Generative Models Erode Human Temporal Learning Through Market Selection

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); General Economics (econ.GN)

arXiv:2606.06572 [cs.LG]

(or arXiv:2606.06572v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.06572 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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