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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.24391 |
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| _version_ | 1866908913344970752 |
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| author | Park, Jeongju Kim, Musu Han, Sekyung |
| author_facet | Park, Jeongju Kim, Musu Han, Sekyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24391 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis Park, Jeongju Kim, Musu Han, Sekyung Computers and Society 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. |
| title | The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2603.24391 |