Path Dependence under Adaptive AI Delegation

TL;DR

This paper presents a mathematical model analyzing how repeated AI assistance influences human skill development and reliance, revealing potential long-term tradeoffs and path-dependent outcomes.

Contribution

It introduces a coupled dynamic framework capturing how adaptive AI delegation affects skill retention and reliance, highlighting the risk of long-term skill degradation.

Findings

AI assistance can improve short-term performance but may harm long-term skill.

Adaptive delegation dynamics can lead to multiple equilibria, including low-skill outcomes.

Increasing AI capability can expand the basin of attraction for low skill, risking skill loss.

Abstract

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 follows a one-dimensional learning-decay process with a single stable equilibrium. With adaptive delegation, the coupled system has two attracting equilibria separated by the stable manifold of an interior saddle. The existence and geometry of this separatrix require a global phase-plane analysis of the coupled dynamics. The system is path-dependent: small differences in initial skill or reliance can lead to different long-run outcomes. We use this characterization to show that AI assistance can improve short-run performance while producing worse long-run performance than a no-AI baseline. Increasing AI capability can enlarge the basin of attraction of the low-skill equilibrium, making delegation appear beneficial for longer while increasing the risk of eventual skill loss. The qualitative picture is observed to persist across alternative specifications. Together, these results show that the risk is not AI assistance itself, but the coupling between performance-driven reliance and use-dependent skill change.