The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis
This provides a quantitative framework for AI governance to prevent irreversible dependency, addressing a critical societal problem in human-AI symbiosis.
The paper tackles the problem of predicting catastrophic human capability loss due to AI delegation by developing a dynamical systems model, which identifies a critical threshold (K* ≈ 0.85) beyond which capability collapses abruptly and shows that periodic AI failures improve capability 2.7-fold and 20% mandatory practice preserves 92% more capability.
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.