paper
2026-01
Kevin A. Bryan
Proposes integrating economic theory into ML via "theory-guided AI." The authors argue that theory aids external validity, but "particular functional forms we fit to get analytic tractability involve many assumptions that go beyond theoretical restrictions" (@Afinetheorem). In other words, using structural restrictions can regularize ML models without imposing ad-hoc functional assumptions.
economics LLM advanced
paper
2025-10
Diane Coyle, John Poquiz
Diane Coyle & John Poquiz discuss how transformative AI challenges current economic statistics. They outline how GDP and productivity measures miss AI-driven outputs and propose new metrics to better capture AI's impact on productivity and output.
economics growth advanced
paper
2025-09
Benjamin Manning, John Horton
Manning & John Horton propose a method to build AI agents grounded in economic theory and data. They create agents using human data from "seed" games and theory-based instructions, then show in 883,320 novel game simulations that these agents predict human play better than standard game-theoretic models. This demonstrates AI's potential to generalize behavioral predictions in new strategic settings.
economics LLM advanced
paper
2025-02
Kevin A. Bryan
An experiment where an entire review paper was drafted by OpenAI's GPT-o3. It discusses how academia might adapt to AI-written text, covers current AI capabilities for drafting and literature surveys, weighs benefits vs. risks (misinformation, plagiarism), provides a 10-year outlook, and concludes with recommendations for journals and researchers to harness AI's benefits while safeguarding integrity (e.g. transparency about AI use).
writing LLM GPT ethics
paper
2025
Daron Acemoglu
Acemoglu evaluates AI's macroeconomic implications using a task-based model. Estimates modest TFP gains (no more than 0.66% over 10 years), arguing early evidence from easy-to-learn tasks may overstate future effects. Published in Economic Policy (2025). See also
presentation slides.
economics growth labor advanced
paper
2024-11
Anton Korinek
Anton Korinek's JEL article on integrating generative AI into research workflows. It serves as a hands-on guide for using LLMs in economics, with emphasis on model reasoning and collaborative tools for economists.
economics LLM tools
paper
2024-07
Melissa Dell
Comprehensive review of deep learning methods for economists. Discusses how CNNs and transformers can impute structure from unstructured data like satellite images or text. Covers classification, record linkage, generative models, and introduces the EconDL companion site with demo notebooks. Emphasizes that with proper tuning, deep nets scale affordably to millions/billions of observations.
economics python LLM advanced
paper
2024-01
Dietrich, Malerba, Gassmann
Dietrich, Malerba & Gassmann introduce a welfare-based evaluation of bias in ML targeting. Using proxy means test models for cash transfers, they weight targeting errors by income level and show that label biases and unstable model weights substantially understate welfare losses, unfairly disadvantaging some groups.
economics development ethics
paper
2023-05
Kevin Bryan
Kevin Bryan's guide (based on a Markus Academy talk) explaining how LLMs like GPT can assist in economics research tasks (coding, literature review, writing, etc.), with examples and practical tips.
economics LLM GPT coding
paper
2023-04
John J. Horton
John J. Horton et al. explore using GPT-3 as "Homo silicus" - a simulated economic agent endowed with preferences and information to run virtual economic experiments. They show LLM agents can replicate classic experimental findings and easily test policy variations in silico.
economics LLM GPT microsimulation
paper
2026-03
Ajay K. Agrawal, John McHale, Alexander Oettl
Characterizes AI as a tool for augmentation through enhanced search over combinatorial spaces. Decomposes knowledge production into a multi-stage process revealing a 'jagged frontier' of AI in science, with differential returns across domains (data-rich biology vs. anomaly-sparse physics) and workflow stages. Shows how AI-expert scientists amplify nonlinear productivity gains.
economics science productivity advanced
paper
2026-03
Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Brent H. Meyer, Michael Sparks, Sonya R. Waddell, Daniel Weitz
Surveys corporate executives to develop an index ranking job functions most negatively affected by AI. Provides firm-level evidence on how AI impacts different workforce roles and productivity, with direct evidence from decision-makers.
economics labor productivity advanced
paper
★
2026-02
Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, et al.
First representative international data on firm-level AI use, surveying ~6,000 executives across the US, UK, Germany, and Australia. Finds ~70% of firms actively use AI but report little impact over the past 3 years, while forecasting AI will boost productivity by 1.4% and cut employment by 0.7% over the next 3 years.
economics labor productivity firms
paper
2026-01
Charles I. Jones
Argues that AI may be fundamentally different from prior general-purpose technologies like electricity or semiconductors, because automating intelligence itself has broader effects. Explores the scenario where machines can perform every cognitive and physical task more cheaply than humans and what economics says about that future.
economics growth automation advanced
paper
★
2026-01
Ruth Appel, Maxim Massenkoff, Peter McCrory, Miles McCain, Ryan Heller, Tyler Neylon, Alex Tamkin
Introduces five foundational measurements—task complexity, skill level, purpose, AI autonomy, and success—to track AI's economic impacts. Based on privacy-preserving analysis of 2 million conversations. Finds more complex tasks see the largest speed-ups, with college-level tasks sped up 12x.
economics LLM productivity measurement
paper
2026-03
Dirk Bergemann, Alessandro Bonatti, Alex Smolin
Develops a framework for optimal pricing and product design of LLMs, where a provider sells menus of token budgets to users who differ in their valuations across a continuum of tasks. Applies mechanism design theory to the economics of AI services.
economics LLM pricing mechanism design
paper
2025
Maryam Feyzollahi, Nima Rafizadeh
Uses a difference-in-differences framework across 25 leading economics journals over 24 years to measure LLM adoption via linguistic footprints. Finds a 4.76 percentage point increase in LLM-associated terms during 2023–2024, more than doubling from 2.85pp in 2023 to 6.67pp in 2024, documenting rapid integration of language models in economics writing.
economics LLM writing adoption
article
2026-03
Alex Imas, Soumitra Shukla
Imas (UChicago Booth) and Shukla (Harvard) argue AI exposure measures are misinterpreted as displacement threats when they indicate task augmentation. The real risk lies in low-dimensional occupations where full automation creates stronger firm incentives to eliminate positions. Uses an O-Ring model to show how partial automation can complement rather than substitute human labor.
economics labor LLM
paper
2026-03
Kevin A. Bryan
Reviews seven books on AI's economic impact, finding strong frameworks for AI as cheap prediction and adoption barriers, but arguing the literature offers little guidance on transformative scenarios — rapid labor churn, scientific acceleration, and existential risk — that policymakers need most.
economics LLM advanced review
paper
2025-12
Jens Ludwig, Sendhil Mullainathan, Ashesh Rambachan
Provides a rigorous econometric framework for using LLMs in empirical research. For prediction tasks, validity requires 'no training leakage.' For estimation, even high-accuracy LLM labels can bias regressions because errors correlate with covariates — the solution is a small human-coded validation sample to debias outputs. Forthcoming in Annual Review of Economics.
economics LLM econometrics advanced
paper
2025-11
Susan Athey, Fiona Scott Morton
Examines how market power in upstream AI affects downstream prices, industry structure, and welfare. Identifies a 'double harm' for displaced workers who face wage cuts from AI adoption and further harm from monopoly AI pricing. Derives an adoption frontier and policy implications for regulating AI usage and access fees.
economics competition labor policy
paper
2026-01
Anton Korinek
Comprehensive guide to building AI agents that autonomously conduct literature reviews, write and debug code, and orchestrate entire research workflows. Includes working code examples readers can immediately use. Shows how researchers can build complete analytical tools from English descriptions, handling everything from data uploads to regression analysis to visualization.
economics LLM agents coding
paper
2026-03
Forecasting Research Institute
Large-scale forecasting exercise surveying 69 leading economists, 52 AI experts, 38 superforecasters, and 401 members of the general public on AI's economic impact. Under rapid AI progress scenarios, economists forecast labor force participation dropping from 62.6% to 55% by 2050, with the richest 10% holding 80% of national wealth. In baseline scenarios, GDP growth and labor participation remain close to today's levels. Over 200 pages of detailed forecasts and methodology.
economics labor growth forecasting LLM
paper
2026-02
Daron Acemoglu, David Autor, Simon Johnson
Defines pro-worker technologies as those that expand worker capabilities and make human skills more valuable. Distinguishes five categories of AI-driven technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Argues that policy and design choices can steer AI development toward complementing workers rather than replacing them. Published jointly with Brookings/Hamilton Project.
economics labor policy automation
paper
2026-02
Nicholas Bloom, Christos Makridis
Uses Gallup Workforce Panel data to examine partisan differences in workplace AI adoption. While Democrats report higher frequent AI use (30.1% vs. 25% for Republicans in Q1 2026), this gap shrinks to statistical insignificance after controlling for education and reverses sign with occupation and industry fixed effects. Suggests the partisan AI divide is driven by educational and occupational sorting, not ideology.
economics labor LLM adoption
paper
2026-02
Serafin Grundl
A 51-page paper from the Federal Reserve Board benchmarking Claude Code as an empirical economist. The subtitle 'Like Humans but Without the Tails' suggests the AI performs comparably to human economists on average but with less variance in output quality.
economics claude LLM coding
paper
2026-03
Douglas K.G. Araujo, Harald Uhlig
Investigates how various LLMs behave in the Ultimatum Game, varying stake sizes and opponent type (human vs. AI). Finds that while some models approximate the rational benchmark, a distinct 'altruistic' mode emerges where LLMs propose hyper-fair distributions (>50%). Highlights the need for careful testing before deploying AI agents in economic settings.
economics LLM game theory behavioral
paper
2025-10
Pascual Restrepo
Argues that compute — not human capability — will be the scarce resource in an AGI economy. Most jobs won't be automated because replacing them wouldn't be worth the computing cost, not because they require uniquely human skills. Forthcoming in 'The Economics of Transformative AI' (Agrawal, Brynjolfsson & Korinek, eds.).
economics AGI labor growth advanced
paper
2026-04
Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Josh Rosenberg, Philip Tetlock, et al.
Elicits forecasts from five groups — academic economists, AI company employees, policy researchers, accurate forecasters, and the general public — about AI's economic impact. Finds expectations of substantial AI capability advances by 2030, modest labor force participation declines, and 2.5% annual GDP growth. Under a rapid AI advancement scenario, forecasters project ~4% GDP growth and labor force participation falling to 55% by 2050. Expert disagreement stems primarily from differing views on highly capable AI's economic effects rather than on the pace of AI progress.
economics forecasting growth labor
paper
2026-04
Grace Liu, Brian Christian, Tsvetomira Dumbalska, Michiel A. Bakker, Rachit Dubey
Large-scale experiments show that after just ~10 minutes of AI-assisted problem-solving, participants gave up more frequently and performed worse once the AI was removed, compared to those who never used it. The persistence costs were concentrated among users who prompted AI to solve tasks directly rather than seeking hints. Effects replicated across arithmetic and reading comprehension, suggesting a general consequence of AI-assisted problem-solving rather than a domain-specific one.
AI cognition experiment productivity
paper
2026-04
Daron Acemoglu, Tianyi Lin, Asuman Ozdaglar, James Siderius
Examines how AI systems that synthesize population beliefs as training data influence social learning. Using an extended DeGroot model with an AI aggregator, introduces a 'learning gap' metric. Key finding: rapid updating degrades learning, while slower updates and localized aggregators trained on specialized information consistently improve outcomes. Consolidating local systems into a single global aggregator diminishes performance.
economics LLM information advanced
paper
2026-04
Michael E. Waugh
Documents international trade patterns in AI-related goods using an LLM classification tool. AI-related products account for 23% of U.S. imports in 2025, with 73% growth since 2023 vs. 3% for non-AI products. Mexico and Taiwan dominate, accounting for roughly half of all U.S. AI-related trade. The U.S. goods trade deficit would have been nearly $200 billion smaller in 2025 without the AI expansion.
economics trade AI measurement
article
2026-04
Ali Hashim, Gizem Kosar, Wilbert van der Klaauw
NY Fed research using the November 2025 Survey of Consumer Expectations. College graduates are more than twice as likely to use AI at work (58.7% vs. 22.9%). Only 15.9% of employers provide AI training despite 38% of workers viewing it as important. Workers without AI training access would accept an 11.4% salary cut to gain it; those with access require a 24.2% raise to give it up.
economics labor AI training adoption
report
2026-04
Jeffrey S. Allen
Federal Reserve FEDS Notes article surveying the state of AI adoption across the U.S. economy. Census data show ~18% of firms adopted AI by year-end 2025, while the Atlanta Fed's Survey of Business Uncertainty estimates 78% of the labor force works at firms that have adopted AI. Adoption is highest in professional services and finance. Newer surveys show a stronger link between adoption rates and firm size.
economics AI adoption measurement Federal Reserve
article
★
2026-04
Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen
St. Louis Fed analysis finding that U.S. firms have a higher share of workers using AI than European firms, and that management practices are a surprisingly powerful predictor of cross-country AI adoption. A one-standard-deviation increase in the management index is associated with a 9.6 percentage point increase in AI adoption. The authors argue that narrowing the U.S.-Europe AI adoption gap may require first narrowing the management gap.
economics AI adoption international management
report
2026-04
Karim Barhoumi, Fabia A. de Carvalho, Michael Gorbanyov, Yosuke Kido, David Koll, Dragana Ostojic, Baoping Shang, Natalia T. Tamirisa, Sally Toms, Era Dabla-Norris, Anh D. M. Nguyen, Yunhui Zhao
IMF staff note synthesizing insights from a high-level workshop and scenario-planning exercise co-organized with EconTAI. Argues AI should be treated as a macro-critical transition rather than a standard technology shock. Macroeconomic outcomes will depend less on frontier capability alone than on the speed and breadth of AI diffusion and institutional readiness. Covers implications for growth, labor markets, equality, financial stability, and governance.
economics AI policy growth IMF
paper
2026-03
Michael Blank, Gregor Schubert, Miao Ben Zhang
Studies generative AI's impact on U.S. households' time allocation using browsing data from 200,000+ home devices (2021–2024). Finds that ChatGPT adoption substantially increases leisure browsing while leaving productive task time unchanged — households primarily use AI for productive non-market tasks (job hunting, travel planning, shopping), freeing up leisure time. Implies large home-productivity gains from generative AI, but raises digital-divide concerns as younger, higher-income users adopt faster.
economics AI productivity households inequality
paper
2026-04
Eleanor W. Dillon, Sonia Jaffe, Nicole Immorlica, Christopher T. Stanton
Field experiment across 66 firms and 7,137 knowledge workers randomly given access to a generative AI assistant integrated into the email, meeting, and writing applications they already used. In the second half of the six-month experiment, the 80% of treated workers who actively used the tool spent two fewer hours per week on email and reduced their time working outside regular hours. Beyond these individual time savings the authors detect no shifts in the quantity or composition of workers' tasks, suggesting that broader reallocation of responsibilities requires institutional and team-level changes rather than just individual AI access.
economics AI productivity field experiment knowledge work
paper
2026-05
Caleb Maresca
Develops a heterogeneous-agent asset pricing model in which transformative AI capable of automating most human labor can lower interest rates even as it dramatically accelerates growth. Under baseline calibrations, the risk-free rate falls to near zero despite growth rising from 2% to 11%, and the equity premium expands from 6% to over 20%. The key mechanism is that labor displacement risk generates massive precautionary saving demand that outweighs the higher productivity effect. Advises caution when interpreting long-term bond yields as a signal of market expectations of transformative AI. Highlighted by
Tyler Cowen.
economics AI AGI interest rates asset pricing macro
paper
2026-05
Tom Davidson, Basil Halperin, Thomas Houlden, Anton Korinek
Develops a semi-endogenous growth model with an innovation network to study when AI-driven automation of research leads to superexponential ('explosive') growth. Derives a condition under which two reinforcing channels — a technological feedback loop across research sectors and an economic feedback loop where higher output finances more research — overcome diminishing returns to ideas. In a simulation calibrated to AI progress trends, fully automating software research plus modest (5%) automation elsewhere produces a singularity within six years. Includes an interactive online simulator for exploring growth paths.
economics AI growth automation singularity
paper
2026-05
Santiago Afonso, Sebastian Galiani, Ramiro H. Gálvez, Raul A. Sosa
Proposes DRIL (Deep Research on a Loop), a methodology that uses AI agents to assemble datasets from publicly available sources. DRIL applies a fixed research instrument across a mapped unit space with a two-stage architecture separating design from implementation. Applied to updating the Global Tax Expenditures Database for eight Latin American and Caribbean countries, producing 129 sources and 136 evidence records at the cost of a standard LLM subscription. Argues that even partial automation of dataset construction can shift the production function of empirical economics.
economics AI data agents methodology
paper
2026-04
Tania Babina
Reviews firm-level data on artificial intelligence and the emerging evidence on AI's economic effects. Argues that measurement is central: different AI datasets capture different objects (invention vs. use, internal capability vs. outsourcing, realized activity vs. investor perceptions) and can therefore lead to different conclusions. Develops a framework for choosing among these measures and surveys available data sources on firm AI efforts. Synthesizes evidence on AI's effects on firm growth, valuation, productivity, risk, labor, competition, and financial markets.
economics AI firms productivity measurement survey
paper
2026-03
Martin Beraja, Eduard Talamàs
Introduces a new metric called VOLT (Value of Organizational Learning Technologies) that measures the potential increase in economic output if firms could learn faster. Using 2023 U.S. Census data on business establishments, finds that VOLT for the American economy is approximately 2.0, meaning AI-driven organizational learning technologies have the potential to double aggregate economic output in the long run. Roughly three-quarters of potential gains come from extending firm lifespans rather than boosting productivity directly. Industry-by-industry analysis reveals that knowing how exposed an industry is to LLMs tells almost nothing about how much it stands to gain from AI as an organizational learning tool.
economics AI growth productivity firms organizational learning
paper
2026-04
Michelle Yin, Hoa Vu, Claudia Persico
Demonstrates that LLM-based occupational AI exposure measures — widely used to estimate AI's labor market effects — are highly fragile. Replicating the dominant rubric with three frontier models on all 18,797 O*NET tasks, mean exposure diverges 3.6-fold (one model rates 14% of tasks as directly exposed while another rates 51%). In difference-in-differences employment regressions, coefficient magnitudes vary 2.4-fold across annotators, and county-level estimates flip sign depending on which model is used. Formalizes this as non-classical measurement error, cautioning against treating evolving LLMs as static instruments.
economics LLM labor measurement methodology
paper
2026-04
Ning Li
Analyzes 953 economics papers — 912 AI-generated from the APE project and 41 human papers published in the AER and AEJ: Economic Policy — to decompose the quality gap into idea quality and execution quality. The idea gap is large (human papers achieve 47.1% mean exceptional probability vs. 16.5% for AI; Cohen's d = 2.23), while the execution gap is smaller (4.38/5.0 vs. 3.84). Idea quality accounts for roughly 71% of the overall quality difference. Only 7 AI papers (0.8%) surpass the median human paper on both dimensions simultaneously, suggesting ideation remains the primary bottleneck to competitive AI-generated economics research.
economics AI research quality ideation methodology
paper
2026-03
Robert Novy-Marx, Mihail Velikov
Published in the Journal of Economic Literature, demonstrates a pipeline for mass-producing academic finance papers using LLMs. After mining 30,000+ potential return predictors from accounting data, the authors use Claude to generate nearly 400 complete, publication-ready papers with distinct theoretical justifications — each indistinguishable from human-authored research. Serves as both a proof of concept for AI-enhanced research efficiency and a cautionary tale about industrializing HARKing (hypothesizing after results are known). Highlighted by
Tyler Cowen.
economics AI LLM finance methodology peer-review
article
2026-05
Thomas Lyttelton, Maxim Massenkoff, Nathan Wilmers
Research from Anthropic's economics team on how AI agents able to execute research end-to-end will reshape social science. It tests coding agents on real social-science tasks and asks to what extent AI may automate innovation and change research productivity.
economics AI LLM labor methodology
paper
2026-05
Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh, Enkelejda Kasneci
A preprint testing whether AI coding agents (Claude Code and Codex) match human methodological diversity in a "many-analysts" design on an immigration and social-policy hypothesis. The agents reproduce human-like method diversity and broadly similar effect estimates, but show a vulnerability at the interpretation layer — a confirmatory prompt flipped Claude Code's verdict from 10% to 90% support without meaningfully changing the coefficients.
economics AI LLM methodology peer-review
paper
2026-06
Jessica Wachter, Jonathan Wachter
The five largest U.S. tech firms spent $380 billion on capex in 2025 and are forecast to roughly double that in 2026 — risking bankruptcy unless expected profits grow commensurately. Embeds this observation in a two-sector open-economy model with rare productivity booms. Calibrating to observed investment implies a boom raises AI-sector productivity by a factor of 2.7, with additional cumulative GDP growth of 5–58 percentage points by 2030 and AI shares of the economy ranging from 8% to 39%.
economics AI investment growth macro asset pricing
paper
2026-06
Alexis Akira Toda
Tests whether AI models (Gemini, Refine, Claude, ChatGPT) can find errors in four published economic theory papers, each containing a known mistake. ChatGPT Pro performed best, occasionally constructing counterexamples and corrected proofs, while other models fared worse. No model located a true error without substantial human guidance. Argues a competent human paired with a frontier model can outperform current peer review, but AI cannot yet refute economic theory on its own. Highlighted by
Tyler Cowen on Marginal Revolution.
economics AI peer-review theory methodology
report
2026-06
Terry Gregory, Najada Feimi, Christina Gathmann, David Marguerit
LISER policy brief drawing on more than 75 million online job vacancies from Belgium, France, Germany, and Luxembourg between 2018 and 2023 to track how employers' skill requirements evolve as occupations become more exposed to AI. It argues AI is reshaping the content of jobs rather than eliminating them: demand is rising fast for AI-related analytical skills, employers increasingly seek workers who combine analytical tools with judgment and leadership, and the adjustment happens largely within existing occupations. Concludes the policy priority should be reskilling and upskilling within current occupational trajectories.
economics AI labor skills Europe policy
paper
2026-06
Pavel Kireyev, Roberto Rafael Maura Rivero
A 26-page guide arguing that the decisions shaping how AI systems are built and aligned are largely microeconomic problems. It opens with a self-contained primer on the RLHF pipeline, showing that reward modeling, preference aggregation, and policy optimization rest on equivalences with discrete choice, social choice, and principal-agent models. It then maps specific competencies (behavioral economics for annotator biases, mechanism design for feedback elicitation, social choice for preference aggregation, contract theory and information design for alignment, game theory for multi-agent safety, production economics for compute scaling laws) to concrete AI research problems, and closes with practical paths into academia and AI labs plus a companion repository.
economics LLM alignment RLHF advanced
article
2026-06
Zoe Hitzig, Maxim Massenkoff, Eva Lyubich, Shaoyi Zhang, Ryan Heller, Peter McCrory
Anthropic Economic Research report based on a privacy-preserving analysis of ~400,000 Claude Code sessions from ~235,000 people (October 2025 to April 2026). It introduces a framework describing what work is done, who does it, and whether it succeeds. Key findings: people make ~70% of planning decisions while Claude makes ~80% of execution decisions; domain expertise (not coding proficiency) drives success, with sessions rated expert reaching verified success more than twice as often as novice ones; non-software occupations succeed within seven points of software engineers; and the estimated value of the typical task rose ~25% over the seven months.
economics Claude Code labor productivity agentic coding
paper
2026-06
Mert Demirer, Leon Musolff, Liyuan Yang
A matched event-study using data on 100,000+ GitHub developers and their AI-usage telemetry to estimate the productivity effects of successive AI coding tools. Autocomplete, interactive coding agents, and autonomous agents raise commits by 40%, 140%, and 180% respectively, but these gains attenuate up the production chain: the 180% commit effect falls to 50% for projects and just 30% for actual releases. The authors interpret this through a weak-link model with an estimated 0.25 elasticity of substitution between AI and human effort (strong complementarity), and confirm the pattern across four app marketplaces. A MIT Sloan working paper.
economics AI tools productivity coding software development
paper
2026-06
Moran Koren
Argues that by 2026 the binding constraint on machine-assisted economic theory is no longer producing mathematics but trusting it, since a fluent model will prove a false theorem as readily as a true one. Proposes a verification-first protocol instantiated as three methods that differ in how work is checked: a single disciplined pass, an adversarial prover-verifier pair (Claude Opus 4.8 proposing, OpenAI Codex refuting, the author triaging), and a structured multi-agent project with a reviewer gate. Demonstrated on a worked mechanism-design example (a Groves/Pigouvian incentive mechanism for the Gans-Kominers grade-inflation model), concluding that external verification, not model capability, is the key design variable.
economics LLM economic theory agents verification
paper
2026-06
Masahiro Kato
Proposes a RAG-based "AI economist agent" that grounds LLM-generated economic narratives in economic theory and real-world data. Rather than producing quantitative claims with the language model alone, agents plan the analysis, retrieve evidence from knowledge graphs, select appropriate models, and generate reports linked to explicit model-based computations. Demonstrated on two applications: economist report generation for U.S. inflation persistence and Federal Reserve policy, and bank stress-test narratives for U.S. commercial real estate refinancing stress.
economics LLM agents RAG macroeconomics
paper
2026-06
Andreas Ferrara
A hands-on guide for researchers new to LLMs, setting out a step-by-step workflow for turning a research idea into working code and data. It describes the four main ways of interacting with an LLM (chat window, editor-integrated assistants, agentic coding tools, and the API) and works through practitioner decisions in sequence: whether an LLM is the right tool, whether data may be sent to one, how to select models, write prompts, manage context limits, and control costs, and finally how to validate, reproduce, document, and correct LLM-generated measures in regression settings. Includes worked examples with replication files, such as classifying emotions in paintings and linking historical census records.
economics LLM coding
paper
2026-07
Matthew O. Jackson, Zafer Kanik
Models how increasingly productive AI reshapes labor reallocation and wages across worker categories: those essential to AI development see faster wage growth, workers displaced by AI experience falling wages in absolute and relative terms, and workers in final-goods production track overall output. Contrasting competitive versus monopolistic AI production (monopoly restricts deployment and slows the transition), it derives optimal tax and regulatory policies that achieve Pareto improvements and shows these differ depending on whether AI production is competitive.
economics AI inequality policy
other
2025
Joshua Gans
An open-access MIT Press monograph treating AI as statistical prediction that enhances (and sometimes enables) decision-making, analysed with microeconomic tools. Gans focuses on impacts within firms and industries rather than economy-wide employment and productivity: the supply and production characteristics of AI, the drivers of demand for prediction, how supply and demand set a price for predictions and how market structure shapes that price, and the key policy trade-offs for antitrust, privacy, and other regulation. The full text is free under a CC BY-NC-ND licence.
economics AI microeconomics book open-access
paper
2026-07
Nicola Borri, Aleh Tsyvinski, Yukun Liu
Using 380 trillion tokens of realized AI consumption from over 400 large language models (OpenRouter data, ~2% of global monthly token use), the authors construct an AI Factor and estimate firm-level AI Betas. High-AI-beta firms earn higher subsequent returns, with a value-weighted strategy earning 64.1 basis points weekly; the premium concentrates in intensive, frontier-oriented AI use, is absent in emerging markets, and interaction-and-communication occupations show higher market-implied AI exposure than analytical roles. The paper offers early evidence of an emerging agentic economy.
economics AI asset pricing
paper
2026-07
C. Castaldi, F. Castellacci, A. Fronzetti Colladon, L. Segneri, F. Venturini
This research note argues that trademark data can complement patents for mapping the development and diffusion of AI. AI trademarks reveal how firms exploit AI to develop new goods and services, and offer a timely, globally available source that spans all economic sectors; the authors illustrate the approach with an empirical exploration of Italian firms and discuss how AI trademarks can be used at different levels of analysis to study AI's spread.
economics AI innovation
paper
2026-07
Chen Zhu, Xiaolu Wang, Weilong Zhang
Proposes a system in which Claude-based agents coordinate through a shared workspace of inspectable intermediate records, with diagnostic gates and human checkpoints, for economic theory development where no cheap, machine-readable correctness signal exists. Across five economic tasks the gated architecture improved mean failure severity from 1.58 to 1.16 and usefulness from 2.60 to 3.10; in one case it rejected a false market-structure premise that the agents would otherwise have perpetuated, preserving human authority over consequential decisions.
economics LLM claude agents
paper
2026-07
David M. Rothschild, Nicole Immorlica, Brendan Lucier, Markus Mobius, Aleksandrs Slivkins
Argues digital search is shifting from human keyword queries toward AI agents that interpret natural-language goals and return recommendations or execute decisions. The authors contend that outcomes will depend less on algorithmic refinements than on system design—information access, option presentation, transparency, and fair competition—and show how relatively modest design choices shape market efficiency, consumer welfare, and competitive dynamics in agent-driven marketplaces.
economics AI markets
paper
2026-07
James M. Zumel Dumlao, Meng Wang, Zhonghan Xie, Junyao Hu, Ivan Bar, George Chaney III, Henry Gold, Misha Teplitskiy
Using 2015–2025 administrative data from a large U.S. university, the authors classify courses by their susceptibility to AI use and estimate the effect of generative-AI availability on outcomes. They find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students, and similarly muted effects on self-reported understanding — evidence against fears of AI-driven grade inflation.
economics AI education
paper
2026-07
Guillaume Coqueret, Joan Llull, Florian Oswald, Christophe Pérignon, Christoph Scheuch, Lars Vilhuber
LLM outputs vary across repeated requests even with unchanged prompts and settings — because of deliberate sampling, silent model updates, numerical rounding, and expert routing — and setting temperature to zero removes only the intentional sampling. Using sentiment analysis of corporate filings as an illustration, the authors argue LLM outputs should be treated as draws from a distribution rather than fixed measurements, and propose concrete reporting standards for researchers, authors, and data editors.
economics LLM reproducibility
paper
2026-07
Annie Liang
Forthcoming in the Journal of Economic Literature, this article examines how machine learning and other computational techniques can contribute to building economic models — clarifying a model's strengths and limitations, identifying prediction failures through adversarial testing, creating hybrid models that combine economic theory with flexible algorithms, and exploring applications of large language models in economics.
economics machine learning economic theory
paper
2026-07
Kevin A. Bryan, Joshua S. Gans
AI predicts, and humans use those predictions — combined with human verification, other statistical models, and further analysis — to make decisions. The authors show that maximizing an AI model's prediction accuracy alone is generally suboptimal once you account for how humans actually use it, and analyze optimal model training (using geometric methods) with heterogeneous users and a monopoly trainer.
economics of AI decision theory AI
paper
2026-08
Taha Choukhmane, Tim de Silva, Weidong Lin, Matthew Akuzawa
Has a representative sample write prompts asking LLMs (GPT-5.2) for spending and investing guidance, then models the life-cycle consequences of following it. Advice would move people closer to life-cycle theory (diversified equity, age-declining equity shares, larger buffers), and equity-share recommendations vary by gender, AI familiarity, and financial literacy — with a two-thirds demand-side vs one-third supply-side decomposition of the gender gap.
economics LLM household finance
paper
2026-08
Yuhao Fu, Nobuyuki Hanaki, Haitao Wang
A laboratory study using a three-stage alternating-offer bargaining game to compare how people negotiate with AI agents versus other humans. Fairness and reciprocity toward AI are weaker and more conditional than toward humans, but partially resurface when the AI’s outcomes affect real people’s payments.
economics AI experimental economics
paper
2026-08
Boone Bowles, Raymond Duch, Sorin Sorescu
Uses LLMs to build and interview "digital twins" of financial influencers reconstructed from their X accounts, extracting measurable stock beliefs in real time. The resulting belief panel predicts large-cap stock returns while avoiding look-ahead bias, illustrating an LLM-based measurement approach for financial economics.
economics LLM finance
paper
2026-07
Zanna Iscenko, Scott Strand, Yiyuan Chen, Guillaume Aimard, Mihai Codreanu, Vivek Sampathkumar, Alex Imas, Julian Jacobs, Evalyne Muiruri, Juan Mateos-Garcia, Jia Jen Ng, Samirah Javed, Josh Martin, Omar Ajmeri, Denis Calin, Andrew Kim, Fabien Curto Millet, James Manyika
Google's counterpart to usage-based AI economic indices: analyzes 15 million de-identified Gemini interactions mapped to over 800 occupations, 4000 tasks, 300 household activities, 150 countries, and 140 languages. Finds adoption spans roughly 88% of US employment but stays shallow and collaborative rather than fully automating tasks, covers about 98% of non-sleep US activities, and correlates with national wealth globally.
economics AI adoption measurement
paper
2026-08
Guillermo Cruces, Diego Fernandez Meijide, Sebastian Galiani, Ramiro Galvez, Maria Lombardi
A randomized online experiment with 1,174 adults aged 25-45 who completed a workplace-style problem-solving task with or without a generative AI assistant, followed by an unassisted module. AI raises performance for everyone, but gains are larger among the less educated, cutting the education-based performance gap by roughly three-quarters while assisted — though the gap partly re-emerges once the AI is removed.
economics AI productivity
paper
2026-08
Victor H. Aguiar
Tests whether a single foundation model can explain economic decision-making across domains. Using a tabular foundation model, the author hides choices in one behavioral domain and recovers them from choices in other domains plus other participants’ labeled data; the model beats baselines, the advantage vanishes when choices are reshuffled, and one random-utility model trained on the learned representation performs well across risk, time, loss aversion, valuation, and social-choice tasks — evidence for a unified structure of economic behavior.
economics machine learning
paper
2026-08
Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga
Analyzes ChatGPT Enterprise usage linked to worker, task, and financial data through March 2026, covering more than 1,500 organizations and 17 million messages at the six-month mark. Finds enterprise adoption growing through both new adopters and deeper engagement by existing users, higher take-up among larger R&D-focused US public firms, use across job levels with especially strong engagement from junior employees, and applications spanning writing, technical work, communication, and information synthesis.
economics AI
paper
2026-08
Gregor Schubert
Uses job-posting data to show that firms which expanded remote hiring during COVID-19 subsequently adopted generative AI at higher rates. Argues this reflects an organizational 'technology ladder' in which shifts toward technical talent and complementary capabilities accelerate later AI integration.
economics AI firms
paper
2026-08
Sebastian Galiani, Federico Ariel López, Raul A. Sosa
Studies how LLMs generate code for econometric tasks along three dimensions: statistical software (Stata, R, Python), prompting strategy (zero-shot vs. few-shot), and level of AI autonomy (chatbot vs. self-executing agent). Moving from a chatbot to a constrained agent raises task success from 74% to 96% at roughly eight additional cents per run. Few-shot prompting helps basic chatbots far more than autonomous agents, and software choice matters for chatbots but becomes far less consequential once an autonomous agent is used.
economics coding LLM stata
paper
2026-08
Joshua S. Gans
Models how regulators should combine staged (delayed or exclusive) access to a dual-use AI model with developer liability, framing a defender and an adversary searching for the same software flaws after release. Liability induces more defensive search but also makes the adversary's search harder, so staged access and liability act as complements in protection. Because defensive effort rises toward the release date, additional delay becomes progressively less productive and optimal evaluation windows are bounded; since defensive search consumes real resources, the efficient liability rate can fall below full internalisation of harm, leaving a distinct role for a timing mandate.
economics AI policy regulation
paper
2026-08
Song Yao
Uses Kaggle competition data spanning 2010-2026 (444,698 participations) to ask whether professional credentials still carry information about skill after generative AI. Finds credentials are informative but perishable, institution-bound, and interdependent: recent medals retain signaling power while older credential stocks decayed sharply (upload-competition medals lost 82% of their informativeness), a shift the author attributes largely to institutional change rather than to AI directly displacing skill.
economics generative AI labor
paper
2026-08
Jorge Fábrega
Studies how delegating tasks to generative AI redistributes control between human and model, distinguishing specified delegation (human direction set before execution) from iterative coproduction (direction supplied during production). Using the Anthropic Economic Index for April-May 2026, it compares direct API traffic with Claude.ai usage and finds that work-oriented interaction is associated with more specified delegation (+2.76 points via API, +1.45 via Claude.ai), shifting attention from how much the AI executes to when human direction leaves observable traces.
economics generative AI AI adoption
paper
2026-02
Christian Catalini, Xiang Hui, Jane Wu
Introduces a macroeconomic framework that models the transition to AGI as a collision between two cost curves: the exponentially falling cost of automating execution and the biologically bounded cost of human verification. Argues the binding constraint on growth becomes verification bandwidth — the capacity to validate, audit and underwrite responsibility when execution is abundant — reframing the story from skill-biased to measurability-biased technical change, and warning that when autonomous execution scales faster than verification the economy accumulates unpriced systemic risk.
economics AGI LLM
paper
2026-08
Matthias Tröbinger, Anil R. Doshi, Sen Chai
Open-access randomized experiment with 310 researchers testing how generative-AI idea suggestions affect their self-evaluation of subsequent research proposals. Experience negatively moderates AI’s effects: less-experienced researchers are more likely to adopt AI suggestions and rate their resulting ideas as more novel and impactful, while senior researchers tend to discount AI-originated ideas — with implications for how AI reshapes knowledge production.
economics LLM research process
paper
2025-12
José Azar, Mireia Giné, Javier Sanz-Espín
Estimates the wage and mobility effects of generative AI exposure using occupation-by-industry data from the Occupational Employment and Wage Statistics alongside job postings and matched employer-employee records from Revelio. Moving from the 10th to the 90th percentile of exposure implies a 4.9% wage decline in OEWS with no detectable employment change, and falls of 8.71% in posted and 10.76% in starting wages in Revelio. Junior workers are hit hardest, and a lower separation elasticity with respect to wages suggests rising employer labor market power.
economics AI labor
paper
2026-07
Jonathan S. Hartley, Filip Jolevski, Vitor Melo, Brendan Moore
Fields a survey measuring US workers perceptions of AI-related job-loss risk and AI adoption (mid-to-high 30% by end of 2025). The average worker believes there is a 20% chance of losing their job to generative AI within two years, but a continuous-treatment difference-in-differences design finds these fears are not borne out: job postings and layoffs in more exposed occupations show no statistically significant response to AI diffusion, and exposure is associated with small positive wage effects. Presented at the NBER Summer Institute 2026 Digitization and AI meeting.
economics AI labor
paper
2026-08
Hamid Firooz, Sylvain Leduc, Zheng Liu
A San Francisco Fed working paper studies how AI affects market competition in a general-equilibrium model with heterogeneous firms and variable markups. It finds a non-monotonic relationship between AI adoption and industry concentration: at low adoption, large incumbents that adopt AI gain share, but as adoption diffuses, entry of new and smaller adopters erodes incumbent dominance and lowers concentration. The calibrated model implies concentration will likely fall as AI adoption rises above current levels, the AI-markup link depends on whether adoption is demand- or supply-driven, and a modest 3% revenue subsidy for AI adopters maximizes social welfare.
economics AI competition
paper
2026-08
Melissa Dell, Ashesh Rambachan
A review of how AI is reshaping economic measurement by cheaply converting unstructured text and images into structured variables — and the new challenge this creates, since researchers must now choose among multiple plausible AI-generated measures that can yield divergent findings. The paper identifies three pipeline stages where AI intervenes (discovery, construct definition, observation), argues for validation anchored to explicit criteria rather than informal 'reasonable proxy' claims, and shows how validation samples enable sound inference despite arbitrary AI prediction bias.
economics AI measurement
paper
2026-08
Nicholas J. Hallman, Zachary T. Kowaleski, Anu Puvvada, Jaime J. Schmidt
Analyzing 713,564 prompts from nearly 4,000 back-office workers across 15 functional areas over eight months in 2025, the authors measure how 'sophisticated' employees' generative-AI use is. Senior employees exhibit more sophisticated use, and sophistication varies considerably across functions (highest in Strategy, Digital Innovation, and Project Management). Strikingly, they find neither improvement in sophistication over time nor lasting gains following formal AI training, suggesting usage patterns are resistant to conventional training.
economics AI productivity
paper
2026-08
Alexander Bick, Adam Blandin, David J. Deming, Tyler R. Schumacher
A nationally representative survey builds the first task-level generative-AI adoption indexes across occupations. It finds adoption is widespread but shallow — genAI is used across many occupations and tasks, yet within most of them fewer than half of workers adopt — with large variation among workers in similar roles.
economics LLM labor adoption
paper
2026-08
Joshua S. Gans
Models how journals should integrate AI-generated manuscript assessments into peer review. Sharing an AI assessment with reviewers can reduce the independent information editors obtain; giving it to only one of two reviewers may better preserve human investigation, and competing policies can be tested via randomized experiments on reviewer disagreement.
economics LLM peer review publishing
paper
2026-08
Pattaraphon Kenny Wongchamcharoen, Kris Gulati, Min Min Fong, Abhishek Nagaraj
Introduces a benchmark that separately measures whether an LLM automates a task versus augments a lower-capacity worker model across seven real-world tasks. Rankings across the two regimes are only modestly correlated — the automation winner loses on augmentation in five of seven tasks — showing automation skill does not predict how well a model assists people.
economics LLM benchmark productivity
paper
2026-09
Isaiah Andrews, Suproteem Sarkar
Tests whether the probabilistic forecasts language models give for economic and real-world events are internally coherent. Building on de Finetti's theorem, the authors compute the largest guaranteed 'Dutch book' arbitrage profit against a model's stated probabilities as a label-free measure of incoherence. They find substantial incoherence that grows with richer logical relationships between events and can jump an order of magnitude when irrelevant context is added.
economics LLM forecasting
paper
2026-09
Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek
Replaces human subjects with LLM agents in a classic double-auction experiment to test whether models can participate in market mechanisms designed for people. Markets populated by LLM agents converge to equilibrium more slowly or not at all, yielding less efficient allocations than human markets, with wide heterogeneity across model families and buyer/seller roles. The authors release their testing framework for future evaluations.
economics LLM experiments
paper
2026-09
Ali Zeytoon-Nejad
A single-authored survey essay arguing that AI is not merely a technological innovation but a major economic phenomenon—one best understood at once as capital, synthetic labor, a general-purpose technology, and economic infrastructure. It maps the channels (automation, augmentation, optimization, prediction, and innovation) through which AI drives productivity, then works through its effects on labor markets, firm structure, competition, and entrepreneurship before turning to welfare and policy. A broad conceptual entry point for economists orienting to the field.
economics AI survey
paper
2026-09
David Autor et al.
A randomized controlled trial with 133 patent lawyers across eleven US intellectual-property firms testing whether an AI drafting tool builds or erodes professional skill. Lawyers using AI improved benchmark work quality at both 10 and 90 days, and still outperformed controls when later working without AI—but the gains split by seniority. The largest durable gains accrued to the lawyers who retained the least, while junior attorneys' outcomes polarized. The authors argue that foundational expertise may be a prerequisite for converting temporary AI assistance into lasting competence.
economics AI expertise labor
paper
2026-09
Jeffrey Clemens and Anwita Mahajan
Pre-analysis plans (PAPs) curb researchers' analytical flexibility, but only if researchers actually follow them. Clemens and Mahajan propose LLMs as a practical, scalable way for both authors and referees to check PAP compliance. Applied to their own work, an LLM identified precommitted methodology, catalogued departures from the plan, and flagged specification shortcomings while sharply cutting manual effort—though results varied across models, so human judgment remains essential. The paper closes with concrete recommendations for implementing AI-assisted PAP review.
economics LLM reproducibility
report
2026-09
Anton Korinek, Charles I. Jones, Szymon Sacher, Tess Cotter, Peter McCrory
A framework from the Anthropic Institute for mapping AI capability trajectories into macroeconomic outcomes through 2030. In the model, AI automates a growing share of cognitive work, raising productivity while displacing workers who must reallocate to other occupations, and it traces the implied paths for GDP, the labor share, wages, and unemployment across three scenarios—modest, substantial, and extreme. Pairs the technical report with an interactive explorer and a survey of Americans expectations about AI.
economics growth automation