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Artificial Intelligence and the Labor Market

Citation. Hampole, M., Papanikolaou, D., Schmidt, L. D. W., & Seegmiller, B. (2025). Artificial Intelligence and the Labor Market (NBER Working Paper No. 33509). National Bureau of Economic Research. https://doi.org/10.3386/w33509

Summary. This NBER working paper constructs firm-, occupation-, and task-level measures of AI and machine-learning exposure from 2010–2023 using resume and job-posting data. It models labor-market effects as direct task substitution, worker reallocation across tasks, and firm-level productivity growth. The authors find task-level substitution alongside offsetting reallocation and firm-growth effects, yielding modest overall employment effects.

Key claims

  • A one-standard-deviation increase in task-level AI exposure within a firm reduces relative demand for skills associated with that task by 2%.
  • In the preferred instrumental-variables specification, a one-standard-deviation increase in mean task exposure reduces an occupation–firm pair’s employment share by about 14.5% over five years, while a comparable increase in exposure concentration raises it by about 7.5%.
  • Strong task-level substitution need not produce large aggregate employment losses because workers can reallocate effort to less-exposed tasks and AI-adopting firms can expand labor demand through productivity growth.

Evidence & limitations

  • The study combines approximately 58 million LinkedIn profiles, 14 million job postings, Compustat outcomes, O*NET task data, and NLP/LLM-derived measures of firms’ AI applications; identification includes an instrument based on historical university hiring networks.
  • The data end in 2023, so the analysis largely excludes effects of broadly available generative AI after late 2022; resume data may also miss firms that outsource AI development.
  • This is an NBER working paper revised in September 2025 and has not been peer reviewed.

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