Generative AI Market Dynamics Accelerate Erosion of Human Temporal Learning

The paper "Generative Models Erode Human Temporal Learning Through Market Selection" (arXiv:2606.06572) models value collapse where verification costs for AI versus human outputs render sustained human effort uneconomic. Cross-domain patterns in publishing and code show evaluators defaulting to surface features once inspection loses justification. This directly ties falling marginal costs of generative models to reduced incentives for path-dependent skill accumulation. Academic citation analyses post-2022 reveal shorter reference chains and shallower problem engagement coinciding with LLM adoption spikes. Legal document drafting platforms report parallel drops in associate training hours as template outputs dominate. Software security audits document increased reliance on unverified generated patches, confirming the four-stage verification erosion sequence. Market selection now rewards price competition over production mode, creating structural pressure against temporal learning independent of alignment improvements.