Does AI Cheapen Talk? Theory and Evidence From Global Entrepreneurship and Hiring

Bo Cowgill (), Pablo Hernandez-Lagos () and Nataliya Wright ()
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Bo Cowgill: Columbia Business School
Pablo Hernandez-Lagos: Sy Syms School of Business at Yeshiva University
Nataliya Wright: Columbia Business School

No 18442, IZA Discussion Papers from IZA Network @ LISER

Abstract: Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants' access to GAI can increase---but also decrease---an evaluator's screening mistakes. This result depends on how GAI affects experts' signals compared to non-experts'. Using experiments in hiring and startup investing, we estimate that senders' access to GAI (ChatGPT) lowers screening accuracy by 4-9% for employers and startup investors. Consistent with our model, senders' access to GAI also improves screening accuracy in some settings---in our case, among senders from non-English-speaking countries. These results show that GAI can profoundly shape screening accuracy.

Keywords: screening; Artificial Intelligence; entrepreneurship; human capital (search for similar items in EconPapers)
JEL-codes: D82 D83 L26 M13 M51 O33 (search for similar items in EconPapers)
Date: 2026-03
New Economics Papers: this item is included in nep-ent, nep-hrm, nep-lma and nep-sbm
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