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Automation, AI, and the Intergenerational Transmission of Knowledge

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  • Enrique Ide

Abstract

Motivated by concerns that AI-driven entry-level automation may deprive new generations of valuable work experience, this paper studies 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 on impact 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, weakening the diffusion of best practices. By contrast, technological improvements that increase the span of control of the most productive experts -- such as those that create new labor-intensive tasks -- strengthen knowledge transmission and raise growth.

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  • Enrique Ide, 2025. "Automation, AI, and the Intergenerational Transmission of Knowledge," Papers 2507.16078, arXiv.org, revised Apr 2026.
    • Handle: RePEc:arx:papers:2507.16078
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