Human-AI Co-Evolution and Epistemic Collapse: A Dynamical Systems Perspective

TL;DR

This paper models human-AI interaction as a coupled dynamical system, revealing how increased reliance on AI can lead to degenerative feedback loops and reduced diversity in knowledge production.

Contribution

It introduces a minimal dynamical model of human-AI co-evolution, identifying regimes that explain how feedback can cause enhancement, fragility, or collapse.

Findings

Increased AI reliance can cause a shift to low-diversity, suboptimal states.

The model reveals three dynamical regimes: co-evolutionary enhancement, fragile equilibrium, degenerative convergence.

Information-theoretic analysis shows entropy reduction as an emergent bottleneck in the feedback loop.

Abstract

Large language models (LLMs) are reshaping how knowledge is produced, with increasing reliance on AI systems for generation, summarization, and reasoning. While prior work has studied cognitive offloading in humans and model collapse in recursive training, these effects are typically considered in isolation. We propose a unified perspective: humans and language models form a coupled dynamical system linked by a feedback loop of usage, generation, and retraining. We introduce a minimal model with three variables -- human cognition, data quality, and model capability -- and show that this feedback can give rise to distinct dynamical regimes. Our analysis identifies three regimes: co-evolutionary enhancement, fragile equilibrium, and degenerative convergence. Through a simple simulation, we demonstrate that increasing reliance on AI can induce a transition toward a low-diversity, suboptimal equilibrium. From an information-theoretic perspective, this transition corresponds to an emergent information bottleneck in the human-AI loop, where entropy reduction reflects loss of diversity and support under closed-loop feedback rather than beneficial compression. These results suggest that the trajectory of AI systems is shaped not only by model design, but by the dynamics of human-AI co-evolution.