Generative AI and the Redefinition of Entry-Level Software Work

Samuel Westby, Alicia Modestino () and Peiran Cheng
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Samuel Westby: Northeastern University
Alicia Modestino: Northeastern University
Peiran Cheng: Northeastern University

No 18723, IZA Discussion Papers from IZA Network @ LISER

Abstract: Generative AI may change how firms define occupations. We study this process in software development, where large language models overlap with tasks commonly assigned to junior workers. Using the near-universe U.S. online vacancy data from Lightcast, we examine how the public release of ChatGPT changed entry-level software hiring standards. Event-study and difference-in-differences estimates show a 14–15 percent relative decline in junior versus senior software developer vacancies, larger than in related technical occupations and absent in mechanical engineering. A shift-share decomposition shows that rising experience requirements were driven primarily by employers asking for more experience within the same job titles, not by asking for a different composition of titles. Remaining junior vacancies shifted toward problem solving, communication, and attention to detail, not AI-specific skills. The results show how generative AI redefines entry-level work by raising the bar for what counts as a qualified junior hire.

Keywords: generative AI; economics of information systems; labor demand; job vacancies; hiring standards; entry-level work (search for similar items in EconPapers)
JEL-codes: D83 J23 J24 L86 M51 O33 (search for similar items in EconPapers)
Date: 2026-06
New Economics Papers: this item is included in nep-ain and nep-lma
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