Automation, AI, and the Intergenerational Transmission of Knowledge

2025-07-21

AI Summary

This paper examines the potential suppressive effects of AI and automation on intergenerational knowledge transfer and long-term economic growth—contrary to mainstream emphasis on short-term productivity gains. It argues that automating entry-level jobs may impede the transmission of tacit knowledge to younger workers, thereby constraining skill accumulation. Method: The study develops a dynamic theoretical model calibrated with rough parameter estimates to quantify the macroeconomic impact of AI-driven displacement of junior roles. Contribution/Results: Findings suggest such displacement could reduce U.S. long-run annual GDP growth by 0.05–0.35 percentage points. The paper innovatively proposes an “AI co-riding system” as a mitigation mechanism; however, it demonstrates that this system’s efficacy is highly conditional—while partially preserving master-apprentice interaction, it may diminish young workers’ hands-on engagement, thereby challenging the optimistic paradigm that AI inherently sustains long-term growth. These results provide a novel intergenerational dimension for technology policy design.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Machine Learning: Life-Long and Continual Learning
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in Artificial Intelligence (AI) have fueled predictions of unprecedented productivity growth. Yet, by enabling senior workers to perform more tasks on their own, AI may inadvertently reduce entry-level opportunities, raising concerns about how future generations will acquire essential skills. In this paper, I develop a model to examine how advanced automation affects the intergenerational transmission of knowledge. The analysis reveals that automating entry-level tasks yields immediate productivity gains but can undermine long-run growth by eroding the skills of subsequent generations. Back-of-the-envelope calculations suggest that AI-driven entry-level automation could reduce U.S. long-term annual growth by approximately 0.05 to 0.35 percentage points, depending on its scale. I also demonstrate that AI co-pilots - systems that democratize access to expertise previously acquired only through hands-on experience - can partially mitigate these negative effects. However, their introduction is not always beneficial: by providing expert insights, co-pilots may inadvertently diminish younger workers' incentives to invest in hands-on learning. These findings cast doubt on the optimistic view that AI will automatically lead to sustained productivity growth, unless it either generates new entry-level roles or significantly boosts the economy's underlying innovation rate.

Problem

• AI reduces entry-level jobs, hindering skill acquisition
• Automation harms long-term growth by eroding future skills
• AI co-pilots may discourage hands-on learning incentives

Innovation

• AI co-pilots democratize expert knowledge access
• Model analyzes automation's impact on skill transmission
• Entry-level automation risks long-term growth decline