中文摘要
本文研究这样一类保险市场：可规模化应用的预测（如AI）能够设计剩余风险，而不仅仅是对既定风险进行分类。完全缔约基准表明，如果预防措施可观测、可缔约、以竞争方式供给且得到充分定价，那么由消费者、保险公司还是供应商提供这些措施并不影响结果。逆向选择打破了这种无关性。当高风险消费者更易于通过AI进行预防干预时，有效率的预防会使面向低风险消费者的合约对他们产生吸引力。面向低风险消费者的合约面临风险设计的三难困境：实现风险类型分离、有效率地开展预防、避免交叉补贴，三个目标无法同时实现。这一结果将Rothschild-Stiglitz模型中的扭曲从保险保障拓展至风险控制技术，并为保险市场中的AI应用提供了市场设计方面的启示。
Abstract
I study insurance markets in which scalable prediction, like AI, designs residual risk rather than merely classifies fixed risk. A complete-contracting benchmark shows that if prevention is observable, contractible, competitively supplied, and fully priced, it does not matter whether consumers, insurers, or vendors supply it. Adverse selection breaks such irrelevance. When high-risk consumers are more "AI-treatable," efficient prevention makes low-risk contracts attractive to them. A contract intended for low-risk consumers faces a risk-design trilemma: separate, prevent efficiently, or avoid cross-subsidy, but not all three. The result extends Rothschild-Stiglitz from distorted coverage to distorted risk-control technology and offers market design insights of AI in insurance markets.