Head of Claude Code: What happens after coding is solved | Boris Cherny

A hundred percent of my code is written by Cloud Code. I have not edited a single line by hand since November. Every day I ship 10, 20, 30 pull requests. So like at the moment, I have like five agents running. While we're recording this? Yeah, yeah. Do 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 minutia. Productivity per engineer has increased 200%. There's always this question, should I learn to code? In a year or two, it's not going to 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? Cloud is starting to come up with ideas. Looking through feedback, it's looking at bug reports, it's looking at telemetry for bug fixes and things to ship. A little more like a co-worker 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 going to be a product manager and everyone codes. The title software engineer is going to start to go away. It's just going to be replaced by builder and it's going to be painful for a lot of people. Today, my guest is Boris Cherny, head of Cloud Code at Anthropic. It is hard to describe the impact that Cloud Code has had on the world. Around the time this episode comes out will be the one year anniversary of Cloud Code. And in that short time, it has 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. Cloud Code itself is also a massive driver of Anthropic's overall growth over the past year. They just raised around at over $350 billion. And as Boris mentions, the growth of Cloud 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 Wen and Mike Krieger for suggesting topics for this conversation. Don't forget to check out lennysproductpass.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 organization leaders 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. With DX, companies like Dropbox, Booking.com, Adyen and Intercom get a deep understanding of how AI is providing value to their developers and what impact AI is having on engineering productivity. To learn more, visit DX's website at getdx.com slash Lenny. That's getdx.com slash Lenny. Applications break in all kinds of ways, crashes, slowdowns, regressions, and the stuff that you only see once real users show up. Sentry catches it all. See what happened, where, and why. Down to the commit that introduced the error, the developer who shipped it, and the exact line of code all in one connected view. I've definitely tried the five tabs and Slack thread approach to debugging. This is better. Sentry shows you how the request moved, what ran, what slowed down, and what users saw. Sear, Sentry's AI debugging agent, takes it from there. It uses all of that Sentry context to tell you the root cause, suggest a fix, and even opens a PR for you. It also reviews your PRs and flags any breaking changes with fixes ready to go. Try Sentry and Sear for free at sentry.io slash Lenny, and use code Lenny for $100 in Sentry credits. That's S-E-N-T-R-Y.io slash Lenny. Boris, thank you so much for being here, and welcome to the podcast. Yeah, thanks for having me on. I wanna 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 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 they saw where AI coding was going, I think, before a lot of people did. So the idea of building good 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 AI coding was the fact that it was so much fun. It was so much fun to work with people, and what I started to realize is what I really missed about Ant was the mission. And that's actually what originally drove me to Ant also. Because before I joined Anthropic, I was working in 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. And the thing that drew me to Anthropic was the mission, and it's all about safety. And 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 gonna be safety. And so this kind of mission-drivenness just really, really resonated with me, and I just know personally it's something I need in order to be happy. And 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 really cool product, it's just not really a substitute for that. So for me, it was actually, it was pretty obvious that I was missing that pretty quick. Okay, so let me follow the thread of just coming back to Anthropic and the work you've done there. This podcast is gonna come out around the year anniversary of launching Cloud Code. So I'm gonna spend 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 this past year and the impact that your work has had. These numbers are just totally crazy, right? 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 craziest 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 quad code's 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 quad code, it was just gonna be like, it was just supposed to be a little hack. You know, we broadly knew at Anthropic that we wanted to ship some kind of coding product. And, you know, for Anthropic 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 Mike Krieger and, you know, Ben Mann, they just kicked this team off again for kind of round two. The team built some pretty cool stuff. So we built quad code, 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 co-work, we're 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 acts. 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 gonna get better from here. So I think for Anthropic, for a long time, there was this feeling that we wanted to build something, but it wasn't obvious what. And so when I joined Ant, I spent one month kind of hacking and built a bunch of weird prototypes. Most of them didn't ship and 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 side 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, 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. But yeah, if you're working in 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 Quad Code. And the very first version of it, I have like a, there's like 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 am I listening to? And this is the craziest thing, right? Cause it's like, there's no, I didn't instruct the model to say, you know, use this tool for this or kind of do whatever. The model was given this tool and it figured out how to use it to answer this question that I had that I wasn't even sure if it could answer what music am I listening to? And so I started prototyping this a little bit more. I made a post about it and I announced it internally and it got two likes. That was the sense of the reaction at the time. Cause 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. And that wasn't really the intention. But, you know, from the start, I built it in a terminal because, you know, for the first couple of 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? Is 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, the quad code 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 model is 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 I released it pretty quickly, it became a hit at Anthropic. And, you know, the daily active users just went vertical. And really early on, actually, before I launched it, Ben Mann nudged 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 chart just went vertical pretty immediately. And then in February, we released it externally. Actually, something that people don't really remember is quad code 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 quad code 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 this way. So you have to kind of be open-minded and you have to learn to use it. And of course now, you know, quad code 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 ID extensions and Slack and GitHub. You know, all of 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 getting 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 it 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 wasn't 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 gonna be written, a hundred percent of code is gonna be written by AI. Everyone's like, no, that's crazy. What are you talking about now? It's like, oh, of course it's happening exactly as they said. It's just the things move so fast and change so fast now. Yeah, it's really fast. Back at Code with Quad back in May, that was like our first, you know, like developer conference that we did as Anthropic. I did a short talk and 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 by the end of the year, you might not need an ID to code anymore. And we're gonna start to see engineers not doing this. And I remember the room like audibly gasped. It was such a crazy prediction. But I think like at Anthropic, like this is just the way we think about things as 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 laws paper. So we really just think in exponentials. And if you kind of look at the exponential, the percent of code that was written by Quad at that point, if you just trace the line, it's pretty obvious we're gonna cross 100% by the end of 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 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 it was really interesting what you just shared there about kind of the journey is this kind of idea of just playing around and seeing what happens. This comes up with OpenClaw 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 to a lot of the biggest innovations in AI is people just sitting around trying stuff to pushing the models further than most other people. I mean, that's the thing about innovation, right? Like 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 a bit. So if the idea is bad, you cut your losses, move on to the next idea. Instead of investing more. In the early days of Quad Code, 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 for 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 sometimes, you know, you find a thread, you just have to pull on it. So at this point, 100% of your code is written by Quad Code. Is that kind of the current state of your coding? Yeah, so 100% of my code is written by Quad Code. I am 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 at Anthropic. Wow, even as head of the team. Yeah, yeah, still do a lot of coding. And so every, you know, every day I ship like 10, 20, 30 pull requests, something like that. Every day? Every day, yeah. Good God. 100% written by Quad Code. I have not edited 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 Quad doing automatic code review for everything. So here at Anthropic, Quad reviews 100% of pull requests. There's still a layer of like human review after it, but you kind of like, you still do want some of 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 gonna run. It's not gonna 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 kind of the next big shift to how software is written that either your team's already operating in or you think we'll head towards? I think something that's happening right now is Quad is starting to come up with ideas. So Quad is looking through feedback. It's looking at bug reports. It's looking at, you know, like telemetry and 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 kinds of programming that I do is just a solved problem because Quad 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 coworker every day now to do all sorts of things that are just not related to coding at all and just to do it automatically. Like, for example, I had to pay a parking ticket the other day. I just had coworker do it. All of my project management for the team, coworker does all of it. It's like syncing stuff between spreadsheets and messaging people on Slack and email and all this kind of stuff. So I think the frontier is something like this. And I don't think it's coding 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 code base, every tech stack 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 are product managers and they're probably sweating. How do you use Quad for this? Do you just talk to it? Is 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 Quad Code or a coworker and point it at a Slack thread. You know, like for us, we have this channel that's all the internal feedback about Quad Code. 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 I would fix every single thing as fast as I possibly could. So like within a minute, within five minutes or whatever. And it's 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 does a lot of the work. So I point it 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, want to take a look at that one. 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. Code review became kind of the next bottleneck with all these 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, what to prioritize. And you're saying that that's where Cloud Code is starting to help you. Has it gotten a lot better with like say Opus 4.6 or what's 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, best coding model in the world and 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 X and it kind of gets better at Y. Yeah, and the gains have just been insane. Like ad-anthropic over the last year, like since we introduced Quad Code, we probably, I don't know the exact number, we probably like 4X the engineering team or something like this. But productivity per engineer has increased 200% in terms of like pull requests. And like this number is just crazy for anyone that actually works in this space and works on dev productivity. Because back in a previous life, I was at Meta and one of my responsibilities was code quality for the company. So this is like all of our code bases, that was 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. 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, something like this. And so nowadays seeing these gains of just hundreds of percentage points, it's just absolutely insane. What's also insane is just how normalized this has all been. Like we hear these numbers, like of course AI is doing this to us. It's just, it's so unprecedented, the amount of change that is happening to software development, to building products, to 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 kinds of downsides that we could talk about. But I think one of them on a personal 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 of months ago where there was a memory leak. And so like what this is, is like quad code, the memory usage is going up and at some point it crashes. This is like a very common kind of engineering problem that 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, 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 quad code do it. And it was like, hey quad, it seems like there's a leak, can you figure it out? And so like quad code did exactly the same thing that I was doing. It took the heap snapshot, it wrote 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 up a pull request 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 mindset shift 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 Claude do it. And it feels like that's exactly what you described with this memory leak is just like, you almost forgot that principle of like, okay, let me see if Claude can solve this for me. There's this interesting thing that happens also when you underfund everything a little bit, because then people are kind of forced to claudify. 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 Claude, 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 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 nowadays it's still very much a principle we have on the team. And if you want to go faster, a really good way to do that is to just have Claude do more stuff. So he 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 be more productive, what you're saying is that you will actually do better if you underfund. 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 like CTOs and kind of all sorts of companies. My advice generally is don't try to optimize. 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, at Anthropic, everyone can use a lot of tokens. We're starting to see this come up as like a perk at some companies. Like 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, maybe you can do it with Haiku or with Sonnet instead of Opus or whatever. But at the beginning, you just want to throw a lot of tokens at it and see if the idea 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 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 at small scale, you're not gonna get a giant bill or anything like this. If it's an individual engineer experimenting, the token cost is still probably relatively low relative to their salary or other costs of running the business. So it's actually not a huge cost. As the thing scales up, so let's say they build something awesome and then it takes a huge amount of tokens. And then the cost becomes pretty big. That's the point at which you want to optimize it. But 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 gonna find and see? You know, at Anthropic, we're starting to see some engineers that are spending hundreds of thousands a month in tokens. So we're starting to see this a little bit. There's some companies that we're starting to see similar things. Yeah. Going back to coding, do you miss writing code? Is this something you're kind of sad about that this is no longer a thing you will do as a software engineer? 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 self-taught. 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 learned to code so that I can cheat on a math test. That was the first thing. We had these graphing calculators and I just programmed the answer into them. TI-83? TI-83 plus, yeah, yeah, exactly. Plus. Plus, yeah, so I programmed the answers in and then the next math test, whatever, the next year, it was just too hard. I couldn't program all the answers in because I didn't know what the questions were. And so I had to write a little solver so that it was a program that would just solve 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 A's. But then we all got caught and the teacher told us to knock it off. But from the very beginning, 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 beauty of programming. So I wrote a book about TypeScript. I started out, actually at the time, it was the world's biggest TypeScript meetup just because I fell in love with the language itself. And I kind of got deep into like functional programming and all the 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 complicated math problem, it's kind of similar when you kind of balance the types or 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 and it's a way to do things. That said, not everyone feels this way. So for example, like there's one engineer on the team, Lina, who was still writing C++ on the weekends by hand because 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 atrophying as an engineer? Is that something you worry about? Or is it just like, you know, this is just how it's gonna go? I think it's just the way that it happens. I don't worry about it too much personally. I think for me, like programming is on a 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 literally pen and paper. It was like a room full of people that were doing math on paper. And so, you know, programming has always changed in this way. In some ways, 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 it just won't really matter. It's just gonna 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 learn 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, you know, this isn't true for everyone. And I think for some people, they're gonna feel a greater sense of, I don't know, maybe like loss or nostalgia or atrophy or something like this. I don't know if you saw this, but Elon was saying that why isn't the AI just writing binary straight to binary? Because what's the point of all this, you know, programming abstraction in the end? Yeah, it's a good question. I mean, it totally can do that if you want it to. Oh man. So what I'm hearing here is in terms, there's always this question, should I learn to code? Should people in school learn to code? What I heard from you is your take is in like a year or two, you don't really need to. My take is, I think 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 gonna matter. I was thinking about what is the right like historical analog for this? Because like somehow we have to situate this thing in history and kind of figure out when have we gone through similar 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 1400s, literacy was actually very low. There was sub 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 that, 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 1,000 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 learning to read and write is, you know, it's quite hard. It takes an education system. It takes free time. It takes like not having to work on a farm all day so that you actually have time for education and things like this. But over the next 200 years, it went up to like 70% 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 like, actually the thing that I don't like doing is 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 this. 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 was not the fun part. The fun part is figuring out what to build and coming up with this. 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'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. I've been doing a bunch of random little projects and it's just like, anytime you get stuck, just like help me figure this out and you get unblocked. Like I used to, I was an engineer for earlier in my career for 10 years. And I just remember spending so much time on like libraries and dependencies and little things and just like, oh my God, what do I do? And then looking on Stack Overflow. And now it's just like, help me figure this out. And here's a step by step, one, two, three, four. Okay, we got this. Yeah, exactly, exactly. I was talking to an engineer earlier today. They're like, they're writing some service and Go and 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 it? And he was like, you know, like I still don't really know Go but, and I think we're gonna start to see more and more of this. It's like, if you know that it works correctly and efficiently, then you don't actually 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 it's gonna 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 gonna expand to pretty much any kind of work that you can do on a computer because the model is just gonna get better and better at this. And, you know, like this is the coworker 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, agentic AI to people that haven't really used it before. And people are starting to just 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, or 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 lost all meaning. But agent actually has like a very specific technical meaning, which is it's a AI, it's a LLM 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 send email, it can run commands on your computer and do all this kind of stuff. So I think like any kind of job where you do, you use computer tools in this way, I think this is gonna be next. This is something we have to kind of figure out as a society. This is something we have to figure out as an industry. And I think for me also, this is one of the reasons it feels very important and urgent to do this work at Anthropic, 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 you're kind of alluding to is jobs and job loss and things like that. There's this concept of Jevons paradox of just, as we can do more, we hire more, and it's not actually as scary as it looks. What have you experienced so far, I guess, with AI becoming a big part of the engineering job? Are you hiring more than if you didn't have AI? And just thoughts on jobs. Yeah, I mean, for our team, we're hiring. So Cloud Code team is hiring. If you're interested, just check out the jobs page on Anthropic. Personally, it's, you know, 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 Cloud Code, because it just makes coding delightful, again. And 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 press is 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 does 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 predicted this. I think it's 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 AI 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 a 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 cloud code team, everyone codes, you know, our product manager codes, our engineering manager codes, our designer codes, our finance guy codes, our data scientist codes, like everyone on the team codes. And then if I look at particular engineers, people often cross different disciplines. So some of the strongest engineers are hybrid 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 loves talking to users and can just really channel 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, they won't just be AI native and they don't just know how to use these tools really well, but also they're curious and they're 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. Do you find these three separate disciplines still useful as a way to think about the team? They're engineering, design, product management. Do you find like those, even though they are now coding and contributing to thinking about what to build, do 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, our PM, does a little bit more coordination or planning or forecasting or things like this. Stakeholder alignment. Stakeholder alignment, exactly. I do think that there is a future where I think by the end of the year, what we're gonna start to see is these start to get even murkier, where I think in some places the title software engineer is gonna start to go away and it's just gonna be replaced by builder or maybe it's just everyone's gonna be a product manager and everyone codes or something like this. time-consuming, alignment is hard, and competition for great talent keeps getting tighter. That's why teams like Eleven Labs, Brex, Repl.it, Deel, and 5,000 other organizations use Metaview, the AI company giving high-performance teams a real unfair advantage in hiring. They give you a suite of AI agents that behave like recruiting coworkers. They find candidates for you based on your exact criteria, take interview notes automatically, gather insights across your hiring process, and help you identify the best candidates in your pipeline. AI handles the recruiting toil and gives you a real source of truth. That means hours saved per hire and a team focused on what matters most, winning the right candidates. Don't let your competitors out-hire you. Metaview customers close roles 30% faster. Try Metaview today for free and get an extra month of sourcing at metaview.ai.lenny. That's m-e-t-a-view.ai.lenny. You talked about how you're enjoying coding more. I actually did this little informal survey on Twitter. I don't know if you saw this where I just asked, I did three different polls. I 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. I thought that was really interesting. That's super interesting. I'd love to talk to these people, you know, both in the more bucket and the less bucket, just to understand. Did you get to follow up with any of them? A few people replied and we're actually doing a follow-up poll that we'll link to in the show notes of going deeper into some of this 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 I didn't hear a lot. So I'm curious what's going on there. Yeah, I'm seeing this a little bit with, at Anthropic, I think everyone is fairly technical. This is something that we screen for, you know, when people join, we have, there's a lot of technical interviews that people go through, even for non-technical functions. And, you know, our designers largely 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 bet it's not uniform like that. Yeah. So maybe if you're listening to this, leave a comment if you're finding your jobs less fun and you're enjoying your job less, because what you're saying and what I'm hearing from most people, 70% of PMs and engineers are loving their job more. That's like, if you're not in 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 learn 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 of latent demand, which I think is just the single most important principle and product. Can you talk about that actually, because I was going to go there, explain what this principle is and just what happens when you unlock this latent 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 learn where to take the product next. So an example of this is 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, it 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 abuse in kind of like a security sense, it's abuse in that no one designed the product for this, but they're kind of figuring it out because it's just so useful for this. And so it was pretty obvious if you build a better product to let people buy and sell, they're going to like it. And it was just very obvious that Marketplace would be a hit from this. 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 looking at each other's profiles on Facebook, 60% of profile views are people that are not friends with each other, that are opposite gender. And so this is kind of like traditional kind of dating setup, but people are just like creeping on each other. So maybe if you can build a product for this, it might work. And so this idea of latent demand, I think, is just so powerful. And for example, this is also where co-work 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. It was like wedding photos. There was someone that was using it for, I think, like, they were using it to analyze an 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 a product for them. And we saw this actually pretty early. Back in maybe May of last year, I remember walking into the office and our data scientist, Brendan, had a quad code on his computer. He just had a terminal up. And I was shocked. I was like, Brendan, what are you doing? You figured out how to open the terminal, which it's a very engineering product. Even a lot of engineers don't want to use a terminal. It's just the lowest level way to do your work. Just really, really in the weeds of the computer. And so he figured out how to use the terminal. He downloaded Node.js. He downloaded quad code. And he was doing SQL 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. It's something that's special purpose for that. I think now there's also this kind of interesting second dimension to latent demand. This is sort of the traditional framing is look at what 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 they kind of put the model in a box. And they were, here's this application that I want to build. Here's the thing that I wanted to do a 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 inverted 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 it can do the things, it can decide which tools to run, it can decide in what order to run them in 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 like what the model is trying to do in product terms, latent demand is just the same exact concept, but applied to a model. You talked about co-work, something that I saw you talk about when you launched that initially is your team built that in 10 days. That's insane. 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 cloud code to build it and that's it. Yeah, it's funny. Cloud code, like I said, when we released it, 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 4, 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. 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 still like wasn't very good. Co-work, when we released it, it was just immediately a hit, much more so than cloud code was early on. I think a lot of the credit honestly just goes to like Felix and Sam and Jenny and the team that built this. It's just an incredibly strong team. And again, the place co-work came from is just this wait and demand. Like we saw people using cloud 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 cloud 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 cloud code to build it. And co-work is actually, there's this very sophisticated security system that's built in. And essentially these guardrails 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 cloud code just wrote all of this code. So we just have to think about, all right, how do we make this a little bit safer, a little more self-guided for people that are not engineers? It was fully implemented with cloud code. Took about 10 days. We launched it early. It was still 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'll 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, release early, learn from users, get feedback, iterate. The fact that it's hard to even know what the AI is capable of and how people will try to use it is a unique reason to start releasing things early. That'll help you, as you exactly described this idea of what is the latent demand in this thing that we didn't really know. Let's put it out there and see what people do with it. Yeah. And for Anthropic as a safety lab, the other dimension of that is safety. Because when you think about model safety, there's a bunch of different ways to study it. The lowest level is alignment and mechanistic interpretability. When we train the model, we want to make sure that it's safe. We, at this point, have pretty sophisticated technology to understand what's happening in the neurons to trace it. For example, if there's a neuron related to deception, we're starting to get to the point where we can monitor it and understand that it's activating. This is alignment. This is mechanistic interpretability. It's the lowest layer. The second layer is evals. This is essentially a laboratory setting. The model is in a petri dish, and you study it. You put it in a synthetic situation and just say, okay, model, what do you do? Are you doing the right thing? Is it aligned? Is it safe? Then the third layer is seeing how the model behaves in the wild. As the model gets more sophisticated, this becomes so important because it might look very good on these first two layers, but not great on the third one. We released Cloud Code really early because we wanted to study safety. 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. This is the first agent, the first big agent that I think folks had released at that point. It was definitely the first coding agent that became broadly used. We weren't sure if it was safe. We actually had to study it internally for a long time before we felt good about that. Even since, 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. For CodeWorks, it's pretty similar. The model's in this new setting. It's doing these tasks that are not engineering tasks. It's an agent that's acting on your behalf. It looks good on alignment. It looks good on evals. We tried it 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. That's why we released a little early. That's why we call it a research preview. It's constantly improving. This is really the only way to make sure that over the long term, the model is 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 the God can escape 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 is 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 tests, evals that tell you this 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 model's brain and see how it's thinking and where it's heading. Yeah, you should at some point have Chris Ola on the podcast, because he's just the industry expert on this. He invented this field of, we call it mechanistic interpretability. And the idea is, at its core, what is your brain? What is it? It's a bunch of neurons that are connected. And so what you can do is in a human brain or an 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 this layer or this neuron maps to this concept, how particular concepts are encoded, how the model does planning, how it thinks ahead. A long time ago, we weren't sure if the model was 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 way to do this are pretty 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 this more sophisticated concept. And it's just something we're learning about all the time. You know, 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 freely 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 Cloud Code also. We call this the race to the top internally. And so for Cloud Code, for example, we released an open source sandbox. And this is a sandbox that you can run the agent in. And it just makes sure that there are certain boundaries and it can't access like everything on your system. And we made that open source. And it actually works with any agent, not just Cloud Code, because we wanted to make it really easy for others to do the same thing. So this is just the same principle, race to the top. We want to make sure this thing goes well. And this is just the lever 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 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, Nisa has a question and answer, or it's like blocked on something, or there's all this productivity I'm losing. I need to wake up and get it going again. Is that something you feel? Is that something your team feels? Do you feel like this is a problem we need to track and think about? I always have a bunch of agents running. So at the moment I have like five agents running. And at any moment, I wake up and I start a bunch of agents. The first thing I did when I woke up was like, oh man, I really want to check this thing. So I opened up my phone, quad iOS app, code tab, agent do blah, blah, blah. Because I wrote some code yesterday and I was like, wait, did I do this right? I was kind of double guessing something and it was correct. But now 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 even in 2026. I love that you describe it as coding still, which is just talking to the to cloud code to code for you essentially. And it's interesting that 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. I remember reading something, this was maybe like very early versions of ACM magazine or something, where people were saying, no, it's not the same thing. This isn't really coding. They called it programming. I think coding is kind of a new word. But I kind of think about this. Back in the, my family is from the Soviet Union. I was born in Ukraine and my grandpa was one of the first programmers in the Soviet Union. And he programmed using punch cards. My mom, growing up, told these stories of when she was growing up, he would bring these punch cards home and there was these big stacks of punch cards. And for her, she would draw all over them with crayons. And that was her childhood memory. But for him, that was 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 was probably this older generation of programmers that just didn't take software very seriously. And they would have been like, well, 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 that. Yeah. Which town? I'm from Odessa. Oh, me too. Yeah, that's crazy. Wow. Incredible. What a moment. Maybe related in some small way. Yeah. What year did you leave and your family leave? We came in 95. Okay. We left in 88, a little earlier. Yeah. What a different life that would have been to not leave, huh? Yeah. I feel so lucky every day that I get to grow up here. Yeah. My family, anytime there's a toaster or a meal, they're just like, to America. It's like, okay, enough about that. But you get it once you start really thinking about what life could have been. Yeah, exactly. We do the same toast, but it's still vodka. It's still vodka. Oh, man. Okay. Let me ask you a couple more things here. You shared some really cool tips for how to get the most out of AI, how to build on AI, how to build great products on AI. One tip you shared is give your team as many tokens as they want. Just let them experiment. You also shared just advice generally of just build towards where the model's going, not to where it is today. What other advice do you have for folks that are trying to build AI products? I'd probably share a few more things. So one is don't try to box the model in. I think a lot of people's instinct when they build on the model is they try to make it behave a very particular way. They're like, this is a component of a bigger system. I think some examples of this are people wearing very strict workflows on the model, for example, to say, you must do step one, then step two, then step three. And you have this 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 the 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 over curate 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 the bitter lesson. And actually for the Quad Code team, we have a, you know, hopefully listeners have read this, but Rich Sutton 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 corollaries to the bitter lesson. And for me, the biggest one is just always bet on the more general model. And you know, over the longterm, like don't, don't try to use tiny models for stuff. Don't try to like fine tune. Don't try to do any of 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. normal model if you can, if you have that flexibility. And so these workflows are essentially a way that, you know, it's not a general model, it's putting the scaffolding around it. And in general, what we see is maybe scaffolding can improve 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 Cloud 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 versions of the product, they just wrote so little of my code because I didn't trust it. Because, you know, it was like Sonnet 3.5, then it was like 3.6 or forget, 3.5 new, whatever name we gave it. These models just weren't very good at coding yet. They were getting there, but it was still pretty early. So back then the model used Git for me. It automated some things, but it really wasn't doing a huge amount of my coding. And so the bet with Cloud Code was at some point the model gets good enough that it can just write a lot of the code. And this is the thing that we first started seeing with Opus 4 and Sonnet 4. And Opus 4 was our first kind of ASL 3 class model that we released back in May. And we just saw this inflection because everyone started to use Cloud Code for the first time. And that was kind of when our growth really went exponential. And like I said, it stayed there. So I think this is advice that I actually give to a lot of folks, especially people building startups. It's gonna be uncomfortable because your product market won't 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 gonna hit the ground running and the product is gonna click and start to work. And when you say build for the model six months out, 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 a 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 gonna get better is it's gonna get better and better using tools and using computers. This is a bet that I would make. Another one is it's gonna get better and better for running for long periods of time. And this is a place, you know, like there's all sorts of studies about this, but if you just trace the trajectory, or, you know, maybe even like for my own experience, when I used Sonnet 3.5 back, you know, a year ago, it could run for maybe 15 or 30 seconds before it started going off the rails. And you just really had to hold its hand through any kind of complicated task. But nowadays with Opus 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 it do something else. And, you know, like I said, I always have a bunch of quads running. And they can also run for hours or even days at a time. I think there were some examples where they ran for many weeks. And so I think over time, this is gonna 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 babysit them anymore. So we just talked about tips for building AI products. Any tips for someone just using Cloud Code, say for the first time, or just someone already using Cloud Code that wants to get better? What are like a couple of pro tips that you could share? I will give a 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 sort of have to find your own path. Luckily, you can ask Cloud Code. It's able to make recommendations. It can edit your settings. It kind of knows about itself. So it 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, but 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 and less token intensive if you use the most capable model because it can just do the same thing much faster with less correction, less hand-holding and so on. So that's the first tip 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. That's it. Like there's actually like 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. It's coming pretty soon to mobile also. And we just launched it 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 it right the first time, almost every time with Opus 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 cloud code, 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, it's like every engineer is different. Everyone that's building is different. Just find the thing that feels right to you. You don't have to use a terminal. It's the same cloud 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 cloud code to me. So that was kind of flattering. 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 an 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 at 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 good product. Maybe a last question. So I talked to Ben Mann, co-founder of Anthropic. What to talk to you about? He had a bunch of suggestions which I've integrated throughout our chat. One question he had for you is what's your plan post AGI? What do you think you're going to be doing? What's your life like once we hit AGI, 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 of times a week, I would like bike to the farmer's market. And you know, you like bike by like rice paddies and stuff. It was just like a totally different speed than 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 is by trading like pickles. So in the town where we lived, it was actually like everyone made like miso, everyone made pickles. And so I actually got like decently good at making miso. And you know, I made a bunch of batches and this is something that I still make. Miso is this interesting thing where it teaches you to think on these long time skills. That's just very different than engineering. Cause like a, you know, like a batch of white miso, it takes like at least three months to make. And a red miso is like, you know, two, three, four years. You just have to be very patient. You kind of mix it up and then you just like wet it's head. You have to be very, very patient. So the thing that I love about it is just thinking in these long time skills. And yeah, I think post-AGI or if I wasn't ad-anthropic, I'd probably be making miso. I love this answer. Ben asked me to ask you about what's the deal with you and miso. And so I love that you answered it. Okay, so the future might be just going deep into miso, getting really good at making miso. Amazing. Boris, this was incredible. I feel like we're brothers now from Ukraine. Before we get to our very exciting lightning round, is there anything else that you wanted to share? Is there anything you want to leave listeners with? Anything you want to double down on? Yeah, I think I would just like underscore, you know, like for anthropic since the beginning, this idea of like starting at coding, then getting to tool 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 or 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 quad code becoming this huge, you know, multi-billion dollar business. And, you know, like now all my friends use quad code and they just text me about it all the time. So just like, you know, this thing getting kind of big and 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 a long time. At the same time, it just feels still very early. You know, like most of the world still does not use quad code. Most of the world still does not use AI. So it just feels like this is 1% done and that there's so much more to go. Now, man, that's insane to think, seeing the numbers that are coming out, you guys just raised a bazillion dollars. I think quad code alone is making $2 billion in 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 in just the numbers we're seeing. Yeah, yeah, yeah. It's crazy. And I mean, like the way that quad code has kept 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 giving feedback. And this is just the single most important thing. And 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. Oh, the miso is like not super involved. It just, you just gotta wait. You just gotta 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'm a big reader. I would start with a technical book. One is, 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 gonna 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 in 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 up. I love this. Never before mentioned book, my favorite. Oh, amazing, amazing. Okay, second one is Accelerando by Strauss. This is probably, you know, like my big genre is sci-fi, like probably sci-fi and fiction. Accelerando is just this incredible book and 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, just the speed of it. And it starts as a liftoff is starting to happen and, you know, it was starting to approach the singularity and it ends with like this like collective lobster consciousness orbiting Jupiter. And, you know, this happens over like the span of a few decades or something. So the pace is just incredible. I really love it. Maybe I'll do one more book. The Wandering Earth, Wandering Earth by Sijun Liu. So he's the guy that did Three-Body Problem. I think a lot of people know him for that. I think Three-Body Problem was awesome, but I actually liked his short stories even more. So Wandering Earth is one of the short story collections and he just has some really, really amazing stories. And it's also just quite interesting to see Chinese sci-fi because it has a very different perspective than Western sci-fi and kind of the way that at least he as a writer thinks about it. So it's just really, really interesting to read and just beautifully written. It's so interesting how sci-fi has prepared us to think about where things are going. Just like it creates these amounts of models of like, okay, I see, I've read about this sort of world. Yeah, I think for me, this is like the reason that I joined Anthropic actually, because like I said, I was living in this rural place. I was thinking these long time scales because everything is just so slow out there, at least compared to SF. 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 go to the farmer's market and it's like it's persimmon season. And you know that because there's like 20 persimmon vendors. And then the next week the season is done and it's like grape season and you kind of see this. So it's like these kind of long time skills. And I 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 scales, 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 Ant. And Ben Mann was also a big part of that too. I feel like I want to do a whole podcast just talking about your time in Japan and 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 Vinge, right? Yeah, it's great. Okay. That one's like, it's like so interesting from an AI, AGI perspective. So a few people have read that. So I've read it myself. Yeah. It's like, it's one I think of a lot. Yeah, yeah, yeah. I like Deepness in the Sky also. I think those purchase the sequel, right? Or yeah. Yeah, yeah, yeah. I think so. Yeah. It's very long and like complex to get into, but so good. Okay. We'll keep going through a lightning round. Do you have a favorite recent movie or TV show you've really enjoyed? So I actually don't really watch TV or movies. I just don't really have time these days. I did watch, I'm going to bring up another Sishin Lou but the Three Body Problem series on Netflix, I really loved. I thought that was like a great rendition of the book series. So the common pattern across AI leaders is no time to watch TV or movies, which I completely understand. Is there a favorite product you've recently discovered that you really love? I'm going to like shill a little bit and just say Co-Work because this is legitimately the one product that's been pretty life-changing for me just because I have it running all the time. And the Chrome integration in particular is just really excellent. So it's been like, it paid a traffic fine for me. It like canceled a couple of subscriptions for me. Just like the amount of like tedious work it gets out of the way is awesome. I also don't know if it's a product but maybe I'll also another podcast that I really love obviously besides Lenny. Obviously. Yeah, it's the Acquired podcast by Ben and David. It's just like super, it's super awesome. 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 a Nintendo episode if you haven't listened to it. Great tip. 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 he had it fill out these like medical forms for him. These like really annoying PDFs where it just like loads up the browser, 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 an agent before. And it just feels very, very similar to me to the Quad Code a year ago. But like I said, it's just growing much faster than Quad Code did in the early days. So I think it's starting to break through a bit. And there's also this Chrome extension that you mentioned that you could just leave standalone that sits in Chrome and you could just talk to Quad looking at your screen, at your browser and have it do stuff, have it tell you about what you're looking at, summarize what you're looking at, things like that. Exactly, exactly. For people that are like just starting 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. The thing that I recommend doing is start by having it use a tool. So clean up your desktop or summarize your email or something like this or respond to the top three emails. It actually just responds to emails for me now too. The second thing is connect tools. So if you connect, if you say, look at my top emails and then send Slack messages or put them in a spreadsheet or something. Or for example, I use it for all my project management. So we have a single spreadsheet for the whole team. There's a row per engineer every week, everyone fills out a status. And every Monday, Co-Work just goes through and it messages every engineer on Slack that hasn't filled out their status. And so I don't have to do this anymore. And this is just one prompt, it'll do everything. And then the third thing is just run a bunch of quads in parallel. So in Co-Work, you can have as many tasks running as you want. So it's like start one task, I have this project management thing running, then I'll have it 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, what was previously Cloud Code and 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 are what people need to hear. I'm just like, oh, wow, I didn't know I could do that. So I'd like to share it with you guys. Yeah, I think a lot of this was also, some of this was also inspired by you, Ani. You had this post about, it was like 50 non-technical use cases for Quad 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, where Co-Work was able to do like 48 out of the 50, they were like, okay, it's pretty good. Wow, I did not know that, that is awesome. I've become an eval. Yeah, how does that feel? Amazing. I feel like I'm valuable to the future of AI. This is like reverse breaking through. Wow, that is so cool. Wow, okay, I wonder what those last two are. Anyway, okay, two more questions. Do you have a favorite life motto that you often come back to in work or in 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 a process without thinking about it. 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 it's probably not a good idea. So I think just this, this is the single advice that I give to coworkers more than anything too. And I feel like that alone could be its own podcast conversation. What is common sense? How do you build it? But we'll keep this short. Final question. So you've gotten more active on Twitter slash X. I'm curious just why and just what's your experience been with Twitter, the world of Twitter, because you get a lot of engagement on Twitter slash X. So for a long time, I used threads exclusively because I actually helped build threads a little bit back in the day. And I 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. So in December, I was in Europe. Started using Twitter, you mean? Oh yeah, yeah, yeah. I started using Twitter because I was bored. So my wife and I were, we were traveling around in Europe for December. We were just kind of nomading 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. Just like code all day. It's the best. And at some point I just kind of got bored and like I ran out of ideas for like a few hours. I was like, okay, what do I want to do next? And so I opened Twitter. I saw some people like tweeting about quad code 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, asked for if people had a bunch of bugs and feedback. And I think they were kind of surprised by like the pace at which we're 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 is good, I would just go and do it. And then I'll go do something else and answer the next thing. But I think for a lot of people, it 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 about bugs, hearing about features. I saw you complaining to Nikita Beer the other day on Twitter of just, you're like posting many threads and it was breaking and just like, oh man, what's going on here? Yeah, there was a bug. I hope it's fixed now. Amazing. Oh man, Boris, I could chat with you for hours. I'll let you go. Thank you so much for doing this. You're wonderful. Where can folks find you online? How can listeners be useful to you? Yeah, find me on threads or on Twitter. That's the easiest place. And please just tag me on stuff. Send bugs, send feature requests. What's missing? What can we do to make the products better? What do you want? I love, love hearing it. Amazing. Boris, thank you so much for being here. Cool. Thanks, Lenny. Bye, 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 a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's podcast.com. See you in the next episode.