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大型语言模型（LLM）非常强大。它们能够快速地为许多问题提供一次性的、看似合理的解决方案。这促使公司大力投入，将日益增长的认知需求转移给LLM。投入精力去验证一个由LLM审查并经过快速冒烟测试、但可能只是一个粗略解决方案的方案，然后由一个疲惫的人类匆匆一瞥，这似乎意义不大。这导致了各处复杂性的蔓延：决策、沟通、代码、文档等等。与认知负债类似，由于思考和决策的细化被外包出去，没有人获得了解决特定细分用例的独特经验。这相当于一个知识渊博的人在知识转移发生之前就离开了公司。你唯一留下的，是别人遗留的、你永远无法完全理解的假设和决策。我已经看到了这种情况的发生。在客服领域，LLM从未真正解决过我的任何客服请求。本应对产品了如指掌的客服，却建议我访问不存在的、被“幻觉”出来的页面和按钮。在代码领域，代码库因不合理的复杂性而膨胀。在产品领域，界面决策被外包给LLM，导致公司在解决自己独特的细分问题时，却采用了千篇一律的解决方案。你是否也观察到了类似的情况？

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LLMs are great. They can quickly one-shot plausible solutions for many problems. This makes companies push hard to offload rising cognitive demands to LLMs.It doesn't make much sense to invest effort into verifying a plausible-looking throwaway solution that is reviewed and smoke-tested by an LLM, with a quick glance from a tired human.This results in complexity creep everywhere: decisions, communication, code, documentation, etc.On par with cognitive debt, as thinking and decision refinement were offloaded, nobody gained unique experience solving a niche use case. This is equal to having a knowledgeable person leave the company before the knowledge transfer occurred. All you're left with is a legacy of someone else's assumptions and decisions you'll never be fully aware of.I already see it happening. In support, LLMs never solved any of my support requests. Support, which should know the product inside out, recommends visiting pages and clicking buttons that are hallucinated and don't exist in the product. In code, codebases are ballooning with unreasonable complexity. In product, interface decisions are outsourced to LLMs, making companies arrive at the same solution for their unique niche problems.Are you observing something similar?

Ask HN：大型语言模型是否正在缓慢地让公司运转失灵？