Essentials
The papers, numbers, people and newsletters the debate on AI's economic impact turns on, sized to the time you have. Picks are chosen for their weight in the debate and span its sides; they are refreshed every two weeks, last on 29 Oct 2026. The reasons are AI-written and evidence-cited; no rankings or scores.
Five papers, the numbers with their exact quotes, five people and three sources.
Papers to read
- Generative AI at WorkErik Brynjolfsson et al. · The Quarterly Journal of Economics · 2025 · Productivity: mixed evidence
Field evidence from 5,172 support agents: an AI assistant raised issues resolved per hour by 15%, most for less experienced workers.
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.
- Experimental evidence on the productivity effects of generative artificial intelligenceShakked Noy and Whitney Zhang · Science · 2023 · Productivity: evidence for the claim
A preregistered experiment: ChatGPT cut writing time by 40% and raised quality, narrowing the gap between weaker and stronger writers.
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.
- The simple macroeconomics of AIDaron Acemoğlu · Economic Policy · 2024 · Productivity: evidence against the claim
The low end of the growth debate: a task-based estimate of at most 0.66% more total factor productivity over ten years.
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.
- GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language ModelsTyna Eloundou et al. · arXiv · 2023 · Productivity: evidence for the claim
The exposure method behind most 'share of jobs affected' headlines: about 80% of US workers have at least 10% of tasks exposed.
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.
- Forecasting the Economic Effects of AIEzra Karger et al. · NBER · 2026 · Productivity: mixed evidence
A 2026 survey of economists, AI staff, forecasters and the public on growth and jobs, showing where expectations agree and where they split.
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.
Of these 3 papers: 1 with evidence for the claim, 1 with evidence against, 1 mixed.
Of these 5 papers: 2 with evidence for the claim, 1 with evidence against, 2 mixed.
Of these 5 papers: 2 with evidence for the claim, 1 with evidence against, 2 mixed.
Figures to know
- +0.66%TFP gain over 10 years (upper bound), 10 years
Daron Acemoğlu, 2024 · Productivity“no more than a 0.66% increase in total factor productivity (TFP) over 10 years”
- +1.5 pp a yearExtra annual labour productivity growth, 10 years
Joseph Briggs and Devesh Kodnani, 2023 · Productivity“~+1.5pp/yr US labor productivity”
- +15%Customer-support issues resolved per hour with an AI assistant
Erik Brynjolfsson et al., 2025 · Productivity“as measured by issues resolved per hour, by 15% on average”
- 47%US jobs at high risk of computerisation
Carl Benedikt Frey and Michael A. Osborne, 2016 · Jobs“According to our estimate, 47 percent of total US employment is in the high-risk category”
- 2047AI researchers' 50% year for machines outperforming humans at every task (2023 survey)
Katja Grace et al., 2025 · Pace“the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047”
Every number is quoted from its source and linked to the paper; estimates differ in method and horizon, so they are not directly comparable.
People to follow
- Erik Brynjolfsson Economist, Stanford University · Documented positions on 4 of 5 questions
Stanford economist behind the customer-support study and the productivity J-curve; documented positions on four of the five questions.
Key work: Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy; The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
- Daron Acemoglu Economist, Massachusetts Institute of Technology · Documented positions on 5 of 5 questions
MIT economist whose task-based estimates set the low end of the growth debate; documented positions on all five questions.
Key work: Robots and Jobs: Evidence from US Labor Markets; The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment
- David Autor Economist, Massachusetts Institute of Technology · Documented positions on 5 of 5 questions
MIT economist who studies how technology changes the mix of jobs and wages; documented positions on all five questions.
Key work: Why Are There Still So Many Jobs? The History and Future of Workplace Automation; The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market
- Anton Korinek Economist, University of Virginia · Documented positions on 5 of 5 questions
Economist who models scenarios in which AI automates ever more tasks, and what that means for wages; documented positions on all five questions.
Key work: Artificial Intelligence and Its Implications for Income Distribution and Unemployment; Excessive Volatility in Capital Flows: A Pigouvian Taxation Approach
- Yoshua Bengio Computer scientist, Université de Montréal · Documented positions on 2 of 5 questions
Computer scientist and Turing Award co-recipient; documented positions on regulation and on how fast the impact arrives.
Key work: Deep learning; Generative adversarial networks
Positions are documented and cited on each question page, never inferred.
Newsletters and sources
- Justified Posteriors (Andrey Fradkin)Newsletter · 7 posts in this guide
An economist's newsletter that discusses new AI-economics papers; the most posts on the five questions among the newsletters followed here.
- Paul KrugmanNewsletter · 5 posts in this guide
A Nobel economist's newsletter with regular posts on AI, growth and inequality.
- Stanford AI Index ReportStanford Institute for Human-Centered AI · report · outside this guide
A yearly report that collects data on AI research, adoption, investment and policy in one place.
- OECD AI Policy ObservatoryOECD · dataset · outside this guide
The OECD's hub for AI policy across countries, with data and its own economic research.
- NBER Working PapersNational Bureau of Economic Research · organization · outside this guide
Where much of the economics in this debate appears first; filter by artificial intelligence for new work each week.