Position: Generative Models Erode Temporal Learning Through Market Selection

This position paper argues that modern machine learning creates structural risks for knowledge and cultural production, operating before AGI thresholds through market selection mechanisms. We use \emph{temporality} operationally for how understanding changes over time and signals left by that process. Representation learning and autoregressive generation approximate output distributions while omitting slow, path-dependent human learning; at scale, these function as general-purpose production technologies. We analyze the link from technical indistinguishability to market selection: when divergence between model and temporal signals is small and verification costly, decision makers cease screening, prices track pooled quality, and temporality-intensive work exits. We call this phenomenon \emph{value collapse}. Recent evidence shows this active: academic publishing has experienced dramatic productivity increases alongside troubling quality trends; cultural production shows explosive AI-generated content adoption. As training data mirror such environments, models absorb their outputs and model collapse risk rises. Alignment is orthogonal: by narrowing observable gaps, it intensifies selection pressures where provenance remains costly.