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

Researchers propose a unified dynamical systems model of human-AI co-evolution, showing that increased reliance on LLMs creates feedback loops between human cognition, data quality, and model capability. The analysis identifies three regimes including a 'degenerative convergence' where over-reliance on AI leads to reduced diversity and an information bottleneck, suggesting AI trajectory depends as much on human behavioral dynamics as on model design.

This theoretical paper addresses a critical but understudied phenomenon: the recursive feedback between human decision-making and AI system development. Rather than treating cognitive offloading and model collapse as separate issues, the researchers model them as coupled dynamics where human reliance on AI for knowledge work shapes the training data quality for future models, which in turn encourages greater human dependence. The framework identifies three possible equilibria, with particular concern for 'degenerative convergence'—a state where closed-loop reinforcement between AI generation and human retraining reduces informational diversity without providing genuine compression or insight.

The information-theoretic framing is particularly significant. The authors argue that what appears as beneficial model compression may actually reflect entropy loss from constrained diversity within the human-AI loop. This distinction matters because it suggests current metrics for model improvement may mask systemic degradation of knowledge quality.

For the broader AI ecosystem, this work signals that architectural improvements alone cannot prevent trajectory degradation if behavioral incentives remain misaligned. Organizations heavily dependent on AI-generated content for training data face systemic risk of quality collapse—a concern extending beyond language models to multimodal systems and scientific research pipelines. The research implies that sustainability requires deliberate human-led knowledge generation, diverse data sources outside AI outputs, and friction mechanisms against pure automation cycles.

Looking forward, practitioners should monitor whether real-world AI systems exhibit the predicted transitions. Measuring data diversity, tracking generative model outputs in training corpora, and assessing human cognitive investment in knowledge work become critical health indicators for AI systems designed for long-term deployment.

• →Human-AI systems form coupled feedback loops that can transition from beneficial co-evolution to degenerative equilibria with reduced diversity.
• →Over-reliance on AI for knowledge work and retraining creates information bottlenecks that reduce diversity without providing genuine compression.
• →Model improvement metrics may mask systemic quality degradation when training increasingly relies on AI-generated rather than human-generated content.
• →Sustainability requires deliberate friction against pure automation cycles and diverse knowledge sources independent of AI outputs.
• →Long-term AI system health depends on monitoring data diversity and human cognitive investment, not just model capability metrics.