Automation, AI, and the Intergenerational Transmission of Knowledge

Abstract: Recent advances in AI have fueled predictions of unprecedented productivity growth. Yet, by enabling senior workers to perform more tasks on their own, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential skills. In this paper, I develop a model to examine how advanced automation affects the intergenerational transmission of knowledge. The analysis reveals that automating entry-level tasks yields immediate productivity gains but can undermine long-run growth by eroding the skills of subsequent generations. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce U.S. long-term annual growth by approximately 0.05 to 0.35 percentage points, depending on its scale. I also demonstrate that AI co-pilots - systems that democratize access to expertise previously acquired only through hands-on experience - can partially mitigate these negative effects. However, their introduction is not always beneficial: by providing expert insights, co-pilots may inadvertently diminish younger workers' incentives to invest in hands-on learning. These findings cast doubt on the optimistic view that AI will automatically lead to sustained productivity growth, unless it either generates new entry-level roles or significantly boosts the economy's underlying innovation rate.