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承保变成审计：没有证据链，就拿不到好保单当风险不再可预测，保险就只能把控制前置，没有证据链就没有保单。条款标准化把边界写清楚之后，保险业真正的下一步不是简单涨价，而是把承保流程变成准入审核。投保企业能不能投保、能投多少额度、免赔多高，越来越取决于企业能不能证明自己具备一套可验证的AI治理机制。例如，像Armilla AI这类新玩家，它专注于为生成式AI和AI代理提供信任与安全验证及保险服务，通过独立模型评估与监管级审计来承保AI暴露，覆盖算法错误、模型漂移与生成式AI幻觉等风险。
Mac 从来都不需要成为 iPad，macOS 也不需要和 iPadOS 融合，生态打通，一切大同。
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.