Danielle Li

Labor as Capital: AI and the Ownership of Expertise

Abstract:   Workplace surveillance generates data that can train AI systems to replicate worker expertise. Using an online survey experiment of U.S. full-time workers, we show that workers report a desire to reduce their knowledge contributions when made aware of this dynamic: they rationally withhold expertise due to career concerns. We formalize this behavior in a model of knowledge supply under surveillance-enabled AI and use it to evaluate alternative policies. Individual data ownership--workers' preferred policy—eliminates knowledge withholding but creates negative externalities: one worker's data strengthens the firm's bargaining position against others, potentially making all workers worse off. In contrast, collective data ownership achieves the first-best outcome, promoting knowledge sharing while allowing workers to benefit from AI-driven productivity gains. These findings highlight the importance of labor agreements in shaping AI adoption in labor markets.