人非孤岛：AI 如何瓦解知识共同体

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.

The Case of Software

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

1. In my professional life, I’m happy to move one level up to become a manager of AI agents. I’m literate, I’m a good technical writer, I can describe what I want and let AI agents write the code.
2. In my own time, I can keep doing the things I care about because I enjoy them intrinsically. This is the “intellectual” side of software engineering: reading technical blog posts and papers, learning new programming languages, designing new programming languages, writing technical essays, writing code for my tiny open source projects.

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

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.

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.

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.

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?

• You write a blog post: who’s going to read it? The next training run will ingest it, marginally improving AI capabilities. Maybe the post was a workaround to some obscure technical problem, so the next time someone encounters that problem, they will ask Claude, who will solve it without crediting you. Maybe you had some insight about how to structure large codebases: who cares? The humans aren’t making those decisions anymore.
• You design a revolutionary new programming language: who cares? Maybe Claude cares, for what that’s worth. But humans don’t write or even read the code anymore. The programming language is an implementation detail the humans no longer have to care about.
• You write a library, and publish it on GitHub: who cares? The AIs might discover it, and use it, but the operator won’t know you exist or did anything.

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.

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.

The Intellectual Life

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.

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:

• Intrinsic motivation: we learn for the sake of learning, we create art from a compulsion we can’t understand, etc.
• Extrinsic motivation: David Chapman defines “nobility” as manifesting glory for the service of others, and using our abilities in service of others. We want our work to be useful to others, we want others to benefit from our work, we want to contribute to a shared human project. Fame and the esteem and good will of your peers are the proxies by which we measure our contribution.

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.

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.

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.

After AI

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.

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.

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.

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. In this view, the collapse of intellectual communities is good, because it sifts the intrinsically-motivated übermenschen from the clout-chasing masses.

I think this view fits with contemporary society: we view intrinsic and extrinsic motivation as high and low status, respectively. A “developed” person is supposed to have a private, inexhaustible reserve of motivation which is causally disconnected from external reward.

But this is not a realistic view of human beings. Humans are social animals who can only flourish in the society of other humans. We care, and we should care, about contributing to the world. And if technology makes our contributions superfluous, then what is left?

Acknowledgements

Thanks to Luke Drago and Andy Matuschak for feedback and conversations.