experts and senior officials. Among these, the Central Bank, the Securities Commission, and the Association of Banks were represented, along with founders of financial technology start-ups. Structural and path relationships were estimated in AMOS, supported by multiple regression. AI has a statistically significant positive effect on growth overall. The total effect is 0.829 at a significance level of 0.000, and the model accounts for 65.9% of the variance in economic growth. CBAs carry a substantial share of that. Its indirect effect is 0.341, or 34.1%, delivered mainly by cutting the time and effort a transaction takes and by holding together an otherwise fragmented set of financial connections, with liquidity rising as a result. The main recommendation of this article follows from this: invest in comprehensive financial connectivity. Financial connectivity and integration proved to be the factor most closely linked with growth, which makes it necessary to invest in the development of flexible technological channels for the growth of digital systems and to ensure the technical simplicity of interactions between Commercial Banking Applications, start-ups, and government agencies.",
   "authors": [
    "Salah Turki Alrawashdeh",
    "Mohammad Yousef Abu Safia",
    "Anas Ahmed Yaseen"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-13",
   "doi": "10.3390/economies14100473",
   "explainer": null,
   "id": "doi:10.3390/economies14100473",
   "institutions": [
    "Al-Balqa Applied University",
    "Al-Zaytoonah University of Jordan"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/economies14100473",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Artificial Intelligence and Economic Growth in Jordan: A Mixed-Methods Study of Commercial Banking Applications as a Mediating Channel",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-economies14100473/",
   "venue": "Economies",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is rapidly transforming global economies by enhancing productivity, enabling innovation, and reshaping labor markets. While advanced economies have integrated AI technologies at scale, emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth (Agrawal, Gans, &amp; Goldfarb, 2019; Acemoglu &amp; Restrepo, 2020). This study provides a comparative assessment of Algeria’s readiness to adopt AI for economic productivity, benchmarking its performance against Morocco, Egypt, and Turkey. Using data from the World Bank (2022), Oxford Insights Government AI Readiness Index, and sector-specific studies, the analysis identifies strengths, gaps, and opportunities for AI-driven economic transformation. Findings reveal that Algeria exhibits significant lag in digital infrastructure, human capital, and institutional frameworks compared to peers, yet targeted investments and policy reforms could accelerate AI adoption and productivity gains (Brynjolfsson, Rock, and Syverson, 2017; McKinsey &amp; Company, 2023). The paper concludes with policy recommendations to foster a conducive environment for AI integration, positioning Algeria to leverage technological advances for sustainable economic growth.",
   "authors": [
    "Lakhdar ZIANE",
    "Moussa Boukrif"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-30",
   "doi": "10.62754/joe.v5i1.7220",
   "explainer": {
    "approach": "A comparative assessment using World Bank data, the Oxford Insights Government AI Readiness Index and sector studies to benchmark Algeria against three peer countries.",
    "findings": "Algeria lags its peers in digital infrastructure, human capital and institutional frameworks, and the paper argues that targeted investment and policy reform could speed AI adoption and productivity gains.",
    "limitations": "The study measures readiness indicators rather than observed productivity effects of AI.",
    "method_type": "policy-analysis",
    "question": "How ready is Algeria to use AI to raise economic productivity compared with Morocco, Egypt and Turkey?"
   },
   "id": "doi:10.62754/joe.v5i1.7220",
   "institutions": [
    "University of Béjaïa"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.62754/joe.v5i1.7220",
   "summary": "Benchmarks Algeria's AI readiness for economic productivity against Morocco, Egypt, and Turkey using World Bank and Oxford Insights Government AI Readiness Index data, finding Algeria lags peers on digital infrastructure, human capital, and institutions. It's a policy-oriented case study illustrating the North Africa/MENA angle on how unevenly AI-driven productivity gains may diffuse to emerging economies.",
   "theme": "Growth & Macro",
   "title": "Artificial Intelligence and Economic Productivity: A Comparative Assessment of Algeria's Readiness and Development Prospects",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-62754-joe-v5i1-7220/",
   "venue": "Journal of Ecohumanism",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is increasingly recognized as a general-purpose technology with significant implications for economic growth, employment, financial stability, and national security. This article examines how AI reshapes economic security, understood as the capacity of households, firms, and states to withstand shocks to income, markets, and critical infrastructure. Using a structured narrative review of academic research, international policy reports, regulatory frameworks, and technical safety studies, the article identifies four principal risk domains: labor-market disruption, market concentration and infrastructure dependence, financial instability, and malicious or geopolitical misuse. The review also examines governance challenges arising from fragmented regulatory approaches, limited institutional capacity, incomplete evidence, and the rapid pace of technological deployment. The findings indicate that AI-related risks are interconnected and that vulnerabilities can spread through shared models, concentrated computing infrastructure, and correlated institutional decisions. In response, the article proposes a layered resilience framework combining adaptive regulation, competition policy, financial supervision, workforce investment, and international coordination. It concludes that economic security depends not only on preventing harmful applications of AI but also on strengthening institutions’ ability to anticipate, absorb, and recover from technological shocks.",
   "authors": [
    "Atabayeva Ruhshona Sadulla qizi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-22",
   "doi": "10.5281/zenodo.23573745",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23573745",
   "institutions": [
    "Tashkent State University of Economics"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23573745",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "ARTIFICIAL INTELLIGENCE AND ECONOMIC SECURITY: EMERGING RISKS, GOVERNANCE CHALLENGES, AND RESILIENCE STRATEGIES",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23573745/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "This study examines the strategic impact of artificial intelligence (AI) on firm productivity, with a focus on how different strategic goals – customer centricity, international market expansion, and environmental sustainability - interact with AI adoption. We combine professional skills data from LinkedIn with information on innovation activities and strategic objectives collected from company websites for a sample of 1800 manufacturing firms. Using an augmented Cobb–Douglas production function and a two-stage estimation approach, our analysis shows that firms with higher levels of AI intensity tend to exhibit higher production efficiency. However, firms emphasizing customer centricity tend to be less productive, while firms combining higher AI intensity with an international market orientation tend to exhibit higher productivity. By contrast, we find no statistically significant link between AI use and productivity when the strategic goal is environmental sustainability. Overall, the study fills a gap in understanding the role of AI in shaping firm productivity across different strategic orientations, offering valuable insights for optimizing AI intensity and aligning it with strategic planning.",
   "authors": [
    "Syed Muhammad Ali Shah",
    "Marco Cucculelli"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-02",
   "doi": "10.1016/j.jsinno.2026.200589",
   "explainer": null,
   "id": "doi:10.1016/j.jsinno.2026.200589",
   "institutions": [
    "Marche Polytechnic University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1016/j.jsinno.2026.200589",
   "summary": "Combining LinkedIn skills data with website-derived strategy information for 1,800 manufacturing firms, the study estimates a production function and finds higher AI intensity is associated with higher production efficiency. The association is strongest for internationally oriented firms and absent for firms whose strategic goal is environmental sustainability.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and firm strategies: A pathway to enhanced productivity",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1016-j-jsinno-2026-200589/",
   "venue": "Journal of Strategy & Innovation",
   "year": 2026
  },
  {
   "abstract": "Drawing on an unbalanced panel of Chinese A-share listed manufacturing firms from 2010 to 2020, this study examines the relationship between firm-specific artificial intelligence capability and total factor productivity (TFP), the accompanying capital and labour adjustments, and heterogeneity across industrial contexts and AI subfields. Using a patent-based measure of AI capability, we estimate firm-level models with high-dimensional fixed effects, while a staggered pilot-zone design provides quasi-experimental corroboration. Stronger AI capability is associated with higher TFP, greater intangible investment, higher headcounts of highly educated employees, and lower headcounts of less-educated employees. The productivity relationship is stronger in high-technology manufacturing and in industries with broader digital application scenarios, while machine learning exhibits the most stable positive association among AI subfields. Reweighting the listed sample to match the official distributions of firm size, industry, and ownership among above-scale manufacturing enterprises leaves the estimate essentially unchanged. The findings document a positive within-firm AI–TFP relationship among listed manufacturers.",
   "authors": [
    "Huiquan Liu",
    "Linxiang Ye"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.1080/00036846.2026.2750976",
   "explainer": null,
   "id": "doi:10.1080/00036846.2026.2750976",
   "institutions": [
    "Nanjing University of Finance and Economics"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.1080/00036846.2026.2750976",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and firm total factor productivity: evidence from digital capital accumulation and labour structure adjustment",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1080-00036846-2026-2750976/",
   "venue": "Applied Economics",
   "year": 2026
  },
  {
   "abstract": "Research hypotheses: H1: The development level of AI has a significant positive direct effect on the green transition of manufacturing. H2a: AI indirectly drives the green transition of manufacturing through green technology innovation. H2b: AI indirectly drives the green transition of manufacturing through improved digital infrastructure support. H3a: The impact of AI on the green transition of manufacturing exhibits a threshold effect contingent on government R&amp;D investment. H3b: The impact of AI on the green transition of manufacturing exhibits a threshold effect contingent on economic development. H4: The effect of AI on the green transition of manufacturing exhibits a positive spatial correlation effect. The data are sourced from the China City Statistical Yearbook, provincial statistical yearbooks, the EPS database, and the National Enterprise Credit Information Publicity System. Missing values for individual observations are imputed using linear interpolation. To mitigate the influence of extreme values, the raw data are winsorized at the 1% level. The study sample comprises 281 prefecture-level and above cities in China, and it covers the period from 2009 to 2023. This yields a total of 4,215 observations.",
   "authors": [
    "Muratbek"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-13",
   "doi": "10.17632/g8zygxps2x",
   "explainer": null,
   "id": "doi:10.17632/g8zygxps2x",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.17632/g8zygxps2x",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and Green Total Factor Productivity in Manufacturing: An Empirical Analysis",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17632-g8zygxps2x/",
   "venue": "Mendeley Data",
   "year": 2026
  },
  {
   "abstract": "This article provides a conceptual analysis of the relationship between the rapid diffusion of artificial intelligence technologies and the emergence of new forms of digital inequality within the startup economy. Against the backdrop of digital transformation, characterised by the rise of platform capitalism, algorithmic governance and the phenomenon of 'artificial sociality', there is an increasing demand for updated theoretical frameworks that can explain structural changes in communication systems and access to economic resources. This study aims to develop such a framework for digital stratification in startup ecosystems, focusing on the interplay between venture capital, technological innovation, and social structures. In addition to conceptual modelling, the research employs a mixed-methods approach, combining quantitative statistical analysis with interpretative theoretical synthesis. The empirical basis draws on data from the Baltic countries (Latvia, Lithuania and Estonia) from 2019 to 2024, including indicators of venture capital investment, startup density and gender distribution among founders. Correlation and regression analyses are used to identify structural dependencies between investment intensity and ecosystem development, and comparative analyses are employed to evaluate cross-country disparities. This methodology combines Niklas Luhmann’s systems theory, Pierre Bourdieu’s theory of capital, and approaches to political economy, platform capitalism (Nick Srnicek) and surveillance capitalism (Shoshana Zuboff). This allows for a multi-layered interpretation of digital inequality that goes beyond purely economic indicators. The results reveal a pronounced concentration of innovation resources. Estonia leads in terms of both venture capital investment per capita (1,100 EUR compared to 153 EUR in Lithuania and 56 EUR in Latvia) and startup density per 100,000 people (82 versus 38 and 24, respectively). A strong relationship has been confirmed between ve",
   "authors": [
    "Владимир Меньшиков",
    "Oksana Ruza",
    "Jelena Semeneca"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-29",
   "doi": "10.30525/2256-0742/2026-12-2-458-468",
   "explainer": null,
   "id": "doi:10.30525/2256-0742/2026-12-2-458-468",
   "institutions": [
    "Daugavpils University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.30525/2256-0742/2026-12-2-458-468",
   "summary": "A conceptual and statistical analysis of digital inequality in the startup ecosystems of Latvia, Lithuania and Estonia from 2019 to 2024. It finds innovation resources are highly concentrated, with Estonia far ahead on venture capital per capita and startup density.",
   "theme": "Inequality & Society",
   "title": "ARTIFICIAL INTELLIGENCE AND INEQUALITY IN THE STARTUP ECONOMY: A CONCEPTUAL FRAMEWORK FOR DIGITAL STRATIFICATION",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-30525-2256-0742-2026-12-2-458-468/",
   "venue": "Baltic Journal of Economic Studies",
   "year": 2026
  },
  {
   "abstract": "Abstract This study develops an agent-based simulation model to investigate how artificial intelligence (AI) and automation technologies affect income inequality under varying redistributive and human capital policies. Drawing on theories of skill-biased technological change, labor market polarization, and capital–labor substitution, the model captures dynamic interactions between heterogeneous agents, sectoral employment shifts, and evolving technological parameters. Simulation results reveal that rapid automation without complementary investment in skills (Scenario S2) exacerbates inequality, depresses labor income shares, and leads to pronounced employment displacement—mirroring trends observed in advanced economies. By contrast, policy configurations combining targeted skill subsidies with progressive taxation (Scenario S5) are shown to significantly reduce both top-end and bottom-end inequality, while facilitating a structural shift toward AI-complementary employment sectors. Sensitivity analysis underscores the importance of initial skill heterogeneity and learning efficiency in shaping long-term distributive outcomes. These findings provide compelling evidence that the inequality effects of AI are not technologically predetermined, but policy-contingent. The study highlights the critical role of redistribution and adaptive human capital strategies in aligning digital transformation with inclusive economic development.",
   "authors": [
    "Hassan Daliri"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-02",
   "doi": "10.1163/15691497-20262006",
   "explainer": null,
   "id": "doi:10.1163/15691497-20262006",
   "institutions": [
    "Golestan University",
    "Golestan University of Medical Sciences"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1163/15691497-20262006",
   "summary": "An agent-based simulation shows that rapid automation without complementary skills investment sharply worsens income inequality and depresses labor's income share, while combining targeted skill subsidies with progressive taxation substantially reduces inequality at both ends of the distribution and shifts employment toward AI-complementary sectors. The core claim — that AI's distributional effects are policy-contingent rather than technologically predetermined — pushes back on deterministic 'AI will increase inequality' narratives.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence and Inequality: Policy Paths in a Polarized Future",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1163-15691497-20262006/",
   "venue": "Perspectives on Global Development and Technology",
   "year": 2026
  },
  {
   "abstract": "Introduction: Artificial Intelligence (AI) is revolutionizing the nature of governance, economic systems, and knowledge infrastructures around the globe.The emergence of generative AI and algorithmic governance is escalating the existing difficulties associated with fragmentation of regulation, economic consolidation, institutional lagging, and disparities in technological capabilities between advanced and developing countries.Purpose: To explore how the AI transformation process influences the evolution of regulatory, economic, and knowledge systems while discussing the limitations of traditional models of governance.Methodology: Mixed methods comparative institutional research design which applies governance mapping, policy analysis, quantitative data, and conceptual systems modeling techniques.Conclusion: The paper argues that AI is creating new opportunities but also poses significant risks, especially for the Global South.In particular, the results show important disparities in AI governance maturity, with the EU exhibiting higher levels of institutional adaptability whereas Global South countries experience governance and infrastructural deficiencies.Moreover, less than ten multinational companies account for almost 78% of AI infrastructures' capacity on the planet, increasing economic concentration and algorithmic dependencies.Contribution: The paper provides a new model of institutional analysis based on a combination of institutional theory, political economy, governance, and AI ethics.",
   "authors": [
    "Goyal Madhu",
    "S Peter Emil Jebakumar",
    "Ms Deepti Singh",
    "Ms Tanya Chopra",
    "Mrs Divesh Sinha",
    "S Pavan Gowtham Kumar"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.64771/jsetms.2026.v03.i06.pp131-146",
   "explainer": null,
   "id": "doi:10.64771/jsetms.2026.v03.i06.pp131-146",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.64771/jsetms.2026.v03.i06.pp131-146",
   "summary": "A comparative institutional analysis argues AI is straining global governance capacity unevenly, finding the EU shows relatively high institutional adaptability to AI governance while Global South countries face bigger governance and infrastructure gaps, and that fewer than ten multinational firms account for nearly 78% of global AI infrastructure capacity. The concentration finding adds an economic-power dimension — not just labor or productivity — to the debate over AI's global governance risks.",
   "theme": "Policy & Governance",
   "title": "Artificial Intelligence and Institutional Transformation across Regulatory Economic and Knowledge Systems",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-64771-jsetms-2026-v03-i06-pp131-146/",
   "venue": "Journal of Science Engineering Technology and Management Sciences",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, significantly influencing economic development, productivity growth, and labor market dynamics worldwide. While developed economies have been at the forefront of AI adoption, developing countries are increasingly experiencing both opportunities and challenges associated with the integration of AI technologies into various sectors. The impact of AI on labor markets in developing countries is complex, involving job creation, job displacement, changes in skill requirements, and shifts in employment structures. This article examines the transformation of labor markets in developing countries under the influence of AI, focusing on employment trends, workforce adaptation, productivity improvements, and policy implications. The study is based on reports and empirical findings published by international organizations, including the International Labour Organization (ILO), World Bank, Organisation for Economic Co-operation and Development (OECD), International Monetary Fund (IMF), and the World Economic Forum (WEF). The findings indicate that while AI may automate routine tasks and threaten certain occupations, it simultaneously creates demand for new skills and occupations, offering significant opportunities for economic development if supported by effective educational and labor policies.",
   "authors": [
    "Sevinch Rakhimjonovna Nortojiyeva"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-31",
   "doi": "10.5281/zenodo.20476468",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20476468",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20476468",
   "summary": "A literature-based review (drawing on ILO, World Bank, OECD, IMF, and WEF reports) on how AI is reshaping labor markets in developing countries, concluding that AI creates both job displacement and demand for new skills, with outcomes depending heavily on education and labor-market policy. It's descriptive synthesis rather than new empirical evidence, but contributes a Global South framing often underrepresented in the AI-economics literature.",
   "theme": "Labor & Work",
   "title": "ARTIFICIAL INTELLIGENCE AND LABOR MARKET TRANSFORMATION IN DEVELOPING COUNTRIES",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20476468/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The rapid expansion of artificial intelligence (AI) is revolutionizing economic systems by reshaping production, labor organization, and productivity patterns. In the construction industry, which remains highly labor-intensive, project-based, and structurally heterogeneous across European countries, AI can support productivity, planning, safety monitoring, cost estimation, and decision-making. However, its implementation also poses specific challenges, including fragmented workflows, heterogeneous construction sites, limited digital skills, poor data interoperability, high adoption costs, resistance to organizational change, and temporary adjustment costs that may delay the visibility of productivity gains. In this paper, we evaluate the effect of Artificial Intelligence (AI) on labor productivity in the construction sector of European Union countries, taking into account labor input and sector output, and search for structural trends from a systems perspective. AI adoption is defined as the share of construction businesses that use at least one AI technology. Productivity is estimated per worker and per hour using a dataset of EU countries from 2023 to 2024. The effects of AI adoption, labor input, and output on productivity are evaluated using multivariate log-linear regressions in SPSS. Hierarchical and K-means clustering reveal groups of countries with comparable digital and labor performance. The results demonstrate that construction output is the key driver of labor productivity. In contrast, AI adoption shows a small, negative association with productivity, consistent with short-term adjustment costs in the early stages of digital transformation. Cluster analysis reveals diverse country profiles in AI use, productivity, and labor intensity. Overall, the findings underscore the need for a gradual approach to managing digital change in construction, supported by complementary organizational and skills-based measures.",
   "authors": [
    "Claudiu George Bocean",
    "Adriana Scrioșteanu",
    "Sorina Gîrboveanu",
    "Marius Mitrache",
    "Mirela Sîrbu",
    "Ionuț-Cosmin Băloi",
    "Adrian Florin Budică-Iacob",
    "Maria Magdalena Criveanu"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-10",
   "doi": "10.3390/systems14070818",
   "explainer": null,
   "id": "doi:10.3390/systems14070818",
   "institutions": [
    "University of Craiova"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/systems14070818",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and Labor Productivity in Construction: A Comparative Systems Analysis Across European Economies",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-systems14070818/",
   "venue": "Systems",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is widely regarded as a transformative general-purpose technology with the potential to reshape production and long-run economic growth. Yet evidence linking national AI development to aggregate productivity remains scarce, reflecting a modern manifestation of the Solow paradox. Existing studies have predominantly examined firm- or task-level outcomes or average cross-country effects, leaving unclear whether the AI-productivity relationship varies across the productivity-growth distribution. This study investigates the relationship between national AI development, measured by the Stanford Global AI Vibrancy Index, and total factor productivity (TFP) using an unbalanced panel of 35 countries over 2017–2023. Results from conventional panel and quantile regression models indicate that lagged AI development is positively related to TFP growth, with the association strengthening toward the upper end of the TFP-growth distribution but remaining weak or insignificant at the lower end. Robustness checks, including a placebo permutation test, further indicate that these findings are unlikely to be driven by random variation. The findings contribute to the emerging macroeconomic literature on AI by providing evidence of distributional heterogeneity in the AI-TFP relationship, with implications for the design and evaluation of AI-related policy interventions.",
   "authors": [
    "Tuan Hock Ng",
    "Chun-Teck Lye",
    "Ying-San Lim"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-12",
   "doi": "10.1016/j.ssaho.2026.103822",
   "explainer": null,
   "id": "doi:10.1016/j.ssaho.2026.103822",
   "institutions": [
    "Multimedia University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1016/j.ssaho.2026.103822",
   "summary": "This study relates national AI development (Stanford's Global AI Vibrancy Index) to total factor productivity across 35 countries from 2017 to 2023. Lagged AI development is positively associated with TFP growth, but the link is strongest in countries already at the top of the productivity-growth distribution and weak or insignificant at the bottom.",
   "theme": "Growth & Macro",
   "title": "Artificial intelligence and national productivity: Evidence of distributional heterogeneity",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1016-j-ssaho-2026-103822/",
   "venue": "Social Sciences & Humanities Open",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping economies, industries, governance, and social systems. Its ability to process vast amounts of data, automate decision-making, enhance productivity, and facilitate innovation has positioned AI as a strategic driver of sustainable economic development. Sustainable economic development seeks to balance economic growth with environmental protection and social equity, ensuring that present development does not compromise the needs of future generations. AI contributes significantly to achieving these objectives by optimizing resource allocation, improving energy efficiency, enhancing healthcare delivery, promoting smart agriculture, supporting climate change mitigation, and fostering financial inclusion. Simultaneously, AI presents considerable challenges, including labor market disruptions, ethical concerns, algorithmic bias, cybersecurity threats, privacy violations, digital inequality, and increasing energy consumption associated with large-scale computational infrastructures. These challenges necessitate robust governance frameworks, ethical standards, and inclusive policy interventions. This article critically examines the relationship between artificial intelligence and sustainable economic development by reviewing contemporary theoretical perspectives and empirical evidence. It explores AI's role across multiple sectors including manufacturing, agriculture, healthcare, education, finance, transportation, and environmental management. Furthermore, the study investigates the policy implications of AI adoption and identifies barriers that may hinder equitable and sustainable implementation, particularly in developing economies. The article concludes by proposing a comprehensive framework integrating technological innovation, institutional governance, environmental sustainability, and social inclusion. Such an integrated approach is essential ",
   "authors": [
    "Dr.Siny Philip Dr. Sreeja K"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-17",
   "doi": "10.69889/yr9gte52",
   "explainer": {
    "approach": "A review of theoretical perspectives and empirical evidence on AI across sectors including manufacturing, agriculture, healthcare, education, finance, transport and environmental management, with attention to developing economies.",
    "findings": "The article lists benefits such as better resource allocation, energy efficiency and financial inclusion, and challenges such as labour market disruption, algorithmic bias, digital inequality and rising energy use. It proposes a framework combining innovation, governance, sustainability and social inclusion.",
    "limitations": "The abstract describes a broad review without new data or quantified estimates.",
    "method_type": "review",
    "question": "How does AI relate to sustainable economic development, and what opportunities and challenges does it bring?"
   },
   "id": "doi:10.69889/yr9gte52",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.69889/yr9gte52",
   "summary": "A broad review of AI's role in sustainable economic development, cataloguing benefits across manufacturing, agriculture, healthcare, finance, and climate mitigation (resource optimization, energy efficiency, financial inclusion) against costs including labor disruption, algorithmic bias, digital inequality, and the rising energy footprint of large-scale compute. Its value to the debate is framing AI-growth tradeoffs through a sustainability lens—arguing gains are contingent on governance, ethics, and inclusive policy—though it is a qualitative synthesis without new empirical estimates.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and Sustainable Economic Development: Opportunities and Challenges",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-69889-yr9gte52/",
   "venue": "Economic Sciences.",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is reshaping the paradigm of marine industrial development during the strategic window of accelerated digital transformation in the global marine economy. In order to investigate the effects of AI on China’s marine economic performance, this study builds a bidirectional assessment system that incorporates AI and the high-quality development of the marine economy using panel data encompassing coastal Chinese provinces and municipalities from 2011 to 2022. Results of the study suggest that AI greatly raises China’s marine economy’s degree of high-quality development. The Bohai Rim, the Pearl River Delta Economic Zone, and economically developed areas all benefit more from AI, according to heterogeneity testing. Further threshold analysis reveals a significant nonlinear relationship between AI and the marine economy, constrained by a single threshold effect of fiscal expenditure. This study offers a fresh viewpoint on the complex relationship between AI and the marine sector and offers policy implications for precise fiscal regulation during intelligent transformation in coastal regions.",
   "authors": [
    "S Liu",
    "Bingjie Huang",
    "Rongbo Wang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-13",
   "doi": "10.3389/fmars.2026.1817479",
   "explainer": null,
   "id": "doi:10.3389/fmars.2026.1817479",
   "institutions": [
    "Guangdong Ocean University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.3389/fmars.2026.1817479",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Artificial intelligence and the high-quality development of China’s marine economy: evidence from coastal provinces",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3389-fmars-2026-1817479/",
   "venue": "Frontiers in Marine Science",
   "year": 2026
  },
  {
   "abstract": "The active introduction of artificial intelligence into the work process causes significant changes in the labor market, accounting and management control systems, as well as in economic growth models. With the advent of new information technologies, it became obvious that national economies need to increase labor productivity through the process of digitalization. This requires scientific substantiation of energy-efficient technologies that affect the use of labor resources in terms of their effectiveness. The purpose of this research was to study the impact of artificial intelligence technologies on labor productivity and changes in the employment structure, especially in the context of the digital economy. The methodology was based on a systematic, logical-structural, comparative and econometric analysis of panel data for the period from 2018 to 2024. The analysis revealed that the introduction of automation, investments in human capital development and spending on scientific research and development have a statistically significant positive effect on labor productivity growth. An increase in the level of automation by 1% leads to a 0.44% increase in productivity, while an increase in spending on education provides an increase of 0.31%. In 2024, the average gross domestic product per capita in developed countries will be $ 133.2 thousand. In countries with economies in transition, this figure is $57.300, while in developing countries it is only $34.000. The digital integration index in developed countries is 0.85, while in developing countries it is only 0.52. The level of accounting automation in economically developed countries is 74%, while in less developed regions this figure is only 29%. The practical value of the results obtained lies in the formation of policies that contribute to the growth of labor productivity, the development of human resources and the improvement of management structures in the conditions of digital transformation.",
   "authors": [
    "MYKHAYLO VIDYAKIN, TETIANA MELNYK, OKSANA NAZARCHUK, NATALIIA POCHERNINA, OLEKSANDR AKIMOV"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-31",
   "doi": "10.5281/zenodo.20497307",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20497307",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20497307",
   "summary": "Using panel data for 2018-2024 across country groups, the study estimates that a 1% rise in automation is associated with a 0.44% rise in labor productivity and a 1% rise in education spending with a 0.31% rise. It links AI adoption to structural change and labor-market polarization across developed and transition economies.",
   "theme": "Growth & Macro",
   "title": "ARTIFICIAL INTELLIGENCE AND THE PRODUCTIVITY PARADOX: MODELING STRUCTURAL TRANSFORMATION AND LABOR MARKET POLARIZATION ACROSS ECONOMIC GROUPS",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20497307/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) and automation are transforming the productive core of the global economy, yet public debate remains dominated by single-number claims about the proportion of jobs “at risk”—claims that collapse heterogeneous technologies, tasks, and adoption conditions into a single misleading index. This thesis develops, verifies, and applies a multidimensional framework for assessing the employment consequences of AI and automation across economic sectors. The framework distinguishes four constructs that previous research has often conflated: (D1) digital AI exposure, defined as the proportion of a sector’s task content that current AI systems can technically support or perform; (D2) physical automation, defined as the automatable share of embodied, manual tasks; (D3) adoption, defined as the actual, observed deployment of AI and automation technologies within a sector; and (D4) net employment impact, defined as the estimated medium-term effect on total employment after accounting for displacement, reinstatement, and the creation of new tasks. The thesis proceeds in four stages. First, it reconstructs the technical foundations of machine learning, generative models, and robotics with sufficient rigor for economic analysis, while tracing the economic theory of technology-induced labor reallocation from Ricardo’s machinery question to the contemporary task-based literature. Second, it formalizes the four-dimensional framework through explicit index definitions, aggregation functions, and uncertainty treatment, deriving each dimension from documented methodologies, including task-exposure assessments, adoption surveys, and causal labor-market studies. Third, it applies the framework to nine sectors: information technology, finance and insurance, healthcare, education, manufacturing, construction, transportation and logistics, hospitality and food services, and retail and e-commerce. This analysis synthesizes approximately 280 verified sources, including ",
   "authors": [
    "Zen Revista"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-15",
   "doi": "10.5281/zenodo.23405018",
   "explainer": {
    "approach": "A thesis that defines four separate measures: digital AI exposure, physical automation potential, actual adoption, and net employment impact after displacement and new tasks. It applies them to nine sectors using about 280 published sources.",
    "findings": "The abstract states the framework is formalized with explicit index definitions and uncertainty treatment and applied across nine sectors from IT to retail; it argues that single-number claims conflate different technologies, tasks and adoption conditions.",
    "limitations": "The abstract does not report the sector results, and the framework draws on secondary sources rather than new data.",
    "method_type": "review",
    "question": "How can the employment effects of AI and automation be assessed without collapsing them into one jobs-at-risk number?"
   },
   "id": "doi:10.5281/zenodo.23405018",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23405018",
   "summary": "A thesis that separates four constructs often conflated in jobs-at-risk estimates: digital AI exposure, physical automation, actual adoption, and net employment impact after displacement and new-task creation. It formalises these as sector-level indices and applies the framework to assess AI's employment consequences across sectors.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence and the Transformation of Work : A Multidimensional Framework for Assessing Technological Exposure, Adoption, and Employment Impacts",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23405018/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "<div> Artificial intelligence is transforming two of the most foundational pillars of modern society: the labor market and the education. This review explores the impact of AI across both domains, highlights the structural dynamics they share, and outlines implications for policy, governance, and educational reform. It first situates AI within the broader trajectory of scientific and technological revolutions. It then examines its effects on the labor market, followed by its influence on higher education. A comparative analysis is subsequently developed to identify common patterns and divergences between these two spheres in the age of AI. Building on this, the review proposes directions for a renewed teaching paradigm, before extending the discussion beyond academia to consider the interplay between AI, labor markets, and the limits of education. It concludes by addressing a key dimension of AI governance; namely, the tension between openness, concentration of power, and inequality in AI research and development. </div>",
   "authors": [
    "Hedi Tabia",
    "Jean-Yves Didier"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-23",
   "doi": null,
   "explainer": null,
   "id": "title:a1609c72529bb91e",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://hal.science/hal-05631192",
   "summary": "A review of how AI is changing both labor markets and higher education, comparing patterns across the two and proposing directions for a new teaching paradigm. It closes on AI governance, especially the tension between openness, concentration of power and inequality in AI research and development.",
   "theme": "Policy & Governance",
   "title": "Artificial Intelligence and the Transformation of Work and Knowledge: A Review of Labor Markets and Higher Education",
   "url": "https://wemustactnow.onrender.com/research/p/title-a1609c72529bb91e/",
   "venue": "HAL (Le Centre pour la Communication Scientifique Directe)",
   "year": 2026
  },
  {
   "abstract": "Abstract One of the most transformative technologies of the twenty first century is Artificial Intelligence (AI) which has had a great impact on the global labour market. The adoption of AI technologies, including machine learning, robotics, natural language processing, and automation systems, into various sectors has reshaped employment landscape, productivity dynamics, and job demands in developed and developing countries. This research paper will analyze the effects of AI on the global labour market, including changes in job trends, job displacement, skill transformation, productivity gains, wage inequality, and labour market polarization. The study also explores sector-specific shifts in manufacturing, healthcare, financial services, retail, logistics, education and information technology. The method of the research is mixed (qualitative and quantitative) and the data used is secondary data obtained from international organizations namely International Labour Organization (ILO), World Economic Forum (WEF), Organization for Economic Co-operation and Development (OECD), International Monetary Fund (IMF) and World Bank. 2018 - 2025 data is analyzed to gain insight into the evolving job market as AI impacts it. The study highlights the positive impact of AI on productivity, efficiency, and economic growth, but also points to the potential challenges posed by AI such as job displacement and technological unemployment, the increasing income gap, and skill mismatches. The results show that only jobs with repetitive or routine tasks are at risk of being automated while jobs that need creativity, emotional intelligence, and high-level analytical skills are still increasing. Additionally, the paper discusses the new job roles in data science, AI engineering, cyber security, digital marketing, and the human-machine interaction. Digital infrastructure, technological readiness and the lack of training of the workforce are all challenges that developing countries have to over",
   "authors": [
    "Sopan Krishna Patil"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-31",
   "doi": "10.5281/zenodo.23719684",
   "explainer": {
    "approach": "Mixed qualitative and quantitative analysis of 2018-2025 secondary data from the ILO, WEF, OECD, IMF and World Bank, with sector-by-sector comparisons.",
    "findings": "The paper reports productivity gains alongside job displacement, a widening income gap and skill mismatches; routine jobs are most at risk while jobs needing creativity, emotional intelligence and advanced analysis keep growing.",
    "limitations": "Relies on secondary aggregates, so it describes associations rather than causal effects; developing countries face infrastructure and training gaps the data cover unevenly.",
    "method_type": "review",
    "question": "How is AI changing jobs, skills, productivity and wages in the global labour market?"
   },
   "id": "doi:10.5281/zenodo.23719684",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23719684",
   "summary": "Using secondary data from the ILO, WEF, OECD and other international organisations, this paper analyses how AI adoption is changing the global labour market, covering job displacement, skill shifts, productivity gains, wage inequality and polarisation. It also looks at sector-specific shifts in manufacturing, healthcare, finance, retail, logistics, education and IT.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence and Transformations in the Labour Market",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23719684/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "In this study, we investigate the impact of artificial intelligence (AI) transformation on within-firm wage disparities in China’s manufacturing sector. Using firm-level text mining to capture both AI adoption and strategic intent, we provide a comprehensive assessment of AI’s distributional consequences. Our findings reveal that AI transformation significantly widens wage gaps between executives and ordinary employees. Two underlying mechanisms drive this effect: technology bias – where productivity gains from AI disproportionately benefit high-skilled, non-routine workers – and labour restructuring, which reduces the employment share of low-skilled, routine-task labour. These disparities are further influenced by firm heterogeneity, with stronger effects observed in capital-intensive firms and those with more advanced digital infrastructure. This study offers policy recommendations focused on reskilling, inclusive labour strategies and sector-specific interventions to mitigate the inequality risks posed by AI in fast-developing economies.",
   "authors": [
    "Yanhui Yin",
    "Meiying Chen",
    "Shasha Fang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-07",
   "doi": "10.16995/ilr.23835",
   "explainer": {
    "approach": "Firm-level analysis of Chinese manufacturers, using text mining of firm documents to measure both AI adoption and strategic intent, then relating that measure to within-firm pay gaps and testing mechanisms and firm heterogeneity.",
    "findings": "AI transformation significantly widens the executive-to-employee wage gap. Two mechanisms are identified: productivity gains that favour high-skilled, non-routine workers, and a falling employment share of low-skilled routine labour. Effects are stronger in capital-intensive firms and those with more advanced digital infrastructure.",
    "limitations": "AI adoption is measured from text in firm documents, which may capture stated intent more than actual use. The abstract does not describe the identification strategy, and results cover one sector in one country.",
    "method_type": "observational",
    "question": "Does AI transformation widen wage gaps between executives and ordinary employees inside Chinese manufacturing firms?"
   },
   "id": "doi:10.16995/ilr.23835",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.16995/ilr.23835",
   "summary": "Using firm-level text mining to measure AI adoption among Chinese manufacturers, the authors find AI transformation significantly widens the wage gap between executives and ordinary employees. They attribute this to skill-biased productivity gains and to a falling employment share of low-skilled routine workers, with stronger effects in capital-intensive and more digitised firms.",
   "theme": "Labor & Work",
   "title": "Artificial intelligence and within-firm wage gaps: Evidence from China’s manufacturing sector",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-16995-ilr-23835/",
   "venue": "International Labour Review",
   "year": 2026
  },
  {
   "abstract": "The article examines the impact of artificial intelligence (AI) technologies on the trajectory of global economic growth, the structure of employment, and the resilience of the world economy in 2016–2026. Based on a comparison of forecasts by international organizations (IMF, World Bank, OECD, ILO) and industry analytics (McKinsey, PwC, IDC, Stanford HAI), and using macroeconomic modeling, scenario and sectoral analysis, the study reconstructs the ranges of the potential contribution of AI to GDP dynamics, productivity, and investment flows. The findings show that the positive effects of AI are accompanied by high macroeconomic uncertainty, exacerbate cross country and intra industry inequality, provoke a structural transformation of the labor market, and create additional threats to economic security (cyber risks, concentration of capital and computing resources, technological and regulatory fragmentation). The scientific novelty of the study lies in the comprehensive integration of macroeconomic and sectoral assessments within a single time horizon, taking into account an “AI readiness” index, which makes it possible to quantitatively characterize the asymmetry in the distribution of benefits and risks across country groups and sectors. It is demonstrated that realizing the potential of AI as a general purpose technology requires coordinated policies by governments and businesses aimed at developing human capital, modernizing digital infrastructure, and shaping institutional mechanisms that minimize social and geoeconomic risks.",
   "authors": [
    "Ачба Любовь Викторовна",
    "Dmitry Agafonov"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-30",
   "doi": "10.37468/2307-1400-2026-2-26-39",
   "explainer": null,
   "id": "doi:10.37468/2307-1400-2026-2-26-39",
   "institutions": [
    "Financial University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.37468/2307-1400-2026-2-26-39",
   "summary": "This article compares forecasts from the IMF, World Bank, OECD, ILO and industry analysts of AI's contribution to GDP, productivity and investment from 2016 to 2026. Using scenario and sectoral analysis it reconstructs ranges for these effects and the risks they pose for economic security.",
   "theme": "Growth & Macro",
   "title": "Artificial intelligence as a factor of the world economy transformation: scenario analysis of risks and consequences for economic security",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-37468-2307-1400-2026-2-26-39/",
   "venue": "National Security and Strategic Planning",
   "year": 2026
  },
  {
   "abstract": "India offers a distinctive setting for examining whether artificial intelligence (AI) can broaden economic opportunity or reinforce pre-existing inequality. The country combines population-scale digital public infrastructure, a rapidly expanding technology and startup ecosystem, large regional and socio-economic disparities, and a workforce in which informal employment remains dominant. This article develops a secondary empirical synthesis of evidence on inequality, digital infrastructure, AI adoption, financial inclusion, labor-market transition, entrepreneurship, and human-capital readiness. It integrates national and international datasets and policy reports with sectoral evidence from finance, public-language infrastructure, education and skills, and technology services. The analysis distinguishes between equalizing mechanisms—lower transaction costs, alternative credit assessment, multilingual access, scalable learning, and distributed entrepreneurship—and dividing mechanisms, including capital-biased productivity gains, uneven compute access, algorithmic exclusion, regional concentration, and skill-premium effects. The central finding is that AI is not intrinsically redistributive. Its distributional effect depends on an institutional complementarity among universal digital access, AI-relevant human capital, inclusive entrepreneurship, responsible governance, and mechanisms that allow workers and small firms to capture productivity gains. The paper proposes an Inclusive AI Capability framework and argues that education systems are a critical transmission mechanism between technological adoption and equitable participation. India’s experience therefore provides a useful model for emerging economies: AI can support inclusive growth, but only when technology policy is integrated with education, skills, public digital infrastructure, and social protection rather than treated as a stand-alone innovation agenda.",
   "authors": [
    "Myra Agarwal",
    "Trasha Gupta"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-07",
   "doi": "10.5281/zenodo.23199554",
   "explainer": {
    "approach": "A secondary synthesis of national and international datasets and policy reports on digital infrastructure, AI adoption, financial inclusion, labor-market transition, entrepreneurship and skills in India.",
    "findings": "AI's distributional effect is not built in: it depends on universal digital access, AI-relevant skills, inclusive entrepreneurship, governance and ways for workers and small firms to capture productivity gains. The paper proposes an Inclusive AI Capability framework.",
    "limitations": "Based on existing data and reports, with no new causal estimates of AI's effect on inequality.",
    "method_type": "review",
    "question": "Will AI broaden economic opportunity in India or deepen existing inequality?"
   },
   "id": "doi:10.5281/zenodo.23199554",
   "institutions": [
    "Delhi Technological University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23199554",
   "summary": "A secondary synthesis of data and policy reports asks whether AI will narrow or widen inequality in India. It contrasts equalising mechanisms such as lower transaction costs and multilingual access with dividing ones such as capital-biased gains and uneven compute access, concluding that the distributional outcome depends on institutions rather than the technology itself.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence as an Equalizer or Divider in India: Digital Public Infrastructure, Human Capital, and Inclusive Technology Adoption",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23199554/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence functions as an engine for increasing the productivity and economic growth of a nation. At the same time, it creates economic inequalities. The rapid development of the AI sector has profoundly transformed global systems, fundamentally altered operational paradigms, and paved novel pathways for economic growth. This study is trying to examine the intricate relationship between AI and economic growth, particularly on both the productivity gains and economic inequalities associated with AI adoption. We frame this scenario through the lens of productivity J-curve analysis. Using a panel of selected OECD countries spanning a period of eight years (2013-2020), the present study seeks to shed some light to examine the association among technological, political, and environmental factors, particularly AI patents and robot installation and their effects on productivity, measured by Total Factor Productivity (TFP). Furthermore, this study also focuses on the complex relationship between political factors, specifically democracy and political stability, and their impact on environmental degradation, measured by carbon emissions. To assess this relationship, we apply a novel econometric technique, the Seemingly Unrelated Regression Equation (SURE) estimation approach. Findings from our empirical study support the productivity J-curve hypothesis, where AI adoption at the early phase exhibits slower productivity growth, and the organizational changes catch up over time. It highlights the necessity for sustained organizational adaptation, realizing the full productivity gain of emerging technologies. Democratic systems frequently face delays in enacting and enforcing stringent environmental regulations due to political negotiations. Governments may prioritize economic development, often postponing environmental regulations to boost economic growth.",
   "authors": [
    "Soumyajit Bhunia",
    "Sneha Dey",
    "Priya Mondal"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-22",
   "doi": "10.31235/osf.io/tmjxw_v1",
   "explainer": null,
   "id": "doi:10.31235/osf.io/tmjxw_v1",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.31235/osf.io/tmjxw_v1",
   "summary": "Using an eight-year OECD country panel and a seemingly-unrelated-regressions approach, finds support for the AI productivity J-curve (slow initial gains from AI patents and robot installations followed by catch-up as organizations adapt), while also showing democratic political systems tend to delay environmental regulation in favor of near-term growth. It links two normally separate strands of the AI debate — productivity diffusion lags and AI's environmental/carbon trade-offs — in one empirical panel.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence at the crossroads: Productivity J-curve and carbon trade-offs in the global economy",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-31235-osf-io-tmjxw-v1/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "With the continuous development of new technologies, these new technologies have a profound impact on the labor market. This paper uses micro-individual data from the China Family Panel Studies (CFPS) from 2016 to 2022 and provincial-level macro-data on artificial intelligence (AI), employing a two-way fixed-effects model to systematically examine the actual impact and underlying mechanisms of AI development on the employment quality of workers. The study shows a significant positive correlation between the level of regional artificial intelligence development and the employment quality of local workers. However, this overall effect at the regional level cannot directly prove the individual-level effect of enterprises or employees using artificial intelligence. Mechanism testing further confirms that the effect of the regional AI development on improving the employment quality of workers is mainly achieved through optimizing the industrial structure and promoting the intelligentization of workers’ working mode. Heterogeneity analysis indicates that the regional artificial intelligence has a greater positive impact on employment quality for male workers, outside-system workers, and workers in the eastern region. Therefore, to achieve higher-quality employment for workers, it is necessary to promote the deep integration of AI with the real economy, build a multi-party collaborative education mechanism involving government, enterprises, and universities, and implement policies to promote digital inclusion and differentiated support.",
   "authors": [
    "Yunting Wu",
    "Xiliang Feng"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-16",
   "doi": "10.3390/su182010528",
   "explainer": null,
   "id": "doi:10.3390/su182010528",
   "institutions": [
    "Capital University of Economics and Business"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/su182010528",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Artificial Intelligence Development and Worker Employment Quality: Evidence from China",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-su182010528/",
   "venue": "Sustainability",
   "year": 2026
  },
  {
   "abstract": "This article explores the impact of artificial intelligence on the labor market, emphasizing the roles AI can potentially automate and those that benefit from human collaboration. Using a framework that aligns specific cognitive abilities with AI tools, the study identifies which tasks within an occupation are most susceptible to AI-driven automation. It highlights the nuanced interaction between human abilities and AI, suggesting that while some tasks may be automated, others require a synergistic blend of human judgment and machine efficiency. The chapter leverages data from the U.S. Bureau of Labor Statistics to assess job functions and estimates the effect of AI on employment dynamics. Findings underscore the importance of training programs focused on both technical and soft skills to prepare employees for a future of AI-enhanced work environments. This framework provides organizations with strategic insights into optimizing human-AI collaboration to improve productivity and job satisfaction.",
   "authors": [
    "Sonia Kherbachi",
    "Naima Benkhider",
    "Nassim Keddari"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-19",
   "doi": "10.4018/979-8-3693-5638-8.ch063",
   "explainer": null,
   "id": "doi:10.4018/979-8-3693-5638-8.ch063",
   "institutions": [
    "University of Béjaïa"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.4018/979-8-3693-5638-8.ch063",
   "summary": "Develops a framework matching specific cognitive abilities to AI capabilities to identify which tasks within U.S. occupations (using Bureau of Labor Statistics data) are most exposed to automation versus most reliant on human-AI collaboration. It's a task-exposure mapping exercise rather than an empirical productivity estimate, aimed at informing workforce training priorities.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence Framework in Labor Market",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4018-979-8-3693-5638-8-ch063/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "The increasing diffusion of AI has generated considerable interest among economists and management scholars, because technological change is a fundamental source of long-term productivity and economic growth. In conventional growth theory, technological progress enables economies to produce more output from a given quantity of labour and capital. AI may influence this process by increasing the efficiency of existing production activities, augmenting workers’ capabilities, accelerating research and development, and improving the allocation of organisational resources. Unlike many earlier digital technologies, AI can perform tasks involving prediction, classification, information processing, content generation and decision support. Consequently, its potential economic effects extend beyond the automation of routine activities to include innovation, organisational transformation, human– machine collaboration and the creation of new products and business models. AI should not be considered an automatic source of economic growth. The productivity consequences of a new technology depend on complementary investments, organisational capabilities, institutional conditions, and the ability of workers and managers to incorporate the technology into production processes. AI can increase aggregate productivity and economic output, but the benefits may not be distributed equally. There are several risks, such as displacement of workers performing automatable tasks, wage polarisation, concentration of technological capabilities, advantages for large firms and divergence between technologically advanced and less-prepared economies.",
   "authors": [
    "Karen Sargsyan"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-19",
   "doi": "10.54503/2579-2903-2026.2-175",
   "explainer": null,
   "id": "doi:10.54503/2579-2903-2026.2-175",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.54503/2579-2903-2026.2-175",
   "summary": "A conceptual paper on the channels through which AI can raise productivity and growth, from task efficiency and worker augmentation to faster R&D. It argues the gains depend on complementary investments, organisational capability and institutions, and flags risks of worker displacement, wage polarisation and divergence between advanced and less-prepared economies.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence Impact on Productivity and Economic Growth",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54503-2579-2903-2026-2-175/",
   "venue": "“Katchar” Collection of Scientific Articles International Scientific-Educational Center NAS RA",
   "year": 2026
  },
  {
   "abstract": "Aim: Artificial intelligence (AI) is increasingly shaping learning processes and work practices, with important implications for productivity and regional competitiveness. However, the benefits of AI adoption may not be evenly distributed across urban and rural contexts. This study examines how individuals living in large cities, small cities, and rural areas perceive and use AI in learning and professional activities, and how they evaluate its economic benefits and associated risks. Method: The study is based on an online survey conducted in 2025 in Lithuania, comprising 17 Likert-scale items measuring perceived productivity benefits, learning support,digital skills, the future relevance of AI, and perceived risks. The final sample comprised 120 valid and fully completed responses (N = 120). Descriptive statistics were used to summarize response patterns, and pairwise Welch’s t-tests were applied to explore differences across residential contexts. Results: The results indicate generally positive attitudes toward AI across all groups, particularly regarding overall time-saving, improved decision-making, and enhanced competitiveness. Statistically significant differences were observed only between respondents living in large cities and small cities, with greater concern about potential job displacement. Conclusions: Overall, the findings of the sample suggest that while AI is widely perceived as a generally productivity-enhancing tool across regions, place-based disparities in skills and perceptions persist, underscoring the need for targeted training, institutional support, and inclusive digital policies to strengthen balanced regional competitiveness.",
   "authors": [
    "Česlovas Christauskas",
    "Algirdas Justinas Staugaitis"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-30",
   "doi": "10.22630/aspe.2026.25.2.5",
   "explainer": null,
   "id": "doi:10.22630/aspe.2026.25.2.5",
   "institutions": [
    "Kauno kolegija Higher Education Institution"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.22630/aspe.2026.25.2.5",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Artificial intelligence in learning and work practices: urban–rural differences in perceived productivity and competitiveness",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-22630-aspe-2026-25-2-5/",
   "venue": "Acta Scientiarum Polonorum - Oeconomia",
   "year": 2026
  },
  {
   "abstract": "A new round of technological revolution centered on artificial intelligence (AI) is reshaping the global labor market. Using panel data for 280 Chinese cities from 2011 to 2023, this study treats the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones (AIIDPZs) as a quasi-natural experiment and employs a staggered difference-in-differences model to examine the impact of AI policy on wage gaps. The results show that the AIIDPZs significantly widen wage gaps within cities. Mechanism tests point to three channels: innovation technology benefit bias, industrial restructuring, and technology diffusion. The effects are stronger in cities in the eastern region, cities with well-developed digital infrastructure, and cities with lower human capital. These findings support incorporating the effects of income distribution into policy evaluation, strengthening skills training and mechanisms for sharing innovation gains, tailoring support to local conditions, and coordinating AI policy with employment policy.",
   "authors": [
    "Jinghai Li",
    "Jingshan Huang",
    "欢欢 杨"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-14",
   "doi": "10.62177/apemr.v3i5.1737",
   "explainer": null,
   "id": "doi:10.62177/apemr.v3i5.1737",
   "institutions": [
    "Guangdong University Of Finances and Economics"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.62177/apemr.v3i5.1737",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Artificial Intelligence Policy and Wage Gaps: Evidence from China",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-62177-apemr-v3i5-1737/",
   "venue": "Asia Pacific Economic and Management Review",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) and digital transformation are rapidly changing the global economic environment and influencing the way businesses, commercial activities, and financial systems operate in modern society. In recent years, organisations across the world have increasingly adopted advanced digital technologies to improve efficiency, productivity, communication, customer service, and strategic decision-making. AI-based systems, automation tools, digital banking platforms, cloud computing, and online business models have transformed traditional business operations into faster, smarter, and more connected systems. At the same time, sustainable economic growth has become one of the most important goals for both developing and developed countries. Technology is now playing a major role in creating employment opportunities, improving financial inclusion, encouraging innovation, supporting entrepreneurship, and strengthening economic development. This research paper examines the impact of Artificial Intelligence and digital transformation on business, commerce, and financial systems while also analysing their contribution toward sustainable economic growth in the modern economy.",
   "authors": [
    "Dr. Rajesh Manikraoji Naik"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-29",
   "doi": "10.5281/zenodo.20443060",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20443060",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20443060",
   "summary": "A descriptive overview of how AI and digital transformation affect business, commerce and financial systems. It argues that these technologies improve efficiency, financial inclusion and innovation and contribute to sustainable economic growth.",
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence, Digital Transformation, and Sustainable Economic Growth: A Multidisciplinary Analysis of Business, Commerce, and Financial Systems",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20443060/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The accelerating integration of artificial intelligence (AI) into virtually every sector of the American economy has created both an extraordinary opportunity and an urgent challenge for national policymakers. This paper investigates the intersection of AI adoption, educational reform, and macroeconomic development, with specific attention to the strategic policy frameworks that federal and state governments have deployed or conspicuously neglected in preparing the United States workforce for an AI-transformed economy. Drawing on a multi-dimensional analytical framework that incorporates human capital theory, systems thinking, and comparative policy analysis, the study evaluates the adequacy, coherence, and implementation fidelity of existing government initiatives, including the National AI Initiative Act of 2020, the CHIPS and Science Act of 2022, and various state-level workforce development programs. The paper argues that while the United States has made measurable progress in AI research and high-skill talent development, a structural mismatch persists between elite-level AI education investments and the scale of workforce transformation needed across mid-skill, service, and manufacturing sectors. The findings reveal that government strategies remain fragmented, inequitably distributed, and insufficiently aligned with the pace of technological displacement. The paper concludes with a policy architecture model, the Adaptive Workforce Ecosystem (AWE) Framework, that proposes a more integrated, equity-centered, and continuously adaptive national strategy for AI-driven workforce readiness and sustainable economic growth.",
   "authors": [
    "Nicholas Teye Otchie"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-12",
   "doi": "10.5281/zenodo.20671667",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20671667",
   "institutions": [
    "Ala-Too International University",
    "International University of Kyrgyzstan"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20671667",
   "summary": "Evaluates U.S. federal and state AI workforce policy (including the National AI Initiative Act and CHIPS and Science Act) and concludes that despite progress on elite AI research talent, there is a structural mismatch with insufficient investment in reskilling mid-skill service and manufacturing workers for AI-driven labor market change. Proposes an 'Adaptive Workforce Ecosystem' policy framework as a corrective, contributing to the policy-design side of the AI-jobs debate rather than new empirical measurement.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence, Education, and Economic Growth: Evaluating Government Strategies for Workforce Readiness and National Development in America",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20671667/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "This study assesses how changes in AI patent stock affect employment, wages, productivity, and labor cost shares. It compares pooled estimates with firm-specific effects for UniCredit and Zerynth in Italy, representing the banking services and industrial tech sectors, respectively. Using a firm-level panel dataset from 2005 to 2024, the study applies task-based and skill-biased technological change frameworks to analyze how exposure to Artificial Intelligence (AI), proxied by AI patent stock, affects labor-market dynamics in the Italian economy. Our empirical strategy used fixed-effects regressions. The results show a dual pattern of technological adjustment. In pooled models, AI-related innovation has a positive and statistically significant effect on labor-market outcomes. However, when firms are analyzed separately, the effects diverge. For UniCredit, AI patent stock reduces employment and labor cost shares while raising productivity growth, with wage effects remaining small or insignificant. On the other hand, for Zerynth, AI patent stock produces consistently negative and significant effects on employment, wages, and labor cost shares. Overall, the findings highlight strong task-level substitution but heterogeneous firm-level outcomes, underscoring the need for targeted reskilling and labor-augmenting innovation policies in Italy’s digital transition.",
   "authors": [
    "Ebrima K. Ceesay",
    "Mamadou Salieu Jallow",
    "Cosimo Magazzino",
    "Alasana Gitteh",
    "Baseedy Bojang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-30",
   "doi": "10.56529/mber.v5i1.591",
   "explainer": null,
   "id": "doi:10.56529/mber.v5i1.591",
   "institutions": [
    "University of Insubria",
    "West African Science Service Centre on Climate Change and Adapted Land Use",
    "Western Caspian University",
    "University of the Gambia"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.56529/mber.v5i1.591",
   "summary": "With a 2005-2024 firm panel for two Italian companies, a bank and an industrial tech firm, the study uses AI patent stock as an exposure proxy in fixed-effects regressions. Pooled estimates are positive, but separately the bank shows lower employment and labor cost shares with higher productivity growth, while the tech firm shows negative effects on employment, wages and labor cost shares.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence, Firm Heterogeneity, and Labor Market Adjustment: Evidence from Service and Industrial Tech Sectors in Italy",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-56529-mber-v5i1-591/",
   "venue": "Muslim Business and Economic Review",
   "year": 2026
  },
  {
   "abstract": "Abstract: Artificial intelligence (AI) is increasingly embedded in production, services, and workforce management. Although AI can raise productivity and output, its distributional effects are uncertain and mediated by institutions and access to complementary resources. This paper investigates how AI may widen capability inequality—inequalities in access to knowledge, digital infrastructure, computational resources, and organizational adoption—thereby shaping income opportunities and socio-economic security for low-income groups. Using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics (NSO/MoSPI GDP estimates, PLFS, HCES) and high-reliability sector evidence (Reuters; Nasscom), the analysis is structured across past, present, and future phases. Evidence indicates accelerating AI adoption among firms in advanced economies and persistent adoption gaps among groups, suggesting unequal access to AI-enabled productivity. OECD (2026) reported, Global frameworks warn that uneven readiness may produce a “Next Great Divergence” between countries. (UNDP, 2025), (WTO, 2025), For labor markets, refined exposure measures imply widespread task transformation rather than uniform job destruction, with accelerated skill change as a central risk for vulnerable workers. (ILO, 2025) India’s macro growth remains robust, yet labor-market segmentation and digital capability gaps create distributional vulnerabilities. (MoSPI–NSO, 2025) In addition, AI-driven efficiency pressures in IT services—an important mobility channel for Indian households—may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers. (Reuters, 2026a) The paper proposes a policy architecture for “shared gains” centered on learning equity, transition protections, accountable algorithmic management, and distribution-sensitive metrics beyond GDP. Keywords: Artifi",
   "authors": [
    "Shrikant Dattatraya Wagh",
    "Dr. Shyam Jivan Salunkhe"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-22",
   "doi": "10.5281/zenodo.20339287",
   "explainer": {
    "approach": "A political-economy framework applied to secondary sources from the OECD, ILO, UNDP, WTO and WEF, Indian official statistics, and sector reporting, organized across past, present and future phases.",
    "findings": "The sources point to faster AI adoption in advanced economies, persistent adoption gaps, and task change rather than uniform job loss; in India, efficiency pressure in IT services may compress billable work and change hiring and wages. The paper proposes policies for shared gains.",
    "limitations": "The paper synthesizes secondary sources and news reports rather than estimating effects; it does not present new data or causal identification.",
    "method_type": "review",
    "question": "Could AI widen inequality in access to knowledge, digital infrastructure and computing, and what would that mean for low-income groups and India's IT workforce?"
   },
   "id": "doi:10.5281/zenodo.20339287",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20339287",
   "summary": "Wagh and Salunkhe synthesize secondary evidence from OECD, ILO, UNDP, WTO, and Indian official statistics (NSO/MoSPI, PLFS, HCES) to argue that AI widens \"capability inequality\" — unequal access to knowledge, compute, digital infrastructure, and organizational adoption — with acute risks for India's segmented labor market and IT services workforce facing AI-driven efficiency pressures. The paper adds to warnings (echoing UNDP's \"Next Great Divergence\" framing) that AI's distributional effects run through institutions and complementary resources rather than technology per se, and that task transformation with accelerated skill change, not mass job destruction, is the central risk for vulnerable workers in emerging economies.",
   "theme": "Labor & Work",
   "title": "ARTIFICIAL INTELLIGENCE, INEQUALITIES OF KNOWLEDGE AND RESOURCES, AND SOCIO-ECONOMIC SECURITY: GLOBAL EVIDENCE WITH IMPLICATIONS FOR INDIA'S LABOR AND IT WORKFORCE",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20339287/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) is a transformational technology with important implications for productivity and economic growth. This paper presents a systematic narrative review of studies published between 2020 and 2026 following the PRISMA 2020 framework to examine the relationship between AI adoption and productivity and growth outcomes. Based on a larger pool of 37 relevant publications, 28 studies were selected and reviewed, with a synthesis of evidence on labour productivity, total factor productivity, labour market reallocation and macroeconomic growth in advanced, emerging and developing economies. The results show that AI creates large productivity gains, but the magnitude of the productivity gain is determined by the digital infrastructure, the human capital, the quality of institutions, and the AI-human complementarity. The firm-level effects are large, but the aggregate growth effects are moderate. The paper develops the Adaptive Diffusion-Complementarity (ADC) Framework to account for these dynamics, and highlights the importance of productivity, labor reallocation and structural transformation in mediating the growth effects of AI. The study highlights the need for supportive policies to maximize the economic benefits of AI.",
   "authors": [
    "Jainendra Kumar Verma",
    "Kamal De Krishna"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.33545/26179210.2026.v9.i6.871",
   "explainer": {
    "approach": "A systematic narrative review following PRISMA 2020 of 28 studies published from 2020 to 2026, selected from 37 relevant publications.",
    "findings": "Firm-level productivity effects are reported as large while aggregate growth effects are moderate, with the size of gains depending on digital infrastructure, human capital, institutions and how well AI complements workers. The paper proposes an Adaptive Diffusion-Complementarity framework.",
    "limitations": "The review covers a small set of 28 studies, and the abstract gives no pooled effect sizes.",
    "method_type": "review",
    "question": "How does AI adoption relate to productivity and economic growth across advanced, emerging and developing economies?"
   },
   "id": "doi:10.33545/26179210.2026.v9.i6.871",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.33545/26179210.2026.v9.i6.871",
   "summary": "A PRISMA-guided narrative review of 28 studies (2020-2026) concludes AI generates substantial firm-level productivity gains, but the aggregate effect on macroeconomic growth is moderate and strongly conditioned by digital infrastructure, human capital, and institutional quality. The authors propose an 'Adaptive Diffusion-Complementarity' framework to explain why large micro-level gains don't automatically translate into large macro-level growth.",
   "theme": "Growth & Macro",
   "title": "Artificial intelligence, productivity, and economic growth: Global evidence and emerging policy implications",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-33545-26179210-2026-v9-i6-871/",
   "venue": "International Journal of Financial Management and Economics",
   "year": 2026
  },
  {
   "abstract": "The first three chapters of the PhD dissertation analyze the labor-market consequences of AI in the United States and Europe. Chapter four investigates the predictors of cross-border employment and its labor-market impact on returnees. Chapter one examines the effects of AI on the U.S. labor market from 2015 to 2022, distinguishing between automation AI—technologies that substitute for labor—and augmentation AI—technologies that enhance workers' output. I construct novel longitudinal measures of occupational exposure to each type of AI by mapping developer activity on Stack Overflow to occupational descriptions. I also introduce a new measure of emerging work based on successive updates of job titles in O*NET. Using an instrumental-variables strategy based on lagged computer science research intensity, I find that augmentation AI stimulates the creation of new work. Automation AI increases employment but depresses wages. These effects vary across skill groups: automation AI harms low-skilled workers, while augmentation AI raises wages and generates new work primarily for high-skilled occupations. In chapter two, we use 75 million online job postings from four European countries over the 2018–2023 period to investigate how skill and occupational demand have shifted in response to AI. Our analysis leverages data science methods to extract and classify skills from job postings across multiple languages. Instrumenting AI exposure with lagged research intensity in computer science, we document significant growth in demand for AI, Data, and Prediction skills within exposed occupations, alongside a decline in Social skills. Guided by our theoretical framework, the results are consistent with the interpretation that AI, Data, and Prediction are AI-exposed skills, while Judgment, Decision-Making, and Leadership are complementary, and Social skills are substituted. We further document an increased co-occurrence of AI-exposed and complementary skill bundles. Our findings indic",
   "authors": [
    "David Marguerit"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-16",
   "doi": null,
   "explainer": null,
   "id": "title:2befea78d9085a1a",
   "institutions": [
    "University of Luxembourg"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://orbilu.uni.lu/handle/10993/68579",
   "summary": "A PhD dissertation on AI and labor markets. Using Stack Overflow-based exposure measures and an instrument based on lagged computer-science research, it finds in the U.S. (2015-2022) that augmentation AI creates new work while automation AI raises employment but depresses wages, hurting low-skilled workers; a second chapter analyzes 75 million European job postings for shifts in skill demand.",
   "theme": "Labor & Work",
   "title": "Artificial Intelligence, Skills, and Labor Mobility: Understanding the Transformation of Work",
   "url": "https://wemustactnow.onrender.com/research/p/title-2befea78d9085a1a/",
   "venue": "Open Repository and Bibliography (University of Luxembourg)",
   "year": 2026
  },
  {
   "abstract": "This paper explores whether artificial intelligence (AI) enhances human cognition or replaces it. It analyzes AI’s impact on education, healthcare, industry, and the global labor market. Results show that AI significantly improves human decision-making and problem-solving abilities, while also raising concerns such as job displacement, ethical risks, and digital dependency. The study concludes that AI should be developed as a tool to augment human intelligence rather than replace it.",
   "authors": [
    "Dildora Aliqulova"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-12",
   "doi": "10.66301/jusr/vol4_issm/art93",
   "explainer": null,
   "id": "doi:10.66301/jusr/vol4_issm/art93",
   "institutions": [
    "Kimyo International University in Tashkent"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.66301/jusr/vol4_issm/art93",
   "summary": null,
   "theme": "Labor & Work",
   "title": "ARTIFICIAL INTELLIGENCE: WILL IT AMPLIFY HUMAN INTELLIGENCE OR REPLACE IT?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-66301-jusr-vol4-issm-art93/",
   "venue": "Journal of Universal Science Research",
   "year": 2026
  },
  {
   "abstract": "This paper explores whether artificial intelligence (AI) enhances human cognition or replaces it. It analyzes AI's impact on education, healthcare, industry, and the global labor market. Results show that AI significantly improves human decision-making and problem-solving abilities, while also raising concerns such as job displacement, ethical risks, and digital dependency. The study concludes that AI should be developed as a tool to augment human intelligence rather than replace it.",
   "authors": [
    "Dildora Aliqulova"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-13",
   "doi": "10.66301/f96jjf26",
   "explainer": null,
   "id": "doi:10.66301/f96jjf26",
   "institutions": [
    "Bose (United States)"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.66301/f96jjf26",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Artificial Intelligence: Will it Enhance or Replace Human Mind?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-66301-f96jjf26/",
   "venue": "Journal of Universal Science Research",
   "year": 2026
  },
  {
   "abstract": "Key takeaways: Overview The adoption of advanced artificial intelligence tools in the workplace has led to widespread concerns about the degradation of work and potential job displacements. While research is mixed on AI’s current labor market impacts, U.S. policymakers should prepare for a range of scenarios that account for the ways in which AI could evolve. Part […] The post Assessing proposed legislation to expand Unemployment Insurance to protect U.S. workers amid AI-related disruptions appeared first on Equitable Growth .",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-23",
   "doi": null,
   "explainer": null,
   "id": "title:8c860df46c9c4b81",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://equitablegrowth.org/assessing-proposed-legislation-to-expand-unemployment-insurance-to-protect-u-s-workers-amid-ai-related-disruptions/",
   "summary": "This Equitable Growth brief assesses proposed U.S. legislation to expand Unemployment Insurance for workers displaced by AI. It notes that research on AI's current labor-market effects is mixed and argues policymakers should prepare for a range of scenarios.",
   "theme": "Labor & Work",
   "title": "Assessing proposed legislation to expand Unemployment Insurance to protect U.S. workers amid AI-related disruptions",
   "url": "https://wemustactnow.onrender.com/research/p/title-8c860df46c9c4b81/",
   "venue": "Washington Center for Equitable Growth (institute research)",
   "year": 2026
  },
  {
   "abstract": "Abstract As artificial intelligence (AI) becomes increasingly integrated into economic activity, its implications for development in sub-Saharan Africa (SSA) require greater empirical attention to country-level differences in productive and institutional capacity. This study examines the relationship between AI and economic growth across 36 SSA countries from 2010 to 2022 using a two-step system generalized method of moments estimator. The results show a positive and significant relationship between AI and economic growth. Mediation analysis indicates that industrial development is an important transmission mechanism, with approximately 23.6% of the total AI–growth effect operating indirectly through industrial development. The results also show that regulatory quality conditions the AI–growth relationship, as the positive interaction between AI and regulatory quality indicates a stronger growth relationship at higher levels of regulatory quality. Heterogeneity analysis reveals no significant AI–growth relationship among low-income countries, whereas the relationship is positive and significant among both lower-middle- and upper-middle-income economies. The study therefore contributes to the emerging macroeconomic literature on AI in SSA by identifying industrial development as a transmission mechanism and regulatory quality as a conditioning factor, while highlighting substantial heterogeneity across income groups. The findings support development strategies that strengthen productive capabilities, institutional quality, and complementary infrastructure alongside AI-related investment.",
   "authors": [
    "Emmanuel Baffour Gyau",
    "Jodian J. Blake",
    "Seidu Musah",
    "Stephen Dokroug Kolog"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-25",
   "doi": "10.1186/s43093-026-01033-y",
   "explainer": null,
   "id": "doi:10.1186/s43093-026-01033-y",
   "institutions": [
    "Jiangsu University",
    "American College of Education"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.1186/s43093-026-01033-y",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Assessing the dynamic role of artificial intelligence in driving economic growth in sub-Saharan Africa",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1186-s43093-026-01033-y/",
   "venue": "Future Business Journal",
   "year": 2026
  },
  {
   "abstract": "We characterize optimal labor and capital income taxation in a task-based model of automation. Workers and machines are perfect substitutes in automatable tasks, which run from the bottom of the skill distribution up to a threshold. Workers supply labor on the extensive margin. Capitalists supply",
   "authors": [
    "Henrik Kleven",
    "Owen M. Zidar"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "September 2026",
   "doi": null,
   "explainer": null,
   "id": "title:c6436cc68d062ce5",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35747",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Automation and Optimal Taxation: A Task-Based Theory",
   "url": "https://wemustactnow.onrender.com/research/p/title-c6436cc68d062ce5/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "We consider a model of automation embedded in a political environment where workers can undertake a revolt (modeled as a global game), and greater inequality between capital and labor increases the likelihood of a revolt. Decentralized automation decisions raise the share of capital in national",
   "authors": [
    "Daron Acemoglu",
    "A. Arda Gitmez",
    "Mehdi Shadmehr"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "June 2026",
   "doi": null,
   "explainer": null,
   "id": "title:e53e3167f9b3fbf3",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35336",
   "summary": "A theoretical NBER model in which automation raises capital's share of national income and so the risk of a worker revolt, modelled as a global game. It studies how decentralised automation decisions interact with that political risk.",
   "theme": "Labor & Work",
   "title": "Automation and Repression",
   "url": "https://wemustactnow.onrender.com/research/p/title-e53e3167f9b3fbf3/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Note: the modeling assumptions and conclusion are Thomas Kwa’s opinion, and others at METR disagree. 1 Also, the math was checked by Claude but not a second human. Introduction Anthropic’s RSI blog post reported that in Q2 2026, Anthropic contributors merged 8× as much code per day as in the 2021-2024 period. What does this imply about the factor by which a researcher’s total effective output increased — the (serial) researcher uplift 2 ? Of course, 8× more code doesn’t mean 8× more research, as coding is only part of the job. However, if we assume each line of code (LoC) has equivalent quality and verbosity to pre-2025 code and make standard economic modeling assumptions, we can conclude a surprising amount: all models predict researcher uplift at Anthropic from coding agents alone is >2×. (Researcher uplift could be even higher, because these numbers assume no uplift on non-code tasks.) Cobb-Douglas predicts that if pre-AI time spent coding is \\(\\beta=50\\%\\) and code output increases by a factor \\(M=8\\), then researcher uplift is \\(M^\\beta = 2.83\\). CES (constant elasticity of substitution) production functions, due to a fun mathematical coincidence, infer a narrow range of about \\([2.75, 2.91]\\) if code is homogeneous. If code is CES but non-homogeneous, such that AI speeds up low-stakes code more than high-stakes code, we obtain a range of \\([2.33, 2.66]\\), lower but still over 2x. However, there are several reasons new code may not be equivalent to old code, which would be at least partially resolvable with internal Anthropic data. Verbosity: \\(M\\) substantially overstates true quality-adjusted code output (e.g. perhaps AI code is >2x more verbose than human code for the same functionality). Barely-useful code: AIs are speeding up people enormously (>20x) on low-stakes code that would never have been written pre-AI, but is only 5%-20% as valuable as normal code. Researcher irrationality: Anthropic researchers are producing code that doesn’t contribute to resear",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-07-08",
   "doi": null,
   "explainer": null,
   "id": "title:cecb7021d4a12286",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/notes/2026-07-08-anthropic-researcher-uplift/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Because 8 ≈ e², Anthropic's researcher uplift is plausibly >2x",
   "url": "https://wemustactnow.onrender.com/research/p/title-cecb7021d4a12286/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Scientific and technological progress has long driven economic growth and led to dramatic improvements in living standards around the world. In his 2025 Nobel lecture , economic historian Joel Mokyr attributed this to a “positive feedback loop”: new technology drives waves of scientific advances that, in turn, fuel further innovation and progress. But he also warns that sustaining this positive loop is never guaranteed. Recent history illustrates Mokyr’s point. The U.S. and E.U. entered the 1990s with relatively similar economic growth rates, but the two have since diverged substantially. The U.S. maintained roughly 2% annual increases in output per capita , largely powered by successive science and technology booms and the broad diffusion of innovation across the economy, while the E.U. has mostly stagnated . Today, the type of scientific progress that drives growth faces significant headwinds. Across semiconductor physics, agriculture, and pharmaceutical R&D, researchers have documented a decrease in productivity that suggests ideas may be “getting harder to find.” Sustaining historical rates of innovation now requires exponentially larger research teams and capital expenditure. Decades of accumulated knowledge have led scientists into narrower specializations, longer paths to PhDs, and prohibitive coordination costs for achieving breakthrough innovations across teams. Overcoming these hurdles is critical because there are still so many open questions, along with many questions not yet asked, whose answers are key for unlocking new possibilities for human progress. There is widespread optimism that artificial intelligence could help counter these headwinds, fulfilling a promise of computational science that researchers have pursued for more than 70 years . The journey began in the 1950s when Alan Turing and John von Neumann first applied foundational concepts of computation to the natural sciences. In the decades that followed, the field progressed from early symb",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-08",
   "doi": null,
   "explainer": null,
   "id": "title:e02ff97271cb9942",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://institute.deepmind.com/essays/bending-the-curve-of-discovery-ai-in-science-today-and-tomorrow/",
   "summary": "A DeepMind Institute essay on AI in scientific discovery. It points to evidence that research productivity has been falling, with ideas getting harder to find, and argues that AI could help restore the science-driven innovation that underpins long-run economic growth.",
   "theme": "Productivity & Firms",
   "title": "Bending the Curve: How AI is Transforming Scientific Discovery",
   "url": "https://wemustactnow.onrender.com/research/p/title-e02ff97271cb9942/",
   "venue": "Google DeepMind Institute (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "The ongoing advancement and adoption of artificial intelligence continues to raise concerns about widespread job losses. Over the past three years, our regional business surveys have asked firms about their AI adoption and its effects on their workforces. This year, we found that AI use among regional businesses has continued to rise sharply, with more than 60 percent of service firms and about half of manufacturers now using AI—a notable increase from 40 percent and 26 percent, respectively, reported in 2025. Despite this rapid adoption, regional firms’ investments in AI are generally modest, usage tends to be concentrated among a small share of workers within firms, and layoffs have remained uncommon. And, while some firms have scaled back hiring due to AI, others have added workers to help them use it. Retraining employees in response to AI remains the primary way firms are adjusting their workforces.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-01",
   "doi": null,
   "explainer": null,
   "id": "title:4a0c20df2764c863",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://libertystreeteconomics.newyorkfed.org/2026/09/businesses-are-using-ai-to-transform-work-not-cut-jobs/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Businesses Are Using AI to Transform Work, Not Cut Jobs",
   "url": "https://wemustactnow.onrender.com/research/p/title-4a0c20df2764c863/",
   "venue": "New York Fed Liberty Street Economics (institute research)",
   "year": 2026
  },
  {
   "abstract": "Workers sometimes enjoy productive tasks and voluntarily devote unpaid time to them. We study O-ring jobs in which firms can either price a complete task bundle or specify paid task floors while workers remain free to add time. For any allocation supported by both hourly contracts, the wage bill is",
   "authors": [
    "Joshua S. Gans"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "June 2026",
   "doi": null,
   "explainer": {
    "approach": "A theoretical model of O-ring jobs in which firms either price a complete task bundle or set paid task floors while workers may add time voluntarily.",
    "findings": "The abstract shows the model compares the wage bill under the two hourly contract types; the full results are not given in the emitted abstract.",
    "limitations": "The emitted abstract is cut short, so the paper's conclusions about automation cannot be summarised from it; the work is theoretical.",
    "method_type": "theory",
    "question": "How do contracts and automation choices change when workers enjoy some of their tasks and give them unpaid time?"
   },
   "id": "title:c3c3f6ce69bbb802",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35309",
   "summary": "Gans builds a contracting model of \"O-ring\" jobs where workers intrinsically enjoy certain tasks and voluntarily contribute unpaid hours, comparing complete task-bundle pricing against paid task floors that let workers top up their own time. The framework shows how automation interacts with task enjoyment in determining which contract form minimizes the wage bill and which tasks firms should automate. It matters for the AI debate because it formalizes a neglected margin: when AI automates tasks workers actually like, firms lose a source of free effort, so the labor-market effects of automation depend on task-level preferences, not just marginal products.",
   "theme": "Labor & Work",
   "title": "But I Like Doing This! Enjoyable Tasks, Contracting, and Automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-c3c3f6ce69bbb802/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Advances in large language models have raised the possibility of automating large swaths of work. But many jobs are physical. AI’s impact on the economy will in part depend on robotics.¹ Predicting the pace of robot advances is difficult, but enumerating capabilities today, we argue, can give insight into the coming years. We develop a measure of job exposure to robots, using Claude to assess how well present-day robots can perform work tasks. A job is more exposed when robots can do more of its tasks in less controlled environments. A robot is cost-competitive when it can do the same task for cheaper than a human worker. We find that robots can already perform 74% of physical tasks in the US, making up 34% of working hours. Robots and LLMs together expose all but one-fifth of employment. But we also find significant barriers to adoption: most robots require highly structured environments, and are cost-competitive with people for just 0.3% of work. If robot price declines follow past trends, it will take 40 years for that share to reach just 10%. After cost, the main barrier is capability, such as the dexterity needed to untangle wires. Human preferences and regulations further limit robot adoption for a significant share of tasks. Our core premise is that jobs are more likely to be impacted when robots can already do their work today. A backtest across 50 years validates this approach: from 1977 to today, jobs that were more exposed to existing robots experienced wage and employment declines in later decades. If the past is any guide, taxi drivers and warehouse packers will see changes sooner than nurses and mechanics. We expect that physical work will first be automated where robots have a foothold today. Most robots today operate in controlled environments like factories. Robots usually need to be programmed to interact with the physical world, whereas humans can adapt to their work environments. This has been a major hurdle for commercially viable robots.² But A",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-30",
   "doi": null,
   "explainer": null,
   "id": "title:c160b75c673f0bef",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/what-work-can-robots-do",
   "summary": "Anthropic builds a measure of job exposure to robots and finds robots can already perform 74% of physical tasks in the US (34% of working hours), but are cost-competitive with people for only 0.3% of work. If past price declines continue, that share would take 40 years to reach 10%; a 50-year backtest links robot exposure to later wage and employment declines.",
   "theme": "Labor & Work",
   "title": "Can we predict the jobs robots will do?",
   "url": "https://wemustactnow.onrender.com/research/p/title-c160b75c673f0bef/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Using a new firm-level measure of AI investment based on AI-skilled employmentspanning machine learning through generative and agentic AIwe show that AI investments are associated with productivity growth in recent years, but not over the previous decade. We trace the productivity gains to the",
   "authors": [
    "Tania Babina",
    "Alex X. He",
    "Renhao Jiang"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "August 2026",
   "doi": null,
   "explainer": null,
   "id": "title:145583735c0b2364",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35684",
   "summary": "Using a firm-level measure of AI investment based on AI-skilled employment, this NBER paper finds that AI investments are associated with productivity growth in recent years but not over the previous decade, and traces the gains to the build-up of organization capital.",
   "theme": "Labor & Work",
   "title": "Canaries in the Gold Mine: Early Productivity Gains from Artificial Intelligence Creating Organization Capital",
   "url": "https://wemustactnow.onrender.com/research/p/title-145583735c0b2364/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Production is a sequence of steps that can be executed (1) manually, (2) augmented with AI, or (3) fully automated within contiguous AI-executed steps called ''chains.'' Firms optimally bundle steps into tasks and then jobs, trading off specialization gains against coordination costs. We characterize the optimal assignment of humans and AI to steps and the firm's resulting job structure, showing that comparative advantage logic can fail with AI chaining. The model implies non-linear productivity gains from AI quality improvements and admits a CES representation at the macro level. Empirical evidence supports the model's key predictions that (1) AI-executed steps co-occur in chains, (2) dispersion of AI-exposed steps lowers AI execution at the job level, and (3) adjacency to AI-executed steps increases the likelihood that a step is AI-executed.",
   "authors": [
    "Mert Demirer",
    "John J. Horton",
    "Nicole Immorlica",
    "Brendan Lucier",
    "Peyman Shahidi"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-14",
   "doi": null,
   "explainer": null,
   "id": "title:daa71d8fc2d188be",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.15960v1",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Chaining Tasks, Redefining Work: A Theory of AI Automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-daa71d8fc2d188be/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Confirmatory test, on an unanalysed week of the Anthropic Economic Index (August 2025), of whether work tasks with more codifiable knowledge are more often delegated to generative AI than iterated on. Pre-registered before analysis. Author: Alberto García-Lluis Valencia (URJC).",
   "authors": [
    "Alberto García-Lluis Valencia"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-24",
   "doi": "10.17605/osf.io/ch67j",
   "explainer": null,
   "id": "doi:10.17605/osf.io/ch67j",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.17605/osf.io/ch67j",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Codifiability and the mode of generative AI use at work",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17605-osf-io-ch67j/",
   "venue": "Open Science Framework",
   "year": 2026
  },
  {
   "abstract": "ABSTRACT The rise of large language models (LLMs) is transforming the trajectory of traditional rule‐based automation, while their global impact on the labor market remains largely unexplored. To assess this transition, we develop a novel bottom‐up framework linking detailed task data to occupational structures across a broad spectrum of economies. Our findings reveal that LLM‐driven automation disproportionately impacts roles centered on information processing, administration, and managerial coordination compared to those in physical or manual domains. At the sectoral level, this impact translates into higher exposure for knowledge‐intensive sectors like finance, education, and professional services, while sectors like agriculture and manufacturing remain more insulated. Because the industrial structure strongly determines a nation's vulnerability, economies reliant on administrative and clerical activities are facing greater exposure. Critically, potential productivity gains from this exposure are concentrated in areas contributing significantly to economic value rather than employment, highlighting a tension between efficiency and equitable labor outcomes. We argue that proactive policies, focusing on fair transitions, skill adaptation, and strategic industrial development, are crucial to avoid this technological shift undermining the sustainable development goals.",
   "authors": [
    "Yuanfan Li",
    "Rongrong Li",
    "Qiang Wang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-02",
   "doi": "10.1002/sd.71238",
   "explainer": null,
   "id": "doi:10.1002/sd.71238",
   "institutions": [
    "China University of Petroleum, East China",
    "Saint Petersburg State University of Economics"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1002/sd.71238",
   "summary": "A bottom-up, task-level framework linking LLM capabilities to occupational structures across countries finds LLM-driven automation disproportionately exposes information-processing, administrative, and managerial roles, concentrating risk in knowledge-intensive sectors like finance and education while leaving agriculture and manufacturing comparatively insulated. It highlights a tension for sustainable-development policy: the sectors gaining the most economic value from AI are not the same as the sectors employing the most people.",
   "theme": "Labor & Work",
   "title": "Cognitive Automation and Sustainable Development: Global Task‐Level Evidence on Large Language Models and Labor Markets",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1002-sd-71238/",
   "venue": "Sustainable Development",
   "year": 2026
  },
  {
   "abstract": "We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition",
   "authors": [
    "Maryam Farboodi",
    "Andrew J. Koh",
    "Anchi Xia"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "June 2026",
   "doi": null,
   "explainer": {
    "approach": "The authors build a dynamic model in which data is heterogeneous and task-specific, accumulates endogenously as a byproduct of economic activity, and spills over so that data generated by one task can raise productivity in another.",
    "findings": "The available abstract describes the model's structure and its analysis of the transition path, but it is cut off before stating the results.",
    "limitations": "This is a theoretical model; its conclusions depend on its assumptions about how data accumulates and spills over, and the available abstract does not report the results.",
    "method_type": "theory",
    "question": "How does data, as an input that builds up from economic activity, shape the automation of tasks over time?"
   },
   "id": "title:311c8a72d5698635",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35320",
   "summary": "This NBER paper builds a dynamic model of automation where task-specific data accumulates endogenously as a byproduct of economic activity and generates cross-task spillovers that raise productivity elsewhere. It matters as a theoretical foundation for treating data — rather than just compute or algorithms — as the accumulable capital driving automation dynamics, with implications for how automation propagates unevenly across tasks and for data policy.",
   "theme": "Productivity & Firms",
   "title": "Data-Driven Automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-311c8a72d5698635/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "This article examines the real-world experience and results of the strategic implementation of the generative artificial intelligence (GenAI) tool \"GitHub Copilot\" into the development ecosystem of Duolingo – the world's leading educational platform with a global audience of over 500 million users [1]. Amid rapid business scaling, the company faced a critical need to optimize code writing processes. The paper details a comprehensive approach to overcoming cognitive friction and workflow fragmentation that inevitably arose from the evolutionary technological transition of the architecture from 3 large monolithic repositories to a complex distributed network of over 400 microservices [1; 2]. Such decentralization significantly complicated codebase navigation and increased engineer onboarding time. To evaluate the effectiveness of the AI assistant's integration, a multidimensional methodology was applied in the study. Based on an in-depth combined analysis of objective quantitative GitHub ecosystem metrics (specifically pull request processing velocity) and subjective qualitative indicators measured using the \"SPACE\" framework (satisfaction, performance, activity, communication, efficiency) [6], the positive impact of the innovation is empirically proven. The results demonstrate that the systematic use of AI assistants in close combination with modern cloud development environments (such as GitHub Codespaces) provides a substantial increase in engineering velocity: 25% among developers working in new or unfamiliar repositories, and a consistent 10% in a familiar environment [1]. Furthermore, the article conceptually shows that GenAI tools today go beyond simple autocompletion. They act as a powerful systemic multiplier of team expertise, reduce cognitive load, and serve as an effective means of maintaining mental flow state while solving complex algorithmic tasks [3; 7; 8]. The findings create a scientific and practical foundation for rethinking the role of artificial ",
   "authors": [
    "Solomiya Ohinok",
    "Андрій Лесюк"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-23",
   "doi": "10.32782/2312-7872.3.2026.18",
   "explainer": null,
   "id": "doi:10.32782/2312-7872.3.2026.18",
   "institutions": [
    "Lviv University",
    "Lviv Polytechnic National University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.32782/2312-7872.3.2026.18",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "DEVELOPER PRODUCTIVITY IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MICROSERVICES: A CASE STUDY OF GITHUB COPILOT INTEGRATION AT DUOLINGO",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-32782-2312-7872-3-2026-18/",
   "venue": "Economics and Management",
   "year": 2026
  },
  {
   "abstract": "Micro, Small, and Medium Enterprises (MSMEs) in Indonesia face persistent challenges in improving labor productivity amid rapid digital transformation and the expansion of artificial intelligence (AI). Although digitalization has increased substantially, its productivity effects remain uneven across provinces and are influenced by institutional, cultural, and religious contexts. Grounded in Ibn Khaldun’s concepts of asabiyyah and tadbir al-‘umran, this study examines the relationship between digitalization, AI adoption, and MSME labor productivity while assessing the moderating role of religiosity. The study employs an explanatory quantitative approach using provincial-level panel data during 2020–2023. The primary estimation uses a fixed-effects model with robust standard errors, complemented by exploratory analyses using Double Machine Learning, Causal Forest, and XGBoost. The findings show that digitalization has a positive and significant effect on labor productivity, whereas AI adoption has not yet generated measurable productivity gains. Religiosity marginally weakens the relationship between digitalization and productivity, indicating transitional adaptation frictions within highly normative environments rather than resistance to technology. These findings suggest that sustainable productivity gains depend not only on technological adoption, but also on institutional capacity, ethical governance, and collective learning mechanisms. The study contributes to the digital transformation literature by extending Ibn Khaldun’s institutional perspective and offers policy insights for strengthening sustainable MSME digital ecosystems in Indonesia. ABSTRAK - Digitalisasi, Adopsi AI, dan Produktivitas UMKM: Perspektif Ibn Khaldun. Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia menghadapi tantangan dalam meningkatkan produktivitas tenaga kerja di tengah percepatan transformasi digital dan perkembangan kecerdasan buatan (artificial intelligence/AI). Meskipun digital",
   "authors": [
    "Feriandy Feriandy"
   ],
   "cited_by": 1,
   "collected_from": "openalex",
   "date": "2026-06-03",
   "doi": "10.22373/share.0060",
   "explainer": null,
   "id": "doi:10.22373/share.0060",
   "institutions": [
    "Universitas Nusa Bangsa"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.22373/share.0060",
   "summary": "Using Indonesian provincial panel data for 2020-2023 and fixed-effects models, the study finds digitalization has a positive, significant effect on small-business labor productivity while AI adoption shows no measurable productivity gain yet. Religiosity marginally weakens the digitalization-productivity link, which the authors read as transitional adaptation friction.",
   "theme": "Policy & Governance",
   "title": "Digitalization, AI Adoption, and MSME Productivity: An Ibn Khaldunian Perspective",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-22373-share-0060/",
   "venue": "Share Jurnal Ekonomi dan Keuangan Islam",
   "year": 2026
  },
  {
   "abstract": "This replication package accompanies the paper “Does Artificial Intelligence Shape Within-Firm Wage Inequality?”, which studies how the adoption of artificial intelligence (AI) affects wage inequality inside Chinese firms. The central research hypothesis is that AI adoption changes a firm’s internal labor demand and pay structure, and that this change narrows the wage gap between management and ordinary employees. More specifically, the paper hypothesizes that AI adoption reduces within-firm wage inequality primarily by shifting labor demand toward high-skilled workers, thereby altering the relative compensation of different employee groups. The analysis is based on panel data on Chinese A-share listed companies from 2007 to 2023. The main dependent variable is a firm-level pay gap, defined as the absolute difference between average management compensation and average ordinary-employee compensation. A smaller value therefore indicates lower within-firm wage inequality. The key independent variable is lnai, the log of AI-related keyword frequency in firms’ annual reports. This is a text-based measure of AI application: firms that discuss AI more intensively in their annual reports are interpreted as having greater AI adoption or AI-related activity. The DID treatment variable is treat_post, the interaction between treatment status and the post-policy period. The policy context is the National AI Innovation Application Pilot Zones (NAAPZ), using official lists from the Ministry. The DID design compares firms in pilot zones before and after the policy with firms not affected by the policy, conditional on fixed effects and controls. The empirical analysis is organized around several tables and Stata do-files. Table 1 reports descriptive statistics for the main variables. Table 2 presents baseline OLS regressions. Column (1) includes industry and year fixed effects; column (2) includes firm and year fixed effects. Table 3 reports the main DID results. Table 4 presents th",
   "authors": [
    "Ao Li"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-12",
   "doi": "10.17632/gjb2hhc4w3",
   "explainer": null,
   "id": "doi:10.17632/gjb2hhc4w3",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.17632/gjb2hhc4w3",
   "summary": "A replication package for a study of AI adoption and within-firm pay gaps in Chinese listed firms from 2007 to 2023, measuring AI use from annual-report keywords and using national AI pilot zones in a difference-in-differences design. The paper's hypothesis is that AI narrows the gap between management and ordinary-employee pay by shifting labour demand toward high-skilled workers.",
   "theme": "Labor & Work",
   "title": "Does Artificial Intelligence Shape Within-Firm Wage Inequality?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17632-gjb2hhc4w3/",
   "venue": "Mendeley Data",
   "year": 2026
  },
  {
   "abstract": "Forecasts of the productivity dividend from artificial intelligence (AI) differ by an order of magnitude, partly because they extrapolate average gains observed among early, highly exposed adopters. Whether those gains scale linearly, flatten, or are competed away is an empirical question about the shape of the return to AI, not its average. This paper develops and validates a design-based framework for measuring that shape with linked firm–worker data. The framework combines a pre-determined, occupation-based measure of firm exposure to generative AI with a continuous-treatment difference-in-differences design around the release of ChatGPT in November 2022, three pre-specified tests of concavity, a decomposition of exposure effects into an adoption margin and a per-adopter return, a doubly robust analysis of use intensity, a staggered-adoption analysis of earlier AI adopters, and a production-function estimator that places AI in the law of motion of productivity. We validate every component on a calibrated synthetic panel of 20,000 enterprises that reproduces the structure of Statistics Canada's linked business microdata and embeds a known concave effect. The estimators recover the true dose–response curve and detect its concavity (slope difference −0.010, standard error 0.003, one-sided p = 0.002); Monte Carlo coverage of the main confidence interval is 0.97, and the concavity test has power of 0.93. The validation also exposes a practical pitfall: pre-trend tests clustered at roughly 40 industries over-reject a true null. The framework, released as open code, is ready for application to Canadian enterprise microdata, where it will inform estimates of potential output and the persistence of Canada's productivity gap.",
   "authors": [
    "Louis Agyekum"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-13",
   "doi": "10.66977/xsci.2610.000y",
   "explainer": null,
   "id": "doi:10.66977/xsci.2610.000y",
   "institutions": [
    "University of Ottawa"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.66977/xsci.2610.000y",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Does the AI Productivity Dividend Diminish? A Validated Framework for Identifying the Shape of Firm-Level Returns to Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-66977-xsci-2610-000y/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "The rapid spread of advanced AI, especially large language models, has renewed the debate over who benefits from technological change. This paper examines whether the current wave of AI adoption is widening economic inequality. To answer it, I pull together recent empirical work from labour economics, macroeconomics, industrial organization, and development economics, focusing specifically on frontier AI systems used across labour markets, firms, financial markets, and national economies. The evidence base here draws on the National Bureau of Economic Research, the IMF, Stanford's Digital Economy Lab, MIT Sloan, PwC's Global AI Jobs Barometer, UNCTAD, Oxfam International, the International AI Safety Report 2026, and a handful of other academic and policy sources. Rather than treating \"inequality\" as single number, I break it down into several moving parts: wage polarization, income inequality, labour's share of national income, capital accumulation, market concentration, wealth concentration, and the growing gap in AI capability between rich and poor countries. I also conducted a cross-country study using the most recent data from the World Bank World Development Indicators for 131 countries for which data were available for both indicators (aggregates and regional groupings excluded), covering research and development expenditure (percent of GDP) and the Gini index. What emerges is a mixed but fairly consistent picture. The evidence is a mixed bag at different levels of the economy. At the firm level, generative artificial intelligence can increase worker productivity and, in some settings, narrow the performance gap between novices and experts. At the same time, skills, capital, profits and computing capacity related to AI are becoming concentrated in certain workers, firms and nations. At the worker level, however, the evidence is mixed with some studies finding productivity gains for less-experienced workers and others finding growing demand for AI-related skill",
   "authors": [
    "Arasmita Mondal"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.5281/zenodo.23692964",
   "explainer": {
    "approach": "Synthesises recent work from labour economics, macroeconomics and development economics, splitting inequality into wages, income, labour share, market and wealth concentration, and the gap between countries, plus a cross-country comparison of R&D spending and the Gini index for 131 countries.",
    "findings": "The picture is mixed: generative AI can raise productivity and narrow novice-expert gaps within firms, while AI-related skills, capital, profits and computing capacity concentrate in certain workers, firms and nations.",
    "limitations": "A single-author synthesis; the cross-country analysis uses R&D spending rather than a direct measure of AI adoption.",
    "method_type": "review",
    "question": "Is the current wave of AI adoption widening economic inequality?"
   },
   "id": "doi:10.5281/zenodo.23692964",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23692964",
   "summary": "A review of recent evidence on whether frontier AI widens inequality, breaking inequality into wage polarization, labour share, capital, market and wealth concentration and cross-country capability gaps, plus a cross-country look at R&D spending and the Gini index for 131 countries. It finds a mixed picture: firm-level productivity gains that can narrow novice-expert gaps alongside concentration of AI skills, capital and compute in certain workers, firms and nations.",
   "theme": "Labor & Work",
   "title": "Does the Rise of Artificial Intelligence Widen Economic Inequality?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23692964/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "We examine the structural transformation of creative industries under generative artificial intelligence, drawing on 374 primary sources spanning policy documents, industry data, creator surveys, and platform analytics. Beginning with the December 2024 release of OpenAI's Sora video model as a watershed event, we trace the historical pattern of creative resistance to technological disruption, then develop an analytical framework -- the Human-AI Agency Continuum for mapping the spectrum of human and machine collaboration in creative work. We present evidence for the \"slop ceiling,\" an audience-imposed quality threshold that constrains AI-generated content to approximately 1--3% of platform streams despite comprising 44% of uploads. Analysis of the UK Government's 2025 consultation on AI and copyright (over 11,500 responses, 88% opposing expanded AI training rights) reveals deep structural tensions between technology firms and creative workers. We investigate how major studios, from Disney's $1 billion OpenAI investment to Netflix's AI-native animation unit, are positioning for an AI-augmented production pipeline. The work covers coordination collapse in creative supply chains, the emergence of new professional roles such as prompt engineers and AI orchestrators, and proposes four principles for navigating the transition: transparency, consent, compensation, and human-centred design. Eight appendices provide quantitative analysis, a glossary, topical bibliography, and deep dives into shadow AI adoption, AI stigma, and algorithmic intent.",
   "authors": [
    "Peter Woodbridge",
    "John J. O'Hare"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-21",
   "doi": "10.48550/arxiv.2606.26114",
   "explainer": null,
   "id": "doi:10.48550/arxiv.2606.26114",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.48550/arxiv.2606.26114",
   "summary": "Drawing on 374 primary sources, this report traces how generative AI is reshaping the creative industries. It reports a 'slop ceiling' where AI content is 44% of uploads but only about 1-3% of streams, and notes that 88% of over 11,500 responses to the UK's AI copyright consultation opposed wider training rights.",
   "theme": "Policy & Governance",
   "title": "Dream machine -- the next creative economy",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2606-26114/",
   "venue": "arXiv (Cornell University)",
   "year": 2026
  },
  {
   "abstract": "This report introduces new metrics of AI usage to provide a rich portrait of interactions with Claude in November 2025, just prior to the release of Opus 4.5. These “primitives”—simple, foundational measures of how Claude is used, which we generate by asking Claude specific questions about anonymized Claude.ai and first-party (1P) API transcripts—cover five dimensions relevant to AI’s economic impact: user and AI skills, how complex tasks are, the degree of autonomy afforded to Claude, how successful Claude is, and whether Claude is used for personal, educational, or work purposes. The results reveal striking geographic variation, real-world estimates of AI task horizons, and a basis for revised assessments of Claude's macroeconomic impact. The data we release alongside this report are the most comprehensive to date, covering five new dimensions of AI use, consumer and firm use, and country and region breakdowns for Claude.ai. In the first chapter, we revisit findings from our previous Economic Index report published in September 2025. We find: While substantial concentration remains, since our last report Claude usage has become noticeably more evenly distributed across US states. If sustained, usage per capita would be equalized across the country in 2-5 years. In the second chapter we discuss the motivation for and introduce our new economic primitives, including how they were selected and operationalized, and their limitations. We additionally present evidence that our primitives capture directionally accurate aspects of underlying usage patterns as compared to external benchmarks. In chapters three and four we use these primitives to further investigate implications for adoption and productivity. We find: These results provide a new window into how AI is currently impacting the economy. Knowing the success rate of tasks gives a more accurate picture of which tasks might be automated, how impacted certain jobs might be, and how labor productivity will change. Me",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-01-15",
   "doi": null,
   "explainer": null,
   "id": "title:c8087e1e0876194e",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/anthropic-economic-index-january-2026-report",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Economic Index report: Economic primitives",
   "url": "https://wemustactnow.onrender.com/research/p/title-c8087e1e0876194e/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "At Anthropic, we’re measuring real-world AI use on an ongoing basis to answer questions exactly like these. Our privacy-preserving analysis method allows us to learn more about conversations on Claude.ai (capturing uses by consumers) and our first-party API (mostly capturing uses by businesses). 1 In past reports, we’ve assessed AI tasks by occupation and wage level , looked more closely at software development , and studied AI use by country and by US state . We’re now adding a new level of detail to our Economic Index. In our fourth report, we’re introducing what we’ve called economic primitives : a set of five simple, foundational measurements to track the economic impacts of Claude over time. Our initial set includes task complexity, skill level, purpose (work, education, or personal use), AI autonomy, and success. 2 We derive these primitives from asking Claude to answer a common set of questions about every conversation in our sample for this report. These primitives provide a leading indicator of AI’s potential economic impacts—and allow us to answer far more complex questions about how AI is already changing jobs. Our latest report, which samples conversations from November 2025 (predominantly using Claude Sonnet 4.5), uses our primitives to explore a wide range of questions that we wouldn’t otherwise be able to answer—including how Claude’s task-level success rates change for more complex tasks, and whether the use of Claude to date might portend a net-deskilling effect on many jobs. You can read the fourth Economic Index report here . Below, we summarize its results. We applied our economic primitives to questions about individual tasks, occupations, and then the possible aggregate impacts of the changes we observe. (Our full methodology—including details on how we tested the accuracy of our primitives—is described in chapter two of the full report .) We found that more complex tasks were sped up the most by Claude. We measure this by what Claude estimates",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-01-15",
   "doi": null,
   "explainer": null,
   "id": "title:45e6bce2da04a680",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/economic-index-primitives",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Economic Index: New building blocks for AI use",
   "url": "https://wemustactnow.onrender.com/research/p/title-45e6bce2da04a680/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Growing anxiety about how AI will impact jobs and the economy is emerging as a major issue across broad swaths of society. It is already a top concern for political candidates and for younger generations – all before the effect of AI shows up in most economic data . AI capabilities have increased tremendously over the last few years, and they show no signs of slowing down. A future economy infused with artificial general intelligence (AGI)—advanced AI systems capable of performing cognitive tasks at least at the level of the average person—could bring huge benefits. This includes improvements in health, leisure time, and higher standards of living. Yet concerns about AI’s economic impacts make sense. In the past, major technological transformations have boosted long-run living standards, granting humanity prosperity and health that would have been unfathomable to our ancestors. Yet in the short term, significant transformations have often also produced lower wages for many, hollowed out regional economies, and deepened inequality . In other words, many people who lived through technological transitions were hurt by the periods of innovation that they lived through. The question is: Can we reduce or avoid such harm this time, while preserving the benefits of technological progress? In the past, countries have struggled to adequately manage productivity-boosting economic transitions. During the Industrial Revolution and the early 20th century, policy often came after the fact, deploying redistributive interventions once the shock and disruption had occurred. In some instances, this may have been due to poor institutions and access to data. In others, slower responses were the result of concerns about acting too early in creating technology-adjustment policy. Previous shocks have also differed from AGI in important ways. Unlike prior technologies that automated routine work and created new industries, by automating intelligence itself, AGI could allow for far more comp",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-16",
   "doi": null,
   "explainer": null,
   "id": "title:4e4b0fbf300bc4be",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://institute.deepmind.com/essays/economic-policy-for-agi/",
   "summary": "A DeepMind Institute essay on economic policy for a world with artificial general intelligence. It notes that past technological transitions raised long-run living standards but lowered wages for many and deepened inequality in the short term, and asks which policies could reduce that harm this time while keeping the benefits.",
   "theme": "Labor & Work",
   "title": "Economic Policy for AGI",
   "url": "https://wemustactnow.onrender.com/research/p/title-4e4b0fbf300bc4be/",
   "venue": "Google DeepMind Institute (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "AI’s near-term economic bite is more likely to be felt in the paychecks of degree-holding workers, according to an Indeed Hiring Lab survey of experts. The post Economists See Slightly Steadier Hiring Ahead, but Offer an AI Wage Warning for College Grads appeared first on Indeed Hiring Lab .",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-24",
   "doi": null,
   "explainer": null,
   "id": "title:ea25dd7e26a821a7",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://hiringlab.indeed.com/2026/09/24/q3-labor-market-outlook-survey/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Economists See Slightly Steadier Hiring Ahead, but Offer an AI Wage Warning for College Grads",
   "url": "https://wemustactnow.onrender.com/research/p/title-ea25dd7e26a821a7/",
   "venue": "Indeed Hiring Lab (institute research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) adoption has become one of the most important economic changes in the United States. The technology is increasingly used in software development, customer service, professional writing, data analytics, health services, logistics, finance, and other knowledge-intensive activities. This paper examines the effect of AI adoption on labour productivity in the United States using a journal-style conceptual and evidence-based approach. The study relies on secondary evidence from the U.S. Census Bureau, U.S. Bureau of Labor Statistics, OECD, IMF, Stanford AI Index, McKinsey Global Institute, NBER, and recent experimental research published from 2020 onward. The paper argues that AI adoption can raise labour productivity by reducing task completion time, improving worker decision-making, automating routine cognitive tasks, supporting software development, and enabling faster knowledge processing. However, the productivity effect is not automatic. It depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI. Evidence from recent experiments shows strong task-level productivity gains, including faster writing, improved customer support performance, and quicker software development. At the same time, macroeconomic evidence remains cautious because AI diffusion is still uneven across industries and many firms are in early adoption stages. The paper concludes that AI adoption is likely to have a positive effect on labour productivity in the United States, but the magnitude will depend on broad diffusion, responsible governance, reskilling, and effective integration into real production processes. Keywords: artificial intelligence, labour productivity, United States, generative AI, digital transformation, economic growth",
   "authors": [
    "Sarah Martinez"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-03",
   "doi": "10.53819/81018102t2581",
   "explainer": {
    "approach": "A conceptual review of secondary evidence from US statistical agencies, the OECD, IMF, Stanford AI Index, McKinsey, NBER and experiments published since 2020.",
    "findings": "Experiments show strong task-level gains in writing, customer support and software development, while macro evidence stays cautious because diffusion is uneven. The paper concludes the effect is likely positive but depends on diffusion, skills, complementary investment and workflow redesign.",
    "limitations": "No new data or estimation; the conclusions rest on the author's reading of existing sources.",
    "method_type": "review",
    "question": "Does AI adoption raise labour productivity in the United States?"
   },
   "id": "doi:10.53819/81018102t2581",
   "institutions": [
    "University of Minnesota System"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.53819/81018102t2581",
   "summary": "A synthesis of secondary evidence (Census, BLS, OECD, IMF, Stanford AI Index, NBER experiments since 2020) arguing that AI adoption raises US labor productivity through faster task completion, better decision-making, and automation of routine cognitive work, but that gains are conditional on firm-level adoption, worker skills, complementary software/data investment, and workflow redesign. It restates the now-familiar micro-macro gap: strong experimental task-level gains (writing speed, customer support, coding) alongside cautious macro evidence because diffusion remains uneven. Useful mainly as a consolidation of the productivity-J-curve consensus rather than new estimates.",
   "theme": "Labor & Work",
   "title": "Effect of Artificial Intelligence Adoption on Labour Productivity in The United States",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-53819-81018102t2581/",
   "venue": "Journal of Economics",
   "year": 2026
  },
  {
   "abstract": "AI progress can feed back into AI research in two ways. Capability improvements allow AIs to perform a larger share of AI research tasks, while efficiency improvements raise AI research output per unit of compute on tasks already automated. In a task automation model of AI research, I show that a software-driven intelligence explosion - a self-sustaining acceleration in AI progress with fixed compute and human labor - requires both margins to improve sufficiently fast, and neither margin can substitute for the other. To see whether these conditions hold, I calibrate both margins with data on recent AI progress. I estimate that capability progress is likely fast enough to support an SIE, while efficiency progress is on the knife-edge. In Monte Carlo simulations, an SIE occurs in 49% of draws, indicating that an SIE is plausible but uncertain based on historical trends in AI progress. The single largest source of uncertainty is the rate of efficiency progress, whose uncertainty is responsible for 45% of the uncertainty about whether an SIE occurs, more than all other parameters combined. Thus, better data on efficiency progress in frontier models is essential for understanding the likelihood of an SIE.",
   "authors": [
    "Karthik Tadepalli"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-24",
   "doi": null,
   "explainer": null,
   "id": "title:821e84ce58d679cd",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.33447v1",
   "summary": "Using a task-automation model of AI research, the paper shows that a self-sustaining acceleration of AI progress with fixed compute and labor requires both AI capability and AI efficiency to improve fast enough, and neither can substitute for the other. Calibrated to recent progress, capability looks fast enough while efficiency is on a knife-edge; in Monte Carlo simulations such an acceleration occurs in 49% of draws, and uncertainty about efficiency progress drives most of the uncertainty.",
   "theme": "Productivity & Firms",
   "title": "Efficiency vs Capability in the Intelligence Explosion",
   "url": "https://wemustactnow.onrender.com/research/p/title-821e84ce58d679cd/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Empirical measures of AI's wage effect typically hold fixed the bundle of activities a worker is paid for at its pre-AI shape. We argue that this assumption hides much of the action. When automation breaks a job apart, firms decide how to recombine the surviving activities; whether they rebundle",
   "authors": [
    "Joshua S. Gans"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "May 2026",
   "doi": null,
   "explainer": {
    "approach": "The authors argue that when automation breaks a job apart, firms decide how to recombine the remaining activities into new jobs, and they treat this rebundling as a choice rather than holding it fixed.",
    "findings": "The abstract argues that holding job bundles at their pre-AI shape hides much of how automation affects wages; the excerpt available does not state specific results.",
    "limitations": "The available abstract is cut short, so the model's assumptions, results and any empirical test cannot be described here.",
    "method_type": "theory",
    "question": "Do measures of AI's wage effects miss something by assuming the bundle of activities a worker is paid for stays the same after automation?"
   },
   "id": "title:b921e97feafcc9b2",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35211",
   "summary": "Gans (NBER) argues that standard estimates of AI's wage effects err by holding workers' task bundles fixed at their pre-AI shape: when automation breaks a job apart, firms endogenously choose how to rebundle the surviving tasks, and that rebundling margin drives much of the wage outcome. This theoretical point cuts at the foundation of the widely used exposure-index literature, suggesting measured wage effects conflate automation with firms' organizational responses and that job redesign, not task displacement alone, determines who gains or loses from AI.",
   "theme": "Labor & Work",
   "title": "Endogenous Task Bundling, Skills and Automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-b921e97feafcc9b2/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "The study aims to assess the factors influencing economic growth in selected Southeast Asian countries, especially the role of artificial intelligence. The research utilizes FGLS regression analysis to avoid autocorrelation and heteroskedasticity. Simultaneously, the study evaluates the likelihood of cross- sectional dependencies using the panel-corrected standard error (PCSE), given the high degree of similarity among countries in terms of economics, investment, labor mobility, and development. The results suggested that artificial intelligence has a positive and significant impact on economic growth. Another possibility, the impact of institutional quality, industrialization, and foreign direct investment on economic growth is positive and significant.",
   "authors": [
    "Nguyen Thi Hong"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-19",
   "doi": "10.59324/ejiss.2026.2(5).05",
   "explainer": null,
   "id": "doi:10.59324/ejiss.2026.2(5).05",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://hal.science/hal-05830624",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Enhancing Economic Growth in Selected Southeast Asian Countries: The Role of Artificaial Intelligence and Other Economic Indicators",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-59324-ejiss-2026-2-5-05/",
   "venue": "HAL",
   "year": 2026
  },
  {
   "abstract": "This study examined the influence of Artificial Intelligence (AI)-driven research methodologies on lecturers' productivity in Lagos State University (LASU), Ojo Campus. The growing integration of AI tools such as Machine Learning (ML), Natural Language Processing (NLP), and ChatGPT into academic research and teaching, alongside concerns about lecturers' research efficiency, publication output, and teaching effectiveness in Nigerian universities, motivated the study. Specifically, the study sought to ascertain lecturers' awareness of AI-driven research methodologies, examine their knowledge of ML, NLP and ChatGPT, determine their perception of these tools in enhancing research quality and academic productivity, and establish the influence of the tools on research efficiency, publication output, and teaching effectiveness. A descriptive survey research design was adopted, with a sample of 165 lecturers drawn from seven faculties and two schools across Lagos State Owned Universities. Data were collected using a researcher-designed instrument, the Artificial Intelligence Driven Research Methodologies and Lecturers' Productivity Questionnaire (AIDRMLPQ). Out of 189 copies of the questionnaire distributed, 165 were properly completed and returned, giving a response rate of 89.2%. Data were analysed using descriptive statistics (mean and standard deviation) and Pearson Product Moment Correlation at the 0.05 level of significance. Findings revealed a significant positive relationship between the adoption of AI-driven research methodologies and lecturers' research efficiency (r = 0.742), between knowledge of ML, NLP and ChatGPT and academic productivity (r = 0.781), between lecturers' perception of AI tools and research quality (r = 0.766), and between AI tools utilisation and research efficiency, publication output, and teaching effectiveness (r = 0.754). The study concluded that AI-driven research methodologies play a significant and positive role in enhancing lecturers' p",
   "authors": [
    "Emmanuel Semako Gbesoevi",
    "Stephens Isaac ENOSAKHAE",
    "Hafsat Ibironke Onasanya",
    "F Salami"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-28",
   "doi": "10.64348/zije.2026634",
   "explainer": null,
   "id": "doi:10.64348/zije.2026634",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.64348/zije.2026634",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Evaluation of Artificial Intelligence Driven Research Methodologies on Lecturers’ Productivity in Lagos State Owned Universities, Nigeria",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-64348-zije-2026634/",
   "venue": "Federal University Gusau Faculty of Education Journal",
   "year": 2026
  },
  {
   "abstract": "We propose a measure of an AI agent’s optimization ability with an “expenditure horizon.” We give an empirical illustration from the NanoGPT speedrun. One difficulty in measuring AI’s ability to accelerate AI R&D is accounting for token cost, experiment compute cost and human labor cost. If we can estimate performance as a function of cost for both humans and agents, we can measure the “expenditure horizon” as the point at which those curves cross: the budget at which humans become more cost-effective than AIs. We illustrate our method with data from the NanoGPT speedrun. We first estimate the returns to human effort in optimizing NanoGPT: on the margin, each 1% improvement costs very roughly \\$2,500 in human labor. We also report the status of preliminary agentic optimization runs against NanoGPT: after more than \\$10K of expenditure we estimate expenditure horizons of \\$0-\\$3K. Introduction A critical question is the degree to which AI will accelerate its own progress. Over the last year there have been many credible claims that AI has begun accelerating algorithmic progress but it is hard to tell by how much, and what to expect in the future. 1 We have many imperfect sources of evidence on AI’s acceleration of AI R&D: AI R&D benchmarks. There are a few high-quality benchmarks which test agents on their ability to optimize AI training algorithms. However most do not report any human baseline, or only a baseline score at 8 hours or 40 hours. Additionally, the benchmark problems are often not reflective of frontier-level AI R&D. It seems plausible that agents can get good solutions to many “textbook” or “toy” problems without being able to contribute to highly-optimized frontier algorithms. Researcher-uplift studies. Ideally we would compare researcher productivity with and without AI help, but it is very difficult to run an experiment that estimates the effect accurately (discussion in Becker et al., 2026 ). We do have some self-reported estimates of productivity i",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-07-21",
   "doi": null,
   "explainer": null,
   "id": "title:68c55ce3e834b77b",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/blog/2026-07-21-expenditure-horizon/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT",
   "url": "https://wemustactnow.onrender.com/research/p/title-68c55ce3e834b77b/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Maria D. Tito Artificial intelligence (AI) technologies have the potential to transform production processes and labor markets, yet their ultimate effects on employment, occupations, and skill demand remain uncertain (Acemoglu and Restrepo, 2018). While research continues to explore implications of AI adoption for productivity and employment, firms' hiring decisions provide a timely measure of employers' evolving skill requirements (Deming and Kahn, 2018; Hershbein and Kahn, 2018; Acemoglu et al., 2022).",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-30",
   "doi": null,
   "explainer": null,
   "id": "title:0952def02e96655a",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.federalreserve.gov/econres/notes/feds-notes/ai-on-the-factory-floor-evidence-from-manufacturing-job-postings-20260930.html",
   "summary": "This Federal Reserve note by Maria D. Tito uses manufacturing job postings to track how AI adoption is changing employers' skill requirements on the factory floor. It treats hiring decisions as a timely signal of AI's still-uncertain effects on employment, occupations and skill demand.",
   "theme": "Labor & Work",
   "title": "FEDS Note: AI on the Factory Floor: Evidence from Manufacturing Job Postings",
   "url": "https://wemustactnow.onrender.com/research/p/title-0952def02e96655a/",
   "venue": "Federal Reserve Board (institute research)",
   "year": 2026
  },
  {
   "abstract": "This article investigates the relationship between financial development and economic growth in Azerbaijan within 2000–2024 period. Findings from the Autoregressive Distributed Lag (ARDL) Bounds Test reveal a significant long-term cointegration among financial development, physical capital investments, and economic growth, suggesting that access to finance for the private sector is a strong accelerator for economic growth. While domestic credit to the private sector shows a weak positive short-run impact, its long-run impact is negative. The divergence between the positive impact of physical capital and the negative impact of credit expansion in the long run implies that even though physical investments enhance productivity, the financial markets might be inefficient in the allocation of resources by moving funds toward non-productive sectors and creating a misallocation constraint on economic growth. To improve the efficiency in financial markets, digitalisation of financial services and integration of AI-based models were evaluated as key strategic priorities. This study also employs a SWOT analysis to explore opportunities and challenges for successful adoption of AI.",
   "authors": [
    "Nazrin Akhundzada",
    "Amiran Gahramanov"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-30",
   "doi": "10.15837/aijes.v20i1.7608",
   "explainer": null,
   "id": "doi:10.15837/aijes.v20i1.7608",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.15837/aijes.v20i1.7608",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "FINANCE-GROWTH NEXUS AND ADOPTION OF ARTIFICIAL INTELLIGENCE IN AZERBAIJAN",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-15837-aijes-v20i1-7608/",
   "venue": "AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES",
   "year": 2026
  },
  {
   "abstract": "Why does massive AI investment fail to generate commensurate productivity gains? We argue the paradox is theoretically generated: prevailing production function frameworks encounter a structural boundary by treating AI as a separable factor of production without modeling the cognitive mediation through which AI generates productive value. This directs investment toward deployment when productivity requires prior development of what we term convergence capacity (C). We propose the Intellectually Converged Human (ICH) framework, a fifth-stage framework for production function theory: H-hat = H[1 + phi(A,C)], where effective productive capacity equals human capital (H) scaled by an augmentation factor [1 + phi], with phi jointly determined by AI utilization intensity (A) and convergence capacity (C), a four-dimensional cognitive construct encompassing embodied understanding, metacognition, temporal integration, and integrative thinking. The production function Y = F(K, H-hat) provides a human-centered mechanism for Solow's TFP residual: A_Solow = [1 + phi(A,C)]^(1-alpha). The framework predicts three augmentation regimes with distinct policy implications. Descriptive cross-national analysis of 20 OECD economies shows the AIxC interaction is associated with 86% of TFP variance versus 31% for AI alone, a pattern-consistent finding in the small-n theoretical tradition. South Korea exemplifies national-scale under-augmentation: high H, substantial A, low C produce phi = 0. We distinguish convergence capacity from adjacent constructs, absorptive capacity, dynamic capability, and human capital, and demonstrate that C constitutes the specific cognitive mediator that prior frameworks have left implicit. We derive C-first policy prescriptions and offer three empirically testable propositions with a falsifiable 10-year forecast.",
   "authors": [
    "Kwan Soo Shin",
    "In Seok Kang"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-18",
   "doi": null,
   "explainer": null,
   "id": "title:3431b3cafbfee886",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.19794v1",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Forecasting AI-Era Productivity: The Intellectually Converged Human Framework and a Missing Cognitive Mediator in Production Function Theory",
   "url": "https://wemustactnow.onrender.com/research/p/title-3431b3cafbfee886/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and privatesector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI.",
   "authors": [
    "Ezra Karger",
    "Otto Kuusela",
    "Jason Abaluck",
    "Kevin A. Bryan",
    "Basil Halperin",
    "Todd R. Jones",
    "Connacher Murphy"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-03-15",
   "doi": null,
   "explainer": {
    "approach": "The authors elicit forecasts from five groups and compare their medians, both unconditionally and conditional on a rapid AI progress scenario in which AI surpasses humans on many cognitive and physical tasks.",
    "findings": "Each group's median expects 2.5% annual GDP growth, above the 2.0% medium-run and 1.7% long-run baselines, with small participation declines. Under rapid progress, experts expect about 4% growth and participation falling from 62% to 55% by 2050, roughly half of that decline attributed to AI.",
    "limitations": "These are elicited beliefs, not measured outcomes; the abstract does not report sample sizes or how respondents were recruited.",
    "method_type": "survey",
    "question": "What do economists, AI company staff, AI policy researchers, accurate forecasters and the public expect AI to do to US GDP growth and labor force participation?"
   },
   "id": "title:1291a6248b6de2d6",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35046",
   "summary": "The authors survey five groups (academic economists, AI company staff, AI policy researchers, top forecasters and the public) on how AI will affect the US economy. The median respondent in each group expects annual GDP growth of 2.5% and small declines in labor force participation; under a rapid-progress scenario, experts expect growth near 4% and participation falling to 55% by 2050, about half of it attributed to AI.",
   "theme": "Labor & Work",
   "title": "Forecasting the Economic Effects of AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-1291a6248b6de2d6/",
   "venue": "NBER Working Paper 35046",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence feels omnipresent, yet the disruption many expect has not fully arrived. The main reason is not model capability, nor even the tools built to harness those models. Rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world. We offer a three-stage lens: Augmentation, Automation, and Reconstruction, and argue that the most consequential disruption resides in the third stage where workflows and markets are rebuilt around delegation, machine-to-machine interaction, continuous monitoring, and auditable constraints. Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization: the complementary investments that produce the familiar \"productivity J-curve\" of general-purpose technologies. We illustrate this transition through examples in consumer markets, education, news, and coding. Finally, we emphasize a normative point: the agentic future is not predetermined. Leaders must both skate to where the puck is going and actively steer it toward a good place, ensuring innovation delivers welfare gains felt by businesses and consumers around the world.",
   "authors": [
    "David M. Rothschild",
    "Jake M. Hofman",
    "Markus Mobius",
    "Brendan Lucier",
    "Eleanor Dillon",
    "Daniel G. Goldstein",
    "Nicole Immorlica",
    "Aleksandrs Slivkins"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-05-28",
   "doi": null,
   "explainer": null,
   "id": "title:78f16185c2cb5276",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2605.29207v1",
   "summary": "A perspective essay arguing AI's expected disruption has not fully arrived because organizations mostly use it to speed up pre-AI workflows. It proposes three stages (augmentation, automation, reconstruction), links the slow transition to complementary investments behind a productivity J-curve, and argues the outcome of the agentic transition can be steered.",
   "theme": "Productivity & Firms",
   "title": "From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place",
   "url": "https://wemustactnow.onrender.com/research/p/title-78f16185c2cb5276/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Practitioner use of generative AI in software engineering has progressed faster than the evidence assessing its effects. This review examined 58 empirical studies published from 2024 to early 2026 on GitHub Copilot, ChatGPT, Claude, and Gemini. Using PRISMA-guided selection from five digital libraries, the studies addressed productivity, role shifts, code quality, adoption behaviour, and evidence gaps. Findings were synthesised into the AI Integration Consequence Model, a conceptual four-layer framework linking adoption conditions, role shifts, quality exposure, and organisational consequences. Evidence indicates that developers increasingly move from code production to code review, as more than half of AI-generated code is discarded after evaluation. Generative AI improves efficiency for boilerplate code, documentation, and unit-test scaffolding, but provides limited benefits for architectural decisions and enterprise debugging. A meta-analysis of 47 studies reported a 45.6-point gap between self-reported and objectively measured productivity. AI-generated code also showed exploitable vulnerabilities at estimated rates of 40–50%, varying by task, tool version, programming language, and measurement method. Social influence strongly predicted adoption intent, while concerns about peer judgement encouraged undisclosed use. Experienced engineers generally received an AI Boost, whereas junior engineers faced an AI Drag. Structured mentorship reduced this gap in one 14-month cohort study. Key evidence gaps include cross-cultural adoption, longitudinal skill development, and unreplicated gender disparities.",
   "authors": [
    "Bassey Echeng Isong",
    "Tshipuke Vahangwele",
    "Nosipho Dladlu"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-24",
   "doi": "10.63158/journalisi.v8i5.1826",
   "explainer": null,
   "id": "doi:10.63158/journalisi.v8i5.1826",
   "institutions": [
    "North-West University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.63158/journalisi.v8i5.1826",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "From Code Author to Code Reviewer: A Systematic Review of Practitioner Experience with Generative AI in Software Engineering",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-63158-journalisi-v8i5-1826/",
   "venue": "Journal of Information Systems and Informatics",
   "year": 2026
  },
  {
   "abstract": "The conflict that erupted in early 2026 continues to be the principal source of macroeconomic risk for the MENAAP region. Key trade routes have been severely disrupted—especially for the oil and gas exporters who depend on the Strait of Hormuz—and energy costs have spiked. Unlike in previous energy shocks, Persian Gulf oil and gas exporters face the largest growth downgrades, while the effects were more contained in middle-income countries. Conflict has compounded humanitarian needs and existing vulnerabilities in fragile settings. In many economies, higher interest rates and increased uncertainty are delaying investment decisions and increased social protection needs are putting pressure on already strained fiscal resources. Against this near-term background, this report examines how artificial intelligence (AI) will reshape labor markets and productivity across the region in the longer term. Wide differences in capacity and readiness will prevent the region from capturing the full potential of AI. Weak private sector dynamism and inadequate innovation ecosystems remain binding constraints. Three mutually reinforcing gaps—in localization, usage, and foundational human and physical capital—need to be addressed simultaneously. Advances in leading economies, such as Saudi Arabia and the United Arab Emirates, can create opportunities for regional collaboration and generate shared gains across the region.",
   "authors": [
    "World Bank"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-06",
   "doi": "10.1596/978-1-4648-2364-0",
   "explainer": null,
   "id": "doi:10.1596/978-1-4648-2364-0",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.1596/978-1-4648-2364-0",
   "summary": null,
   "theme": "Labor & Work",
   "title": "From Divide to Opportunity: AI, Jobs, and Growth",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1596-978-1-4648-2364-0/",
   "venue": "World Bank, Washington, DC eBooks",
   "year": 2026
  },
  {
   "abstract": "Generative artificial intelligence can reduce the cost of producing text, software, and other digital outputs, yet improved task performance does not necessarily produce a viable business model. This review examines the mechanisms connecting generative capabilities to value creation, delivery, and capture. A purposive synthesis brings business-model and digital-transformation theory into dialogue with experimental productivity and creativity studies and recent business-use-case research. The paper distinguishes operating improvements within an existing model from changes to the customer promise, activity system, or revenue logic. It develops a conceptual framework in which useful generation creates potential value, complementary assets enable delivery, verification determines acceptable quality, and bargaining conditions influence who captures the resulting surplus. Four business-model archetypes are examined through their revenue mechanisms, necessary complements, and failure conditions. An explicit cost-per-accepted-outcome model and a hypothetical service example show how inference savings can be offset by review, rework, and provider dependence. Four testable propositions and explicit cost thresholds connect task verifiability, complementary assets, differentiation, and supplier portability to business outcomes. No new field data, causal estimates, or commercial forecasts are reported. The review concludes that research should measure accepted outcomes and distribution of value across stakeholders rather than infer business-model innovation from tool adoption, usage growth, or faster content production alone.",
   "authors": [
    "Bankatesh Choudhary",
    "Meenakshi Sharma"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-26",
   "doi": "10.20944/preprints202610.2554.v1",
   "explainer": null,
   "id": "doi:10.20944/preprints202610.2554.v1",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.20944/preprints202610.2554.v1",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "From Generative AI Capability to Business Value: A Critical Review of Verification Complements and Value Capture",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-20944-preprints202610-2554-v1/",
   "venue": "Preprints.org",
   "year": 2026
  },
  {
   "abstract": "Large Language Models (LLMs) promise significant efficiency gains across information-intensive operations by automating repetitive language-based tasks. However, adoption decisions remain largely opportunistic, as organizations struggle to move from pilots to sustainable industrialization. Without systematic economic frameworks, practitioners cannot assess whether LLM investments create value in a given context. This paper develops a general economic model to support informed LLM adoption decisions in operational information systems. Facing high uncertainty, we represent parameters as random variables centered on authoritative benchmarks, and evaluate the model by Monte Carlo simulation across pessimistic-to-optimistic assumptions on LLM efficiency. The ROI distributions show the probability of profitability and the Value at Risk for each configuration, together with a sensitivity analysis of its main drivers. As a first instantiation, we apply the model to Security Operations Centers (SOCs), mapping workflows onto established standards to identify tasks suited to LLM augmentation. The model balances productivity gains, driven by alert volume, analyst effort, and LLM efficiency, against operational costs, including inference, infrastructure, and error remediation, covering both cloud API and self-hosted deployments. Results show that adoption economics are context-dependent, and that automation is an exploitable lever, gated by accuracy, while the cost of errors and the model's price are secondary. Across the analyzed models and prices, most configurations are unprofitable, with profitability limited to high-accuracy, low-uncertainty deployments. The model gives organizations a quantitative basis for evidence-based adoption decisions, by locating the boundary between profit and loss, and the loss risked beyond it.",
   "authors": [
    "Pellegrino Casoria",
    "Andrea Araldo",
    "Rosario Patanè",
    "Simon Pietro Romano"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-23",
   "doi": null,
   "explainer": null,
   "id": "title:0773c0c1ee6f4175",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.33352v1",
   "summary": "The paper builds a Monte Carlo model of the returns to adopting large language models in operational information systems, weighing productivity gains against inference, infrastructure and error-remediation costs. Applied to security operations centres, most configurations of model and price turn out unprofitable, with profit limited to high-accuracy, low-uncertainty deployments.",
   "theme": "Productivity & Firms",
   "title": "From Hype to ROI: An Economic Model for LLM Adoption in Information Systems and Security Operations",
   "url": "https://wemustactnow.onrender.com/research/p/title-0773c0c1ee6f4175/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "The economic impact of technological change will critically depend on how future workers invest in their human capital. Yet, little is known about how future workers themselves evaluate and choose their educational and occupational paths in light of emerging technologies. This paper examines how adolescents currently at the schoolto-work transition stage value working with generative artificial intelligence (GenAI) in their future occupations, and how automation risk and opportunities for continuing education shape these preferences. We field a discrete-choice experiment among a nationally representative sample of over 7,000 Swiss adolescents aged around 15. We find that adolescents generally exhibit an aversion to collaborating with GenAI at work, with females consistently more averse than males. However, preferences are nuanced: adolescents welcome greater GenAI collaboration, provided that GenAI usage levels remain moderate and that it is not accompanied by increases in jobautomation risk. Finally, continuing education opportunities in occupations improve attitudes towards working with GenAI across genders. Our results challenge simple narratives of technology acceptance or rejection, revealing that adolescents’ willingness to work with GenAI depends on how it is implemented — its intensity, associated displacement risks, and accompanying skill development — rather than the technology itself. Our findings suggest that the way future workers value GenAI collaboration in their career choices critically depends.",
   "authors": [
    "Christian Gschwendt",
    "Martina Viarengo",
    "Thea Zöllner"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": null,
   "explainer": null,
   "id": "title:bccce6021f96cc19",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "http://repository.graduateinstitute.ch/record/321942",
   "summary": "A discrete-choice experiment with over 7,000 Swiss 15-year-olds on how they value working with generative AI in future jobs. Adolescents are on average averse to GenAI collaboration, girls more than boys, but accept moderate use when it does not raise automation risk, and continuing-education opportunities improve their attitudes.",
   "theme": "Labor & Work",
   "title": "Generative AI and career choices",
   "url": "https://wemustactnow.onrender.com/research/p/title-bccce6021f96cc19/",
   "venue": "Graduate Institute Geneva Institutional Repository (Graduate Institute of International and Development Studies)",
   "year": 2026
  },
  {
   "abstract": "Generative artificial intelligence (AI) is expected to transform work, but less is known about how firms reorganize labor demand as the technology diffuses. Existing research has largely focused on which occupations are exposed to AI or whether exposed jobs decline. We extend this debate by examining whether firms adjust by changing where they hire, what jobs contain, or both. Using a nationwide dataset of job postings in the United States, covering all sectors of the economy, we construct a dynamic, posting-level measure of generative AI exposure with a two-stage large language model pipeline. The pipeline identifies the tasks described in each posting and classifies the extent to which generative AI can perform or assist them. We then decompose changes in aggregate exposure into two margins: reallocation of demand across jobs and redesign of tasks within jobs. We document three main findings. First, generative AI exposure is dynamic rather than fixed, changing substantially over time. Second, labor demand adjusts through both margins. Hiring reallocation explains the largest share of the aggregate decline in exposure, accounting for 52% on average, while within-job redesign becomes increasingly important, accounting for 39.5%. A complementary Oaxaca-Blinder decomposition shows that shifts in occupational composition account for about 90% of the exposure change attributable to observable job characteristics. Third, adjustment differs across the job ladder. Senior jobs adjust earlier and mainly through reallocation, whereas junior jobs adjust through a broader mix of reallocation, redesign, and their interaction. These findings suggest that labor-market adjustment to generative AI is a process of organizational reconfiguration, in which firms reshape both hiring demand and the task architecture of work.",
   "authors": [
    "Fangyan Wang",
    "Zaiyan Wei",
    "Yang Wang"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-05-22",
   "doi": null,
   "explainer": {
    "approach": "Using nationwide US job postings across all sectors, the authors measure each posting's generative AI exposure with a two-stage language-model pipeline, then decompose changes in exposure into reallocation across jobs and redesign within jobs, plus an Oaxaca-Blinder decomposition.",
    "findings": "Exposure changes substantially over time. Hiring reallocation explains about 52% of the aggregate decline in exposure and within-job redesign 39.5%. Senior jobs adjust earlier and mainly through reallocation, junior jobs through a broader mix.",
    "limitations": "Job postings show what firms advertise, not who is hired or how work is actually done, and the exposure measure depends on a language-model classification of tasks.",
    "method_type": "observational",
    "question": "As generative AI spreads, do US firms adjust by changing which jobs they hire for, by redesigning the tasks inside jobs, or both?"
   },
   "id": "title:b551925f1997ce2f",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2605.23159v1",
   "summary": "Wang, Wei, and Wang build a dynamic posting-level GenAI exposure measure over nationwide U.S. job postings and decompose the aggregate decline in exposure into hiring reallocation across jobs (52% on average) versus within-job task redesign (39.5%), with occupational composition shifts explaining about 90% of the exposure change attributable to observables; senior roles adjust earlier via reallocation while junior roles adjust through a broader mix. The decomposition matters because it shows firms actively reorganize labor demand rather than passively shedding exposed jobs, implying static occupation-level exposure indices misread how the AI shock actually propagates.",
   "theme": "Labor & Work",
   "title": "Generative AI and the Reorganization of Labor Demand",
   "url": "https://wemustactnow.onrender.com/research/p/title-b551925f1997ce2f/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Generative artificial intelligence (GenAI) is the first automation wave to reach high-cognitive tasks at scale, yet its effects on intra-urban inequality remain largely unknown. Using 5 million job postings from Beijing (2018--2024), we construct a neighborhood-level GenAI Exposure Index by aggregating task-level assessments from five leading large language models. We examine the spatial, structural and causal mechanisms of this shock. We find that GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide. Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers -- a \"high-skill trap.\" This wage penalty is driven by task de-skilling and intensified labor-market crowding. A difference-in-differences design centered on ChatGPT's release supports a causal interpretation. These findings challenge the prevailing theory of skill-biased technological change and provide a basis for inclusive AI governance in global technology hubs.",
   "authors": [
    "Xiliu He",
    "Haoxiang Zhao",
    "Mingyi Ma",
    "Edward Wen Chuan Lai",
    "Koei Enomoto",
    "Anni Hu",
    "Jiatong Li",
    "Lingyun Chu",
    "Yuan Lai"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-05-25",
   "doi": null,
   "explainer": {
    "approach": "The authors use 5 million Beijing job postings from 2018 to 2024 to build a neighbourhood-level GenAI exposure index from task assessments by five large language models, and use a difference-in-differences design around ChatGPT's release.",
    "findings": "GenAI exposure is concentrated in the city's core districts. Since 2023, high-exposure neighbourhoods have seen wage stagnation while still attracting high-skilled workers, which the authors call a high-skill trap, driven by task de-skilling and labour-market crowding.",
    "limitations": "The data are job postings from a single city, which may not reflect realised wages or other cities. Exposure is measured through language models' own task assessments.",
    "method_type": "observational",
    "question": "How does exposure to generative AI affect wages and inequality across neighbourhoods within Beijing?"
   },
   "id": "title:1b8acf883a6f873f",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2605.25505v1",
   "summary": "Using 5 million Beijing job postings (2018-2024) and an LLM-derived neighborhood-level GenAI Exposure Index, the authors show exposure concentrates in core districts and that since 2023 high-exposure neighborhoods have suffered wage stagnation even while attracting high-skilled workers — a \"high-skill trap\" driven by task de-skilling and labor-market crowding, causally identified with a diff-in-diff around ChatGPT's release. This is a direct empirical challenge to skill-biased technological change as the operative model for GenAI, and one of the first studies of intra-urban (rather than cross-occupation) AI inequality.",
   "theme": "Labor & Work",
   "title": "Generative AI impacts on intra-urban inequality and skill premium in Beijing",
   "url": "https://wemustactnow.onrender.com/research/p/title-1b8acf883a6f873f/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Generative Artificial Intelligence (AI) is reshaping business analytics by enabling organizations and governments to generate insights, improve decision-making, optimize resources, and support data-driven economic planning. This study examines the role of generative AI-driven business analytics in national economic development, with particular attention to macroeconomic performance, financial inclusion, human capital, and sustainable growth. Using secondary data from the World Bank World Development Indicators, specifically the Economic Policy and Debt and Financial Sector datasets, the study analyzes five major economies: the United States, China, India, Japan, and Germany. A quantitative and comparative research approach was applied using Python, Tableau, and Microsoft Excel for data cleaning, descriptive analysis, correlation assessment, regression estimation, and visual interpretation. Key indicators included GDP growth, adjusted net national income, education expenditure, financial account ownership, unemployment, and gross savings. The findings indicate that stronger digital readiness, financial inclusion, and investment in education are positively associated with economic performance and innovation capacity. Emerging economies such as China and India demonstrate stronger growth-oriented effects from AI-enabled digital transformation, while developed economies such as the United States, Japan, and Germany show more stable patterns linked to efficiency, productivity, and sustainability. However, the study also identifies critical risks, including data inequality, automation-related job displacement, algorithmic bias, privacy concerns, and weak ethical governance. The results suggest that generative AI in business analytics represents both a strategic opportunity and a governance challenge. Sustainable economic progress will depend on how effectively countries balance AI innovation with human capital development, inclusive digital access, ethical regulation, and",
   "authors": [
    "Md Sultanul Arefin Sourav"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-06",
   "doi": "10.32996/jcsts.2026.8.8.2",
   "explainer": {
    "approach": "Descriptive, correlation and regression analysis of World Bank development indicators for the US, China, India, Japan and Germany.",
    "findings": "Digital readiness, financial inclusion and education investment are positively associated with economic performance; China and India show stronger growth-oriented patterns, while the US, Japan and Germany show more stable efficiency-linked patterns.",
    "limitations": "The indicators used do not measure generative AI directly, and five-country correlations cannot isolate an AI effect.",
    "method_type": "observational",
    "question": "What role can generative AI-driven business analytics play in national economic development?"
   },
   "id": "doi:10.32996/jcsts.2026.8.8.2",
   "institutions": [
    "Trine University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.32996/jcsts.2026.8.8.2",
   "summary": "Using World Bank WDI data across the US, China, India, Japan, and Germany, this study correlates generative-AI-enabled business analytics readiness with macro indicators (GDP growth, financial account ownership, education spending, savings) and finds digital readiness, financial inclusion, and education investment associated with stronger economic performance, with emerging economies (China, India) showing growth-oriented AI effects versus efficiency/stability effects in developed ones. For the AI-economics debate it supports the view that AI's growth dividend is mediated by complementary national infrastructure, while flagging data inequality and automation risks, though the correlational cross-country design limits causal weight.",
   "theme": "Growth & Macro",
   "title": "Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-32996-jcsts-2026-8-8-2/",
   "venue": "Journal of Computer Science and Technology Studies",
   "year": 2026
  },
  {
   "abstract": "Generative artificial intelligence is gradually infiltrating contemporary socio-technical systems, affecting work practices, labor market participation, and the dynamics of labor-related costs. In this paper, we analyze cross-sectional relationships between generative AI use among active individuals and two macro-level labor-market indicators: the labor force participation rate and the evolution of nominal unit labor costs. The research is based on comparative European data. It analyses the use of AI in personal, professional, educational, and recent contexts as an expression of the interaction of human agency, technological infrastructure, and the institutional environment. The methodology combines multivariate modelling and regression with exploratory factor analysis and clustering to examine the statistical associations between AI use and labour-market indicators, as well as the emergent typologies of countries with diverse profiles. Results indicate stable links between the extent of generative AI use and employment, and unit labour cost disparities, suggesting that generative AI use is associated with broader socio-economic development patterns, without implying a causal relationship. The results highlight the need for an integrative view of AI adoption, linking human behaviors to organizational procedures and governance structures to ensure a responsible and fair transition.",
   "authors": [
    "Claudiu George Bocean",
    "Luminiţa Popescu",
    "Dalia Simion",
    "Natalița Maria Sperdea",
    "Carmen Puiu",
    "Roxana Marinescu",
    "Enescu Maria"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-09",
   "doi": "10.3390/systems14070815",
   "explainer": null,
   "id": "doi:10.3390/systems14070815",
   "institutions": [
    "University of Craiova"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/systems14070815",
   "summary": "A cross-sectional analysis of European countries finds generative AI use is statistically associated with labor force participation rates and unit labor cost trends, and clusters countries into distinct AI-adoption/labor-market typologies — though the authors are explicit the associations are not causal. It's a descriptive contribution to the cross-country empirical base rather than a causal identification study.",
   "theme": "Labor & Work",
   "title": "Generative AI in the Labor Force: Multivariate Modeling and Cluster Typologies of Participation and Cost Dynamics",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-systems14070815/",
   "venue": "Systems",
   "year": 2026
  },
  {
   "abstract": "Purpose: This study aims to analyze the impact of Generative Artificial Intelligence (GenAI) on the marketing industry with a focus on content transformation, consumer engagement, and firm performance. The research examines how GenAI is reshaping traditional marketing practices through automation, personalization, and integration with Marketing Technology (MarTech) systems. It also seeks to understand the opportunities, strategic implications, and challenges associated with the adoption of AI-driven marketing technologies. Methodology: The study adopts an exploratory and analytical research approach based on secondary data collected from peer-reviewed journals, academic publications, industry reports, and digital sources. Various analytical frameworks such as SWOC (Strengths, Weaknesses, Opportunities, Challenges), ABCD (Advantages, Benefits, Constraints, Disadvantages), and PESTEL (Political, Economic, Social, Technological, Environmental, Legal) analyses are used to evaluate the internal and external factors influencing the adoption of Generative AI in marketing. The study also incorporates stakeholder perspectives to understand the impact of GenAI on businesses, consumers, policymakers, and investors. Results &amp; Analysis: The findings indicate that Generative AI significantly improves content creation, marketing automation, personalization, customer interaction, and operational efficiency. AI-driven tools enable businesses to generate scalable and data-driven marketing strategies that enhance customer engagement and improve return on marketing investment. The SWOC and ABCD analyses highlight major advantages such as efficiency, innovation, personalization, and competitive advantage, while also identifying challenges including ethical concerns, algorithmic bias, cybersecurity risks, and data privacy issues. The PESTEL analysis further emphasizes the role of political, economic, social, technological, environmental, and legal factors in shaping the future of AI-",
   "authors": [
    "Diya Devadiga",
    "P. S. Aithal"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-29",
   "doi": "10.64818/pijmess.3107.4626.0050",
   "explainer": null,
   "id": "doi:10.64818/pijmess.3107.4626.0050",
   "institutions": [
    "Poornaprajna Institute of Scientific Research"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.64818/pijmess.3107.4626.0050",
   "summary": "A qualitative industry analysis (using SWOC/ABCD/PESTEL frameworks over secondary sources) argues generative AI is improving marketing content creation, personalization, and operational efficiency while raising algorithmic-bias, privacy, and cybersecurity concerns. It's a practitioner-oriented synthesis rather than new empirical measurement of firm performance effects.",
   "theme": "Productivity & Firms",
   "title": "Generative AI in the Marketing Industry: An Industry Analysis of Content Transformation, Consumer Engagement, and Firm Performance",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-64818-pijmess-3107-4626-0050/",
   "venue": "Poornaprajna International Journal of Management Education & Social Science (PIJMESS)",
   "year": 2026
  },
  {
   "abstract": "Abstract Research Summary Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative, entrepreneur‐centered review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment–Entrapment Framework, which not only catalogs GenAI's benefits and costs throughout the entrepreneurial process but also identifies potential trade‐offs underlying GenAI's double‐edged role. For example, GenAI may improve venture idea quality yet produce hallucinations and biases; boost entrepreneurial self‐efficacy yet heighten overconfidence; increase functional breadth and self‐sufficiency yet reduce prosociality; and enhance productivity yet diminish cognitive engagement, learning, and memory. We also identify features of GenAI that underlie these empowering and entrapping effects. Moreover, we explore boundary conditions that moderate GenAI's effects, including entrepreneurs' expertise, approaches to GenAI use, and personal traits and values. Beyond these theoretical contributions, our review and framework offer practical guidance for entrepreneurs seeking to use GenAI strategically while managing its risks. Managerial Summary We review how GenAI influences entrepreneurs at each stage of the entrepreneurial process: opportunity recognition and ideation, opportunity evaluation and commitment, resource assembly and mobilization, and venture launch and growth. Based on this review, we propose the Empowerment–Entrapment Framework to help entrepreneurs understand GenAI's trade‐offs at each stage. The future of entrepreneurship will depend less on whether entrepreneurs use GenAI than on how they use it. Used thoughtfully, GenAI may empower e",
   "authors": [
    "Jackson G. Lu",
    "Gerui Gloria Zhao",
    "Anna Zheng"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.1002/sej.70050",
   "explainer": null,
   "id": "doi:10.1002/sej.70050",
   "institutions": [
    "Massachusetts Institute of Technology",
    "Tsinghua University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.1002/sej.70050",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Generative AI use in entrepreneurship: An integrative review and the Empowerment–Entrapment Framework",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1002-sej-70050/",
   "venue": "Strategic Entrepreneurship Journal",
   "year": 2026
  },
  {
   "abstract": "This paper reviews current published research on how generative artificial intelligence affects firm productivity. Rather than conducting new firm-level data analysis, it synthesizes evidence from artificial intelligence research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies. The central question is why GenAI produces measurable productivity gains in some work settings but uneven or limited firm-level effects in others. The review develops a task-to-firm conversion framework, arguing that GenAI should not be treated as a standalone productivity shock. Its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure. Existing studies show that GenAI can improve writing, customer support, consulting, and software-development tasks, but they also reveal risks of overreliance, misalignment, and uneven performance. For Chinese firms, the review suggests that productivity gains are most likely when GenAI adoption is supported by cloud infrastructure, data readiness, skilled labor, workflow redesign, and strong digital ecosystems.",
   "authors": [
    "Jinliang Mai"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-15",
   "doi": "10.54254/2754-1169/2026.bl34568",
   "explainer": null,
   "id": "doi:10.54254/2754-1169/2026.bl34568",
   "institutions": [
    "The University of Melbourne"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.54254/2754-1169/2026.bl34568",
   "summary": "A literature synthesis proposing a 'task-to-firm conversion' framework to explain why generative AI produces measurable productivity gains in some settings (writing, customer support, software development) but uneven effects elsewhere, arguing the payoff depends on model capability, task fit, and complementary assets like cloud infrastructure and data readiness. It applies this lens specifically to the digital-economy conditions Chinese firms would need for GenAI to pay off at the firm level.",
   "theme": "Labor & Work",
   "title": "Generative AI, Digital Infrastructure, and Firm Productivity: A Task-to-Firm Conversion Framework for Chinese Firms",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54254-2754-1169-2026-bl34568/",
   "venue": "Advances in Economics Management and Political Sciences",
   "year": 2026
  },
  {
   "abstract": "This study examines the global impacts of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) through a conceptual review of the related literature. AGI refers to AI systems that may match human cognitive ability across multiple domains, while ASI refers to systems that may surpass human intelligence across scientific, social, strategic, and creative tasks. Recent advances in large language models, agentic systems, and general-purpose AI have intensified concern about the gap between rapid technological development and slower adaptation in economic structures, ethical principles, and governance frameworks. This review synthesizes economic, ethical, and governance literature to examine how AGI and ASI may affect productivity, labor markets, inequality, human autonomy, safety, and institutional accountability. The literature presents competing economic scenarios, ranging from modest productivity gains to explosive growth and a possible post-labor economy marked by wage decline and weakened aggregate demand. The review also identifies risks related to value alignment, catastrophic misuse, algorithmic bias, surveillance, moral dependency, and existential harm. These risks may deepen global inequality by concentrating wealth, computational power, and decision-making authority among technologically advanced nations and dominant technology firms. The study concludes that current institutional structures remain insufficient for managing the systemic disruptions associated with AGI and ASI. A stable transition will require proactive international cooperation, stronger governance frameworks, social protection, AI capital taxation, and alignment strategies that reflect collective human values.",
   "authors": [
    "Mehmet Orhan",
    "Zeynep Orhan",
    "Brady Lund",
    "Nayana P. Madali"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-06",
   "doi": "10.20944/preprints202607.0329.v1",
   "explainer": {
    "approach": "A conceptual review of economic, ethical and governance literature.",
    "findings": "The literature ranges from modest productivity gains to explosive growth and a possible post-labor economy; the review argues current institutions are insufficient and calls for international cooperation, social protection and AI capital taxation.",
    "limitations": "A conceptual review of scenarios about systems that do not yet exist; it offers no new estimates.",
    "method_type": "review",
    "question": "How might artificial general intelligence and superintelligence affect the global economy, ethics and governance?"
   },
   "id": "doi:10.20944/preprints202607.0329.v1",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.20944/preprints202607.0329.v1",
   "summary": "A conceptual review of AGI/ASI's global economic, ethical, and governance impacts, mapping scenarios ranging from modest productivity gains to explosive growth and a post-labor economy with wage decline and weakened aggregate demand, alongside risks of misalignment, misuse, and concentration of wealth and compute among a few nations and firms. Its contribution to the debate is the institutional conclusion: current governance structures are judged insufficient for AGI-scale disruption, pushing the case for proactive international coordination rather than reactive national policy.",
   "theme": "Labor & Work",
   "title": "Global Impacts of Artificial General Intelligence and Artificial Superintelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-20944-preprints202607-0329-v1/",
   "venue": "Preprints.org",
   "year": 2026
  },
  {
   "abstract": "In our new analysis, we argue that AI-driven productivity gains will vary widely across countries and are expected to raise per capita real income growth – our measure of welfare – by 0.1–0.95 percentage points annually over the next decade in our central scenario.",
   "authors": [
    "Francesco Filippucci",
    "Peter Gal",
    "Katharina Laengle",
    "Matthias Schief",
    "Muhammed Yildirim"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-05-15",
   "doi": null,
   "explainer": null,
   "id": "title:8dc714d697184683",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.suerf.org/wp-content/uploads/2026/05/SUERF-Policy-Brief-1463_Filippucci-Gal-Laengle-Schief-Yildirim.pdf",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Global trade linkages and the cross-country distribution of the gains from AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-8dc714d697184683/",
   "venue": "SUERF Policy Brief No. 1463",
   "year": 2026
  },
  {
   "abstract": "This study examines the global artificial intelligence market through the lens of innovation and technology governance, asking how institutions are responding to rapid capital concentration, regulatory divergence, and early labor-market adjustment. It combines the market valuation and forecast published by Next Move Strategy Consulting, which places the market at USD 629.97 billion in 2026 and projects USD 3,796.05 billion by 2035 (21.3% CAGR), with company disclosures, government instruments, central bank research, and reporting from approved news organizations published between October 2025 and October 2026. The analysis finds that growth rests on a highly concentrated infrastructure layer, with four hyperscalers projected to spend more than USD 700 billion on capital equipment in 2026. It also finds that governance is fragmenting between delay and simplification in the European Union, preemption of state law in the United States, and voluntary coordination in India, and that early labor evidence points to concentrated, modest effects on young workers rather than broad displacement. The study argues that realization of the forecast depends less on model capability than on grid capacity, regulatory predictability, and the institutional ability to manage distributional effects. It closes with implications for policy and practice and a statement of the evidence's limits.",
   "authors": [
    "Sanyukta Deb"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-14",
   "doi": "10.17613/kpm41-q0355",
   "explainer": null,
   "id": "doi:10.17613/kpm41-q0355",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.17613/091pe-jcr89",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Governing the Compute Frontier: Institutional Responses to Capital Concentration, Regulatory Divergence, and Labor Adjustment in the Global Expansion of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17613-kpm41-q0355/",
   "venue": "Knowledge Commons (Lakehead University)",
   "year": 2026
  },
  {
   "abstract": "Both China and the United States have developed complex artificial intelligence (AI) governance frameworks; however, neither system is able to fulfill its stated objectives. This review article employs a comparative political-economy approach to explore how each nation's foundational political structure leads to unique, intractable regulatory failures. In China, the Party-state model creates an innovation-control paradox: the imperatives of information sovereignty embedded in its generative AI regulations and cross-border data transfer restrictions ultimately undermine the open research environments essential for advancing frontier AI development. This situation disproportionately impacts smaller technology companies and academic researchers while reinforcing the dominance of established industry leaders. Conversely, the United States grapples with severe regulatory fragmentation, leaving existing governance gaps that enable persistent cross-sectoral AI risks, as industry lobbying effectively reframes necessary safety regulations as economically unpatriotic. By examining specific legislative instruments, including the Cyberspace Administration of China's 2023 Interim Measures for Generative AI, Executive Order 14110, and using the EU AI Act as a comparative benchmark, this paper evaluates critical friction points in compliance cost distribution, institutional coordination failures, and the macroeconomic repercussions these frameworks impose on emerging markets in Southeast Asia, Sub-Saharan Africa, and Latin America. The paper concludes that meaningful governance progress requires separating information control from technical AI oversight in China, establishing a unified federal AI statute in the United States, and creating multilateral regulatory coalitions. These coalitions would enable emerging economies to maintain policy autonomy while striving for interoperability with leading AI ecosystems.",
   "authors": [
    "Jimmy Kinyonyi Bagonza"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-15",
   "doi": "10.30574/wjarr.2026.30.3.1617",
   "explainer": {
    "approach": "A review article using a comparative political-economy lens. It examines China's 2023 Interim Measures for Generative AI, US Executive Order 14110, and uses the EU AI Act as a benchmark, focusing on compliance costs, coordination failures and effects on emerging markets.",
    "findings": "China's Party-state model creates an innovation-control paradox in which information-sovereignty rules undermine open research and favour incumbents. The US suffers from regulatory fragmentation that leaves cross-sector gaps. The paper recommends separating information control from technical oversight in China, a federal AI statute in the US, and multilateral coalitions.",
    "limitations": "A qualitative review: the abstract reports no original data or measured outcomes, so its conclusions rest on the authors' reading of legislation and existing literature.",
    "method_type": "policy-analysis",
    "question": "Why do the AI governance frameworks of China and the United States fall short of their own stated goals, and what does that mean for emerging economies?"
   },
   "id": "doi:10.30574/wjarr.2026.30.3.1617",
   "institutions": [
    "University of the Cumberlands"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.30574/wjarr.2026.30.3.1617",
   "summary": "This comparative review argues that neither China's nor the United States' AI governance framework can meet its stated aims. It describes an innovation-control paradox in China's Party-state model and regulatory fragmentation shaped by industry lobbying in the United States.",
   "theme": "Policy & Governance",
   "title": "Governing the Ungovernable: Structural Contradictions, Institutional Limitations, and the Constraints of State Authority in AI Regulation in China and the United States",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-30574-wjarr-2026-30-3-1617/",
   "venue": "World Journal of Advanced Research and Reviews",
   "year": 2026
  },
  {
   "abstract": "The deployment of artificial intelligence (AI) in Nigeria's informal economy is accelerating without adequate governance frameworks to mediate its risks. This paper investigates the intersection of AI deployment and informality in Nigeria, a country where the informal sector accounts for approximately 58% of GDP and over 93% of employment. Drawing on a mixed-methods study comprising semi-structured interviews (n=60) with AI-deploying fintechs, regulators, and informal workers, and a structured survey (n=800) across four Nigerian states, the study maps AI deployment patterns, documents algorithmic harms, and critically analyses the adequacy of existing national and continental governance frameworks. Findings reveal a three-dimensional governance gap: AI systems operate without risk classification or algorithmic auditing; informal workers lack legal standing or digital literacy to contest algorithmic decisions; and regulators lack both technical capacity and jurisdictional mandate to oversee informal sector AI deployment. Seventy-one percent of surveyed informal workers reported experiencing at least one form of AI-mediated harm, including credit exclusion (43%), non-transparent algorithmic decisions (38%), and data extraction without meaningful consent (29%). In response, the paper proposes the Informal Economy AI Governance (IEAIG) Framework — a four-layer co-regulatory model comprising community accountability, platform-level obligations, state co-regulation, and continental interoperability — validated through a Delphi panel of 25 experts. The framework offers the first governance model specifically designed for low-formality, data-sparse environments in Sub-Saharan Africa and contributes original theoretical and policy tools to the responsible AI literature.",
   "authors": [
    "Chukwuma Okpaka"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-14",
   "doi": "10.59256/ijrtmr.20260603031",
   "explainer": {
    "approach": "Mixed methods: 60 semi-structured interviews with AI-deploying fintechs, regulators and informal workers, plus a survey of 800 people across four Nigerian states. A proposed governance framework was validated with a Delphi panel of 25 experts.",
    "findings": "The study describes a three-part governance gap: no risk classification or auditing, workers unable to contest algorithmic decisions, and regulators lacking capacity and mandate. 71% of surveyed informal workers reported at least one AI-mediated harm, most often credit exclusion (43%). It proposes a four-layer co-regulatory framework.",
    "limitations": "Harms are self-reported in a survey covering four states, so prevalence may not generalise nationally. The framework is validated by expert consensus rather than tested in practice.",
    "method_type": "survey",
    "question": "How is AI being deployed in Nigeria's informal economy, what harms do informal workers report, and what governance model fits low-formality settings?"
   },
   "id": "doi:10.59256/ijrtmr.20260603031",
   "institutions": [
    "Veritas University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.59256/ijrtmr.20260603031",
   "summary": "Drawing on 60 interviews and a survey of 800 people in four Nigerian states, the study maps AI deployment in Nigeria's informal economy and finds a three-part governance gap in risk classification, workers' ability to contest decisions, and regulatory capacity. Seventy-one percent of surveyed informal workers reported at least one form of AI-mediated harm, most often credit exclusion.",
   "theme": "Policy & Governance",
   "title": "Governing the Ungoverned: A Framework for Responsible AI Deployment in Nigeria’s Informal Economy",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-59256-ijrtmr-20260603031/",
   "venue": "International Journal Of Recent Trends In Multidisciplinary Research",
   "year": 2026
  },
  {
   "abstract": "In the next post in this series, we’ll discuss how we might improve the accuracy and relevance of LEAP forecasts. There is major disagreement and uncertainty about the trajectory of AI progress, with thoughtful and engaged researchers looking at similar evidence and drawing very different conclusions. For example, Ajeya Cotra and Timothy B. Lee disagree by about 40 years on when they expect self-sufficient AI systems to exist. Andrej Karpathy has argued that achieving AGI would have little to no impact on GDP, while Epoch researchers have modeled that AI automation could lead to economic growth in excess of 30% a year. 2 These disagreements are large, but even they only capture a relatively narrow section of the huge range of views that exist on AI progress. At the Forecasting Research Institute, we want to bring more rigor to the AI progress discussion and help policymakers understand where we’re headed. We have been eliciting forecasts on AI progress since our Existential Risk Persuasion Tournament in mid-2022 and have continued to gather AI progress forecasts in specific domains, including biorisk and cyber. In mid-2025 we launched the Longitudinal Expert AI Panel (LEAP)—a monthly survey that gathers AI progress forecasts from a panel of leading experts and superforecasters. We want to clarify what’s likely to happen with the future of AI, help people understand where forecasts are more or less reliable, and identify particularly accurate forecasting approaches that might give us more insight on the future. In this post, we’ll provide an update on how accurate forecasters—both domain experts and generalist forecasters with a strong track record (“superforecasters”)—have been across FRI studies. In the next post in this series, we’ll take a look at a few different approaches to improving the accuracy and relevance of LEAP forecasts. In this post, we considered forecasts across all relevant FRI studies from mid-2022 to August 2026: our Existential Risk Persuasion T",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-22",
   "doi": null,
   "explainer": null,
   "id": "title:44e8921b6419e833",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://forecastingresearch.org/research/ai-progress-accuracy-update",
   "summary": "The Forecasting Research Institute checks how accurate domain experts and superforecasters have been on AI progress across its studies from mid-2022 to August 2026, including its monthly expert panel, against a backdrop of wide disagreement about AI's trajectory and its economic effects.",
   "theme": "Growth & Macro",
   "title": "How Accurate Have AI Progress Forecasts Been So Far?",
   "url": "https://wemustactnow.onrender.com/research/p/title-44e8921b6419e833/",
   "venue": "Forecasting Research Institute (institute research)",
   "year": 2026
  },
  {
   "abstract": "Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute tasks end to end. Using production data from Perplexity's Search and Computer products, we study this transition by examining how AI agents accelerate and reshape knowledge work. Three key empirical findings emerge. First, using sessions with near-identical initial query pairs as natural experiments for the same underlying task attempted with both products, Computer performs 26 minutes of autonomous work per user session, versus 33 seconds for Search. Computer automates task decomposition and execution that Search users might otherwise manually orchestrate and implement. As a result, Computer shifts follow-up query distribution toward higher-order work such as verification and extension. Autonomy also increases execution quality, with per-query dissatisfaction rates 55% lower on Computer than on Search. Second, due to its autonomy advantage, Computer reduces completion time from 269 to 36 minutes on matched tasks, lowering estimated time and cost by 87% and 94%, respectively, compared to humans equipped with Search alone. Third, Computer changes the scope of work that users attempt: Computer queries more often cross occupational boundaries, require higher-order cognition, draw on broader expertise, take the form of composite tasks that bundle interdependent subtasks into a single query, and unlock work activities that are essentially absent from Search usage among the same users. Together, the evidence indicates that AI agents accelerate workflows, enhance output quality, reduce costs, and expand the breadth and depth of automated work.",
   "authors": [
    "Jeremy Yang",
    "Kate Zyskowski",
    "Noah Yonack",
    "Jerry Ma"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-05",
   "doi": null,
   "explainer": {
    "approach": "Uses production data from Perplexity's Search and Computer products. Sessions with near-identical first queries on both products serve as natural experiments for the same task; the study compares autonomous work time, completion time, dissatisfaction rates and the kinds of tasks users attempt.",
    "findings": "The agent product performs about 26 minutes of autonomous work per session versus 33 seconds for search, cuts matched-task completion time from 269 to 36 minutes, and has 55% lower per-query dissatisfaction. Estimated time and cost fall by 87% and 94% versus humans using search alone, and users attempt broader, more composite tasks.",
    "limitations": "The data come from one company's own products and users, and the matched-query design does not randomise who uses which product. Time and cost savings versus humans are estimates rather than direct measurements.",
    "method_type": "observational",
    "question": "How do autonomous AI agents change the speed, quality and scope of knowledge work compared with a conversational search assistant?"
   },
   "id": "title:9772c630751b4990",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.07489v2",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope",
   "url": "https://wemustactnow.onrender.com/research/p/title-9772c630751b4990/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "When experts hand their work to an AI agent, the usual assumption is that they still make the decisions that matter. The agent writes the code, runs the numbers, and offers advice, but the expert decides what to measure, how to interpret ambiguous evidence, and what the next steps should be. Often there is more than one reasonable answer to these questions, and the decision will come down to the experience and values of whoever makes the call. We wanted to look at whether experts really do make those important decisions, or whether in practice they defer to their AI agents. In a study we report in a new paper , we asked 50 professional analysts to do a realistic statistical analysis with Claude Code. They all had experience in causal inference—that is, working out from data whether one thing causes another. Their task was to advise a farming cooperative on whether an advisory service it was running was effective and should be expanded. Along the way, the analysts had to make four choices between two reasonable options, such as whether to measure productivity as crop yield per hectare or as yield per unit of operating cost, and then make a final decision. To see whether they were making these calls themselves, we set up each analyst’s agent to favor one option on each decision, which the analysts weren’t told about. Which way the agents would “lean” would be decided by coin flip. The agent was told to make the best honest case for its lean, to change its mind if the evidence demanded it, and never to mislead. Across the four analytical choices, analysts went with their agent’s lean 86% of the time. If the agent’s input made no difference, we’d expect about 50%. When analysts had already expressed their own preference before the agent weighed in, they switched away from it 60% of the time if the agent disagreed, compared to 6% if it agreed. Afterward, when asked to explain their choices, 77% of analysts used the agents’ own arguments—sometimes word for word—but only 1",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-27",
   "doi": null,
   "explainer": null,
   "id": "title:38402cee00e1171c",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/agents-judgment-calls",
   "summary": "In a randomized study, 50 professional analysts did a statistical analysis with Claude Code, whose agent was secretly set to favor one option on each analytical choice by coin flip. Analysts went with the agent's lean 86% of the time, and the leans alone predicted 82% of their final recommendations. It is evidence on how AI agents shape micro-level decisions in skilled work; the sample is small and the task fictional.",
   "theme": "Productivity & Firms",
   "title": "How AI agents shape experts’ judgment calls",
   "url": "https://wemustactnow.onrender.com/research/p/title-38402cee00e1171c/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "The widespread adoption of generative AI in software development can impact junior developers as AI tools can help with coding, testing, debugging, and documentation tasks. This paper will explore how AI influences the job, the necessary skills, and the career paths of junior software engineers, and why this can vary across different countries, such as China and the USA. Experiments, job-vacancy studies, surveys, government statistics, and industry evidence are the bases for the analysis. While results have been inconsistent for different tasks and contexts, research indicates that AI can boost developer productivity, with one field experiment finding it has a greater impact on less-experienced developers. There is more code being written, more code to be reviewed, and more code to be communicated and understood because of AI's increasing automation of repetitive tasks and coding. While software developers should still be in demand, competition for jobs in this field might increase. The direct evidence of changes in junior hiring is more pronounced in the United States, while China has a massive developer community and a growing domestic AI industry. In conclusion, the evidence points to the evolution of the junior developer position, not its demise, due to the influence of AI.",
   "authors": [
    "Yuxiang Zou"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-19",
   "doi": "10.54254/2754-1169/2026.37790",
   "explainer": null,
   "id": "doi:10.54254/2754-1169/2026.37790",
   "institutions": [
    "Thomas More University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.54254/2754-1169/2026.37790",
   "summary": "A review of experiments, job-vacancy studies and surveys on how generative AI affects junior software developers in China and the United States. It finds AI can raise developer productivity, more so for less-experienced developers, with clearer signs of changed junior hiring in the US, and concludes the junior role is changing rather than disappearing.",
   "theme": "Labor & Work",
   "title": "How Artificial Intelligence Is Reshaping the Work and Career Paths of Junior Software Developers in China and the United States",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54254-2754-1169-2026-37790/",
   "venue": "Advances in Economics Management and Political Sciences",
   "year": 2026
  },
  {
   "abstract": "How has AI restructured work and employment at Anthropic? As a company at the frontier of AI adoption, Anthropic could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy. What transformations of work and employment can we expect under increasingly powerful AI? Anthropic is worth studying because it has thoroughly molded its workflows around AI. Agents flit through Slack chats and schedule meetings. They autonomously fix bugs and collectively tackle huge engineering projects. Agent hours worked outnumber human hours 6 to 1. Changes happening at Anthropic now give advanced signals about a possible future of work and employment. This report draws on comprehensive internal Anthropic data covering employment, AI use, output metrics, employee surveys, internal communication patterns, employee calendar data, and hiring decision records. It contextualizes these quantitative data with 17 employee interviews. We find that even as the company grows, relative hiring in junior technical positions has slowed. Productivity for both engineers and non-technical employees has leapt up and did so sharply when they adopted coding agents. Hiring managers increasingly emphasize applicants’ autonomy and judgment relative to specific technical expertise. A defining feature of working in an AI-heavy organization is constant pressure to streamline oneself out of this bottleneck position when possible. One engineer described work as facing “an army of Claudes, with every one asking for my attention.” Employees at Anthropic remain critical. But the nature of their contributions has changed. Anthropic has responded to these changes by setting up an early warning system to track jobs at risk of disruption, and as jobs evolve the company has prioritized internal redeployments. These actions, taken at the frontier of both AI development and workplace change, show how specific organizational commitments can shape the impact of AI on employment",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-16",
   "doi": null,
   "explainer": null,
   "id": "title:4d77c6d5ab05e1c4",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/employment-at-anthropic",
   "summary": null,
   "theme": "Labor & Work",
   "title": "How employment is changing at Anthropic",
   "url": "https://wemustactnow.onrender.com/research/p/title-4d77c6d5ab05e1c4/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Exposure indices that map technical capabilities onto occupational abilities have become a standard tool for anticipating how AI may reshape work, but existing measures either cover software AI or proxy robotics through patents and installation data that reflect the industrial robots of past decades. We construct the first abilities-based exposure measures for robotics and physical AI, and the first to span occupations, industries, and geographies. Following the framework of Felten et al. (2018, 2021), we build a robotics capability taxonomy of 16 capabilities in eight domains from 78 academic, industry, and standards sources, use a large language model to score the relatedness of each capability to the 52 ONET work abilities, and aggregate to Robotics Occupational Exposure (ROE) for 774 occupations, Robotics Industry Exposure (RIE) for 250 NAICS industries, and county-level Robotics Geographic Exposure (RGE). We find that robotics exposure concentrates in physically intensive work such as construction, farming, transportation, and production, and in industries such as logging, mining, trucking, and warehousing. Unlike generative-AI exposure, ROE is higher in occupations with lower wages, lower education requirements, and less creative-ability content. Physical collaboration and assistance, terrain-based locomotion, grasping and handling, and task and functional reasoning move the exposure ranking most, yet no single capability carries the index. Because the measures capture technical relatedness rather than readiness, cost, or adoption, they should be read as an upper bound on where physical automation could affect work.",
   "authors": [
    "Sebastian Sartor",
    "Robert Seamans",
    "Neil Thompson"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-16",
   "doi": null,
   "explainer": {
    "approach": "Following Felten et al., the authors build a taxonomy of 16 robotics capabilities from 78 sources, use a large language model to score each against 52 O*NET work abilities, and aggregate to exposure scores for 774 occupations, 250 industries and US counties.",
    "findings": "Exposure is highest in physically intensive work such as construction, farming, transport and production, and industries such as logging, mining, trucking and warehousing. Unlike generative-AI exposure, robotics exposure is higher in lower-wage, lower-education jobs.",
    "limitations": "The measures capture technical relatedness, not readiness, cost or adoption, so the authors present them as an upper bound; scores rely on a language model's ratings.",
    "method_type": "dataset-or-tool",
    "question": "Which occupations, industries and places are most exposed to robotics and physical AI?"
   },
   "id": "title:f2c0eff3ffc3a117",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.24267v1",
   "summary": "The authors build the first abilities-based measures of exposure to robotics and physical AI, scoring 16 robotics capabilities against O*NET work abilities with a language model and aggregating to 774 occupations, 250 industries and US counties. Exposure concentrates in physically intensive work such as construction, farming, transportation and warehousing, and unlike generative-AI exposure it is higher in lower-wage, lower-education jobs. The measures capture technical relatedness, not cost or adoption, so the authors present them as an upper bound.",
   "theme": "Labor & Work",
   "title": "How exposed is work to robotics? Occupational, industry, and geographic measures",
   "url": "https://wemustactnow.onrender.com/research/p/title-f2c0eff3ffc3a117/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence will probably generate major changes in the US economy, although the nature, timing, and magnitude of those changes are highly uncertain.We analyze a set of long-term scenarios involving different combinations of faster productivity growth, greater income inequality, job displacement, and a higher capital share of income.For each scenario, we assess the implications for federal debt and potential policy responses related to faster economic growth, the distribution of income, support for workers who are laid off, and taxation and ownership of capital.Given the uncertainty surrounding AI's economic effects, policies that are robust to different scenarios would be especially valuable.",
   "authors": [
    "Karen Dynan",
    "Douglas Elmendorf",
    "Louise Sheiner"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-01",
   "doi": "10.3386/w35437",
   "explainer": null,
   "id": "doi:10.3386/w35437",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.3386/w35437",
   "summary": "This NBER paper lays out long-term US scenarios combining faster productivity growth, greater income inequality, job displacement and a higher capital share of income, and works through what each would mean for federal debt. It then weighs policy responses on growth, income distribution, support for laid-off workers and the taxation and ownership of capital, favouring policies that hold up across scenarios given how uncertain AI's effects are.",
   "theme": "Labor & Work",
   "title": "How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3386-w35437/",
   "venue": "National Bureau of Economic Research",
   "year": 2026
  },
  {
   "abstract": "Retraining workers is the most popular policy option for mitigating labor market disruption from AI. In this report, the authors investigate whether these programs would work in the face of significant labor market disruption. The review is part of our Economic Research team's work on AI's effects on the economy. Our Economic Index tracks how AI is being used across occupations and industries. Earlier this year, we published a framework for measuring AI's effects on the labor market and identifying the jobs most likely to be affected. Our Economic Policy Framework sets out possible policy responses, including worker retraining, across a range of scenarios. We want to understand the evidence behind each of these policy responses. The review draws on 56 randomized US studies, combined in a new meta-analysis, along with experimental evidence from Europe. On average, job training programs produce positive but modest effects: for each person offered a training slot, employment rises by two to three percentage points and earnings by roughly $1,000 a year, against a cost of about $13,000. Counting the added tax revenue and reduced benefit payments, the government recovers more than half of what it spends, and programs roughly break even overall. A small set of “sector programs”—programs that partner with employers in a high-demand industry and place people directly into jobs in that field—produce gains several times larger. But attempts to replicate them have often failed. The authors conclude that if AI displaces workers at scale, existing retraining programs would likely fall short. Their central recommendation is to invest now in demonstrating, evaluating, and scaling the most promising programs, including through an effort to rapidly expand a leading program for a specific group of workers and rigorously measure the results. Our Economic Futures Research Fund is designed to fund investigations into questions like these.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-08-12",
   "doi": null,
   "explainer": null,
   "id": "title:33a0771f4c65c95d",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/reviewing-the-evidence-on-worker-retraining-programs",
   "summary": null,
   "theme": "Labor & Work",
   "title": "How well do job retraining programs work?",
   "url": "https://wemustactnow.onrender.com/research/p/title-33a0771f4c65c95d/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Has generative AI changed how labor markets value human capital? We study this question using contract-level data from Upwork, a large online labor market. We represent worker profiles with high-dimensional text embeddings, allowing us to capture rich human capital information from unstructured profile text. We then compute the predictive importance of workers' human capital information and posted hourly rates for client demand, and incorporate these measures into a difference-in-differences design around the release of ChatGPT. We find that in more AI-exposed job categories, the importance of human capital declines and the importance of price rises, suggesting a commoditization effect of AI on labor. Two additional findings support commoditization as a mechanism: The demand premium enjoyed by workers with strong human capital declines in more AI-exposed categories, and demand reallocates toward lower-priced workers. Our results have implications for the design of online labor markets, workers' incentives to invest in human capital, and labor welfare.",
   "authors": [
    "Auyon Siddiq",
    "Niuniu Zhang"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-20",
   "doi": null,
   "explainer": null,
   "id": "title:efb98a938e2d8bd7",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.21880v2",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Human Capital, AI, and Labor Commoditization",
   "url": "https://wemustactnow.onrender.com/research/p/title-efb98a938e2d8bd7/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "This report examines a central economic concern about artificial intelligence: when firms replace workers with AI to reduce costs, they may also reduce the purchasing power of the very consumers who sustain market demand. The original idea, known as the AI layoff trap, argues that individual firms may find it rational to automate aggressively, even though the collective result may weaken demand and make both workers and firms worse off. The report does not reject this argument. Instead, it refines it. It argues that AI adoption and worker displacement are not the same thing. Some AI systems fully replace human labor, while others assist, supervise, or augment workers without eliminating their role. Because of this, the economic risk does not come from AI use in general, but from the extent to which AI removes worker income from the economy. The report introduces a broader way to think about automation by distinguishing between how widely AI is used, how deeply it replaces human labor, and whether it also improves productivity. This distinction matters because human-in-the-loop systems may preserve employment and soften the demand loss, while full-replacement systems may intensify it. The report also shows that competitive pressure may push firms toward more replacement-heavy AI designs than would be socially or collectively desirable. The main policy implication is that regulation should not treat all AI adoption as harmful. A better approach is to focus on worker-equivalent displacement: the actual amount of labor income removed by AI deployment. Policies such as income replacement, wage insurance, retraining, worker profit-sharing, and carefully designed displacement-based taxes may be more appropriate than broad taxes or restrictions on AI itself. Overall, the report presents a more nuanced account of the AI layoff trap. It preserves the original insight that decentralized automation decisions can create harmful demand effects, while showing that the severity of ",
   "authors": [
    "Ambarish Moharil"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-10",
   "doi": "10.5281/zenodo.20633148",
   "explainer": {
    "approach": "A conceptual report that revisits the AI layoff trap argument and separates how widely AI is used, how deeply it replaces human labor, and whether it raises productivity.",
    "findings": "The report argues the demand risk comes from labor income removed by AI rather than AI use as such; human-in-the-loop systems may soften the demand loss, while competition may push firms toward more replacement-heavy designs than is collectively desirable.",
    "limitations": "The argument is conceptual; the abstract reports no data or estimates. Policy suggestions such as wage insurance or displacement-based taxes are proposed rather than evaluated.",
    "method_type": "theory",
    "question": "Does replacing workers with AI risk a fall in consumer demand that leaves both workers and firms worse off, and does it matter whether AI replaces or assists workers?"
   },
   "id": "doi:10.5281/zenodo.20633148",
   "institutions": [
    "Tilburg University",
    "Eindhoven University of Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20633148",
   "summary": "Responding to the 'AI layoff trap' argument, this report distinguishes AI adoption breadth, depth of labor replacement, and productivity effects, arguing that the demand externality (automation eroding the consumer purchasing power firms depend on) is driven not by AI use per se but by worker-equivalent displacement, the actual labor income removed from the economy; human-in-the-loop systems soften the externality while full-replacement designs amplify it, and competition pushes firms toward more replacement-heavy designs than is collectively optimal. This reframes the automation-tax debate: policy should target displaced labor income rather than AI adoption broadly, giving regulators a margin that penalizes replacement without discouraging augmentation.",
   "theme": "Labor & Work",
   "title": "Human-in-the-Loop Automation and the Demand Externality: A Generalized Response to The AI Layoff Trap",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20633148/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) has emerged as a transformative technology influencing employee productivity and managerial decision-making in contemporary organisations. This study examines the impact of AI adoption on workplace efficiency, employee performance, and the quality of managerial decisions. AI-powered tools facilitate task automation, data analysis, workflow optimisation, and real-time information processing, enabling employees to improve productivity and managers to make faster, evidence-based decisions. However, the effectiveness of AI depends on factors such as technological infrastructure, employee competencies, organisational readiness, and ethical implementation. The study also considers potential challenges, including job displacement, algorithmic bias, data privacy concerns, and excessive dependence on automated systems. It emphasises the importance of human–AI collaboration in achieving sustainable organisational performance. The findings are expected to provide insights into how organisations can strategically integrate AI into business operations while maintaining accountability, employee engagement, and managerial effectiveness. The study contributes to understanding AI-driven workplace transformation and its implications for organisational growth and competitiveness",
   "authors": [
    "Mr. Jainish Gotecha, Mr. Sandip Rawool, CA Dhvani Shah"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-24",
   "doi": "10.48175/ijarsct-40132",
   "explainer": null,
   "id": "doi:10.48175/ijarsct-40132",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.48175/ijarsct-40132",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Impact of Artificial Intelligence on Employee Productivity and Managerial Decision-Making",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48175-ijarsct-40132/",
   "venue": "International Journal of Advanced Research in Science Communication and Technology",
   "year": 2026
  },
  {
   "abstract": "In order to solve the workplace-identified problems, this research major objective is to ascertain the nature and actual impact of Artificial Intelligence (AI) on employee’s productivity. This study adopted a descriptive survey research method through the use of primary data source instruments of questionnaires shared with over 150 employees of technological firms in Delta State. This study found that Artificial intelligence (AI) has the potential to revolutionize the workplace by greatly increasing worker engagement and productivity. The study empirically proved that Artificial Intelligence impacts positively and significantly on the sustainability of employee engagement. Secondly, the study empirically revealed that there is a positive and significant connection between Artificial Intelligence and employees' attitudes toward work in organizations. This means that the AI introduction has made employees to develop a positive work attitude in order to keep their jobs from being taking over by automation powered AI machines. The study analyses proved that Artificial Intelligence (AI) has a positive and significant effect on employee’s productivity. AI may assist organizations in more efficiently accomplishing their objectives by automating repetitive processes, customizing employee experiences, and encouraging improved communication and teamwork. To guarantee that AI's advantages are fully realized, it is crucial to solve the difficulties and moral dilemmas related to its application. It was observed that Artificial Intelligence integration to the workplace must be human-centric before it can lead to more employee engagement and increase in productivity. The study concludes that the welfare of employees should always come first when using AI, making ethical issues very important. The study recommends that companies aim for a human-centric strategy, making sure AI complements human strengths rather than takes their place in the workspace.",
   "authors": [
    "Ernest Jebolise Chukwuka"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-21",
   "doi": "10.6084/m9.figshare.32749662",
   "explainer": null,
   "id": "doi:10.6084/m9.figshare.32749662",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.6084/m9.figshare.32749662",
   "summary": "A questionnaire survey of over 150 employees of technology firms in Delta State, Nigeria, reports a positive and significant association between AI use and employee productivity, engagement and work attitudes, and recommends a human-centric approach to AI adoption.",
   "theme": "Productivity & Firms",
   "title": "Impact of Artificial Intelligence on Employee’s Productivity in Technological Organizations in Delta State, Nigeria",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-6084-m9-figshare-32749662/",
   "venue": "Figshare",
   "year": 2026
  },
  {
   "abstract": "The modern Software Development Lifecycle (SDLC) is undergoing a profound paradigm shift driven by the rapid maturation of generative artificial intelligence, Large Language Models (LLMs), and automated program synthesis engines. While historical transitions—from linear Waterfall models to Agile sprints and continuous DevOps pipelines—focused primarily on procedural orchestration and organizational agility, the integration of Artificial Intelligence introduces cognitive automation directly into the engineering fabric. This research paper presents an exhaustive empirical and methodological investigation into the impact of AI across the end-to-end SDLC, evaluating its tangible ramifications on developer productivity, cognitive workflow dynamics, code quality engineering, and enterprise security. Employing a rigorous triangulated mixed-methods research design, this study combines: (i) quantitative repository mining across 1,200,000 commits spanning 45 enterprise software projects; (ii) a controlled randomized quasi-experiment comprising N = 120 professional software engineers measuring task velocity and cognitive workload via the NASA-TLX inventory; and (iii) longitudinal static analysis tracking post-release defect density and Common Weakness Enumerations (CWE). Our findings demonstrate that AI assistance compresses coding lead times by 55.8% and accelerates developer flow states, yielding substantial improvements across DORA deployment frequency and change velocity metrics. However, ungoverned AI code generation introduces acute quality paradoxes, including a 19.4% surge in code churn, subtle logic regression risks, and elevated vulnerabilities in insecure deserialization and unverified package dependencies. To resolve this tension, we propose a multi-tier Enterprise AI-Augmented SDLC (A-SDLC) Framework coupling Abstract Syntax Tree (AST) policy guardrails, local context retrieval-augmented generation (RAG), automated mutation test synthesis, and continuous AIOps obs",
   "authors": [
    "Neha Jethwa"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.5281/zenodo.23696548",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23696548",
   "institutions": [
    "G.S. Science, Arts And Commerce College"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23696548",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Impact of Artificial Intelligence on Modern Software Development Lifecycle: Methodological Advancements, Developer Productivity, and Quality Engineering",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23696548/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "This article examines the impact of introducing artificial intelligence (AI) technologies into enterprise operations on labor productivity, with particular attention to the mediating and enabling roles of employees’ digital competencies. The study aims to identify opportunities to improve efficiency through AI-driven automation, data-informed decision-making, and business process optimization, as well as to systematize the skills required to realize these opportunities. The analysis draws on a comparative review of scholarly literature, a systems approach, and logical synthesis. Digital competencies are considered in terms of searching for, analyzing, and interpreting data; using AI tools appropriately; critically assessing generated outputs; complying with cybersecurity requirements; and collaborating in digital environments. The analysis suggests that technological infrastructure alone does not guarantee greater productivity. Outcomes also depend on workforce readiness, management practices, organizational culture, and the extent of workflow redesign. AI can reduce time spent on routine tasks, limit errors, and improve resource allocation. However, insufficient skills may increase risks associated with excessive reliance on technology, information security, and the quality of managerial decisions. To address these challenges, the article proposes an approach that combines initial competency assessment, role-specific training programs, practice-based learning, and regular evaluation against labor productivity indicators. Suggested indicators include output per employee, task completion time, and quality measures. The conclusions provide a conceptual basis for aligning enterprise investment in AI with human capital development and for designing management decisions that support sustainable productivity growth.",
   "authors": [
    "Maftuna Samiyeva"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": null,
   "explainer": null,
   "id": "title:a2888bc2d5a90c14",
   "institutions": [
    "Tashkent State University of Economics"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://brightmindpublishing.com/index.php/ev/article/view/3031",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "IMPLEMENTING ARTIFICIAL INTELLIGENCE AND LABOR PRODUCTIVITY IN ENTERPRISES: THE ROLE OF DIGITAL COMPETENCIES",
   "url": "https://wemustactnow.onrender.com/research/p/title-a2888bc2d5a90c14/",
   "venue": "EduVision: Journal of Innovations in Pedagogy and Educational Advancements",
   "year": 2026
  },
  {
   "abstract": "The article analyses organisational and economic mechanisms for ensuring economic growth based on digital transformation and artificial intelligence (AI) in Uzbekistan, using data of the National Statistics Committee, Oxford Insights and WIPO. In 2020–2025 the volume of communication and information services rose from UZS 13.9 to 79.5 trillion, and its ratio to GDP from 1.96% to 4.30%, with the sector growing about three times faster than GDP. At the same time, 72.7% of digital services are concentrated in Tashkent city, and a substantial gap remains between current indicators and the 2030 AI strategy targets. Evidence-based recommendations on governance, financial, infrastructure, human capital and regional mechanisms are proposed.",
   "authors": [
    "Абдумалик Бектемиров",
    "Worldly Knowledge Publishing Centre"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-06",
   "doi": "10.5281/zenodo.23178646",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23178646",
   "institutions": [
    "Samarkand Institute of Economics and Service"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23178646",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "IMPROVING ORGANISATIONAL AND ECONOMIC MECHANISMS FOR ENSURING ECONOMIC GROWTH BASED ON DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23178646/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The findings point to a user base that applies AI more heavily in professional contexts, delegates more autonomy to it, and brings Claude tasks that are substantially more time-consuming to complete without assistance. Higher shares of complex tasks that humans could not complete alone suggest that Indian users are using the technology at the frontier. India ranks second among all countries by share of total Claude.ai use, trailing only the United States. However, on a per-capita basis, adjusting for the working-age population, India ranks 101st out of 116 countries with sufficient observation volume, below other countries in Asia such as Singapore or Malaysia. This gap suggests that India’s high Claude use overall reflects the sheer size of its population, not that the average person is using Claude heavily. This points to significant opportunities to increase adoption. Use is concentrated in a small number of highly economically active states. Maharashtra, Tamil Nadu, Karnataka, and Delhi together account for over half of India’s total Claude.ai use. This pattern closely mirrors India’s IT sector geography and urban economic output. The concentration in these four states—home to Bangalore, Hyderabad, Chennai, Mumbai, and Delhi NCR—suggests that current AI adoption is driven primarily by India’s established technology workforce rather than broad-based consumer uptake. The occupational mix of Indian Claude.ai use, inferred by mapping tasks to related occupations, skews towards software development and engineering roles, consistent with the country’s large IT services sector. The most common O*NET tasks performed by Indian users confirm the software-heavy profile: India ranks 1st globally in the share of AI use devoted to software-related tasks (45.2% of all O*NET-mapped tasks), ahead of Vietnam (42.1%) and Egypt (39.2%). The presence of educational tasks among the most common individual tasks (see Table 1) and when aggregating tasks to occupation groups (see Figure ",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-02-16",
   "doi": null,
   "explainer": null,
   "id": "title:797da37dd58e6a27",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/india-brief-economic-index",
   "summary": null,
   "theme": "Labor & Work",
   "title": "India Country Brief: Anthropic Economic Index",
   "url": "https://wemustactnow.onrender.com/research/p/title-797da37dd58e6a27/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "The rapid development of artificial intelligence is profoundly reshaping the global labor market landscape. This article is based on a systematic literature review and summarizes the four core theoretical mechanisms of substitution, complementarity, new task creation, and skill mismatch. It analyzes the direct impact of artificial intelligence on employment structure, occupational tasks, and skill demand, as well as its indirect effects on job mobility, cross-border and industry differences, and policy interventions. Research has shown that artificial intelligence is primarily driven by substitution effects in the short term, but will generate complementary and creative effects in the long term, with significant cross national, cross industry, and cross regional heterogeneity in its impact. Skill mismatch constitutes the core contradiction of labor force transformation. There are still significant shortcomings in existing research in terms of local empirical evidence, micro task mechanisms, and the impact of cutting-edge AI. This article aims to provide systematic literature support for subsequent research and adaptive policy formulation.",
   "authors": [
    "Yuehan Cai"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.54254/2754-1169/2026.bl34049",
   "explainer": null,
   "id": "doi:10.54254/2754-1169/2026.bl34049",
   "institutions": [
    "Hong Kong Baptist University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.54254/2754-1169/2026.bl34049",
   "summary": "A literature review organising AI's labor-market effects into substitution, complementarity, new task creation and skill mismatch. It concludes that substitution dominates in the short term, complementary and creative effects grow in the long term, and skill mismatch is the central problem of the transition.",
   "theme": "Labor & Work",
   "title": "Influence of Artificial Intelligence in the Labor Market",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54254-2754-1169-2026-bl34049/",
   "venue": "Advances in Economics Management and Political Sciences",
   "year": 2026
  },
  {
   "abstract": "The objective of this research was to analyze the impact of artificial intelligence in the labor field, in order to determine whether it represents a risk or an opportunity for the future of employment, as well as to evaluate the transformation of the labor market in the face of the growing presence of this technology. A systematic literature review was conducted based on a structured process that included the identification, selection, and evaluation of scientific studies, starting from a set of 350 research articles obtained from recognized academic databases, published between 2020 and 2025. After a rigorous exclusion process, which involved the analysis of titles, abstracts, removal of duplicates, and the exclusion of studies not related to the variable of interest, 40 articles were selected for analysis. The results showed that artificial intelligence improved efficiency and optimized processes, although it also raised concerns about the possible displacement of jobs. Likewise, it was evidenced that the development of digital competencies and soft skills contributed to mitigating the risks of unemployment and promoting the creation of new jobs. In this sense, artificial intelligence represents an opportunity if strategies are implemented to strengthen employability and facilitate adaptation to technological changes.",
   "authors": [
    "Angela Luciana Laura Vega",
    "Daniel Josué Yataco Blas",
    "Jorge Andrés Tipiani Yactayo",
    "Ruth Gissela Perez Cabrejos"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-15",
   "doi": null,
   "explainer": null,
   "id": "title:f12adff5c1af7314",
   "institutions": [
    "Universidad Privada San Juan Bautista"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://rcci.uci.cu/index.php/RCCI/article/view/13242",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Inteligencia Artificial y la transformación de la empleabilidad en el trabajo del futuro",
   "url": "https://wemustactnow.onrender.com/research/p/title-f12adff5c1af7314/",
   "venue": "Revista Cubana de Ciencias Informáticas",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is transforming both the content of work and the capabilities workers present to the labor market. This study examines whether AI-oriented human capital updating, annual increases in AI-related signals in resume-related text, is associated with subsequent turnover. Drawing on strategic human capital research and signaling theory, I argue that AI-oriented updating makes AI-related human capital visible to external employers, thereby increasing workers’ marketability and likelihood of subsequent turnover. I further examine whether this relationship is conditioned by the AI exposure of individuals’ current and desired occupations and by traditional human capital characteristics, including education and tenure. Using 106,822 person-year observations from 29,341 jobseekers, drawn from annual resume and work-history records from a large Chinese online recruitment platform from 2020 through 2025, I construct a person-year panel and estimate first-event discrete-time hazard models. The results show that AI-oriented human capital updating is positively associated with subsequent first observed turnover, and this relationship remains significant when updating is lagged by two and three years. The positive relationship is stronger among individuals with greater accumulated work experience, but it does not vary significantly by education, desired-occupation AI exposure, or current-occupation AI exposure. Supplementary analyses further indicate that the association between AI-oriented updating and turnover became stronger in more recent years. By documenting the labor-market value of AI-related human capital, examining its consequences across occupational contexts, and demonstrating its consistent role in worker mobility, this study advances research on strategic human capital, labor-market signaling, turnover, and the role of AI in shaping careers and workforce flows.",
   "authors": [
    "Qizhong Yang"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-07",
   "doi": "10.7282/t3-pmdy-ex04",
   "explainer": null,
   "id": "doi:10.7282/t3-pmdy-ex04",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.proquest.com/LegacyDocView/DISSNUM/32903904",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Keeping Up with AI, Leaving Jobs? AI-Oriented Human Capital Updating, Occupational AI Exposure, and Employee Turnover",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-7282-t3-pmdy-ex04/",
   "venue": "Rutgers University Community Repository (Rutgers University)",
   "year": 2026
  },
  {
   "abstract": "Massachusetts has built an unusually prosperous economy around high household costs, concentrated knowledge-intensive employment, and institutions sustained by substantial recurring economic activity. This paper examines a structural vulnerability in that arrangement: the earning power that justifies the Commonwealth's cost premium may become less reliable even as household commitments, public obligations, and physical infrastructure remain slow to adjust. Artificial intelligence is examined not as a prediction of mass unemployment, but as a potential change in the amount of human labor required to produce knowledge-intensive output. The analysis distinguishes employment, economic value, carrying wage capacity, and household income headroom. Using public economic data, illustrative wage-compression scenarios, demographic trends, fiscal conditions, and the historical transformation of the Route 128 technology corridor, it identifies a conditional feedback mechanism through which declining household margin and rigid institutional obligations could reinforce one another without an immediate collapse in employment, population, or economic output. The paper does not establish that such deterioration is already underway. Instead, it argues that Massachusetts' long-term resilience depends on how technological productivity translates into durable household and fiscal capacity. Its proposed direction combines greater physical anchoring of knowledge-intensive economic activity with reductions in the recurring costs of sustaining ordinary households. The central question is not whether Massachusetts can continue creating extraordinary wealth, but whether that prosperity can continue supporting the population and institutions built around it.",
   "authors": [
    "Scott Jellen"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-18",
   "doi": "10.5281/zenodo.23458090",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23458090",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23458090",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Killing Yourself to Live: Massachusetts, Artificial Intelligence, and the Cost of Staying",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23458090/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The rapid diffusion of AI is generating a wave of research measuring and forecasting its impacts on labor markets. But the track record of past approaches gives reason for humility. For example, a prominent attempt to measure job offshorability identified roughly a quarter of US jobs as vulnerable, but a decade on, most of those jobs maintained healthy employment growth. The government’s own occupational growth forecasts, while directionally correct, have added little predictive value beyond linear extrapolation of past trends. Even in hindsight, the impact of major economic disruptions on the labor market is often unclear. Studies on the employment effects of industrial robots reach opposing conclusions, and the scale of job losses attributed to the China trade shock continues to be debated. 1 In this paper, we present a new framework for understanding AI’s labor market impacts, and test it against early data, finding limited evidence that AI has affected employment to date. Our goal is to establish an approach for measuring how AI is affecting employment, and to revisit these analyses periodically. This approach won't capture every channel through which AI could reshape the labor market, but by laying this groundwork now, before meaningful effects have emerged, we hope future findings will more reliably identify economic disruption than post-hoc analyses. It is possible that the impacts of AI will be unmistakable. This framework is most useful when the effects are ambiguous—and could help identify the most vulnerable jobs before displacement is visible. Causal inference is easier when the effects are large and sudden. The COVID-19 pandemic and accompanying policy measures caused economic disruption so stark that sophisticated statistical approaches were unnecessary for many questions. For example, unemployment jumped sharply in the early weeks of the pandemic, leaving little room for alternative explanations. The impacts of AI, however, might be less like COVID an",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-03-05",
   "doi": null,
   "explainer": null,
   "id": "title:1da31a2b3a49544c",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/labor-market-impacts",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Labor market impacts of AI: A new measure",
   "url": "https://wemustactnow.onrender.com/research/p/title-1da31a2b3a49544c/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "This chapter examines how artificial intelligence (AI) changes labor markets and the distribution of income. It begins by distinguishing skill-biased, routine-biased, and task-biased technological change, then explains why generative AI requires a further shift in emphasis from occupations to tasks, workflows, and organizational control. The chapter argues that AI affects inequality through several channels at once: direct task displacement, productivity gains, the creation of new tasks, changes in market power, and differences in worker bargaining power. A central claim is that exposure to AI is not the same as technological unemployment. The distributional consequences depend on adoption costs, complementary investments, institutions, and the allocation of productivity rents inside firms and across markets. This chapter, therefore, treats AI neither as an automatic engine of mass unemployment nor as a neutral productivity tool, but as a general-purpose technology whose labor market effects are mediated by economic structure and policy.",
   "authors": [
    "Ceyhun Elgin"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-20",
   "doi": "10.4324/9781042011162-4",
   "explainer": null,
   "id": "doi:10.4324/9781042011162-4",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.4324/9781042011162-4",
   "summary": "A book chapter on how AI changes labour markets and income distribution, moving from skill- and routine-biased technical change to a task- and workflow-based view of generative AI. It argues exposure is not unemployment and that distributional outcomes depend on adoption costs, complementary investment, institutions, bargaining power and how productivity rents are shared.",
   "theme": "Labor & Work",
   "title": "Labor markets and inequality",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4324-9781042011162-4/",
   "venue": "Routledge eBooks",
   "year": 2026
  },
  {
   "abstract": "Purpose. Law is a profession built on text, which makes it an obvious target for language models, and one in which a confident error can end up in a court filing. This article asks where artificial intelligence stands in legal services in 2026 and where the profession may be by about 2035. Design and methods. A review combining the share of the legal research literature that involves AI (OpenAlex, 2010–2025), Eurostat's survey of AI use in EU professional-services enterprises (2021–2025), randomised trials with law students, audits of the accuracy of legal AI tools, a public database of court decisions on fabricated citations, and the regulatory position in the EU and the United States. Claims taken from the literature were checked against the sources cited. The scenarios are judgements built on this evidence. Findings. The share of legal works that mention AI rose from 0.7% in 2018 to 7.0% in 2025, 10.6 times as much and the second-largest increase of the 20 industries, and language-model works from 93 to 3,598. In the EU, 37.9% of enterprises in the wider professional-services sector used AI in 2025, up from 18.1% in 2021 and nearly twice the 19.9% for all enterprises. Randomised trials with law students show real gains: a recent trial found productivity gains of 50% to 130% on five of six tasks and better quality of work with a retrieval-based legal tool and a reasoning model, while an earlier trial with GPT-4 found large and consistent speed gains but only slight, inconsistent gains in quality. The risk is equally documented. Leading commercial legal research tools hallucinated between 17% and 33% of the time in a preregistered audit, general-purpose models at least 58% of the time on questions about federal cases, and a public database had logged 2,202 court decisions involving hallucinated content by October 2026. We expect AI-assisted drafting and research to be routine in firms by 2028, the profession to settle norms of verification and disclosure in 2029–20",
   "authors": [
    "P. Ghorbani"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-21",
   "doi": "10.5281/zenodo.23517309",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23517309",
   "institutions": [
    "Shahid Beheshti University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23517309",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Legal Services and Artificial Intelligence: Real Productivity Gains, Fabricated Citations, and a Profession Learning to Verify",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23517309/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "How much liability should AI providers bear when their services enable both attack and defence? Liability can improve welfare while increasing harm. Providers sell a common input to productive users, attackers and defenders. Within a defended contest, a higher common price reduces effort without",
   "authors": [
    "Joshua S. Gans"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "September 2026",
   "doi": null,
   "explainer": null,
   "id": "title:d56211e7a9037887",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35828",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Liability and Pricing of Dual-Use AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-d56211e7a9037887/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "OBJECTIVES: To investigate the longitudinal effects of an ambient artificial intelligence (AI) scribe use in primary care on outcomes related to documentation time, note writing patterns, and clinician financial productivity. MATERIALS AND METHODS: We conducted a quasi-experimental study with an interrupted time-series design to ascertain the effect of AI scribe use on note writing time, time outside of scheduled hours (TOSH), pajama time (5:30PM-7:00aM), note length, note closure <24 h, and work relative value units (wRVU). For each primary care clinician (physician, advanced practice provider), date of initial access was day 0; each measure was calculated for 150 days prior (pre-period) and after (post-period) day zero. RESULTS: Two hundred and twenty clinicians (mean age = 43.7, 70.9% females; 56.4% physicians) from 36 clinics, conducting 314 845 patient encounters were included. Compared to the pre-period, there was 7% decrease in note writing time at day 0 (incidence rate ratio [IRR] 0.93, 95% credible interval [CI] [0.89, 0.98]), increasing to 16% decrease by day 150 (IRR 0.84, 95% CI [0.82, 0.87]). There was no change in pajama time, TOSH, or wRVU at day 0; however, at day 150, there was a 19% decrease in pajama time (0.81, 95% CI [0.73, 0.90]), 13% decrease in TOSH (0.87, 95% CI [0.77, 0.98]), and a 2% increase in wRVU (1.02, 95% CI [1.01, 1.04]). At day 0, there was a 5% increase in note closures (1.05, 95% CI [1.02, 1.08]), with no difference to pre-period levels by day 150. There were no changes in note length at day 0 or day 150. DISCUSSION AND CONCLUSION: There were persistent changes in AI scribe use measures over time as clinicians situated these tools within their workflows.",
   "authors": [
    "R. J. Waken",
    "Sunny S. Lou",
    "Mackenzie R. Hofford",
    "Elise Eiden",
    "Carrie Burk",
    "Seunghwan Kim",
    "Jared Esker",
    "Linying Zhang",
    "Thomas M. Maddox",
    "Joanna Abraham",
    "Albert M. Lai",
    "Sam B. Bhayani"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-18",
   "doi": "10.1093/jamia/ocag151",
   "explainer": null,
   "id": "doi:10.1093/jamia/ocag151",
   "institutions": [
    "Washington University in St. Louis"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.1093/jamia/ocag151",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Longitudinal effects of ambient artificial intelligence scribe use on documentation burden and financial productivity in primary care: a quasi-experimental study",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1093-jamia-ocag151/",
   "venue": "Journal of the American Medical Informatics Association",
   "year": 2026
  },
  {
   "abstract": "When not told to condition on a progress scenario, experts, superforecasters, and the public broadly agree on near-term GDP growth, with median forecasts of 2.7%, 2.5%, and 2.5% annualized real GDP growth for 2025–2030, respectively—broadly in line with recent historical rates. Scenario assumptions make a substantial difference, however. Under Rapid Progress, the median expert expects 4.0% annualized growth to 2030 and 5.0% to 2050, while under Slow Progress those figures fall to 2.3% and 2.5%.",
   "authors": [
    "Connacher Murphy",
    "Josh Rosenberg",
    "Jordan Canedy",
    "Zach Jacobs",
    "Nadja Flechner",
    "Rhiannon Britt",
    "Alexa Pan"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-04-15",
   "doi": null,
   "explainer": null,
   "id": "title:bc335413ca61c322",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://leap.forecastingresearch.org/reports/wave6",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Longitudinal Expert AI Panel, Wave 6: Economic Effects of AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-bc335413ca61c322/",
   "venue": "Forecasting Research Institute, LEAP Report 6",
   "year": 2026
  },
  {
   "abstract": "Frontier-AI governance today faces a problem structurally analogous to the one banking regulation faced pre-2008, and which post-2008 reforms (Basel III, Dodd-Frank) have since addressed. Two gaps recur: discovering a risk is not tantamount to acting on it, and individual-model review is unlike managing correlated build-up across the sector. Drawing on the Basel III framework and the U.S. financial-stability architecture, I propose a macro-prudential early warning and response system (\"MEWRS\") for internal frontier AI. These are systems deployed for labs' own internal research, testing, and production workflows, as distinct from externally released products. Layer A adapts the finder-coordinator-defender early-warning model to route structured reports on dual-use capabilities, autonomy indicators, and security compromises through a government clearinghouse to domain-specific defender working groups. Layer B calibrates operational controls via three quantitative buffer metrics, namely Effective Compute-at-Risk (ECAR), Cumulative Red-Team Hours (CRTH), and an Alignment Robustness Score (ARS), so that faster capability scaling automatically triggers stronger safeguards, analogously to how risk-weighted assets drive capital ratios under Basel III. I outline the reporting schema, map six Basel III mechanisms onto AI-governance analogues, identify seven failure modes with concrete mitigations, and sketch an exercise-based validation plan. MEWRS is designed to detect correlated risk build-ups across the frontier-AI sector and create pre-committed off-ramps before a cascade unfolds.",
   "authors": [
    "Pranav Mehta"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-07-03",
   "doi": null,
   "explainer": null,
   "id": "title:ebc49a21d835f998",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2607.03542v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Macro-Prudential AI Governance: A Two-Layer Early Warning and Response System for Frontier AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-ebc49a21d835f998/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "We examine how firms acquire artificial intelligence (AI) capabilities and how their AI sourcing decisions relate to workforce outcomes. Using Swedish enterprise surveys from 2019 and 2021 linked to financial statements and employer–employee data, we directly observe whether firms adopt AI and whether they outsource AI-related activities. AI adoption is concentrated among larger firms. Among adopters, pre-existing cost pressure is associated with outsourcing. Relative to non-adopters, both in-house adopters and outsourcers have higher subsequent employment and persistently higher shares of university-educated and computer-science (CS)-educated workers, with no further change in workforce composition in the year after adoption. Workforce outcomes are statistically indistinguishable between in-house adopters and outsourcers.",
   "authors": [
    "Oscar Stålnacke",
    "Ebrahim Bazrafshan",
    "Amin Sofla"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-14",
   "doi": "10.1080/13504851.2026.2749673",
   "explainer": null,
   "id": "doi:10.1080/13504851.2026.2749673",
   "institutions": [
    "Ecole des Hautes Etudes Commerciales du Nord"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.1080/13504851.2026.2749673",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Make or buy AI? Evidence on AI sourcing decisions and workforce outcomes",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1080-13504851-2026-2749673/",
   "venue": "Applied Economics Letters",
   "year": 2026
  },
  {
   "abstract": "A new OpenAI report maps how AI could reshape jobs across the EU, highlighting which occupations may face automation, growth, or workflow changes.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-06-29",
   "doi": null,
   "explainer": null,
   "id": "title:bc6ee0897584c485",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://openai.com/index/mapping-ai-jobs-transition-eu",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Mapping Europe’s AI Workforce Opportunity",
   "url": "https://wemustactnow.onrender.com/research/p/title-bc6ee0897584c485/",
   "venue": "OpenAI (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "This document presents a preliminary mathematical framework to evaluate the interaction between Artificial Intelligence (AI) accelerated automation and demographic growth trends within the active population. The objective is to determine the critical time (t_crit) it would take for a theoretical economy to reach a social crisis threshold due to structural unemployment generated by the mismatch between labor supply and demand.",
   "authors": [
    "JOSE ANGEL MARTIN CARBALLO"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-21",
   "doi": "10.5281/zenodo.20433889",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20433889",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20433889",
   "summary": "A preliminary mathematical model of how AI-accelerated automation interacts with growth of the working-age population, used to estimate the time a theoretical economy would take to reach a social-crisis threshold of structural unemployment.",
   "theme": "Labor & Work",
   "title": "Mathematical Modeling of the Impact of Artificial Intelligence on the Labor Market and Demographics",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20433889/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Summary In February–April 2026, we ran a survey of 349 technical workers (including 87 software engineers, 71 researchers, 129 academics and PhD students, and 48 founders and managers) about their usage of AI tools. Compared to previous work, our survey is one of the more detailed surveys of technical workers’ self-reported gains from frontier AI tools. 1 We attempt to capture gains due to AI in terms of ‘value’ (how much more value are you creating with AI), rather than ‘speed’ (how long would it have taken you to do these tasks without AI). These can give different answers in principle, in particular if using AI changes the distribution of tasks you work on. For example, researchers could use AI to quickly build an interactive dashboard for their data, which would have taken significantly longer without AI but isn’t that important for their project. We provide more detail on the distinction between value and speed gains in our previous research . We think that the distinction between ‘value’ and ‘speed’ gains is important because value is closer to the idea that survey designers typically care about, whereas our sense is that it is common for respondents to think in terms of speed, and we expect that speed changes would typically overstate value changes. See methodological details here . Participants self-reported a median 1.4–2x change in the value in their work due to AI tools. The median self-reported speed change (which we expect to be higher than value change) is 3x. Using the same question wording, respondents retrospectively estimate 1.3x value of work in March 2025, estimate 2x in March 2026, and forecast 2.5x for March 2027. Responses are broadly internally consistent – correlations between different value change measures are moderate, only 3% of respondents showed anomalies in their answers that we feel is sufficient to remove them. There are tentative reasons to be skeptical of the magnitude of responses. METR staff give the lowest change in value answe",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-05-11",
   "doi": null,
   "explainer": null,
   "id": "title:36232cb6c2348f90",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/blog/2026-05-11-ai-usage-survey/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity",
   "url": "https://wemustactnow.onrender.com/research/p/title-36232cb6c2348f90/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Men's employment grew faster than women's in occupations most exposed to AI, a gap driven largely by workers without a bachelor's degree.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-22",
   "doi": null,
   "explainer": null,
   "id": "title:5cdbcdf4048278dd",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.pewresearch.org/short-reads/2026/10/22/men-are-experiencing-more-job-growth-than-women-in-ai-exposed-occupations/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Men Are Experiencing More Job Growth Than Women in AI-Exposed Occupations",
   "url": "https://wemustactnow.onrender.com/research/p/title-5cdbcdf4048278dd/",
   "venue": "Pew Research Center (institute research)",
   "year": 2026
  },
  {
   "abstract": "A new framework analyzes 921 occupations and 148 million U.S. jobs to identify which roles face automation risk, reorganization, growth, or minimal AI disruption.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-04-25",
   "doi": null,
   "explainer": null,
   "id": "title:45f989c6b108b294",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://openai.com/index/modeling-ai-jobs-transition",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Modeling an AI jobs transition",
   "url": "https://wemustactnow.onrender.com/research/p/title-45f989c6b108b294/",
   "venue": "OpenAI (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Open-weight AI models (OWMs), or models released with publicly-available weights, are distributing rapidly and approaching the performance levels of leading closed-weight AI models (CWMs). While OWMs offer substantial scientific and economic benefits, their release introduces distinct risk factors for which existing evaluation practices, largely designed for CWM deployment, fail to account. In this paper, we argue that these risk factors demand distinct proportional evaluation (PE) approaches: evaluating without system-level safeguards (PE1), assessing robustness to modifications that undo model-level safeguards (PE2), testing selective capability amplification (PE3), and proxying worst-case misuse (PE4). We systematically review current evaluation practices of OWMs released in 2025 through April 2026, finding that only one of the 37 families of models reviewed fulfills PE1-4 and most do not fulfill any. This paper targets policymakers, funders, and researchers involved in AI evaluation. As OWMs grow increasingly capable, their evaluation warrants close attention from developers, funders, and governance bodies alike.",
   "authors": [
    "Patricia Paskov",
    "Christopher Rodriguez",
    "Sunishchal Dev",
    "Stephen Casper"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-18",
   "doi": null,
   "explainer": null,
   "id": "title:d2b8a659c4ab86a2",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.19890v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Open Weight AI Models Require Proportional Evaluation Approaches",
   "url": "https://wemustactnow.onrender.com/research/p/title-d2b8a659c4ab86a2/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "When will advances in AI lead to economic transformation? We develop a model of how intelligence, the ability to achieve goals, matters for organizations, the key unit of economic production. Agents (human or AI) with multidimensional capabilities sort endogenously into competing firms. A firm's productivity depends on its agents' capabilities; we distinguish competence, which shifts productivity levels, from coordination, which governs how productivity scales with size. Two differences between AI and humans drive transformation: AI expertise is reproducible while humans' are in fixed supply, and AIs may not face the same limits for coordination as humans. We analyze how equilibrium market structure, productivity, employment, and wages evolve as AI capabilities improve. When AI is more capable than the average human, organizations of complementary humans can nonetheless outperform AI. Increases in AI competence can change the composition of workers within firms and decrease firm size. As AI coordination improves, however, this can break the Coasean ceiling on firm size and the market structure undergoes a phase transition toward large, concentrated AI-heavy firms. Changes in human employment are gradual when AI displaces workers within mixed human-AI firms, or sudden when AI firms outcompete human firms. We decompose wage changes into a technology effect, which changes the value of humans within existing firms, and a reorganization effect as firms adjust size and composition. Reorganization can reverse the technology effect on wages.",
   "authors": [
    "Alex Imas",
    "Andrew Koh",
    "Shane Legg"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-10-29",
   "doi": null,
   "explainer": {
    "approach": "An equilibrium model in which agents with multidimensional capabilities sort into competing firms; competence shifts productivity levels while coordination governs how productivity scales with size, and AI is reproducible at the cost of compute.",
    "findings": "Organizations of complementary humans can outperform AI that beats the average human. Higher AI competence can change firm composition and reduce firm size; better AI coordination can break the ceiling on firm size and cause a phase transition to concentrated AI-heavy firms. Employment changes are gradual or sudden depending on the mechanism.",
    "limitations": "A theoretical model; results follow from its assumptions about capability profiles and coordination and are not estimated from economic data.",
    "method_type": "theory",
    "question": "How do improvements in AI capability change market structure, productivity, employment and wages once firms can mix humans and AI agents?"
   },
   "id": "title:1c3b2763dde06c65",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://storage.googleapis.com/deepmind-media/papers/organizing-intelligence.pdf",
   "summary": "A theoretical model of how humans and AI agents sort into competing firms, separating competence (which raises productivity levels) from coordination (which sets how productivity scales with firm size). It finds that teams of complementary humans can beat more capable AI, that better AI competence can shrink firms, and that better AI coordination can trigger a sudden shift to large AI-heavy firms, with wage effects split into a technology effect and a reorganization effect that can reverse it.",
   "theme": "Labor & Work",
   "title": "Organizing Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/title-1c3b2763dde06c65/",
   "venue": "Google DeepMind",
   "year": 2026
  },
  {
   "abstract": "Despite the accelerating growth, the effects of A.I. on GDP per person are remarkably small for the next 30 years: incomes are only 4% higher in 2050 than they would be if growth remained constant rather than accelerating.",
   "authors": [
    "Charles I. Jones",
    "Christopher Tonetti"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-01-15",
   "doi": null,
   "explainer": null,
   "id": "title:4cc690f5d8da29fa",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://web.stanford.edu/~chadj/JonesTonetti_Automation.pdf",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Past Automation and Future A.I.: How Weak Links Tame the Growth Explosion",
   "url": "https://wemustactnow.onrender.com/research/p/title-4cc690f5d8da29fa/",
   "venue": "Working paper (Stanford GSB)",
   "year": 2026
  },
  {
   "abstract": "The fast integration of artificial intelligence (AI) into business operations and labour processes is reshaping global economic landscapes. The study examines the effects of AI adoption on labour productivity and sales growth in selected sub-Saharan African countries using a firm-level dataset from the World Bank Enterprises from 2007 to 2024. The study employs feasible generalised least squares (FGLS), robust ordinary least squares (OLS), and high-dimensional fixed effects (HDFE) linear regression techniques. The results show that AI has a significant positive relationship with firm labour productivity and sales growth in the selected sub-Saharan African countries. However, nuances differ across countries due to varying industrial structures. Results vary across the 10 selected countries due to differences in technological readiness. These results underscore the importance of targeted policy interventions, such as upskilling initiatives and supportive regulatory frameworks, to harness AI&rsquo;s benefits while mitigating adverse impacts on workers. This research contributes to the growing body of literature on technology adoption in developing economies, offering policymakers and business leaders in sub-Saharan Africa valuable insights.",
   "authors": [
    "Olanrewaju Adewole Adediran"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-19",
   "doi": "10.5256/f1000research.198472.r488903",
   "explainer": null,
   "id": "doi:10.5256/f1000research.198472.r488903",
   "institutions": [
    "Institute of Livelihood Research and Training"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5256/f1000research.198472.r488903",
   "summary": "A peer-review report on a study of AI adoption in firms across ten sub-Saharan African countries, using World Bank Enterprise Survey data from 2007 to 2024. The underlying study reports a significant positive association between AI adoption and both labour productivity and sales growth, varying across countries with technological readiness.",
   "theme": "Labor & Work",
   "title": "Peer Review Report For: Estimation of Firm Labour Productivity and Sales Growth from Artificial Intelligence in Sub-Saharan African Countries [version 1; peer review: 1 approved with reservations]",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5256-f1000research-198472-r488903/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "The integration of artificial intelligence (AI) into information technologies is reshaping competency requirements for IT specialists. However, AI competency penetration into the early phases of the Software Development Life Cycle (SDLC) – requirements analysis and planning – remains underexplored. Existing studies either rely on static competency models lacking empirical validation or analyze the labor market at an aggregated occupational level without differentiation across SDLC functional clusters. The research objective is to empirically test the hypothesis of AI competency penetration into the skill sets associated with early SDLC phases. The scientific novelty lies in the methodological transition from static competency models to a process-oriented analysis. Roles are cross-referenced with SDLC phases through weighted clustering with normative alignment to SWEBOK v3.0, BABOK v3.0, and ISO/IEC/IEEE 12207:2017. The empirical basis comprises 182,447 unique IT job postings from the hh.ru platform for the period March–December 2025, covering 19 SDLC-relevant roles. A reference list of 307 AI competencies was compiled via LLM-based preliminary screening followed by expert verification. Eight functional clusters are identified. Weighted demand S(s) is distributed as follows: analytics – 16.0%, architecture – 16.0%, development – 13.6%, management – 12.2%, documentation – 12.2%, testing – 12.0%, support – 9.1%, design – 8.9%. Weighted penetration P(s) ranges from support (0.34%) to design (1.70%). The hypothesis is confirmed: penetration in the Analytics cluster (0.69%) corresponds to the market average, while in the Management cluster (0.76%) it exceeds it, though early SDLC phases lag behind the Design, Development, and Architecture clusters in absolute terms. The findings are applicable to corporate early-warning strategies, educational curriculum revision, and updating professional standards in the context of the proliferation of generative AI.",
   "authors": [
    "O. V. Stoyanova",
    "Ivan S. Okuskov"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-30",
   "doi": "10.17323/2587-814x.2026.2.81.93",
   "explainer": null,
   "id": "doi:10.17323/2587-814x.2026.2.81.93",
   "institutions": [
    "National Research University Higher School of Economics"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.17323/2587-814x.2026.2.81.93",
   "summary": "Analyzing 182,447 Russian IT job postings (hh.ru, March-December 2025) across 19 software-development roles, finds AI-skill penetration into early-phase software work (requirements analysis, planning) is lower than in later phases like design and development, with weighted AI-competency penetration ranging from 0.34% (support) to 1.70% (design). It's a granular labor-market data point on where, within a single knowledge-work pipeline, AI skills are actually showing up in job postings versus where they aren't yet.",
   "theme": "Labor & Work",
   "title": "Penetration of artificial intelligence into the software development life cycle: An empirical labor market analysis",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17323-2587-814x-2026-2-81-93/",
   "venue": "Business Informatics",
   "year": 2026
  },
  {
   "abstract": "This chapter synthesizes the preceding analysis into a policy toolkit for the artificial intelligence (AI) economy. It argues that AI should not be treated as a single policy domain, but as a cross-cutting transformation that changes the production structure, the organization of firms, the allocation of risk, and the administrative capacity of the state. The appropriate response is therefore not one instrument, such as a robot tax or a narrow AI safety rule, but a coordinated package: skills and labor market institutions, competition and data governance, public investment in research and compute, risk-based regulation, public procurement, fiscal adaptation, and democratic accountability. The chapter emphasizes that policy must shape the direction of AI rather than merely compensate for its consequences. Advanced, emerging, and developing economies face different constraints, but all require a sequencing strategy that combines experimentation with institutional safeguards. The central policy question is not whether AI should be promoted or restricted, but which kinds of AI should be promoted, under which governance arrangements, and for whose benefit.",
   "authors": [
    "Ceyhun Elgin"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-20",
   "doi": "10.4324/9781042011162-9",
   "explainer": null,
   "id": "doi:10.4324/9781042011162-9",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.4324/9781042011162-9",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Policy toolkits for the AI economy",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4324-9781042011162-9/",
   "venue": "Routledge eBooks",
   "year": 2026
  },
  {
   "abstract": "Generative AI changes the scarcity of different forms of human capital. It makes codified knowledge cheaply available at the point of production, while judgment acquired through practice remains tied more closely to the worker. We ask what this does to two familiar prices in the wage structure: the return to schooling and the return to experience. In a short-run framework, machine-supplied codified knowledge substitutes directly for the codified component of worker human capital, lowering the schooling gradient. If codified and practice-based knowledge are complements, the return to experience rises where learning through practice is fastest; technology-specific experience can also become obsolete, rotating the experience profile rather than shifting it uniformly. Using US Current Population Survey earnings data from 2018 through June 2026, we compare occupations in the top and bottom quartiles of the pre-ChatGPT Felten-Raj-Seamans exposure index. After the public release of ChatGPT, the return to a year of schooling falls by 0.55 log points in exposed occupations. The return to a year of experience rises by 0.26 log points at three years of potential experience, is unchanged at fifteen, and falls by 0.12 log points at twenty-five; under the baseline quadratic, the effect crosses zero at 18.1 years. These breaks appear with the release, disappear under a pandemic placebo, survive a common censoring rule across the full sample, and remain when each occupation is allowed its own pre-shock schooling and experience gradients. The pattern is a repricing of human capital inside exposed occupations: codifiable knowledge becomes less scarce, while the value of experience depends increasingly on whether it transfers to the new technology.",
   "authors": [
    "Eduardo Levy Yeyati",
    "Darío Judzik",
    "Juan Ignacio Barbiero"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-26",
   "doi": null,
   "explainer": null,
   "id": "title:6d11e1693e4be833",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.36008v1",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Practice Makes Perfect: Tacit Knowledge, Generative AI, and Returns to Schooling and Experience",
   "url": "https://wemustactnow.onrender.com/research/p/title-6d11e1693e4be833/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "The integration of artificial intelligence into organizational workflows represents neither inevitable workforce decimation nor frictionless productivity gains, but rather a complex transformation requiring deliberate strategic responses. This article synthesizes evidence from labor economics, organizational psychology, and management practice to examine how enterprises and workers can navigate AI adoption. Analysis reveals that AI's organizational impact depends critically on implementation choices: whether firms deploy AI to augment human capability or merely automate existing roles. Drawing on research spanning multiple industries and geographies, we identify evidence-based interventions including transparent communication frameworks, skills recalibration programs, distributed leadership models, and human-AI collaboration protocols. Organizations that proactively invest in workforce readiness—through hybrid skill development, psychological contract renegotiation, and inclusive change management—position themselves to capture AI's productivity potential while maintaining workforce stability and organizational trust. The article concludes with a framework for building long-term organizational resilience through continuous learning systems, purpose-driven culture, and adaptive governance structures.",
   "authors": [
    "Jonathan Westover"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.70175/hclreview.2020.35.3.6",
   "explainer": null,
   "id": "doi:10.70175/hclreview.2020.35.3.6",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.70175/hclreview.2020.35.3.6",
   "summary": "A practitioner-oriented synthesis of labor economics, organizational psychology and management research on preparing workforces for AI. It argues the impact depends on whether firms use AI to augment or to automate roles, and outlines interventions such as transparent communication, reskilling programs and human-AI collaboration protocols.",
   "theme": "Labor & Work",
   "title": "Preparing the Workforce for AI Integration: Evidence-Based Strategies for Organizations and Workers",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-70175-hclreview-2020-35-3-6/",
   "venue": "Human capital leadership.",
   "year": 2026
  },
  {
   "abstract": "The most widespread promise of generative artificial intelligence (GenAI) for solo entrepreneurs is time savings. Experimental evidence documents substantial gains on well-defined tasks, but large-scale adoption studies find modest self-reported savings, and at least one randomized trial with experienced professionals found an increase, rather than a reduction, in completion time. This theoretical essay argues that part of this discrepancy stems from conflating gross productivity — the time saved in generating an output — with net productivity — the time and value saved after accounting for task specification, verification, correction, rework, context switching, and the expected cost of errors that escape review. For solo entrepreneurs, this distinction is especially relevant because the same person generates, reviews, corrects, and answers for errors, with no division of labor to absorb these costs. Integrating the economics of productivity and time allocation, the literature on quality costs and rework, research on automation and overreliance, human–AI interaction studies, and recent empirical evidence on GenAI at work, we propose an accounting model of net productivity, with explicit cost components and boundary conditions, and derive eight propositions. We discuss implications for solo entrepreneurs, limitations, and a study design with task-level randomization and an illustrative power calculation. The article presents no empirical data; its propositions are intended for testing.",
   "authors": [
    "Dani Hayoshi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.69849/gm2sb559",
   "explainer": null,
   "id": "doi:10.69849/gm2sb559",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.69849/gm2sb559",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Produtividade líquida no empreendedorismo individual: quanto a IA economiza depois da revisão e do retrabalho? Ensaio teórico com proposições e agenda de pesquisa",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-69849-gm2sb559/",
   "venue": "Revista fisio&terapia.",
   "year": 2026
  },
  {
   "abstract": "Technological principles of openness, interoperability, and decentralization are integral to building an AI economy that is accountable to the public and truly responsive to local needs and constraints. However, Big Tech's firm control over essential elements of the stack (such as chips, cloud, models, data, and talent) limits valuable alternatives from reaching the scale necessary for sustained growth. Worse still, hyperscalers continue to enclose and extract value from publicly funded and volunteer-driven technologies without adequately distributing these returns. We see consolidated power within a few entities and \"enshittification\" of once-transformative innovations. Embedding ethical micro-level tools (like open data repositories or small-language models) into workflows is promising, but without a consistent innovation strategy, they also often end up perpetuating and being appropriated by the same exploitative trajectories. Instead, this whitepaper outlines a three-pillared holistic and intentional policy approach that i) actively pushes for the adoption of cooperative technologies in existing and emerging systems, ii) sets up collective institutional structures to sustain and maintain them in the long-term, and iii) introduces regulatory proposals that limit private entities from disempowering them through uncontrolled enclosure and extraction. Through desk research and purpose-specific case studies, we argue for the importance of such an approach in the Global South, where structural constraints around infrastructure, financing, and policy governance make it difficult for AI commons and cooperative AI tools to emerge and survive. The paper concludes with key open questions for campaigners, policy advocates, researchers, and grassroots organizers to consider and explore while working towards peoples' solidarity within and between Global South countries.",
   "authors": [
    "Abhineet Nayyar",
    "Mallory Knodel",
    "Kimberlee Weatherall",
    "Gunn Jiravuttipong"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-16",
   "doi": null,
   "explainer": null,
   "id": "title:1bfa21f195a78394",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.23212v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Regulating for an AI Commons: Adopting, sustaining, and defending people-first innovation trajectories",
   "url": "https://wemustactnow.onrender.com/research/p/title-1bfa21f195a78394/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Against the backdrop of intensifying global market competition and China’s pursuit of high-quality development in manufacturing, the improvement of labour productivity has become a critical lever for firms to achieve sustainable growth. Using a sample of 32,914 firm-year observations of Chinese A-share listed companies in Shanghai and Shenzhen from 2011 to 2024, this paper empirically investigates the impact, influencing mechanisms and heterogeneous characteristics of artificial intelligence (AI) application on firm labour productivity with a two-way fixed-effects model and instrumental-variable estimation. The results show that AI application significantly improves firm labour productivity, and this finding remains valid after a battery of robustness and endogeneity tests. Mechanism tests indicate that AI empowers firm labour productivity primarily through optimising organisational management efficiency (lowering the management expense ratio); this channel accounts for most of the total effect. Although AI also increases corporate innovation output and firm digitalisation, these two factors do not significantly transmit the productivity effect in our sample. Heterogeneity analysis demonstrates that the productivity-enhancing effect of AI is more pronounced among non-state-owned enterprises, large-sized firms and enterprises located in Eastern China. By contrast, small- and medium-sized enterprises and firms in Western China have not yet fully reaped AI-driven productivity dividends. This study enriches research on the microeconomic consequences of AI and provides empirical references for firms and policymakers.",
   "authors": [
    "廖信林",
    "Yi Liu"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-12",
   "doi": "10.9734/ajeba/2026/v26i102409",
   "explainer": null,
   "id": "doi:10.9734/ajeba/2026/v26i102409",
   "institutions": [
    "Anhui University of Finance and Economics"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.9734/ajeba/2026/v26i102409",
   "summary": "Using 32,914 firm-year observations of Chinese listed companies from 2011 to 2024, this paper finds that AI application significantly raises firm labour productivity, a result that holds under instrumental-variable estimation. Most of the effect runs through lower management expenses rather than through innovation or digitalisation.",
   "theme": "Productivity & Firms",
   "title": "Research on the Impact of Artificial Intelligence Application on Firm Labor Productivity: Empirical Evidence from Chinese A‑share Listed Companies",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-9734-ajeba-2026-v26i102409/",
   "venue": "Asian Journal of Economics Business and Accounting",
   "year": 2026
  },
  {
   "abstract": "The rapid proliferation of intelligent automation technologies - encompassing artificial intelligence (AI), machine learning, and robotic process automation (RPA) - is fundamentally reshaping the global workforce landscape. This chapter examines the critical imperatives of reskilling and upskilling as strategic responses to technological displacement and the evolving demands of the labor market. Drawing on contemporary empirical research and theoretical frameworks, the chapter explores how organizations, educational institutions, and policymakers can collaboratively design and implement effective workforce development interventions. Special attention is given to the role of lifelong learning, digital literacy, and human-centric competencies in sustaining employability. The chapter also addresses equity dimensions, highlighting disparities in access to training opportunities across demographic and socioeconomic groups. Practical models and evidence-based recommendations are provided for building resilient, future-ready workforces capable of thriving alongside intelligent machines.",
   "authors": [
    "Mohan Kumar",
    "Mohit Yadav",
    "Nandan Kumar Jha",
    "Azadeh Amoozegar"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.4018/979-8-2600-0393-0.ch004",
   "explainer": null,
   "id": "doi:10.4018/979-8-2600-0393-0.ch004",
   "institutions": [
    "Shree Guru Gobind Singh Tricentenary University",
    "O. P. Jindal Global University",
    "Villa College",
    "Galgotias University",
    "INTI International University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.4018/979-8-2600-0393-0.ch004",
   "summary": "This book chapter examines reskilling and upskilling as responses to job displacement from AI and automation, and how employers, educators and policymakers can design workforce training together. It highlights unequal access to training across demographic and socioeconomic groups.",
   "theme": "Labor & Work",
   "title": "Reskilling and Upskilling in the Age of Intelligent Automation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4018-979-8-2600-0393-0-ch004/",
   "venue": "IGI Global eBooks",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) systems increasingly assist human experts, but the consequences of AI assistance on productivity can be heterogeneous. Caplin, Deming, S. Li, Martin, Marx, Weidmann, and Ye (2025b) provide evidence that two characteristics, ability and belief calibration, help to determine the returns to AI assistance. This note shows that their results replicate to a setting where professional radiologists analyze chest X-rays with access to state-of-the-art machine learning predictions. I leverage the public Collab-CXR data repository described by Moehring, Kutwal, Huang, Banerjee, Jacobi, Eber, Mendoza, Chung, Dayan, Gupta, Bui, Truong, Pareek, Langlotz, Lungren, Agarwal, Rajpurkar, and Salz (2025) and first analyzed for human-AI collaboration by Agarwal, Moehring, Rajpurkar, and Salz (2023). To faithfully reproduce the analysis in Caplin, Deming, S. Li, Martin, Marx, Weidmann, and Ye (2025b), I use the radiologist assessments from the repeated-case designs, which include 68 radiologists and 11,420 paired radiologist-patient-pathology observations. The results of this replication support the external validity of their core findings: lower baseline ability and higher calibration predict larger incremental value from AI.",
   "authors": [
    "Daniel Martin"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-10",
   "doi": null,
   "explainer": null,
   "id": "title:ad1fbf8b98671bd7",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.12585v1",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Revisiting the ABCs of Working with AI: A Replication with Radiologists",
   "url": "https://wemustactnow.onrender.com/research/p/title-ad1fbf8b98671bd7/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Automation affects public budgets through wages and the tax base, and also through inflows into social insurance. We estimate the effect of industrial robot exposure on Social Security Disability Insurance (SSDI) applications using confidential commuting-zone data and a shift-share design that instruments U.S. exposure with earlier European robot diffusion. One additional robot per 1,000 workers lowers applications by about 8 per 100,000 working-age residents, with the largest declines among workers aged 55 to 64. Employment-to-population ratios do not fall in exposed commuting zones, which weighs against broad local displacement as the sole explanation. A year's flow of averted applications corresponds to about \\$3.4 billion in expected SSDI and Medicare obligations, or about \\$17,000 per robot-year, in present value rather than realized cash. Displacement adds to social-insurance costs, whereas robot exposure here reduces them, an offset that assessments built only on displacement leave unpriced.",
   "authors": [
    "Duha T. Altindag",
    "Reem El Cheikh Taha",
    "John M. Nunley",
    "R. Alan Seals"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-03",
   "doi": "10.48550/arxiv.2607.02892",
   "explainer": null,
   "id": "doi:10.48550/arxiv.2607.02892",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.48550/arxiv.2607.02892",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Robots and the Public Finance of Disability Insurance",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2607-02892/",
   "venue": "arXiv (Cornell University)",
   "year": 2026
  },
  {
   "abstract": "This paper investigates how robotization affects local- and worker-level labor market outcomes in Turkey for 2014-2021. We estimate shift-share specifications, instrumenting Turkish industry-level robot adoption with the same indicator in eight leading European countries, utilising a combined dataset of administrative employer-employee data and industry-level robot stocks. Contrary to evidence from advanced economies, we find positive effects of robot exposure on district-level employment growth, concentrated in manufacturing and driven by the automotive industry. This pattern is consistent with the theoretical insight that the labor market effects of automation depend on an economy's position relative to the global productivity frontier. We complement the local-level analysis with an intensive-margin, worker-level exercise that tracks the 2014 manufacturing-worker cohort through 2021. The results reveal that incumbent workers in more-exposed industries experience a reduction in cumulative workdays at their original plants and are unlikely to transition outside manufacturing. The aggregate employment gains, therefore, accrue through firm expansion and new worker entry rather than through intensive-margin expansion of the incumbent workforce.",
   "authors": [
    "Uğur Aytun",
    "Yılmaz Kılıçaslan",
    "Oytun Meçi̇k",
    "Ümit Yapıcı"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-11",
   "doi": "10.22440/wjae.12.1.3",
   "explainer": null,
   "id": "doi:10.22440/wjae.12.1.3",
   "institutions": [
    "Middle East Technical University",
    "Anadolu University",
    "Eskişehir Osmangazi University",
    "Ahi Evran University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.22440/wjae.12.1.3",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Robots, Employment and Wages: Evidence from Turkish Labor Markets",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-22440-wjae-12-1-3/",
   "venue": "World Journal of Applied Economics",
   "year": 2026
  },
  {
   "abstract": "This article systematically studies the impact of artificial intelligence technologies on labor market transformation and the mechanisms of their impact on economic efficiency. The research used economic and statistical analysis, comparative analysis, and regression modeling methods. Based on international databases, including the World Bank, International Labor Organization, and OECD, the dynamics of labor productivity and employment in sectors where artificial intelligence has been introduced were analyzed. The results show that artificial intelligence increases production efficiency, but leads to a reduction in low-skilled jobs.",
   "authors": [
    "Erkin To'rayev",
    "Ne'mat Normamatov",
    "Sitora Ziyatova"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-26",
   "doi": "10.5281/zenodo.20401973",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20401973",
   "institutions": [
    "Agrobiotechnology Institute",
    "University of Mindanao"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20401973",
   "summary": "Using World Bank, ILO and OECD data with regression models, this article analyses labor productivity and employment in sectors that introduced AI and finds that AI raises production efficiency but reduces low-skilled jobs.",
   "theme": "Labor & Work",
   "title": "ROLE OF ARTIFICIAL INTELLIGENCE IN LABOR MARKET TRANSFORMATION AND EFFICIENCY ANALYSIS",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20401973/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "AbstractSmall and Medium Enterprises (SMEs) are essential contributors to global economic growth, innovation, and employment, yet they face unique barriers in their digital transformation journeys. The emergence of generative artificial intelligence (AI) presents a significant opportunity for SMEs to enhance productivity, strengthen customer engagement, and build competitive advantage. Applications of AI in areas such as process automation, predictive analytics, and personalised services offer potential for innovation and long-term growth. However, adoption remains limited due to persistent challenges, including high implementation costs, shortage of technical expertise, organisational resistance to change, and cybersecurity vulnerabilities. This paper synthesises findings from recent studies (2022–2025) to examine both the opportunities and challenges associated with AI integration in SMEs. The review highlights that while awareness of AI technologies is rising, practical implementation is slow, indicating a critical awareness–adoption gap. To bridge this divide, SMEs must adopt phased and strategic approaches, supported by employee training, enabling policy incentives, and human-centred AI practices that balance technological potential with ethical considerations.",
   "authors": [
    "Komal Amre",
    "K Vaishnav Gandhi",
    " Shriya Mhatre",
    "Nandini Vyas"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-04",
   "doi": "10.5281/zenodo.21187433",
   "explainer": {
    "approach": "A review synthesizing studies published between 2022 and 2025 on AI integration in SMEs.",
    "findings": "Awareness of AI is rising but practical implementation is slow, an awareness-adoption gap driven by implementation costs, lack of technical expertise, resistance to change and cybersecurity risks.",
    "limitations": "A literature synthesis with no new data; the abstract does not state how studies were selected.",
    "method_type": "review",
    "question": "What opportunities and obstacles does generative AI bring for small and medium enterprises?"
   },
   "id": "doi:10.5281/zenodo.21187433",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.21187433",
   "summary": "A literature synthesis of 2022–2025 studies on generative AI in SMEs, finding a persistent \"awareness–adoption gap\": SMEs increasingly know about AI's productivity and customer-engagement potential but implementation lags due to cost, skills shortages, organizational resistance, and cybersecurity concerns. It matters because SMEs are the bulk of employment in most economies, so if AI diffusion stalls at the SME level, aggregate productivity gains from AI will be concentrated in large firms and slow to show up in macro data—reinforcing the diffusion-lag view of AI's economic impact.",
   "theme": "Productivity & Firms",
   "title": "Role of Generative AI in Small and Medium Enterprises: Opportunities & Challenges",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-21187433/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Abstract Falk and Tsoukalas show that competitive firms may choose an excessive automation endpoint because each firm captures its own labour-cost saving while internalising only part of the product-demand loss generated by worker displacement. This paper studies a second choice margin: the temporal staging of displacement. We first establish a full-horizon null. Under stationary exogenous recovery, all finite displacement paths reaching the same automation endpoint generate identical complete-lifetime unresolved exposure, despite potentially different peaks. Timing becomes consequential only when the transition changes subsequent recovery. We introduce state-dependent reabsorption and show that, under an increasing, concave total service flow with a stable residual process, an undivided displacement batch weakly maximises complete-tail exposure; genuine splitting is strictly beneficial under strict residual convexity. A declining individual recovery hazard alone is insufficient. At a fixed endpoint, partial private internalisation produces a signed staging distortion: excessive front-loading where marginal common timing cost is positive and excessive delay where it is negative. We then solve the joint endpoint-staging problem, establish unique private and cooperative targets, and derive a target-specific two-rate correction with a controlled return to the source model. The analysis is theoretical and uncalibrated; it does not establish that current AI adoption is excessive or prescribe numerical policy rates. Research status Working paper, version 0.1 (September 2026). Not peer reviewed. Independent technical review and empirical validation have not been completed. AI assistance and research transparency OpenAI ChatGPT was used substantively during the research and manuscript-development process, including theoretical exploration, mathematical formulation, proof and counterexample analysis, literature exploration, drafting and editing, and the development and execu",
   "authors": [
    "Antonios Frangotsinos"
   ],
   "cited_by": 3,
   "collected_from": "openalex",
   "date": "2026-10-16",
   "doi": "10.5281/zenodo.23423760",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23423760",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23423760",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Scale and Staging in Competitive Automation: Demand Externalities with State-Dependent Reabsorption",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23423760/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Anthropic’s Economics team built a model of how AI might affect jobs, growth, and unemployment in the US in coming years. Read about possible economic futures and make your own predictions about AI capabilities to see the economy they imply. While our Economic Index measures how AI is being used across the economy right now, this scenario explorer is about looking ahead. Based on our technical report, Economic Scenarios for Transformative AI (Korinek et al., 2026), this explorer gives you a chance to find out what the economy might look like as AI continues to get more capable. In scenarios ranging from business as usual to an economy where AI increases growth to about twice the normal rate, unemployment stays within the historical range and wages remain flat or rise depending on the industry. But in scenarios where growth is faster than anything in economic history, there are adverse impacts on wages and job prospects for knowledge workers. In those scenarios, society is far wealthier, so the challenge is making sure that the gains are broadly shared. As you scroll down, you’ll see an overview of how AI affects the economy. Then, you can plug in your expectations for how capable AI will be, and how extensively it will be used across the economy in the future. The model will show you what the economy in 2030 might look like if your predictions come true—and how your predictions compare to others. You can think of any job as a bundle of tasks that someone does. She does rounds to check on a sick patient. She draws blood. She triages incoming patients, charts patients’ vitals, and orders supplies for the ward. That’s just the start of the list. Each of the tasks listed are based on the US Department of Labor’s O*NET taxonomy, listing the tasks for each occupation. Tasks leave the bundle ( hardly anyone hand-writes paper charts anymore ) and new tasks arrive ( 30 years ago, no one monitored patients remotely ). The bundle of tasks isn’t static, and the job changes as t",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-23",
   "doi": null,
   "explainer": null,
   "id": "title:55df1892a701d20c",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/institute/econ-scenarios",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Scenarios for our Economic Future",
   "url": "https://wemustactnow.onrender.com/research/p/title-55df1892a701d20c/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "We develop an agent-based, stock-ow-consistent model of an economy undergoing automation, built to ask which scal instrument reaches the durable surplus that articial intelligence creates. The model separates two channels: a competitive return on reproducible robotic capital, and a mobile, foreign-held intellectual-property rent earned by AI. Production is an endogenous nested-CES technology; wealth concentration is microfounded through heterogeneous, persistent returns to wealth; and taxation and capital mobility are modelled as behavioural responses. The central result is that the durable surplus is the foreign-held AI rent, a cross-border licence fee that corporate, robot, and compute or token taxes largely miss and that only a source-based levy (a digital-services-style tax or a withholding) reaches. The appropriate policy depends decisively on whether a country owns the automation or imports it: for a host that owns the rent the problem is domestic inequality, reached by progressive and wealth taxes; for a rent-importing host the problem is base erosion and a gradual transfer of capital ownership abroad, which a residence-based wealth tax cannot reach. We report conditional orderings, stress-tested with global (Sobol) sensitivity and a formal stability analysis, rather than point forecasts.",
   "authors": [
    "Leonard Wossnig"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-07",
   "doi": null,
   "explainer": null,
   "id": "title:3347bb38bfccc2c8",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.20649v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Simulating a Post-Automation Economy",
   "url": "https://wemustactnow.onrender.com/research/p/title-3347bb38bfccc2c8/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "This piece builds upon two prior pieces published on the TechTank blog: “ Borrowed expertise: Why AI’s productivity boom may not survive the generation that built it” (July 10, 2026) and “ Repaying the inheritance: How education and research policy can address AI’s borrowed expertise” (July 20, 2026) . On Sept. 8, OpenAI announced that an unreleased internal model had produced a proposed solution to the Navier-Stokes existence and smoothness problem, one of the seven Millennium Prize Problems —a set of complex, unsolved problems selected by the Clay Mathematics Institute in 2000 that offer a $1 million prize for the first correct solution for each. Roughly 10,000 agents ran in parallel for 88 hours. The compute bill ran millions of dollars, which OpenAI’s Sébastien Bubeck described in the press briefing as roughly a thousand times what the company had previously spent on a single problem. The proof is not verified. Mathematicians are reading it now, and under Clay’s rules a proposed solution must appear in a qualifying publication, wait two years, and win general acceptance in the field before the prize is considered at all. OpenAI says it does not intend to claim it. That was five weeks ago, and it is already the smaller story. On Oct. 6, OpenAI released 722 manuscripts in a single GitHub drop, claiming progress on 372 open problems across algebra, geometry, number theory, and theoretical computer science, all produced by the same unreleased internal model. The company estimates the average result cost roughly three hours of ChatGPT Pro compute. Only 162 of the manuscripts arrived with the machine-checkable Lean formalizations that let a computer confirm a proof, and mathematicians pushed back immediately: A group calling itself the Association for Human Mathematics described the release as “not a demonstration of scholarship, but a demonstration of power.” A day later, OpenAI withdrew three of the manuscripts after an internal audit caught a sign error that invali",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-29",
   "doi": null,
   "explainer": null,
   "id": "title:7cb3a4acc2238021",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/spending-it-faster-what-a-10000-agent-proof-means-for-ais-borrowed-expertise/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Spending it faster: What a 10,000-agent proof means for AI's borrowed expertise",
   "url": "https://wemustactnow.onrender.com/research/p/title-7cb3a4acc2238021/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "Think of a marketing professional using a chatbot to draft ad copy, an IT support specialist troubleshooting a user’s problem with one, or a teacher trying to spot homework written by AI. A few years after ChatGPT’s launch, generative AI has quickly become part of everyday white-collar work. Has it changed how much workers earn, or how many people firms hire? Our research provides one of the first comprehensive answers, based on surveys of 25,000 Danish workers across 11 highly exposed occupations, linked to their monthly administrative records through December 2024. The headline result is a precise null: no effects on earnings or hours, either for workers who use chatbots or for the firms that adopt them. Beneath this still surface, however, work is already being substantially reorganized. Experts disagree sharply about how generative AI will affect work, both in direction and in size. Anthropic CEO Dario Amodei (2025) warns that much white-collar work could be displaced within a few years, while Nobel laureate Daron Acemoglu (2025) expects modest productivity gains spread over decades. The stakes are especially high for young workers: is this the worst or the best time to start a career in an AI-exposed occupation? In an influential paper, Brynjolfsson, Chandar, and Chen (2025) document that early-career employment has fallen sharply in AI-exposed occupations, which has raised concerns that AI may already be displacing entry-level jobs. By contrast, a more optimistic view, advanced by Autor (2024) , is that generative AI may put expertise that used to be scarce within reach of more workers, opening paths to better-paid careers. Until now, there has been little direct evidence to weigh these views against each other. In this article, we describe how workplaces are absorbing AI chatbots: the employer policies, tools, and training now surrounding the technology, and the new job tasks it is creating. We then ask whether these changes have surfaced in earnings, hours, ",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-06",
   "doi": null,
   "explainer": null,
   "id": "title:a9990b7e7ca1b638",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/still-waters-rapid-currents-early-labor-market-transformation-under-generative-ai/",
   "summary": "Linking surveys of 25,000 Danish workers in 11 highly exposed occupations to administrative records through December 2024, the authors find a precise null effect of chatbot use or firm adoption on earnings and hours. Beneath that, they report that work is already being substantially reorganized.",
   "theme": "Labor & Work",
   "title": "Still waters, rapid currents: Early labor market transformation under generative AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-a9990b7e7ca1b638/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "This paper introduces Symbiotic Diplomacy as a normative and operational framework for governing the human-AI transition in anticipation of the Singularity — the projected moment when artificial intelligence systems surpass human cognitive capacity in a general and irreversible manner. Drawing on thirteen years of direct observation of labor automation in Chinese manufacturing contexts, certification in AI management systems (ISO/IEC 42001), and an applied book-writing experiment conducted in collaboration with a large language model, the paper argues that the central challenge of the AI transition is not technical but diplomatic: the negotiation of boundaries, responsibilities, and benefits between people, models, and institutions. The framework articulates four structural pillars — technological sovereignty, governance as architecture, human-impact metrics, and humanized productivity — and maps their implementation across a Quadruple Helix of actors: government, academia, industry, and civil society. The paper also presents the book-writing process itself as a case study in humanized productivity, demonstrating that AI-augmented knowledge creation can preserve and amplify human authorship rather than replace it. The work emerges from a practitioner perspective situated in the Global South, offering a vantage point distinct from both Silicon Valley and European regulatory discourse.",
   "authors": [
    "Mikael Soares Martins"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-20",
   "doi": "10.5281/zenodo.20315444",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20315444",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20315444",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Symbiotic Diplomacy A Practitioner-Generated Framework for Human-AI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20315444/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Does generative artificial intelligence (AI) expand the set of tasks that middle-skilled, early-career workers can successfully perform? In a field experiment with 673 final-year IT apprentices at twelve German vocational schools, we randomly assign access to AI for completing occupation-specific tasks. To identify task expansion, we study tasks both within the apprenticeship curriculum and deliberately beyond it. AI raises productivity substantially across all task types, with gains tending to be larger on tasks where performance without AI is lowest. Crucially, productivity increases by 26–31 percentage points on tasks beyond apprentices’ formal training, providing direct evidence of AI-enabled task expansion. These productivity gains do not come at the cost of reduced immediate task comprehension. Treatment effects are generally larger among apprentices with above-median AI literacy.",
   "authors": [
    "Hanna Brosch",
    "Christina Langer",
    "Philipp Lergetporer",
    "Sandra Mueller",
    "Harald Ulrich Pfeifer",
    "Nele Tschoepe"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-07",
   "doi": "10.65864/euu6lxmt4t",
   "explainer": {
    "approach": "A field experiment with 673 final-year IT apprentices at twelve German vocational schools, randomly assigning AI access for occupation-specific tasks both within and beyond their curriculum.",
    "findings": "AI raised productivity across all task types, with larger gains where unaided performance was lowest, and by 26-31 percentage points on tasks beyond formal training. Immediate task comprehension did not fall, and effects were larger for apprentices with above-median AI literacy.",
    "limitations": "The setting is German IT apprentices completing set tasks, so results may not carry over to other occupations, experience levels or real workplace output over time.",
    "method_type": "experiment",
    "question": "Does generative AI let middle-skilled, early-career workers successfully perform tasks they could not do before?"
   },
   "id": "doi:10.65864/euu6lxmt4t",
   "institutions": [
    "Friedrich-Alexander-Universität Erlangen-Nürnberg",
    "Stanford University",
    "Technical University of Munich",
    "Maastricht University",
    "Federal Institute for Vocational Education and Training"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.65864/euu6lxmt4t",
   "summary": "A field experiment randomly gave 673 final-year IT apprentices at twelve German vocational schools access to generative AI for occupation-specific tasks. AI raised productivity across all task types, most where unaided performance was lowest, and by 26-31 percentage points on tasks beyond the apprentices' formal training, without reducing immediate task comprehension.",
   "theme": "Labor & Work",
   "title": "Task Expansion with Generative AI: Experimental Evidence on Middle-Skilled, Early-Career Workers",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-65864-euu6lxmt4t/",
   "venue": "CESifo",
   "year": 2026
  },
  {
   "abstract": "Summary: We describe three different definitions of the productivity impact of AI (AKA uplift), and show there’s reason to expect: \\[\\text{uplift on old tasks} \\leq \\text{uplift in value} \\leq \\text{uplift on new tasks}\\] Three Measures of Uplift One complication in measuring AI’s effect on productivity is that it has different effects on different tasks, and this causes people to change how they allocate their time between tasks. This makes it more difficult to talk about the effect of AI on overall productivity. We use “old tasks” to mean the set of tasks you’d do in a typical day before AI is available – your average workday in 2021, say. “New tasks” means the set of tasks you’d do in a typical day after AI is available. Not all new tasks necessarily use AI; they’re just the tasks you choose knowing AI is an option. We have found it important to distinguish between three measures of AI’s uplift: Uplift on old tasks: The factor by which pre-AI time exceeds post-AI time to complete the old tasks. Uplift on new tasks: The factor by which pre-AI time exceeds post-AI time to complete the new tasks. Uplift in value: The factor by which post-AI value exceeds pre-AI value, allowing for reshuffling of tasks between the pre-AI and post-AI cases. In some cases value has a natural definition; in others, it can be operationalized using related definitions discussed more in the accompanying note. This note discusses the distinction and its implications for interpreting AI productivity estimates. We also have an accompanying short note with more mathematical details. Ranking the Three Types of Uplift Under some simplifying assumptions, the following inequalities hold: \\[\\text{uplift on old tasks} \\leq \\text{uplift in value} \\leq \\text{uplift on new tasks}\\] These inequalities correspond to classic inequalities in the economic theory of price indices, which estimate the effects on welfare of changes in prices based on how consumers change their expenditure in response to the pri",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-05-08",
   "doi": null,
   "explainer": null,
   "id": "title:bf259a8ea5664621",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/blog/2026-05-08-task-substitution-and-uplift/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Task Substitution and Uplift",
   "url": "https://wemustactnow.onrender.com/research/p/title-bf259a8ea5664621/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) reshapes workers comparative advantage by altering the tasks they perform and the skills those tasks require. We develop a dynamic task-based model to quantify the general-equilibrium effects of task-specific technical change. Workers have multidimensional skills, choose",
   "authors": [
    "Lukas Althoff",
    "Hugo Reichardt"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "June 2026",
   "doi": null,
   "explainer": null,
   "id": "title:a1260cec22112750",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35353",
   "summary": "This NBER paper develops a dynamic task-based model to quantify the general-equilibrium effects of AI as task-specific technical change, which shifts workers' comparative advantage by changing the tasks they do and the skills those tasks need. Workers in the model have multidimensional skills; the available abstract is cut off before the results.",
   "theme": "Labor & Work",
   "title": "Task-Specific Technical Change and Comparative Advantage",
   "url": "https://wemustactnow.onrender.com/research/p/title-a1260cec22112750/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Understanding how AI is reshaping work requires an accurate account of the tasks workers perform. Workers are often the first to see how their jobs are changing, as new responsibilities and tools often appear in everyday work before these changes reach formal descriptions. Interviews with workers can help capture these emerging changes in job tasks. Yet existing methods support standardized ratings across workers, while open-ended conversations with workers remain costly to collect and synthesize at scale. We introduce the Stanford Task Network (TaskNet), an occupational taxonomy grounded directly in workers' accounts of their everyday work. Every task statement is written to O*NET's standards and validated by job analysts and workers. Across 132 worker interviews, 2,726 task reports and 7,923 ratings from 318 raters in six occupations, TaskNet holds 202 task statements: 79 new to O*NET, 123 overlapping with O*NET, while 30 O*NET tasks never came up in interviews.",
   "authors": [
    "Jennifer Wang",
    "Andy Haupt",
    "Isaac Robinson",
    "Phil Lewis",
    "Erik Brynjolfsson",
    "Diyi Yang"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-01-01",
   "doi": null,
   "explainer": {
    "approach": "The authors built TaskNet from 132 worker interviews, 2,726 task reports and 7,923 ratings from 318 raters across six occupations. Each task statement is written to O*NET standards and validated by job analysts and workers.",
    "findings": "TaskNet holds 202 task statements: 79 are new to O*NET and 123 overlap with it, while 30 existing O*NET tasks never came up in the interviews.",
    "limitations": "The inventory covers six occupations, so it does not yet describe the wider labour market, and the abstract does not report how the tasks relate to AI exposure or use.",
    "method_type": "dataset-or-tool",
    "question": "Can interviews with workers be turned, at scale, into an occupational task list that captures how jobs are changing with AI before formal job descriptions catch up?"
   },
   "id": "title:bd6eef81951c26ae",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://digitaleconomy.stanford.edu/project/tasknet/",
   "summary": "Stanford's TaskNet builds an occupational task taxonomy directly from worker interviews, with each task statement written to O*NET standards and validated by job analysts and workers. Across 132 interviews in six occupations it yields 202 task statements, 79 of them new to O*NET, while 30 existing O*NET tasks never came up.",
   "theme": "Labor & Work",
   "title": "TaskNet: Building Occupational Task Inventories from Worker Interviews at Scale",
   "url": "https://wemustactnow.onrender.com/research/p/title-bd6eef81951c26ae/",
   "venue": "Stanford Digital Economy Lab",
   "year": 2026
  },
  {
   "abstract": "Relevance . The introduction of artificial intelligence and robotisation technologies determines the competitiveness of any country’s economy at the present stage and determines changes in the architecture of direct and indirect taxation of entities using these technologies. The search for a multi-level, differentiated and integrated model of fiscal policy towards persons developing or using AI and robotisation technologies will enable the development of this economic segment. The purpose of this study is to assess the current state of taxation of economic activities related to the development or use of AI and robotisation in domestic and foreign legal orders and to identify relevant regulation. Objectives : to identify the differences between artificial intelligence and a robot; to analyse expert assessments on the integration of AI and robotisation into the economies of Russia and foreign countries. Methodology . To achieve the stated objective, the author applied methods of scientific inquiry, namely analysis, synthesis, systemic-analytical, formal-logical and systemic-legal methods. Using these methods, the author carried out an analytical review of domestic and foreign legislation, determined the specific features of the legal relations under study, and proposed solutions to the identified legal problems. Results . The central conclusion is that fiscal policy with respect to economic activities involving AI and robotisation should be divided into: (1) supportive tax policy for persons engaged in the development and training of AI and robotisation; (2) conservative (or progressive) tax policy for persons carrying out activities using AI and robotisation. Conclusion . The study proposes legal constructs for a system of taxation of economic activities related to the development or use of artificial intelligence and robotisation.",
   "authors": [
    "O. A. Tarasenko"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-06",
   "doi": "10.21869/2223-1501-2026-16-2-33-43",
   "explainer": null,
   "id": "doi:10.21869/2223-1501-2026-16-2-33-43",
   "institutions": [
    "Kutafin Moscow State Law University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.21869/2223-1501-2026-16-2-33-43",
   "summary": "The article reviews how activities that develop or use artificial intelligence and robotics are taxed in Russia and other legal systems. It distinguishes AI from robots, draws on expert assessments of their integration into national economies, and looks for a differentiated, multi-level fiscal policy toward the firms involved.",
   "theme": "Policy & Governance",
   "title": "Taxation of economic activities related to the development or use of artificial intelligence and robotics",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-21869-2223-1501-2026-16-2-33-43/",
   "venue": "Proceedings of the Southwest State University Series History and Law",
   "year": 2026
  },
  {
   "abstract": "While AI promises major benefits, its development and deployment can shift costs onto others, including environmental pressures on local communities, labor and creative displacement, and systemic risks from rapid frontier development. Taxation is an integral part of policy design, and recent academic, industry, and policy debates have begun to consider whether tax instruments can help address these harms. In this paper, we explore the viability of AI taxation. More broadly, AI taxation should not be understood only as Pigouvian correction. In the AI context, taxation can also correct harmful activity, redistribute unevenly borne costs and gains, and fund regulatory capacity. We discuss the main externalities associated with AI and survey possible tax instruments, including corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities. We further assess the benefits and pitfalls of these instruments, including feasibility, measurement problems, incidence, leakage, and innovation costs. Because AI externalities differ in nuanced ways, tax policy must be carefully designed and matched to the specific harms and policy objectives.",
   "authors": [
    "Juliette Faivre",
    "Sarah H. Cen"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-07-02",
   "doi": null,
   "explainer": null,
   "id": "title:428ce738c7f7bc77",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2607.02144v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Taxing Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/title-428ce738c7f7bc77/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) could raise productivity across a wide range of occupations and tasks. But it could also replace workers who currently perform some of those tasks. How many workers AI might replace, and how quickly, remains an open question. These questions matter for the federal budget because the U.S. tax system relies heavily on income earned by workers. Individual income and payroll taxes generate roughly 85% of federal tax receipts . The individual income tax draws heavily from wages, salaries, and tips and is the single largest source of federal revenue. The payroll tax that funds Social Security and Medicare applies only to wage and self-employment income. If AI shifts income from workers to business profits and other capital income, some of that income could face lower effective tax rates. A sustained shift could erode federal revenues amid growing demands for public spending to assist displaced workers. In response, lawmakers are proposing AI-specific taxes with a range of stated goals: maintaining tax revenues given the changing economy; raising additional revenues to support displaced workers ; addressing the power and water infrastructure investment AI facilities require, and slowing down the pace of AI activity and growth. Several distinct tax designs have been proposed or discussed, each aimed at a different point in the AI production process, from the computing power used to train AI models to the profits they will eventually generate. The proposals differ substantially, however, in timing and scope, potential revenue and economic costs, and ease of administration. An AI-specific tax is targeted and easy to explain to the public. That is a fair political consideration, but it is not an economic one. Achieving the above goals does not strictly require a tax specific to AI. Reforming how capital income is taxed, introducing a broad-based consumption tax such as a value-added tax ( VAT ), a carbon tax , or a corporate cash-flow tax could add",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-27",
   "doi": null,
   "explainer": null,
   "id": "title:933a8bf321f5805e",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/taxing-artificial-intelligence-whats-on-the-table-and-whats-at-stake/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Taxing artificial intelligence: What’s on the table, and what’s at stake",
   "url": "https://wemustactnow.onrender.com/research/p/title-933a8bf321f5805e/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "AI is often described as a software story, but the boom is also a massive physical investment in data centers, power systems, and specialized chips. A new BPEA paper projects that this buildout will total $10.3 trillion from 2025 to 2032, an average of 3.63% of U.S. GDP per year and larger relative to the economy than past booms in railroads, highways, and telecommunications. On this episode of the B rookings Podcast on Economic Activity , Aaron Klein talks to the paper’s author, Stijn Van Nieuwerburgh about what this means for transparency and risk. EBERLY: Welcome to season nine of the Brookings Podcast on Economic Activity. I’m Jan Eberly, the James R. and Helen D. Russell Professor of Finance at Northwestern University. And this season, we’ll be listening to discussions about papers from the fall 2026 Brookings Papers on Economic Activity conference, hosted by Brookings in September. STEINSSON: And I’m Jón Steinsson, Marek Professor of Public Policy and Economics at the University of California, Berkeley. We are the co-editors of the Brookings Papers on Economic Activity , a semi-annual conference and journal that pairs rigorous research with real-time policy analysis to address the most urgent economic challenges of the day. EBERLY: Once again, we have a great lineup of timely topics, energy markets, inflation, migration’s effects on labor and housing markets, financing AI, and more. Over the course of this season, we’ll hear a lot more about the conference papers and importantly, how decision-makers should be thinking about the topics they explore. STEINSSON: This episode is about the AI boom, but it’s about an aspect of the AI boom that is not always at the center of attention, how we’re actually paying for all the AI investments that are going on at the moment. Aaron Klein, senior fellow in Economic Studies at Brookings, speaks with author Stijn Van Nieuwerburgh of Columbia Business School about his new paper, “Financing the AI Build-Out.” EBERLY: The number",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-08",
   "doi": null,
   "explainer": null,
   "id": "title:fd24854bcab69530",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/the-10-trillion-question-financing-the-ai-buildout/",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "The $10 trillion question: Financing the AI buildout",
   "url": "https://wemustactnow.onrender.com/research/p/title-fd24854bcab69530/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "This paper identifies and formalizes the Acceleration Paradox: a structural economic phenomenon in which defensive market interventions by organized labor—specifically mandatory wage inflation, rigid employment guarantees, and operational vetoes—inadvertently lower the financial and operational threshold for capital to adopt generative AI and automation technologies. Traditional labor economics posits that wage increases induce capital-labor substitution along a smooth production frontier. However, in the era of high-fixed-cost, low-marginal-cost cognitive automation, labor market rigidity introduces non-linear administrative friction that transforms capital's technology adoption decision from a gradual evaluation into an immediate strategic necessity. Drawing on task-based frameworks of technological change, game-theoretic modeling, and generational political economy, this study demonstrates how institutional protectionism accelerates the obsolescence of the very workforce it seeks to preserve.",
   "authors": [
    "Min Jinseong"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-11",
   "doi": "10.5281/zenodo.23290520",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23290520",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23290520",
   "summary": "A theoretical paper arguing that labour protections such as mandated wage increases and rigid employment guarantees can lower the threshold for firms to adopt generative AI and automation. Drawing on task-based and game-theoretic models, it argues such protections can speed up the replacement of the workers they aim to protect.",
   "theme": "Labor & Work",
   "title": "The Acceleration Paradox: How Coercive Protectionism Catalyzes Technological Replacement in the Age of Generative AI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23290520/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Policymakers are asking whether generative AI is reshaping labour demand, and the literature has found mixed results so far. This column links Denmark’s official IT-use survey to monthly employer–employee records and compares firms that started using AI in 2023 with those that did not. Early adopters fall behind their own pre-adoption employment trend. The decline is most visible in AI-exposed jobs and among new hires, and only significant among small firms. The results indicate that AI is affecting the labour market, even though the effect is not visible in the aggregate statistics.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-08",
   "doi": null,
   "explainer": null,
   "id": "title:19023260b7e7da66",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://cepr.org/voxeu/columns/adjustment-danish-labour-market-ai-has-begun",
   "summary": "Linking Denmark's official IT-use survey to monthly employer-employee records, this column finds that firms which started using AI in 2023 fell behind their own pre-adoption employment trend. The decline is concentrated in AI-exposed jobs and new hires and is significant only among small firms, an effect not yet visible in aggregate statistics.",
   "theme": "Labor & Work",
   "title": "The adjustment of the Danish labour market to AI has begun",
   "url": "https://wemustactnow.onrender.com/research/p/title-19023260b7e7da66/",
   "venue": "CEPR VoxEU (institute research)",
   "year": 2026
  },
  {
   "abstract": "How will AI reshape the economy in the years ahead? AI capabilities are advancing rapidly, and the possibility that future AI models may be able to build their own successors (recursive self-improvement) could further accelerate the rate of progress. How quickly this might materialize and to what extent is unclear. Forecasts vary widely and are fiercely debated. What is the core source of disagreement about AI's future impact on the economy? Is it principally due to differences in beliefs about how quickly AI capabilities will improve? Or does it reflect disagreement about how quickly and in what form those capabilities translate to real-world adoption? Which views on the future path of AI are consistent with rapid economic growth? This note presents new survey evidence on these questions. In August 2026, we surveyed two groups: a sample of US adults that was nationally representative and Anthropic staff. 1 The questions covered, among other things, AI’s capabilities, how widely it will be used, how much time it saves, how autonomous it will be, and how difficult it will be for a displaced worker to find a job in a different occupation. 2 Anthropic staff build and use frontier AI every day. The second survey therefore shows how people closest to the technology anticipate its future, both in capabilities and in broader economic impact. We report the following main findings. First, the two groups largely agree about what AI can do today but disagree about the pace of future progress. Anthropic staff expect capabilities to advance faster, AI to be used more widely and with less human involvement, and the time it saves on a task to be much larger. For example, 91% of staff expect AI to be able to make a Nobel-level discovery by 2030, compared to 39% of US adults. Second, there are sharp differences concerning people's own jobs. On average, staff members expect AI to be able to do 75% of their current work by 2030, compared to 28% for US workers overall. Third, the two g",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-16",
   "doi": null,
   "explainer": null,
   "id": "title:7fbab8a2a029f14e",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/ai-expectations-gap",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The AI expectations gap",
   "url": "https://wemustactnow.onrender.com/research/p/title-7fbab8a2a029f14e/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence is widely expected to raise productivity, yet its macroeconomic gains remain uncertain, uneven, and institutionally mediated. This paper develops an original Dynamic Institutional Absorptive Capacity (DIAC) model to explain why the same AI shock can produce divergent outcomes in small open economies. The central argument is that AI does not translate directly from firm-level task efficiency into national productivity. Its effect is filtered through complementary intangible investment, skills formation, data governance, competition policy, labor-market mobility, and social insurance. The paper formalises an AI productivity transmission gap between technical adoption and inclusive productivity realisation. Using analytical theory-building, it identifies three regimes: adoption without absorption, constrained complementarity, and adaptive complementarity. It also derives propositions on threshold effects, productivity J-curve dynamics, distributional stress, and policy sequencing. The model implies that small open economies should not maximise AI adoption as an isolated target, but should build institutional absorptive capacity that converts AI exposure into productivity, worker mobility, and shared prosperity.",
   "authors": [
    "Kwan Hong Tan"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-06",
   "doi": "10.5281/zenodo.21220426",
   "explainer": null,
   "id": "doi:10.5281/zenodo.21220426",
   "institutions": [
    "University of Suffolk",
    "University of West London",
    "Graham International Implant Institute",
    "Singapore University of Social Sciences"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.21220426",
   "summary": "A theoretical paper proposing a Dynamic Institutional Absorptive Capacity model of why the same AI shock can yield different productivity outcomes in small open economies. It argues firm-level efficiency gains are filtered through intangible investment, skills, data governance, competition policy, labor mobility and social insurance, and derives propositions on threshold effects, J-curve dynamics and policy sequencing.",
   "theme": "Productivity & Firms",
   "title": "The AI Productivity Transmission Gap in Small Open Economies: A Dynamic Institutional Absorptive Capacity Model",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-21220426/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "This combined title brings together the major themes of the research paper: AI-driven automation, changing employment patterns, emerging skill requirements, economic opportunities, and the social challenges associated with technological progress. 2. Alternative titles 1. The AI Tsunami and the Future of Employment: Opportunities, Risks, and Workforce Transformation in the Digital Era 2. Artificial Intelligence and the Changing World of Work: Automation, Job Displacement, and Emerging Career Opportunities 3. Beyond Automation: Rethinking Employment, Human Skills, and Economic Growth in the Age of Artificial Intelligence 4. Artificial Intelligence and Labour-Market Transformation: A Critical Review of Employment Risks and Reskilling Strategies 5. The Future of Work in the AI Era: Balancing Technological Innovation, Workforce Adaptability, and Inclusive Growth 6. AI-Driven Economic Transformation: Implications for Employment, Education, Productivity, and Social Equity 7. From Job Displacement to Job Creation: Understanding the Socioeconomic Impact of Artificial Intelligence 8. Artificial Intelligence, Workforce Disruption, and the Skills Revolution: Challenges and Opportunities for India 9. Human–AI Collaboration in the Digital Economy: Redefining Professional Competencies and Employment Structures 10. Managing the AI Revolution: A Framework for Responsible Automation, Workforce Reskilling, and Sustainable Employment 11. The AI Tsunami in India: Employment Vulnerability, Digital Skills, and Strategies for an Inclusive Technological Transition 12. Artificial Intelligence and the Next Industrial Transformation: Labour-Market Restructuring and the Future of Human Capital 3. Suggested subtitles The following subtitles can be used with the main title depending on the emphasis of the intended publication. A Narrative Review of Automation, Job Transformation, and Emerging Workforce Skills Assessing Employment Risks, Productivity Gains, and Reskilling Requirements A Socioecono",
   "authors": [
    "Sudhakar Geruganti"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-13",
   "doi": "10.5281/zenodo.23342959",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23342959",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23342959",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The AI Tsunami: Transforming Employment, Skills, and the Future of Work in the Digital Economy — An Integrated Analysis of Automation, Human–AI Collaboration, Workforce Reskilling, and Socioeconomic Transformation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23342959/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Abstract We examine how firm age affects the adoption of artificial intelligence (AI) within the workforce. Drawing on a novel dataset developed by Babina et al. (2024) combining resume and job posting data for U.S. firms, we find that older firms often encounter difficulties integrating AI talent, possibly due to entrenched practices, outdated systems, and resistance to change. Specifically, a rise in firm age by one standard deviation reduces the share of AI workers by 5.2%. However, investments in R&amp;D and infrastructure can mitigate these challenges, enabling modernization and attracting skilled AI professionals. Importantly, our analysis reveals that board composition, particularly the presence of female and minority directors, impacts AI adoption. In older firms, risk-averse decision-making and slower consensus-building among diverse boards may hinder the integration of AI talent. Firms that successfully integrate AI talent achieve significant improvements in market valuation and operational efficiency. Our study provides practical insights for managers and policymakers aiming to guide mature organizations through the challenges of technological transformation. Our findings relate to internal AI capability development and may not fully capture firms’ reliance on external AI solutions.",
   "authors": [
    "Pattanaporn Chatjuthamard",
    "Pornsit Jiraporn",
    "Pandej Chintrakarn",
    "Woraphon Wattanatorn"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-11",
   "doi": "10.1007/s11573-026-01271-0",
   "explainer": null,
   "id": "doi:10.1007/s11573-026-01271-0",
   "institutions": [
    "Chulalongkorn University",
    "Pennsylvania State University",
    "Mahidol University",
    "Thammasat University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1007/s11573-026-01271-0",
   "summary": "Using resume and job-posting data on U.S. firms (Babina et al. 2024), the authors find that older firms employ a smaller share of AI workers: a one-standard-deviation increase in firm age lowers the AI-worker share by 5.2%. R&D and infrastructure investment mitigate this, board composition matters, and firms that integrate AI talent show higher market valuation and operational efficiency.",
   "theme": "Labor & Work",
   "title": "The AI workforce and firm maturity: old firms, new tech",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1007-s11573-026-01271-0/",
   "venue": "Journal of Business Economics",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence is increasingly reshaping workplaces, occupations, and organisational practices across the global economy. While advocates highlight its potential to drive innovation, productivity, and economic growth, concerns remain about its implications for job quality, worker autonomy, skills, inequality, and the future of decent work. These tensions sit at the heart of Sustainable Development Goal 8, which calls for sustained economic growth alongside productive employment and decent work for all. This webinar brings together two complementary perspectives on the relationship between AI and work. The first seminar explores the lived experiences of creative workers navigating the rise of generative AI, revealing complex tensions between efficiency and creativity, democratisation and precarity, as well as automation and human agency. It raises important questions about identity, meaning, and authorship in an AI-enabled workplace, arguing for worker-centred approaches to technological adoption. The second seminar takes a broader view, examining emerging evidence on AI's labour market impacts across occupations, organisations, and economies. Drawing on recent scholarship, it highlights how AI's effects on employment, skills, income distribution, and workplace power relations are shaped not by technology alone, but by organisational choices, institutional frameworks, and policy interventions. Together, the two talks move beyond simplistic narratives of technological optimism or pessimism. They invite us to consider how AI can be developed, governed, and implemented in ways that support both innovation and human flourishing, opening a critical discussion about whether AI can genuinely contribute to the ambitions of SDG 8 and the creation of more inclusive, productive, and decent workplaces. Re-evaluating creative labour in the age of artificial intelligence This session is based on our qualitative study of how generative AI reshapes creative workers' self-un",
   "authors": [
    "Balca Arda",
    "Yunus Emre Öztaş",
    "Dario Guarascio",
    "Jelena Reljic"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-08",
   "doi": "10.52843/cassyni.hvtplc",
   "explainer": {
    "approach": "A recorded webinar of two talks: one on creative workers' lived experience with generative AI, one reviewing recent research on AI's labour-market impacts.",
    "findings": "The talks describe tensions between efficiency and creativity, and argue that AI's effects on employment, skills, income distribution and workplace power depend on organisational choices, institutions and policy.",
    "limitations": "It is a seminar recording that summarises other work; the abstract reports no new data or estimates.",
    "method_type": "commentary",
    "question": "Can AI support Sustainable Development Goal 8 on growth with productive employment and decent work?"
   },
   "id": "doi:10.52843/cassyni.hvtplc",
   "institutions": [
    "Kadir Has University",
    "University of Leeds",
    "Sapienza University of Rome"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.52843/cassyni.hvtplc",
   "summary": "A two-part webinar on AI and Sustainable Development Goal 8 (decent work). One talk covers creative workers' experiences with generative AI; the other reviews evidence that AI's effects on employment, skills, income distribution and workplace power depend on organisational choices, institutions and policy rather than technology alone.",
   "theme": "Labor & Work",
   "title": "The AI Workplace: Can Artificial Intelligence Deliver on SDG 8?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-52843-cassyni-hvtplc/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "Abstract Artificial Intelligence (AI) is widely recognised as a transformative general-purpose technology with the potential to reshape productivity dynamics across firms, industries, and economies. Despite growing empirical research, the evidence remains fragmented due to variation in definitions, measurement approaches, and contexts, making it difficult to draw consistent conclusions about productivity outcomes. This study addresses this gap by systematically synthesising 76 peer-reviewed studies published between 2015 and 2025. The review suggests a positive directional pattern between AI-related technologies and productivity outcomes across much of the literature, although the magnitude and consistency of effects vary considerably depending on how AI and productivity are operationalised, as well as across organisational, sectoral, and institutional contexts. Many reviewed studies examine AI as part of broader digital transformation initiatives, including robotics, automation, digital infrastructure, broadband expansion, and related technological systems. Through thematic inductive analysis, the study identifies key enablers of productivity, including organisational capabilities, workforce skills, complementary innovation pathways, and digital infrastructure. It also highlights recurring barriers, such as data limitations, cultural resistance, regulatory constraints, and high adoption costs, demonstrating that gains are neither uniform nor automatic. By synthesising heterogeneous empirical evidence and linking mechanisms to outcomes, the review provides a nuanced understanding of AI’s role in shaping productivity and offers a foundation for future research and policy aimed at maximising AI’s productivity potential.",
   "authors": [
    "Andreas Cebulla",
    "Ehsan Abedin",
    "Janice Jones"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-09",
   "doi": "10.1007/s11123-026-00813-6",
   "explainer": {
    "approach": "A systematic review of 76 peer-reviewed studies published from 2015 to 2025, synthesised with thematic inductive analysis.",
    "findings": "Most studies point in a positive direction, but the size and consistency of effects vary with how AI and productivity are measured and with context. Enablers include organisational capabilities, skills, complementary innovation and digital infrastructure; barriers include data limits, cultural resistance, regulation and adoption costs.",
    "limitations": "Many reviewed studies bundle AI with robotics, broadband and wider digital transformation, so the review cannot isolate AI's own effect.",
    "method_type": "review",
    "question": "What does the empirical literature say about the relationship between AI and productivity in firms, industries and economies?"
   },
   "id": "doi:10.1007/s11123-026-00813-6",
   "institutions": [
    "Flinders University",
    "University of South Australia",
    "The University of Adelaide"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1007/s11123-026-00813-6",
   "summary": "A systematic review synthesizing 76 peer-reviewed studies (2015-2025) on AI and productivity finds a broadly positive but highly inconsistent relationship, with effect size and direction depending heavily on how AI and productivity are measured and on organizational, sectoral, and institutional context. It maps a fragmented empirical landscape rather than producing a new estimate, identifying workforce skills, complementary innovation, and digital infrastructure as recurring enablers and data limitations, cost, and regulation as recurring barriers.",
   "theme": "Labor & Work",
   "title": "The artificial intelligence-productivity relationship: a systematic literature review and research agenda",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1007-s11123-026-00813-6/",
   "venue": "Journal of Productivity Analysis",
   "year": 2026
  },
  {
   "abstract": "The study explores how the labor market would be upset by the widespread use of Large Language Models (LLMs) and multimodal AI in the professional creative and technical industries.Whereas past instances of automation involved mainly routine manual activity, the present wave aims at non-routine cognitive activity, in this case, coding, technical writing, and graphic design.This paper measures the change in the skill premium using a difference-indifferences design on freelance websites worldwide.Early evidence indicates an offsetting effect in that AI is reducing the productivity difference between beginner and expert employees, which has the potential to lower wages on entry-level thinking jobs and soar the worth of the so-called human-in-theloop supervision and strategic planning.We simulate the Elasticity of Substitution between human intuition and the output of an algorithm, which offers a template of how firms ought to reorganize internal promotion ladders when the positions of the socalled juniors are significantly automated.",
   "authors": [
    "Anika Singh"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-22",
   "doi": "10.34218/ijeco_03_01_003",
   "explainer": {
    "approach": "A difference-in-differences design on freelance platforms worldwide, plus a simulation of the elasticity of substitution between human work and algorithm output.",
    "findings": "Early evidence indicates AI narrows the productivity gap between beginner and expert workers, which could lower wages for entry-level cognitive jobs and raise the value of human supervision and strategic planning.",
    "limitations": "The abstract gives no sample size, platforms, period or effect sizes, and calls the evidence early.",
    "method_type": "observational",
    "question": "How does generative AI change the returns to skill among high-skilled freelance workers in coding, technical writing and design?"
   },
   "id": "doi:10.34218/ijeco_03_01_003",
   "institutions": [
    "Mayo Clinic in Arizona"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.34218/ijeco_03_01_003",
   "summary": "Using a difference-in-differences design on global freelance platforms, this study finds generative AI compresses the productivity gap between novice and expert cognitive workers—lowering the skill premium and entry-level wages in coding, technical writing, and design—while raising the value of human-in-the-loop supervision and strategic work; it also models the elasticity of substitution between human judgment and algorithmic output. This is direct quasi-experimental evidence for the leveling-vs-displacement question, and its implication that junior-role automation breaks internal promotion ladders is central to debates about how firms will rebuild talent pipelines.",
   "theme": "Labor & Work",
   "title": "THE ASYMMETRIC IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON THE RETURNS TO COGNITIVE LABOR: A QUASI-EXPERIMENTAL STUDY OF HIGH-SKILLED FREELANCE MARKETS",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-34218-ijeco-03-01-003/",
   "venue": "International Journal of Economics",
   "year": 2026
  },
  {
   "abstract": "In CBO’s projections, annual TFP growth from 2026 to 2036 is 0.1 percentage point higher than it would have been without the additional contribution from generative AI diffusion; that faster growth increases the level of output in the nonfarm business sector by 1 percent in 2036.",
   "authors": [
    "Congressional Budget Office"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-02-11",
   "doi": null,
   "explainer": null,
   "id": "title:065791a81ada9d66",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.cbo.gov/publication/62105",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Budget and Economic Outlook: 2026 to 2036",
   "url": "https://wemustactnow.onrender.com/research/p/title-065791a81ada9d66/",
   "venue": "Congressional Budget Office",
   "year": 2026
  },
  {
   "abstract": "Recent evidence suggests that artificial intelligence is displacing workers at an accelerating pace across multiple industries, with over 100,000 technology workers laid off in 2025 alone due to AI adoption. This article examines a critical yet underappreciated market failure: when firms automate in competitive environments, each captures the full cost savings while bearing only a fraction of the resulting demand destruction, creating a strategic externality that harms both workers and firm owners. Drawing on game-theoretic models and recent empirical observations, we demonstrate that competitive pressure traps rational, forward-looking firms in an automation arms race that exceeds collectively optimal levels. Neither wage flexibility, profit-sharing arrangements, nor voluntary coordination mechanisms can eliminate this distortion. Only policy interventions that directly address the per-task automation margin—specifically, Pigouvian automation taxes calibrated to uninternalized demand losses—can restore efficiency. The analysis reveals that \"better\" AI paradoxically amplifies rather than resolves the problem, and that fragmented markets suffer disproportionately. These findings suggest policy discourse should shift from managing displacement consequences to correcting the competitive incentives driving excessive automation.",
   "authors": [
    "Jonathan Westover"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-01",
   "doi": "10.70175/hclreview.2020.36.1.7",
   "explainer": {
    "approach": "Game-theoretic reasoning combined with recent observations on AI-related layoffs, focusing on how each firm keeps its cost savings while sharing the loss of consumer demand with others.",
    "findings": "The article argues competitive pressure leads to more automation than is collectively optimal, that wage flexibility, profit-sharing or voluntary coordination do not remove this, and that taxes on automation set to the uninternalized demand loss could restore efficiency.",
    "limitations": "The abstract does not describe the empirical data behind its layoff figures or test the model against outcomes; the tax proposal is derived from the model rather than evaluated.",
    "method_type": "theory",
    "question": "Can competition push firms to automate with AI beyond the level that is best for workers and firm owners as a group?"
   },
   "id": "doi:10.70175/hclreview.2020.36.1.7",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.70175/hclreview.2020.36.1.7",
   "summary": "Westover formalizes a strategic externality in AI automation: citing over 100,000 tech layoffs attributed to AI in 2025, he uses game-theoretic models to show that each firm captures the full cost savings from automating while bearing only a fraction of the aggregate demand destruction, trapping rational firms in an automation arms race above the collectively optimal level, with the perverse results that better AI worsens the problem and fragmented markets suffer most. The policy claim is stark: wage flexibility, profit-sharing, and voluntary coordination cannot fix the distortion, so only Pigouvian automation taxes calibrated to uninternalized demand losses restore efficiency, sharpening the case for taxing the per-task automation margin rather than managing displacement after the fact.",
   "theme": "Labor & Work",
   "title": "The Competitive Trap: How AI-Driven Automation Creates Collective Market Failure",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-70175-hclreview-2020-36-1-7/",
   "venue": "Human capital leadership.",
   "year": 2026
  },
  {
   "abstract": "AI coding agents could change whether organizations buy software or build the functionality they need. We focus on organizations and professional buyers whose domain knowledge can help them specify and evaluate tools for their work. We define the software substitution frontier as the functions customers could build and maintain at an acceptable standard for less than buying them. Using public data, we compare growing capacity to build with indicators of market change. Since 2023, the length of benchmark tasks that leading agents can complete at 50 percent success, measured in human working time, has doubled about every 129 days. A broad search identified 575 potential open-source alternatives to selected commercial products, although some functional matches still require independent validation. The Craft suite produced 12 application repositories over 12.0 days of public development, but these remain early versions with unverified completeness and reliability. Market evidence is mixed: purchased software gained US investment share and tracked commercial installations grew. Yet 32 percent of respondents to an organizational survey reported forgoing at least one software purchase because their organization could build the functionality with coding agents. These self-reports do not establish the spending displaced or the reliability of the resulting tools. The gap motivates a warning: customers' capacity to build could affect individual buying decisions before a broad purchasing shift becomes visible. We examine this possibility using benchmark projections and observable market tests. The projections are an extrapolation, not a forecast of customer switching.",
   "authors": [
    "Ripon Chandra Malo",
    "Zimeng Yu",
    "Tong Qiu"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-21",
   "doi": null,
   "explainer": null,
   "id": "title:52010a17bf9435ce",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.30307v1",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Customer Is the Builder: How AI Coding Agents Are Rewriting the Economics of Software",
   "url": "https://wemustactnow.onrender.com/research/p/title-52010a17bf9435ce/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "35.2% of task time in world employment is at least partly delegable to AI agents in 2026, and only 2.5% can be handed over in full. Existing measures score exposure to generative AI, mostly for the United States. We build the Agent Delegability Index (ADI): two language models rate all 18,796 O*NET 30.2 task statements on a frozen, preregistered rubric of delegation levels and barriers; tasks are mapped to 433 ISCO-08 unit groups and weighted by ILOSTAT employment in 146 countries. Four findings. First, delegable employment rises with income, from 22.5% in low-income to 48.7% in high-income countries, a slope of 7.23 percentage points per log point of GDP per capita. This delegation divide is compositional: the physical barrier dominates the jobs poorer countries employ most. Second, at the occupation level ADI tracks exposure closely (r = 0.93 with Eloundou et al. and 0.89 with Felten et al.), above the preregistered bound of 0.80; ADI adds delegation levels and barriers on top of exposure, for the world, not a new ranking of occupations. Third, full delegation is rare and peaks for tasks that take a human about twelve minutes (11.8 minutes). Fourth, the preregistered adoption gap is not supported: across 101 countries, delegability and observed AI usage correlate at r = 0.86, and the gap between them is not wider in middle-income countries (p = 0.27). Model agreement is kappa = 0.77; a blind human rating of 300 tasks agrees with the model consensus at kappa = 0.85, and an agentic panel with web search at kappa = 0.87. We release the index, the atlas, the prompts and an MCP server at https://github.com/Cespial/delegation-divide.",
   "authors": [
    "Cristian Espinal Maya",
    "Santiago Jiménez Londoño"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-10-22",
   "doi": null,
   "explainer": null,
   "id": "title:15af22afcead85f0",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2610.30752v1",
   "summary": "The paper builds an Agent Delegability Index by having two language models rate all O*NET tasks for how far they could be handed to AI agents, weighted by employment in 146 countries. It finds 35.2% of task time worldwide is at least partly delegable but only 2.5% fully, and delegable employment rises from 22.5% in low-income to 48.7% in high-income countries.",
   "theme": "Labor & Work",
   "title": "The Delegation Divide: An Open Index of AI-Agent Delegability across Occupations and Countries",
   "url": "https://wemustactnow.onrender.com/research/p/title-15af22afcead85f0/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Generative AI (GenAI) is reshaping creative work, yet little empirical research has examined how novelists understand the socio-economic implications of GenAI for writers and readers. Drawing on focus group data and a survey with 384 literary creatives across the UK, including 258 published novelists, this article explores the concerns of creatives around the expanding production and consumption of AI-generated fiction. It argues that these concerns can be conceptualised under the umbrella of ‘the discrimination loop’, a process by which AI-generated fiction may intensify inequality in the creative writing economy and society. The loop has four interrelated dimensions. First, market saturation may make it harder for new or diverse voices to be discovered. Second, declining income from fiction and related freelance work may increase reliance on personal resources, privileging writers with more time, wealth, or an existing profile. Third, a two-tier market may emerge in which human-written fiction becomes a premium good, while cheaper AI-generated content becomes more widely and cheaply consumed. Fourth, AI-generated fiction may reproduce discriminatory, stereotyped or homogenised narratives, with consequences for the voices and experiences represented. By conceptualising the discrimination loop, this article contributes to critical data studies, science and technology studies, and sociology by offering a framework for future longitudinal research on GenAI, creative labour, and inequality.",
   "authors": [
    "Clementine Collett"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-22",
   "doi": "10.17863/cam.136140",
   "explainer": null,
   "id": "doi:10.17863/cam.136140",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.repository.cam.ac.uk/handle/1810/409988",
   "summary": null,
   "theme": "Inequality & Society",
   "title": "The discrimination loop: The concerns of literary creatives around the socio-economic implications of GenAI on novelists and readers",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17863-cam-136140/",
   "venue": "Apollo (University of Cambridge)",
   "year": 2026
  },
  {
   "abstract": "The impact of AI on the employment prospects of recent college graduates is hotly debated with no consensus on the magnitude of impacts nor even the timing of those potential impacts. Using CPS microdata, we provide the first estimates of the effects of AI on the unemployment of recent college",
   "authors": [
    "Robert W. Fairlie",
    "Jane Wu"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "September 2026",
   "doi": null,
   "explainer": null,
   "id": "title:a848882fa027ff93",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35796",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Early Impacts of AI on Employment among Recent College Graduates",
   "url": "https://wemustactnow.onrender.com/research/p/title-a848882fa027ff93/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Abstract: Artificial intelligence (AI) is transforming global labor markets by automating routine tasks, reshaping employment patterns, and creating significant economic and organizational challenges. This study investigates the multidimensional consequences of AI-induced workforce displacement by examining employment trends, wage polarization, worker experiences, organizational adaptation strategies, and relevant theoretical perspectives. An explanatory sequential mixed-methods design was adopted, combining quantitative analysis of survey data from 2,350 workers across 15 industries in 12 countries between January 2021 and June 2022 with longitudinal employment statistics and regression modelling, alongside qualitative analysis of 142 semi-structured interviews with displaced workers, organizational leaders, and HR professionals. Data were analyzed using SPSS v28, Stata v17, and NVivo v12 following institutional ethical approval (Protocol No. WUC-IRB-2021-047_ Dated 7 January 2021), with inter-coder reliability of κ=0.83. Results showed that routine-intensive occupations faced a 67% higher displacement risk, while manufacturing (β=-0.43, p<0.001) and administrative services (β=-0.38, p<0.001) experienced significant employment declines associated with AI adoption. Displaced workers reported an average income reduction of 26.7% and employment transitions lasting 16.8 months. Qualitative findings highlighted psychological distress, organizational culture shifts toward AI adoption, inadequate reskilling infrastructure, and insufficient institutional support, whereas organizations implementing proactive reskilling programs achieved 64% higher employee retention. Overall, AI-driven workforce displacement extends beyond job losses, requiring coordinated policy interventions, institutional support, and continuous workforce reskilling to promote equitable labor market transitions, organizational resilience, sustainable employment, and inclusive economic growth in increasin",
   "authors": [
    "Shankar S. Iyer",
    "Raman Subramanian",
    "Rajesh Arora",
    "Dr Brinitha Raji",
    "Veena Christy",
    "Aaryan Kapur",
    "RADHAKRISHNAN NATARAJAN"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-19",
   "doi": "10.18282/hrms6882",
   "explainer": null,
   "id": "doi:10.18282/hrms6882",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.18282/hrms6882",
   "summary": "A mixed-methods study combining a survey of 2,350 workers in 12 countries with 142 interviews reports that routine-intensive occupations faced higher displacement risk and that manufacturing and administrative services saw employment declines associated with AI adoption. Displaced workers reported average income losses of 26.7% and transitions lasting about 17 months.",
   "theme": "Labor & Work",
   "title": "The Economic and Organizational Consequences of AI-Induced Workforce Displacement: A Mixed-Methods Investigation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-18282-hrms6882/",
   "venue": "Human Resources Management and Services",
   "year": 2026
  },
  {
   "abstract": "The rapid iterative development of generative artificial intelligence technology is fundamentally reshaping production methods and labor forms. Its ability to penetrate non-routine cognitive tasks breaks the impact boundary of traditional automation technologies, and also promotes task content analysis method to become the mainstream research paradigm in this field. This paper systematically sorts out the theoretical evolution and research progress of the economic impact of generative AI from the perspective of task content. From the two dimensions of industrial development and labor market, it integrates the research results of core topics such as enterprise adoption, productivity effect, market structure, labor complementarity and substitution, and changes in employment structure. It focuses on clarifying the sources of divergence and applicable boundaries of different research conclusions. Studies show that existing literature has formed a basic consensus that generative AI exerts impact mainly through the reconstruction of intra-occupational tasks. However, there are still significant divergences in the total employment effect, the implementation degree of productivity gains, and the mechanism of cross-country heterogeneity. The main limitations of current research in this field include the short research time span, lack of evidence from developing economies, and insufficient research on long-term dynamic mechanisms. The contribution of this paper is to restore the seemingly divergent literature conclusions to differences in methodological premises with a unified three-effect coordinate, and put forward corresponding testable designs for each type of research gap. Future research needs to be further deepened around the two core dimensions of industry and labor market, improve localized measurement systems, and expand long-term dynamic analysis.",
   "authors": [
    "Jingyi Shi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.70267/fer.v3n5.107119",
   "explainer": {
    "approach": "A systematic review of studies on firm adoption, productivity, market structure, labour substitution and complementarity, and employment structure, organised by a common three-effect framework.",
    "findings": "The literature broadly agrees that generative AI acts by reshaping tasks within occupations, but disagrees on total employment effects, how far productivity gains are realised, and differences across countries.",
    "limitations": "The reviewed research covers short time spans, has little evidence from developing economies and little on long-run dynamics.",
    "method_type": "review",
    "question": "What does task-based research say about generative AI's effects on industries and labour markets, and why do studies disagree?"
   },
   "id": "doi:10.70267/fer.v3n5.107119",
   "institutions": [
    "Central University of Finance and Economics"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.70267/fer.v3n5.107119",
   "summary": "A review of research on generative AI's economic impact through the lens of task content, covering firm adoption, productivity, market structure, labour substitution and complementarity, and employment structure. It finds a basic consensus that generative AI works mainly by reshaping tasks within occupations, and sets out where and why studies still disagree.",
   "theme": "Labor & Work",
   "title": "The Economic Impact of Generative Artificial Intelligence: A Review of Research from the Perspective of Task Content",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-70267-fer-v3n5-107119/",
   "venue": "Financial economics research.",
   "year": 2026
  },
  {
   "abstract": "This study examines the economic implications of artificial intelligence (AI) for employment and labour productivity using published evidence from 2020–2025. It synthesizes enterprise AI-adoption statistics, occupational-exposure assessments, and empirical workplace studies to distinguish technological diffusion, potential labour-market transformation, and reported productivity outcomes. The reviewed evidence indicates expanding business adoption of AI and productivity improvements in selected customer-support and professional-writing tasks. However, occupational exposure does not necessarily translate into actual job displacement, and productivity gains observed in specific workplace settings cannot be directly generalized to national labour productivity. Potential labour-market adjustment pressures vary across income groups and genders, while some evidence suggests that less experienced workers may benefit disproportionately from AI assistance. Overall, the findings indicate that task transformation and worker augmentation provide a more balanced interpretation of AI's employment implications than the assumption of uniform labour replacement. Key policy priorities include improving digital infrastructure, strengthening relevant workforce skills, supporting workers during occupational transitions, and protecting workers' interests. As a descriptive evidence synthesis rather than an original causal analysis, this study highlights the need for more harmonized AI-adoption data and further research into employment, wages, and long-term productivity outcomes. References Acemoglu, D. (2025). The simple macroeconomics of AI. Economic Policy, 40(121), 13–58. https://doi.org/10.1093/epolic/eiae042 Acemoglu, D., & Johnson, S. (2024). Learning from Ricardo and Thompson: Machinery and labor in the early Industrial Revolution, and in the age of AI (NBER Working Paper No. 32416). National Bureau of Economic Research. https://doi.org/10.3386/w32416 Acemoglu, D., & Restrepo, P. (2",
   "authors": [
    "Seerat Ali",
    "Abdul Majeed",
    "Surayya Mukhtar",
    "Salwa Ashraf",
    "Md. Shahidur Rahman Khan"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-11",
   "doi": "10.63544/ijss.v5i5.339",
   "explainer": null,
   "id": "doi:10.63544/ijss.v5i5.339",
   "institutions": [
    "Kohat University of Science and Technology",
    "Islamia University of Bahawalpur",
    "International Islamic University, Islamabad",
    "University of Gujrat",
    "Z. H. Sikder University of Science and Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.63544/ijss.v5i5.339",
   "summary": "A descriptive synthesis of 2020-2025 evidence on AI adoption, occupational exposure and workplace studies. It finds productivity gains in specific tasks such as customer support and writing, but argues that exposure does not equal job loss and task-level gains cannot be read as national productivity growth.",
   "theme": "Labor & Work",
   "title": "The Economic Implications of Artificial Intelligence for Employment and Labour Productivity: A Descriptive Evidence Synthesis (2020–2025)",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-63544-ijss-v5i5-339/",
   "venue": "Inverge Journal of Social Sciences",
   "year": 2026
  },
  {
   "abstract": "Background: The possibility of an economic singularity---a finite‑time explosion of AI capability, financial capital, and inequality---has been discussed in futurist and economic literature, but endogenous feedback loops between AI, finance, and labour markets remain underexplored. Methods: This paper presents a dimensional reduction of a dynamical systems model of an economy with recursively self‑improving artificial intelligence and financial autocatalysis. By transforming to three economically meaningful ratios---financial depth per AI capability ($x = K_f/A$), AI capital per financial capital ($u = K_{ai}/K_f$), and employment odds ($z = L_p/(1-L_p)$)---the original four‑variable explosive system reduces to a bounded, analytically tractable three‑dimensional system. Results: The reduced model possesses a saddle‑type equilibrium that separates two asymptotic regimes: one in which AI dominates ($x\\to0$) and one in which finance explodes ($x\\to\\infty$, Neofeudalism). The critical financial depth is $x_c = (\\lambda+\\beta)/\\gamma_F$, so that the economy tips into Neofeudalism whenever the initial $x_0 &gt; x_c$. Numerical simulations confirm the saddle structure and the threshold behaviour. Conclusions: The analysis yields sharp policy recommendations: regulate financial autocatalysis to raise the safety threshold, and intervene early to keep the financial‑to‑AI ratio below the critical value. Redistribution alone cannot substitute for structural regulation.",
   "authors": [
    "Mykola Yaremenko"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-10",
   "doi": "10.62762/jnda.2026.489975",
   "explainer": null,
   "id": "doi:10.62762/jnda.2026.489975",
   "institutions": [
    "National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.62762/jnda.2026.489975",
   "summary": "A theoretical dynamical-systems model of an economy with self-improving AI, financial capital and labour, reduced to three ratios. It finds a tipping point in financial depth per unit of AI capability: above it the economy drifts toward runaway finance ('neofeudalism'), below it AI dominates. The author draws policy recommendations to curb financial feedback loops early.",
   "theme": "Policy & Governance",
   "title": "The Economic Singularity: Dimensional Reduction, Invariants, and Stability Analysis of the Financialization Threshold",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-62762-jnda-2026-489975/",
   "venue": "Journal of Nonlinear Dynamics and Applications",
   "year": 2026
  },
  {
   "abstract": "This book offers a rigorous but accessible economic map of the artificial intelligence (AI) economy. Moving beyond both technological hype and alarmist predictions of mass unemployment, Ceyhun Elgin explains how AI changes the allocation of tasks between humans and machines; why productivity gains may be delayed or uneven; and how wages, inequality, market power, public finance, financial stability, and global development are likely to be transformed. AI, the book argues, is not simply a new tool that makes existing work faster. It is a general‑purpose, task‑reallocating technology that changes the organization of production itself. Bringing together macroeconomics, labor economics, industrial organization, finance, public finance, development economics, and political economy, this book provides a unified framework for understanding the most important economic transformation of our time. It examines why AI may generate a productivity boom in some sectors while producing displacement and concentration in others; why superstar firms may become even more powerful; how tax systems and welfare states must adapt when value shifts from labor toward data, software, and intangible capital; and why developing economies may face both new opportunities and new risks in an AI‑driven world. This book’s central message is clear: AI’s economic future is not predetermined. The same technology can support broad‑based prosperity, deepen inequality, strengthen public services, entrench corporate power, expand access to knowledge, or intensify social and geopolitical divides. Outcomes will depend on institutions, competition, education, regulation, public investment, and the distribution of bargaining power. Designed for researchers, graduate students, policymakers, business leaders, and intellectually engaged readers, this book explains not only what AI can do, but how it changes the economic foundations of growth, work, welfare, and power.",
   "authors": [
    "Ceyhun Elgin"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-20",
   "doi": "10.4324/9781042011162",
   "explainer": null,
   "id": "doi:10.4324/9781042011162",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.4324/9781042011162",
   "summary": "Ceyhun Elgin's book sets out an economic framework for AI as a general-purpose technology that reallocates tasks between people and machines. It covers why productivity gains may be delayed or uneven and how wages, inequality, market power, public finance and development could change.",
   "theme": "Labor & Work",
   "title": "The Economics of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4324-9781042011162/",
   "venue": "Routledge eBooks",
   "year": 2026
  },
  {
   "abstract": "How do capability improvements transform the economy, and when? In this essay, we define thresholds of economic capabilities, e.g., minimal AGI, as a function of how they transform the economy. We then track how the economy (market structure, employment, and wages) changes during the transition across capability regimes. By examining how intelligence functions inside organizations, the answer to when the economy transforms depends on when two key capability thresholds are crossed. Competence: gains in individual skills like math, writing, or coding will smoothly shift wages, prices and the mix of humans and AI agents in a firm while leaving industry structure largely intact. Coordination: gains in the ability of agents to work together within a firm alter how organizations scale. Once coordination reaches a critical threshold, it triggers large, abrupt jumps in market concentration, employment, and wages. We term these regimes minimal AGI, supercompetent AI, supercooperative AI, and Artificial Super Intelligence (ASI). When participants are paired to complement each other to solve a specific task, they outperform random pairs by 32%. Because wage effects arrive before organizational restructuring, macro statistics of employment rates provide poor evidence that AI is not having an effect on the economy. Wages and job roles, not headline GDP or industry concentration, remain the earliest signals of change. The essay draws on the paper Organizing Intelligence, which presents the full economic model and derives the results formally.",
   "authors": [
    "Alex Imas",
    "Andrew Koh",
    "Shane Legg"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-10-29",
   "doi": null,
   "explainer": {
    "approach": "A conceptual essay drawing on a formal economic model (the companion paper Organizing Intelligence) in which firms combine humans and AI agents; capability is split into competence and coordination, plus a 100-person quiz on how complementary pairings perform.",
    "findings": "Competence gains shift wages and prices smoothly while firms stay small; coordination past a threshold makes larger firms more productive and causes abrupt jumps in concentration and wage drops in scalable markets. Complementary human pairs outperformed random pairs by 32%. Wage effects arrive before restructuring.",
    "limitations": "Essay-length summary of a theoretical model; the scenarios are not estimated from economic data, and the regimes depend on capability thresholds that current benchmarks do not measure.",
    "method_type": "theory",
    "question": "When does improving AI capability actually transform market structure, employment and wages, rather than just individual task performance?"
   },
   "id": "title:ca02022cd2e65350",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://institute.deepmind.com/essays/the-economics-of-intelligence/",
   "summary": "A DeepMind Institute essay arguing that AI's economic impact depends on two separate capability thresholds: individual competence, which shifts wages and prices gradually, and agent coordination, which past a tipping point could abruptly concentrate markets and cut wages. It sketches four regimes (minimal AGI, supercompetent, supercooperative, superintelligence) and argues that wages and job roles, not GDP or employment totals, will move first.",
   "theme": "Labor & Work",
   "title": "The Economics of Intelligence: How and when does AGI transform the economy?",
   "url": "https://wemustactnow.onrender.com/research/p/title-ca02022cd2e65350/",
   "venue": "DeepMind Institute",
   "year": 2026
  },
  {
   "abstract": "We (Parker and Tom) recently coauthored a paper, “The Economics of Recursive Self-Improvement” , with 7 other economists. The paper walks through a series of simple models of how AI may accelerate AI R&D, and we thought it’s worth highlighting some context and takeaways: We care about Recursive Self-Improvement (RSI) because we want to forecast capabilities. METR’s priority is to assess risk from frontier AI development, and one input is how capable AI systems will be in the future. Capabilities have been growing rapidly over the past 5 years, and we want to know whether to expect an acceleration. 1 The term RSI has been used with very different definitions. Unfortunately a lot of confusion has been caused by different definitions of RSI. Everyone agrees that RSI refers to feedback from model capabilities to model improvements, but some have said that RSI occurs when there’s any feedback ( Karpathy , Patel , Musk , LessWrong ), while others reserve it for when the feedback is strong enough to cause super-exponential growth ( Lambert ) or fully autonomous growth ( Favaro & Clark ). We decided not to use the term RSI in a technical sense, to avoid confusion. Instead we focus on the strength of feedback effects, and whether they are sufficiently strong for “self-sustaining acceleration.” (We have a longer survey of definitions here ). The effect on capabilities acceleration depends on the strength of feedback effects. The model gives a simple way of quantifying the strength of overall feedback effects through decomposing into individual effects. The most uncertain relationship is how an increase in model capabilities would increase the rate of algorithmic progress. We can’t rule out a substantial acceleration. We discuss a variety of reasons why there could be an acceleration in capabilities that fizzles out: bottlenecks on data, training compute, inference compute, or experiments; algorithmic-specific capabilities; and R&D-specific capabilities. However, we do not thi",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-07-22",
   "doi": null,
   "explainer": null,
   "id": "title:fb6d53620b428fdb",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/notes/2026-07-22-economics-of-recursive-self-improvement/",
   "summary": null,
   "theme": "Other",
   "title": "The Economics of Recursive Self-Improvement",
   "url": "https://wemustactnow.onrender.com/research/p/title-fb6d53620b428fdb/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "This thesis examines the impact of artificial intelligence (AI) on labor markets and employment. It analyzes how AI affects job opportunities, workforce demand, and economic development. The study shows that while AI increases productivity and creates new types of jobs, it may also reduce employment in routine occupations. The research emphasizes the importance of developing digital skills and adapting labor policies to meet the changing requirements of the modern economy.",
   "authors": [
    "Dilbar Rajabova"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-24",
   "doi": "10.5281/zenodo.20822719",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20822719",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20822719",
   "summary": "This thesis reviews how AI affects job opportunities, workforce demand and economic development. It concludes that AI raises productivity and creates new kinds of jobs but may reduce employment in routine occupations, and argues for digital skills and adapted labor policy.",
   "theme": "Labor & Work",
   "title": "THE EFFECT OF ARTIFICIAL INTELLIGENCE ON LABOR MARKETS AND EMPLOYMENT",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20822719/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "We argue that AI systems used in conducting foreign policy tasks - broadly enacting 'statecraft' - should be a priority test case for technical AI governance research. In enacting foreign policy, we refer to the formulation and implementation of external objectives by political actors. Statecraft is a high-consequence deployment domain, with extreme downside risks and structural properties that standard evaluation practices handle poorly. These features include partial observability, unbounded action spaces, contested ground truth, and multidimensional objectives. This paper advocates for a literature-grounded research agenda. Our contribution is threefold: (i) a claim about the structural conditions of foreign policy that combine catastrophic tail risk with technical evaluation complexities, (ii) an ECOSYSTEM review that highlights the asymmetric focus on ASSESSMENT features over ACCESS, VERIFICATION, SECURITY, and OPERATIONALIZATION, and (iii) a demand-side evaluation framework that decomposes foreign-policy workflows into bounded, evaluable sub-tasks with human recombination. As AI systems are already being deployed in the conduct of war and peace, amid limited public evaluation infrastructure from the technical AI governance community, this agenda is an urgent priority.",
   "authors": [
    "Charles Pozniak",
    "Jeba Sania"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-07-03",
   "doi": null,
   "explainer": null,
   "id": "title:206d5c950ef7360a",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2607.02955v1",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "The Foreign Policy AI Evaluation Gap",
   "url": "https://wemustactnow.onrender.com/research/p/title-206d5c950ef7360a/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) has emerged as a transformative technology influencing economic growth, labour markets, and rural development across the world. In developing countries like India, where a significant proportion of the population depends on agriculture and rural occupations, AI is reshaping employment patterns through automation, digitalization, and smart technologies. This paper examines the opportunities and challenges associated with AI in the future of rural employment. The study is based entirely on secondary data collected from research articles, government reports, policy documents, and publications of international organizations. The findings reveal that AI offers substantial opportunities in areas such as precision agriculture, rural entrepreneurship, digital services, healthcare, education, and skill development. At the same time, challenges including job displacement, digital illiteracy, inadequate infrastructure, and socio-economic inequalities continue to hinder inclusive AI adoption in rural areas. The study concludes that appropriate policy interventions, digital infrastructure development, and skill enhancement programmes are essential for ensuring sustainable and inclusive rural employment in the age of AI.",
   "authors": [
    "Vijaykumar mallikarjunamath",
    "Kaviraj Chippalakatti"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-31",
   "doi": "10.5281/zenodo.20068256",
   "explainer": {
    "approach": "A study based entirely on secondary data from research articles, government reports, policy documents and international organisations.",
    "findings": "It identifies opportunities in precision agriculture, rural entrepreneurship, digital services, healthcare, education and skills, and challenges including job displacement, digital illiteracy, weak infrastructure and socio-economic inequality. It concludes that policy, infrastructure and skills programmes are needed for inclusive adoption.",
    "limitations": "Relies only on secondary sources, with no original data or measured employment effects.",
    "method_type": "review",
    "question": "What opportunities and challenges does AI create for rural employment in developing countries such as India?"
   },
   "id": "doi:10.5281/zenodo.20068256",
   "institutions": [
    "Government Dental College and Hospital"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20068256",
   "summary": "This secondary-data review surveys how AI is reshaping rural employment in developing economies, especially India, finding meaningful upside in precision agriculture, rural entrepreneurship, digital healthcare, and education, but persistent barriers from job displacement risk, digital illiteracy, and weak infrastructure. It matters as a reminder that most of the AI-labor debate is calibrated to advanced-economy office work, while the inclusiveness of AI gains for agriculture-dependent populations hinges on policy, connectivity, and skilling rather than model capability.",
   "theme": "Labor & Work",
   "title": "The Future of Rural Employment in the Age of Artificial Intelligence: Opportunities and Challenges",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20068256/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The rapid advancement of Artificial Intelligence (AI) has significantly transformed labor markets byenabling AI-driven gigification, where digital platforms organize work through task-based employmentmodels and algorithmic management systems. This study examines the implications of AI-driven gigwork for sustainable development, focusing on employment quality, worker well-being, autonomy,sustainability outcomes, and Sustainable Development Goal 8 (SDG 8): Decent Work and EconomicGrowth. The study adopts a qualitative descriptive research design based on a systematic review of literaturepublished between 2022 and 2026, including academic studies, policy reports, and industry publications.The findings reveal that AI-enabled gig platforms enhance labor market participation, flexibility,productivity, and economic opportunities, particularly for youth and workers in developing economies.However, challenges such as income instability, limited social protection, algorithmic control, reducedworker autonomy, and uncertain career sustainability remain significant. The study highlights that AIdriven gigification can support sustainable development only when accompanied by effective regulatoryframeworks, transparent algorithmic practices, and innovative Human Resource Management (HRM)strategies. The research concludes that balancing technological innovation with worker protection isessential to achieving inclusive economic growth and the objectives of SDG8.",
   "authors": [
    "ideal research review"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.5281/zenodo.21341011",
   "explainer": null,
   "id": "doi:10.5281/zenodo.21341011",
   "institutions": [
    "Artificial Intelligence in Medicine (Canada)"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.21341011",
   "summary": "A literature review (2022-2026) on AI-managed gig work and the UN goal of decent work. It reports that AI-enabled platforms widen participation and flexibility, especially for young workers in developing economies, but bring income instability, limited social protection and algorithmic control.",
   "theme": "Labor & Work",
   "title": "The Gig Economy in the Age of Artificial Intelligence: Implications for Sustainable Development",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-21341011/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) is becoming more accessible via cloud platforms and generative AI tools. However, the skills needed to develop, manage, and regulate advanced AI are still concentrated in a few countries and major tech companies. This study introduces the emerging Global AI Divide, defined as a capability gap driven by unequal access to compute power, advanced semiconductors, data, capital, energy, models, and specialised human expertise. Through qualitative policy analysis and comparative case studies, it explores the technological, economic, and geopolitical challenges to meaningful AI participation. The research distinguishes between democratising AI creation and democratising AI use, emphasising that wider access to AI applications does not automatically mean true democratisation. It proposes a Six-Pillar Framework for AI Democratisation, covering compute, data, models, innovation, human capability, and governance. The study also points to an emerging policy paradox: expanding fair access to AI while ensuring that advanced frontier systems are developed within proper safety, accountability, and human oversight. Ultimately, it suggests that AI democratisation should be seen as enhancing society’s ability to access, adapt, develop, and govern AI responsibly, not just increasing its consumption.",
   "authors": [
    "Rakesh Kumar",
    "Shilpi Shrivastawa"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-14",
   "doi": "10.63002/assm.405.1779",
   "explainer": null,
   "id": "doi:10.63002/assm.405.1779",
   "institutions": [
    "Pandit Deendayal Energy University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.63002/assm.405.1779",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "The Global AI Divide: Infrastructure, Access and Policy Pathways for Democratising Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-63002-assm-405-1779/",
   "venue": "Advances in social sciences and management.",
   "year": 2026
  },
  {
   "abstract": "Artificial Intelligence (AI) is increasingly viewed as a major driver of productivity growth and economic transformation. This paper challenges the assumption that AI-related technological expansion necessarily generates widespread macroeconomic productivity gains. Drawing on endogenous growth theory and the productivity paradox literature, it introduces the Relative Economic Growth Illusion (REGI) framework, which argues that contemporary AI-driven growth may operate through concentrated and intangible-intensive economic structures rather than through broad productivity diffusion across the economy. Using cross-country evidence from OECD Productivity, OECD STAN, OECD Patents, INTAN-Invest, and Functional Urban Areas (FUAs) databases, the study combines descriptive analysis with panel and robust regression techniques to examine the relationship between AI innovation, productivity, and intangible capital accumulation. The results show that AI patent intensity has weak and statistically insignificant associations with aggregate Total Factor Productivity (TFP), while intangible capital accumulation remains strongly linked to localized sectoral productivity gains. Evidence also reveals a marked geographical concentration of AI-related innovation within a limited number of technologically advanced economies. These findings suggest that AI-driven technological progress generates localized efficiency improvements while diffusing only weakly across the broader economy. As a result, observed economic expansion may increasingly reflect concentrated growth driven by intangible capital and technological concentration rather than broad-based productivity improvements. JEL classification numbers: O33, O47, D43. Keywords: Artificial Intelligence, Economic Growth, Productivity Paradox, Intangible Capital, Market Concentration, Technological Diffusion, Relative Growth.",
   "authors": [
    "Gianmario Strappati"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-19",
   "doi": "10.47260/amae/1659",
   "explainer": null,
   "id": "doi:10.47260/amae/1659",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.47260/amae/1659",
   "summary": "A cross-country panel study using OECD productivity, patent and intangible-capital data to test whether AI innovation raises aggregate productivity. It finds AI patent intensity has weak, statistically insignificant links to total factor productivity, while intangible capital is tied to localized sectoral gains, suggesting AI's efficiency effects diffuse only weakly across economies.",
   "theme": "Growth & Macro",
   "title": "The Illusionary Model of Relative Economic Growth in the Era of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-47260-amae-1659/",
   "venue": "Advances in Management and Applied Economics",
   "year": 2026
  },
  {
   "abstract": "This study examined the fundamental association between Artificial Intelligence, Information and Communication Technology (ICT) and economic growth in some West African countries. The study acquired data from the International Telecommunication Union (ITU) for the selected countries and analysed these data sets for comparison. The findings of the study show that countries with high investment in AI and ICT have better economies, with high GDP as compared to countries that have little or no investment in both. We used several variables such as mobile telephony penetration rates, rate and number of other several ICT acquisitions; these data were acquired from the International Telecommunication Union (ITU) and world development indicators from the World Bank databases. Our findings went further to show that two major telephone systems, the fixed telephone line and mobile phone penetration together are highly able to promote economic growth. This shows that ICT is a catalyst when it comes to economic development of nations and their economies. It also shows that while some variables could improve economies or lead to arithmetic growth, AI and ICT will give rise to geometric growth. The West African countries should prioritize the allocation of resources to the development of the various ICT infrastructure and artificial intelligence digital tools, while minimizing the negative effects of inflation and government wasteful consumption.",
   "authors": [
    "John Paul Enebeli",
    "Helen Okparaji Onungwe"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-23",
   "doi": "10.64388/irev10i4-1724149",
   "explainer": null,
   "id": "doi:10.64388/irev10i4-1724149",
   "institutions": [
    "University of Port Harcourt"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.64388/irev10i4-1724149",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "The Impact of AI and ICT on the Economic Development of Some West African Countries",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-64388-irev10i4-1724149/",
   "venue": "Iconic Research and Engineering Journals",
   "year": 2026
  },
  {
   "abstract": "This study discusses the role of institutional factors in the effects of artificial intelligence and automation on labor marketinequalities. A panel data set covering the period 2005–2024 was used, comprising 7 developed countries (South Korea,Japan, Germany, the United States, France, Italy, and Spain) and 6 developing countries (China, Turkey, Poland, Hungary,Mexico and Brazil). The findings indicate that technological transformation and the spread of artificial intelligence andautomation have increased wage inequality in the labor market, though this effect varies across countries. The empiricalstrategy, tested using fixed-effects panel regression and interaction models, examines three main hypotheses: (H1) AI andautomation increase wage inequality; (H2) This effect is more pronounced in developed countries; (H3) Institutional qualitysignificantly moderates this relationship.The interaction model (β₃ = 0.89, p < 0.001) showed that institutional quality significantly mitigated the effects oftechnological transformation on inequality. Industrial robotization has significantly increased inequality in advancedeconomies. In contrast, this effect was less pronounced in developing economies. The results suggest that inequality isshaped not only by technological progress but also by institutional factors.",
   "authors": [
    "Ubeydullah ŞENER"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-02",
   "doi": "10.5281/zenodo.20698861",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20698861",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20698861",
   "summary": "A fixed-effects panel study of 13 developed and developing countries over 2005-2024 finding that AI and automation increased wage inequality, with industrial robotization raising inequality more in advanced economies. Institutional quality significantly moderated the effect, so inequality outcomes depend on institutions as well as technology.",
   "theme": "Labor & Work",
   "title": "The Impact of Artificial Intelligence and Automation on Labor Markets: The Role of Organizations in Advanced and Developing Economies",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20698861/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The article presents a comprehensive analysis of the systemic impact of artificial intelligence and automation processes on the modern labor market. In particular, on the basis of philosophical, sociological and economic approaches, the processes of reduction and transformation of professions, the formation of new professional competencies and the restructuring of human capital are studied. The study compares the approaches of technological determinism and social constructivism, revealing new forms of labor arising as a result of automation and their impact on the human subject. The article also substantiates that in the digital economy, knowledge, creativity and adaptability become the main capital.",
   "authors": [
    "Kuyliyev"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-16",
   "doi": "10.5281/zenodo.20722126",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20722126",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20722126",
   "summary": "Drawing on philosophical, sociological and economic approaches, the article analyzes how AI and automation eliminate and transform occupations and create new skill requirements. It compares technological determinism with social constructivism and argues that knowledge, creativity and adaptability become the main forms of capital.",
   "theme": "Labor & Work",
   "title": "THE IMPACT OF ARTIFICIAL INTELLIGENCE AND AUTOMATION ON THE LABOR MARKET: THE LOSS OF OCCUPATIONS AND THE EVOLUTION OF NEW SKILLS",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20722126/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The article analyzes the impact of artificial intelligence (AI) and digital transformation on economic growth through the labor market and productivity channels. The study uses statistical-comparative, structural and logical analysis methods based on official and international statistical data for Uzbekistan for 2023–2026, the “Digital Uzbekistan – 2030” strategy and the Strategy for the Development of Artificial Intelligence Technologies until 2030, as well as foreign empirical literature. The results show that AI affects growth in three ways: by raising total factor productivity, by reallocating tasks, and by creating new occupations. At the same time, the share of small business in employment (74.9%) is markedly higher than its share in gross domestic product (52.2%), which means that relative labor productivity in this sector is about 70% of the average level. This shows that diffusing AI tools precisely to low-productivity sectors is a major opportunity and, at the same time, a skills challenge. The article concludes with proposals on retraining of personnel, adoption of digital technologies in small business, data infrastructure, and measurement of the effects of AI.",
   "authors": [
    "Malikov Elbek Abdurayim ugli",
    "Worldly Knowledge Publishing Centre"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-06",
   "doi": "10.5281/zenodo.23192798",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23192798",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "http://www.arabianjournalofscience.com/index.php/index/article/view/1098",
   "summary": "This article examines how AI and digital transformation affect growth in Uzbekistan through productivity and the labour market, using official statistics for 2023-2026. It argues AI acts through higher total factor productivity, task reallocation and new occupations, and notes that small businesses hold 74.9% of employment but only 52.2% of GDP.",
   "theme": "Labor & Work",
   "title": "THE IMPACT OF ARTIFICIAL INTELLIGENCE AND DIGITAL TRANSFORMATION ON ECONOMIC GROWTH: THE CASE OF THE LABOR MARKET AND PRODUCTIVITY",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23192798/",
   "venue": "Arabian journal of science.",
   "year": 2026
  },
  {
   "abstract": "This article examines how artificial intelligence (AI), conceptualized as a general-purpose technology (GPT), shapes economic growth and structural transformation through a structured literature review covering the period from 2015 to 2025. The study adopts a structured, mechanism-oriented synthesis approach grounded in transparent search, screening, and thematic classification procedures rather than formal meta-analytic protocols. It develops an integrative innovation ecosystem framework that links three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics. The findings indicate that AI adoption does not generate uniform or automatic growth effects. Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts. In most cases, outcomes depend less on the technology itself and more on complementary conditions—human capital formation, digital and data infrastructure, institutional coordination, and governance capacity—that enable effective diffusion. Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes. Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development. The study contributes by distinguishing foundational theoretical perspectives from the contemporary 2015–2025 evidence base, clarifying the relationship between task transformation and structural transformation, and emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.",
   "authors": [
    "Sultan Salur Kucuk"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-25",
   "doi": "10.3390/economies14070239",
   "explainer": {
    "approach": "A structured, mechanism-oriented literature review of work from 2015 to 2025, using transparent search, screening and thematic classification rather than a formal meta-analysis, organised into an innovation ecosystem framework.",
    "findings": "The review links three channels: total factor productivity, task reallocation and labour-market restructuring, and innovation dynamics. It reports that growth effects are not uniform, estimates vary across contexts, and outcomes depend mostly on complementary conditions such as human capital, data infrastructure and governance capacity.",
    "limitations": "It is a narrative synthesis without meta-analytic pooling, so it does not produce a single estimate of AI's growth contribution.",
    "method_type": "review",
    "question": "Through which channels does AI, viewed as a general-purpose technology, affect economic growth and structural change?"
   },
   "id": "doi:10.3390/economies14070239",
   "institutions": [
    "Sinop University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/economies14070239",
   "summary": "A structured review of 2015–2025 evidence framing AI as a general-purpose technology working through three channels—TFP, task reallocation, and innovation dynamics—and concluding that growth effects are neither uniform nor automatic: outcomes hinge on complementary conditions like human capital, data infrastructure, and institutional governance capacity rather than the technology itself. This conditional, ecosystem-centered reading helps explain why similar AI investments produce divergent macro outcomes across countries, a key corrective to deterministic AI-growth forecasts.",
   "theme": "Growth & Macro",
   "title": "The Impact of Artificial Intelligence as a General-Purpose Technology on Economic Growth and Structural Transformation: An Innovation Ecosystem Perspective",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-economies14070239/",
   "venue": "Economies",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) is rapidly changing our economies, labor markets and global balance of power. The present paper studies AI from geographical and geopolitical perspectives, drawing together findings from literature on the uneven spatial diffusion of AI and global competition over its development. This evidence indicates that AI influences are unevenly distributed, with greater intensity in urban, high-skill regions, and so amplify regional and socio-economic inequality. AI is also becoming an object of global politics, amplifying geopolitical competition between the US, China and the EU, who approach the development of the technology with different policy visions. This paper advocates for a multi-level perspective on the development of AI, ranging from local to global levels, to understand new forms of inequality, governance issues and security concerns.",
   "authors": [
    "Aishwarya Upadhye"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-14",
   "doi": "10.5281/zenodo.20687138",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20687138",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20687138",
   "summary": "A literature review of AI from geographic and geopolitical angles. It argues that AI's effects concentrate in urban, high-skill regions, widening regional and socio-economic inequality, and that AI has become an object of competition between the US, China and the EU, calling for a multi-level view from local to global governance.",
   "theme": "Labor & Work",
   "title": "The Impact of Artificial Intelligence on Global Power and Geopolitics",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20687138/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI)-particularly Generative AI -has the potential to raise productivity but could reshape workplaces across sectors. Using occupational microdata, this paper assesses the potential impact of Generative AI on Israel’s labor market and benchmarks it against selected European economies. The results indicate that most Israeli workers are likely to benefit from AI adoption. However, about one-fifth of the workforce faces high exposure with low complementarity, indicating their vulnerability to displacement. To assist workers at risk of job displacement, a comprehensive lifelong learning strategy—including reskilling, upskilling, and mid-career training—is essential.",
   "authors": [
    "Ece Ozge Emeksiz"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-01",
   "doi": "10.5089/9798229052931.018",
   "explainer": {
    "approach": "Uses occupational microdata to measure workers' exposure to generative AI and its complementarity with their work, benchmarked against European economies.",
    "findings": "Most Israeli workers are likely to benefit from AI adoption, but about one-fifth of the workforce has high exposure with low complementarity, which the authors read as vulnerability to displacement.",
    "limitations": "Exposure measures indicate potential effects, not observed job changes; the abstract does not give the timing of any impact.",
    "method_type": "observational",
    "question": "How exposed is Israel's labor market to generative AI, compared with selected European economies?"
   },
   "id": "doi:10.5089/9798229052931.018",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.5089/9798229052931.018",
   "summary": "This IMF Selected Issues paper applies occupational exposure-complementarity measures to Israeli microdata and finds most Israeli workers stand to benefit from generative AI, but roughly one-fifth of the workforce is highly exposed with low complementarity and thus displacement-vulnerable, benchmarked against selected European economies. It extends the Cazzaniga et al.-style IMF exposure framework to a highly tech-intensive labor market and grounds the policy case for lifelong learning, reskilling, and mid-career training as the main buffer against AI-driven displacement.",
   "theme": "Labor & Work",
   "title": "The Impact of Artificial Intelligence on Israel’s Labor Market",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5089-9798229052931-018/",
   "venue": "Selected Issues Papers",
   "year": 2026
  },
  {
   "abstract": null,
   "authors": [
    "Iñaki Aldasoro",
    "Sebastian Doerr",
    "Leonardo Gambacorta",
    "Daniel M. Rees"
   ],
   "cited_by": 2,
   "collected_from": "openalex",
   "date": "2026-09-28",
   "doi": "10.1016/j.econmod.2026.107858",
   "explainer": null,
   "id": "doi:10.1016/j.econmod.2026.107858",
   "institutions": [
    "Bank for International Settlements",
    "Centre for Economic Policy Research"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.1016/j.econmod.2026.107858",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The impact of artificial intelligence on output and inflation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1016-j-econmod-2026-107858/",
   "venue": "Economic Modelling",
   "year": 2026
  },
  {
   "abstract": "Artificial intelligence (AI) has rapidly moved from a specialized technological field into a major component of modern economic activity. Its application in manufacturing, finance, education, healthcare, logistics, customer service, and other sectors is changing the way organizations produce goods and services and how workers perform their tasks. While AI can increase productivity, reduce operational costs, and create new occupations, it also raises concerns about job displacement, changing skill requirements, wage inequality, and the future of traditional employment. This article examines the relationship between artificial intelligence and the labour market, focusing on both the opportunities and challenges created by the increasing adoption of AI. The analysis suggests that the main economic effect of AI is not simply the elimination of jobs but the transformation of occupational tasks. Jobs that involve repetitive and predictable activities are particularly exposed to automation, whereas occupations requiring creativity, complex decision-making, interpersonal communication, and contextual understanding are more likely to be complemented by AI. The article argues that education, lifelong learning, reskilling, and effective labour-market policies will be essential for ensuring that technological progress contributes to inclusive economic development.",
   "authors": [
    "Nuriddinova Ma'suda Lutfilla qizi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-19",
   "doi": "10.5281/zenodo.23474211",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23474211",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23474211",
   "summary": "An article on AI and the labour market arguing that AI's main effect is the transformation of occupational tasks rather than outright job elimination. Repetitive, predictable work is most exposed, while creative and interpersonal work is more likely to be complemented, so the author stresses education, reskilling and labour-market policy.",
   "theme": "Labor & Work",
   "title": "THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE LABOUR MARKET AND EMPLOYMENT",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23474211/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Abstract This study investigates the impact of Artificial Intelligence (AI) adoption on the performance of small and medium-sized enterprises (SMEs) in Kosovo, a context where firms face resource constraints but play a pivotal role in economic resilience. Using survey data from 210 SMEs and applying correlation and multiple regression analyses, the research explores how AI integration affects internal management processes, strategic adaptability, and overall organizational outcomes. The objectives were to assess the extent to which AI influences human resource management (HRM), decision-making quality, and customer experience, and to identify enabling factors such as digital readiness and employee training. The findings reveal a strong positive relationship between AI adoption and internal performance indicators, particularly HRM efficiency, where AI training emerged as the strongest predictor of improved outcomes. Decision-making quality was also positively associated with AI, albeit more modestly. However, results indicate a negative relationship between AI usage and customer experience, suggesting that current deployments prioritize internal efficiency at the expense of personalized service delivery. The study concludes that while AI can act as a catalyst for productivity and innovation, its benefits in customer-facing functions remain underutilized. These insights highlight the importance of aligning technological investment with workforce upskilling and customer-centric strategies. The paper contributes to the broader discourse on digital transformation in developing economies by offering recommendations for SME leaders and policymakers to foster inclusive and sustainable AI-driven growth.",
   "authors": [
    "Blerim Dragusha",
    "Saso Josimovski"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-25",
   "doi": "10.2478/sues-2026-0017",
   "explainer": null,
   "id": "doi:10.2478/sues-2026-0017",
   "institutions": [
    "University of Prishtina",
    "Ss. Cyril and Methodius University in Skopje"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.2478/sues-2026-0017",
   "summary": "A survey of 210 small and medium enterprises in Kosovo finds AI adoption is positively associated with internal performance, especially HR management efficiency, and more modestly with decision quality. AI use is negatively associated with customer experience, and AI training is the strongest predictor of better outcomes.",
   "theme": "Productivity & Firms",
   "title": "The Impact of Artificial Intelligence on the Performance of Small and Medium Enterprises in Kosovo",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-2478-sues-2026-0017/",
   "venue": "Studia Universitatis „Vasile Goldis” Arad – Economics Series",
   "year": 2026
  },
  {
   "abstract": "Green total factor productivity (GTFP) plays an important role in urban sustainability. In the context of rapid advances in artificial intelligence (AI), this study examines whether and how the accumulation of AI technologies affects urban GTFP. We construct a city-level AI development index based on patent data. Using panel data on 120 Chinese prefecture-level cities from 2010 to 2023, we employ two-way fixed-effects models, instrumental-variables estimation, and multiple robustness checks to identify the impact of AI on urban GTFP. The results show that AI significantly improves urban GTFP. A one-standard-deviation increase in AI development is associated with an increase of approximately 0.75 in GTFP, which represents a substantial improvement within the distribution of GTFP. Mechanism analysis indicates that this effect operates primarily through green innovation and industrial upgrading. The positive impact is more pronounced in cities with stronger economic and institutional foundations.",
   "authors": [
    "X. B. Wang",
    "Minghuan Wu",
    "Yuan Feng",
    "Liang Zhao"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-02",
   "doi": "10.3390/su18115616",
   "explainer": null,
   "id": "doi:10.3390/su18115616",
   "institutions": [
    "Guangdong Ocean University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.3390/su18115616",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Impact of Artificial Intelligence on Urban Green Total Factor Productivity—Evidence from Chinese Cities",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3390-su18115616/",
   "venue": "Sustainability",
   "year": 2026
  },
  {
   "abstract": "This article analyzes the impact of artificial intelligence on Uzbekistan's labor market based on public perceptions. The study is based on a survey conducted through the Telegram channel of the \"Digital Education Center\", involving 8,500 participants. The findings show that 72% of respondents believe that AI will transform Uzbekistan's labor market. The occupations expected to be most affected are accounting (58%), translation (52%), programming and IT (47%), and marketing (43%). Moreover, 49% believe that AI will create new jobs, while 61% expect it to have a positive impact on their professional development. The study emphasizes the need to adapt education to the AI era, strengthen young people's digital and cognitive skills, and promote responsible AI literacy.",
   "authors": [
    "Abduqodirova Elnora Odiljon qizi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-06",
   "doi": "10.5281/zenodo.23262520",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23262520",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23262520",
   "summary": null,
   "theme": "Labor & Work",
   "title": "THE IMPACT OF ARTIFICIAL INTELLIGENCE ON UZBEKISTAN'S LABOUR MARKET: PUBLIC PERCEPTIONS, OCCUPATIONAL TRANSFORMATION, AND PROSPECTS FOR HUMAN CAPITAL DEVELOPMENT",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23262520/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The accelerated development of future technologies and artificial intelligence (AI) profoundly impacts the global economic system, revolutionizing the functioning of corporations, governmental agencies, as well as private citizens' life and work. Technologies like machine learning, robotics, blockchain, cloud computing, biotechnology, quantum computing, and the Internet of Things (IoT) have become indispensable aspects of economic growth in both developed and underdeveloped countries. Technological innovations allow for raising productivity levels, promoting innovations, decreasing expenses and helping businesses to better compete in the international business environment. Artificial intelligence is an integral part of the contemporary technological landscape. The capabilities of intelligent machines and software systems include the analysis of vast amounts of data, identification of patterns, prediction of outcomes and performance of actions which usually can only be performed by human intelligence. Corporations widely employ AI to automate production processes, provide clients with adequate services, streamline logistics and make strategic decisions. Governmental agencies also utilize smart technologies in the spheres of health care, education, transportation and public management.",
   "authors": [
    "Norqulova Sevinch"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-17",
   "doi": "10.5281/zenodo.20259859",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20259859",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20259859",
   "summary": "A descriptive overview of how AI and other emerging technologies (robotics, blockchain, cloud computing, IoT) are changing the work of firms, governments and households. It discusses productivity, innovation and cost reductions without presenting new data.",
   "theme": "Productivity & Firms",
   "title": "THE IMPACT OF FUTURE TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE ON THE GLOBAL ECONOMY",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20259859/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Frontier AI's labor-market effects matter to workers, firms, and policymakers, but current evidence generally comes from a handful of high-income economies. The capabilities of frontier AI are jagged across work tasks and national economies diverge in how they allocate human labor. We introduce a national AI exposure metric that combines occupation-level exposure scores and international employment data for 141 countries. We find that high income countries are substantially more exposed than low income countries and that Europe and Central Asia are 50 percent more exposed than Sub-Saharan Africa. We also find a gender gap: women are more exposed than men in 91 percent of countries, driven by their concentration in white-collar and sales occupations. The exceptions are countries where women's employment remains concentrated in agriculture and household enterprises. We validate our national AI exposure estimates by showing they predict national AI adoption statistics published by Anthropic, Microsoft, and OpenAI. Beyond direct exposure, we identify a new mechanism for indirect exposure due to cross-country income dependencies. Some nations such as Tajikistan depend heavily on foreign workers remitting money back to their home countries: Tajikistan's direct exposure to frontier AI is below-average but because 37 percent of Tajikistan GDP is Russian remittance and Russia is very exposed, Tajikistan's remittance-accounted exposure becomes above-average. Our research shows that national variation in exposure is large enough that policy responses calibrated to U.S. or European labor markets will not generalize.",
   "authors": [
    "Arul Murugan",
    "Tomás Aguirre",
    "Abhishek Nagaraj",
    "Rishi Bommasani"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-08",
   "doi": null,
   "explainer": {
    "approach": "The authors build a national AI exposure metric by combining occupation-level exposure scores with international employment data for 141 countries, validate it against AI adoption statistics published by Anthropic, Microsoft and OpenAI, and add indirect exposure through cross-country remittance dependencies.",
    "findings": "High-income countries are substantially more exposed than low-income ones, and Europe and Central Asia are 50 percent more exposed than Sub-Saharan Africa. Women are more exposed than men in 91 percent of countries. Remittances can raise exposure: Tajikistan becomes above-average because 37 percent of its GDP is remittances from highly exposed Russia.",
    "limitations": "Exposure measures which jobs AI could affect, not realised effects on employment or wages. The occupation-level scores and the validation rely on available adoption statistics, which the abstract does not detail.",
    "method_type": "observational",
    "question": "How exposed are different national economies to frontier AI, given that AI's capabilities are uneven across tasks and countries employ people in different occupations?"
   },
   "id": "title:e055f319cd9c365f",
   "institutions": [
    "Berkeley College",
    "University of California, Berkeley"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2607.05404",
   "summary": "The authors build a national AI exposure metric spanning 141 countries by combining occupation-level exposure scores with international employment data, finding high-income countries far more exposed than low-income ones (Europe/Central Asia 50% more exposed than Sub-Saharan Africa) and women more exposed than men in 91% of countries due to concentration in white-collar and sales work. They also introduce an indirect-exposure channel via remittances — Tajikistan's below-average direct exposure becomes above-average once its 37%-of-GDP dependence on Russian remittances is accounted for. This matters because it shows AI labor-market policy calibrated to US/European evidence will misfire elsewhere, and it validates the metric against Anthropic, Microsoft, and OpenAI adoption data.",
   "theme": "Labor & Work",
   "title": "The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies",
   "url": "https://wemustactnow.onrender.com/research/p/title-e055f319cd9c365f/",
   "venue": "arXiv (Cornell University)",
   "year": 2026
  },
  {
   "abstract": "This article examines the deep integration of the Internet and digital technologies into all spheres of society as one of the key trends of global development in the 21st century. It also analyzes the structural changes taking place in the labor market, the emergence of new professions, and the evolving requirements for existing occupations. The study highlights the impact of digital technologies, artificial intelligence, automation, and the platform economy on the labor market, emphasizing the growing importance of digital skills and the professions expected to be in high demand in the future. Furthermore, the paper proposes scientific and practical recommendations for aligning the education system with labor market needs, enhancing the professional and digital competencies of specialists, improving their qualifications, and developing a lifelong learning system.",
   "authors": [
    "Ziyadullayeva Ruxshona Ruxiddin qizi",
    "Worldly Knowledge Publishing Centre"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-07-09",
   "doi": "10.5281/zenodo.21276749",
   "explainer": null,
   "id": "doi:10.5281/zenodo.21276749",
   "institutions": [
    "Tashkent University of Information Technology",
    "Tashkent State University of Economics",
    "World Scientific (Singapore)"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.21276749",
   "summary": "An article on structural change in the labor market from digital technologies, AI, automation and the platform economy, including emerging professions and changing skill requirements. It recommends aligning education with labor market needs and building lifelong learning systems.",
   "theme": "Labor & Work",
   "title": "THE LABOR MARKET AND EMERGING PROFESSIONS IN THE CONTEXT OF THE DIGITAL ECONOMY",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-21276749/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Generation Beta (2025-2039) is being born into a world already mid-transformation, in which artificial intelligence is anchored not as a tool but as the connective tissue of education, work, and the economy. This analysis synthesizes research from the International Monetary Fund, the World Economic Forum, the McKinsey Global Institute, the University of Oxford, and other institutions, and tests it against the concrete corporate events of early and mid 2026. The finding is consistent: between 40 and 60 percent of jobs in advanced economies face meaningful disruption from AI, and the social safety structures required to manage that transition do not exist at the necessary scale in any country on earth. The mass layoffs at Oracle, Amazon, Meta, Cloudflare, and DeepL in 2026 are not the result of failing businesses. They are the result of businesses succeeding with fewer people. The transition is not hypothetical. It is happening, now, at a speed no previous technological upheaval has matched, and one that neither educational systems nor social systems were built to manage.",
   "authors": [
    "George A. Rauscher"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-26",
   "doi": "10.5281/zenodo.20394043",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20394043",
   "institutions": [
    "Starnberg Hospital"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20394043",
   "summary": "An essay synthesizing reports from the IMF, World Economic Forum, McKinsey and others, concluding that 40 to 60 percent of jobs in advanced economies face meaningful disruption from AI. It points to 2026 layoffs at several technology firms as evidence and argues that no country has social safety structures at the scale the transition requires.",
   "theme": "Labor & Work",
   "title": "The Last Generation to Learn a Trade: How Artificial Intelligence Is Reshaping the Labor Market at a Speed History Has Never Seen, and Why the Transition Will Spare Almost Nobody",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20394043/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "We measure how artificial intelligence (AI) affects the economy through its impact on software engineering productivity. We use information from financial markets to develop a forward-looking measure that is available in real time. We estimate the sensitivity of each firms stock return to an AI",
   "authors": [
    "Alex Blumenfeld",
    "Jonathon Hazell",
    "Chen Lian",
    "Andreas Schaab"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "September 2026",
   "doi": null,
   "explainer": null,
   "id": "title:3ae9ea7febbaafc9",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35793",
   "summary": "This NBER paper sizes AI's macroeconomic effect through its impact on software engineering productivity, using firms' stock-return sensitivity to AI news to build a forward-looking, real-time measure.",
   "theme": "Productivity & Firms",
   "title": "The Macroeconomic Effect of AI: Sizing the Software Engineering Channel",
   "url": "https://wemustactnow.onrender.com/research/p/title-3ae9ea7febbaafc9/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "In an analysis of over 200 media industry job vacancies referencing artificial intelligence (AI) skills, we identified key evolutions in employer requirements for candidates between 2023 and 2025. Our findings indicate that proficiency in AI has transitioned from a supplementary skill to a fundamental competency essential for media professionals. Employers now prioritize candidates who possess not only foundational knowledge of AI functionalities and an active interest in the technology but also practical experience with primary tools used for content creation and management. The demand for such roles is predominantly for full-time employment, with the scope of positions requiring AI competence expanding significantly. By 2025, roles encompassing AI expertise include copywriter, social media manager, public relations specialist, and designer, whereas in 2023, these roles were confined to editors and journalists. The repertoire of available positions is also being augmented to include roles such as AI translator, AI editor, AI designer, AI content manager, and AI trainer. Consequently, media professionals are expected to acquire familiarity with these emerging tools and demonstrate capabilities in content creation, editing, fact-checking, and generation. Additionally, proficiency in data analysis and creative ideation are increasingly requisite.",
   "authors": [
    "K. L. Zuykina",
    "Daria V. Sokolova"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-24",
   "doi": "10.25205/1818-7919-2026-25-6-22-33",
   "explainer": null,
   "id": "doi:10.25205/1818-7919-2026-25-6-22-33",
   "institutions": [
    "Lomonosov Moscow State University",
    "Moscow State University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.25205/1818-7919-2026-25-6-22-33",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Media Labor Market: The New AI Skills",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-25205-1818-7919-2026-25-6-22-33/",
   "venue": "Vestnik NSU Series History and Philology",
   "year": 2026
  },
  {
   "abstract": "This study examines how the dominant industry narrative presents generative artificial intelligence (GenAI) as a trigger for total software automation and positive exponential outcomes. Empirical evidence from codebases and recent software-engineering studies, however, suggests a paradox: rapid generation can increase review overhead, architectural decay, and critical edge-case failures. By analysing the ArtCraft case study alongside quantitative reports from GitClear, DORA, and the empirical software-engineering literature, this paper formulates the Ninety Percent Trap: GenAI can accelerate initial scaffolding from zero to one while shifting the burden of production dependability to human experts. The paper also presents the True Cost of AI Quality (TCAQ) framework, which evaluates GenAI productivity through generation time, expert review, edge-case resolution, and technical-debt cost rather than output volume alone. An illustrative calculation shows that a two-hour generation phase can become a 112-hour end-to-end engineering effort after review, edge-case resolution, and debt mitigation. The findings indicate that domain expertise remains essential for architectural reasoning, security verification, and dependable delivery.",
   "authors": [
    "Husam Altahier Mohammed Amhimmid",
    "Milad Areir"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-30",
   "doi": "10.14738/tecs.1405.12484",
   "explainer": null,
   "id": "doi:10.14738/tecs.1405.12484",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.14738/tecs.1405.12484",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Ninety Percent Trap: Empirical Documentation on Why Generative AI Requires Deep Domain Expertise",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-14738-tecs-1405-12484/",
   "venue": "Transactions on Engineering and Computing Sciences",
   "year": 2026
  },
  {
   "abstract": "This analysis explores how varying market structures and institutional frameworks shape both the development trajectory of artificial intelligence (AI) technologies and their subsequent impact on labor markets and economic development across different market economies. Building upon the varieties of capitalism framework, the study examines how institutional configurations in liberal market economies (LMEs) and coordinated market economies (CMEs) create distinct patterns of AI innovation, labor market adaptation, and power dynamics. The findings reveal that AI development is not a technologically determined phenomenon but is rather institutionally mediated, leading to divergent socioeconomic outcomes. LMEs exhibit rapid, breakthrough innovations in AI alongside skill-biased technological change and labor market disruption, while CMEs demonstrate more systematic integration of AI technologies with incremental adaptation strategies and stronger worker protection mechanisms.",
   "authors": [
    "Habib Badawi",
    "Ayman Majzoub"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-19",
   "doi": "10.4018/979-8-3693-5638-8.ch062",
   "explainer": null,
   "id": "doi:10.4018/979-8-3693-5638-8.ch062",
   "institutions": [
    "Lebanese University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.4018/979-8-3693-5638-8.ch062",
   "summary": "This book chapter applies the varieties-of-capitalism framework to argue that AI's development and labor-market effects are mediated by institutions. It describes liberal market economies as pairing rapid AI breakthroughs with skill-biased disruption, and coordinated market economies as integrating AI more incrementally with stronger worker protections.",
   "theme": "Labor & Work",
   "title": "The Political Economy of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-4018-979-8-3693-5638-8-ch062/",
   "venue": null,
   "year": 2026
  },
  {
   "abstract": "Based on the realistic background of the current employment situation, this paper combs the dual impact of artificial intelligence on the labor market. Combined with the research findings of scholars, AI has a two-way effect of employment substitution and creation. At the same time, AI has changed the employment structure, generated a wealth of new types of high skilled industries, promoted the transfer of labor to the tertiary industry and optimized the allocation of industrial employment, and also exacerbated the structural contradictions of job contraction, skill polarization and unequal income distribution. Finally, this paper summarizes the existing research, points out the lack of industry group heterogeneity and policy adaptation, and looks forward to the future, which should be combined with the development of new quality productivity, in-depth research on the medium and long-term transmission mechanism of the scene, so as to provide theoretical support for resolving structural employment contradictions and achieving high-quality employment.",
   "authors": [
    "Wenjing Chen"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-01",
   "doi": "10.54254/2753-7048/2026.34013",
   "explainer": null,
   "id": "doi:10.54254/2753-7048/2026.34013",
   "institutions": [
    "Beijing Institute of Fashion Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.54254/2753-7048/2026.34013",
   "summary": "A review of research on AI's dual effect on employment, substituting some jobs while creating others. It reports that AI shifts labour toward services and new high-skill industries but also deepens job contraction, skill polarization and income inequality, and notes gaps in research on industry differences and policy adaptation.",
   "theme": "Labor & Work",
   "title": "The Positive and Negative Impacts on Employment in the Context of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54254-2753-7048-2026-34013/",
   "venue": "Lecture Notes in Education Psychology and Public Media",
   "year": 2026
  },
  {
   "abstract": "Who is exposed to generative AI in a developing-country labour market? We map three occupational AI-exposure indices to India's redesigned Periodic Labour Force Survey (2025) and document a steep caste gradient among 83,000 employed graduates: graduates from the Scheduled Castes and the Scheduled Tribes are 0.24--0.37 standard deviations less exposed than upper-caste graduates within the same district. Two channels drive the gap: one in four SC and one in three ST graduates work in farm or elementary occupations untouched by AI, and those in white-collar work are underrepresented in managerial, software, and finance occupations. Because exposure commands a wage premium of up to 20 per cent, generative AI stands to widen, not narrow, India's caste earnings gap.",
   "authors": [
    "Kaibalyapati Mishra"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-11",
   "doi": null,
   "explainer": null,
   "id": "title:391afea656110815",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.13314v2",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Privilege of Exposure: Caste and Generative AI in India's Graduate Labour Market",
   "url": "https://wemustactnow.onrender.com/research/p/title-391afea656110815/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "In Albania, youth face significant challenges in accessing stable and meaningful employment, particularly in rural areas where educational opportunities and technological infrastructure remain limited (World Bank, 2022). This study examines how education, complemented by the strategic application of Artificial Intelligence (AI), can facilitate the integration of young people into the labor market by enhancing both employability and social inclusion (OECD, 2021). Adopting a mixed analytical approach based on literature review and empirical evidence, the study evaluates current educational frameworks, labor market dynamics, and the role of emerging AI-driven tools. The findings indicate a persistent mismatch between skills acquired through formal education and labor market requirements, especially in digital competencies (European Commission, 2023). AI-based solutions—such as intelligent career guidance systems, personalized learning platforms, and job-matching algorithms—demonstrate strong potential in bridging this gap and improving school-to-work transitions (UNESCO, 2021). Furthermore, the study highlights the social dimension of youth employment, emphasizing that equitable access to education and AI resources contributes to reducing regional disparities and promoting inclusive development (International Labour Organization, 2022). However, challenges related to digital infrastructure, institutional capacity, and unequal access to technology continue to limit the effective implementation of AI in education and employment systems. The paper provides policy-relevant recommendations, including strengthening digital education, fostering collaboration between academia and industry, and promoting inclusive access to AI tools. These measures are essential for empowering youth and supporting the development of a more adaptive and inclusive labor market in Albania.",
   "authors": [
    "Leons Hasani",
    "Suela KOCIBELLINJ",
    "Eriona Deda"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-05-29",
   "doi": "10.5281/zenodo.20574916",
   "explainer": null,
   "id": "doi:10.5281/zenodo.20574916",
   "institutions": [
    "Agricultural University of Tirana",
    "Thi Qar University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.20574916",
   "summary": "A study combining a literature review with empirical evidence on youth employment in Albania, finding a persistent mismatch between skills taught in formal education and labor market demands, especially digital skills. It argues that AI tools such as career guidance and job matching could ease school-to-work transitions, but infrastructure and unequal access limit their use.",
   "theme": "Labor & Work",
   "title": "THE ROLE OF EDUCATION AND ARTIFICIAL INTELLIGENCE IN YOUTH LABOR MARKET INTEGRATION IN ALBANIA",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-20574916/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Popular narratives regarding Generative Artificial Intelligence (GAI) emphasize the egalitarian democratization of high-tier cognitive execution, proposing that conversational interfaces allow non-experts to command complex technical domains seamlessly. This paper refutes this techno-utopian paradigm by constructing a mathematical and institutional framework around two structural constraints: The Scarcity of Sockets and The Commander’s Paradox. We demonstrate that while GAI reduces the marginal cost of information synthesis to near zero, it exponentially increases the economic premium on verified domain intuition and tail-risk ownership. Because strategic decision-making positions (\"sockets\") remain structurally bounded by organizational hierarchies and risk concentration, AI integration compresses mid-tier knowledge work. The resulting equilibrium is a hyper-polarized dual labor market comprising a tiny, highly compensated elite of System Orchestrators who bear risk and audit synthetic outputs, and a disposable mass of Biological Interfaces relegated to routine physical or prompt-driven execution.",
   "authors": [
    "Min Jinseong"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-16",
   "doi": "10.5281/zenodo.23421898",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23421898",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23421898",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Scarcity of Sockets and Structural Labor Polarization in the Generative AI Era: Modeling the Dual Economy of System Orchestrators and Biological Interfaces",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23421898/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "We analyze usage data from OpenAI's Codex tool to present large-scale evidence of how agentic AI technology, which can take actions on a user's behalf, changes how people work. We use an automated, privacy-protecting pipeline to contrast usage across three populations: external personal-account users, external organizational-account users, and workers within OpenAI. We find that agentic AI usage is growing rapidly: the number of active users has grown more than fivefold in the first half of 2026, with the most rapid increase occurring outside the initial audience of software developers. Uptake is uneven: within OpenAI, Codex usage is nearly universal and has largely replaced business usage of ChatGPT. We document a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven. In addition to headline usage figures, we observe measures of sophistication, and find that a growing number of users have used Codex to change their workflows substantially. More than 10% of users manage three or more concurrent Codex agents at some point each week and that 26.6% use skills, which allow users to share instructions for complex workflows. Alongside these changes in usage practices, request complexity has increased: since the start of the year, the share of individual Codex users who submit at least one request for a task estimated to require more than eight hours for an experienced human to complete has increased nearly tenfold. Concurrently, output has grown rapidly -- in June 2026, the median OpenAI employee in a legal role generated 13 times more monthly output tokens across Codex and ChatGPT than they did in November 2025, while the median researcher generated more than 50 times as many. We conclude by discussing the implications of these patterns for productivity, job reorganization, and workforce restructuring.",
   "authors": [
    "Drew Johnston",
    "David Holtz",
    "Alex Martin Richmond",
    "Christopher Ong",
    "Prasanna Tambe",
    "Aaron Chatterji"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-25",
   "doi": null,
   "explainer": {
    "approach": "The authors analyse Codex usage data through an automated, privacy-protecting pipeline, comparing external personal-account users, external organizational users and OpenAI's own staff, and track usage volume, sophistication, request complexity and output.",
    "findings": "Active users grew more than fivefold in the first half of 2026. Within OpenAI, Codex use is nearly universal and has largely replaced business use of ChatGPT. Over 10% of users run three or more concurrent agents weekly; the share submitting tasks estimated at over eight hours of human work rose nearly tenfold. Median output tokens rose 13-fold for legal staff and 50-fold for researchers.",
    "limitations": "The data come from one company's product and its own employees, an early-adopting group, and output tokens measure usage rather than productivity or job outcomes.",
    "method_type": "observational",
    "question": "How is agentic AI, which takes actions on a user's behalf, changing how people work, judged from usage of OpenAI's Codex tool?"
   },
   "id": "title:44241cbc59e57883",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.26959v1",
   "summary": "Analyzing OpenAI Codex telemetry across personal, organizational, and internal-OpenAI users, the authors document active users growing more than fivefold in the first half of 2026 (fastest outside software developers), over 10% of users managing 3+ concurrent agents weekly, 26.6% using shareable skills, and a near-tenfold rise in the share of users submitting tasks estimated at 8+ human-hours. Inside OpenAI, Codex has largely displaced business ChatGPT use, offering an early look at what near-frontier agentic adoption implies for workflow restructuring — though external organizational uptake remains far more uneven, underscoring the diffusion gap central to AI-productivity forecasts.",
   "theme": "Labor & Work",
   "title": "The Shift to Agentic AI: Evidence from Codex",
   "url": "https://wemustactnow.onrender.com/research/p/title-44241cbc59e57883/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Purpose. The software industry both builds artificial intelligence and was among the first to use it on its own work, through coding assistants. This article asks where AI stands in software in 2026, what controlled studies say about developer productivity, and where the industry may be by about 2035. Design and methods. A review combining the share of the software research literature that involves AI (OpenAlex, 2010–2025), Eurostat's survey of AI use in EU enterprises in programming, consultancy and information services (2021–2025), three randomised studies of coding assistants with different results, a user study of security, and a study of early-career employment. Claims taken from the literature were checked against the sources cited. The scenarios are judgements built on this evidence. Findings. The share of software works that mention AI rose from 7.4% in 2018 to 34.6% in 2025, 4.7 times as much, and language-model works from 198 to 4,052, 46.3% of AI-related works, the highest share of the 20 industries. In the EU, 65.7% of enterprises in the sector used AI in 2025, the highest of the twelve sectors with Eurostat data and more than three times the 19.9% for all enterprises, and 42.6% used three or more technologies. Controlled studies disagree about productivity, and the disagreement is informative. In a laboratory experiment, developers with an AI pair programmer completed a programming task 55.8% faster; in three field experiments with 4,867 developers at three large companies, the number of completed tasks rose by 26.08%, with larger gains for less experienced developers; but in a randomised trial with 16 experienced open-source developers working on their own mature projects, allowing AI tools increased completion time by 19%, while the developers believed that it had cut their time by 20%. A user study found that participants with an AI assistant wrote significantly less secure code and were more confident in it. US payroll data show employment of worker",
   "authors": [
    "P. Ghorbani"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-21",
   "doi": "10.5281/zenodo.23517385",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23517385",
   "institutions": [
    "Shahid Beheshti University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23517385",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Software Industry and Artificial Intelligence: Nearly Universal Use, Evidence That Ranges from Large Gains to a Slowdown, and the Question of Who Gets Hired",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23517385/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "This dissertation investigates the displacement of human actors by generative AI performers in the fastest-growing segment of the screen entertainment economy: the vertical drama, or micro-drama, industry. The study is organized around a triggering case widely reported in October 2026, in which a nineteen-year-old actress, Hannah Lowery, was reportedly recast out of an ice-skating-themed series when the production shifted entirely to AI-generated performers. Using the video report as an entry point, the dissertation triangulates its claims against primary trade reporting, including Business Insider (June 2026), trade publications such as Variety, Mashable, and The Economic Times, regulatory documents, and a broad interdisciplinary literature spanning cultural economics, labor sociology, media theory, and machine learning. Methodologically, the dissertation combines (i) documentary and content analysis of the video case and its verification status, examining each claim individually; (ii) economic analysis of production cost structures, drawing on reported estimates of US$1,000–US$3,000 per AI-generated episode compared with US$100,000–US$300,000 for equivalent productions featuring human casts; (iii) institutional analysis of the emerging regulatory landscape, including the 2023 SAG-AFTRA agreements, California Assembly Bill 2602 (AB 2602), Tennessee’s ELVIS Act, the proposed federal NO FAKES Act, the European Union’s AI Act, and China’s provisions governing deep synthesis and generative AI; and (iv) the construction of forward-looking scenarios for the coming decade. The central argument is threefold. First, vertical dramas constitute a genuine entry-level market for synthetic performance in a manner consistent with disruption theory: their economics are driven by cost minimization and production volume rather than the premium emotional differentiation characteristic of high-end productions. Second, the displacement pressure documented in 2026 is driven less by audi",
   "authors": [
    "Zen Revista"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-13",
   "doi": "10.5281/zenodo.23328647",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23328647",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23328647",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Synthetic Performer : Economic, Labor, Technological, Legal and Ethical Dimensions of AI-Generated Actors in the Vertical Drama Industry",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23328647/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "The rapid diffusion of artificial intelligence (AI) across industries has intensified concerns regarding labor substitution, yet scholarly literature on AI-driven employment termination remains fragmented. This study systematically identifies displacement mechanisms, maps vulnerable sectors and workforce categories, and synthesizes mitigation strategies. Following the PRISMA 2020 protocol, a Systematic Literature Review (SLR) was conducted via Google Scholar across three search sessions, yielding 25 eligible peer-reviewed articles (2016–2026) from 520 initial records. Data were analyzed using thematic synthesis complemented by keyword co-occurrence bibliometric mapping in VOSviewer. Results indicate four interrelated displacement patterns: hierarchical task substitution, skill degradation and labor market polarization, acceleration coinciding with generative AI, and opaque algorithmic layoff practices. Service, information technology, and labor-intensive sectors are identified as most vulnerable, with middle-skilled workers experiencing the greatest structural pressure. Effective mitigation requires a simultaneous three-pillar approach: competency-based reskilling, algorithmic accountability regulation, and collaborative labor union engagement, with proactive timing highlighted as critical. These findings offer a synthesized evidence base for policymakers and organizations navigating an AI-augmented labor market. Given that only one reviewed study directly examined Indonesia, these findings serve as contextual implications rather than direct empirical assertions, underscoring the necessity for future primary research in developing national contexts.",
   "authors": [
    "Ilham Prasetya Wibowo",
    "Cucu Hayati",
    "Tri Wahjoedi"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-10",
   "doi": "10.24123/gesdr.v30i1.9077",
   "explainer": {
    "approach": "A systematic review following PRISMA 2020: 25 peer-reviewed articles (2016-2026) selected from 520 Google Scholar records, analysed by thematic synthesis and keyword co-occurrence mapping.",
    "findings": "The review identifies four displacement patterns: task substitution up the hierarchy, skill degradation and polarisation, acceleration with generative AI, and opaque algorithmic layoffs. Service, IT and labour-intensive sectors and middle-skilled workers face the most pressure.",
    "limitations": "Only 25 studies from one search engine, and only one study on Indonesia, so the authors present the findings as context rather than direct evidence for developing countries.",
    "method_type": "review",
    "question": "How does AI automation lead to job terminations, which workers are most exposed, and what can limit the harm?"
   },
   "id": "doi:10.24123/gesdr.v30i1.9077",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.24123/gesdr.v30i1.9077",
   "summary": "A PRISMA systematic review of 25 peer-reviewed articles (2016-2026) on AI-driven job terminations. It identifies four displacement patterns, including hierarchical task substitution and opaque algorithmic layoffs, finds service, IT and labor-intensive sectors and middle-skilled workers most exposed, and proposes reskilling, algorithmic accountability rules and union engagement as mitigations.",
   "theme": "Labor & Work",
   "title": "The Threat of AI Automation on Employment Termination: A Systematic Literature Review",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-24123-gesdr-v30i1-9077/",
   "venue": "Global Economic Social and Development Review",
   "year": 2026
  },
  {
   "abstract": "The green and digital transitions do not automatically produce inclusive growth. They interact through distinct labour-market processes: green structural change reallocates employment across sectors and places, whereas artificial intelligence (AI) reorganises tasks and authority within workplaces. This article develops a process-based conceptual framework for examining when their interaction can support AI-enabled green growth. An integrative review brings together peer-reviewed research and recent comparative reports. The framework follows a directional sequence in which green restructuring and AI deployment generate sectoral reallocation and task/workplace reorganisation. Labour-market adjustment arrangements and structural context then shape how these mechanisms lead to environmental, productivity, employment, job-quality, and distributive outcomes. These elements are not specified as independent, dependent, mediator, or moderator variables because the article does not estimate a statistical model. The framework distinguishes an enabling pathway based on labour augmentation and shared productivity gains from an adverse pathway involving displacement, surveillance, unequal value capture, and ecological externalisation. Six propositions identify relationships for subsequent empirical examination. A renewable-electricity value-chain application illustrates why AI exposure and labour effects differ across manufacturing, construction, and operations. The contribution is a power-sensitive account that treats environmental improvement and decent work as joint and institutionally contingent outcomes.Keywords: twin transition, artificial intelligence, labour-market adaptation, just transition, green growth.",
   "authors": [
    "Suad Muvakit"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-23",
   "doi": "10.54752/ct.1973941",
   "explainer": null,
   "id": "doi:10.54752/ct.1973941",
   "institutions": [
    "Gaziantep University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.54752/ct.1973941",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Twin Transition and Labor Market Transformation: A Conceptual Framework for AI-Enabled Green Growth",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-54752-ct-1973941/",
   "venue": "Çalışma ve Toplum",
   "year": 2026
  },
  {
   "abstract": "Automation and artificial intelligence (AI) are reshaping labor demand unevenly across space, creating an urgent imperative for place-sensitive education and workforce policy. This study asks whether regional exposure to automation and to AI relates to local employment and wages in opposite ways, and whether those relationships differ between urban and rural regions -- two questions whose answers carry direct implications for how skills training and digital education should be targeted. Using a region-by-year panel and shift-share measures of technological exposure built from baseline industry and occupation composition, we estimate two-way fixed-effects and instrumental-variable models that interact exposure with an urban indicator. The framework distinguishes automation exposure, concentrated in routine work, from AI exposure, concentrated in cognitive work -- a distinction that maps directly onto the types of skills that education systems need to develop or preserve. Estimates show automation exposure lowering employment and wages, with the employment loss cushioned in cities, while AI exposure raises wages and concentrates in urban regions. Technology therefore reshapes, rather than simply widens, the divide. The findings argue for place-sensitive policy: weighting reallocation and reskilling support toward routine-exposed rural regions, while extending digital infrastructure and AI-complementary skills outward so that rural workers can share AI's wage gains rather than absorb only automation's losses.",
   "authors": [
    "CHAU TRAN BAO",
    "Khoi Nguyen Dinh Nguyen",
    "Ha Nguyen Manh",
    "Ngan Nguyen Thi Thuy"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-22",
   "doi": null,
   "explainer": null,
   "id": "title:a20cebc033b49983",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.22833",
   "summary": "Using a region-by-year panel with shift-share exposure measures, finds automation exposure (concentrated in routine work) lowers regional employment and wages with losses cushioned in cities, while AI exposure (concentrated in cognitive work) raises wages and concentrates in urban areas — meaning technology reshapes rather than simply widens the urban-rural divide. The paper argues for place-sensitive reskilling policy that directs support toward routine-exposed rural regions rather than a one-size-fits-all national program.",
   "theme": "Labor & Work",
   "title": "The Urban-Rural Divide in the Age of Artificial Intelligence: Assessing the Effects of Technology and Automation on Regional Labor Markets",
   "url": "https://wemustactnow.onrender.com/research/p/title-a20cebc033b49983/",
   "venue": "arXiv (Cornell University)",
   "year": 2026
  },
  {
   "abstract": "This essay examines how artificial intelligence transforms productivity, competition and economic value. It explores how AI reduces production costs, reshapes competitive advantages, expands opportunities and challenges the distribution of wealth. It argues that the future of the AI economy depends not only on what we can produce, but on who creates, captures and benefits from the value generated.",
   "authors": [
    "Gungor"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-30",
   "doi": "10.5281/zenodo.23794049",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23794049",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23794049",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Worm in the Apple: Artificial Intelligence, Capital and the Economy of Value",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23794049/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Purpose We examine how artificial intelligence (AI) reshapes the relative economic positioning of entry-level and experienced workers within industries. Rather than focusing on job elimination, we investigate whether AI changes the returns to experience. Design/methodology/approach We construct an industry-quarter panel from 337 million US Lightcast job postings across 16 NAICS2 industries, with finer-grained inference and decomposition across 86 NAICS3 industries (Q1 2017 to Q1 2026). Difference-in-differences models use a leave-own-industry-out shift-share measure, with industry and quarter fixed effects and small-cluster-robust inference. Lightcast AI-skills rate and Felten task-exposure provide comparisons, with Census firm-reported AI adoption and BLS occupational data providing triangulation. Findings Entry-level posting shares decline modestly but not differentially with AI-exposure. By contrast, the experienced-entry pay gap widens with greater shift-share AI exposure; a one-percentage-point increase in exposure predicts a significant $2,780 widening. Occupational reallocation toward higher-paid jobs accounts for 61% of the widening, and stronger within-occupation wage growth concentrated among experienced workers accounts for 39%. Research limitations/implications The findings suggest AI and labour-market research should distinguish potential task exposure from realized adoption and examine how occupational restructuring alters within-industry career stage inequality. Originality/value We shift attention from displacement to experience repricing, introducing career-stage-biased technical change as a two-channel account in which AI may alter both occupational demand and the rewards to experience-mediated capabilities.",
   "authors": [
    "Eric Shaunn Mattingly",
    "Steven James Hyde",
    "Andrew Steven Manikas"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-08",
   "doi": "10.1108/ijm-04-2026-0378",
   "explainer": null,
   "id": "doi:10.1108/ijm-04-2026-0378",
   "institutions": [
    "Boise State University",
    "University of Louisville"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1108/ijm-04-2026-0378",
   "summary": "Using 337 million US job postings from 2017 to 2026, the authors find that industries more exposed to AI saw the pay gap between experienced and entry-level postings widen, by about $2,780 per percentage point of exposure. Entry-level posting shares fell only modestly and not more in exposed industries; most of the widening comes from a shift toward higher-paid occupations.",
   "theme": "Labor & Work",
   "title": "Tools of the trade: AI and the restructuring of the experience premium",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1108-ijm-04-2026-0378/",
   "venue": "International Journal of Manpower",
   "year": 2026
  },
  {
   "abstract": "Employment in AI-exposed jobs like software development and customer service has dropped by more than 4% this year, while occupations that are reliant on AI automation are showing slowing or shrinking job growth. Those are some of the key findings from a new set of “dashboards” launched in June 2026 by Stanford HAI’s Digital Economy Lab (DEL). Updated monthly, these dashboards – called AI Economic Indicators – track AI’s economic footprint in real time. Unlike official statistics that report on longer-term trends, these new tools capture swift shifts as they happen. “At DEL, we’re really interested in understanding the economic impact of AI not only because it has amazing potential, but also because it’s been adopted perhaps more rapidly than any other technology ever,” says Connacher Murphy , who spearheaded the Indicators project. “The issue we’ve kept running into is that it’s very hard to get a big, aggregate view of what’s actually happening, what’s just hype, and what is genuine economic change. Our Indicators can help us get a clearer picture.” The project pulls data from private companies, academic researchers, and government statistical agencies and organizes it into three primary dashboards that track trends in employment and wages, aggregate outcomes, consumer surplus (the difference between what someone is willing to pay and the actual price they pay), hiring and skills, and AI usage. One dashboard, the Transformation Tracker, monitors 12 macroeconomic indicators to gauge if any significant changes can be attributed to AI. “We’re asking, ‘Do we see anything happening in aggregate with these bigger, moving statistics?’” Murphy explains. True to its name, the Canaries Dashboard is designed as an early-warning indicator to detect emerging workforce trends. It tracks employment across occupations , with workers grouped according to high and low AI exposure. The dashboard was built in collaboration with ADP Research, a New Jersey-based, data-driven institute ",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-21",
   "doi": null,
   "explainer": null,
   "id": "title:5add2354b9e5bd3c",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://hai.stanford.edu/news/tracking-ais-real-time-footprint-on-the-labor-market",
   "summary": "Stanford HAI's Digital Economy Lab describes monthly AI Economic Indicators dashboards that track employment, wages, aggregate outcomes, hiring and AI usage. Early readings show employment in AI-exposed jobs such as software development and customer service down more than 4% this year.",
   "theme": "Labor & Work",
   "title": "Tracking AI’s Real-Time Footprint On The Labor Market",
   "url": "https://wemustactnow.onrender.com/research/p/title-5add2354b9e5bd3c/",
   "venue": "Stanford HAI (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Purpose of Review Artificial intelligence (AI) is rapidly changing the tasks performed by physicians, raising questions about how graduate medical education should prepare trainees for future practice and whether residency workforce planning should change as AI adoption expands. This brief review examines the implications of AI for residency education and physician workforce planning, using radiology as an early model of technology-driven task transformation while considering broader effects across diagnostic, procedural, and relationship-based specialties. Recent Findings AI is already being used for clinical documentation, image interpretation, predictive analytics, administrative workflows, and large-scale data analysis. AI-assisted scribing has particular relevance to chronic pain and other documentation-intensive specialties, where it may reduce clerical burden and preserve clinician attention for patient care. However, current evidence more strongly supports task redistribution and physician augmentation than complete replacement. Specialties built around standardized digital information may experience earlier automation, whereas procedural skill, complex communication, longitudinal judgment, and responsibility for final clinical decisions remain difficult to substitute. At present, broad reductions in residency positions based only on anticipated AI capabilities seem premature. Summary Residency programs should adapt curricula now by incorporating AI literacy, verification skills, data interpretation, and safeguards against deskilling. Changes in residency position numbers should occur more cautiously and should be based on durable, specialty-specific workforce evidence, including hiring patterns, patient access, workload, productivity, and regional need. The goal should be to train physicians who can safely supervise and integrate AI while preserving the human expertise that remains central to high-quality care.",
   "authors": [
    "Madelyn J. Reilly",
    "Jamal J. Hasoon",
    "Alaa A Abd-Elsayed"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-26",
   "doi": "10.52965/001c.172700",
   "explainer": null,
   "id": "doi:10.52965/001c.172700",
   "institutions": [
    "University of Wisconsin–Madison",
    "The University of Texas Health Science Center at Houston"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.52965/001c.172700",
   "summary": "A brief review of how AI is changing physicians' tasks, using radiology as an early case, and what that means for residency training and workforce planning. It finds current evidence supports task redistribution and augmentation more than replacement, and argues cuts to residency positions based on anticipated AI capabilities are premature.",
   "theme": "Labor & Work",
   "title": "Training for Jobs That May Not Exist: Artificial Intelligence and the Future of Medical Residency",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-52965-001c-172700/",
   "venue": "Orthopedic Reviews",
   "year": 2026
  },
  {
   "abstract": "Firms ask workers to train AI systems that will replace them. We model this with a worker who can shirk, cooperate with training, or obstruct training while maintaining productivity. Because training ends the job but only cooperation completes training, dismissal becomes a weaker penalty for",
   "authors": [
    "Joshua S. Gans",
    "Peter Landry"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "October 2026",
   "doi": null,
   "explainer": {
    "approach": "A model in which a worker can shirk, cooperate with training, or obstruct training while keeping up productivity. Training ends the job, but only cooperation completes it.",
    "findings": "Because completing training ends the job, the threat of dismissal becomes a weaker penalty for the worker, which the model uses to study how training incentives affect the pace of automation.",
    "limitations": "The available abstract is cut short, so further results and any calibration or empirical test cannot be described here.",
    "method_type": "theory",
    "question": "How do worker incentives shape the pace of automation when firms ask workers to train the AI systems that will replace them?"
   },
   "id": "title:0209d7613d9307e4",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35950",
   "summary": "This NBER paper models firms asking workers to train the AI systems that will replace them, where a worker can shirk, cooperate with training, or obstruct it while staying productive. Because finishing the training ends the job, the threat of dismissal loses force as a penalty, which shapes how fast automation can proceed.",
   "theme": "Productivity & Firms",
   "title": "Training Your Replacement: Worker Incentives and the Pace of Automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-0209d7613d9307e4/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Skills and jobs in business and economics are being reshaped by globalization, technological change, demographic shifts, and the green transition. The World Economic Forum’s Future of Jobs Report 2025 estimates that employers expect 39% of workers’ core skills to change or become outdated by 2030; this is a five-year expectation, not a forecast for 2026 (World Economic Forum [WEF], 2025, p. 32).These changes have intensified with the diffusion of generative artificial intelligence (GenAI), digital platforms, and knowledge-intensive production. The WEF estimate describes expected skill instability among surveyed employers and should not be interpreted as meaning that 39% of all jobs will disappear. The OECD’s Skills Outlook 2025 highlights unequal opportunities to develop and use foundational, information-processing, and social-emotional skills, while the IMF’s October 2025 World Economic Outlook discusses the productivity potential of AI alongside the need for policies that help workers adapt (OECD, 2025; IMF, 2025).This paper examines the relationship between technological change, skill formation, and occupational restructuring, with particular attention to digital competence, analytical reasoning, and socio-emotional skills. It uses a documentary and conceptual analysis of selected international reports and academic literature, and applies an illustrative case analysis to reconstruction and development in Azerbaijan’s Karabakh and Eastern Zangezur regions. It discusses how digitalization, smart infrastructure, logistics, and renewable energy may alter skill needs, while recognizing that the paper does not estimate employment effects using original quantitative data.",
   "authors": [
    "Kh.F. HUSEYNOVA"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.17866/rd.salford.34790931",
   "explainer": null,
   "id": "doi:10.17866/rd.salford.34790931",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.17866/rd.salford.34790931",
   "summary": "A documentary and conceptual analysis of how technological change, including generative AI, reshapes skills and jobs in business and economics, drawing on WEF, OECD and IMF reports and a case from Azerbaijan. It does not estimate employment effects with original data.",
   "theme": "Labor & Work",
   "title": "Transformation of Skills and Jobs in Business and Economics",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-17866-rd-salford-34790931/",
   "venue": "University of Salford Institutional Repository (University of Salford)",
   "year": 2026
  },
  {
   "abstract": "Transformative AI promises to accelerate growth through productivity enhancement and automation. Yet automation on such a scale would raise the share of investment in output, potentially starving AI innovation of returns from a shrinking consumer market.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-12",
   "doi": null,
   "explainer": null,
   "id": "title:ec2a33ab22951f67",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.bis.org/publications/working-paper-1381-transformative-ai-and-demand-bottlenecks",
   "summary": "A BIS paper arguing that while transformative AI could speed growth through productivity and automation, automation at that scale would raise the investment share of output and could starve AI innovation of returns from a shrinking consumer market.",
   "theme": "Productivity & Firms",
   "title": "Transformative AI and demand bottlenecks",
   "url": "https://wemustactnow.onrender.com/research/p/title-ec2a33ab22951f67/",
   "venue": "Bank for International Settlements (institute research)",
   "year": 2026
  },
  {
   "abstract": "Over the past decade, U.S. policies have increasingly aimed to preserve artificial intelligence (AI) leadership by promoting domestic free-market policies while controlling global technological chokepoints, particularly advanced semiconductors and computational infrastructure. These measures raised the cost of Chinese AI development, but they also increased the strategic value of open and locally adaptable AI systems. Before raising export controls on high-performance chips, both the U.S. and China promoted policies that included support for open-source AI. During the period following major U.S. export-control shocks, China increasingly embedded open-source AI into national technology strategy through proposed ecosystem building, standards coordination, and resilience-oriented deployment. Moreover, Chinese developers increased engagement with open-source large language model repositories substantially more than U.S. developers did, consistent with a shift toward open infrastructure under geopolitical constraints. Subsequently, Chinese-origin open models diffused widely through open-source communities and scientific research. Even though such models remained largely absent from U.S. patent disclosures, American commercial entities use them in open-access research, suggesting their undermeasured importance within the foundation of U.S. commercial activity. These findings suggest that technological containment policies may unintentionally accelerate open innovation ecosystems as a competitive response, with implications for global leadership in both academic and commercial artificial intelligence.",
   "authors": [
    "Wang Jin",
    "Nadav Kunievsky",
    "Bowen Lou",
    "Tianshu Sun",
    "James Evans"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-06-14",
   "doi": null,
   "explainer": {
    "approach": "Analysis of policy documents and of developer activity on open-source large language model repositories in the U.S. and China before and after major export-control changes, plus diffusion of Chinese open models in research.",
    "findings": "After the export-control shocks, China embedded open-source AI in national strategy and Chinese developers increased engagement with open model repositories more than U.S. developers; Chinese open models spread widely in research and are used by U.S. firms in open-access research.",
    "limitations": "The evidence is described as consistent with a shift rather than a causal estimate; the abstract notes the models are largely absent from patent disclosures, so commercial use may be undermeasured.",
    "method_type": "observational",
    "question": "Did U.S. export controls on AI chips push China toward open-source AI, and with what effects?"
   },
   "id": "title:e0716c83fbc005b6",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2606.15999v1",
   "summary": "Wang, Kunievsky, Lou, Sun, and Evans document that after U.S. export controls on advanced chips, China pivoted open-source AI into national strategy and Chinese developers increased engagement with open LLM repositories substantially more than U.S. developers, with Chinese-origin open models then diffusing widely through research communities — including into U.S. open-access commercial research, even while absent from U.S. patent disclosures. The finding matters for the AI-policy debate because it suggests technological containment can backfire, accelerating a rival's open innovation ecosystem and leaving its contribution to U.S. commercial activity systematically undermeasured.",
   "theme": "Other",
   "title": "U.S. Policies Unintentionally Accelerated China's Open AI Ecosystems",
   "url": "https://wemustactnow.onrender.com/research/p/title-e0716c83fbc005b6/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "This study examines how generative artificial intelligence is transforming professional work and the processes through which individuals acquire professional expertise. Rather than focusing exclusively on occupational displacement, it addresses a less visible but fundamental question: How will future professionals develop expertise when AI increasingly performs tasks traditionally assigned to early-career workers? Drawing on empirical research from the United States, Denmark, and Germany, the paper distinguishes technological exposure, actual adoption, productivity gains, employment changes, and professional learning. Recent experimental evidence reveals contrasting outcomes: AI assistance can enhance productivity and expand the range of tasks individuals can perform, but improved assisted performance does not necessarily translate into equivalent gains in independent knowledge or professional judgment. The study introduces the \"experience gap\" as a provisional conceptual framework encompassing three dimensions: execution, knowledge transfer, and professional development trajectories. It examines how AI may accelerate, replace, or reorganize the experiences through which professional competence is acquired. Particular attention is given to the implications for university education, emphasizing the importance of disciplinary foundations, supervised practice, transferable competencies, and the development of independent professional judgment. Argentina is examined as a contextual case, exploring how international evidence can inform educational and institutional adaptation without assuming uniform technological trajectories or deterministic predictions. The paper also proposes an institutional observation framework and a testable pilot research design to investigate the relationship between technological change, professional learning, and university education. Research type: Documentary and analytical study with an international comparative perspective. Publication st",
   "authors": [
    "Hugo Roger Paz"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-27",
   "doi": "10.5281/zenodo.23688109",
   "explainer": null,
   "id": "doi:10.5281/zenodo.23688109",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.5281/zenodo.23688109",
   "summary": null,
   "theme": "Labor & Work",
   "title": "University Education and the Transformation of Professional Work: Task Change, Early-Career Learning, and Institutional Adaptation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-5281-zenodo-23688109/",
   "venue": "Zenodo (CERN European Organization for Nuclear Research)",
   "year": 2026
  },
  {
   "abstract": "Subject. Application of artificial intelligence technologies in the operations of the Chinese internet insurance company ZhongAn – the first professional online insurer in China. Objectives. To conduct a comprehensive assessment of the impact of artificial intelligence technologies on the operational efficiency of the insurance company and to identify factors limiting the full realization of the potential of digital transformation. Methods. The study employed comparative analysis of operational indicators for 2019–2024, trend analysis of the dynamics of investments in research and development, financial analysis of performance indicators, SWOT analysis, and content analysis. Results. It has been established that the automation of the insurance company’s key business processes through artificial intelligence has reached extremely high levels: the underwriting automation rate is 99%, the reduction in claims settlement time is 87.5%, and employee productivity has increased 30‑fold. A paradox of efficiency has been identified: the growth in operational performance is accompanied by a persistently high level of consumer complaints, the opacity (non‑interpretability) of algorithms, risks of homogenization, and a shortage of personnel. A similar phenomenon has also been observed in the Russian insurance market. The hypothesis has been confirmed that technological efficiency alone is not a sufficient condition for sustainable profitability and consumer trust. Conclusions. For Russian insurers, ZhongAn’s experience demonstrates the need for the simultaneous development of technological infrastructure, human capital, and a customer‑oriented culture. The key success factor is the transformation from a technological pioneer into a responsible leader by ensuring the transparency of artificial intelligence systems and striking a balance between innovation and trust.",
   "authors": [
    "Youzhi WANG",
    "Yuliya A. SOLOV'EVA"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-28",
   "doi": "10.24891/lomhbt",
   "explainer": null,
   "id": "doi:10.24891/lomhbt",
   "institutions": [
    "Financial University"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.24891/lomhbt",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Using artificial intelligence in insurance: ZhongAn Company as a case study",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-24891-lomhbt/",
   "venue": "Finance and Credit",
   "year": 2026
  },
  {
   "abstract": "METR previously published a paper which found the use of AI tools caused a 20% slowdown in completing tasks among experienced open-source developers, using data from February to June 2025. To understand how AI is impacting developer productivity over time, we started a new experiment in August 2025 with a larger pool of developers using the latest AI tools. Unfortunately, given participant feedback and surveys, we believe that the data from our new experiment gives us an unreliable signal of the current productivity effect of AI tools. The primary reason is that we have observed a significant increase in developers choosing not to participate in the study because they do not wish to work without AI, which likely biases downwards our estimate of AI-assisted speedup. We additionally believe there have been selection effects due to a lower pay rate (we reduced the pay from $150/hr to $50/hr), and that our measurements of time-spent on each task are unreliable for the fraction of developers who use multiple AI agents concurrently. Based on conversations with study participants, we believe it is likely that developers are more sped up from AI tools now — in early 2026 — compared to our estimates from early 2025. However, because of the selection effects in our experiment, our data is only very weak evidence for the size of this increase. Our raw results show some evidence for speedup. Our early 2025 study found the use of AI causes tasks to take 19% longer, with a confidence interval between +2% and +39%. For the subset of the original developers who participated in the later study, we now estimate a speedup of -18% with a confidence interval between -38% and +9%. Among newly-recruited developers the estimated speedup is -4%, with a confidence interval between -15% and +9%. However the true speedup could be much higher among the developers and tasks which are selected out of the experiment. Some developers self-report very high speedups, though as we documented in our ea",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-02-24",
   "doi": null,
   "explainer": null,
   "id": "title:567264eccbf82438",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://metr.org/blog/2026-02-24-uplift-update/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "We are Changing our Developer Productivity Experiment Design",
   "url": "https://wemustactnow.onrender.com/research/p/title-567264eccbf82438/",
   "venue": "METR (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Our recent survey study with 81,000 Claude users provides a way to connect people’s economic concerns with what we’ve quantified in Claude traffic. The survey asked people about their visions and fears around advances in AI. Many of the thoughts that people shared touched on economic topics. We learned that many people fear job displacement—though they also feel more productive and empowered at work. In some cases, AI has enabled them to start businesses, or given them time for more important things; in others, AI feels stifling, or imposed on them by their employers. The survey’s results provide initial evidence that observed exposure (our measure of AI displacement risk) is correlated with economic concern around AI. People in highly exposed occupations—as defined by the tasks Claude is observed performing—were more nervous about economic displacement. This is consistent with people being broadly aware of AI’s diffusion and potential impacts. We expand on our findings below. “Well like anyone who has a white collar job these days I'm 100% concerned, pretty much 24/7 concerned about losing my job eventually to A.I.”—Software engineer. 1 One fifth of the respondents in our survey voiced concern about economic displacement. Some worried about this in the abstract: one software developer cautioned about “the possibility of AI in its current state being used to replace junior positions.” Others lamented that their jobs, or aspects of their jobs, were being automated away. One market researcher said, “In terms of improving my capability, it's no doubt. [B]ut in the future AI may replace my work.” In some jobs, people felt it made their work harder. One software developer observed that “when AI arrived, the project managers started giving harder and harder tickets and bugs to solve.” Throughout this report, we use Claude-powered classifiers to infer people’s attributes and sentiments from their responses. For example, many participants mention their line of work in passi",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-04-22",
   "doi": null,
   "explainer": null,
   "id": "title:73408bef8de53650",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/81k-economics",
   "summary": null,
   "theme": "Labor & Work",
   "title": "What 81,000 people told us about AI economics",
   "url": "https://wemustactnow.onrender.com/research/p/title-73408bef8de53650/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "Key Takeaways: Overview The adoption of artificial intelligence in the workplace has led to predictions of U.S. labor market disruptions. In response, policymakers have increased attention on the fitness of the nation’s social insurance programs. Of particular interest is the Unemployment Insurance system, the joint federal-state program that provides temporary and partial wage replacement or […] The post What happens to U.S. workers without access to Unemployment Insurance amid economic downturns or disruptions related to AI? appeared first on Equitable Growth .",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-02",
   "doi": null,
   "explainer": null,
   "id": "title:a9db8f2aad797fab",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://equitablegrowth.org/what-happens-to-u-s-workers-without-access-to-unemployment-insurance-amid-economic-downturns-or-disruptions-related-to-ai/",
   "summary": null,
   "theme": "Labor & Work",
   "title": "What happens to U.S. workers without access to Unemployment Insurance amid economic downturns or disruptions related to AI?",
   "url": "https://wemustactnow.onrender.com/research/p/title-a9db8f2aad797fab/",
   "venue": "Washington Center for Equitable Growth (institute research)",
   "year": 2026
  },
  {
   "abstract": "The five largest U.S. technology firms spent $380 billion on capital expenditure in 2025 and are forecast to spend roughly double that in 2026. These firms risk bankruptcy unless expected profits grow commensurately. We embed this observation in a two-sector open-economy model with rare productivity",
   "authors": [
    "Jessica Wachter",
    "Jonathan Wachter"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "June 2026",
   "doi": null,
   "explainer": null,
   "id": "title:e569790f5acf45ca",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35290",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "What Investment Data Implies about the AI Transition",
   "url": "https://wemustactnow.onrender.com/research/p/title-e569790f5acf45ca/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Speak with any number of friends, and many would surely volunteer to work fewer hours for the same pay, if they could. Economics often treats work as a ‘ disutility’ — something that weighs negatively on overall wellbeing. Yet others love what they do, often because they find their work important and meaningful, for its own sake . What explains the huge divergences in the ways that people relate to their work? Today, there is rightfully significant public concern about the potential for AI to displace workers, shifting the demand for labor across tasks. Existing evidence suggests that, as AI capabilities improve, the technology will diffuse across the economy, disrupting some forms of work while creating others. This transition brings with it risks of increasing inequalities and stagnation of real wages, as well as declining material wellbeing for displaced workers, as the history of technological shocks suggests . In response to AI’s potential impacts on the labor market, proposals generally emphasize the importance of income transfers. Yet the non-monetary social, cultural, and psychological value of work is often under-explored in these discussions. In a series of new papers from Google DeepMind, we study the value that work provides people and what it might mean for policymakers. In “What is Work For? Labor, Politics, and Meaning in the Age of AI” (Section 1), we study the non-monetary value of employment through a large representative survey across four countries. Meanwhile, in “ Work, Wellbeing, and Choice: Empirical Lessons for AI Futures ” (Section 2), we investigate the existing empirical literature on work and wellbeing in real-world transitions (e.g. unemployment, winning the lottery). And in another upcoming paper (Section 3), we explore the ways in which work offers a bundle of benefits, and its implications for human dignity. Collectively, our research explores how people’s work is intimately connected with values beyond material needs. Beyond ensuring",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-29",
   "doi": null,
   "explainer": null,
   "id": "title:f6edb6f32225d679",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://institute.deepmind.com/essays/what-is-work-for-human-wellbeing-and-the-value-of-work-in-the-agi-era/",
   "summary": "A DeepMind Institute essay introducing new Google DeepMind papers on the value of work for human wellbeing. It argues that policy responses to AI displacing workers focus on income transfers, while the social, cultural and psychological value people get from work is under-explored.",
   "theme": "Labor & Work",
   "title": "What is work for? Human wellbeing and the value of work in the AGI era",
   "url": "https://wemustactnow.onrender.com/research/p/title-f6edb6f32225d679/",
   "venue": "Google DeepMind Institute (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "We measure how workers use genAI for their jobs in a nationally representative survey linking genAI adoption to detailed occupations and tasks. Our data provide the first task-level genAI adoption indexes, which we show can inform analyses of genAIs labor market impact. Exposure scores explain some,",
   "authors": [
    "Alexander Bick",
    "Adam Blandin",
    "David J. Deming",
    "Tyler R. Schumacher"
   ],
   "cited_by": 0,
   "collected_from": "nber",
   "date": "August 2026",
   "doi": null,
   "explainer": {
    "approach": "A nationally representative survey of workers linking generative AI adoption to detailed occupations and tasks, used to build task-level adoption indexes.",
    "findings": "The authors report the first task-level generative AI adoption indexes and show they can inform analyses of labor market impact; exposure scores explain only part of observed adoption.",
    "limitations": "The available abstract is truncated, so the size of the gap between exposure and adoption and other results are not described here. Survey self-reports may not match measured use.",
    "method_type": "survey",
    "question": "Which job tasks do workers actually use generative AI for, and does task-level adoption help explain its labor market impact?"
   },
   "id": "title:ae92eb1281024d07",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w35677",
   "summary": "Using a nationally representative survey that links generative AI use to detailed occupations and tasks, the authors build the first task-level indexes of genAI adoption. They show these adoption measures can sharpen analyses of genAI's labor-market impact, and that standard exposure scores explain only part of actual use.",
   "theme": "Labor & Work",
   "title": "What Work Does Generative AI Do?",
   "url": "https://wemustactnow.onrender.com/research/p/title-ae92eb1281024d07/",
   "venue": "NBER Working Papers",
   "year": 2026
  },
  {
   "abstract": "Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in these logs are not the workforce. We show that platform-derived exposure scores combine task-level AI applicability with the occupational composition of the platform's user base. Holding the empirical design fixed, changing only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9, and consumer and enterprise channels within the same vendor disagree in sign. We formalize the resulting non-classical measurement error, decompose it into between- and within-occupation selection, and construct workforce-reweighted partial-identification bounds. Reweighting to Bureau of Labor Statistics employment shares attenuates estimates by 42 to 93 percent. The bias captures augmentation among observed users more directly than substitution in the workforce.",
   "authors": [
    "Michelle Yin",
    "Burhan Ogut"
   ],
   "cited_by": 0,
   "collected_from": "arxiv",
   "date": "2026-05-20",
   "doi": null,
   "explainer": null,
   "id": "title:2c3e752739371cc2",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://arxiv.org/abs/2605.21743v2",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure",
   "url": "https://wemustactnow.onrender.com/research/p/title-2c3e752739371cc2/",
   "venue": "arXiv",
   "year": 2026
  },
  {
   "abstract": "Generative artificial intelligence (GenAI) is frequently presented as a linguistic equalizer in workplaces where English mediates recruitment, collaboration, documentation, and customer interaction. Productivity research partly supports this claim, particularly for less-experienced workers. This conceptual piece argues that such gains do not constitute linguistic equality and that, for business communication scholarship, the key issue is how GenAI redistributes communicative authority, adaptation, and credibility within organizations. I call this process Algorithmically Mediated Unequal Englishes (AMUE). It operates through four connected mechanisms: normative compression into AI-polished English, unequal linguistic and affective labor, automated misrecognition and screening, and a credibility trap surrounding authorship and disclosure. These mechanisms offer resources while preserving institutional ‘standards’ and listener expectations. Linguistically equitable AI governance must therefore move beyond providing access to tools. It must redefine effective communication through task relevance, intelligibility, accuracy, reciprocal accommodation, worker control over voice, and institutional responsibility for technological risk.",
   "authors": [
    "Veronico N. Tarrayo"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-10-29",
   "doi": "10.1177/23294884261500740",
   "explainer": null,
   "id": "doi:10.1177/23294884261500740",
   "institutions": [
    "University of Santo Tomas"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://doi.org/10.1177/23294884261500740",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Whose English Does Generative AI Improve? Algorithmically Mediated Unequal Englishes in the Workplace",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1177-23294884261500740/",
   "venue": "International Journal of Business Communication",
   "year": 2026
  },
  {
   "abstract": "The paper summarized here is part of the fall 2026 edition of the Brookings Papers on Economic Activity , the leading conference series and journal in economics for timely, cutting-edge research about real-world policy issues. Research findings are presented in a clear and accessible style to maximize their impact on economic understanding and policymaking. The editors are Brookings Nonresident Senior Fellows Janice Eberly and Jón Steinsson . See the fall 2026 BPEA event page to watch paper presentations and read summaries of all the papers from this edition. Submit a proposal to present at a future BPEA conference here . Three papers presented on September 25 at the Brookings Papers on Economic Activity (BPEA) fall conference examine potential risks and rewards of the boom in artificial intelligence (AI): Why is AI so contentious, The vanishing advantage of specialization , and Financing the AI buildout . Why is AI so contentious examines why many people have a negative view of AI, despite its enormous economic potential. Many people, either through direct or pension-fund investments, have a financial stake in AI, the paper notes. Yet, according to survey results, around half of U.S. adults are more concerned than excited about increased use of AI in daily life. “We have argued that AI is so contentious—and the conflict over it so visceral—because on top of the economic and existential concerns that every transformative technology raises, it raises a moral one: that it has built from people’s words, creations and expertise, taken without consent,” the authors write. They write that they are not passing judgment on whether AI developers did something illegal and that that question will be decided by the courts. Their aim, they write, is to point out that policies most often discussed—universal basic income, retraining programs, and pauses in AI development—“will at best be incomplete” because they address people’s economic concerns but not AI’s “moral grievance.” “A",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-23",
   "doi": null,
   "explainer": null,
   "id": "title:7943140b91ffb099",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/why-is-ai-so-contentious/",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Why is AI so contentious?",
   "url": "https://wemustactnow.onrender.com/research/p/title-7943140b91ffb099/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "Rising income inequality in Western high-income countries threatens democratic governance and social cohesion, and AI risks making it worse. Drawing on a forthcoming book, this column argues for worker-managed firms as a durable remedy. With no external owners taking profits, worker cooperatives protect against the rising bargaining power of employers and provide an enhanced sense of self-worth and self-determination for employees. Furthermore, the data suggest that well-designed worker cooperatives compete with conventional firms in free markets with no evidence of a productivity gap. Because they are underprovided by the market, it calls for government action to support worker cooperatives.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-07",
   "doi": null,
   "explainer": null,
   "id": "title:d890bc9b2e919159",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://cepr.org/voxeu/columns/worker-managed-firms-can-curb-inequality-and-protect-free-markets",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Worker-managed firms can curb inequality and protect free markets",
   "url": "https://wemustactnow.onrender.com/research/p/title-d890bc9b2e919159/",
   "venue": "CEPR VoxEU (institute research)",
   "year": 2026
  },
  {
   "abstract": "Abstract Generative artificial intelligence (GenAI) is transforming society, including its economic foundations. This study examines the drivers and barriers shaping perceptions of GenAI’s influence on sustainable economic development (SED) among the working population. We adopt a conceptual framework in which these perceptions are shaped by four cognitive factors: three derived from the Technology Readiness and Acceptance Model, namely technological optimism, technological insecurity, and habitual use of GenAI, and a fourth related to the perceived need for regulation. The framework also incorporates three professional and sociodemographic characteristics: entrepreneurial status, gender, and membership in digital versus non-digital generations. Using a survey from Spain, we employ an analytical approach that combines ensemble tree–based machine learning models with Shapley Additive Explanations (SHAP) to enhance interpretability and explanatory insight. Among the evaluated models, XGBoost and Bayesian Additive Regression Trees achieve the strongest predictive performance. Random Forest exhibits comparatively weaker generalization performance. SHAP analyses consistently reveal that cognitive factors related to technology perceptions and habit in GenAI use play a central role in shaping evaluations of its contribution to sustainable economic development, whereas entrepreneurial status and generational cohort exert a secondary influence. Gender, however, shows greater explanatory relevance than perceptions regarding the need for regulation and reveals a pronounced gender gap in GenAI acceptance, with women exhibiting systematically less favorable perceptions than men. Although entrepreneurship exerts a relatively small effect, entrepreneurs tend to hold more favorable views regarding GenAI’s role in fostering sustainable economic development. Overall, the findings demonstrate that ensemble-based machine learning models, combined with SHAP, provide a powerful and trans",
   "authors": [
    "Jorge Andrés-Sánchez",
    "Teresa Torres-Coronas"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2026-06-18",
   "doi": "10.1007/s11135-026-02916-6",
   "explainer": null,
   "id": "doi:10.1007/s11135-026-02916-6",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1007/s11135-026-02916-6",
   "summary": "A survey study of workers in Spain that uses ensemble machine-learning models with SHAP explanations to identify what shapes perceptions of generative AI's contribution to sustainable economic development. Technological optimism, insecurity and habitual use matter most; women hold systematically less favorable views than men, and entrepreneurs slightly more favorable ones.",
   "theme": "Labor & Work",
   "title": "Workers’ perceptions of the economic impact of generative AI on sustainable economic development: evidence from ensemble tree models and explainable machine learning",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1007-s11135-026-02916-6/",
   "venue": "Quality & Quantity",
   "year": 2026
  },
  {
   "abstract": "The debate over AI and work is often framed as a choice between mass unemployment and widespread human augmentation. The economic evidence supports neither conclusion. Instead, the literature suggests that AI will likely produce sharply divergent labor-market outcomes on dramatically different timelines, rapidly displacing some forms of work while gradually augmenting others. These effects will also likely vary substantially across occupations, industries, and even employers. The economic literature’s central contribution is not merely to show that AI adoption will be divergent and uneven. It is also that AI exposure is the wrong organizing principle for workforce policy. What matters is not just whether a certain occupation is exposed, but also how AI changes the value of human work, and whether it enhances human expertise and/or lowers barriers to economic opportunities. Recent economic research offers four key findings that inform these questions and can help shape how policymakers respond to them. First, AI exposure does not necessarily translate into commercially viable automation or augmentation. Second, the central question should not be simply whether AI creates or destroys jobs, but also how it changes the value of human expertise. Third, successful AI adoption requires not only knowing how to use AI but knowing when to trust it. Fourth, as models become more reliable and capable of completing longer, complex tasks, AI adoption may become more autonomous, and commercially viable across a broader range of workflows. These findings argue against both complacency and an immediate economy-wide sweeping intervention. Workforce policy should instead preempt the worst potential downsides of AI and develop a targeted policy framework that can adapt to evolving labor-market changes. It should prioritize the areas where policy can have the greatest economic impact : supporting workers who are actually displaced and expanding access to newly created high-return opport",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-09-15",
   "doi": null,
   "explainer": null,
   "id": "title:a3cf4f94d015c5a0",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.brookings.edu/articles/workforce-policy-for-the-age-of-ai/",
   "summary": "This policy piece argues the evidence supports neither mass unemployment nor universal augmentation: AI is likely to displace some work quickly and augment other work gradually, with outcomes varying by occupation, industry and employer. It contends that exposure is the wrong basis for workforce policy, which should instead focus on how AI changes the value of human expertise and access to opportunity.",
   "theme": "Labor & Work",
   "title": "Workforce policy for the age of AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-a3cf4f94d015c5a0/",
   "venue": "Brookings (institute research)",
   "year": 2026
  },
  {
   "abstract": "Anthropic staff and US workers don’t seem to have different theories about how AI affects jobs. Instead, they have different experiences of what AI capabilities are. Those different experiences seem closely related to perceptions about what will happen to work in the future. Anthropic is a company at the frontier of AI adoption, and that adoption has changed the way we hire. We find that over the past year, we're hiring for a smaller share of junior positions, and a smaller share of our employees work in technical roles. At the same time, the rate of hours worked by AI agents has shot up. By March 2026, agents worked more hours than employees. By August, there were six times more agents than employees at Anthropic, and productivity increased. That’s led to Anthropic producing more code per person, but that’s a notoriously imperfect measure. We see signs of increased productivity elsewhere: we’ve closed more sales per sales representative, and processed more contracts. When our researchers follow the same employee over time, they find the employee’s output rises as their adoption of AI tools increases. All this acceleration means that, increasingly, the bottleneck is human attention. As a result, employees spend more time building ways to better monitor work their agents are doing. Anthropic could be a leading indicator of how AI will shift work organization, hiring, and labor demand across the economy. An AI lab is different from more traditional businesses in how it adopts AI into its work, but research findings about our own workplace may still be instructive about broader transformations, including dynamics for future AI-native companies. Because Anthropic employees experience AI’s effect on work so differently, it stands to reason that they might have different predictions about AI’s effect on the future of work. That’s what our second report investigates. We surveyed Anthropic employees, and compared them to an August survey of almost 11,000 US adults, fielded ",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2026-10-16",
   "doi": null,
   "explainer": null,
   "id": "title:40b4ad64472a5de6",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/expectations-for-the-future",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Working at the frontier shapes expectations for the future",
   "url": "https://wemustactnow.onrender.com/research/p/title-40b4ad64472a5de6/",
   "venue": "Anthropic (AI lab research)",
   "year": 2026
  },
  {
   "abstract": "As a result, global growth may be lifted by as much as 0.3 percentage points in 2026 and between 0.1 and 0.8 percentage points per year in the medium term, depending on the speed of adoption and improvements in AI readiness globally.",
   "authors": [
    "International Monetary Fund"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2026-01-19",
   "doi": null,
   "explainer": null,
   "id": "title:bb06138a09d71a0a",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.imf.org/-/media/files/publications/weo/2026/january/english/text.pdf",
   "summary": null,
   "theme": "Other",
   "title": "World Economic Outlook Update, January 2026: Global Economy: Steady amid Divergent Forces",
   "url": "https://wemustactnow.onrender.com/research/p/title-bb06138a09d71a0a/",
   "venue": "IMF World Economic Outlook Update",
   "year": 2026
  },
  {
   "abstract": "A more reasonable scenario might be one in which AI boosts annual productivity growth by 0.3 percentage points for the next decade.",
   "authors": [
    "Mark A. Wynne",
    "Lillian Derr"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-06-24",
   "doi": null,
   "explainer": null,
   "id": "title:84007dc0f67b34d6",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.dallasfed.org/research/economics/2025/0624",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Advances in AI will boost productivity, living standards over time",
   "url": "https://wemustactnow.onrender.com/research/p/title-84007dc0f67b34d6/",
   "venue": "Federal Reserve Bank of Dallas",
   "year": 2025
  },
  {
   "abstract": "We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution. (Added Nov 22 2025, to prevent misunderstandings: we don't know exactly when AGI will be built. 2027 was our modal (most likely) year at the time of publication, our medians were somewhat longer.",
   "authors": [
    "Daniel Kokotajlo",
    "Scott Alexander",
    "Thomas Larsen",
    "Eli Lifland",
    "Romeo Dean"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-04-03",
   "doi": null,
   "explainer": null,
   "id": "title:31c9c7acd8c554ee",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://ai-2027.com/",
   "summary": null,
   "theme": "Other",
   "title": "AI 2027",
   "url": "https://wemustactnow.onrender.com/research/p/title-31c9c7acd8c554ee/",
   "venue": "AI Futures Project",
   "year": 2025
  },
  {
   "abstract": "We find that the medium-term productivity gains for Europe as a whole are likely to be modest, at around 1 percent cumulatively over five years.",
   "authors": [
    "Florian Misch",
    "Ben Park",
    "Carlo Pizzinelli",
    "Galen Sher"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-04-04",
   "doi": null,
   "explainer": null,
   "id": "title:ebfc7dbb5534860b",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.imf.org/en/publications/wp/issues/2025/04/04/ai-and-productivity-in-europe-565924",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "AI and Productivity in Europe",
   "url": "https://wemustactnow.onrender.com/research/p/title-ebfc7dbb5534860b/",
   "venue": "IMF Working Paper WP/25/67",
   "year": 2025
  },
  {
   "abstract": "According to a new nationally representative Gallup survey conducted in partnership with the Special Competitive Studies Project (SCSP), 80% of U.S. adults believe the government should maintain rules for AI safety and data security, even if it means developing AI capabilities more slowly. In contrast, 9% say the government should prioritize developing AI capabilities as quickly as possible, even if it means reducing rules for AI safety and data security. Almost all Americans (97%) agree that AI safety and security should be subject to rules and regulations, but views diverge on who should be responsible for creating them.",
   "authors": [
    "Gallup",
    "Special Competitive Studies Project"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-09-01",
   "doi": null,
   "explainer": null,
   "id": "title:82ddbde477c0f198",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://news.gallup.com/poll/694685/americans-prioritize-safety-data-security.aspx",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "Americans Prioritize AI Safety and Data Security",
   "url": "https://wemustactnow.onrender.com/research/p/title-82ddbde477c0f198/",
   "venue": "Gallup News (with the Special Competitive Studies Project)",
   "year": 2025
  },
  {
   "abstract": "Today, we’re releasing our second research report from the Index, covering usage data on Claude.ai following the launch of Claude 3.7 Sonnet —our newest and most capable model with strengths in agentic coding and a new “extended thinking” mode. In addition, we’re releasing a first-of-its-kind bottom-up taxonomy of usage on Claude.ai. This new dataset covers 630 granular categories ranging from “Help resolve household plumbing, water, and maintenance issues” to “Provide guidance on battery technologies and charging systems.” We hope this bottom-up taxonomy will be useful for researchers, and reveal use-cases that might be missed by top-down approaches which map usage onto a list of predefined tasks. Last month, we introduced Claude 3.7 Sonnet, our most capable model yet with an “extended thinking mode”. We reran our previous analysis on data from the 11 days following the launch, covering 1 million anonymized Claude.ai Free and Pro conversations. The vast majority of the data we analyzed was from Claude 3.7 Sonnet, as it is set as the default on Claude.ai and our mobile app. As a reminder, our privacy-preserving analysis tool, Clio , maps each conversation to one of 17,000 tasks in the U.S. Department of Labor’s O*NET database. We then look at the overall patterns in the occupations and high-level occupational categories associated with those tasks. When looking at the breakdown of these 1 million conversations, we see that the proportion of usage in several occupational categories has increased modestly, including coding, education and the sciences. While this increase in coding usage was expected due to Claude 3.7 Sonnet’s improved scores on coding benchmarks, the increase in these other categories could reflect either ongoing diffusion of AI throughout the economy, novel applications of coding to those domains, or unexpected capability improvements in the model. Claude 3.7 Sonnet features a new “extended thinking” mode which, when activated by the user, enables th",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-03-27",
   "doi": null,
   "explainer": null,
   "id": "title:ae671e3029a05615",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/anthropic-economic-index-insights-from-claude-sonnet-3-7",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Anthropic Economic Index: Insights from Claude 3.7 Sonnet",
   "url": "https://wemustactnow.onrender.com/research/p/title-ae671e3029a05615/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "While a majority supports using robots and AI to make decisions at work, 84% of Europeans think that AI requires careful management to protect privacy and ensure transparency in the workplace.",
   "authors": [
    "European Commission"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-01-01",
   "doi": null,
   "explainer": null,
   "id": "title:4766f2c9941e1c1f",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://europa.eu/eurobarometer/surveys/detail/3222",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial Intelligence and the future of work",
   "url": "https://wemustactnow.onrender.com/research/p/title-4766f2c9941e1c1f/",
   "venue": "Special Eurobarometer",
   "year": 2025
  },
  {
   "abstract": "AI could wipe out half of all entry-level white-collar jobs — and spike unemployment to 10-20% in the next one to five years, Amodei told us in an interview from his San Francisco office.",
   "authors": [
    "Jim VandeHei",
    "Mike Allen"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-05-28",
   "doi": null,
   "explainer": null,
   "id": "title:f674a460efa49d41",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.axios.com/2025/05/28/ai-jobs-white-collar-unemployment-anthropic",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Behind the Curtain: A white-collar bloodbath",
   "url": "https://wemustactnow.onrender.com/research/p/title-f674a460efa49d41/",
   "venue": "Axios (interview with Dario Amodei, Anthropic)",
   "year": 2025
  },
  {
   "abstract": "We find that since the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment even after controlling for firm-level shocks.",
   "authors": [
    "Erik Brynjolfsson",
    "Bharat Chandar",
    "Ruyu Chen"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-08-26",
   "doi": null,
   "explainer": {
    "approach": "The authors compare employment trends for workers by age and by their occupation's exposure to AI, controlling for firm-level shocks.",
    "findings": "Workers aged 22-25 in the most AI-exposed occupations experienced a 13 percent relative decline in employment since the widespread adoption of generative AI, even after controlling for firm-level shocks.",
    "limitations": "The available abstract is a single sentence; data source, period and the other five facts are not described.",
    "method_type": "observational",
    "question": "Has employment changed for workers in AI-exposed occupations since generative AI was widely adopted?"
   },
   "id": "title:66a325dcbbf90863",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://siepr.stanford.edu/publications/working-paper/canaries-coal-mine-six-facts-about-recent-employment-effects-artificial",
   "summary": "Using payroll data, the authors find that since generative AI spread, workers aged 22-25 in the most AI-exposed occupations saw a 13% relative decline in employment, even after controlling for firm-level shocks.",
   "theme": "Labor & Work",
   "title": "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/title-66a325dcbbf90863/",
   "venue": "Stanford Digital Economy Lab working paper",
   "year": 2025
  },
  {
   "abstract": "It may be a gradual thing that happens, but we'll have something that we could sort of reasonably call AGI, that exhibits all the cognitive capabilities humans have, maybe in the next five to 10 years, possibly the lower end of that.",
   "authors": [
    "Demis Hassabis"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-08-04",
   "doi": null,
   "explainer": null,
   "id": "title:31f2ff2f89d77bb4",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.theguardian.com/technology/2025/aug/04/demis-hassabis-ai-future-10-times-bigger-than-industrial-revolution-and-10-times-faster",
   "summary": null,
   "theme": "Other",
   "title": "Demis Hassabis on our AI future: 'It'll be 10 times bigger than the Industrial Revolution – and maybe 10 times faster'",
   "url": "https://wemustactnow.onrender.com/research/p/title-31f2ff2f89d77bb4/",
   "venue": "The Guardian (interview)",
   "year": 2025
  },
  {
   "abstract": "That doesn’t mean these are the most popular tasks: software engineering is still by far in the lead in almost every state and country in the world. Instead, it means that people in Massachusetts have been more likely to ask Claude for help with scientific research than people elsewhere – or, for instance, that Claude users in Brazil appear to be particularly enthusiastic about languages: they use Claude for translation and language-learning about six times more than the global average. These are statistics we found in our third Anthropic Economic Index report . In this latest installment, we’ve expanded our efforts to document the early patterns of AI adoption that are beginning to reshape work and the economy. We measure how Claude is being used differently… We summarize the report below. In addition, we’ve designed an interactive website where you can explore our data yourself. For the first time, you can search for trends and results in Claude.ai use across every US state and all occupations we track, to see how AI is used where you live or by people in similar jobs. Finally, if you’d like to build on our analysis, we’ve made our dataset openly available , alongside the data from our previous Economic Index reports. We've expanded the Anthropic Economic Index to include geographic data. Below we cover what we've learned about how Claude is used across countries and US states. The US uses Claude far more than any other nation. India is in second place, followed by Brazil, Japan, and South Korea, each with similar shares. However, there is huge variation in population size across these countries. To account for this, we adjust each country’s share of Claude.ai use by its share of the world’s working population. This gives us our Anthropic AI Usage Index , or AUI. Countries with an AUI greater than 1 use Claude more often than we’d expect based on their working-age population alone, and vice-versa. From the AUI data, we can see that some small, technologically adva",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-09-15",
   "doi": null,
   "explainer": null,
   "id": "title:9a828c2f0c06594c",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/economic-index-geography",
   "summary": "Anthropic's third Economic Index report maps Claude use across countries and US states, with an AI Usage Index that adjusts for working population. Software engineering leads almost everywhere, the US uses Claude most, and the data are released openly.",
   "theme": "Labor & Work",
   "title": "Economic Index: AI's role in the US and global economy",
   "url": "https://wemustactnow.onrender.com/research/p/title-9a828c2f0c06594c/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "Historically, new technologies took decades to reach widespread adoption. Electricity took over 30 years to reach farm households after urban electrification. The first mass-market personal computer reached early adopters in 1981, but did not reach the majority of homes in the US for another 20 years. Even the rapidly-adopted internet took around five years to hit adoption rates that AI reached in just two years. 2 Why is this? In short, it takes time for new technologies—even transformative ones—to diffuse throughout the economy, for consumer adoption to become less geographically concentrated, and for firms to restructure business operations to best unlock new technical capabilities. Firm adoption, first for a narrow set of tasks, then for more general purpose applications, is an important way that consequential technologies spread and have transformative economic effects. 3 In other words, a hallmark of early technological adoption is that it is concentrated —in both a small number of geographic regions and a small number of tasks in firms. As we document in this report, AI adoption appears to be following a similar pattern in the 21st century, albeit on shorter timelines and with greater intensity than the diffusion of technologies in the 20th century. To study such patterns of early AI adoption, we extend the Anthropic Economic Index along two important dimensions, introducing a geographic analysis of Claude.ai conversations and a first-of-its-kind examination of enterprise API use. We show how Claude usage has evolved over time, how adoption patterns differ across regions, and—for the first time—how firms are deploying frontier AI to solve business problems. In the first chapter of this report, we identify notable changes in usage on Claude.ai over the previous eight months, occurring alongside improvements in underlying model capabilities, new product features, and a broadening of the Claude consumer base. For the first time, we release geographic cuts of Cla",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-09-15",
   "doi": null,
   "explainer": null,
   "id": "title:b51da6dcfe1a2f0d",
   "institutions": [],
   "lab_report": true,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/anthropic-economic-index-september-2025-report",
   "summary": "Anthropic's Economic Index report on early AI adoption adds a geographic analysis of Claude.ai use and a first look at enterprise API use. It finds adoption concentrated in a few regions and tasks, following past technology diffusion patterns but faster and more intensely.",
   "theme": "Productivity & Firms",
   "title": "Economic Index: Uneven AI adoption",
   "url": "https://wemustactnow.onrender.com/research/p/title-b51da6dcfe1a2f0d/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "Extrapolating these estimates out suggests current-generation AI models could increase US labor productivity growth by 1.8% annually over the next decade — roughly twice the run rate in recent years. But this isn’t a prediction of the future, since we don’t take into account the rate of adoption or the larger productivity effects that would come from much more capable AI systems. Our analysis has limits. Most notably, we can’t account for additional time humans spend on tasks outside of their conversations with Claude, including validating the quality or accuracy of Claude's work. But as AI models get better at time estimation, we think our methods in this research note could become increasingly useful for understanding how AI is shaping real work. This gives us a new lens for understanding how AI’s economic impacts over time, which we will track going forward as part of our Economic Index : Computing these estimates based on real-world Claude conversations gives us a new lens to understand AI productivity. This complements other approaches, like lab studies in narrow domains, or government statistics which provide more coarse-grained insights. We will track how these estimates change over time to get an evolving picture of these issues as capabilities and adoption continue to progress. As part of the Anthropic Economic Index , we have documented how people use Claude across different tasks, industries, and places. We’ve captured the breadth of uses—how people use Claude for legal, scientific, and programming tasks—but not their depth . How substantial are the tasks for which people use Claude, and how much time does Claude save them? The current version of the Economic Index can't capture this within-task heterogeneity —for instance, it can’t distinguish report-writing tasks that take five minutes from those that take five days, or financial modeling tasks that take an afternoon from those that take a few weeks. This makes it difficult to assess AI's economic effec",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-11-25",
   "doi": null,
   "explainer": null,
   "id": "title:28f3f1f09453e3b9",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/estimating-productivity-gains",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Estimating AI productivity gains",
   "url": "https://wemustactnow.onrender.com/research/p/title-28f3f1f09453e3b9/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "OpenAI is launching a Jobs Platform and new Certifications to connect workers with jobs, training, and certifications. Learn how we’re expanding economic opportunity and making AI skills more accessible.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-09-04",
   "doi": null,
   "explainer": null,
   "id": "title:17c4b2afd653c69a",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://openai.com/index/expanding-economic-opportunity-with-ai",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Expanding economic opportunity with AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-17c4b2afd653c69a/",
   "venue": "OpenAI (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation. GATE combines three key ingredients that have not been brought together in previous work: (1) a compute-based model of AI development, (2) an AI automation framework, and (3) a semi-endogenous growth model featuring endogenous investment and adjustment costs. The model allows users to simulate the economic effects of the transition to advanced AI across a range of potential scenarios. GATE captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms. This paper explains the model's structure and functionality, emphasizing AI development for economists and economic modeling for the AI community. The model is implemented in an interactive sandbox, enabling users to explore the impact of AI under different parameter choices and policy interventions. The modeling sandbox is available at: www.epoch.ai/GATE.",
   "authors": [
    "Ege Erdil",
    "Potlogea, Andrei",
    "Tamay Besiroglu",
    "Roldan, Edu",
    "Ho, Anson",
    "Jaime Sevilla",
    "Matthew Barnett",
    "Vrzla, Matej",
    "Sandler, Robert"
   ],
   "cited_by": 0,
   "collected_from": "openalex",
   "date": "2025-03-06",
   "doi": "10.48550/arxiv.2503.04941",
   "explainer": null,
   "id": "doi:10.48550/arxiv.2503.04941",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "http://arxiv.org/pdf/2503.04941",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "GATE: An Integrated Assessment Model for AI Automation",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2503-04941/",
   "venue": "RePEc: Research Papers in Economics",
   "year": 2025
  },
  {
   "abstract": "Abstract We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.",
   "authors": [
    "Erik Brynjolfsson",
    "Danielle Li",
    "Lindsey Raymond"
   ],
   "cited_by": 591,
   "collected_from": "openalex",
   "date": "2025-02-04",
   "doi": "10.1093/qje/qjae044",
   "explainer": {
    "approach": "The authors study the staggered introduction of a generative AI conversational assistant across 5,172 customer-support agents, measuring issues resolved per hour, quality, learning, and customer behaviour.",
    "findings": "Access to the assistant raises issues resolved per hour by 15% on average. Less experienced and lower-skilled agents gain in both speed and quality, while the most experienced see small speed gains and small quality declines. Gains are largest on moderately rare problems, and customers become more polite and less likely to ask for a manager.",
    "limitations": "The evidence comes from one setting, customer support, with one AI tool, so results may not carry over to other occupations or tools. Effects vary substantially across workers.",
    "method_type": "observational",
    "question": "Does giving customer-support agents a generative AI assistant change their productivity, and for whom?"
   },
   "id": "doi:10.1093/qje/qjae044",
   "institutions": [
    "National Bureau of Economic Research",
    "Stanford University",
    "Massachusetts Institute of Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1093/qje/qjae044",
   "summary": "Studying the staggered rollout of a generative AI assistant to 5,172 customer-support agents, finds it raised productivity (issues resolved per hour) by 15% on average, with the biggest gains for less experienced and lower-skilled workers, who saw both faster and higher-quality output; the most experienced agents saw little speed gain and slightly lower quality. Because the assistant appears to transfer top performers' know-how to novices, the paper is widely cited as evidence that generative AI can compress skill gaps within an occupation rather than simply replacing workers.",
   "theme": "Productivity & Firms",
   "title": "Generative AI at Work",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1093-qje-qjae044/",
   "venue": "The Quarterly Journal of Economics",
   "year": 2025
  },
  {
   "abstract": "We expect AI will provide a 3%-9% increase to economic growth per year in the near future",
   "authors": [
    "Jack Wiseman",
    "Duncan McClements"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-01-17",
   "doi": null,
   "explainer": null,
   "id": "title:ef1b68e68dd48349",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://inferencemagazine.substack.com/p/how-much-economic-growth-from-ai",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "How much economic growth from AI should we expect, how soon?",
   "url": "https://wemustactnow.onrender.com/research/p/title-ef1b68e68dd48349/",
   "venue": "Inference Magazine",
   "year": 2025
  },
  {
   "abstract": "Both the public and experts largely worry the U.S. government will not go far enough in regulating AI. About six-in-ten U.S. adults and 56% of experts surveyed say they're more concerned about this than about the government going too far.",
   "authors": [
    "Colleen McClain",
    "Brian Kennedy",
    "Jeffrey Gottfried",
    "Monica Anderson",
    "Giancarlo Pasquini"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-04-03",
   "doi": null,
   "explainer": null,
   "id": "title:3e9d99dadb343281",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/",
   "summary": null,
   "theme": "Policy & Governance",
   "title": "How the U.S. Public and AI Experts View Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/title-3e9d99dadb343281/",
   "venue": "Pew Research Center",
   "year": 2025
  },
  {
   "abstract": "Innovation related to artificial intelligence (AI) could displace 6-7% of the US workforce if AI is widely adopted. Goldman Sachs Research estimates that unemployment will increase by half a percentage point during the AI transition period as displaced workers seek new positions.",
   "authors": [
    "Goldman Sachs Research"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-01-01",
   "doi": null,
   "explainer": null,
   "id": "title:138a12cac1923e71",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce",
   "summary": null,
   "theme": "Labor & Work",
   "title": "How Will AI Affect the Global Workforce?",
   "url": "https://wemustactnow.onrender.com/research/p/title-138a12cac1923e71/",
   "venue": "Goldman Sachs Insights",
   "year": 2025
  },
  {
   "abstract": "The Index’s initial report provides first-of-its-kind data and analysis based on millions of anonymized conversations on Claude.ai , revealing the clearest picture yet of how AI is being incorporated into real-world tasks across the modern economy. We're also open sourcing the dataset used for this analysis, so researchers can build on and extend our findings. Developing policy responses to address the coming transformation in the labor market and its effects on employment and productivity will take a range of perspectives. To that end, we are also inviting economists, policy experts, and other researchers to provide input on the Index. Our new paper builds on a long line of research on the labor market impact of technologies, from the Spinning Jenny of the Industrial Revolution to the car-manufacturing robots of the present day. We focus on the ongoing impact of AI. We don’t survey people on their AI use, or attempt to forecast the future; instead, we have direct data on how AI is actually being used. Our research began with an important insight from the economics literature : sometimes it makes sense to focus on occupational tasks rather than occupations themselves . Jobs often share certain tasks and skills in common: for example, visual pattern recognition is a task performed by designers, photographers, security screeners, and radiologists. Certain tasks lend themselves better to being automated or augmented by a new technology than others. We’d therefore expect AI to be adopted selectively for different tasks across different occupations, and that analyzing tasks—in addition to jobs as a whole—would give us a fuller picture of how AI is being integrated into the economy. This research was made possible by Claude insights and observations, or \" Clio \", an automated analysis tool that allows us to analyze conversations with Claude while preserving user privacy 1 . We used Clio on a dataset of approximately one million conversations with Claude (specifically, Fre",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-02-10",
   "doi": null,
   "explainer": null,
   "id": "title:f104611084576ae9",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/the-anthropic-economic-index",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Introducing the Anthropic Economic Index",
   "url": "https://wemustactnow.onrender.com/research/p/title-f104611084576ae9/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "I think my bear case on all of this is 3 percent, and my bull case is something like 5 percent.",
   "authors": [
    "Jack Clark",
    "Tyler Cowen"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-05-01",
   "doi": null,
   "explainer": null,
   "id": "title:2cd090bc16ae6306",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://conversationswithtyler.com/episodes/jack-clark/",
   "summary": null,
   "theme": "Other",
   "title": "Jack Clark on AI's Uneven Impact (Ep. 242)",
   "url": "https://wemustactnow.onrender.com/research/p/title-2cd090bc16ae6306/",
   "venue": "Conversations with Tyler (interview)",
   "year": 2025
  },
  {
   "abstract": null,
   "authors": [
    "Anders Humlum",
    "Emilie Vestergaard"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-04-25",
   "doi": "10.3386/w33777",
   "explainer": null,
   "id": "doi:10.3386/w33777",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.nber.org/papers/w33777",
   "summary": "Humlum and Vestergaard link surveys on AI chatbot use to Danish administrative records for 25,000 workers in 11 exposed occupations. Despite fast adoption and reported time savings, they find no significant effects on earnings or recorded hours in any occupation two years after ChatGPT's release, ruling out effects larger than 1%.",
   "theme": "Labor & Work",
   "title": "Large Language Models, Small Labor Market Effects",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-3386-w33777/",
   "venue": "NBER Working Paper 33777",
   "year": 2025
  },
  {
   "abstract": "The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon in G7 economies. It builds on our previous work that introduced a micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates. This paper refines and extends the estimates from the United States to other G7 economies, in particular by harmonising current adoption rate measures among firms and updating future adoption path estimates. Across the three scenarios considered, the estimated range for annual aggregate labour productivity growth due to AI range between 0.4-1.3 percentage points in countries with high AI exposure – due to stronger specialisation in highly AI-exposed knowledge intensive services such as finance and ICT services – and more widespread adoption (e.g. United States and United Kingdom). In contrast, projected gains in several other G7 economies are up to 50% smaller, reflecting differences in sectoral composition and assumptions about the relative pace of AI adoption.",
   "authors": [
    "Francesco Filippucci",
    "Péter Gál",
    "Katharina Laengle",
    "Matthias Schief"
   ],
   "cited_by": 13,
   "collected_from": "openalex",
   "date": "2025-06-26",
   "doi": "10.1787/a5319ab5-en",
   "explainer": {
    "approach": "Extends an earlier micro-to-macro framework from the United States to other G7 countries, harmonising firm adoption measures and updating future adoption paths across three scenarios.",
    "findings": "Annual labour productivity growth from AI is estimated at 0.4 to 1.3 percentage points in high-exposure countries such as the US and UK; gains in several other G7 economies are up to 50% smaller due to sectoral mix and slower adoption.",
    "limitations": "Results are scenario projections sensitive to adoption assumptions and to how micro-level gains are scaled up.",
    "method_type": "macro-projection",
    "question": "How do expected AI-driven productivity gains differ across the G7 economies?"
   },
   "id": "doi:10.1787/a5319ab5-en",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1787/a5319ab5-en",
   "summary": "Extending its micro-to-macro framework from the United States to the other G7 economies, this OECD paper estimates AI could add 0.4-1.3 percentage points a year to labour productivity growth in highly exposed economies such as the US and UK. Projected gains elsewhere in the G7 are up to 50% smaller because of sectoral composition and slower assumed adoption.",
   "theme": "Growth & Macro",
   "title": "Macroeconomic productivity gains from Artificial Intelligence in G7 economies",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1787-a5319ab5-en/",
   "venue": "OECD artificial intelligence papers",
   "year": 2025
  },
  {
   "abstract": "Analysis provides insights into ChatGPT’s impact on the economy. OpenAI also launches new research collaboration to study AI’s broader effects on the labor market and productivity.",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-07-22",
   "doi": null,
   "explainer": null,
   "id": "title:407597efcc9305a6",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://openai.com/global-affairs/new-economic-analysis",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "OpenAI’s new economic analysis",
   "url": "https://wemustactnow.onrender.com/research/p/title-407597efcc9305a6/",
   "venue": "OpenAI (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "AI could raise annual total factor productivity (TFP) growth by around 0.3-0.7 percentage points in the United States over the next decade.",
   "authors": [
    "Francesco Filippucci",
    "Peter Gal",
    "Katharina Laengle",
    "Matthias Schief",
    "Filiz Unsal"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-06-15",
   "doi": null,
   "explainer": null,
   "id": "title:a51f396b4561570f",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.productivity.ac.uk/wp-content/uploads/2025/07/IPM_48_OECD_Final.pdf",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Opportunities and risks of artificial intelligence for productivity",
   "url": "https://wemustactnow.onrender.com/research/p/title-a51f396b4561570f/",
   "venue": "OECD Economics Department",
   "year": 2025
  },
  {
   "abstract": "Since launching the Anthropic Economic Index , we've observed an important shift in AI use. Users are becoming increasingly likely to delegate full tasks to Claude , “collaborating” with Claude less. As AI models continue to work independently for longer periods of time, and as more employers adopt AI to improve their productivity, we expect this trend to accelerate. The implications for the workforce are uncertain. How should policymakers respond? This is not an easy question, nor is it one that any single actor can answer. There is great uncertainty about the scale of the transition ahead, and a wide range of views about how to manage it. But it is imperative to begin formulating ideas now for the economic scenarios we might find ourselves in. Over the past year, we’ve worked with economists and policy experts from around the world (including members of our Economic Advisory Council and participants in our first Economic Futures Symposium ) to move this discussion forward. To generate a broad range of ideas, we’ve engaged with both non-partisan thinkers and those from across the political spectrum. Below, we briefly explore nine of these categories of ideas, covering workforce development, permitting reform, fiscal policy, and social services. While we don’t know what the optimal policies will prove to be, we’re committed to sharing ideas in the open, and to being transparent about the economic effects of advanced AI. The rate, scale, and form of AI's economic effects will determine the policy responses that are necessary across the world. Accordingly, we've organized these initial ideas into three broad categories: Policy ideas for nearly all scenarios , including those where negative effects on the labor market remain modest. These are policies that their advocates argue merit consideration almost regardless of how significant the disruption of AI proves to be. Given this, many of these proposals have been suggested in other contexts before. They include upskill",
   "authors": [],
   "cited_by": 0,
   "collected_from": "lab",
   "date": "2025-10-14",
   "doi": null,
   "explainer": null,
   "id": "title:71945a404a9d280d",
   "institutions": [],
   "lab_report": true,
   "labels": [],
   "open_access": true,
   "source_url": "https://www.anthropic.com/research/economic-policy-responses",
   "summary": null,
   "theme": "Labor & Work",
   "title": "Preparing for AI's economic impact",
   "url": "https://wemustactnow.onrender.com/research/p/title-71945a404a9d280d/",
   "venue": "Anthropic (AI lab research)",
   "year": 2025
  },
  {
   "abstract": "However, this growth is expected to be offset by the displacement of the equivalent of 8% (or 92 million) of current jobs, resulting in net growth of 7% of total employment, or 78 million jobs.",
   "authors": [
    "World Economic Forum"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-01-07",
   "doi": null,
   "explainer": null,
   "id": "title:34be7abe3c9209c4",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.weforum.org/publications/the-future-of-jobs-report-2025",
   "summary": null,
   "theme": "Labor & Work",
   "title": "The Future of Jobs Report 2025",
   "url": "https://wemustactnow.onrender.com/research/p/title-34be7abe3c9209c4/",
   "venue": "World Economic Forum",
   "year": 2025
  },
  {
   "abstract": "We estimate that AI will increase productivity and GDP by 1.5% by 2035, nearly 3% by 2055, and 3.7% by 2075. AI's boost to annual productivity growth is strongest in the early 2030s but eventually fades, with a permanent effect of less than 0.04 percentage points due to sectoral shifts.",
   "authors": [
    "Alex Arnon"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-09-08",
   "doi": null,
   "explainer": null,
   "id": "title:26d44dbe87b6a698",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://budgetmodel.wharton.upenn.edu/p/2025-09-08-the-projected-impact-of-generative-ai-on-future-productivity-growth/",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Projected Impact of Generative AI on Future Productivity Growth",
   "url": "https://wemustactnow.onrender.com/research/p/title-26d44dbe87b6a698/",
   "venue": "Penn Wharton Budget Model",
   "year": 2025
  },
  {
   "abstract": "In October 2023, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace, nature and impacts of AI progress. Significant steps were taken to minimize and evaluate bias. In evaluations of participation bias, we found that most groups responded at similar rates. The participants estimated that several milestones had at least a 50% chance of being feasible for AI by 2028, including constructing a payment processing site and fine-tuning an LLM. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027 and 50% by 2047—13 years earlier than in our 2022 survey (N = 738). The chance of all occupations becoming fully automatable, however, was not expected to reach 10% until 2037, and 50% until 2116 (compared to 2164 in the 2022 survey. Most respondents expressed substantial uncertainty about long-term impacts: While 68% in 2023 thought good outcomes from high-level machine intelligence AI were more likely than bad ones, 48% of these net optimists gave at least a 5% chance of extremely bad outcomes. Conversely, 59% of net pessimists gave 5% or more to extremely good outcomes. Depending on how we asked, between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that “substantial” or “extreme” concern is warranted about AI increasing misinformation, boosting authoritarian control, worsening inequality, and other scenarios. There was broad agreement that research aimed at minimizing risks from AI systems ought to be more prioritized.",
   "authors": [
    "Katja Grace",
    "Julia Fabienne Sandkühler",
    "Harlan Stewart",
    "Ben Weinstein-Raun",
    "Stephen Thomas",
    "Zach Stein-Perlman",
    "John Salvatier",
    "Jan Brauner",
    "Richard C. Korzekwa"
   ],
   "cited_by": 82,
   "collected_from": "openalex",
   "date": "2025-10-06",
   "doi": "10.1613/jair.1.19087",
   "explainer": {
    "approach": "A survey of 2,778 researchers in October 2023, with steps to minimize and evaluate participation bias, compared with the authors' 2022 survey of 738 respondents.",
    "findings": "Respondents gave a 50% chance of unaided machines outperforming humans in every task by 2047, 13 years earlier than in 2022, but full automation of all occupations only reached 50% by 2116. 68% thought good outcomes from high-level machine intelligence more likely than bad ones.",
    "limitations": "Forecasts are expert opinion with wide stated uncertainty, and answers depended on how questions were framed.",
    "method_type": "survey",
    "question": "What do researchers who publish in top AI venues expect about the pace, nature and impacts of AI progress?"
   },
   "id": "doi:10.1613/jair.1.19087",
   "institutions": [
    "University of Bonn",
    "University of Oxford"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1613/jair.1.19087",
   "summary": "A survey of 2,778 researchers who published in top AI venues in October 2023 put a 50% chance on machines outperforming humans at every task by 2047, 13 years earlier than the 2022 survey. Full automation of all occupations was not expected to reach 50% probability until 2116, and most respondents expressed substantial uncertainty about long-term impacts.",
   "theme": "Labor & Work",
   "title": "Thousands of AI Authors on the Future of AI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1613-jair-1-19087/",
   "venue": "Journal of Artificial Intelligence Research",
   "year": 2025
  },
  {
   "abstract": "I've gone on record as suggesting that AI will boost economic growth rates by half a percentage point a year.",
   "authors": [
    "Tyler Cowen"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2025-02-01",
   "doi": null,
   "explainer": null,
   "id": "title:f890de1e935b6e3d",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://marginalrevolution.com/marginalrevolution/2025/02/why-i-think-ai-take-off-is-relatively-slow.html",
   "summary": "Tyler Cowen explains why he expects AI take-off to be relatively slow, restating his estimate that AI will add about half a percentage point a year to economic growth.",
   "theme": "Growth & Macro",
   "title": "Why I think AI take-off is relatively slow",
   "url": "https://wemustactnow.onrender.com/research/p/title-f890de1e935b6e3d/",
   "venue": "Marginal Revolution (blog)",
   "year": 2025
  },
  {
   "abstract": "In this note we use two alternative approaches to estimate the macroeconomic impact of artificial intelligence (AI) on productivity growth over the next decade. The first approach exploits the parallel between the AI revolution and past technological revolutions. The second approach follows Acemoglu (2024) and the task-based framework, which we revisit using our own reading of the existing empirical literature on the various component of the task-based formula. Based on the first approach, we estimate that the AI revolution should increase aggregate productivity growth by between 0.8 and 1.3pp per year over the next decade. Using the second approach but with our own reading of the recent empirical literature on the various components of the task-based formula, we obtain a median estimate of 0.68pp additional annual total factor productivity (TFP) growth. Our estimates do not take into account the fact that AI automates tasks not only in the production of goods and services, our focus in this note, but also in the production of ideas.",
   "authors": [
    "Philippe Aghion",
    "Simon Bunel"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2024-06-01",
   "doi": null,
   "explainer": {
    "approach": "Two approaches: an analogy with past technological revolutions, and the task-based formula from Acemoglu (2024) recomputed with the authors' own reading of the empirical literature.",
    "findings": "The historical-analogy approach implies 0.8 to 1.3 percentage points of extra annual productivity growth; the task-based approach gives a median of 0.68 points of extra annual TFP growth.",
    "limitations": "The estimates exclude AI's effects on the production of ideas and rest on the authors' interpretation of existing micro evidence.",
    "method_type": "macro-projection",
    "question": "How much could AI raise aggregate productivity growth over the next decade?"
   },
   "id": "title:e78842657b0f08e8",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.frbsf.org/wp-content/uploads/AI-and-Growth-Aghion-Bunel.pdf",
   "summary": "The authors estimate AI's effect on productivity growth over the next decade with two approaches. Comparing AI with past technological revolutions gives 0.8-1.3 percentage points of extra annual productivity growth, while a task-based approach in the spirit of Acemoglu (2024), using their own reading of the evidence, gives a median of 0.68 percentage points of extra TFP growth.",
   "theme": "Productivity & Firms",
   "title": "AI and Growth: Where Do We Stand?",
   "url": "https://wemustactnow.onrender.com/research/p/title-e78842657b0f08e8/",
   "venue": "Working paper (Federal Reserve Bank of San Francisco conference)",
   "year": 2024
  },
  {
   "abstract": "Although our assessments are inherently uncertain, we think that a productivity boost of 0.2%– 0.3% is plausible over the next decade, with gains of 0.5%–0.9% attainable in the lead-up to 50% adoption.",
   "authors": [
    "Irina Tytell"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2024-07-01",
   "doi": null,
   "explainer": null,
   "id": "title:d4fcadac7bad8822",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.fidelity.ca/content/dam/fidelity/en/documents/other-pdfs/ai-x-factor-new-regime-en.pdf",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "Artificial intelligence: An X-factor in a new investment regime",
   "url": "https://wemustactnow.onrender.com/research/p/title-d4fcadac7bad8822/",
   "venue": "Fidelity Investments, Asset Allocation Research Team",
   "year": 2024
  },
  {
   "abstract": "a dream scenario—perhaps a goal to aim for—would be 20% annual GDP growth rate in the developing world, with 10% each coming from AI-enabled economic decisions and the natural spread of AI-accelerated technologies",
   "authors": [
    "Dario Amodei"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2024-10-11",
   "doi": null,
   "explainer": null,
   "id": "title:43a0cbc140b1faaa",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.darioamodei.com/essay/machines-of-loving-grace",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Machines of Loving Grace: How AI Could Transform the World for the Better",
   "url": "https://wemustactnow.onrender.com/research/p/title-43a0cbc140b1faaa/",
   "venue": "Essay",
   "year": 2024
  },
  {
   "abstract": "The paper studies the expected macroeconomic productivity gains from Artificial Intelligence (AI) over a 10-year horizon. It builds a novel micro-to-macro framework by combining existing estimates of micro-level performance gains with evidence on the exposure of activities to AI and likely future adoption rates, relying on a multi-sector general equilibrium model with input-output linkages to aggregate the effects. Its main estimates for annual aggregate total-factor productivity growth due to AI range between 0.25-0.6 percentage points (0.4-0.9 pp. for labour productivity). The paper discusses the role of various channels in shaping these macro-level gains and highlights several policy levers to support AI's growth-enhancing effects.",
   "authors": [
    "Francesco Filippucci",
    "Peter Gal",
    "Matthias Schief"
   ],
   "cited_by": 50,
   "collected_from": "openalex",
   "date": "2024-11-22",
   "doi": "10.1787/b524a072-en",
   "explainer": {
    "approach": "A micro-to-macro framework combining micro-level performance gains, exposure of activities to AI and projected adoption rates, aggregated with a multi-sector general equilibrium model with input-output links.",
    "findings": "Annual aggregate TFP growth due to AI is estimated at 0.25 to 0.6 percentage points, or 0.4 to 0.9 points for labour productivity, with several policy levers discussed.",
    "limitations": "Projections depend on assumed adoption paths and on transferring micro-level estimates to the whole economy.",
    "method_type": "macro-projection",
    "question": "How large are the likely macroeconomic productivity gains from AI over a 10-year horizon?"
   },
   "id": "doi:10.1787/b524a072-en",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1787/b524a072-en",
   "summary": "This OECD paper combines micro-level performance gains, exposure of activities to AI and expected adoption rates in a multi-sector general-equilibrium model with input-output links. It estimates AI will add 0.25-0.6 percentage points to annual aggregate TFP growth (0.4-0.9 points for labour productivity) over ten years.",
   "theme": "Productivity & Firms",
   "title": "Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1787-b524a072-en/",
   "venue": "OECD artificial intelligence papers",
   "year": 2024
  },
  {
   "abstract": "We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If the distribution of task complexity exhibits a sufficiently thick infinite tail, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages.",
   "authors": [
    "Anton Korinek",
    "Donghyun Suh"
   ],
   "cited_by": 1,
   "collected_from": "openalex",
   "date": "2024-03-17",
   "doi": "10.48550/arxiv.2403.12107",
   "explainer": {
    "approach": "A theoretical model in which work is split into tasks of differing complexity, automation reaches ever more complex tasks, and wages depend on a race between automation and capital accumulation.",
    "findings": "If task complexity has a thick unbounded tail, there is always work for humans and wages may keep rising; if human-performable complexity is bounded and full automation arrives, wages collapse. Wages can fall earlier if automation outpaces capital accumulation.",
    "limitations": "Results are scenario-based and depend on assumed distributions of task complexity, not on estimates from data.",
    "method_type": "theory",
    "question": "How might output and wages evolve as technology progresses toward AI that can perform every task humans can?"
   },
   "id": "doi:10.48550/arxiv.2403.12107",
   "institutions": [
    "Brookings Institution"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "http://arxiv.org/abs/2403.12107",
   "summary": "In a task-based model where AI automates ever more complex tasks, wages depend on a race between automation and capital accumulation. If task complexity has an unbounded tail, wages can rise indefinitely; if full automation is reached, wages collapse, and they may fall earlier if automation outpaces capital accumulation.",
   "theme": "Productivity & Firms",
   "title": "Scenarios for the Transition to AGI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2403-12107/",
   "venue": "arXiv (Cornell University)",
   "year": 2024
  },
  {
   "abstract": "We could see economic growth rates of 30%/year and beyond, quite possibly multiple doublings a year. This follows fairly straightforwardly from economists’ models of economic growth.",
   "authors": [
    "Leopold Aschenbrenner"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2024-06-04",
   "doi": null,
   "explainer": null,
   "id": "title:9f55b963203782db",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://situational-awareness.ai/wp-content/uploads/2024/06/situationalawareness.pdf",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Situational Awareness: The Decade Ahead",
   "url": "https://wemustactnow.onrender.com/research/p/title-9f55b963203782db/",
   "venue": "Essay series",
   "year": 2024
  },
  {
   "abstract": null,
   "authors": [
    "Zheyuan Cui",
    "Mert Demirer",
    "Sonia Jaffe",
    "Leon Musolff",
    "Sida Peng",
    "Tobias Salz"
   ],
   "cited_by": 53,
   "collected_from": "openalex",
   "date": "2024-01-01",
   "doi": "10.2139/ssrn.4945566",
   "explainer": null,
   "id": "doi:10.2139/ssrn.4945566",
   "institutions": [
    "Princeton University",
    "Massachusetts Institute of Technology",
    "Microsoft Research (United Kingdom)",
    "Public Policy Institute of California",
    "University of Pennsylvania",
    "Microsoft (Finland)",
    "National Bureau of Economic Research"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.2139/ssrn.4945566",
   "summary": "Reports on three separate field experiments (at Microsoft, Accenture, and a Fortune 100 firm) measuring how GitHub Copilot access affects software developers' output, finding sizable increases in completed tasks per week that are largest for less-experienced programmers. As one of the largest employer-scale randomized trials of a generative AI coding tool to date, it adds enterprise-level evidence to the debate over whether AI narrows or widens skill gaps among workers.",
   "theme": "Labor & Work",
   "title": "The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-2139-ssrn-4945566/",
   "venue": "SSRN Electronic Journal",
   "year": 2024
  },
  {
   "abstract": "We predict TFP gains of 2.9% in the medium run (say in the next ten years) in the euro area, equivalent to an additional 0.29 percentage points per year.",
   "authors": [
    "Antonin Bergeaud"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2024-01-01",
   "doi": null,
   "explainer": null,
   "id": "title:6932eebaa7a3b52e",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.ecb.europa.eu/pub/pdf/sintra/ecb.forumcentbankpub2024_Bergeaud_paper.en.pdf",
   "summary": null,
   "theme": "Productivity & Firms",
   "title": "The Past, Present and Future of European Productivity",
   "url": "https://wemustactnow.onrender.com/research/p/title-6932eebaa7a3b52e/",
   "venue": "ECB Forum on Central Banking (Sintra) conference volume",
   "year": 2024
  },
  {
   "abstract": "SUMMARY This paper evaluates claims about the large macroeconomic implications of new advances in Artificial intelligence (AI). It starts from a task-based model of AI’s effects, working through automation and task complementarities. So long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: Gross Domestic Product (GDP) and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear non-trivial but modest – no more than a 0.66% increase in total factor productivity (TFP) over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.53%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labour income inequality. Instead, AI is predicted to widen the gap between capital and labour income. Finally, some of the new tasks created by AI may have negative social value (such as the design of algorithms for online manipulation), and I",
   "authors": [
    "Daron Acemoğlu"
   ],
   "cited_by": 189,
   "collected_from": "openalex",
   "date": "2024-08-06",
   "doi": "10.1093/epolic/eiae042",
   "explainer": {
    "approach": "Using a task-based model and a version of Hulten's theorem, the paper estimates aggregate gains from the share of tasks exposed to AI and average task-level cost savings, drawing on existing exposure and productivity estimates, and analyses wage and inequality effects.",
    "findings": "Estimated TFP gains are no more than 0.66% over ten years, and below 0.53% once harder-to-learn tasks are accounted for. AI is unlikely to raise inequality as much as earlier automation but shows no sign of reducing labour income inequality, and is predicted to widen the gap between capital and labour income.",
    "limitations": "The estimates depend on existing measures of exposure and task-level gains, much of which comes from easy-to-learn tasks. The framework covers cost savings in existing tasks and the paper notes some new AI-created tasks may have negative social value.",
    "method_type": "macro-projection",
    "question": "How large are AI's likely effects on total factor productivity, GDP, wages and inequality over the next ten years?"
   },
   "id": "doi:10.1093/epolic/eiae042",
   "institutions": [
    "Massachusetts Institute of Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1093/epolic/eiae042",
   "summary": "Using a task-based model and Hulten's theorem, estimates that AI's macroeconomic effects over the next decade will be 'non-trivial but modest' — no more than a 0.66% increase in total factor productivity, and likely under 0.53% once harder-to-learn tasks are accounted for — far below headline projections from industry reports. Acemoglu also argues AI could widen the gap between capital and labor income even without worsening cross-group wage inequality, making this one of the most-cited skeptical counterweights in the AI-growth debate.",
   "theme": "Labor & Work",
   "title": "The simple macroeconomics of AI",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1093-epolic-eiae042/",
   "venue": "Economic Policy",
   "year": 2024
  },
  {
   "abstract": null,
   "authors": [
    "Nicholas G. Otis",
    "Rowan Philip Clarke",
    "Solène Delecourt",
    "David Holtz",
    "Rembrand Michael Koning"
   ],
   "cited_by": 30,
   "collected_from": "openalex",
   "date": "2024-01-01",
   "doi": "10.2139/ssrn.4671369",
   "explainer": null,
   "id": "doi:10.2139/ssrn.4671369",
   "institutions": [
    "University of California, Berkeley",
    "Harvard University",
    "Massachusetts Institute of Technology"
   ],
   "lab_report": false,
   "labels": [],
   "open_access": true,
   "source_url": "https://doi.org/10.2139/ssrn.4671369",
   "summary": null,
   "theme": "Other",
   "title": "The Uneven Impact of Generative AI on Entrepreneurial Performance",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-2139-ssrn-4671369/",
   "venue": "SSRN Electronic Journal",
   "year": 2024
  },
  {
   "abstract": "We examined the productivity effects of a generative artificial intelligence (AI) technology, the assistive chatbot ChatGPT, in the context of midlevel professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 453 college-educated professionals and randomly exposed half of them to ChatGPT. Our results show that ChatGPT substantially raised productivity: The average time taken decreased by 40% and output quality rose by 18%. Inequality between workers decreased, and concern and excitement about AI temporarily rose. Workers exposed to ChatGPT during the experiment were 2 times as likely to report using it in their real job 2 weeks after the experiment and 1.6 times as likely 2 months after the experiment.",
   "authors": [
    "Shakked Noy",
    "Whitney Zhang"
   ],
   "cited_by": 1426,
   "collected_from": "openalex",
   "date": "2023-07-13",
   "doi": "10.1126/science.adh2586",
   "explainer": {
    "approach": "A preregistered online experiment: 453 college-educated professionals were given occupation-specific, incentivized writing tasks, with half randomly given access to ChatGPT and the rest working without it.",
    "findings": "Access to ChatGPT raised productivity substantially: average task time fell by 40% and output quality rose by 18%. Gaps between stronger and weaker writers narrowed, and exposed workers were about twice as likely to use the tool in their real jobs two weeks later.",
    "limitations": "The tasks were short, self-contained writing assignments done online for pay, not full jobs — effects may differ for longer or more specialized work. The study measures task output, not employment or wages, and the follow-up window is a couple of months.",
    "method_type": "experiment",
    "question": "Does using a generative AI chatbot change how fast and how well professionals complete everyday writing tasks?"
   },
   "id": "doi:10.1126/science.adh2586",
   "institutions": [
    "Massachusetts Institute of Technology"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1126/science.adh2586",
   "summary": "A preregistered experiment assigning 453 college-educated professionals writing tasks found that access to ChatGPT cut average completion time by 40% and raised output quality by 18%, with the gap between weaker and stronger writers narrowing. Two-thirds of exposed workers reported using ChatGPT in their actual jobs weeks later, making this one of the earliest rigorous demonstrations that generative AI both raises output and reduces within-occupation inequality for a common white-collar task.",
   "theme": "Labor & Work",
   "title": "Experimental evidence on the productivity effects of generative artificial intelligence",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1126-science-adh2586/",
   "venue": "Science",
   "year": 2023
  },
  {
   "abstract": "We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.",
   "authors": [
    "Tyna Eloundou",
    "Sam Manning",
    "Pamela Mishkin",
    "Daniel L. Rock"
   ],
   "cited_by": 544,
   "collected_from": "openalex",
   "date": "2023-03-17",
   "doi": "10.48550/arxiv.2303.10130",
   "explainer": {
    "approach": "The authors rate occupations' tasks against LLM capabilities with a new rubric, combining human expert judgments with GPT-4 classifications, and compare the exposure of LLMs alone with LLMs plus software and tooling built on them.",
    "findings": "About 80% of US workers could have at least 10% of their tasks affected and about 19% at least half. With an LLM alone, about 15% of all tasks could be done much faster at equal quality; with LLM-powered software this rises to 47-56%. Exposure spans all wage levels and is higher for higher-income jobs.",
    "limitations": "The study measures potential exposure, not actual adoption or outcomes, and makes no predictions about when LLMs will be developed or adopted. Part of the classification relies on GPT-4's own judgments.",
    "method_type": "observational",
    "question": "How much of the work done in US occupations could large language models and software built on them affect?"
   },
   "id": "doi:10.48550/arxiv.2303.10130",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "http://arxiv.org/abs/2303.10130",
   "summary": "Combining human ratings with GPT-4 classifications, finds that roughly 80% of the U.S. workforce could see at least 10% of their tasks affected by large language models, and about 19% could see over half their tasks affected, with higher-wage occupations facing greater exposure than lower-wage ones. With an LLM alone about 15% of all worker tasks could be completed significantly faster, rising to 47-56% when LLM-powered software tools are included — evidence often cited to argue AI functions as a general-purpose technology with economy-wide reach rather than a narrow labor-saving tool.",
   "theme": "Labor & Work",
   "title": "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2303-10130/",
   "venue": "arXiv (Cornell University)",
   "year": 2023
  },
  {
   "abstract": "The projection labeled \"Level\" assumes that generative AI raises the level of productivity and output by an additional 18% over ten years, as suggested by the illustrative numbers we discussed for the first channel. After ten years, growth reverts to the baseline rate. The third projection labeled \"Level+Growth\" additionally includes a one percentage point boost in the rate of growth over the baseline rate, resulting from the additional innovation triggered by generative AI.",
   "authors": [
    "Martin Neil Baily",
    "Erik Brynjolfsson",
    "Anton Korinek"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2023-05-10",
   "doi": null,
   "explainer": {
    "approach": "The authors compare illustrative projections against a baseline: a Level scenario in which generative AI raises productivity and output by an extra 18% over ten years, and a Level+Growth scenario that adds a one percentage point boost to the growth rate from additional innovation.",
    "findings": "In the Level scenario output is 18% higher after ten years, after which growth returns to its baseline rate. The Level+Growth scenario additionally raises the growth rate by one percentage point through innovation triggered by generative AI.",
    "limitations": "The figures are illustrative projections built on assumed channels rather than measured effects, so the outcome depends on whether those assumptions hold.",
    "method_type": "macro-projection",
    "question": "How much could generative AI raise productivity and output over the next decade?"
   },
   "id": "title:69fa922e0c6f92ed",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.brookings.edu/articles/machines-of-mind-the-case-for-an-ai-powered-productivity-boom/",
   "summary": "The authors argue that generative AI could drive a productivity boom and illustrate it with projections: one where AI raises the level of productivity and output by an additional 18% over ten years, and another that adds a one-percentage-point boost to the growth rate from AI-triggered innovation.",
   "theme": "Productivity & Firms",
   "title": "Machines of Mind: The Case for an AI-Powered Productivity Boom",
   "url": "https://wemustactnow.onrender.com/research/p/title-69fa922e0c6f92ed/",
   "venue": "Brookings Institution",
   "year": 2023
  },
  {
   "abstract": null,
   "authors": [
    "Fabrizio Dell’Acqua",
    "Edward McFowland",
    "Ethan Mollick",
    "Hila Lifshitz‐Assaf",
    "Katherine C. Kellogg",
    "Saran Rajendran",
    "Lisa Krayer",
    "François Candelon",
    "Karim R. Lakhani"
   ],
   "cited_by": 714,
   "collected_from": "openalex",
   "date": "2023-01-01",
   "doi": "10.2139/ssrn.4573321",
   "explainer": null,
   "id": "doi:10.2139/ssrn.4573321",
   "institutions": [
    "Harvard University",
    "University of Pennsylvania",
    "University of Warwick",
    "Innovation Research Center",
    "Massachusetts Institute of Technology",
    "Boston Consulting Group (United States)",
    "Internet Society",
    "Quantitative BioSciences"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.2139/ssrn.4573321",
   "summary": "In a field experiment with several hundred Boston Consulting Group consultants completing realistic case tasks, access to GPT-4 raised completion speed and quality substantially for tasks within the model's capability frontier, but consultants using AI on a task just outside that frontier were markedly more likely to produce wrong answers than those without it. The 'jagged frontier' finding — that AI's capability boundary is uneven and hard for users to perceive in the moment — has become a touchstone for warnings that AI productivity gains are highly task-dependent rather than universal.",
   "theme": "Productivity & Firms",
   "title": "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-2139-ssrn-4573321/",
   "venue": "SSRN Electronic Journal",
   "year": 2023
  },
  {
   "abstract": "My best guess, and I do stress that word guess, is that advanced artificial intelligence will boost the annual US growth rate by one-quarter to one-half of a percentage point.",
   "authors": [
    "Tyler Cowen"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2023-08-01",
   "doi": null,
   "explainer": null,
   "id": "title:12dabdbb8ce2ecc3",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://marginalrevolution.com/marginalrevolution/2023/08/the-economic-impact-of-ai.html",
   "summary": "Tyler Cowen gives his best guess that advanced AI will raise the annual US growth rate by a quarter to half a percentage point, stressing that it is a guess.",
   "theme": "Other",
   "title": "The economic impact of AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-12dabdbb8ce2ecc3/",
   "venue": "Marginal Revolution (blog; Bloomberg Opinion column)",
   "year": 2023
  },
  {
   "abstract": null,
   "authors": [
    "Michael Chui"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2023-01-01",
   "doi": null,
   "explainer": null,
   "id": "title:8eb5621fccffbc7f",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier",
   "summary": "McKinsey Global Institute estimates that generative AI could add $2.6 to $4.4 trillion a year in value across 63 business use cases, with about three quarters of it in customer operations, marketing and sales, software engineering and R&D. It also projects that generative AI and other technologies could automate work activities absorbing 60 to 70% of employees' time today.",
   "theme": "Productivity & Firms",
   "title": "The Economic Potential of Generative AI: The Next Productivity Frontier",
   "url": "https://wemustactnow.onrender.com/research/p/title-8eb5621fccffbc7f/",
   "venue": "McKinsey Global Institute",
   "year": 2023
  },
  {
   "abstract": "Generative AI tools hold promise to increase human productivity. This paper presents results from a controlled experiment with GitHub Copilot, an AI pair programmer. Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible. The treatment group, with access to the AI pair programmer, completed the task 55.8% faster than the control group. Observed heterogenous effects show promise for AI pair programmers to help people transition into software development careers.",
   "authors": [
    "Sida Peng",
    "Eirini Kalliamvakou",
    "Peter Cihon",
    "Mert Demirer"
   ],
   "cited_by": 251,
   "collected_from": "openalex",
   "date": "2023-02-13",
   "doi": "10.48550/arxiv.2302.06590",
   "explainer": {
    "approach": "A controlled experiment: recruited developers were asked to implement an HTTP server in JavaScript as quickly as possible, with the treatment group given access to GitHub Copilot and the control group working without it.",
    "findings": "The group with Copilot completed the task 55.8% faster than the control group. The authors also observe heterogeneous effects that suggest AI pair programmers could help people move into software development careers.",
    "limitations": "A single, well-defined programming task under time pressure; speed on this task may not reflect productivity on larger, real-world codebases or code quality over time.",
    "method_type": "experiment",
    "question": "Does access to an AI pair programmer (GitHub Copilot) make software developers complete a coding task faster?"
   },
   "id": "doi:10.48550/arxiv.2302.06590",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "http://arxiv.org/abs/2302.06590",
   "summary": "A controlled experiment with recruited developers building a JavaScript HTTP server found that those with GitHub Copilot access finished the task 55.8% faster than a control group without it, with especially strong effects for less experienced programmers. It remains one of the most-cited direct measurements of an AI coding assistant's productivity effect and is frequently used as a benchmark figure in broader estimates of AI-driven productivity growth.",
   "theme": "Productivity & Firms",
   "title": "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-2302-06590/",
   "venue": "arXiv (Cornell University)",
   "year": 2023
  },
  {
   "abstract": null,
   "authors": [
    "Joseph Briggs",
    "Devesh Kodnani"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2023-01-01",
   "doi": null,
   "explainer": null,
   "id": "title:5d84297ca034e372",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.gspublishing.com/content/research/en/reports/2023/03/27/d64e052b-0f6e-45d7-967b-d7be35fabd16.html",
   "summary": "This widely circulated Goldman Sachs research note estimates that generative AI could eventually raise global GDP by roughly 7% (about $7 trillion) over a 10-year period and lift annual productivity growth by around 1.5 percentage points, while exposing roughly two-thirds of current U.S. jobs to some degree of AI automation. As one of the most-cited 'optimistic' macro estimates in the public debate, it is frequently contrasted with more conservative academic estimates such as Acemoglu's.",
   "theme": "Growth & Macro",
   "title": "The Potentially Large Effects of Artificial Intelligence on Economic Growth",
   "url": "https://wemustactnow.onrender.com/research/p/title-5d84297ca034e372/",
   "venue": "Goldman Sachs Global Economics Analyst",
   "year": 2023
  },
  {
   "abstract": "The aggregate forecast time to a 50% chance of HLMI was 37 years, i.e. 2059 (not including data from questions about the conceptually similar Full Automation of Labor, which in 2016 received much later estimates). This timeline has become about eight years shorter in the six years since 2016, when the aggregate prediction put 50% probability at 2061, i.e. 45 years out.",
   "authors": [
    "Katja Grace",
    "Zach Stein-Perlman",
    "Benjamin Weinstein-Raun",
    "John Salvatier"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2022-08-03",
   "doi": null,
   "explainer": null,
   "id": "title:4c8e761f28b6948f",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://aiimpacts.org/2022-expert-survey-on-progress-in-ai/",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "2022 Expert Survey on Progress in AI",
   "url": "https://wemustactnow.onrender.com/research/p/title-4c8e761f28b6948f/",
   "venue": "AI Impacts",
   "year": 2022
  },
  {
   "abstract": "General purpose technologies (GPTs) like AI enable and require significant complementary investments. These investments are often intangible and poorly measured in national accounts. We develop a model that shows how this can lead to underestimation of productivity growth in a new GPTs early years and, later, when the benefits of intangible investments are harvested, productivity growth overestimation. We call this phenomenon the Productivity J-curve. We apply our method to US data and find that adjusting for intangibles related to computer hardware and software yields a TFP level that is 15.9 percent higher than official measures by the end of 2017. (JEL E22, E23, G31, L63, L86)",
   "authors": [
    "Erik Brynjolfsson",
    "Daniel Rock",
    "Chad Syverson"
   ],
   "cited_by": 584,
   "collected_from": "openalex",
   "date": "2020-12-30",
   "doi": "10.1257/mac.20180386",
   "explainer": {
    "approach": "The authors build a model in which general-purpose technologies require large, poorly measured intangible investments, and apply it to US data on intangibles related to computer hardware and software.",
    "findings": "The model implies productivity growth is underestimated in a new technology's early years and overestimated later, when the intangible investments pay off: a Productivity J-curve. Adjusting for hardware and software intangibles gives a US TFP level 15.9% above official measures by the end of 2017.",
    "limitations": "The empirical application covers computer hardware and software intangibles up to 2017, not AI specifically, and depends on how the unmeasured intangible investment is estimated.",
    "method_type": "theory",
    "question": "Do official statistics mismeasure productivity growth while a general-purpose technology like AI is being adopted?"
   },
   "id": "doi:10.1257/mac.20180386",
   "institutions": [
    "Stanford University",
    "University of Pennsylvania",
    "University of Chicago"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1257/mac.20180386",
   "summary": "Argues that general-purpose technologies like AI require large, often-intangible complementary investments that official statistics undercount, producing a 'Productivity J-curve': measured productivity growth is understated early in a technology's diffusion and overstated later as those investments pay off. Applying the model to U.S. data on computer hardware and software, the authors estimate 2017 total factor productivity was 15.9% higher than official figures once intangible capital is included — a key reference point for economists arguing today's modest measured AI productivity gains understate its eventual impact.",
   "theme": "Productivity & Firms",
   "title": "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1257-mac-20180386/",
   "venue": "American Economic Journal Macroeconomics",
   "year": 2020
  },
  {
   "abstract": "This study focuses on the risk of automation and its interaction with training and the use of skills at work. Building on the expert assessment carried out by Carl Frey and Michael Osborne in 2013, the paper estimates the risk of automation for individual jobs based on the Survey of Adult Skills (PIAAC). The analysis improves on other international estimates of the individual risk of automation by using a more disaggregated occupational classification and identifying the same automation bottlenecks emerging from the experts’ discussion. Hence, it more closely aligns to the initial assessment of the potential automation deriving from the development of Machine Learning. Furthermore, this study investigates the same methodology using national data from Germany and United Kingdom, providing insights into the robustness of the results. The risk of automation is estimated for the 32 OECD countries that have participated in the Survey of Adult Skills (PIAAC) so far. Beyond the share of jobs likely to be significantly disrupted by automation of production and services, the accent is put on characteristics of these jobs and the characteristics of the workers who hold them. The risk is also assessed against the use of ICT at work and the role of training in helping workers transit to new career opportunities.",
   "authors": [
    "Ljubica Nedelkoska",
    "Glenda Quintini"
   ],
   "cited_by": 909,
   "collected_from": "openalex",
   "date": "2018-03-08",
   "doi": "10.1787/2e2f4eea-en",
   "explainer": {
    "approach": "An OECD study that applies the Frey and Osborne expert assessment to individual jobs in the Survey of Adult Skills (PIAAC) for 32 countries, with a finer occupational classification and robustness checks on German and UK data.",
    "findings": "The study estimates the share of jobs likely to be significantly disrupted by automation in each country and describes the jobs and workers most at risk, relating risk to ICT use at work and to training.",
    "limitations": "Estimates rest on expert judgments of technical feasibility from 2013 and do not model adoption, costs or new jobs; the abstract does not report the headline shares.",
    "method_type": "observational",
    "question": "What share of jobs in OECD countries face a high risk of automation, and how does that risk relate to skills use and training?"
   },
   "id": "doi:10.1787/2e2f4eea-en",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1787/2e2f4eea-en",
   "summary": "This OECD study estimates the risk of automation for individual jobs in 32 countries using the Survey of Adult Skills (PIAAC), building on the Frey and Osborne expert assessment with a finer occupational classification. It also examines which workers hold high-risk jobs and how training and ICT use relate to that risk.",
   "theme": "Labor & Work",
   "title": "Automation, skills use and training",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1787-2e2f4eea-en/",
   "venue": "OECD social employment and migration working papers",
   "year": 2018
  },
  {
   "abstract": null,
   "authors": [
    "James Manyika",
    "Susan Lund",
    "Michael Chui",
    "Jacques Bughin",
    "Jonathan Woetzel",
    "Parul Batra",
    "Ryan Ko",
    "Saurabh Sanghvi"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2017-11-28",
   "doi": null,
   "explainer": null,
   "id": "title:052d5309abfc2052",
   "institutions": [],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages",
   "summary": "McKinsey Global Institute models automation scenarios to 2030 across 46 countries and 800 occupations. It estimates that 400 to 800 million workers worldwide could be displaced by automation, while demand growth could create enough new jobs to offset this if workers can move between occupations.",
   "theme": "Labor & Work",
   "title": "Jobs lost, jobs gained: Workforce transitions in a time of automation",
   "url": "https://wemustactnow.onrender.com/research/p/title-052d5309abfc2052/",
   "venue": "McKinsey Global Institute",
   "year": 2017
  },
  {
   "abstract": "Global GDP will be up to 14% higher in 2030 as a result of the accelerating development and take-up of AI – the equivalent of an additional $15.7 trillion.",
   "authors": [
    "PwC"
   ],
   "cited_by": 0,
   "collected_from": "manual",
   "date": "2017-06-27",
   "doi": null,
   "explainer": null,
   "id": "title:8f841d92e42b03d9",
   "institutions": [],
   "lab_report": false,
   "labels": [],
   "open_access": false,
   "source_url": "https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html",
   "summary": null,
   "theme": "Growth & Macro",
   "title": "Sizing the prize: What's the real value of AI for your business and how can you capitalise?",
   "url": "https://wemustactnow.onrender.com/research/p/title-8f841d92e42b03d9/",
   "venue": "PwC",
   "year": 2017
  },
  {
   "abstract": "Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.",
   "authors": [
    "Katja Grace",
    "John Salvatier",
    "Allan Dafoe",
    "Baobao Zhang",
    "Owain Evans"
   ],
   "cited_by": 285,
   "collected_from": "openalex",
   "date": "2017-05-24",
   "doi": "10.48550/arxiv.1705.08807",
   "explainer": {
    "approach": "A large survey of machine learning researchers asking for forecasts of when AI will exceed human performance on named activities and on all tasks and jobs.",
    "findings": "Researchers predicted AI would outperform humans at translating languages by 2024, writing high-school essays by 2026 and working as a surgeon by 2053, with a 50% chance of outperforming humans in all tasks in 45 years and automating all jobs in 120 years. Asian respondents expected these dates sooner than North Americans.",
    "limitations": "The forecasts are expert opinion from 2016-2017; the abstract does not report response rates or sample size.",
    "method_type": "survey",
    "question": "When do machine learning researchers expect AI to outperform humans at specific activities and at all tasks?"
   },
   "id": "doi:10.48550/arxiv.1705.08807",
   "institutions": [
    "Institute for the Future",
    "Yale University"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "http://arxiv.org/abs/1705.08807",
   "summary": "A survey of machine learning researchers on when AI will surpass humans at specific activities, from translating languages to working as a surgeon. Respondents gave a 50% chance of AI outperforming humans in all tasks within 45 years and automating all jobs within 120 years, with Asian respondents expecting these dates sooner than North Americans.",
   "theme": "Labor & Work",
   "title": "When Will AI Exceed Human Performance? Evidence from AI Experts",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-48550-arxiv-1705-08807/",
   "venue": "arXiv (Cornell University)",
   "year": 2017
  },
  {
   "abstract": null,
   "authors": [
    "Carl Benedikt Frey",
    "Michael A. Osborne"
   ],
   "cited_by": 8667,
   "collected_from": "openalex",
   "date": "2016-10-01",
   "doi": "10.1016/j.techfore.2016.08.019",
   "explainer": null,
   "id": "doi:10.1016/j.techfore.2016.08.019",
   "institutions": [
    "University of Oxford"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": false,
   "source_url": "https://doi.org/10.1016/j.techfore.2016.08.019",
   "summary": "Frey and Osborne estimate how likely 702 US occupations are to be computerised, using expert judgements of which tasks machines can do. They classify about 47% of US employment as at high risk of automation over an unspecified number of years, a widely cited estimate of exposure rather than of jobs actually lost.",
   "theme": "Labor & Work",
   "title": "The future of employment: How susceptible are jobs to computerisation?",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1016-j-techfore-2016-08-019/",
   "venue": "Technological Forecasting and Social Change",
   "year": 2016
  },
  {
   "abstract": "In recent years, there has been a revival of concerns that automation and digitalisation might after all result in a jobless future. The debate has been fuelled by studies for the US and Europe arguing that a substantial share of jobs is at “risk of computerisation”. These studies follow an occupation-based approach proposed by Frey and Osborne (2013), i.e. they assume that whole occupations rather than single job-tasks are automated by technology. As we argue, this might lead to an overestimation of job automatibility, as occupations labelled as high-risk occupations often still contain a substantial share of tasks that are hard to automate. Our paper serves two purposes. Firstly, we estimate the job automatibility of jobs for 21 OECD countries based on a task-based approach. In contrast to other studies, we take into account the heterogeneity of workers’ tasks within occupations. Overall, we find that, on average across the 21 OECD countries, 9 % of jobs are automatable. The threat from technological advances thus seems much less pronounced compared to the occupation-based approach. We further find heterogeneities across OECD countries. For instance, while the share of automatable jobs is 6 % in Korea, the corresponding share is 12 % in Austria. Differences between countries may reflect general differences in workplace organisation, differences in previous investments into automation technologies as well as differences in the education of workers across countries.",
   "authors": [
    "Melanie Arntz",
    "Terry Gregory",
    "Ulrich Zierahn"
   ],
   "cited_by": 879,
   "collected_from": "openalex",
   "date": "2016-05-14",
   "doi": "10.1787/5jlz9h56dvq7-en",
   "explainer": {
    "approach": "The authors estimate automatability for 21 OECD countries using a task-based approach that allows for differences in tasks among workers in the same occupation, in contrast to the occupation-based method of Frey and Osborne.",
    "findings": "On average across the 21 countries, 9% of jobs are automatable, far fewer than occupation-based estimates suggest. The share ranges from 6% in Korea to 12% in Austria.",
    "limitations": "The estimate measures technical automatability, not actual job losses, and predates generative AI; differences between countries may reflect workplace organisation, past automation investment and education.",
    "method_type": "observational",
    "question": "How many jobs in OECD countries are actually at risk of automation once the mix of tasks within each occupation is taken into account?"
   },
   "id": "doi:10.1787/5jlz9h56dvq7-en",
   "institutions": [
    "Centre for European Economic Research"
   ],
   "lab_report": false,
   "labels": [
    "AI-written, evidence-cited editorial content; see the methods and labeling rules at /about/"
   ],
   "open_access": true,
   "source_url": "https://doi.org/10.1787/5jlz9h56dvq7-en",
   "summary": "Using a task-based approach that accounts for differences in tasks within occupations, the authors estimate that on average 9% of jobs across 21 OECD countries are automatable, far fewer than occupation-based estimates suggest. The share ranges from 6% in Korea to 12% in Austria.",
   "theme": "Labor & Work",
   "title": "The Risk of Automation for Jobs in OECD Countries",
   "url": "https://wemustactnow.onrender.com/research/p/doi-10-1787-5jlz9h56dvq7-en/",
   "venue": "OECD social employment and migration working papers",
   "year": 2016
  }
 ]
}