Working with AI feels more like leadership than coding

与 AI 协作更像是一种领导力，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. 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.

在我的职业生涯的大部分时间里，代码给了我确定性。程序只会按照指令行事。如果相同的输入产生了不同的结果，我们称之为漏洞。人却从来不是这样。作为领导者，我可以解释一项任务并得到我要求的结果。我也可能得到更好的结果，因为同事理解了请求背后的意图。有时结果会表明，我并没有我想象的那么清晰。

Working with AI feels closer to the second experience. 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. This is frustrating when I treat AI like a compiler. It becomes more useful when I treat the interaction as a form of collaboration.

与 AI 协作的感觉更接近第二种体验。AI 运行在软件之上，但与之协作并非完全可预测。相同的请求可能会产生不同的答案。它可能建立有用的联系，遗漏明显的要点，或者用我没考虑过的方法让我感到惊讶。当我把 AI 当作编译器时，这令人沮丧。当我把这种互动视为一种协作形式时，它变得更有用。

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. The same habits improve my work with AI.

这并不意味着 AI 是一个人。它没有亲身经历、责任感或人类判断力。这种比较是关于我们的工作方式。优秀的领导者不仅仅是发布指令。他们分享背景信息，解释期望的结果，设定边界，并对反馈做出回应。同样的习惯也改善了我与 AI 的工作。

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. The investment is not in pretending that AI is human. It is in becoming better at expressing intent. 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.

好的提示词有帮助，但共享的工作背景更有帮助。示例、纠正和可复用的指令可以减少误解。随着时间的推移，系统会变得更好地与我的思维方式以及我对它的需求保持一致。这种投入并不是假装 AI 是人类。而是变得更好地表达意图。我们花了多年时间学习如何告诉计算机确切该做什么。现在我们也需要解释为什么这项工作重要，什么是好的结果，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. 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.

对我来说，这就是转变。AI 正在让软件工作变得不那么像向机器发布命令，而更像通过对话进行领导。技术是新的。但领导力技能不是。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；赞同、批评和不同的经历为这个想法增添了比我在这里能容纳的更多的内容。