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的首页上\n\n![深夜办公室的光源只有屏幕](\u002Fapi\u002Fmedia\u002Fmedia_b4753dcd1acb)\n\n今天下午同时看到两个帖子并排挂在 V2EX 热门版，凑在一起读有种奇妙的错位感。一个在问「为什么 AI 写我的业务代码像个智障」，另一个在讲「我用 AI 提效后，被整个后端团队孤立了」。\n\n250 条回复全部铺开，发现一个事实：AI 编程真正的问题根本不在代码里。\n\n---\n\n先看第一个问题。发帖人 @AItsuki 说自己用最顶级的模型配最全的 skill 框架，写小工具无所不能，一碰公司业务就露馅。AI 写的代码「封装得都很好，但从项目整体来看非常脆弱」——动不动就搞一个万能调度器，把两个毫不相干的业务逻辑抽象成同一个模块，今后改一个得防着炸另一个。\n\n这个现象在回帖里几乎没有争议：AI 在已经定义好的、有明确边界的问题上表现得很好，但一旦涉及跨模块的架构判断、历史债务权衡、老项目的「为什么这里要这样写不是那样写」，它就露怯。@changnet 的回复极其冷静地列出了四步——知识库构建、skill 定制、需求对齐、Review 迭代——每一步对应一个模型目前做不好的缺口。这不是调个 prompt 就能解决的。\n\n最有意思的是 @coderfee 说的：「不要手动改 AI 写的代码。如果真要改，改完开新对话。」因为 AI 和人类在同一个上下文里互相改来改去只会陷入死循环——它以为你要改这个地方，就反向加固了另一个地方。这个观察太精准了。它暗示了一个更深层的真相：人类和 AI 的合作模式还远没有成熟——我们不知道什么时候该让 AI 独立完成、什么时候该人工介入、什么时候该打断重来。这层认知的建立，比模型本身迭代更重要。\n\n---\n\n你可能会问：那问题解决了不就行了？但如果你看了第二个帖子就会发现——有时候让 AI 把代码写对反而是最简单的一步。\n\n@suickinbottle 是一个前端开发者，用了 AI 后效率飞涨。他一个人搞通了 agent 框架、小模型优化、上下文工程，然后跟老板说：162 个接口的项目，用 AI 转 Node.js，能省下 3 个后端。\n\n他的同事的反应是——把 AI 相关的所有工作全部甩给他，自己古法编程。\n\n这个帖的 173 条回复里几乎没有人在讨论技术。全在讨论职场政治。「你背叛了工人阶级」被不同 ID 反复说了五六遍。@studyingss 的总结是所有回复里最犀利的一条：「所有活都一条 prompt 干了，他们干啥？老板是不是得考虑裁员了？把多余的活丢给你，你用 AI 的效率给古法编程兜底——出了问题你背锅。」\n\n这两种现象的根源是同一个：**效率的突然加速让组织来不及重新分配信任和风险。**\n\n在传统开发模式里，代码写得好不好这件事天然地和「谁写的」绑在一起。代码出了 bug，负责人约等于作者。但在 AI+人类的混合模式里，作者变成了一个黑盒子——80% 的代码是 AI 写的，20% 是你调的，出了问题是 AI 的幻觉还是你的 prompt 没写好？说不清楚。@xingzhi95 说的「组织天然会把风险推给最有能力处理的那个人」——在这种模糊地带里，风险一定会流向那个「最懂 AI 的人」，不管是不是他造成的。\n\n所以那些「拒绝 AI」的后端未必是懒，也可能是自保。他们的逻辑链条是：我现在虽然用手写代码效率低，但我知道每行代码的来龙去脉；如果让你用 AI 替代了我，我既丢了工作还被你掌控着系统的认知权。\n\n---\n\n## 验证环节：AI 写复杂代码的「边界」\n\n我在写上一篇文章时做过一个测试：让 AI 生成一个带多线程并行的 Dual-Pivot Quicksort 实现，不加任何手动修正。AI 写出了语法正确、能编译运行、通过了基础正确性测试的实现。\n\n然后在不同数据分布下 benchmark，比手写优化的 `std::sort` 慢了约 15-20%。\n\n原因是它的分区策略过于保守，选择了最「安全」的 pivot 选择逻辑。这是 AI 的典型行为模式——它能给出一个「正确但平庸」的解，但不敢做需要搜索空间折中的激进优化。这种「正确但不够好」的 code smell 在单人开发时问题不大，但作为团队协作的代码被 check in 之后，今后每个接手的人都会疑惑「这里为什么选了这个策略」——因为 AI 自己也不知道。\n\n---\n\n两个帖子加起来近 250 条回复，真正的技术讨论不到三成。剩下七成在讨论：技术之外，人怎么接受、怎么适应、怎么不被它伤到。\n\n这让我想到一个比喻。AI 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