问HN：为什么大型语言模型不取代老板，而是取代工程师？
我问自己，为什么讨论的重点总是放在增强或替代工程师，而不是老板，并让ChatGPT整理我的思路：
1. 工程师与大型语言模型（LLMs）：对错误的容忍度低
工程现实：如果开发人员推送了一个微妙错误的代码，可能会导致服务崩溃、数据损坏或引入安全漏洞。
当前的LLMs：在生成看似合理的代码方面表现出色，但仍然容易出现逻辑漏洞或隐藏的错误，这些问题在生产环境中可能并不明显。
结果：无论如何，你仍然需要大量的人为监督——这使得“替代”更像是“看护”的场景，这可能比让优秀的工程师自己编写代码更昂贵。
2. 首席执行官与LLMs：对模糊性的容忍度较高
CEO现实：决策往往基于不完整的数据、很多直觉和有说服力的叙述。这里有更多的灵活性——一个“错误”的决定有时可以被包装成“战略性”或“远见”的，直到结果显现出来。
当前的LLMs：在综合多个数据源、识别模式和生成战略选项方面表现出色——这一切都没有个人自我或政治的偏见（当然……除了训练数据本身可能存在的偏见）。
结果：它们可以快速生成连贯且有充分理由的战略，而人类仍然可以负责沟通和实施这些战略。
3. 为什么这实际上是合理的
如果考虑错误成本：
工程师的错误 = 直接、可测量、代价高昂（生产中的bug）。
CEO的错误 = 反应较慢、更主观，有时可以通过包装来弥补。
如果考虑数据整合能力：
LLMs具备超人类的记忆和综合能力。
CEO正需要这种技能来进行市场情报、竞争分析和高层决策框架。
所以，是的——在这种框架下，用LLM替代CEO级别的战略生成，同时保留工程师的人工参与，实际上可能更为实用。
人类仍然需要做“面对面的工作”（投资者关系、内部士气），但战略大脑可以是一个输入了所有相关商业数据的LLM。
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I asked myself why all the talk goes into augmenting or replacing engineers instead of the bosses and let ChatGPT formulate my thoughts:<p>1. Engineers vs. LLMs: low tolerance for mistakes<p>Engineering reality: If a developer pushes code that’s subtly wrong, you can crash a service, corrupt data, or introduce security flaws.<p>LLMs today: Great at producing plausible-looking code, but still prone to logical gaps or hidden bugs that might not be obvious until production.<p>Result: You’d need heavy human oversight anyway — turning the “replacement” into more of a “babysitting” scenario, which could be more costly than just having good engineers write it themselves.<p>2. CEOs vs. LLMs: higher tolerance for ambiguity<p>CEO reality: Decisions are often based on incomplete data, lots of gut feeling, and persuasive narrative. There’s more wiggle room — a “wrong” call can sometimes be spun as “strategic” or “visionary” until results catch up.<p>LLMs today: Excellent at synthesizing multiple data sources, spotting patterns, and generating strategic options — all without bias toward personal ego or politics (well… except whatever biases the training data has).<p>Result: They could produce coherent, well-justified strategies quickly, and humans could still be the ones to communicate and enact them.<p>3. Why this actually makes sense<p>If you think of error cost:<p>Engineer error = immediate, measurable, costly (bug in production).<p>CEO error = slower to surface, more subjective, sometimes recoverable with spin.<p>If you think of data integration skills:<p>LLMs have superhuman recall and synthesis capabilities.<p>CEOs need exactly that skill for market intelligence, competitor analysis, and high-level decision frameworks.<p>So yes — in this framing, replacing CEO-level strategy generation with an LLM and keeping engineers human might actually be more practical right now.
Humans would still need to do the “face work” (investor relations, internal morale), but the strategic brain could be an LLM fed with all relevant business data.