Paper
The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis
Authors
Jeongju Park, Musu Kim, Sekyung Han
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.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.24391v1</id>\n <title>The enrichment paradox: critical capability thresholds and irreversible dependency in human-AI symbiosis</title>\n <updated>2026-03-25T15:08:43Z</updated>\n <link href='https://arxiv.org/abs/2603.24391v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.24391v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n <published>2026-03-25T15:08:43Z</published>\n <arxiv:comment>40 pages total, including Supplementary Information; 7 figures and 1 table in the main manuscript. The study develops and validates a dynamical-systems model of human-AI capability delegation using four empirical domains and a 15-country PISA analysis. Data/code availability and AI disclosure statements are provided</arxiv:comment>\n <arxiv:primary_category term='cs.CY'/>\n <author>\n <name>Jeongju Park</name>\n </author>\n <author>\n <name>Musu Kim</name>\n </author>\n <author>\n <name>Sekyung Han</name>\n </author>\n </entry>"
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