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Artificial Intelligence and the Labor Market

Author

Listed:
  • Menaka Hampole
  • Dimitris Papanikolaou
  • Lawrence D.W. Schmidt
  • Bryan Seegmiller

Abstract

We use advances in natural language processing to construct new measures of workers’ task-level exposure to artificial intelligence (AI) and machine learning from 2010 to 2023, capturing variation across firms, occupations, and time. Tasks with higher AI exposure subsequently experience reduced labor demand. To interpret these patterns, we develop a model that separates direct substitution from indirect reallocative effects of labor-saving technologies. Two variables summarize the impact of AI on within-firm labor demand: the mean exposure of an occupation’s tasks, which depresses demand, and the concentration of exposure in a few tasks, which offsets losses by enabling workers to reallocate effort. Using an instrument based on historical university hiring networks, we find causal evidence consistent with these predictions. Despite strong substitution at the task level, overall employment effects are modest, as reduced demand in exposed occupations is offset by productivity-driven increases in labor demand at AI-adopting firms.

Suggested Citation

  • Menaka Hampole & Dimitris Papanikolaou & Lawrence D.W. Schmidt & Bryan Seegmiller, 2025. "Artificial Intelligence and the Labor Market," NBER Working Papers 33509, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:33509
    Note: CF EFG LS PR
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    Cited by:

    1. Zhang, Su & Wang, Xiaolin & Xia, Yan & Wang, Huijuan, 2025. "The AI Redemption: How technology is rewriting the rules of cross-industry labor mobility," International Review of Economics & Finance, Elsevier, vol. 103(C).
    2. Yongheng Hu, 2025. "Heterogeneous Agents in the Data Economy," Papers 2509.09656, arXiv.org.
    3. Wang, Mingyue & Sun, Tianshi & Liu, Xiuyan, 2025. "Peace or menace? Robots and social conflict," China Economic Review, Elsevier, vol. 92(C).
    4. Katharina Hartinger & Erik Sarrazin & David J. Streich, 2025. "Banking for Boomers – A Field Experiment on Technology Adoption in Financial Services," Working Papers 2505, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 02 Sep 2025.

    More about this item

    JEL classification:

    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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