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No Man Is an Island

In this post, I argue that individual intellectual activity can only be sustained in an intellectual community of other humans. AI dissolves these communities, which in turn makes private intellectual activity rarer.

在这篇文章中，我主张个人的智力活动只有在由他人组成的智力共同体中才能得以维持。人工智能瓦解了这些共同体，进而使得私人的智力活动变得更加稀缺。

Some years ago, when it became clear that AI would solve software engineering, my thinking was:

几年前，当人工智能将解决软件工程这一事实变得明朗时，我的想法是：

The second point has not quite worked out. What actually happened? First, the discourse of software engineering became worse. As I wrote earlier:

第二点并没有完全实现。实际上发生了什么？首先，软件工程的讨论质量变差了。正如我之前所写：

Claude Code was released a little over a year ago. In that short time, software engineering has been completely transformed. Materially, it might be positive: higher productivity, though at the cost of a messier codebase. Socially, it has been a disaster.

Claude Code 在一年多前发布。在如此短的时间内，软件工程已被彻底改变。从物质层面看，这可能是积极的：生产力提高了，尽管代价是代码库变得更加混乱。从社会层面看，这简直是一场灾难。

The discourse around software engineering has gotten dumber. It’s like everyone in the industry lost 30 IQ points. People used to talk about compilers, type systems, logic. Now they talk about “prompts”, “harnesses”, “loops”. The discourse is narrower, shallower, and more repetitive. There’s only so many times I can hear about “agentic harnesses” before I lose my mind.

围绕软件工程的讨论变得更愚蠢了。就像行业里的每个人都损失了 30 点智商。人们过去谈论编译器、类型系统、逻辑。现在他们谈论“提示词”、“工具框架”、“循环”。讨论变得更狭隘、更肤浅、更重复。在发疯之前，我实在听不了太多次关于“代理框架”的讨论了。

Then there’s the loss of human capital formation: there is nothing to learn. Prompting is not a skill, at least, it’s a much shallower skill than software engineering. The instrumental dimension of the work has improved in that people can get more output per unit of effort, but the dimension of work that’s about building up human capital has collapsed. And maybe this is rational. Why learn to code at all? The computers can do that for us. And so the rigorous, systematic thinking you need to practice in order to be a good programmer: all gone. The machines can be rational for us. We can just vibe.

此外还有人力资本形成的缺失：没什么可学的了。提示词编写并非一项技能，至少它比软件工程要肤浅得多。工作的工具性维度得到了改善，因为人们可以用单位努力获得更多产出，但关于积累人力资本的工作维度却崩溃了。也许这是理性的。为什么还要学编程呢？计算机可以为我们代劳。因此，成为一名优秀程序员所需要练习的严谨、系统的思维：全都消失了。机器可以替我们理性思考。我们只需要随波逐流。

Second, contributing to the commons of software engineering is increasingly pointless. Before AI, you could publish open-source code, write blog posts to share ideas or inspire other people, write expository texts like tutorials, forum posts, textbooks etc. to teach people. After AI, what’s the point?

其次，为软件工程的公共领域做出贡献变得越来越毫无意义。在人工智能出现之前，你可以发布开源代码，撰写博客文章来分享想法或启发他人，编写教程、论坛帖子、教科书等说明性文本来教导他人。在人工智能之后，意义何在？

It’s not just “you can’t get GitHub stars or traffic to your blog”; rather, there is no sense of a common human project you can contribute to. There’s your own private garden of code, which you can grow infinitely in all directions with the help of AI, but you never have to leave the garden and go to the bazaar to trade with people. Under these conditions, it’s hard to care or do anything.

这不仅仅是“你无法获得 GitHub 星标或博客流量”的问题；而是说，不再有一种你可以参与的共同人类事业。你拥有自己的私人代码花园，可以在人工智能的帮助下向各个方向无限生长，但你永远不必离开花园去集市与人交易。在这种条件下，很难去关心或做任何事情。

But does it actually matter? Does it matter if we stop writing blog posts about obscure JavaScript features, and designing new programming languages? Maybe writing code was always drudgery, and now we can move on to higher things, like math—oh, wait.

但这真的重要吗？如果我们停止撰写关于晦涩 JavaScript 特性的博客文章，停止设计新的编程语言，这重要吗？也许编写代码一直都是苦差事，现在我们可以转向更高层次的事物，比如数学——哦，等等。

From observing what happened to software engineering, and what’s currently happening to mathematics, I think we can derive some general insights about intellectual practices in general.

通过观察软件工程所发生的一切，以及目前数学领域正在发生的事情，我认为我们可以得出关于一般智力实践的一些普遍见解。

We tend to think of intellectual activity as private and solitary: the philosopher sitting in his armchair, deriving the world ab initio. But intellectual activity has two inputs that can’t be acquired in isolation: a shared body of work to build upon, and motivation. The shared body of work is communal, unless you want to recapitulate the entire tech tree. Motivation we can break down into two components:

我们倾向于认为智力活动是私人的、孤独的：哲学家坐在扶手椅上，从头开始推导世界。但智力活动有两个无法在孤立状态下获得的输入：可供构建的共享工作成果，以及动力。除非你想重演整个技术树，否则共享的工作成果必然是共同的。我们可以将动力分解为两个部分：

We tend to think of intrinsic motivation as the purest kind: endogenous, self-created, unmotivated by material or social gain. But it’s an emotion, and, like all emotions, it’s transient and short-lived. And this is rational: otherwise, we’d all be stuck in life-long unproductive obsessions. So, we need something to fill the gaps between moments of divine inspiration. Extrinsic motivation serves this function.

我们倾向于认为内在动力是最纯粹的：内生的、自我创造的、不受物质或社会利益驱动的。但它是一种情绪，像所有情绪一样，它是短暂且易逝的。这是合理的：否则，我们都会陷入终身无成效的执念中。因此，我们需要一些东西来填补灵感迸发时刻之间的空白。外在动力就起到了这个作用。

Private intellectual activity that is sustained, complex, and long-term requires an external intellectual community to provide material and motivation, like fuel and oxidizer. That private activity, in turn, sustains the community: by publishing papers, textbooks, code, etc., you add to the shared body of work for others to build on top of; by citing someone’s paper or contributing to their repository, you give them the recognition and honor that confirms they are doing useful work, which in turn motivates them to keep contributing.

持续、复杂且长期的私人智力活动需要一个外部智力共同体来提供材料和动力，就像燃料和氧化剂一样。反过来，这种私人活动也维持着共同体：通过发表论文、教科书、代码等，你为他人构建的基础增加了共享成果；通过引用他人的论文或为他们的仓库做出贡献，你给予他们认可和荣誉，确认他们正在做有用的工作，这反过来又激励他们继续贡献。

Without community, you don’t get isolated individuals each working on their own things: you get nothing. The inputs to intellectual activity dry up: no one is adding to the shared body of work, and there are no peers to benefit from your own intellectual activity. Without this extrinsic motivation, you get less intellectual activity because, again, intrinsic motivation is fleeting.

没有共同体，你得到的不是各自做事的孤立个体：你什么也得不到。智力活动的输入枯竭了：没有人再增加共享成果，也没有同行能从你的智力活动中受益。没有这种外在动力，你的智力活动就会减少，因为再次强调，内在动力是转瞬即逝的。

After AI, intellectual contributions become unnecessary or redundant. In the case of software: the AIs write all the code, so what’s the point of writing either code or prose? The audience for those things is now severely diminished. Humans don’t write code anymore, so they won’t read blog posts about how to write code, or tutorials, or try new libraries or programming languages. In the case of mathematics: the AIs can prove theorems, write papers, explain papers, tutor students, and in the near future, they might write entire textbooks better than humans. So what’s the point of writing a paper, or a textbook? It’s superfluous.

在人工智能之后，智力贡献变得不必要或多余。以软件为例：人工智能编写了所有代码，那么编写代码或文章还有什么意义？这些内容的受众现在严重减少了。人类不再编写代码，因此他们不会阅读关于如何编写代码的博客文章、教程，也不会尝试新的库或编程语言。以数学为例：人工智能可以证明定理、撰写论文、解释论文、辅导学生，在不久的将来，它们甚至可能编写出比人类更好的整本教科书。那么，撰写论文或教科书还有什么意义？那是多余的。

If intellectual activity is unnecessary—if there’s no consequence to designing a new programming language or publishing a paper, or if there’s simply no community to contribute to—then it won’t happen. There’s no point.

如果智力活动是不必要的——如果设计一种新的编程语言或发表一篇论文没有任何后果，或者根本没有可以贡献的共同体——那么它就不会发生。毫无意义。

Now apply this to every other domain of intellectual activity, and you see what the future looks like. There may be individuals building new libraries and programming languages, but no shared culture of software engineering; there may be individual students and practitioners of mathematics, but no living community of mathematicians.

现在将其应用到其他每一个智力活动领域，你就会看到未来是什么样子。可能会有个体在构建新的库和编程语言，但不再有共享的软件工程文化；可能会有个体的数学学生和从业者，但不再有活跃的数学家共同体。

I’ve spoken to people who think AI will have a positive effect on the life of the mind, and their thinking is that right now too many people are doing intellectual activity for instrumental reasons: citations, clout, etc.

我曾与一些认为人工智能将对精神生活产生积极影响的人交谈，他们的想法是，目前有太多人出于工具性原因进行智力活动：引用率、影响力等等。