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.
Enrique Ide (2025) studied this question.