Head of Claude Code What happens after coding is solved

 

100% of my code is written by quadcode. I have not edited it a single line by hand since November. Every day I should 10, 20, 30 forecasts. So like at the moment I have like 5 agents running. While recording this. Yeah, you miss writing code. I have never enjoyed coding as much as I do today because I don't have to deal with all the minutiae productivity per engineer has increased 200%. There's always this question, should I learn to go in a year or two, it's not gonna matter. Coding is largely solved. I imagine a world where everyone is able to program. Anyone can just build software anytime. What's the next big shift to how software is written? Quad is starting to come up with ideas. Looking through feedback, it's looking at bug reports, it's looking a telemetry for bug fixes and things to ship a little more like a coworker or something like that. A lot of people listening to this are product managers and they're probably sweating. I think by the end of the year everyone's gonna be a product manager and everyone codes. The title software engineer is gonna start to go away. It's just gonna be replaced by builder and it's gonna be painful for a lot of people. Today my guest is Boris Turny, head of Claude Code at Anthropic. It is hard to describe the impact that Claude Code has had on the world. Around the time this episode comes out will be the one year anniversary of Claude Code. And in that short time, it is completely transformed the job of a software engineer and it is now starting to transform the jobs of many other functions in tech, which we talk about. Claude Code itself is also a massive driver of Anthropics over all growth over the past year. They just raised around it over $350 billion. In his Boris mentions, the growth of Claude Code itself is still accelerating. Just in the past month, their daily active users has doubled. Boris is also just a really interesting, thoughtful, deep thinking human.

And during this conversation, we discover we were born in the same city in Ukraine. That is so funny, I had no idea. A huge thank you to Ben Mann, Jenny Went and Mike Krieger for suggesting topics for this conversation. Don't forget to check out Lenny's productpast.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. Let's get into it after a short word from our wonderful sponsors. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers to thrive in the AI era, organizations need to adapt quickly. But many organizations need to struggle to answer pressing questions like which tools are working, how are they being used, what's actually driving value? DX provides the data and insights that leaders need to navigate this shift. 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It also reviews your PRs and flags any breaking changes with fixes ready to go. Try sentry and seeer for free at sentry.io/Lenny and use code Lenny for $100 in sentry credits. That's scmtr-y.io/Lenny. Boris, thank you so much for being here and welcome to the podcast. Yeah, thanks for having me on. I want to start with a spicy question. About six months ago, I don't know if people even remember this. You actually left anthropic. You joined cursor and then two weeks later, you went back to anthropic. What happened there? I don't think I've ever heard the actual story. It was the fastest job change that I've ever ever had. I joined cursor because I'm a big fan of the product and honestly I met the team and I was just really impressed. They're an awesome team. I still think they're awesome and they're just building really cool stuff and kind of they saw where AI coding was going. I think before a lot of people did. So the idea of building could product was just very exciting for me. I think as soon as I got there, what I started to realize is what I really missed about ant was the mission. That's actually originally drove me to ant also. Before I joined anthropic, I was working big tech and then I was at some point I wanted to work at a lab to just help shape the future of this crazy thing that we're building in some way. The thing that drew me to anthropic was the mission and it's all about safety. When you talk to people at anthropic, just find someone in the hallway if you ask them why they're here, the answer is always going to be safety. This mission driven us just really resonated with me. I just know personally it's something I need in order to be happy. That's just a thing that I really missed and I found that whatever the work might be no matter how exciting even if it's building a real equal product. It's just not really a substitute for that. So for me it was actually, it was pretty obvious that that was missing that pretty quick. Okay, so let me follow the thread of just coming back to anthropic in the work you've done there. This podcast is going to come out around the year anniversary of launching cloud code. So when it's been a little time just reflecting on the impact that you've had, there's this report that recently came out that I'm sure you saw by semi analysis that showed that 4% of all GitHub commits are authored by cloud code now and they predicted it'll be a fifth of all code commits on GitHub by the end of the year. The way they put it is while we blinked AI consumed all software development. The day that we're recording this Spotify just put out this headline that their best developers haven't written a line of code since December. Thanks to AI, more and more of the most advanced senior engineers, including you, are sharing the fact that you don't write code anymore that it's all AI generated. And many aren't even looking at code anymore is how far we've gotten. In large part, thanks to this little project that you started and that your team has scaled over the past year. I'm curious just to hear your reflections on on this past year and the impact that your work has had. These numbers are just totally crazy. Like 4% of all commits in the world is just way more than I imagined and like you said, it still feels like the starting point. These are also just public commits. So we actually think if you look at private repositories, it's quite a bit higher than that. And I think the crazy thing for me isn't even the number that we're at right now, but the pace at which we're growing. Because if you look at cloud codes, growth rate kind of across any metric, it's continuing to accelerate. So it's not just going up, it's going up faster and faster. When I first started quadcode, it was just going to be like, it was just supposed to be a little hack. You know, we broadly knew it on Thropic that we wanted to ship some kind of coding product. And, you know, for Thropic for a long time, we were building the models in this way that kind of fit our mental model of the way that we build safe HAI. Where the model starts by being really good at coding, then it gets really good at tool use, then it gets really good at computer use. Roughly, this is like the trajectory. And, you know, we've been working on this for a long time. And, when you look at the team that I started on, it was called the Anthropic Labs team, and actually my Krieger, and, you know, then they just kick the steam off again for kind of round two. The team built some pretty cool stuff. So we built quadcode, we built MCP, we built the desktop app.

So you can kind of see the seeds of this idea, you know, like it's coding, then it's tool use, then it's computer use. And the reason this matters for Anthropic is because of safety, it's kind of, again, just back to that AI is getting more and more powerful. It's getting more and more capable. The thing that's happened in the last year is that for at least for engineers, the AI doesn't just write the code, it's not just a conversation partner, but it actually uses tools, it acts in the world. And I think now with coworker starting to see the transition for non-technical folks also. For a lot of people that use conversational AI, this might be the first time that they're using the thing that actually act, it can actually use your Gmail, it can use your Slack. You can do all these things for you, and it's quite good at it. And it's only going to get better from here. So I think for Anthropic for a long time, there's this feeling that we wanted to build something, but it wasn't obvious what. And so when I joined down, I spent one month kind of hacking and, you know, built a bunch of weird prototypes, most of them didn't ship, and, you know, weren't even close to shipping. It was just kind of understanding the boundaries of what the model can do. Then I spent a month doing post-training, so to understand kind of the research ahead of it. And I think honestly, that's just for me as an engineer, I find that to do good work, you really have to understand the layer under the layer at which you work. And with traditional engineering work, you know, if you're working on product, you want to understand the infrastructure, the runtime, the virtual machine, the language, kind of whatever that is, the system that you're building on it. But yeah, if you're working AI, you just really have to understand the model to some degree to do good work. So I took a little detour to do that, and then I came back and just started prototyping what eventually became quadcode. And the very first version of it, I have like a, there's a video recording of the summer, because I recorded this demo, and I posted it, it was called Quad CLI back then. And I just kind of showed off how it used a few tools and the shocking thing for me was that I gave it a bash tool. And it just was able to use that to write code to tell me what music I'm listening to when I asked it, like, what music I'm listening to. And this is the craziest thing, right, because it's like, there's no, I didn't instruct the model to say, you know, use, you know, this tool for this or kind of do whatever the model was given this tool and I figured out how to use it to answer this question that I had that I wasn't even sure if I could answer what music I'm listening to. And so I started prototyping this a little bit more, and made a post about it, and I announced it internally, and it got two likes. That's the, that was like, that was like sense of the reaction at the time. Because I think people internally, you know, like when you think of coding tools, you think of like, you think of IDEs, you think about kind of all these pretty sophisticated environments. No one thought that this thing could be terminal based, that's sort of a weird way to design it. That wasn't really the intention. But, you know, from the story, I built it in a terminal because, you know, for the first couple months, it was just me. So it was just the easiest way to build. And for me, this is actually a pretty important product lesson, right? It's like you want to under resource things a little bit at the start. Then we started thinking about what other form factors we should build. And we actually decided to stick with the terminal for a while. And the biggest reason was the model is improving so quickly. We felt that there wasn't really another form factor that could keep up with it.

And honestly, this was just me kind of like struggling with kind of like what should we build, you know, like for the last year, quadcode has just been all I think about. And so just like late at night, this is just something I was thinking about. Like, okay, the models continuing to improve, what do we do, how can we possibly keep up? And the terminal was honestly just the only idea that I had. And yeah, it ended up catching on after, after I released it pretty quickly, it became a hit at anthropic. And, you know, the, the daily act of users just went vertical. And it really early on, actually, before I launched it, then man, not just me to make a DAU chart. And I was like, you know, it's kind of early, maybe, you know, should we really do it right now? And he was like, yeah. And so the the chart just went vertical pretty immediately. And then in February, we released it externally. Actually, something that people don't really remember is quadcode was not initially a hit when we released it. It got a bunch of users. There was a lot of early adopters that got it immediately. But it actually took many months for everyone to really understand what this thing is. Just again, it's like, it's just so different. And when I think about it, kind of part of the reason quadcode works is this idea of latent demand, where we bring the tool to where people are and it makes existing workflows a little bit easier. But also because it's in a terminal, it's like a little surprising. It's a little alien in a way. So you have to, you have to kind of be reminded and you have to learn to use it. And of course, now, you know, quadcode is available, you know, in the iOS and Android quad app, it's available in the desktop app, it's available on the website, it's available as IDE extensions and Slack and GitHub, you know, all these places where engineers are, it's a little more familiar. But that wasn't the starting point.

So yeah, I mean, at the beginning, it was kind of a surprise that this thing was even useful. And, you know, as the team grew, as the product grew, as it started to become more and more useful to people, just people around the world from, you know, small startups to the biggest thing companies started using it and they started giving feedback. And I think just reflecting back, it's been such a humbling experience because we just, we keep learning from our users and just the most exciting thing is like, you know, none of us really know what we're doing. And we're just trying to figure out along with everyone else. And the single best signal for that is just feedback from users. So that's just been the best. I've been surprised so many times. It's incredible how fast something can change in today's world. You launched this a year ago, and it was the first time people could use AI to code. But in a year, the entire profession of software engineering has dramatically changed. Like, there's all these predictions. Oh, AI is going to be in 100% AI's, of course, going to be run by AI ever. It's like, no, that's crazy. What do you talk about now? Of course, it's happening exactly as they said. It's just things move so fast and change so fast now. Yeah, it's really fast. Back at code with clogged back in May, those like our first, you know, like developer conference that we did as on Thropic. I did a short talk in the Q&A after the talk, people were asking what are your predictions for the end of the year. And my prediction back in May of 2025 was, but the end of the year, you might not need to ID to code any more. And we're going to start to see engineers not doing this in the I remember the room like a lot of weight gasped. This is a crazy prediction. But I think like at an anthropic, like this is just the way the way we think about things is exponentials. And this is like very deep in the DNA, like if you look at our co-founders, like three of them were the first three authors on the scaling loss paper. So we really just think in exponentials. And if you kind of look at the exponential of the percent of code that was written by quad at that point, if you just trace the line, it's pretty obvious we're going to cross a hundred percent by then to the year, even if it just does not match intuition at all. And so all I did was trace the line. And yeah, in November, that, you know, that happened for me personally. And that's been the case since and we're starting to see that for a lot of different customers too. I thought that was really interesting, which you just shared there about kind of the journey is this kind of idea of just playing around and seeing what happens. This came up comes up with open claw a lot, just like Peter was playing around and just like a thing happened. And it feels like that's a central kind of ingredient a lot of the biggest innovations in AI's people just sitting around trying stuff to pushing the models further than most other people. I mean, this is the thing about innovation, right? Like you can't, you can't force it. There's no roadmap for innovation. You just have to give people space. You have to give them maybe the word is like safety. So it's like psychological safety that it's okay to fail. It's okay if 80% of the ideas are bad. You also have to hold them accountable that's so if the idea is bad, you know, you cut your losses move on to the next idea instead of investing more. In the early days of quadcode, I had no idea that this thing would be useful at all because even in February, when we released it, it was writing maybe, I don't know, like 20% of my code, not more. And even in May, it was writing maybe 30%. I was still using, you know, cursor from most of my code. And it only crossed 100% in November. So it took a while. But even from the earliest day, it just felt like I was onto something. And I was just spending like every night, every weekend, hockey on this. And luckily, my, you know, my wife was very supportive. But it just felt like it was onto something. It wasn't obvious what. And then sometimes, you know, you find a threat. You just have to pull on it. So at this point, 100% of your code is written by cloud code. Is that, is that kind of the current state of your coding? Yes. Well, 100% of my code is written by cloud code. I'm a fairly prolific coder. And this has been the case, even when I worked back at Instagram, I was like one of the top few most productive engineers. And that's actually that's still the case. Here it on Thropic. Wow. And that's all out of the team. Yeah, yeah, do it. Still do a lot of coding. And so every, you know, every day, I feel like 10, 20, 30, 4 requests, something like that every day, a hunt, every day. Yeah, good. 100% written by cloud code. I have not added a single line by hand since November. And yeah, that's been it. I do look at the code. So I don't think we're kind of at the point where you can be totally hands-off, especially when there's a lot of people, you know, like running the program, you have to make sure that it's correct. You have to make sure it's safe and so on. And then we also have cloud doing automatic code review for everything. So here it in Thropic cloud reviews 100% of whole requests. There's still a layer of like human review after it. But you kind of like you still do want somebody these checkpoints. Like you still want a human looking at the code. Unless it's like pure prototype code that, you know, it's not going to run, it's not going to run anywhere. It's just a prototype. What's kind of the next frontier. So at this point, 100% of your code is being written by AI. This is clearly where everyone is going in software engineering. That felt like a crazy milestone. Now it's just like, of course, this is the world now. What's, what's kind of the next big shift to how software is written that either your team's already operating and or you think we'll head towards. I think something that's happening right now is cloud is starting to come up with ideas. So cloud is looking through feedback. It's looking at bug reports, it's looking at, you know, like telemetry and then things like this. And it's starting to come up with ideas for bug fixes and things to ship. So it's just starting to get a little more, you know, like a little more like a coworker or something like that. I think the second thing is we're starting to branch out of coding a little bit. So I think at this point, it's safe to say that coding is largely solved. At least for the kind of programming that I do, is just a solved problem because cloud can do it. And so now we're starting to think about, okay, like, what's next? What's beyond this? There's a lot of things that are kind of adjacent to coding.

And I think this is going to be coming, but also just, you know, general tasks, you know, like, I use cowork every day now to do all sorts of things that are just not related to coding at all and just to do it automatically. For example, I had to pay a parking ticket the other day. I just had to co-work do it. All of my project management for the team. Co-work does all of it. It's like syncing stuff between spreadsheets and messaging people on Slack and email and all of those kind of stuff. So I think the frontier is something like this. And I don't think it's coding because because I think coding is, you know, it's pretty much solved. And over the next few months, I think what we're going to see is just across the industry. It's going to become increasingly solved. You know, for every kind of codebase, every text tag that people work on. This idea of helping you come up with what to work on is so interesting. A lot of people listening to this or product managers.

And I thought they're probably sweating. How do you use cloud for this? Do you just talk to it? Does there anything clever you've come up with to help you use it to come up with what to build? Honestly, the simplest thing is like open quadcode or core can point it out as a fact thread. You know, like for us, we have this channel that that's all the internal feedback about quadcode. Since we first released it, even in like 2024 internally, it's just been this fire hose of feedback. And it's the best. And like in the early days, what I would do is anytime that someone sends feedback, I would just go in and out fix every single thing as fast as I possibly could. So like within a minute within five minutes or whatever. And this is just really fast feedback cycle. It encourages people to give more and more feedback. It's just so important because it makes them feel heard. Because you know, like usually when you use a product, you get feedback, it just goes into a black hole somewhere, and then you don't get feedback again. So if you make people feel heard, then they want to contribute. And they want to help make the thing better. And so now I kind of do the same thing, but quad, honestly, it does a lot of the work. So I pointed at the channel and it's like, okay, here's a few things that I can do. I just put up a couple of PRs. I want to take a look at that. I'm like, yeah. Have you noticed that it is getting much better at this? Because this is kind of the holy grail right now. It's like cool building solved. Coder v became kind of the next bottleneck, the least PRs who's going to review them all. The next big open question is just like, okay, now we need to. Now humans are necessary for figuring out what to build, which are prioritizing. You're saying that's where Claw and Cod is starting to help you. Has it gotten a lot better with like, is there a purpose for six or what's it been the trajectory there?

Yeah, yeah, it's improved a lot. I think some of it is kind of like training that we do specific to coding. So obviously, you know, best coding model in the world and, you know, it's getting better and better. Like 4.6 is just incredible. But also actually a lot of the training that we do outside of coding translates pretty well too. So there is this kind of like transfer where you teach the model to do, you know, X and it kind of gets better at why. Yeah, and the gains have just been insane. Like, I don't throw up it. Over the last year, like since we introduced Claw Code, we probably, I don't know the exact number. We probably like 4x, then generic team, or something like this. But productivity per engineer has increased 200%. In terms of like four requests. And like this number is just crazy for anyone that actually works in the space and works on deaf productivity. Because back in the previous life, I was at meta and, you know, one of my responsibilities was code quality for the company. So this is like all of our code bases, those my responsibility, like Facebook, Instagram, WhatsApp, all this stuff. And a lot of that was about productivity. Because if you make the code higher quality, then engineers are more productive. And things that we saw is, you know, in a year with hundreds of engineers working on it, you would see a gain of like a few percentage points of productivity. So I'm going to make this. And so now we're using these gains of just hundreds of percentage points. It's just absolutely insane. What's also insane is just how normalize this has all been. Like we hear these numbers. Like of course, yeah, I was doing this to us. It's just, it's so unprecedented. The amount of change that is happening to software development, to building products, it's just the world of tech. It's just like so easy to get used to it. But it's important to recognize this is crazy. This is something like, I have to remind myself once in a while, there's sort of like a downside of this because the model changes. So there's actually like, there's many kind of downsides that we could talk about. But I think one of them on a person level is the model changes so often that I sometimes get stuck in this like old way of thinking about it. And I even find that like new people on the team or even new grads that join do stuff in a more kind of like AGI forward way than I do. So like sometimes, for example, I had this case like a couple months ago where there was a memory week. And so like what this is is, you know, like quadcode the memory usage is going up and at some point it crashes. This is like a very common kind of engineering problem that, you know, every engineer has debugged a thousand times. And traditionally the way that you do it is you take a heap snapshot, you put it into a special debugger, you kind of figure out what's going on, you know, use these special tools to see what's happening. And I was doing this and I was kind of like looking through these traces and trying to figure out what was going on. And the engineer that was newer on the team, just had quadcode do it. It was like, hey, quad, it seems like there's a week and you figure it out. And so like quadcode did exactly the same thing that I was doing. It took the heap snapshot, erode a little tool for itself. So it can kind of like analyze it itself. It was sort of like a just in time program. And it found the issue and put a paperwork was faster than I could. So it's something where like for those of us that have been using the model for a long time, you still have to kind of transport yourself to the current moment and not get stuck back in an old model because it's not sonnet 3.5 anymore. The new models are just completely completely different. And just this this mindset shift is is very different.

I hear you have these very specific principles that you've codified for your team that when people join you, you kind of walk them through them. I believe one of them is what's better than doing something, having cloud do it. And it feels like that's exactly what you describe with this memory leak is just like you almost forgot that principle of like, okay, let me see if cloud can solve this for me. There's this interesting thing that happens also when you when you underfund everything a little bit, because then people are kind of forced to qualify. And this is something that we see. So, you know, for work where sometimes we just put like one engineer on a project and the way that they're able to ship really quickly, because they want to ship quickly, this is like an intrinsic motivation that comes from within. It's just wanting to do a good job. If you have a good idea, you just really want to get it out there. No one has to force you to do that. That comes from you. And so if you have cloud, you can just use that to automate a lot of work. And that's kind of what we see over and over. So I think that's kind of like one principle is underfunding things a little bit. I think another principle is just encouraging people to go faster. So if you can do something today, you should just do it today. And this is something we really, really, really encourage on the team. Early on, it was really important because it was just me. And so our only advantage was speed. That's the only way that we could ship a product that would compete in this very crowded coding market. But now it is, it's still very much a principle we help on the team. And if you want to go faster, a really good way to do that is to just have cloud do more stuff. So it just very much encourages that. This idea of underfunding. It's so interesting because in general, there's this feeling like AI is going to allow you to not have as many employees, not have as many engineers. And so it's not only you can do more productive, which you're saying is that you will actually do better if you're underfunding. It's not just that AI can make you faster. It's you will get more out of the AI tooling if you have fewer people working on something. Yeah, if you hire great engineers, they'll figure out how to do it. And especially if you empower them to do it. This is something I actually talk a lot about with, you know, with Lake CTOs and kind of all sorts of companies. My advice, generally, is don't try to optimize. Don't don't try to cost cut at the beginning. Start by just giving engineers as many tokens as possible. And now you're starting to see companies like, you know, identropic. We have, you know, everyone can use a lot of tokens. We're starting to see this come up as like a perk at some companies. Right, if you join, you get unlimited tokens. This is a thing I very much encourage because it makes people free to try these ideas that would have been too crazy. And then if there's an idea that works, then you can figure out how to scale it. And that's the point to kind of optimize and to cost cut figure out, like, you know, maybe you can do it with Hiku or with Sonic instead of purpose or whatever. But at the beginning, you just want to throw a lot of tokens at it and see if they do works and give engineers the freedom to do that. So the advice here is just be loose with your tokens with the cost on using these models. People hearing this may be like, of course, he works at anthropic. You want us to use as many tokens as possible. But what you're saying here is that the most interesting innovative ideas will come out of someone just kind of taking it to the max and seeing what's possible. Yeah. And I think the reality is like, at small scale, like, you know, you're not going to get like a giant bill or anything like this, like if it's an individual engineering experimenting, the token costs are still probably relatively low relative to their salary or other costs of running the business. So it's actually like not a huge cost. As the things scales up, so like, let's say, you know, they build something awesome and then it takes a huge amount of tokens. And then the cost becomes pretty big. That's the point out which you want to optimize it. But don't don't do that too early. Have you seen companies where their token cost is higher than their salary? Is that a trend you think we're going to find and see? You know, I don't think we're starting to see some engineers that are spending, you know, like hundreds of thousands of months in tokens. So we're starting to see this a little bit. There's some companies that are we're starting to see some more things. Yeah. Going back to coding. Do you mess writing code? Is this something you're kind of sad about that there's no longer thing you'll do as a self-ranger? It's funny. For me, when I learned engineering for me, it was very practical. I learned engineering so I could build stuff. And for me, I was a self-taught, you know, like I studied economics in school, but I didn't study CS. But I taught myself engineering kind of early on, I was programming in middle school. And from the very beginning, it was very practical. So I actually, like, I've learned to code so that I can cheat on a map test. It was like the first thing we had these like graphing calculators and the, you know, I just programmed the answer into 383. 383 plus, yeah, yeah, exactly. Plus, yeah, it's like I programmed the answer is in. And then the next like math test, whatever, like the next year, they was just like too hard, like I couldn't program all the answers in because I didn't know what the questions were. And so I had to write like a little solver so that it was a program that would just like solve these, like, you know, these algebra questions or whatever. And then I figured out you can get a little cable, you can give the program to the rest of the class, and then the whole class gets is, but then we all got caught in the teacher told us to not get off. But from the very beginning, it's, it's always just been very practical for me. Where programming is a way to build a thing. It's not the end in itself. At some point, I personally fell into the rabbit hole of kind of like the, the beauty of, of programming. So like I wrote a book about TypeScript. I sort of, the actually at the time it was the world's biggest TypeScript, you know, just because I fell enough with the language itself. And I kind of got in deep into like functional programming and all this stuff. I think a lot of coders, they get distracted by this. For me, it was always sort of there is a beauty to programming and especially to functional programming. There's a beauty to type systems. There's a certain kind of like this like buzz that you get. Like when you solve like a really, a really complicated math problem, it's kind of similar when you kind of balance the types or you know, the program is just like really beautiful. But it's really not the end of it. I think for me coding is very much a tool in a, it's a way to do things. That said, not everyone fills this way. So for example, you know, like there's one engineer on the team Lina who, you know, was still writing C++ on the weekends by hand. Because, you know, for her, she just really enjoys writing C++ by hand. And so everyone is different. And I think even as this field changes, even as everything changes, there's always space to do this. There's always space to enjoy the art and to kind of do things by hand if you want. Do you worry about your skills atrophing as an engineer? Is that something you can worry about or is it just like, you know, this is just how it's going to go? I think that's just the way that it happens. I don't worry about it too much personally. I think for me, like programming is on the continuum and, you know, like way back in the day, you know, like software actually is like relatively new, right? Like if you look at the way programs are written today, like using software that's running on a virtual machine or something, this has been the way that we've been writing programs since probably the 1960s. So, you know, it's been, you know, like 60 years or something like that. Before that it was punch cards, before that it was switches, before that it was hardware, and before that it was just, you know, like literary pen and paper. It was like a room for people that were doing math on paper. And so, you know, programming has always changed in this way. In some way, as you still want to understand the layer under the layer, because it helps you be a better engineer, and I think this will be the case maybe for the next year or so. But I think pretty soon, I just won't really matter. It is just going to be kind of like the assembly code running under the program or something like this. At an emotional level, you know, I feel like I've always had to have our new things. And as a programmer, it's actually not, it doesn't feel that new because there's always new frameworks, there's always new languages, it's just something that we're quite comfortable with in the field. But at the same time, I, you know, this isn't true for everyone. And I think for some people, they're going to feel a greater sense of, I don't know, maybe like loss or nostalgia or after-fear or something like this. And I don't know if you saw this, but Elon was saying that why isn't the, I just writing binary straight to binary, because what's the point of all this, you know, programming the abstraction in the end? Yeah, it's a good question. I mean, it totally can do that if you wanted to. Oh, man. So what I'm hearing here is in terms, there's always a question, should I learn to code, should people on the school learn to code? What I heard from you is, their take is, in that like a year or two, you don't really need to. My take is, I think for for people that are using, that are using quad code that are using agents to code today, you still have to understand the layer under. But yeah, in a year or two, it's not kind of matter. I was thinking about, um, what is the right like historical analog for this? 'Cause like, like somehow we have to situate this thing in history and kind of figure out when have we gone through some more transitions? What's the right kind of mental model for this? I think the thing that's come closest for me is the printing press. And so, you know, if you look at Europe in, you know, like in the mid, the mid, the mid, 1400s, literacy was actually very low. There was some 1% of the population. It was scribes that, you know, they were the ones that did all the writing. They were the ones that did all the reading. They were employed by like lords and kings, that often were not literate themselves. And so, you know, it was their job of this very tiny percent of the population to do this. And at some point, you know, Gutenberg and the printing press came along. And there was this crazy stat that, in the 50 years after the printing press was built, there was more printed material created than in the thousand years before. And so, the volume of printed material just went way up. The cost went way down. It went down something like 100x over the next 50 years. And if you look at literacy, you know, it actually took a while because of learning to read on writers. You know, it's quite hard. It takes education system. It takes free time. It takes like not having to work on a farm all day. So you actually have time for education and things like this. But over the next 200 years, it went up to like 70 percent globally. So I think this is the kind of thing that we might see is a similar kind of transition. And there was actually this interesting historical document where there was an interview with some like scribe in the 1400s about like how do you feel about the printing press? And they were actually very excited because they were actually the thing that I don't like doing this copying between books. The thing that I do like doing is drawing the art in books and then doing the book binding. And I'm really glad that now my time is freed up. And it's interesting like as an engineer, I sort of felt like a peril with us like this is sort of how I feel where I don't have to do the tedious work anymore of coding because this has always been sort of the detail of it. It's always been the tedious part of it and kind of like messing with a kid and kind of using all these different tools that those not the fun part. The fun part is figuring out what to build and kind of coming up with us. It's it's talking to users. It's thinking about these big systems. It's thinking about the future. It's collaborating with other people on the team and that that's what I get to do more of now. And what's amazing is that the tool you're building allows anybody to do this. People that have no technical experience can do exactly what you're describing. Like I've been doing a bunch of random little projects and it's just like any time you get stuck just like help me figure this out. And you got on block like I used to yeah I was an engineer for an earlier my career for 10 years and I just remember spending so much time on like libraries and dependencies and things and just like oh my god what do I do and then look again stack overflow and now it's just like help me figure this out and there's step by step one two three four okay we got this. Yeah exactly like I was talking to an engineer earlier today they're like they're writing some service and go and you know it's been like a month already and they built up the service like it's working quite well and then I was like okay so like how do you feel writing and you're like you know like I still don't really know go but and I think we're going to start to see more and more of this. It's like if you know that it works correctly and efficiently then you don't have to know all the details. Clearly the life of a software engineer has changed dramatically it's like a whole new job now as of the past year or two. What do you think is the next role that will be most impacted by AI within either within tech like you know product managers designers or even outside tech just like what do you think where do you think AI is going next?

I think this is going to be a lot of the roles that are adjacent to engineering so yeah it could be like product managers it could be design could be data science. It is going to expand to pretty much any kind of work that you can do on a computer because the model is just going to get better and better at this and you know like this is the core product is kind of the first way to get at this but it's just the first one and it's the thing that I think brings AI to a agentic AI to people that haven't really used it before and people are starting just to to get a sense of it for the first time. When I think back to engineering a year ago no one really knew what an agent was no one really used it but nowadays it's just the way that you know we do our work and then when I look at non-technical work today so you know like you know or like maybe semi-technical like product work and you know like data science and things like this. When you look at the kinds of AI that people are using it's always these like conversational AI it's like a chatbot or whatever but no one really has used an agent before and this word agent just gets thrown around all the time and it's just like so misused it's like vast all meaning but agent actually has like a very specific technical meaning which is it's a it's an AI it's an LM that's able to use tools so it doesn't just talk it can actually act and it can interact with your system and you know this means like it can use your Google Docs and it can it can send email it can run commands on your computer into all this kind of stuff so I think like any kind of job where you do use computer tools in this way I think this is going to be next is this is something we have to kind of figure out as a as a society this something we have to figure out as an industry and I think for me also this is one of the reasons it it feels very important an urgent to do this work add-on-thropic because I think we take this very very seriously and so now you know we have economists we have policy folks we have social impact folks this is something we just want to talk about a lot so as a society we can kind of figure out what to do because it shouldn't be up to us so the big question which is you're kind of alluding to his jobs and job loss and things like that there's this concept of Jeven's paradox of just as we can do more we hire more and it's not actually as scary as it looks what did you experience so far I guess with AI becoming a big part of the engineering job just are you hiring more than if you didn't have AI and just thoughts on jobs yeah I mean for our timber we're hiring so quad-coating this hiring if you're interested just check out the jobs page on on-thropic personally it's you know all all this stuff has just made me enjoy my work more I have never enjoyed coding as much as I do today because I don't have to deal with all the minutia so for me personally it's been quite exciting this is something that we hear from a lot of customers where they love the tool they love quad-coated because it just makes coding delightful again and that's just that's just so fun for them but it's hard to know where this thing is gonna go and again I just like I have to reach for these historical analogs and I think the printing process just such a good one because what happened is this technology that was locked away to a small set of people like knowing how to read and write became accessible to everyone it was just inherently democratizing everyone started to be able to do this and if that wasn't the case then something like the Renaissance just could never have happened because a lot of the Renaissance it was about like knowledge spreading it was about like written records that people used to communicate you know because there were no phones or anything like this there's no internet at the time so it's about like what does this enable next and I think that's the very optimistic version of it for me and that's the part that I'm really excited about it's just unimaginable you know like we couldn't be talking today if the printing press hadn't been invented like our microphones wouldn't exist none of the things around us would exist it just wouldn't be possible to coordinate such a large group of people if that wasn't the case and so I imagine a world you know a few years in the future where everyone is able to program and what is that unlock anyone can just build software anytime and I have no idea it's just the same way that you know in the 1400s no one could have protected this I think is the same way but I do think in the meantime it's gonna be very disruptive and it's gonna be painful for a lot of people and again as a society this is a conversation that we have to have and this is the thing that we have to figure out together so for folks hearing this that want to succeed and you know make it in this crazy turmoil we're entering any advice is it you know play with the eye tools get really proficient at the latest stuff is there anything else that you recommend to help people stay ahead yeah I think that's pretty much it experiment with the tools get to know them don't be scared of them just you know dive in try them be on the bleeding edge be on the frontier maybe the second piece of advice is try to be a generalist more than you have in the past for example in school a lot of people that study CS they learn to code and they don't really learn much else maybe they learn our little bit of systems architecture or something like this but some of the most effective engineers that I work with every day and some of the most effective you know like product managers and so on they cross over disciplines so on the quadcode team everyone codes you know our product manager codes or engineering manager codes or designer codes or finance guy codes or data scientists codes like everyone on the team codes and then in then if I look at particular engineers people often cross different disciplines so some of the strongest engineers are hybrid a product and infrastructure engineers or product engineers with really great design sense and they're able to do design also or an engineer that has a really good sense of the business and can use that to figure out what to do next or an engineer that also love stalking to users and can just really channel what what users want to figure out what's next so I think a lot of the people that will be rewarded the most over the next few years there won't just be AI native and they don't just know how to use these tools really well but also their curious and their generalists and they cross over multiple disciplines and can think about the broader problem they're solving rather than just the engineering part of it you find these three separate disciplines still useful as a way to think about the team they're you know engineering design product management you find like those even though they are now coding and contributing to thinking about what to build you feel like those are three roles that will persist long term at least at this point I think in the short time it'll persist but one thing that we're starting to see is there's maybe a 50% overlap in these roles where a lot of people are actually just doing the same thing and some people have specialties for example I code a little bit more versus cat rpm does a little bit more you know coordination or planning or you know forecasting or things like that. stakeholder alignment stakeholder alignment is actually I do think that there is a future where I think by the end of the year what we're going to start to see is these start to get even work or work your where I think in some places that title software engineer is going to start to go away and it's just going to be replaced by builder or maybe it's just everyone's going to be a product manager and everyone codes or something like this who says hiring has to be fair. 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at metavue.ai/lany that's m-e-t-a-view.ai/lany you talked about how you're enjoying coding more actually did this little informal survey and Twitter I don't know if you saw this where I just asked and did three different polls asked engineers are you enjoying your job more or less since adopting AI tools and then I did a separate one for PMs and one for designers and both engineers and PMs 70% of people said they're enjoying their job more and about 10% said they're enjoying their job less designers interestingly only 55% said they're enjoying their job more and 20% said they're enjoying their job less that that was really interesting that's super interesting I'd love to talk to these people you know both in the more bucket in the west bucket just understand did you get to follow up with any of them they a few people replied and were actually doing a follow-up that will link to in the show notes of going deeper into some of the stuff but a lot of there's like you know the factors that make it more fun and less fun the designers they didn't share a lot actually of just like the people that are actually asked just like why are you enjoying your job less and in here lots I'm curious what's going on there yeah I'm seeing this a little bit with a I don't think I think everyone is fairly technical this is something that we screen for you know when when people join we have there's a lot of technical interviews that people go go through even for non-technical functions and you know our designers are actually code so I think for them this is something that they have enjoyed from what I've seen because now instead of bugging engineers they can just like go in and code and even some designers that didn't code before have just started to do it and for them it's great because they can unblock themselves but I'd be really interested just to hear more people's experiences because I I bet it's not you know for my thought yeah so maybe if you're listening to this leave a comment if you're finding your job's less fun and you're enjoying your job less because what you're saying and what I'm hearing from most people 70% of PM's engineers are loving their job more that's like if you're not on that bucket you could something's going on yeah yeah we do see that people use also different tools so for example our designers they use the quad desktop app a lot more to do their coding so you just download the desktop app there's a code tab it's right next to cowork and it's actually the same exact quad code so it's like the same agent and everything we've had this for you know for many many months and so you can use this to code in a way that you don't have to open a bunch of terminals but you still get the power of quad code and the biggest thing is you can just run as many you know quad sessions in parallel as you want we can you know we call this multi-quoting so this is a it's a little more native I think for folks that are not engineers and really this is back to bringing the product to where the people are you don't want to make people use a different workflow you don't want to make them go out of their way to earn a new thing it's whatever people are doing if you can make that a little bit easier then that's just going to be a much better product that people enjoy more and this is just this principle blatant demand which I think is just the single most important principle in product can you talk about that actually because I was going to go there explain what this principle isn't and and just what happens when you unlock this lean demand latent demand is this idea that if you build a product in a way that can be hacked or can be kind of misused by people in a way it wasn't really designed for to do kind of something that they want to do then this helps you as the product builder or learn where to take the product next so in example this is a Facebook Marketplace so the manager for the team Fiona she was actually the founding manager for the Marketplace team and she talks about this a lot Facebook Marketplace is started based on the observation back in this must have been like 2016 or something like this that 40% of posts in Facebook groups are buying and selling stuff so this is crazy it's like people are abusing the Facebook groups product to buy and sell and it's not it's not abuse and kind of like a security sense it's abuse and that no one designed the product for this but they're kind of figuring it out because it's it's just so useful for this and so it's pretty obvious if you build a better product to what people buy and sell they're going to like it and it was just very obvious that Marketplace would be a hit from us and so the first thing was buy and sell groups so kind of special purpose groups to let people do that and the second product was Marketplace. Facebook dating I think started in a pretty similar place and I think that the observation was if you look at people looking if you look at profile views so people will can't each other's profiles on Facebook 60% of profiles or people that are not friends with each other that are opposite gender and so this is this kind of like you know like traditional kind of dating setup but you know people are just like creeping on each other so maybe if you can build a product for this it's you know it might work and so this idea of leading demand I think is just so powerful and for example this is also where coworkers came from we saw that for the last six months or so a lot of people using quad code were not using it to code there was someone on Twitter that was using it to grow tomato plants there was someone else using it to analyze their genome someone was using it to recover photos from a corrupted hard drive those like winning photos there was someone that was using it for I think like they were using it to analyze a MRI so there's just all these different use cases that are not technical at all and it was just really obvious like people are jumping through hoops to use a terminal to do this thing maybe we should just build the product for them and we saw this actually pretty early back in maybe may have last year I remember walking to the office in our data scientist Brendan was had a quad code on his computer he just had a terminal up and I was like I was shocked I was like Brendan what what are you doing like you you figured out how to open the terminal which is you know it's a very engineering product even a lot of engineers don't want to use a terminal it's just like a it's like just like the lowest level way to do your work um just really really kind of in the weeds of the computer and so he figured out how to use the terminal he downloaded no chase he downloaded quad code and he was doing sequel analysis in the terminal it was crazy and then the next week all the data scientists were doing the same thing so when you see people abusing the product in this way using it in a way that it wasn't designed in order to do something that is useful for them it's just such a strong indicator that you should just build a product and people are going to like that is something that's special purpose for that I think now there there's also this kind of interesting second dimension to latent demand this is sort of the traditional framing is look at where people are doing make that a little bit easier empower them the modern framing that I've been seeing in the last six months is a little bit different and it's look at what the model is trying to do and make that a little bit easier and so when we first started building quad code I think a lot of the way that people approached designing things with LLMs is the kind of put the model in a box and there are here's this application that I want to build here's the thing that I wanted to do model you're going to do this one component of it here's the way that you're going to interact with these tools and APIs and whatever and for quad code we invert it that we said the product is the model we want to expose it we want to put the minimal scaffolding around it give it the minimal set of tools so you can do the things they can decide which tools to run I can decide in what order to run them and so on and I think a lot of this was just based on kind of latent demand of what the model wanted to do and so in research we call this being on distribution you want to see what the model is trying to do in product terms latent demand is just the same exact concept but apply to the model you talked about co-work something that I saw you talk about when you launched that initially as you your team built that in 10 days that's insane yeah I think it came out I think it was like you know used by millions of people pretty quickly something like that being built in 10 days anything there any stories there other than just it was just you know we used to call code to build it that's it yeah it's funny uh quad code like I said when we were we said it was not immediately a hit it became a hit over time and there was a few inflection points so one was you know like opus four it just really really inflected and then in November it inflected and it just keeps inflecting the growth just keeps getting steeper and steeper and steeper every day but you know for the first few months it wasn't a hit uh people used it but a lot of people couldn't figure out how to use it they didn't know what it was for the model so like wasn't very good co-work when we were released it it was just immediately a hit much more so than quad code was early on I think a lot of the credit honestly just goes to like Felix and and Sam and the and Jenny and the the team that built us is just an incredibly strong team and again the the place co-work came from is just the sweet and demand like we saw people using quad code for these non-technical things and we're trying to figure out what do we do and so for a few months the team was exploring they were trying all sorts of different options and in the end someone was just like okay what if we just take quad code and put it in the desktop app and that's essentially the thing that worked and so over 10 days they just completely used quad code to build it and you know co-work is actually there's this very sophisticated security system that's that's built in and essentially these guard rails to make sure that the model kind of does the right thing it doesn't go off the rails so for example we ship an entire virtual machine with it and quad code just wrote all of this code so we just had to think about all right how do we make this a little bit safer a little more self guided for uh people that are not engineers it was fully implemented with quad code took about 10 days we launched it early you know it was so pretty rough and it's still pretty rough around the edges but this is kind of the way that we learn both on the product side and on the safety side is we have to release things a little bit earlier than we think so that we can get the feedback so that we can talk to users we can understand what people want and that will shape where the product goes in the future yeah I think that point is so interesting and it's so unique there's always been this idea really thoroughly learn from users get feedback iterate the fact that it's hard to even know what the AI's capable of and how people will try to use it is like is a unique reason to start releasing things early to that will help you as you exactly describe this idea what is it like in the man in this thing that we didn't really know let's put it out there and see if people do with it yeah and in front of it is a safety of the other dimension of that is safety because you know like when you think about model safety there's a bunch of different ways to study it sort of the lowest level is alignment and mechanistic and interpretability so this is when we train the model we want to make sure that safe we at this point have like pretty sophisticated technology to understand what's happening in the neurons to trace it and so for example like if there's a neuron related to deception we can start we're starting to get to the point where we can monitor it and understand that it's activating and so this is just this is alignment this is mechanistic and interpretability it's like the lowest wear the second wear is evolves and this is essentially a laboratory setting the model is in a petri dish and you study it and you put in this synthetic situation and just say okay like model what do you do and are you doing the right thing is it aligned is it safe and then the third wear is seeing how the model behaves in the wild and as the model gets more sophisticated this this becomes so important because it might look very good on these first two wearers but not great on the third one we released quad code really early because we wanted to study safety and we actually used it within anthropic for I think four or five months or something before we released it because we weren't really sure like this is the first agent that you know the first big agent that I think folks had released at that point it was definitely the first you know coding agent that became brought we used and so we weren't sure if it was safe and so we actually had to study it internally for a long time before we felt good about that and even since you know there's a lot that we've learned about alignment there's a lot that we've learned about safety that we've been able to put back into the model back into the product and for co-work it's pretty similar the models in this new setting it's you know doing these tasks that are not engineering tasks it's an agent that's acting on your behalf he looks good on alignment it looks good on e-values we try to internally it looks good we tried it with a few customers it looks good now we have to make sure it's safe in the real world and so that's why we released a little early that's what we call it a research preview but yeah it's just it's constantly improving and this is really the only way to to make sure that over the long term the models aligned and it's doing the right things it's such a wild space that you work in where there's this insane competition and pace at the same time there's this fear that if you get the you know the god Guinness gap and cause damage and just finding that balance must be so challenging what I'm hearing is there's kind of these three layers and I know there's like this could be a whole podcast conversation it's how you all think about the safety piece but just what I'm hearing is there's these three layers you work with there's kind of like observing the model thinking and operating there's e-tests e-values that tell you is doing bad things and then releasing it early I haven't actually heard a ton about that first piece that is so cool so you guys can there's an observability tool that can let you peek inside the models brain and see how it's thinking in words heading yeah you should at some point have Chris Ola on the podcast because he he's used the industry expert on this he invented this field of we call it mechanistic interpretability and that the idea is you know like at its core like what is your brain like whatever what is it it's like it's a bunch of neurons that are connected and so what you can do is like in a human brain or animal brain you can study it at this kind of mechanistic level to understand what the neurons are doing it turns out surprisingly a lot of this does translate to models also so model neurons are not the same as animal neurons but they behave similarly in a lot of ways and so we've been able to learn just a ton about the way these neurons work about you know this layer or this neuron maps to this concept how particular concepts are encoded how the model does planning how it how it thinks ahead you know like a a long time ago we weren't sure if the model is just predicting the next token or is doing something a little bit deeper now I think there's actually quite strong evidence that it is doing something a little bit deeper and then the structures that we'd do this are pre-sophisticated now where as the models get bigger it's not just like a single neuron that corresponds to a concept a single neuron might correspond to a dozen concepts and if it's activated together with other neurons this is called superposition and together it represents a more sophisticated concept and it is just something we're learning about all the time you know and for anthropic as we think about the way this space evolves doing this in a way that is safe and good for the world is just this is the reason that we exist and this is the reason that everyone is at anthropic everyone that is here this is the reason why they're here so a lot of this work we actually open source we publish it a lot and you know we publish very free we to talk about this just so we can inspire other labs that are working on similar things to do it in a way that's safe and this is something that we've been doing for quadcode also we call this the race to the top internally and so for quadcode for example we release to open source sandbox and this is a sandbox they can run the agent in it just make sure that there are certain boundaries and they can't access like everything on your system and we made that open source and it actually works with any agent not just quadcode because we wanted to make it really easy for others to do the same thing so this is just the same principle of race to the top we we want to make sure this thing goes well and this is just the this is the we were that we have incredible okay I definitely want to spend more time on that I will follow up with this suggestion something else that I've been noticing in the in the field across engineers product managers others that work with agents is there's this kind of anxiety people feel when their agents aren't working there's a sense that like oh man these are has a question and answer or it's like blocked on something or it's or I just like I I'm like there's all this productivity I'm losing it yeah like I need to wake up and get it going again is that's something you feel that something your team feels do you feel like this is a problem you do track and think about I always have a bunch of agents running so like at the moment I have like five agents running and at any moment like you know like I wake up and I start a bunch of agents like the first thing I did when I woke up was like oh man I want everybody want to check thithing so like I opened up my phone quad i was a code tab you know like agent do do bobbleball because I wrote some code yesterday and I was like wait did I do this right I was like kind of double guessing something and it was correct but it's just like so easy to do this so I don't know there is this little bit of anxiety maybe I personally haven't really felt it just because I have agents running all the time and I'm also just like not locked into a terminal anymore maybe a third of my code now is in the terminal but also a third is using the desktop app and then a third is the iOS app which is just so surprising because I did not think that this would be the way that I code in even in 2020 2006 and I love this is described as coding still which is just talking to the to cloud code to code for you essentially and it's interesting that this is like this is now coding coding now is describing what you want not writing actual code I kind of wonder if the people that used to code using punch cards or whatever if you show them software what they would have said is that I couldn't I I remember reading something this was maybe like very early versions of like ACM like magazine or something where people were saying no it's not the same thing like this isn't really coding and you know like they called a program I think coding is kind of a new word but I kind of think about those like in the back in the you know my family from the Soviet Union I you know I I was born in Ukraine and my grandpa was actually one of the first programmers in the Soviet Union and he programmed using punch cards and you know like he he told my mom growing up told these stories of like or she she told these words I when she was growing up he would bring these punch cards home and there's these like big stacks of punch cards and for her she would like draw all over them with crayons and that was a curcheltered memory but for him that was like his experience of programming and he actually never saw the software transition but at some point it did transition to software and I think there's probably this older generation of programmers that just didn't take software very seriously and that they would have been like well you know it's not really coding but I think this is a field that just has always been changing in this way I don't think you know this but I was born in Ukraine also oh I don't know yeah yeah which I'm from I'm from Odessa oh me too he's one yeah that's crazy wow incredible what a moment maybe related and some small way not what year did your did you leave and your family leave we came in 95 okay we left in 88 little earlier oh yeah what are different life that would have been to not to not leave huh yeah I just I feel I feel so lucky every day but uh get get to corp here yeah my family any time there's like a toaster meal they're just like to America it's like okay enough about that but you get it you know once you start really thinking about what life could have been yeah yeah yeah we do that we do the same toast but it's still vodka and it's still vodka so no man okay let me ask you a couple more things here you share some really cool tips for how to get the most out of AI had a build on AI had a build great products and AI one tip you share it is give your team as many tokens as they want just like let them experiment you also share just advice generally of just build towards the model or the model is going not to where it is today what other advice do you have for folks that are trying to build AI products I probably share a few more things so one is don't try to box the model in I think a lot of people is instinct when they build on the model is they try to make it behave a very particular way they're like you know this is a component of a bigger system I think some examples of this are people wearing like very strict workflows on the model for example you know to say like you must do step one then step two then step three and you have this like very fancy orchestrator doing this but actually almost always you get better results if you just give the model tools you give it a goal and you let it figure it out I think a year ago you actually needed a lot of this scaffolding but nowadays you don't really need it so you know I don't know what to call this principle but it's like you know like ask not what the model can do for you maybe it's something like this just think about how do you give the model the tools to do things don't try to overture it don't try to put it into a box don't try to give it a bunch of context up front give it a tool so that it can get the context it needs you're just going to get better results I think a second one is maybe actually like a more even more general version of this principle is just a bit of lesson and actually for the quadcode team we have a you know hopefully hopefully listeners have have read this but research something had this blog post maybe 10 years ago called the bitter lesson and it's actually a really simple idea his idea was that the more general model will always outperform the more specific model and I think for him he was talking about like self-driving cars and other domains like this but actually there's just so many core areas to the bitter lesson and for me the biggest one is just always bet on the more general model and you know over the long term like don't don't try to use tiny models for stuff don't try to like find to don't try to do any this stuff there's like some applications you know there's some reasons to do this but almost always try to bet on the more general model if you can if you have that flexibility and so these workloads are essentially a way that you know it's not it's not a general model it's putting the scaffolding around it and in general we see as maybe scaffolding can improve for performance maybe 10-20% something like this but often these gains just get wiped out with the next model so it's almost better to just wait for the next one and I think maybe this is a final principle and something that quad code I think got right in hindsight from the very beginning we bet on building for the model six months from now not for the model of today and for the very early version to the product they just wrote so little of my code because I didn't trust it because you know it was like son of 3.5 then it was like 3.6 or forget 3.5 new whatever whatever maybe we give it these models just weren't very good at coding yet they were they were getting there but it was still pretty early so back then the model did you used get for me it automated some things but it really wasn't doing a huge amount of my coding and so the bet with quad code was at some point the model gets good enough that it can just write a lot of the code and this is a thing that we first started seeing with opus 4 and son of 4 and opus 4 was our first kind of ASL 3 class model that we really speck and may and we just saw this inflection because everyone started to use quad code for the first time and that was kind of when our growth really went exponential and like I said it's kind of it's stayed there so I think this is something this advice that I actually give to a lot of folks especially people building startups it's going to be uncomfortable because your product market will be very good for the first six months but if you build for the model six months out when that model comes out you're just going to hit the ground running and the product is going to click and start to work and when you say build for the model six months out what is what is it that you think people can assume will happen is it just generally it will get better at things is it just like okay it's like almost good enough and that's sign that it'll probably get better at that thing is there any advice there I think that's a good way to do it like you know obviously within an AI lab we get to see the specific ways that it gets better so it's a little unfair but we also we try to talk about this so you know like one of the ways that it's going to get better is it's going to get better and better using tools and using computers this is a bet that I would make another one is it's going to get better and better for long for running for long periods of time and this is a place you know like there's also some studies about this but if you just trace that trajectory or you know maybe even like for my own experience when I used on a 3.5 back you know a year ago it could run for baby 15 or 30 seconds or for sort of going off the rails and just really had to hold the tan through any kind of complicated task but nowadays with open 4.6 you know on average it'll run maybe 10 30 20 30 minutes unattended and I'll just like start another quad and have a do something else and you know like I said it always have a bunch of quad running and they can also run for hours or even days at a time I think there are some examples where they ran for many weeks and so I think over time this is going to become more and more normal where the models are running for a very very long period of time and you don't have to sit there and maybe set them anymore. So you just talked about tips for building AI products and he tips for someone just using cloud code for say for the first time or just someone already using cloud code that wants to get better what are like a couple prototypes that you could share. We'll give it caveat which is there's no one right way to use cloud code so I can share some tips but honestly this is a dev tool developers are all different developers have different preferences they have different environments so there's just so many ways to use these tools there's no one right way you you sort of have to find your own path luckily you can ask cloud code it's able to make recommendations they can edit your settings it kind of knows about itself so it can help you can help with that a few tips that generally I find pretty useful so number one is just use the most capable model currently that's Opus 4.6 I have maximum effort enabled always the thing that happens is sometimes people try to use a less expensive model like Sonnet or something like this the because it's less intelligent it actually takes more tokens in the end to do the same task and so it's actually not obvious that it's cheaper if you use a less expensive model often it's actually cheaper in less token intensive if you use the most capable model because it can just do the same thing much faster with the less correction was a was handholding on so on so the first step is just use the best model the second one is use plan mode I start almost all of my tasks in plan mode maybe like 80% and plan mode is actually really simple all it is is we inject one sentence into the model's prompt to say please don't write any code yet and so like there's actually nothing fancy going on it's just the simplest thing and so for people that are in the terminal it's just shift tab twice and that gets you into plan mode for people in the desktop app there's a little button on web there's a little button is coming pretty soon to mobile also and we just want you to for the slack integration too so plan mode is the second one and essentially the model would just go back and forth with you once the plan looks good then you let the model execute I auto accept edits after that because if the plan looks good it's just going to one shot it it'll get right the first time almost every time with the open 4.6 and then maybe the third tip is just play around with different interfaces I think a lot of people when they think about quadcode they think about a terminal and you know of course we support every terminal we support like Mac windows you know like whatever terminal you might use it works perfectly but we actually support a lot of other form factors too like you know we have like iOS and Android apps we have a desktop app there's you know the slack integration there's all sorts of things that we support so I would just like play around with these and again take every engineer is different everyone that's building it's different just find the thing that feels right to you and use that you don't have to use a terminal it's the same quad agent running everywhere amazing okay just a couple more questions to round things out what's your take on codex how do you feel about that product how do you feel about where they're going just kind of competing in this very competitive space in coding agents yeah I actually haven't really used it but I think I did use it maybe when it came out it looked a lot like quadcode to me so that was kind of flattering it's I think it's actually good you know to have more competition because people should get to choose and hopefully it forces all of us to like do a even better job honestly for our team though we're just focused on solving the problems that users have so for us you know we don't spend a lot of time looking and competing products we don't really try the other products I you know you kind of you want to be aware of them you want to know they exist but for me I just I love talking to users I love making the product better I love just acting on feedback so it's really just about building a building a good product maybe a last question so I talked to a bandman co-founder of anthropic what what to talk to you about here about just suggestions which have integrated throughout our chat one question you had for you is what's your plan post a GI what do you think you're going to be doing with your life like once we hit a GI whatever that means so before I joined anthropic I was actually living in rural Japan and it was like a totally different lifestyle I was like the only engineer in the town I was the only English speaker in the town it was just like a totally different vibe like a couple times a week I would like bike to the farmers market and you know you like bike by like race paddy isn't stuff you just like a totally different speed then it just complete opposite of San Francisco one of the things that I really liked is a way that we got to know our neighbors and we kind of built friendships by trading like pickles so in that in the town where we lived it was actually like everyone made like me so everyone made pickles and so I actually got like decently good at making me so and you know I made up into batches and this is something that I still make me so is this interesting thing where it teaches you to think on these a long time skills that's just very different than engineering because like a you know like a batch of white me so it takes like at least three months to make and I read me so it's like you know two three four years you have to be very patient it kind of makes it up and then you just like let it sit you have to be very very patient so I the thing that I love about is just thinking in these long times skills and yeah I think post a GI or if I wasn't an anthropic I'd probably be making me so I love this answer Ben asked me to ask you about what's the deal with you and me so and so I love that okay so the future the future might be just going deep into me so getting really good at making me so amazing Boris this is incredible I feel like we're brothers now from Ukraine before we get to a very exciting ladyground is there anything else that you wanted to share is there anything you want to leave listeners with anything you want you want to double down on yeah I think I would just like underscore you know like for philanthropic since the beginning this idea of like starting at coding then getting to two use then getting to computer use has just been the way that we think about things and this is the way that we know the models are going to develop you know the way that we want to build our models and it's also the way that we get to learn about safety study it and improve it the most so you know everything that's happening right now around you know just like quadcode becoming this huge you know multi-billion dollar business and you know like now all my friends use quadcode and they just text me about it all the time so just like you know this thing getting kind of big in some ways it's a total surprise because this isn't kind of the we didn't know that it would be this product we didn't know that it would start in a terminal or anything like this but in some ways it's just totally unsurprising because this has been our belief as a company for for a long time at the same time it just feels still very early you know like most of the world still does not use quadcode most of the world still does not use AI so it just feels like this is one percent on and there is so much more to go you know man that's insane to think seeing the numbers that are coming out you guys just raised a bazillion dollars I think quadcode alone is making $2 billion revenue you think anthropic I think the number you guys put out you're making 15 billion in revenue it's insane to just think this is how early it still is and just the numbers we're seeing yeah yeah yeah it's crazy and I mean like the the way that quadcode has got growing is honestly just the users like we so many people use it they're so passionate about it they fall in love with the product and then they tell us about stuff that doesn't work stuff that they want and so like the only reason that it keeps improving is because everyone is using it everyone is talking about it everyone keeps getting feedback and this is just the single most important thing and you know for me this is the way that I love to spend my days just talking to users and making it better for them and making me so and making me so you know the me so is like not super involved it just you just got to wait well Boris with that we've reached our very exciting lightning round I've got five questions for you are you ready let's do it first question what are two or three books that you find yourself recommending most to other people I am a big reader I would start with a technical book one it it is functional programming in scala this is the single best technical book I've ever read it's very weird because you're probably not going to use scala and I don't know how much this matters in the future now but there's this just elegance to functional programming and thinking and types and this is just the way that I code and the way that I can't stop thinking about coding so you know you could think of it as a historical artifact you could think of it as something that will level you on I love this never before mentioned book my favorite the whole amazing amazing okay second one is accelerando by straws this is probably you know like my my dig genre is sci-fi like probably sci-fi and fiction accelerando is just this incredible book and it it's just so fast-paced the pace gets faster and faster and faster and I just feel like it captures the essence of this moment that we're in more than any other book that I've read is the speed of it and it starts as a lift off is starting to happen and you know it's starting to approach the singularity and it ends with like this like collected lobster consciousness orbiting Jupiter and you know this happens over like the span of a few decades or something so the the pace is just incredible I really love it maybe I'll do one more book the wandering earth wandering earth by cation loo so he's the guy that did a three body problem I think a lot of people know for that I actually I think three body problems awesome but I actually like to short stories even more so wandering earth is one of the short story collections and it just has some really really amazing stories and it's also just quite interesting to see a Chinese sci-fi because it has a very different perspective than western sci-fi and kind of the way that at least he has a writer thinks about it so it's just really really interesting to read it in just beautifully written it's so interesting how sci-fi is prepared us to think about where things are going just like it creates these wapmouths and models of like okay I see I've read about this sort of world yeah I think I think for me this is like the reason that I joined inthropic actually because you know like I said I was living in this rural place I was thinking these long time skills because everything is just so slow out there at least compared to us half and just like all the things that you do are based around the seasons and it's based around this food that takes many, many months that's the way that kind of like social events are organized that's the way you kind of organize your time you like you go to the farmer's market and it's like its persimmon season and you know that because there's like 20 persimmon vendors and then the next week the season is done and then it's like grapes you send them you kind of see this so it's like these kind of long time skills and it was also reading a bunch of sci-fi at the time and just like being in this moment I was like you know just thinking about these long time skills I know how this thing can go and I just I felt like I had to contribute to it going a little bit better and that's actually why I ended up at Anton then man it was also a big part of that too I feel like I want to do a whole podcast just talking about your timeage pan in the journey of Boris through Japan to anthropic but we'll keep it short I'll quickly recommend a sci-fi book to you if you haven't read it have you read fire upon the deep this is vintage right yeah yeah okay that one's like it's like so interesting from an AI, AI perspective so a few people have read that so I love that yeah it's like I really like the yeah yeah yeah I like a deepness in the sky also I think those approaches the sequel later yeah yeah yeah I think so yeah very long like complex to get into but so good okay we'll keep going through a lighting around yeah a favorite recent movie or TV show you really enjoyed so I actually don't really watch TV or movies I just don't really have time these days um I did watch I I'm gonna bring up another season loop with the three body problem series on Netflix I I really loved um I thought it was like a great rendition of the books series so the common pattern across AI leaders is no time to watch TV or movies which I completely understand uh is there a favorite product you recently discovered that you really love I'm gonna like chill a little bit and just hate co-work because I just this is a legitimate really the the one product that's been pretty life-changing for me just because I have a running all the time and the the chrome integration in particular is just really excellent so it's been like you paid a traffic fine for me it like canceled a couple of subscriptions for me uh just like the amount of like tedious work it gets out of the way is awesome and I also don't know if it's a product but maybe I'll uh also another podcast that I really love obviously besides besides 20 is uh yeah it's uh it's the acquired uh podcast by then Ben and David it's it's just like super it's super awesome um I feel like the way that they get into like business history and bring it alive is really really good and I would start with an intendo episode if uh if you haven't listened to it and great tip uh with co-work just so people understand if they haven't tried this like basically you type something you want to get done and it can launch chrome and just do things for you I saw one of the someone went on pat leave from anthropic and you had it fill out these like medical forms for them these like really annoying PDFs or it just like loads up the browser and logs in fills about some bits of yeah exactly exactly and it actually just kind of works like we tried this experiment like a year ago and it didn't really work as the model wasn't ready but now it actually just works and it's amazing I think a lot of people just don't really understand what this is because they haven't used to agent before and it just feels very very similar to me to the quadcode a year ago um but like I said it's just growing much faster than quadcode did in the early days so I think it's starting to it's starting to break through a bit and there's also this chrome extension that you mentioned that you could just leave stand alone that's it's in chrome and you could just talk to clawed looking at your screen at your browser and have it do stuff have it tell you about what you're looking at summarized which you're looking at things like that exactly exactly for people they're like just learning to use co-work the thing I recommend is so you download the quad desktop app you go to the co-work tab it's right next to the code tab um the thing that I recommend doing is like start by having it use a tool so like clean up your desktop or like summarized or email or something like this or you know like respond to the top three emails like it actually just response to emails from me now too the second thing is connect tools so like if you connect like if you say look at my top emails and then sends back messages or you know like put them in a spreadsheet or something or for example like I use it for all my project management so we have a single spreadsheet for the whole team there's like a row per engineer every week everyone fills out a status and every Monday code just goes through and it messages every engineer on swag that hasn't filled out their status and so I don't have to do this anywhere and this is just one problem to it'll do everything and then the third thing is just run a bunch of quads and parallel so it can co-work you can have as many tasks running as you want so it's like start one task you know I have this project management think running then I'll have to do something else then something else and then I'll kick these off and then I just go get a coffee while it runs there's a post I'll link to that shares a bunch of ways people use uh what was previously cloud code or now just you could do through co-work because a lot of this is just like oh wow I hadn't thought I could use it for that and once you see like these examples I think it where people need to hear I've just like oh wow I didn't know I could do that yeah I think a lot of this was also some of this was also inspired by you honey you you had this post about uh it was like 50 non-technical use cases for co-work code or something like this so we actually one of our PMs used that as a way to evaluate co-work before we released it and I think at the point where we hit work work was able to do like 48 out of the 50 they were okay it's pretty good wow I did not know that bad it's also uh it's I've become an eval yeah how did that go I'm amazing I feel like I'm valuable to the future oh yeah this is like reverse breaking through wow that is so cool wow okay I wonder what does last you are anyway okay two more questions um do you have a favorite life motto the often come back to in worker and life use common sense I think a lot of the failures that I see in especially in a work environment is people just failing to use common sense like they follow process without thinking about it um they just do a thing without thinking about it or they're working on a product that's like not a good product or not a good idea and they're just following the momentum and not thinking about it I think the best results that I see are people thinking from first principles and just developing their own common sense like if something smells weird then you know it's probably not a good idea so I think I think just this this is the single advice that I give you know to co-workers more than anything too and I feel like that alone could be some podcast conversation what is common sense how do you build but we'll keep this short final question so you've been got more active on twitter slash x um here's just uh why and just what's your experience been with with twitter the world of twitter uh because you get a lot of engagement on twitter slash x so for one time I use threads x was really because I actually helped they build threads a little bit back in the day um and also just like the design it's like a very clean product I just really like that I started using threads because actually I was bored um so in the in December I was in your starting time yeah yeah yeah yeah I started I started using a twitter because I was bored so my wife and I were we were traveling around in in Europe for December we're just kind of no-mitting around we went to like Copenhagen went to like a few different countries and for me it was just like a coding vacation so every day I was coding and that's like my favorite kind of vacation was just like cold all day it's the best and at some point I just kind of got bored and like I ran out of ideas for you know like a few hours I was like okay widow on a dude next and so open twitter I saw some people like tweeting about quadcode and then I just started responding and then I was like okay maybe actually a thing I should do is just like look for people look for bugs that people have maybe people have like bugs or kind of feedback they have and so kind of introduced myself as for people how to bunch of bugs and feedback and I think they were kind of surprised by like the pace at which we were able to address feedback nowadays for me it's just like so normal like if someone has a bug like I can probably fix it within a few minutes because I just sort of quad and as long as the description goes go to it it would just go and do it and then I'll all go do something else and answer the next thing but I think for a lot of people was pretty surprising so it was really cool and yeah the experience on twitter has been pretty great it's been awesome just engaging with people and seeing what people want hearing hearing about bugs hearing about features I say complaints in a key to beer the other day on twitter just you could you're like posting many threads and it was bridging and just like oh man let's come on here yeah yeah there there was a bug I hope it's fixed now amazing oh man Boris I could chat with you for hours all that you go thank you so much for doing this you're wonderful work in folks funny online how can listeners be useful to you yeah find me on threads or on twitter that's the that's the easiest place and please just tag me on stuff then send bugs send feature requests what's missing what can we do to make the products better what do you like what do you want I love love hearing it amazing Boris thank you so much for being here cool thanks funny everyone thank you so much for listening if you found this valuable you can subscribe to the show on apple podcasts Spotify or your favorite podcast app also please consider giving us a rating or leaving review as that really helps other listeners find the podcast you can find all past episodes.