How Retrainable are AI-Exposed Workers?

Benjamin Hyman, Benjamin Lahey, Karen Ni and Laura Pilossoph

No 34174, NBER Working Papers from National Bureau of Economic Research, Inc

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 US 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 the average earnings return to training among AI-exposed workers is high, around $1,470 per quarter. Low-exposure trainees capture higher returns, and trainees who target AI-intensive work face a 29% earnings return penalty relative to their high exposure peers who pursue more general training. We estimate that between 25% to 40% 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.

JEL-codes: E0 E2 J6 (search for similar items in EconPapers)
Date: 2025-08
New Economics Papers: this item is included in nep-ain, nep-hrm and nep-lab
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Working Paper: How Retrainable Are AI-Exposed Workers? (2025)
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