The AI Industrial Revolution: Building Your Own Factory

The job of the engineer being something where you just show up to work, you ship the output directly, and everything inside the company was—'how good is person A at shipping output B?' And now what's happening is, the way I'm judging you as an engineer is, 'are you producing the factory that will produce multiplicative outputs B through Z?'

I've just been completely ham-fisted with them. I get frustrated at them and have found myself typing less and less information, doing less and less work as time goes on, because I just assume I can brute-force my way through it. I'll throw Codex, Claude, and Gemini at the same problem over and over and just waste tokens to save time.

Is pure software dead? Is pure software engineering an obsolete thing? It's like saying speaking English. The models now speak English. We had to learn code to communicate with them. Now the models speak English—fuzzy, sloppy English, like a human—and they understand things. So where's the moat for a founder? Hardware?

I haven't written a single line of code in quite a while now. Partly that's because my job is different. But also—since December, I've built a huge amount of software that I now use every day. All these projects I'd kind of fantasized about for years that I'm now using—that I've actually built. I didn't write any of that.

It completely changes the role of software and hardware developers. From day one we tried to take a lot of traditional engineering workflows—hardware engineering workflows—and turn them into software. ... What we've gotten into now is a mind-blowingly different model: the software engineers create the architectures, because they understand systems, algorithms, and division of concerns. Then the hardware engineers can vibe-code their pieces because they know hardware engineering.

No. This is an argument I had yesterday at dinner. One person at the table was claiming you'll just use DeepSeek for 97% of things because it's so cheap, and if you need more intelligence you'll just run it over and over again—the same problem. You'll only use OpenAI, Anthropic, etc. for the most advanced tasks. I was kind of like, 'I don't know.' I think intelligence is an unalloyed good. You always want more intelligence.

For many things, you can't buy it, so you have to make it somehow. ... The closer our products get to being a single block of covalently bonded matter, the better they'll be. Lower power, smaller, higher performance, longer lasting. The components aren't available. In order to do that type of integration—to actually innovate beyond just piecing together things you can buy off the shelf, which is really very limiting—you have to learn to do it yourself.

One of the things we've seen related to regulatory—it massively reduces change aversion and improves iteration. ... The first-order effect is you save a lot of time. The second-order effect is, if you change the specification of the airplane, it now takes minutes, not months. So you can actually be willing to change. And the third-order effect is you can get rid of the not-very-great engineers and have a small number of really creative ones who can iterate rapidly, because the cost of change goes down.

This is why there's so little innovation in medical right now. The FDA approval process is a nightmare. In fact, the two biggest advancements in tech in Silicon Valley in the last decade—AI and, before that, crypto—they're both in the math domain, because that's the last unregulated domain.

It's totally asymmetric. If you approve a bad thing, your career is over. If you block a good thing, nobody notices. It creates an asymmetric slowdown. I think that is the most important problem to solve in the regulatory state.

A lot of our infrastructure is already autonomous. We have a capability that fires off upon finding anomalies—I recommend everyone create a version of this, or Vercel offers one. ... We've automated a lot of the SRE job—Site Reliability Engineering. Any metric that slows down, speeds up, or changes throughput fires an anomaly alert, an agent investigates, and the agent can decide to create an incident.

I stopped all project work across the entire company for a week and said, 'Everybody, from the receptionist to the engineers, build whatever you think is the most important thing to build. Your only requirements: you have to use AI, and you have to demo it for the whole company when you're done.' I expected a large number of silly projects and a small number of needle-movers. We got the opposite—a large number of needle-movers and very few silly projects.

I think of art as conveying emotion—something you felt, transmitted to another person; you create an object that captures an emotion you felt inside. By that definition a computer is almost incapable of it: the exact same piece of art without intent behind it is meaningless.

The really deep question: is it possible for an LLM or transformer to go out of distribution—to have a new idea that wasn't present in the training set?