Across schooling, workplace, and modeling studies, AI's performance gains evaporate or reverse once assistance is withdrawn. Bastani et al. 2025 found students "actually perform worse than those who never had access" after GPT-4 removal; Wu et al. 2025 found the "performance augmentation effect did not persist in subsequent tasks performed independently by humans"; and Park et al. 2026's model predicts "20% mandatory practice preserves 92% more capability."
All three quotes are verbatim: Bastani (students perform worse than those never given access after removal), Wu (augmentation effect did not persist in independent tasks), and Park (20% mandatory practice preserves 92% more capability), consistent with gains evaporating or reversing once assistance is withdrawn.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 3 Oct, 04:46
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction in grades for GPT Base)”
Abstract · no page number · evidence E25
- 02
“However, this performance augmentation effect did not persist in subsequent tasks performed independently by humans.”
Abstract · no page number · evidence E26
- 03
“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”
Full-text passage · p. 1 · evidence E3
The enrichment paradox: critical capability thresholds and irreversible dependency in human–AI symbiosis Jeongju Park1, Musu Kim 1, Sekyung Han 1,* 1Department of Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea *Corresponding author: skhan@knu.ac.kr 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 thresholdK ∗∼0.85 (scopedependent; broader AI scope lowersK∗) 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).
Passage read from arxiv.org, which may be a preprint rather than the published version.