We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while receiving AI-generated content, and AI systems retrain on the aggregate social information they influence. This interaction generates two feedback forces: an AI contagion channel, through which distortions diffuse across the network, and an AI social distortion multiplier, through which retraining amplifies past errors. Despite the high dimensionality of the environment, we show that the long-run behavior of the system admits a two-dimensional representation whose spectral radius determines whether AI-mediated information systems are dynamically stable or unstable. We characterize a sharp regulatory frontier identifying the minimum filtering required for stability and show how network topology shapes systemic informational risk.

翻译：我们研究人工智能（AI）如何与社交沟通网络相互作用，从而塑造集体知识的稳定性。智能体通过网络交换信息，同时接收AI生成的内容，而AI系统则在其所影响的社会聚合信息上进行再训练。这种相互作用产生两种反馈力：一是AI传染渠道，通过该渠道，信息扭曲在网络中扩散；二是AI社会扭曲乘数，通过该乘数，再训练会放大过去的错误。尽管环境具有高维特性，我们证明系统的长期行为存在一个二维表征，其谱半径决定了AI中介的信息系统是动态稳定还是不稳定的。我们刻画了一个清晰的监管边界，该边界确定了实现稳定性所需的最低过滤力度，并展示了网络拓扑如何塑造系统性信息风险。