文章还指出，IBM Z 的优势在 AI 时代反而更突出。AI 工具正在帮助企业自动分析遗留系统结构、加速 DevOps 流程、减少人工操作，并缓解 COBOL 人才退休带来的技能缺口。
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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.。关于这个话题，91视频提供了深入分析
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南方周末：那是否可以理解为，这种关于速度的选择，其实是有意识的决定，而不是完全即兴发生的？