Practice Makes Perfect: Tacit Knowledge, Generative AI, and Returns to Schooling and Experience

Eduardo Levy Yeyati (), Darío Judzik () and Juan Ignacio Barbiero

School of Government Working Papers from Universidad Torcuato Di Tella

Abstract: Generative AI changes the scarcity of different forms of human capital. It makes codified knowledge cheaply available at the point of production, while judgment acquired through practice remains tied more closely to the worker. We ask what this does to two familiar prices in the wage structure: the return to schooling and the return to experience. In a short-run framework, machine-supplied codified knowledge substitutes directly for the codified component of worker human capital, lowering the schooling gradient. If codified and practice-based knowledge are complements, the return to experience rises where learning through practice is fastest; technologyspecific experience can also become obsolete, rotating the experience profile rather than shifting it uniformly. Using US Current Population Survey earnings data from 2018 through June 2026, we compare occupations in the top and bottom quartiles of the pre-ChatGPT Felten–Raj–Seamans exposure index. After the public release of ChatGPT, the return to a year of schooling falls by 0.55 log points in exposed occupations. The return to a year of experience rises by 0.26 log points at three years of potential experience, is unchanged at fifteen, and falls by 0.12 log points at twenty-five; under the baseline quadratic, the effect crosses zero at 18.1 years. These breaks appear with the release, disappear under a pandemic placebo, survive a common censoring rule across the full sample, and remain when each occupation is allowed its own pre-shock schooling and experience gradients. The pattern is a repricing of human capital inside exposed occupations: codifiable knowledge becomes less scarce, while the value of experience depends increasingly on whether it transfers to the new technology.

Keywords: Inteligencia Artificial; Capital humano; Salarios; Experiencia laboral; Cambio tecnológico; Artificial intelligence; Human capital; Wages; Work experience; Technological change (search for similar items in EconPapers)
JEL-codes: I26 J24 J31 O33 (search for similar items in EconPapers)
Pages: 66 pages
Date: 2026-10
References: Add references at CitEc
Citations:

Downloads: (external link)
https://repositorio.utdt.edu/items/af55d1d2-5d03-4581-9815-5214b3b788ea (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:udt:wpgobi:wp_gob_2026_15

Access Statistics for this paper

More papers in School of Government Working Papers from Universidad Torcuato Di Tella
Bibliographic data for series maintained by Fiorela Navarro Duymovich ().