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.

翻译：随着人工智能承担认知劳动，尚无定量框架能预测人类能力丧失何时会变得灾难性。我们提出了一个耦合能力（H）与授权（D）的双变量动态系统模型，该模型基于三条公理：学习需要能力、实践与荒废导致遗忘。在四个领域（教育、医疗、导航、航空）进行校准后，模型识别出一个临界阈值K*≈0.85（范围依赖；AI范围越广，K*越低），超过该阈值能力会骤然崩溃——即“能力悖论”。基于15个国家PISA数据验证后（102个数据点，R²=0.946，3个参数，最低贝叶斯信息准则），模型预测：周期性AI故障能使能力提升2.7倍，而20%的强制练习能比模拟基线（包含5%的背景AI故障率）多保留92%的能力。这些发现为AI能力阈值治理提供了定量基础。