François Chollet：推理模型正在改变编程工作的投入产出比
如今我不再读写代码，只向大型推理模型（LRM）下达指令。但这并不是因为我觉得 LRM 生成的代码质量完美，甚至也不是因为我觉得质量很好；同样，这也不是因为我认为自己的指令总能被完美执行——我知道事实并非如此。
关键在于：LRM 不仅能帮你写代码，还让你能用新的方式掌控正在做的事情、保持对系统的理解，而这些方式的效果与阅读代码大致相当。你可以更充分地测试代码库，审查特定组件，快速生成可视化，让 LRM 开展红队测试，等等。这一切都能以很快的速度完成，而以前做不到。读写代码带来的好处，如今可以通过新的方式获得。
总体而言，手工编写代码的投入产出比已经不再显得那么理想。这不是因为 LRM 变得完美了，而是因为它们做事足够快，让你能够围绕它们建立新的工作流程，并最终比旧流程更高效。
对照原文
I don't read/write code these days, I only instruct an LRM. But it's not because I think LRM code quality is perfect, or even good. And it's not because I think my instructions are always being perfectly acted upon -- I know they aren't. The thing is: LRMs don't just write your code, they unlock new ways to control what you're doing and maintain understanding of your system, which are about as effective as reading code. You can better test your codebase, you can audit specific components, you can spin up visualizations, you can ask the LRM to red team, etc. You can do all of these things at high speed -- you couldn't before. The benefits of reading/writing code can now be achieved in new ways. All in all, the ROI of writing code by hand is no longer looking good, not because LRMs got perfect but because they're so fast at doing things that you can develop new workflows around them, workflows that end up being more productive than the old workflows.