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

• URL: http://arxiv.org/abs/2603.24391v1
• Date: Wed, 25 Mar 2026 15:08:43 GMT
• Title: The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis
• Abstract summary: We present a two-variable dynamical systems model coupling capability (H) and delegation (D)<n>Calibrated to four domains (education, medicine, navigation, aviation), the model identifies a critical threshold K* approximately 0.85.<n>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.
• Score: 1.0832844764942349
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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