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: As artificial intelligence (AI) capabilities advance, will workers best adapt by reskilling into AI-complementary work or by sorting into occupations less exposed to AI? To answer this question, we assemble a new dataset of 1.9 million occupational training spells funded by the U.S. Workforce Innovation and Opportunity Act from 2012–2024. We link pre- and post-training occupations to task-level AI exposure measures and estimate returns to training by comparing trainees to matched workers who sought workforce services but received only job search assistance. While trainees from low AI-exposure occupations earned high quarterly returns throughout the sample period, returns for workers from high-exposure occupations rose sharply—from about $900 quarterly before 2020 to $2,900 by 2022–2024. We attribute these gains primarily to transitions into less AI-exposed occupations and, to a lesser extent, to the expansion of training programs that build AI-complementary skills. To quantify when training into higher AI exposure work pays off, we construct a new AI Retrainability Index (AIR) and find that a large share of occupations are “AI-retrainable,” pointing to broad potential for adaptation.

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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