Coupling Physical Entropy Production and Semantic Diversity in Generative AI Ecosystems: A Preliminary Predictive Framework

Generative artificial intelligence is increasingly embedded in recursive informational ecosystems in which outputs are produced, published, retrieved, summarized, copied and pasted into new human or machine production. This paper proposes a preliminary predictive framework for Dissipative Semantic Homogenization (DSH), a possible re-gime in which recursive generative AI ecosystems dissipate physical energy while corpus-level semantic diversity contracts and saturates. The framework does not iden-tify thermodynamic entropy with semantic entropy. Instead, it treats them as opera-tionally coupled variables: semantic distributions are transformed by physically im-plemented computation, while energy dissipation provides a macroscopic cost proxy. We model semantic diversity as Shannon entropy over a corpus-level partition of se-mantic states and introduce modal amplification, independent novelty injection, and AI assimilation of nominally human production as control variables. The model yields empirically testable implications: semantic contraction should occur only when effec-tive independent novelty falls below a stability threshold; contraction should be scale-dependent; and cumulative semantic loss should saturate even while physical entropy production continues. The framework is not presented as an empirical law, but as a testable theoretical model for future longitudinal and controlled studies.