Path Dependence under Adaptive AI Delegation
Repeated AI assistance can improve immediate task performance while reducing the skill available for future independent work. We develop a mathematical framework for this long-run tradeoff. The model tracks two state variables: a latent human skill level governing expected independent performance, and a delegation level representing the learner's evolving tendency to rely on AI. Skill changes through error-driven learning under practice and decay under delegation; delegation responds to observed performance, increasing when AI-assisted work appears to outperform independent work. We analyze the resulting dynamics and contrast them with fixed delegation. With fixed delegation, skill follow...
| Authors | Lingxiao Huang, Nisheeth K. Vishnoi |
|---|---|
| Primary category | cs.CY |
| Published | 3 March 2026 |
| Last updated at source | 7 May 2026 |
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