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

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—and also decrease—an evaluator’s screening mistakes. This result depends on how GAI affects experts’ signals compared with nonexperts’. Using experiments in hiring and start-up investing, we estimate that senders’ access to GAI (ChatGPT) lowers screening accuracy by 4%–9% for employers and start-up 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. This paper was accepted by Anindya Ghose, information systems. Funding: We are grateful for the Columbia Business School Digital Future Initiative Grant for helping fund this project. B. Cowgill thanks the Kauffman Foundation Emerging Scholars Program, the Columbia Center for Political Economy, the NET Institute, and the Stellar Development Foundation. P. Hernandez-Lagos thanks the Yeshiva University Sy Syms Dean’s Research Fund. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07027 .

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