When I Tried Doing Everything With AI, It Backfired

当我尝试用 AI 完成一切时，结果适得其反

There was a phase where I started pushing AI into everything. Writing with AI. Planning with AI. Coding with AI. Research with AI. Even thinking through decisions with AI. At first, it felt like leverage at scale. Why not use an extraordinary tool for everything? That seemed rational. But slowly, something strange happened. The more I tried using AI everywhere… the worse some of my thinking became.

曾有一段时间，我开始将 AI 应用到所有事情中。用 AI 写作、规划、编程、研究，甚至用它来辅助决策。起初，这感觉像是大规模的杠杆效应。既然有如此强大的工具，为什么不把它用在所有地方呢？这看起来很合理。但慢慢地，奇怪的事情发生了：我越是尝试在所有地方使用 AI，我的某些思考能力反而变得越差。

Breaking the Expectation

打破预期

We tend to assume maximum tool usage means maximum advantage. Use AI more. Get more value. Simple. But I started realising that assumption breaks down. Because not everything improves through automation. Some things degrade. Especially when over-optimised. And thinking is one of them.

我们往往认为，工具使用得越多，优势就越大。多用 AI，就能获得更多价值，这很简单。但我开始意识到，这种假设是站不住脚的。因为并非所有事物都能通过自动化得到改善，有些事物反而会退化，尤其是当过度优化时。而“思考”就是其中之一。

The Insight

洞察

What backfired wasn’t AI itself. It was my attempt to make it universal. I was treating AI as the answer to every cognitive task. And that created subtle problems: I accepted first answers too quickly; I explored fewer original paths; I began outsourcing rough thinking, not just repetitive work. That was the mistake. Because rough thinking, the messy early stage, is often where the best ideas form. And I was bypassing it. Efficiency was starting to eat into originality.

适得其反的并不是 AI 本身，而是我试图让它“万能化”的尝试。我把 AI 当作解决所有认知任务的答案，这导致了一些隐蔽的问题：我太快接受了第一个答案；我探索的原创路径变少了；我开始外包那些粗略的思考过程，而不仅仅是重复性工作。这就是错误所在。因为粗略的思考——那个混乱的早期阶段——往往是最佳创意形成的地方，而我却绕过了它。效率开始蚕食原创性。

What I Realized

我的感悟

Some tasks should be accelerated. Others should be wrestled with. That distinction matters. AI is exceptional for: expanding options, reducing mechanical effort, stress-testing ideas. But there are moments where speed is the enemy. Moments where slowness produces depth. And I was losing that.

有些任务应该被加速，而另一些则需要我们去反复推敲。这种区别至关重要。AI 在以下方面表现卓越：扩展选项、减少机械劳动、对想法进行压力测试。但有些时刻，速度是敌人；有些时刻，缓慢反而能带来深度。而我正在失去这种深度。

The Bigger Pattern

更大的趋势

I think many people are doing something similar. Using AI not as leverage, but as default cognition. That feels advanced. But often it is overdependence disguised as sophistication. Just because AI can be inserted into every part of work… doesn’t mean it should be. That was a hard lesson.

我认为许多人正在做类似的事情：将 AI 不再视为杠杆，而是视为默认的认知方式。这看起来很先进，但往往是伪装成“精明”的过度依赖。仅仅因为 AI 可以被插入到工作的每一个环节，并不意味着它就应该被这样使用。这是一个深刻的教训。

The Reflection

反思

I still use AI heavily. But with much more restraint. Because I’ve come to believe this: Good use of AI is not about putting it everywhere. It’s knowing where it should stop. When I tried doing everything with AI, it backfired because I confused amplification with substitution. And those are very different things. Some of our best thinking still happens in the parts no tool should touch.

我仍然大量使用 AI，但更加克制。因为我开始相信：善用 AI 并不在于把它用在所有地方，而在于知道它应该在哪里止步。当我尝试用 AI 完成一切时，结果适得其反，因为我混淆了“放大”与“替代”。这两者有着本质的区别。我们最优秀的思考，依然发生在那些任何工具都不应触碰的领域。