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

• 成果类型: Article; Early Access
• 署名作者: Cowgill, Bo; Hernandez-Lagos, Pablo; Wright, Nataliya Langburd
• 署名单位: University of Toronto; Yeshiva University; Columbia University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.07027
• 发表日期: 2026
• 关键词: Screening; Artificial intelligence; entrepreneurship; human capital

摘要

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.