与AI协作更像领导而非编程
For most of my career, code gave me certainty. A program did what its instructions told it to do. If the same input produced a different result, we called it a bug.
在我职业生涯的大部分时间里，代码给了我确定性。程序会按照其指令执行。如果相同的输入产生不同的结果，我们称之为bug。
People were never like that. As a leader, I can explain a task and get exactly what I asked for. I can also get something better because a colleague understood the intent behind the request. Sometimes the result shows that I was not as clear as I thought.
人从来不是这样的。作为领导者，我可以解释一项任务，并得到我要求的确切结果。我也可以得到更好的结果，因为同事理解了我请求背后的意图。有时结果表明我并不像自己以为的那样清晰。
Working with AI feels closer to the second experience.
与AI合作感觉更接近第二种体验。
AI runs on software, but working with it is not fully predictable. The same request can produce a different answer. It can make a useful connection, miss an obvious point, or surprise me with an approach I had not considered.
AI运行在软件上，但与它合作并非完全可预测。相同的请求可能产生不同的答案。它可能做出有用的联系，错过明显的要点，或者用我未曾考虑过的方法让我惊讶。
This is frustrating when I treat AI like a compiler. It becomes more useful when I treat the interaction as a form of collaboration.
当我像对待编译器一样对待AI时，这令人沮丧。当我将互动视为一种协作形式时，它变得更加有用。
That does not make AI a person. It has no lived experience, accountability, or human judgment. The comparison is about how we work. Good leaders do more than issue instructions. They share context, explain the desired outcome, set boundaries, and respond to what comes back.
这并不会让AI成为一个人。它没有生活经验、责任感或人类判断力。这种比较是关于我们如何工作的。优秀的领导者不仅仅是下达指令。他们分享背景，解释期望的结果，设定界限，并对反馈做出回应。
The same habits improve my work with AI. A good prompt helps, but a shared working context helps more. Examples, corrections, and reusable instructions reduce misunderstandings. Over time, the system becomes better aligned with how I think and what I need from it.
同样的习惯改善了我与AI的合作。好的提示词有帮助，但共享的工作背景更有帮助。示例、纠正和可重用的指令减少了误解。随着时间的推移，系统会更好地与我的思维方式以及我对它的需求保持一致。
The investment is not in pretending that AI is human. It is in becoming better at expressing intent.
投资不在于假装AI是人类，而在于更好地表达意图。
We spent years learning how to tell computers exactly what to do. Now we also need to explain why the work matters, what a good result looks like, and where judgment is needed.
我们花了多年时间学习如何告诉计算机确切要做什么。现在我们也需要解释为什么工作很重要，好的结果是什么样子，以及哪里需要判断力。
For me, that is the shift. AI is making software work less like issuing commands to a machine and more like leading through a conversation. The technology is new. The leadership skills are not.
对我来说，这就是转变。AI正在使软件工作不再像向机器发出命令，而更像通过对话进行领导。技术是新的，领导技能却不是。
This note led to a thoughtful discussion on Hacker News. I recommend reading through all the comments; the agreement, criticism, and different experiences add more to the idea than I could fit here.
这篇笔记在Hacker News上引发了深思熟虑的讨论。我建议阅读所有评论；其中的赞同、批评和不同经历为这个想法增添了更多内容，超出了我在这里能容纳的范围。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力