Not yet human-reviewed
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Automation, AI, and the Intergenerational Transmission of Knowledge
Citation. Ide, E. (2025). Automation, AI, and the Intergenerational Transmission of Knowledge. SSRN working paper. https://doi.org/10.2139/ssrn.5360803
Summary. This working paper develops a task-based overlapping-generations model in which novices acquire tacit knowledge by working with experts. It argues that entry-level automation can increase immediate output while weakening long-run growth and welfare if it reallocates novices away from the most productive mentors. The countervailing case is automation that expands the number of novices learning from highly productive experts.
Key claims
- Entry-level automation can raise output at adoption while lowering subsequent growth and welfare, even when it does not reduce entry-level employment, by worsening the composition of novice-expert matches.
- In the model, slower diffusion of best practices follows when automation shifts novices from the most productive experts to lower-quality mentors.
- Technological changes that increase the number of novices working with the most productive experts strengthen tacit-knowledge transmission and long-run growth.
Evidence & limitations
- The paper is a theoretical task-based overlapping-generations model rather than a causal empirical study; its conclusions depend on assumptions about tacit knowledge, incomplete contracts, and mentor matching.
- The ingested Drive PDF exactly matches the public arXiv version (2507.16078); the canonical DOI is a 2025 SSRN working-paper record, so the work is not treated as peer-reviewed.