Identifies a 'critical threshold' in human-AI symbiosis beyond which human capability collapses abruptly and irreversibly due to over-delegation.
March 26, 2026
Original Paper
The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis
arXiv · 2603.24391
The Takeaway
Validated against global PISA data, the model predicts that delegation beyond 85% of scope leads to catastrophic skill loss. This provides a quantitative foundation for AI governance and 'mandatory practice' policies in high-stakes fields like medicine and aviation.
From the 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 low