Generative Models Erode Human Temporal Learning Through Market Selection
This paper argues that modern generative models, even below AGI capability, create structural risks for knowledge and cultural production. It defines Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement. Generative outputs increasingly resemble HTL-intensive work in surface features, making verification of genuine human learning costly relative to benefits. When verification loses economic justification, evaluators reward outputs indiscriminately, forcing human learners to compete on price with near-zero-cost AI outputs. The authors formalize this 'value collapse' with a costly-inspection framework. Cross-domain evidence from academia, law, content platforms, and software security maps four stages of verification erosion. Alignment success is orthogonal: better-aligned models narrow observable human-AI gaps, making source verification harder and intensifying competitive pressure on HTL-intensive work.
[2606.06572] Generative Models Erode Human Temporal Learning Through Market Selection
[Submitted on 4 Jun 2026]
Title:Generative Models Erode Human Temporal Learning Through Market Selection
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Abstract:We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement with problems over time. Generative outputs increasingly resemble HTL-intensive work in surface features, so verifying whether a given output reflects genuine human learning grows costly relative to its expected benefit. Once verification loses economic justification, evaluators reward outputs regardless of production mode, and producers who invested years of learning compete on price against outputs that cost almost nothing to generate. We call this pathway value collapse and formalize it through a costly-inspection framework. Cross-domain evidence from academic publishing, legal practice, content platforms, and software security maps onto four stages of verification erosion. Alignment success is orthogonal. Better-aligned models narrow observable gaps between human and AI outputs, making source verification harder and intensifying competitive pressure against HTL-intensive work even when individual AI outputs improve.
Comments: Accepted at ICML 2026
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); General Economics (econ.GN)
Cite as: arXiv:2606.06572 [cs.LG]
(or arXiv:2606.06572v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06572
arXiv-issued DOI via DataCite (pending registration)
Journal reference: Forty-third International Conference on Machine Learning Position Paper Track (2026)
Submission history
From: Wenjun Cao [view email] [v1] Thu, 4 Jun 2026 17:59:06 UTC (52 KB)
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