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.

翻译：大语言模型（LLMs）正在重塑知识生产方式，人类日益依赖人工智能系统进行生成、总结和推理。尽管已有研究关注人类的认知外包行为以及递归训练中的模型崩塌效应，但这些效应通常被孤立地考察。我们提出一个统一视角：人类与语言模型通过使用、生成和再训练的反馈回路构成一个耦合动力系统。我们引入一个包含三个变量——人类认知能力、数据质量与模型能力——的最小模型，并证明该反馈机制可产生不同的动力系统状态。通过分析，我们识别出三种状态：共同进化增强态、脆弱平衡态与退化收敛态。通过简单模拟，我们发现对人工智能的过度依赖可引发向低多样性、次优平衡态的相变。从信息论角度审视，这种相变对应着人机回路中涌现的信息瓶颈：在闭环反馈下的熵减表征的是多样性丧失与支持度下降，而非有益的压缩效应。这些结果表明，人工智能系统的发展轨迹不仅受模型设计影响，更受人类与AI共同进化动力学的塑造。