Bohea

I use AI heavily. The machine still can't hurt you. 我大量用 AI,机器依然伤不了人

I use AI heavily in my work — and every time I ask it to review its own code, it finds new bugs. Sit with that for a second: probability-based AI, enormous coding power, and a physical world where one bug is a human–machine risk. Not using AI means being generationally crushed on productivity. Using it carries risk on every pass. How do you weigh that? 我在工作中大量使用 AI——而每次让它审查它自己写的代码,它总能再找出新 bug。停在这个事实上想一秒:基于概率的 AI、巨大的编码产能,和一个"一个 bug 就是人机风险"的物理世界。不用 AI,生产力会被代际碾压;用它,每一轮都带着风险。这怎么权衡?

AISafety-Critical安全关键How I Think我怎么想Type Systems类型系统

The realization: I will never finish fixing bugs想明白的那一刻:bug 我永远修不完

I fix bugs because I know they're there. The things I don't know — I have no way to know them. Which means I will never finish fixing bugs. Neither will AI: it just finds a different unfinished subset. So the answer can't live in anyone's vigilance — mine or a model's. 我修 bug,是因为我知道它们在那里。而我不知道的东西——我没有任何办法知道。这意味着 bug 我永远修不完。AI 也一样:它只是找到另一个修不完的子集。所以答案不可能存放在任何人的警觉里——无论是我的,还是模型的。

The answer: gates, like a type system答案:闸门,像类型系统那样

Don't fix bugs with your own cognition. Instead, classify what the machine can do wrong: 别用自己的认知去修 bug。相反,给"机器可能出的错"分类:

Class by class, build hard checks the way Java, Go or Rust type systems work: wrong type, you don't get to move. The software will be wrong someday — the design's job is to make sure "wrong" has no capability to become "harm." 一类一类地,像 Java、Go、Rust 的类型系统那样建硬校验:类型不对,就不许动。软件总有一天会出错——设计的职责,是确保"出错"没有任何能力升级成"伤害"。

What this unlocks about AI这为 AI 解锁了什么

Once the gates exist in a layer neither I nor the model can author or bypass, AI's productivity becomes safe to harvest: I use it heavily, on the non-core, after my decisions. The division of labor is explicit — AI generates, I decide, the gates enforce. Safety stops depending on how careful anyone was on a given Tuesday. 一旦闸门存在于一个我和模型都无法编写、也无法绕过的层里,AI 的生产力就可以安全地收割:我大量用它,用在非核心处,用在我的决策之后。分工是明确的——AI 生成,我决策,闸门执行。安全,从此不再取决于某个星期二谁有多小心。

What you're actually buying你真正买的是什么

Not "AI or no AI." A person who carries responsibility for the result: which risks get called out loud up front, which ones the design strips of the capability to do harm, which aren't worth fixing yet — a UI flicker gets its impact scope stated and a slot after delivery. My name is on the work. 你买的不是"用 AI 还是不用 AI",而是一个为结果扛责任的人:哪些风险开工前就大声说出来,哪些被设计剥夺了作恶的能力,哪些暂时不值得修——一个 UI 闪烁,会被讲清影响范围,排进交付后的时段。这份工作上,签着我的名字。

Written from real force-controlled robotics work; all client, vendor, and product specifics removed. And in the spirit of the piece: AI helped me write this page — the thinking is mine. 写自真实的力控机器人工作;所有客户、供应商与产品细节均已移除。并且秉承本文的精神:这一页有 AI 帮我写——思考是我的。

This is the personal-practice piece of a trilogy. The method in full: Industrial reliability: change the denominator. The macro argument: Where value goes when building is free. 这是三部曲里"个人实践"的一篇。方法全文:工业可靠性:换掉分母。宏观论证:当"造东西"变免费,价值去了哪。

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