How Retrainable Are AI-Exposed Workers?

Benjamin Lahey, Benjamin Hyman, Karen Ni () and Laura Pilossoph ()
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Laura Pilossoph: https://scholars.duke.edu/person/Laura.Pilossoph

No 1165, Staff Reports from Federal Reserve Bank of New York

Abstract: 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.

Keywords: artificial intelligence; active labor market policies; job training; labor markets (search for similar items in EconPapers)
JEL-codes: J08 M53 O31 (search for similar items in EconPapers)
Pages: 34
Date: 2025-08-01
New Economics Papers: this item is included in nep-ain, nep-hrm and nep-mac
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Working Paper: How Retrainable are AI-Exposed Workers? (2025)
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DOI: 10.59576/sr.1165

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