Coding is basically the pinnacle of what you could reasonably automate with AI, and yet we still need human engineers to oversee agents for them to be effective. The AI models are trained on an incredible amount of sophisticated code. The users are highly technical and can use the latest tools quickly. The work is “verifiable” because you can test an app. The outcomes are often removed from the quality of the code (you can have sloppy code but the app can still work). And the context for the agent is often already digitized and sitting in the codebase. That’s an incredible amount of benefits that AI coding agents get to work with. Some of those apply to knowledge work, but most don’t in areas where the work needs to be fully reviewed to be useful, or where data isn’t as abundantly digitized. This makes the job for agents in knowledge work more complicated. So if with all of that, engineers still remain in very high demand, the risks are going to be less than what’s perceived for other areas of knowledge work. Agents will let people do far more than they did before, but the people don’t go away.
编程基本上已经是你可以 reasonably（相当合理地）用 AI 自动化的顶峰了，但即便如此，我们仍然需要人类工程师来监督 agent，agent 才能真正有效。AI 模型是在数量惊人的复杂代码上训练出来的。用户本身也高度技术化，能够快速使用最新工具。这项工作是“verifiable”（可验证的），因为你可以测试一个 app。最终结果往往与代码质量并不完全绑定（代码可以写得很 sloppy，但 app 仍然能运行）。而且，agent 所需的上下文常常早已数字化，并且就存在于 codebase 中。这些都是 AI coding agent 能够利用的巨大优势。其中有些优势也适用于知识工作，但在大多数领域并不成立，尤其是那些必须经过完整审查才有用的工作，或者数据还没有被如此充分数字化的领域。这就让 agent 在知识工作中的任务变得更加复杂。所以，如果在具备所有这些有利条件的情况下，工程师依然处于非常高的需求之中，那么其他知识工作领域面临的风险就会比人们想象的小。agent 会让人们做到比以前多得多的事，但人并不会消失。