Steve Yegge

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we are moving into a world very quickly this year where proof of work is so important and I mean proof of work not in the Bitcoin sense but your proof of what you have done, your resume. And I don't mean your resume because nobody's going to believe that. I mean the actual work that you did which has to be visible

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But, I can tell you that there are at least two more cycles left in this, and that means they will be at least 16 times smarter than they are today, and that is going to cause all of knowledge work to be subsumed by this stuff.

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The bitter lesson is don't try to be smarter than the AI, okay? You think that you've got special knowledge. The humans bring special domain knowledge to this problem and we're going to teach it something to AI and it will be smarter. What we found was bigger is smarter. Always.

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My my feeling is that probably people have a low tolerance for non-determinism. And these things are fundamentally non-deterministic. So, they can't just go replace customer call center software because they they could be wrong.

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So, I think what's happening is we're all looking at the big companies going, "When are you going to give us something?" And the answer is we're looking at the big dead companies. We just don't know they're dead yet.

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In fact, innovation died on the vine like all together and since I don't know, 2008, there has been no innovation from Google. It's all been acquisitions. They have they've created nothing new.

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let's say an engineer can be 100 times as productive, just just for sake of argument, all right? Who captures that value? If the if the engineer goes to work and works for 8 hours a day and produces 100 times as much, the company captured all of that value.

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You might only get three productive hours out of a person at max vibe coding speed. And yet they're still 100 times as productive as they would have been without AI. So, do you let them work for 3 hours a day? And the answer is yeah, you better.

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There's a vampiric effect happening with AI where it gets you excited and you work really really hard and you're capturing a ton of value. For me, I'm doing it all for myself and it's still kind of like pushing me to my ragged edge.

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But right now I think it's sitting somewhere between half million and five million lines of code, somewhere in there. Probably more on the half million side right now and with the next drop of an Anthropic model, we're probably going to see it jump up to a few million lines.

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If you're a monolith, you're kind of hosed because I told you the ceiling's going up for what they can do, but it ain't ever going to hit your monolith. They will never fit in the context window and you're never going to be able to never in the next 18 months be able to tell a model go fix my monolith. You have to break it up.

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if chat is completions in a loop, basically, and agents are basically chat in a loop, well, then we're going to put we're going to put agents in a loop, and that'll be an orchestrator, right?

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I think that by the end of this year and we'll see demos of it like right away, but by the end of this year most people will be programming by talking to a face. A face as in a face on the screen.

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So I think we're in a weird limbo for the rest of this year okay where until the UIs arrive that are good enough for everybody who can't read everybody who can't read is going to be a severe disadvantage.

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What probably the single most important proxy metric that you can have it in a company today is token burn. Because what token burn says is your engineers are trying to do stuff or your non-engineers.

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I think a lot of the people coming out of knowledge work are just anti-AI. And those people are going to struggle. I'm sorry, but if you're anti-AI at this point, it's like being anti-the the sun. You're going to have to go live underground, right?

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it's also enabling engineers who have seen the light and believe the curves are going to continue to go up to actually get together in groups of two and five and 10 and 20 and 30 people and start to do things that rival the output of these big companies that are tripping over themselves.

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I'm mad at Amazon for laying off 16,000 people and blaming AI without an AI strategy for it. Those people are not going to be able to find jobs, by and large, and they're the first of many to come and nobody has a plan for this.

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And so, what's happening is everybody on average is setting that dial to about 50% and we're going to lose about half the engineers from big companies, which is scary.

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Because they're all starting to spend their own salaries in tokens. And so, at least for a while, if you want your engineers to be as productive as possible, you're going to have to get rid of half of them to make the other half maximally productive.

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I had a lot of my own ego and identity wrapped up in my sort of compiler background. It's all It's interesting, right? But it's it's not useful in any meaningful sense anymore.

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they they're like, "Yeah, you know, I use Cursor, and I I ask it questions sometimes, and I'm really impressed with the answers, and then I review its code really carefully, and then I check it in." And I'm like, "Dude, you're going to get fired, and you're one of the best engineers I know."

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Excellent book on concurrency.

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This is a great book for exactly, precisely the reason that all O'Reilly books are great: it covers complex material in a way that's accessible to uncomplex people.

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I've read a ton of data-structures and algorithms books, and I've never found one like this one. Its unconventional approach is refreshing and keeps you interested.

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This is an odd little book. It's frequently mistaken for an introductory programming book, which inevitably leads to frustration.

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This is without question one of the weirdest technical books you'll ever encounter. But don't be put off by the format.

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and it's by far the hardest book in the list so far, even more so than The Algorithm Design Manual, in no small part because it's extremely dense.

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But WikiWikiWeb is still cool, and in particular it's cool because it has a lot of smart people agonizing over hard issues.

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The thing is, Google has a well-known false negative rate, which means we sometimes turn away qualified people, because that's considered better than sometimes hiring unqualified people.