Automation, AI, and the Intergenerational Transmission of Knowledge

Abstract

Motivated by concerns that AI-driven entry-level automation may disrupt early-career learning, this paper examines how technological change affects the intergenerational transmission of tacit knowledge -- practical, hard-to-codify skills acquired through workplace interaction. I develop a task-based overlapping-generations model in which novices acquire tacit knowledge by working alongside experts. Knowledge-transfer contracts are incomplete because tacit knowledge is embodied and non-verifiable. In equilibrium, endogenous growth arises because only the most knowledgeable experts manage production and transmit their expertise to multiple novices, diffusing best practices. I show that improvements in entry-level automation increase output upon adoption but can reduce growth and welfare, even without reducing entry-level employment. This occurs when such improvements reallocate novices away from the most productive experts, slowing the diffusion of best practices. By contrast, technological improvements that increase the number of novices learning from the most productive experts strengthen knowledge transmission and raise growth.