Automation, AI, and the Intergenerational Transmission of Knowledge

Recent advances in Artificial Intelligence (AI) have sparked expectations of unprecedented economic growth. Yet, by enabling senior workers to accomplish more tasks independently, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential expertise. This paper develops a model to examine how advanced automation affects the intergenerational transmission of tacit knowledge -- practical insights that resist codification and are critical for workplace success. The analysis shows that the competitive equilibrium features socially excessive automation of early-career tasks and reveals a critical trade-off: while such automation delivers immediate productivity gains, it can undermine long-term growth by hindering younger workers' acquisition of tacit skills. Back-of-the-envelope calculations suggest AI-driven entry-level automation could lower the long-run annual growth rate of U.S. per capita output by 0.05 to 0.35 percentage points, depending on its scale. The analysis further shows that AI co-pilots -- systems providing access to tacit-like expertise once obtained only through direct experience -- can partially offset these losses by assisting individuals who fail to develop adequate skills early in their careers. However, co-pilots are not always beneficial, as they may also weaken junior workers' incentives to engage in hands-on learning. These findings challenge the view that AI will automatically lead to higher economic growth, highlighting the need to safeguard -- or deliberately create -- entry-level opportunities to fully realize AI's potential.