Authors: Ben Hyman, Benjamin Lahey, Karen X. Ni, and Laura Pilossoph
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Staff Reports
How Retrainable Are AI-Exposed Workers?
Number 1165
August 2025
August 2025
JEL classification: J08, M53, O31
We document the extent to which workers in AI-exposed occupations can successfully retrain for AI-intensive work. We assemble a new workforce development dataset spanning over 1.6 million job training participation spells from all U.S. Workforce Investment and Opportunity Act programs from 2012-2023 linked with occupational measures of AI exposure. Using earnings records observed before and after training, we compare high AI exposure trainees to a matched sample of similar workers who only received job search assistance. We find that AI-exposed workers have high earnings returns from training that are only 25 percent lower than the returns for low AI exposure workers. However, training participants who target AI-intensive occupations face a penalty for doing so, with 29 percent lower returns than AI-exposed workers pursuing more general training. We estimate that between 25 percent to 40 percent of occupations are “AI retrainable” as measured by its workers receiving higher pay for moving to more AI-intensive occupations—a large magnitude given the relatively low-income sample of displaced workers. Positive earnings returns in all groups are driven by the most recent years when labor markets were tightest, suggesting training programs may have stronger signal value when firms reach deeper into the skill market.
Author Disclosure Statement(s)
Benjamin Hyman
Ben Hyman has not received any source of research support nor has he any financial relationships or potential conflict of interests to report in conducting this research. The views expressed here are the authors’ and are not necessarily the views of the Federal Reserve Bank of New York or the Federal Reserve System. The Federal Reserve Bank of New York reviewed the contents of this disclosure prior to submission.
Ben Lahey
Ben Lahey has not received any source of research support nor has he any financial relationships or potential conflict of interests to report in conducting this research.
Karen Ni
Karen Ni gratefully acknowledges support from the Institution of Education Sciences, U.S. Department of Education, through grant R305B150012 to Harvard University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education.
Laura Pilossoph
Laura Pilossoph has not received any source of research support nor has he any financial relationships or potential conflicts of interest to report in conducting this research.
Ben Hyman has not received any source of research support nor has he any financial relationships or potential conflict of interests to report in conducting this research. The views expressed here are the authors’ and are not necessarily the views of the Federal Reserve Bank of New York or the Federal Reserve System. The Federal Reserve Bank of New York reviewed the contents of this disclosure prior to submission.
Ben Lahey
Ben Lahey has not received any source of research support nor has he any financial relationships or potential conflict of interests to report in conducting this research.
Karen Ni
Karen Ni gratefully acknowledges support from the Institution of Education Sciences, U.S. Department of Education, through grant R305B150012 to Harvard University. The opinions expressed are those of the authors and do not represent the views of the Institute or the U.S. Department of Education.
Laura Pilossoph
Laura Pilossoph has not received any source of research support nor has he any financial relationships or potential conflicts of interest to report in conducting this research.
Suggested Citation:
Hyman, Benjamin, Benjamin Lahey, Karen Ni, and Laura Pilossoph. 2025. “How Retrainable Are AI-Exposed Workers?” Federal Reserve Bank of New York Staff Reports, no. 1165, August. https://doi.org/10.59576/sr.1165
Hyman, Benjamin, Benjamin Lahey, Karen Ni, and Laura Pilossoph. 2025. “How Retrainable Are AI-Exposed Workers?” Federal Reserve Bank of New York Staff Reports, no. 1165, August. https://doi.org/10.59576/sr.1165
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