The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis

TL;DR

This paper develops a dynamical systems model to identify critical thresholds in human-AI collaboration, revealing how excessive reliance on AI can lead to irreversible human capability loss, and proposes governance strategies to mitigate this risk.

Contribution

The paper introduces a novel two-variable model linking human capability and AI delegation, identifying a critical threshold causing capability collapse, validated with real-world data and offering governance insights.

Findings

Identifies a critical threshold K* (~0.85) for capability collapse.

Periodic AI failures can enhance human capability 2.7-fold.

Mandatory practice preserves significantly more human capability.

Abstract

As artificial intelligence assumes cognitive labor, no quantitative framework predicts when human capability loss becomes catastrophic. We present a two-variable dynamical systems model coupling capability (H) and delegation (D), grounded in three axioms: learning requires capability, practice, and disuse causes forgetting. Calibrated to four domains (education, medicine, navigation, aviation), the model identifies a critical threshold K* approximately 0.85 (scope-dependent; broader AI scope lowers K*) beyond which capability collapses abruptly-the "enrichment paradox." Validated against 15 countries' PISA data (102 points, R^2 = 0.946, 3 parameters, lowest BIC), the model predicts that periodic AI failures improve capability 2.7-fold and that 20% mandatory practice preserves 92% more capability than the simulation baseline (which includes a 5% background AI-failure rate). These findings provide quantitative foundations for AI capability-threshold governance.