The Geometry of Learning Under AI Delegation

2026-03-03

AI Summary

This study investigates how AI collaboration influences the long-term evolution of human skills, with a focus on whether AI assistance leads to skill atrophy. The authors model human skill and AI delegation as a coupled dynamical system: skill improves with use and decays with disuse, while delegation levels adapt dynamically based on relative performance to jointly minimize task error. Through dynamical systems theory, stability analysis, and phase-space geometry, they demonstrate the existence of a stable low-skill equilibrium, where early delegation decisions become irreversible due to basin boundaries. Although AI enhances short-term performance, it may induce long-term performance degradation—a phenomenon rooted in system stability rather than misaligned incentives. This mechanism remains robust under stochastic perturbations and asymmetric trust updating. Crucially, AI quality significantly shifts basin boundaries, highlighting a critical window for design interventions.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

As AI systems shift from tools to collaborators, a central question is how the skills of humans relying on them change over time. We study this question mathematically by modeling the joint evolution of human skill and AI delegation as a coupled dynamical system. In our model, delegation adapts to relative performance, while skill improves through use and decays under non-use; crucially, both updates arise from optimizing a single performance metric measuring expected task error. Despite this local alignment, adaptive AI use fundamentally alters the global stability structure of human skill acquisition. Beyond the high-skill equilibrium of human-only learning, the system admits a *stable* low-skill equilibrium corresponding to persistent reliance, separated by a sharp basin boundary that makes early decisions effectively irreversible under the induced dynamics. We further show that AI assistance can strictly improve short-run performance while inducing persistent long-run performance loss relative to the no-AI baseline, driven by a negative feedback between delegation and practice. We characterize how AI quality deforms the basin boundary and show that these effects are robust to noise and asymmetric trust updates. Our results identify stability, not incentives or misalignment, as the central mechanism by which AI assistance can undermine long-run human performance and skill.

Problem

• AI delegation
• human skill decay
• learning dynamics
• performance loss
• skill acquisition

Innovation

• coupled dynamical system
• skill-delegation feedback
• basin boundary
• stable low-skill equilibrium
• AI-induced performance loss