From IDEs to AI Agents with Steve Yegge

26:21Yeah. And I think as professionals, we really ought to just at least try. You get first-hand experience and then you can make your decision. Steve's point about token burn is really interesting. The companies that win are the ones that experiment the most. And if you want to bring that same experimental mindset to your product, not just your AI usage, that's exactly what our presenting sponsor, Static, is built for. Static gives you the complete toolkit without building it yourself. You get feature flags, experimentation, and product analytics all in one platform and tied to the same underlying user assignments and data.

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27:27Statsik has a general street tier to get started, and pro pricing for Teams starts at$150 per month. To learn more and get a 30-day enterprise trial, go to statstig.com slash pragmatic. With that, let's get back to Steve's take on the state of Gastown. Now, there's a huge problem with people not knowing how to try and they say, oh, let me do something. And then it does the wrong thing because they always do. And then they're like, well, this is garbage. So, you know, you have to teach them that it's a shovel and you don't go shovel dig like in Fantasia, right? Like make the brooms walk around.

27:56No, you pick up a shovel and you dig with it, but it's a shovel that you didn't have before you were using your hands. Like it's a really, really simple analogy, but people just don't get it. they don't get it and i think and i'm going to say something that's contentious but in like it's it's just the reality of the world most people can't read i've ruined much much of my work in my life i've just completely gone down wrong path by overestimating people's ability to read and i think that reading is if anything getting harder to come by as a skill these days and uh and this is the situation that we're in right now is that cloud code makes you read a lot 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 tell me a little bit more about your observation a lot of people a lot of developers cannot read because you were at amazon that place supposedly is running on six pagers and people actually reading does it i mean most dude most people can't read i don't know if you know this man like i they just they read really slow.

29:00Okay. And, and the AI is, I mean, come on to most people, five paragraphs as an essay, remember five paragraph scenes in high school is a thing we have in America, I guess, maybe years were a hundred paragraphs in Amsterdam, but to us, five paragraphs is a lot. And that's like, that's the AI just clearing its throat. Right. You know, you've got to be able to read waterfalls of text. And so we're looking at a world where that won't work. And so you're going to need recursive summarization. You're going to need a factory. And it's funny because like, this is why, I mean, trying UIs is so important because Gaston right now, the reason I say you can't use it is that it's a factory filled with workers and you're talking to it through a telephone.

29:38You can also go and look through the window and count on it and talk to the workers, but it's not like you're in it, right? With a UI, you're in it and you can, you can see what's going on. And right. It's all invisible and yes, by and large, right. You know, hard to see. And so I really do think, and I'm going to, I'm just going to make a bold prediction. I think that by the end of this year, and we'll see demos of it right away, but by the end of this year, most people will be programming by talking to a face. A face as in? A fake person. Your AI, like the Gastown Mayor, will be a fox talking to you.

30:11And you'll say, why doesn't it work? And they'll say, I'll go look at it. And it'll go spin off its workers just like it's doing, but you're talking to a face. And it will talk back. Yeah. I think that's the only thing that's going to work for most people. Fascinating. Let's write this down to prediction. Why do you... Go build it. I'm not going to. Let's talk about Gastown. You mentioned Gastown. What, for those that a lot of people have heard about, what is Gastown? Gastown is an orchestrator. So 2023 was completions. Code completions. Yeah, autocomplete. Yeah. That's when we said it's a fact.

30:44Completion acceptance rate card. You remember that? Oh my yeah. People were measuring it. Stupid metric, by the way. The second one was, but it was close. It was a proxy for, are they trying? Right? Then there was chat. That was 2024, right? And then agents was 2025. We knew you could just look at that curve and go, okay, well, if chat is completion is in a loop, basically, and agents are basically chat in a loop, well, then we're going to put agents in a loop and that'll be an orchestrator, right? And a bunch of them started coming out and I built one of my own, my own vision, but that's all it is.

31:15It's agents running agents. and can you talk through a software engineer to us architecture like how is it organized how can i imagine you know this setup sure i mean look um gastown is really complicated and it's been really broken all week because i'm migrating it to dolt and that's where i actually learned how complicated it was it has a lot of features you're migrating it to to dolt it's a a new database oh okay yeah Yeah, Dolt is amazing. Dolt is a Git back to database. It's a Git database. Beads is just Git plus database crammed together badly. And there's actually a database that does this.

31:51So I'm migrating to it. But yeah, anyway, Gastown is what it should be is one mayor that you talk to. That's your person. And then whatever else needs to get done, they're just going to fire off workers. okay it's a little a little bit more complicated than that because there are really i think there are two kinds of work that that people go back and forth on and people are arguing about whether they're the right one some people at anthropic told me it's the minimaxing context argument okay there are people who believe that you should maximize your context window and fill it with rich juicy context so that the ai is wise and all-knowing when it's talking to you they want to like you know just right at the edge of the context and then there are others who are like task kill it task kill it i want the shortest window because of the quadratic x you know increase in in um cost yeah combined with the dramatic drop off in cognition as the tokens go up right yeah losing their track and stuff so so what which one's right and we've got people who are like full on in the in the in the minimizing and the maxers and and i looked at my work workflow and i was like well polecats are the min and crew are the max i have two fundamental role worker roles in gas towns.

33:01So you have the really simple one, which is the small content from the forecast. If you have a really well-specified task, all broken down into subtasks, then you can find, and it's like, it's self-contained. It says what to do. Then you can give it to a worker and have it go do it, right? Meanwhile, you have a really difficult design problem. You're going to have to have a series of conversations about this. I maximize context. I'm like, read all these docs and then we'll talk, right? So it's just two workflows. And like, I like the idea. I mean, it sounds like I think it's so easy to imagine like it's a little town, you know, like it was wild, wild west.

33:36There's the mayor, like the crew, the workers, everyone's buzzing and going around and the houses are being built. In practice, how does this work? Like, how has it worked for you? What are you hearing people get projects done versus not getting it done versus turning into absolute chaos? What have you learned with Gastown? It's been a great experiment. I mean, I've really enjoyed it. It's been an experiment, right? Well, yeah. I mean, right? I mean, I went out and built something that deliberately doesn't work. It's too hard. It's too hard for the models. Even Opus 4.5 is barely enough. And it's funny because the folks at Anthropic told me they like it, but they're kind of embarrassed, some of them, because it feels like I've got all these workarounds for bugs in their model.

34:13Which it kind of is, right? But it's not a bug. It's that their model was never trained to be a factory worker. And it will be soon. So a lot of gas time is going to disappear. A lot of the complexity, a lot of the roles that are monitoring, all they're trying to do is tell Opus 4.5 to be smarter, and that's being on the wrong side of the bitter lesson, right? So Gastown is going to simplify and flatten into just minimax roles. Crew for your max and your polecats for your mins, and I think that's the natural shape, and they'll just scale up. And could that be the polecats? They might just be subagents at some point, for example?

34:44Well, subagents, I mean, polecats are subagents. It's just that they're more, they're first class. They have their own identity inbox. You can talk to them. You can actually see how they performed over time by computing skill vectors on their work and things like that. So a little bit more than that than subagents. I think subagents have the problem of being opaque. I'm going to fire off a bunch of subagents to go do this work. And then you're like, okay, let me know when you're done. Whereas with Gastown, you can go look at them and be like, dude, your polecat's not working. I'm going to poke it.

35:12Right? So Gastown gives you a lot of hands-on, I don't know, steering. Right? It doesn't try to get out of your way. It's in your way, Gastown. It's really fun, though. I miss it. It's been down for a few days for me. And I tell you, man, working with regular Claude just stinks by comparison. Because it's like an idea factory. Once it's actually running and all booted up and everything, you can have so many things going on at once. And actually track them reasonably well. Now, it can suck you into a mode where you don't sleep, you don't eat, and you start, it's not good for you. And I actually wanted to talk to you a little bit about what's happening in the industry at some point.

35:47but but Gastown itself i mean like it was all calculated all the characters you know the naming why did i even do Gastown right why is it why because i wanted to move the over to the window right because people last year when i would say orchestration's coming they'd say no agents aren't aren't no swarms no orchestration whatever everything you're saying is just not true and now what they're saying is bro you're being pretty aggressive right which is a different conversation they're like now they're like well your swarm i don't know maybe your swarm can't do bubble but it's just completely shifted the conversation from the realm of impossibility to the realm of possibility so is it fair to say that you took on more than you you reasonably thought you could chew you took on this more ambitious ones because you wanted to both stress test what these models can do and find out find out and honestly just have some fun have some fun find out what's next and i'm continuing to do that so my next thing is i'm going to string 100 gas towns together we have a community a discord and if moltbook can get people to pitch in tokens for fun like they paying they're paying you're paying for the inference of your your agent on moltbook right so if i string 100 gas towns together and we decide to build something together we will learn the mechanics of federation we're probably retracing ethereum steps but we will and uh and we're going to come up with something remarkable it's like the people version of multa uh right multbook whatever it is and what what are misconceptions about gastown or what it's trying to do that you feel it's kind of you know gone off a little bit of rails and it's good to clean up well i mean for starters i don't think people should be using it and they are and i really mean it well i'm always saying people should not be using it, like should not be using it except if you're doing research or if you're like actually understand that this is just a proof of concept.

37:41So some very, very clever people that I've been talking to have been searching their problem spaces for subsets, categories that Gastown could productively use today at a big company, a big Fortune 50 company, say. Wow. And they've identified some problem spaces that you could put Gastown on today. And I was like, oh, that's pretty clever thinking. One of them was this company I talked to that sets up bespoke data centers for you, okay, in any region you want, which is something AWS has never been able to do. Google's always tried. And they say it's just three months of miserable button presses to try to install the software and check that it all works.

38:15And the acceptance criteria are very clear. It's, you know, it's almost a Ralph loop. But they think Gastown could swarm it and eventually converge on a data center that works and save all the people the trouble. You know what I mean? And I was like, all right. AI, and this could potentially meaningful move the needle on their ability to open up more of these data centers for people, right? Yeah, go figure. And the same guy was telling me that he's been looking at production incidents and he's realized their system is already in an indeterminate, unknown, broken state when they're down. So how much worse can AI actually make it?

38:45Now, I cautioned him and said, actually, it can make it a lot worse. But he's thinking on the long lines that there are certain categories of outages where you could have them in investigation mode or whatever, right, where they could speed things up. So people are looking for the fuzzy problems there was a third one that came along i forget what it was but there's there's a class of problems emerging for which you can swarm them because you don't care that the results are messy it's the cumulative work that right but that's actually how i code now i mean like right i mean like i code myself i mean i bit off more than i could chew there's no question about it man gas town is a huge mess right now and everybody's going he's going to vibe code himself into a corner and come crying out you know they're pretty close to true although i did manage at just before we got on the plane to get it back on track and it's working again right so one interesting thing about Gastown is you said you don't look at the code you have the agents write the code and which is very very unlike what your career has been right you cared about craft code elegance why did you decide to do it and what are the results I mean are the results as bad as I would think they would because this is right like like if if you imagine we're going to put like a thousand interns on a project like we've kind of seen that in the past and the result has been well eventually Josie and your engineer comes in and cleans up the mess.

39:53And I'm just curious, like how is it better or worse? Well, so the ceiling of what it can actually build productively before it just dissolves into a mess is going up. But right now, I think it's sitting somewhere between a 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, which is pretty good size. But it's nothing compared to what enterprises have. Right. Nothing. Enterprises are very, very, very, very big. They have hundreds of millions to billions of lines.

40:24Yeah, but not in one code base. Like having a few million lines of code is already a big code base and you'll typically have 50 plus people, sometimes 100 plus, 200 plus working on it. Right. What it really comes down to, just to summarize this conversation, get to the end, is how well you're going to be able to take advantage of AI totally depends on whether you're a monolith or not. If you're a monolith, which almost every company is a monolith, they have one monolith and then a bunch of microservices, right? 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.

40:52That 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. Okay. If you want to take advantage of AI or rewrite it from scratch, it's starting to get faster at this point to think about rewriting your stack. Yeah. One thing you mentioned even before we started that AI can really drain you. It can drain your energy. It can pull you and it can suck you. Can you tell me about this? Dude, there is something happening that we need to start talking about as a community, as an industry.

41:20Okay. 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. I find myself napping during the day, but I'm talking to friends at startups and they're finding themselves napping during the day. It's funny. They literally try to load each other up with enough context to force the other one into a nap, almost like a compaction event. It's so weird. And we're starting to get tired and we're starting to get cranky. And I started talking to people in the industry and they're starting to get tired and cranky.

41:59And what's happening is, see, companies are set up to extract value from you and then pay you for it. Right. But the way all companies have always been set up is that they will give you more work until you break. if you can do it, they'll just happily just say, give you more, give you more until your plate flows over and you die. And people have to learn the art of pushing back, right? And that's been a thing for a long time, but it's changed the equation. The way you push back, the reasons to push back and all that have changed very dramatically and are changing right now because you've got all these people now who can be super productive.

42:33And it's like, let's say an engineer can be a hundred times as productive, just for sake of argument, all right? Who captures that value? If the engineer goes to work and works for eight hours a day and produces 100 times as much, the company captured all of that value. And that is not a fair capture exchange. I think we can argue, unless if they have early, say, sharp, and they have a meaningful equity, that's a bit different. Yes, yes. But for all the rest of you. But that's not the majority of people, right? It's a minority. Yeah. Yeah. We're probably getting there pretty quickly. We did notice one thing, and you probably saw this as well, about six months ago, we talked about a lot.

43:10the 996 problem at AI startups. And we were like, oh, it's interesting. AI startups, people are working really frigging long hours and they're posting that they're in the office at 3 a.m. And you could tell - I'll share with people what 996 is who don't know. 996 is 9 a.m. to 9 p.m. six days a week, if I'm not mistaken. Which is 996 is the standard you're expected to work in most of Southeast Asia, as far as I know. I haven't been to China or India, but I assume it's pretty much similar there too, right? There's another group of people who are capturing all of the value for themselves. They go in and they work for 10 minutes a day and they get 100 times as much done and they don't tell anyone and they've captured all the value.

43:51And that's not really ideal either. So at least in terms of if you're thinking in terms of how can groups of people be successful, it's best if they're all contributing. So what do you do? and i think that the answer is each and every one of us has to learn how to say no real fast and get real good at it and we need to learn how to start capturing and the correct this is the new work-life balance okay it's how much of the value are you going to capture from being 100 times it's productive and how much of it are you going to pass along to your employer and this is a really difficult place to be because we don't have any cultural all our cultural expectations are pointed in the wrong way for us to work harder and they want us to right everyone to extract extract extract at.

44:32And so I seriously think founders and company leaders and engineering leaders at all levels, all the way down to line managers, you're going to have to be aware of this and realize that getting your engineers onto this treadmill is pulling them into a, they're using much, much more of their system too. They're doing much, much more of that hard thinking now. The easy stuff is getting automated. So you're actually draining them at a higher rate. Their batteries are draining at a higher rate. You might only get three productive hours out of a person at max vibe coding speed. And yet they're still a hundred times as productive as they would have been without AI.

45:06So do you let them work for three hours a day? And the answer is, yeah, you better. Or your company's going to break. It's very interesting. It's also like the value extraction, I think, I can see it speeding up. And we see it with a few prominent people. Peter Steinberger single-handedly pushes out so much more value output, you name it, commits. In any way, that would have been a team of 10 pretty good engineers before. And he, you know, like in all fairness, he is capturing it in the sense that he's, it's his project, it's his baby. He does not sleep much. So, so that, that's definitely showing, but the value capture there is kind of okay.

45:41But I agree with you that this could be something like in the past, whenever there was a technology shift where people were more, more efficient, we couldn't, in your lifetime. Have you seen this where injuries became more efficient and suddenly you could do a lot more with a lot less? And what happened at that time? People got mad. Yeah. I'll give you an example. Pearl. The Perl programming language was a massive accelerator. Amazon's website was built in Perl, probably still is. I think Facebook's technically is too. PHP is a fake Perl. And you can quote me on that. And both of them were incredible productivity accelerators.

46:14And everybody just could see it. You don't want to build websites and see. You just don't. Amazon tried it and they gave up, right? So that caused a huge rift, a huge schism. There were second-class citizens. All kinds of cultural dynamics happened there, right? I'm curious about how some AI companies deal with this. Can we talk about how Antrofic works? Yeah. From what I know. From what you know from the outside, I know that you talk with people across the industry, but Antrofic is a very interesting place. One interesting thing that Dario recently said is he thinks compensation specifically for their staff, the people who are building all these things and they're actually using the models and doing, he said something interesting that maybe we should have compensation where people are compensated even after they leave the company for the value that they created, which is just something completely unheard of.

47:06But it's clear that he's thinking about this thing that is changing where individuals can create massive value in a relatively short amount of time. Google, you can send me a check for all that stuff you never paid me for. Okay, just got to get that out of the way. I like that idea. Anthropic is unlike any company on earth right now. They're operating in a space that is really fragile and they're very protective of it and they need to be because they've created a hive mind. They're running the company, as far as I can tell, like a pure functional data structure. Remember Chris Okasaki's book that was such a mind-blowing.

47:41You can make data structures that never mutate, then how do you mutate them, right? And the answer is you just keep adding. It's improv. Yes, and. Yes, and. Right? And that's how they operate. And when you say hive mind, what do you mean by that? It's like the markets today. Vibes. Everything's vibes. It just shifts. It's just, right? It's vibing. It's kind of hard to explain, but you see, here's the thing, right? We used to build products by, like, making a spec and then implementing it and then complaining about it and then shipping it. Oh, and having a roadmap and planning for it. Waterfall.

48:13And timing it for the company annual events. Right. And Apple, right? Once a year. The way you work with, like, systems like Gastown, and they've got their own internal orchestrators, is you create it. And your founders, the one that like the co-founder that was non-technical, you create the prototype and that's your product and you start building it and you just make it the product until it's right. So everybody just gathers around the prototype like a campfire and builds it. And that is what Anthropik's doing at scale with thousands of people. So you're saying that the playbook of a successful tech product might have changed because the traditional wisdom since the lean startup in like 2010 or so was you use your prototype to get signaled, then you throw it away and then you build a lot more Polish stuff.

48:53right and we used to i think every software engineer who's been around you don't ship a prototype you tell people it's a throwaway you start again you make it productionally scalable that kind of stuff because you don't want to give a bad experience to people yeah what changed though just the ability to do an infinite number of prototypes so instead you make prototypes until you get a great one and you're like let's launch this and so apparently claude co-work happened in 10 days somebody went hey i did a prototype and they were like we're gonna launch this and 10 days later they launched it so i mean it works but i guess one one important context there when i talked with boris cherny about a feature that they did about how they did the tasks in clutch in cloud code the task list of how it completes he told me that in two days he built 20 different prototypes that were all working thanks to ai i didn't know that but he's doing what i'm talking about they call it slot machine programming right you do 20 implementations and is that what he's doing something like that i don't want to put words in his mouth but but i was i was just floor because building 20 working prototypes, that would have been two weeks and you would have stopped at three, right?

49:55That's in our book, actually, if I can pitch the book for a moment. FAFO, F-A-A-F-O, is the dimensions of value that you get from VibeCoding and the O is optionality, which is the ability to create lots of prototypes. What it lets you do is defer your decision until you know what the right answer is, which is cheating. So, of course, everybody does it, right? And it's going to fundamentally change the way that companies are run. It's going to change the way that people organize to create software. And it's going to happen this year. It's just fascinating how these changes are coming. But what enables these changes?

50:30Is it the fact that we can iterate faster with these things? Look, I saw a phenomenon happen at Google. This is kind of a big company question. There's a big company and a small company answering your question, right? Something happened at Google. I went through the golden age at Google where it was like anthropic it was a hive mind it was nobody was mean everybody was innovating and it was wonderful yeah this was a time where like the founders were pretty close you you'd go to the cafeteria and larry and sergey be sitting there and you'd hang out with them and just chat and it was like golden age right yeah and then it changed rather abruptly we made a few pivots and it became not that company anymore and in fact innovation died on the vine like all together and since I don't know, 2008, there has been no innovation from Google.

51:16It's all been acquisitions. They've created nothing new. I mean, they did Gemini a few years later, right? Yeah. Okay, sure. They created LLMs and then did nothing with them. That's a perfect example of why innovation dies there. For five years. Right? Five years, they did nothing. So I don't count Gemini. That's a different Google. We're talking about the Google that screwed up. I don't want philanthropic to screw up this way again the way that google did google put safeguards in place to try to keep them from turning into the company that they turned into which was ossified you know territorial nobody could i hired a brilliant dude from microsoft brought him into google and said figure out what you're going to do take as long as you need it took him six months to find something that nobody else had claimed already people claim work and then never do it at google so i'm going to tell you something I've never said before.

52:04This is a brand new take. I think what happened at Google was when Larry Page became CEO and he said, we're going to put more wood behind fewer arrows. That was a motto. And he put a halt to innovation. Before then, there was more work than people. And after that, there were more people than work. And so people started to fight over the work. And that's where people started to do land grabs and backstabbing and territoriality and empire building and all the bad stuff you see, all the politics that you see is about fighting over work. Going back to Anthropic, they're at a frontier and there's infinite work and literally all of them have too much to do.

52:45And a friend of mine, a friend of mine in Amazon once told me that we don't have a lot of the problems that Google has because everyone at Amazon is always slightly oversubscribed. They have too much work. I've heard similar with Apple as well. That's kind of deliberate. Interesting thing. I mean, if we assume I am seeing productivity gains for myself, so I'm not disputing that agents actually make you more productive. And I think we can agree on by how much. But for me, it's a lot. But if this happens, a lot of companies, people can actually do a lot more work. Do you think a lot of companies that are larger will see politics show up, which typically happens when.

53:19If the catalyst for the bad stuff beginning is more people than work, and all of a sudden people can do all the work, then the company's biggest problem is going to be finding more work or they're going to have to get rid of people, which is kind of bad, right? But it's not unlike Gastown in this mall. My biggest problem with Gastown is feeding it because it works so fast. I have to work really hard to come up with good designs for it, right? That's what I spend on my – which is why I'm taking naps all day long because I'm trying to come up with difficult work for it, right? Other people have said this too.

53:49This is the problem with gas. And this is the problem with everybody who's going to use any orchestrator. It doesn't have to be gas town. That thing will be dead in four months, probably, right? I mean, it's the shape that worked in December 2025. That's not going to be the shape that works in four months, right? One thing that I think, you know, where it might sound that we're talking really abstract, especially for people who have not done this type of work in themselves, is like, well, we're talking about orchestrators. They're like all productive. Can you point to something that has been built with an orchestrator or with this higher productivity that is a production software?

54:16either you built it or you've observed someone build it that could show like actually this is way more productive and we can actually see the output or turning it the other way around like we're still not seeing that much more output from companies teams that you would expect okay like a lot of them are having more productivity but like from the outside it's easy to be skeptical when we're seeing not much has changed in terms of our data live the app so you know we're seeing signals here and there, but nothing major. Like, why might that be? Yeah, that's fair. My feeling is that probably people have a low tolerance for non-determinism, and these things are fundamentally non-deterministic.

55:00So they can't just go replace customer call center software because they could be wrong. And it doesn't seem to matter that humans are also wrong very often, and AIs can, these days can very easily get to the same level as a human, as an average human in the job. But I think there's still a lot of risk aversion, right? So I think that the companies that are actually running with this are actually starting to see the results and it's going to be reflected in the quarterly earnings invisibly and in other ways at first. Could it be that we're focusing on building the tools? I'll turn it around and I'll say, what if what we're actually observing is that innovation at large companies is now dead.

55:37And we are only going to see innovation from small places, which is kind of what happened when cloud came out. And Facebook was a college kid at one point. Facebook feels like the biggest company in the world right now, but it was one dude. Okay. And so when a new enabling platform technology substrate appears, you're going to see innovation at the fringes because of the innovator's dilemma. Big companies can't innovate. They're all running into this problem. They may have hyperproductive engineers who are producing at a very, very high rate, But the company itself can't absorb that work downstream.

56:08They're just hitting bottlenecks and these engineers are getting shut down and they're quitting, right? 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. Do you think they're dead because, for example, it can now be cheaper to do something? Like we can just say the internal punching bag, Zendesk customer support. They have been the de facto place to do your customer support because your agent can sign up. They get this UI, they get this workflow, et cetera.

56:34And for AI native companies that are using MCPs and whatnot, it makes no sense for them because they just want an API, which Zendesk does not want to give to you because they want to charge extraordinary amounts for you to come to their platform and buy their AI for, you know, 10 times the cost. That model is going to struggle a lot in coming years because people will build their own stuff bespoke with APIs. This is my platform rant in real life, right? If Zendesk doesn't make themselves a platform, then they're going to build a product of themselves out of existence, I think. and the platform for the for looking ahead it's is it apis is it i think mcps i mean as far as we can no maybe not mcp right i mean what did anthropic found that what works better than mcp is having the ai write its own api to call the mcp because they're so good at writing code but then nothing really changes because platforms always apis from the beginning right so why do we need mcp well we needed some way to declare what the tool does in an ai way but i mean like Like I just, it's so loose and so flexible.

57:32Integration is going to be really easy. I don't know. I'm not following that space well enough to know if MCP is going to continue to be an important dominant player or if the AIs just use stuff directly like via command line tools, right? Or APIs. But either way, we're moving into this world where the innovation is coming out of new shops who have adopted and adapted. and I see big companies struggling really bad right now with this. I wonder if we will see a lot more of these building blocks that we didn't know we needed. I think we're going to see a huge ecosystem of building blocks for people who are non-technical, who want to build stuff and they need those APIs.

58:15You know what I mean? Like for storage or for matching or for whatever it is they need to do. So I guess if you're in tech and if you're looking for an idea either because your job is looking a bit shaky or you actually just want to do something, now could be a great time to start building some of these building blocks that work. Reliable building blocks will probably be in need that have states, that have SLAs, whatever, that have some importance, right? That's not trivial to do. That's right, because AIs are lazy. With good reason, they don't want to burn tokens if they don't have to. So if you provide a service that's going to make something convenient for them, they'll absolutely use it.

58:50Yeah, especially if it's a service that you need to maintain, for example, like you need to keep up with may that be regulation or changes or logging or whatever yeah that's kind of a lot of work to do even to prompt like to and go back every day to prompt again to like update and all that also as humans we're also lazy yeah i mean well larry wall called it right it's that's one of the virtues of a programmer yeah i want to go back to one of another one of your essays from 2012 uh which was called the borderlands gun collector club pa Steve Yegge. You're the one that read that one. I got recommended on Blue Sky and a lot of people liked it and I read it and I realized I didn't read it.

59:28And this was a really interesting essay because seemingly it has nothing to do with what we're talking about. But you talked about gamification and you talked about how this Borderlands game, which you played apparently, right? or back in the day you mentioned how after you completed the game there was this weird thing that the game developers probably accidentally put in there people kept coming back to have like custom guns and these were like a meta goal that the designers probably never thought of but it actually made the game pretty kind of addictive and you called this as a i think it was like some sort of elder game or something like that and you were kind of saying that hey this was pretty smart there was an accident from the game designers but maybe more game designers should do this because it just makes the game addictive and you know like not saying that but since that it was in 2012 we've seen so many games just have like deliberate gamification and not just games but but a lot of other things yeah a lot of them found that mechanic eventually what who is it did the borderlands um take two or i forget anyway they figured it out early then they didn't capitalize on it but uh yeah so interestingly i think yeah gamification uh gamification is kind of rearing its head people have pointed out that like people are making game fun into gastown right i mean why don't i make it a game like come on man i mean like look we have literally we have games for running factories imagine you're running an actual factory how cool is that right that's what guess what gastown is that's why it's so fun actually do you think that one of the reason that some of the agents are more successful than others looking at specifically cloud code is they also there's some gamification where there's always something showing there right there's a tinkering there's the different things that keeps talking to you.

1:01:06There's always is some of maybe accidentally or maybe deliberately? Oh, they have the best product managers in the world and they have done absolute magic with command line UIs and stuff that they've done. It's wild. But look, I mean, come on. Right? That's not going to work for most devs. So that's why Cloud Cowork is so cool, right? Because it's showing us the direction that things are going to evolve, I think. I think developers will use Cloud Cowork or something more like it. With traditional software, we have tag depth, and we know how to deal with it, and we've talked so much of this. In fact, if we think about what we spent, we're very busy with the 2010s, tag depth, collecting it, paying it off, migrations, yada, yada, yada.

1:01:51Now that we're doing a lot of Vibe coding, or you call it Vibe coding, but agentic engineering just churning out a lot of code, how do you think we will recognize or deal with, or do we need to deal with this Vibe coding depth? Oh, yeah. You do. You do. One of my upcoming blog posts is about this, actually. I've discovered that there's a thing. I've given it the name of, it's called a heresy, okay, that happens in Vibe-coded code bases that you're not looking at, where an idea can take root among the agents that's incorrect. It's a wrong architecture or a wrong data flow or whatever that's causing an impedance mismatch for the rest of your code.

1:02:29And what happens is I call it a heresy because they have a tendency to grow and to come back. And they're really hard to weed out. I had a bunch of them in Gastown. There was a polecat heresy that kept coming back. And so what would happen was it's invisible and your product stops working properly along the edges. And you don't know why. and you start having the agents dig into it and you realize you've got a fracture. You've got a fault line. You have like, say, two complete databases that are both live and operational and you're randomly choosing between the two of them, right? And you didn't realize this until just now, right?

1:03:07You find terrible things in your code, right? And you try to get them all out, but there'll be one reference to it in some doc somewhere that an agent picks up on and goes, oh, that makes sense. It's the heresy and it returns. And the agent does the wrong thing and goes off and rebuild the heresy and it starts to spread again. It comes back, right? It's like the agents want the system to work this certain way. And you're telling them, no, I want it to work this other way. And you're fighting with them. And what you have to do is you have to actually document the heresy in the beginning of your prompting and say, this is one of the ways that you can go wrong on my project.

1:03:40Don't do that, right? And then you have to remind it periodically or even put in tooling to keep it from doing that. Another heresy is that my agents all think they should be doing PRs. It's like, I'm the maintainer of this code, man. just push to main, right? Or a branch or something. Don't make a PR. It's just polluting the PR space. That's for contributors. They can't get this today. Now, I could put a bunch of hacks in, but that's fighting the bitter lesson. Opus 5 will be fine. Opus 5 will be, oh, you don't want PRs? I won't do PRs. What is the bitter lesson? Oh, the bitter lesson, yes. Richard Sutton wrote a very, very short essay.

1:04:11It's like 800 words. It's one of the best essays ever called the bitter lesson where he's like, yeah, we're AI researchers and we learned a bitter lesson and you need to learn this lesson. The bitter lesson is don't try to be smarter than the AI. Okay. You think that you've got special knowledge, that humans bring special domain knowledge to this problem and we're going to teach it so that the AI will be smarter. What we found was bigger is smarter always. And this is more data, right? Yeah. And so like when they're going into Australia right now, you know, you've seen the drawings, you know, how big open AI's training center was, how big Anthropics training center was and now the training centers that are being are you know 10 times larger they're massive they're in australia because they have all the energy in the land and everything but they are going to make models that are 10 times and or more smarter than the ones we have today right we talked about the the vibe that but does it not pain you i mean as someone who has built software you know how to build good software you you went in there to clean up the mess of junior teams or like messes you you were you could clean it up and put your eyes closed or maybe you had to keep it open.

1:05:11Does it not pain you that when you describe, oh, the AI going off trail and doing it, if you scaled it back and said, like, hang on, like, let me step in, let me make these decisions, let me be the architect, it would not happen. Yeah, well, see, the thing is, I've also been a vice president at big companies of engineering. True. And so, when I'm working with a team of 80 agents, it's not very different from working with a team of 80 engineers. Any one of them can screw up too, engineers. Oh, and you've done that, right? I have. And I'm telling you, they are, isomorphic. So what is the bitter lesson?

1:05:42The bitter lesson is don't try to be smart, just try to be large. Okay. Now that's not the only way to make the AI smarter. They can also make them smarter in a couple of other important frontiers that are also getting developed. And so to tie it full circle to a beginning of our conversation, everyone who believes right now that the curve is S-shaped, they're a hundred percent correct. They are a hundred percent correct. It is S-shaped. Eventually, we will run out of resources. The world will be out of resources and it will flatten, right? But I can tell you that there are at least two more cycles left in this.

1:06:18And 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. Before we go all the way there, let's talk about how all this, the better models, more productive, could impact personal software. things that people can build themselves. This is what I thought you were asking about earlier when you said you wanted an API from Zendesk. Think about it. Everyone's going to want to build their own software. Oh, I was talking about a business for now. Not personal, but... Oh, businesses, yeah. But yeah, but also personal software.

1:06:51Like what would the future look like when everyone could have like OpenClaw running in their closet or gas town or they don't have to run it on their thing, but they can turn to this agent? Yeah. How could that change like both personal software, but also the software industry as a whole. Because for a long time, personal software was the privilege of us engineers who could build it, and we built our tools, and we had open source, and we had some billion-dollar companies grow out of some of the cool things. But what do you think could happen now that this will be democratized to some extent? How do you think open source could change?

1:07:23Open source? How would open source change? Could have changed, because one interesting thing that I'm seeing is a lot of remixing happening. So people, you know, now a lot of open source projects don't really take pull requests because there's a lot of not great ones. But a lot of people are just remixing. They're just taking the open source project. They're telling the AI, make this change. And they publish it as open source as well. Often no one looks at it. But now they don't need to ask for permission. A lot of people are like weaving things together. They say, take this project, take this thing.

1:07:49And it's actually a lot more open source. I see what you're saying. In the old days, the F word, fork you, used to be like kind of a declaration of war. Yep. Like if you forked somebody's project, it meant you had had enough of them. Like RueCode forked Klein and then somebody else forked RueCode. And it's just like, I think it's now going to be an everyday occurrence. Right? Because it used to be that to fork, it would be a lot of time and effort to maintain a fork, to merge back the thing. Cursor is a fork, isn't it? It is. Yeah. That's a lot of work. That's a lot of work. Yeah. A lot less work now.

1:08:23Right? So yeah, everyone's going to be forking. So, yeah, no, I think that's a natural consequence of everybody writing code. Yeah. Just like everyone can take a picture now. That didn't used to be true. Yeah. What are some of your beliefs from early on in your career that held really well until recently and now you just abandoned because of AI? Engineers are special. Here's one. Come on, we are special. I think we're so special. Yeah, sure. We learned how to do something by hand that computers can do now. Kind of cool, I guess. What about the engineering mindset? We have that. It's not just coding that we do, right?

1:09:02Well, for one thing is I believe that our thirst for new software will never, ever, ever diminish. It will only grow. And so we're at the beginning of software. All the software we have right now is garbage. That right there, OBS especially. And we're going to see a new world over the next 10 years where software is commonplace and good. and you'll have your choice and it won't be i have to pick and choose between three really bad oauth solutions or or company hr systems or whatever stupid ass thing right like today the selection is terrible sass is awful the whole the whole right airline apps airline apps right uh i mean we we ran a vibe coding workshop in sydney where a dude actually wrote an airline check-in app for himself and got into the android queue before southwest realized and shut him down because as a bot but that's what people want they want personal bespoke software and they're gonna get it and so yeah i think you're gonna see that's why when jeffrey emmanuel forked beads i was like you go you go he's i i feel so bad about it i'm like dude this is the new world man fork fork fork let's have beads in every language i don't care right i mean in all fairness like just looking at it from the positive side like i wouldn't mind just having good software for the stuff that i use day to day my utility provider is somewhat is getting better the government websites that i have to access my paying my parking fine the other day i tried to send a package to canada from the netherlands and the post like the official post has been broken they cannot send anything for a week and i see the exception they cannot fix it so i had to go dhl and pay a bunch more money that's right and like there's a lot of bad software out there and your agent will be dealing with it not you yeah but i think people who write software that agents like and prefer and choose and then they find a way to market it and get the agents aware of it, they're going to win big because everyone will use agents.

1:10:53We'll all be dependent on it. Well, plus also, I guess, software or ways of making agents write quality software, because I have a feeling like you will want to do better stuff that if you do the same, you're not going to have a business, right? Yeah. So, I mean, look, I think businesses will compete on more and more complex software. The ceiling will just keep going. We're building like we're going to until we build the Death Star or whatever. Right. I mean, like we're building bigger and bigger things. oddly enough I am an optimist through all of this that's my first belief I think first and foremost is that it's all going to work out so asking the optimist now I got this question of I think it was on Blue Sky this person asked like how do you think the software industry will continue to exist if we get to the point that any software could be trivially cloned yeah where will that leave us what cannot be cloned what is the moat just we just jump ahead we assume that these things actually can do it?

1:11:44Human connections are probably the biggest one. As, you know, kind of almost counterintuitively, as software does more and more automated for you, people are going to be like, oh, well, yeah, but that's just automated. I want a human to do it. And they will literally want a human to bring their thing instead of a drone. You know, they'll want humans to curate things for them. And I think that's going to be, humans will be a moat. Do you think if you look back at some history, like from, you know, the history, the rest of history, Like, have we seen some changes that felt a bit like this? And then we saw some professions thrive because of either more automation or, you know, like stack overflow.

1:12:20I don't know. I mean, like that one jumped to mind, Mechanical Turk. I mean, like we've seen a bunch of big step functions. It's just that we're about to see a whole bunch of them at once. Right. I mean, look at the news lately. I mean, like you're like, this is the funny thing is where I was like, where's all the innovation? And then in the news all day long, they're seeing all this innovation in AI. It's just not coming from, you know, the Walmarts and Microsofts. It's coming from random individuals, right? But the innovation is there. And from the startups that I've been talking to, you know, I've been talking to anywhere from two, five to 20 person startups.

1:12:53I think we're going to see some really impressive stuff launching in the next couple of months. Are you seeing these small startups change how they work? Oh, God, it's so different, dude. Tell me how. It's so different. Okay, for starters, for starters, I think in the new world, I'm convinced of this, okay? Everything that you do will either have to be fully transparent or you're hiding it for a reason. Tell me more. In other words, if you don't want people to see what you're doing, just don't show it to them and they will never see it. And if you do want them to see what you're doing, then you had better get it out in front of them as you do it instantly or else the train will pass you by.

1:13:29So what they're saying is like, so I told the story on my blog. People have heard it, but they yelled at a teammate. They were mad because he implemented a feature that they'd asked for two hours before. And they were like, two hours ago, it's changed too much since then, right? And he's like, well, what do I do? What's happening is they're getting into this mode where they realize that stuff moves so fast that everything is invisible, effectively, from the volume. And so you have to be extremely loud and transparent and intentional about saying everything that you're doing so that if anybody else is doing it, they can stop you right then.

1:14:01And if they need to integrate with you, they can start right then. And we're talking about startups that are looking for product fit, looking for customers. They actually just want to get what we call product market fit, where the traditional wisdom was build something amazing and then release it to the world. That's right. That's right. Try to find product market fit in secret as much as you can and then launch it and then tune, right? That's the formula. And many people fail at it. It used to be. Now, like you're saying, with Gastown, I realized I'm not going to find product market fit by myself.

1:14:31So I launched it as soon as it kind of worked and was like, help me. And that's how I found out about the adult database, which was a big change. And people fixed a bunch of bugs. I got 100 plus PRs the first couple of days. Right. And so it found its way closer to product market fit just by me getting it out there. And would you say that has brought you, like on one end, people look at you, well, yeah, it's just one other open source project. But is it bringing actually opportunity? If you wanted to, could you turn this into a business? Has it brought you the things? where I'm getting at is these things that take off either open source projects, like can they actually turn into actual businesses?

1:15:06Are we at that stage? I promise you, if you had made Gastown, you would be shaking venture capitalists off you like ticks right now. I am. They're finding me everywhere. And I tell you, it's because there's a lot of money out there right now sniffing, wanting to find its way into it. I didn't know something big's going to happen. right and it's looking and you can see it in all these different micro economies that are springing up but nowhere can you see it more clearly than when you launch something cool like jeff huntley did ralph wiggum vcs right you know everyone want to talk to you just got to be real careful because anything you build probably has a real short shelf life at this point right a real short one i don't know i'm not attached to gas town in any way because i think it'll be supplanted by something better within six months if not sooner right so so too attached so let's assume the staff engineer is listening to this podcast or watching it on their commute and they're at the type of company where they have co-pilots still there's people like this and and they're using it and they're they want to believe you but they're not sure they can what would you tell them what is the the thing that they can do to get proof that you're actually right and this thing is is working we're not at 100 we're not even at 50 for for people like a lot of people who are in this field i'll have tried it out but there's there's a lot of oh yeah no i would say probably still 70 percent aren't aren't doing it yeah um so like what would i say i had a really good message for them oh yeah get out get out um so here's the thing right copilot is uh if you were to line up all the tools you know from best to worst right copilot is like you're a line right doesn't even know about the line right but it used to be the best four years ago in 2021 right yeah and which i was it was another competition even maybe two and a half years ago i was quite stunned that that somebody asked, does anybody use Copilot at an AI Tinkers meeting?

1:16:58And somebody raised their hand and he goes, do you have to? And everyone laughed. And I was like, what happened, right? The brand just tanked. But I'm serious. If you're working at a company that gave you Copilot, they think that they're starting to move faster. And there's a barbarian horde of people using Opus 4.5 that are going to destroy your company sooner or later. So what you need to do is go into the crazy part of crazy town and figure this stuff out and start building. and because we are moving into a world very quickly this year where proof of work is so important. And I mean, proof of work, not the Bitcoin sense, but your proof of what you have done, your resume.

1:17:34And 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 back to our transparency, right? I think everyone's going to be bringing their work with them. I mean, the notion of proprietary work is starting to like be threatened, I think, because it's so easy to fork. It's so easy to clone it's so easy to route around if you have anything proprietary you become this this thing that everybody just wants to run around you and so right so big big changes are afoot but man if you're working with co-pilot right now you are going to get left behind and so what you need to do is get get yourself find a half an hour a day to go play with with cloud code right and uh and and and and it's like i said or if you're a company make your token burn as high as your investors will let you go right because that token burn is your practice it's your it's your sorting things out so i want to ask you the other way around let's assume you're just wrong in terms of the the curve and we're at the peak and it will not be 10x it will plot to at 3x or let's just say the next model is inexplicably dumber than opus 5 and we've peaked what would happen to the person who takes your advice and they go all in and they learn things what's the worst thing that could happen to them?

1:18:48If these things take off, it's a great investment, right? But what would happen to them if they followed your advice and the models didn't follow? Where would that lead them? Exactly where they need to go because the damage is done. Opus 4.5 made this officially an engineering problem. We don't need you AI researchers anymore. Thank you. You can make smarter models, I guess, but we don't need them because we have something that can take a bite size chunk out of a mountain and it's a bite size about town size now. And so we can eat mountains. Okay. It's purely an engineering problem at this point.

1:19:22It's like fire or steam. It's a, it's a force. It's a power. And we wrap layer, layer, layer, layer. I worked on a nuclear reactor. I was in the Navy. I know how these things work. Okay. We are going to put all right, uh, layers around Opus 4.5, if that's the smartest model ever, and that will do all of the engineering from now on. So it's done. So it's okay to jump into the pool now. Your first job was about debuggers or not debuggers, but you worked at this amazing company. You told me they had the best debugger tools. What was the name? It was GeoWorks and the debugger was called SWAT and it was amazing.

1:19:53Time machine and all that. And on the first Pragmatic Engineer interview, when we talked, this is in the newsletter, you are actually saying that you, to this date, you've not seen as good of a debugger, but you're kind of determined to like build at some point and help build that. I did build a debugger enclosure for the JVM called Ganja. It was actually pretty cool. But then I got an argument with Rich Hickey about how well he wanted to support the JVM, and he doesn't. But anyway, you're a guy who is passionate about... I had to tell the story somewhere, though. You're passionate about debugging.

1:20:24What will happen with debugging? What will happen with debugging tooling? What do you think the future of debugging is with agents? When I see agents say, I'm going to debug this, they all use printfs. So, you know, I'm curious. it could very well be that they just haven't been trained on debuggers yet and that they'll all wake up in six months and go oh i should have been using this but it could also be that we don't need them anymore i don't know and another step further what do you think the future of the developer workstation like our our rigs our machines will be right like do you think it'll phone i want gas down on my phone i almost have i have it but i just haven't worked on it peter steinberger told me that he had vibe tunnel where he could do it from your phone he said he stopped it because it became too addictive oh yeah no tail scale and yeah actually the only thing that's keeping me from just being addicted to it all day long is it's too hard to enter control characters in but that's going to get fixed at some point programming on your phone will be a thing but but so do you think that developer workstations can be just lightweight chromebook whatnot or we actually want beefy ones which can run our local agents whatnot like where do you think it'll be headed on the short term and then maybe on the longer term yeah see what I mean, local models.

1:21:34Yeah, no, I look, I love my laptop. I've been programming 40 years. I get the local thing, but I've been saying for at least 15 years that we don't need this stuff locally. Google had an amazing client in the cloud, high speed network connection. And what you can do with it. Cider, right? SITSI was the base and then Cider was built way up on a higher layer. But when you get something like that and you're not restrained by that, especially in the world where you can run kind of unlimited agents based on your pocketbook. uh yeah people are not going to be wanting to work on their laptops and i've already gastown has already completely stressed out my laptop to the wire you know because cloud code actually takes quite a bit of memory so yeah i think we're moving to a world where uh people will work on servers and and on mobile devices probably less less and ipads not on um laptops as much in the past you've said that one of the most important kind of predictors of the productivity is language design well-designed languages are easier to work with you think this has completely erased or do you think it might come back at some point either purpose-built languages i think there will probably be purpose-built languages by ais for ais maybe but right now we're in a funny place where the some languages work better than others still because they have better training data but in the fullness of time all the languages will work equally well i push back on that like if if a new language never has training there how would it work no i mean i'm sorry all the existing ones oh yeah type script it struggles with type script today Yeah, it does.

1:22:59But it's not going to in one or two model releases. It won't matter. So could we see a stagnation? Just fewer languages or no languages launching because they just get the job done. And launching a new language seems a bit suicidal unless you bring a bunch of training data with it, right? Man, that's a loaded question. I mean, part of me... I didn't mean to make it a loaded question. No, it's a good question, right? Part of me says languages just don't matter anymore, right? Any more than assembly languages matter, except for a few people who are trying to optimize really important things. And then everybody else, it just doesn't matter.

1:23:30But then part of me says, well, energy is the most constrained and important resource on this planet, and it's only going to get worse. So finding better algorithms, finding better ways to solve problems is often a language problem. Finding a DSL, you know. So I think from an optimization perspective, an efficiency perspective, the search for new languages will probably continue. But for pragmatic, for everyday, I don't think, it doesn't matter what you pick. you might not even ask your agent what language it's using. So as a software professional who like loves the crafts, is into, you know, languages, debuggers, tooling, et cetera, a lot of what we talked about is pretty sad because, you know, like a lot of the beauty, the challenges that we worked, it seems they might be going away if we continue and if this continues as well.

1:24:18How did you work through this yourself? And also, what is the thing that actually excites you looking ahead? Right. So I had the benefit of going through 30 years of graphics evolution. And so I saw the sadness and I saw the resulting much better games we got after all that happy stuff we were doing by hand moved into the hardware. We're sad because we're used to it. Change is part of life. OK. And we're, you know, at one point I had to say goodbye to assembly language. Right. I was like, let's compile the writers. They finally caught up. Right. And then we were mad. But then we were happier because compilers are obviously way better than writing an assembly language.

1:24:56Anybody would be stupid to say, oh, God, yeah, no, you're not a good engineer if you can't write an assembly language today. But that was actually what we were saying in 1992. And then you had the blog post out in 2012 as well. Yeah, no, I'm just saying stuff changes. What you need to know as an engineer will change. And you can't rest on your laurels. And we're going through a period of faster change now. But you have helpers called agents that can actually help you through this change. So stop complaining and just go do it. Yeah. And I think just recognize we're in this industry where change is a thing.

1:25:25And that's right. Now, with that said, I did go through the five phases of grief, right? The five stages of grief. I mean, like I went through, I don't know if I, I don't know about anger. I was angry, really angry for a lot of reasons two years ago. But, but no, I mean, like if you've ever truly grieved, if you've like lost someone, you know that it hits you in a lot of weird ways where you feel reality is disconnected. You feel sick. You feel stunned. You feel all day long. The world goes monochrome, all color disappears, all kind of weird stuff, right? And I went through that for about, I don't know, six or seven days.

1:25:57It didn't take me that long to get through it, fortunately. Or maybe that was the peak, and I was surrounded by a few months of it on either side. But there was a period that I went through it where I was checking off things that no longer mattered that I had really cared about, like my ability to memorize or my ability to write or my ability to compute or whatever. All those things, anything computing related. I was very sad, right? because those things made me special somehow, right? But then to your question, what makes me excited? Like, as soon as I got through that, I was like, but wait, I'm writing 10 times more code than I ever was and I'm having fun and why should I be sad, right?

1:26:33And so I realized it's just me holding onto the old, just like I did in graphics. And there's no point because the future is actually more fun than the present. It's gonna be. You're known for your predictions and I'd like to put it to a test. Let's give some specific predictions for next year in 2027. things that you think will happen either with how we develop or, or how the industry works. I think that my wife is going to be the top contributor to our video game. Ooh, bold claim. Summer of next year. And she is not a developer, I'm guessing. No, oh no, no, no. But she loves our game and she has lots of ideas, right?

1:27:10Amazing. Yeah. In fact, I think my whole family might be in on it. I'm serious, man. Programming is going to be for everybody and it's going to be the most amazing thing because you know how much fun we've been having all the those years and we've been telling people it's really fun but now they're going to get to experience it right i look at my kids and how they look at ai they're having so much fun with it creating they're just prompting gemini or any of these with their imagination and they actually have they don't think it's weird i think it's weird so i never would think of it but they just enhance our photos with like squirrels on my head and it it just made me laugh and fun and you realize like there's just a lot of fun and new things with it when you let go or or you never knew what was before.

1:27:49It's given the people the ability to do very sophisticated mashups of anything. And mashups are really where innovation happens, right? Innovation comes from taking things and putting them together and seeing where it goes, right? We're going to see everybody innovating, man. And it's going to be the most amazing thing ever. And then we're going to need ecosystems of agents that can go find stuff that you like, because there'll be so much content. How are you going to find the stuff that's really like, that you like? You're going to have an agent that knows you really well. I think any software engineer who wants to get, go make a big business right now should go start working on agents that know how to go and search the new world, everything that's coming out.

1:28:25I don't even know what we call it, right? The work pile for, for, uh, software that you like for experiences that you like. If everybody's creating it, think about it. When, when the internet came out and everybody can make a webpage and upload shit, we needed aggregators. We needed, you know, we needed search engines. We needed ways to organize and find and surface the good stuff, right? None of that exists right now, but everybody's about to start coding. Like, right? You know, and so like, you can get ahead of this. This is why I keep saying, just believe the curves, pick a point on the curve and aim for it.

1:28:58And you will land there and you'll be first when the AIs are ready for your thing. Yeah. And I think as engineers, we already can build. We don't need permission. We can use this whole super efficiently. Right now. And we are ahead of the rest of the world right now. Right now. Well, it's exciting times. Well, Steve, we'll have to check back on how, if that prediction will come through with your wife contributing more. But this has been, I think, really eye-opening. And sometimes I think it's good to go through the has-been and the can-be. Yeah. Well, thanks. I hope you enjoyed this conversation as much as I did.

1:29:32An interesting thought from Steve is his parallel between the graphics industry and what's happening in software engineering right now. In 1992, Steve was learning to calculate where individual pixels go on a line. Two years later, the same course was teaching animation. The work in graphics went from writing device drivers to building game worlds and physics engines. It all just moved up the abstraction layer. Steve's argument is that software engineering is going through exactly that same shift right now, except it's faster. Instead of asking, will engineers have jobs at all? A better question might be, what will the new jobs we do as software engineers look like?

1:30:06Another thing was the grief of this change. Steve is someone who spent 40 years building his identity around compilers, debuggers, elegant code. And then one day he sat down and started checking off one by one the things that made him special that no longer mattered. His world went monochrome, as he said. Within a week or so, he came out from the other side and realized he was writing 10 times more code and that he was having more fun doing it. Still, I think a lot of engineers are quietly going through something similar right now, and it's usually taking longer than a week to digest all of this.

1:30:37Finally, one thing I found really honest from Steve was his point about value capture. If you become 100 times more productive with AI, who benefits? If you work 8 hours and produce 100 times the output, the company captured all of that. But if you just work 10 minutes in a day and produce the same value as before, you technically captured all of it and your company captured none of it. Now, neither extreme is sustainable. Steve is saying that this new work-life balance is a question that we'll need to figure out. We don't have the cultural norms for any of this, and it's going to be messy as we figure it out.

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