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

I have not edited a single line by hand since November. Every day I ship 10, 20, 30 forecasts. So like at the moment, I have like five agents running. Well, recording this. Yeah, you miss writing code.

I have never enjoyed coding as much as I do today.

Because I don't have to deal with all the minutiae. Productivity per engineer has increased 200%. There's always this question. Should I learn to go in a year or two? It's not 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?”

Quad is starting to come up with ideas. Looking through feedback. It's looking at bug reports. It's looking a telemetry for bug fixes and things to ship. A little more like a coworker or something like that.

A lot of people listening to this or 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 Terny, 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 is completely transformed the job of a software engineer. And it is now starting to transform the jobs

“of many other functions in tech, which we talk about.”

Cloud Code itself is also a massive driver of Anthropics overall growth over the past year.

They just raised around it 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 bored 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 Lenny's productpast.com for an incredible set of deals available exclusively

to Lenny's newsletter subscribers. Let's get into it after a short word from our wonderful sponsors. Today's episode is brought to you by DX, the developer intelligence platform designed by leading researchers to thrive in the AI era, organizations need to adapt quickly.

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It also reviews your PRs and flags any breaking changes with fixes ready to go. Try Century and Sear for free at centri.io/Lenny and use code Lenny for $100 in Century credits. That's SCNTRY.io/Lenny. Boris, thank you so much for being here and welcome to the podcast.

Yeah, thanks for having me on. I want to start with a spicy question. About six months ago, I don't know if people even remember this. You actually left anthropic, you joined cursor, and then, two weeks later, you went back to anthropic.

What happened there? I don't think I've ever heard the actual story. It was the fastest job change that I've ever ever had. I joined cursor because I'm a big fan of the product.

“And honestly, I met the team and I was just really impressed.”

They're an awesome team. I still think they're awesome and they're just building really cool stuff. And kind of they saw where AI coding was going. I think before a lot of people did. So the idea of building could product was just very exciting for me.

I think as soon as I got there, what I started to realize is what I really missed about Ant was the mission. 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 when you talk to people at anthropic, just like,

“find someone in the hallway if you ask them why they're here,”

the answer is always going to be safety.

And so this kind of like mission driven us 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, you know, 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 that was missing that pretty quick. Okay, so let me follow the thread of just coming back to anthropic in the work. You've done there. The spot guess is going to come out around the year anniversary of launching cloud code.

So when it's been a little time just reflecting on the impact that you've had. There's this report that recently came out that I'm sure you saw by semi analysis, that show 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, or sharing the fact that you don't write code anymore, that it's all AI generated.

And many aren't even looking at code anymore is how far we've gotten in large part. Thanks to this little project that you started and that your team has scaled over the past year. I'm curious just to hear your reflections on on this past year and the impact that your work has had. These numbers are just totally crazy, like 4% of all commits in the world is just way more than I imagined. Like you said, it still feels like the starting point.

These are also just public commits. So we actually think if you look at private repositories, it's quite a bit higher than that.

“And I think the crazy thing for me isn't even the number that we're at right now,”

but the pace at which we're growing. Because if you look at cloud codes, growth rate kind of across any metric, it's continuing to accelerate. So it's not just going up, it's going up faster and faster.

When I first started quote code, it was just going to be, it was just supposed to be a little hack.

You know, we broadly knew it on the topic that we wanted to get a, 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 HCI. 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 lab steam. And actually my Krieger and, you know, then they just kick the steam off again for kind of round two. The team built some pretty cool stuff. So we built 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 coworker starting to see the transition for non technical folks also.”

For a lot of people that use conversational AI, this might be the first time that they're using the thing that actually act. And actually use your Gmail, you can use your Slack. You can do all these things for you. And it's quite good at it. And it's only going to get better from here. So I think for anthropic for a long time, there's this feeling that we wanted to build something, but it was not just what. And so when I joined in, I spent one month kind of hacking and, you know, built a bunch of weird prototypes.

Most of them didn't ship and, you know, weren't even close to shipping. It was just kind of understanding the boundaries of what the model can do. Then I spent a month doing post training, so to understand kind of the research 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, you know, if you're working on product, you want to understand the infrastructure, the runtime, the virtual machine, the language, kind of whatever that is, the system that you're building on it. But yeah, if you're working AI, you just really have to understand the model to some degree to do good work. So I took a little detour to do that and then I came back and just started prototyping what eventually became quadcode. And the very first version of it, I have like a, there's 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 I'm listening to. And this is the craziest thing, right? Because it's like, there's no, I didn't instruct the model to say, you know, use, you know, this tool for this or kind of do whatever the model was given this tool and it figured out how to use it to answer this question that I had that I wasn't even sure if I could answer what music I was listening to.

I made a post about it and I announced it internally and it got two likes. That's the, those like, sense of the reaction at the time. Because I think people internally, you know, like when you think of coding tools, you think of like, you think of IDEs, you think about kind of all these pretty sophisticated environments. No one thought that this thing could be terminal based. That's sort of a weird way to design it and that wasn't really the intention.

But, you know, from the story, I built it in a terminal because, you know, for the first couple months it was just me.

So it was just the easiest way to build.

“And for me, this is actually a pretty important product lesson, right?”

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. But for the last year, quadcode has just been all I think about. And so just like late at night, this is just something I was thinking about. Like, I'll get the models continuing to improve. What do we do? How can we possibly keep up?

And the terminal was honestly just the only idea that I had. And yeah, it ended up catching on. After, after I released it pretty quickly, it became a hit at anthropic. And, you know, the daily active users just went vertical. And it really early on actually before I launched it, then man.

Nudge 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 quadcode was not initially a hit. When we released it. It got a bunch of users. There were some other early adopters that got it immediately. But it actually took many months for everyone to really understand what this thing is. Just again, it's like, it's just so different. And when I think about it,

kind of part of the reason quadcode works is this idea of latent demand. Where we bring the tool to where people are and it makes existing workflows a little bit easier. But also because it's in the terminal, it's like a little surprising.

“It's a little alien in a way. So you have to have to kind of be reminded and you have to learn to use it.”

And of course, now, you know, quadcode is available, you know, in the iOS and Android quad app. It's available in the desktop app, it's available on the website. It's available as ID extensions and Slack and GitHub. You know, all these places where engineers are, it's a little more familiar. But that wasn't the starting point.

So yeah, I mean, at the beginning, it was kind of a surprise that this thing was even useful. And, you know, as the team grew, as the product grew, as it started to become more and more useful to people. Just people around the world from small startups to the biggest thing companies started using it. And they started getting feedback. And I think just reflecting back is been such a humbling experience. Because we just keep learning from our users and just the most exciting thing is like, you know, not of us really know 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's going to be written 100% AI's of code is going to be written by AI.

“Everyone's like, no, that's crazy. What are you talking about now?”

And so, of course, that's happening exactly as they said. So things move so fast and change so fast now. Yeah, it's really fast. You can get a back at code with Quad back in May. Those like our first, you know, like developer conference that we did as on Thropic. I did a short talk in the Q&A after the talk.

People are asking what are your predictions for the end of the year. And my prediction back in May of 2025 was, but then to the year, you might not need to ID to code anymore. And we're going to start to see engineers not doing this in the I remember the room like a lot of week asked. This is a crazy prediction. In fact, I don't think this is just the way the way we think about things is exponentials.

And this is like very deep in the DNA. Like if you look at our co-founders, like three of them were the first three authors on the scaling loss paper.

So we really just think in exponentials. And if you kind of look at the exponential of the percent of code that was written by Quad at that point, if you just trace the line. It's pretty obvious we're going to cross 100% by then to the year, even if it just does not match intuition at all. And so all I did was trace the line. And yeah, in November, that, you know, that happened for me personally. And that's been the case since and we're starting to see that for a lot of different customers too.

I thought it was really interesting which you just shared there about kind of the journey is this kind of idea of just playing around and seeing what happens. This came up comes up with open claw a lot, just like Peter was playing around and just like a thing happened. And it feels like that's a central kind of ingredient, a lot of the biggest innovations in AIs.

I mean, this is the thing about innovation, right? Like you can't, you can't force it. There's no roadmap for innovation.

“You just have to give people space. You have to give them, maybe the word is like safety.”

So it's like psychological safety that it's okay to fail. It's okay if 80% of the ideas are bad. You also have to hold them accountable if it's so if the idea is bad, you know, you cut your losses move on to the next idea. Instead of investing more, in the early days of quadcode, I had no idea that this thing would be useful at all. Because even in February when we released it, it was writing maybe, I don't know, like 20% of my code, not more. And even in May, it was writing maybe 30%, I was still using, you know, cursor from most of my code.

And it only crossed 100% in November. So it took a while, but even from the early estate, 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 threat. You just have to pull on it. So at this point, 100% of your code is written by cloud code, is that, is that kind of the current state of your coding?

Yeah, so 100% of my code is written by cloud code. I'm a fairly prolific coder. And this has been the case even when I worked back at Instagram. I was like one of the top few most productive engineers. And that's actually, that's still the case here at Anthropic. Wow, I'm going to sort of add up the team. Yeah, yeah, do it. Still do a lot of coding. And so every, you know, every day, I feel like 10, 20, 30 work was something like that.

Every day. Every day. Yeah. Good guy. 100% written by cloud 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 cloud doing automatic code review for everything. So here at Anthropic cloud reviews 100% of whole requests. There's still a layer of like human review after it. But you kind of like you still do want some of these checkpoints. Like you still want a human who can't the code.

Unless it's like pure prototype code that, you know, it's not going to run. It's not going to run anywhere. It's just a prototype. What's kind of the next frontier. So at this point, 100% of your code is being written by AI. This is clearly where everyone is going in software engineering.

That felt like a crazy milestone. Now it's just like, of course, this is the world now. What's what's kind of the next big shift to how software is written that either your team is already operating and or you think we'll head towards.

“I think something that's happening right now is cloud is starting to come up with ideas.”

So cloud is looking through feedback. It's looking at bug reports, it's looking at, you know, like telemetry and things like this. And it's starting to come up with ideas for bug fixes and things to ship. So it's just starting to get a little more, you know, like a little more like a coworker or something like that.

I think the second thing is we're starting to branch out of coding a little bit.

So I think at this point it's safe to say that coding is largely solved. At least for the kind of programming that I do is just solve problem because cloud can do it. And so now we're starting to think about, okay, like, what's next? What's beyond this? There's a lot of things that are kind of adjacent to coding and I think this is going to be coming. But also just, you know, general tasks, you know, like an I use cowork every day now to do all sorts of things that are just not related to coding at all and just to do it automatically.

For example, I had to pay a parking ticket the other day, I just had to co-work do it. All of my project management for the team. Yeah, it's like, correct, it's like sinking stuff between spreadsheets and messaging people on Slack and email and all of those kind of stuff. So I think the frontier is something like this. And I don't think it's coding because 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 text that people work on. This idea of helping you come up with what to work on is so interesting. It's something to this or product managers and they're probably sweating. How do you use cloud for this?

Do you just talk to it? Does there anything clever you've come up with to help you use it to come up with what to build? Honestly, the simplest thing is like open quad code or a core can point it out as a fact thread. You know, like for us, we have this channel that that's all the internal feedback about 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 any time that someone sends feedback. I would just go in and out fix every single thing as fast as I possibly could. So like within a minute, within five minutes or whatever. And this is just really fast feedback cycle. It encourages people to give more and more feedback. It's just so important because it makes them feel heard.

Because, you know, like, usually when you use a product, you get feedback, it just goes into a black hole somewhere and then you don't get feedback again. So if you make people feel heard, then they want to contribute and they want to help make the thing better. And so now I kind of do the same thing, but quad honestly does a lot of the work.

I just put up a couple of PRs. Want to take a look at them and 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 cold building solved. Code review became kind of the next bottleneck, the least PRs who's going to review them all. The next big open question is just like, okay, now we need to. Now humans are necessary for figuring out what to build, which is prioritizing. You're saying that that's where cloud code is starting to help you.

Has it gotten a lot better with, like, say, up as 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, you know, best coding model in the world and, you know, it's getting better and better.

Like 4.6 is just incredible.

But also actually a lot of the training that we do outside of coding translates pretty well too. So there is this kind of like transfer where you teach the model to do, you know, X and it kind of gets better at why. Yeah, and the gains have just been insane. Like, I don't throw up it. Over the last year, like, since we introduced what code we probably, I don't know the exact number. We probably like 4x, then generic team or something like this.

But productivity per engineer has increased 200%. In terms of, like, four requests. And like, this number is just crazy for anyone that actually works in the space and works on deaf productivity. Because back in the previous life, I was at meta and, you know, one of my responsibilities was code quality for the company. So this is like, all of our code bases, those my responsibility, like Facebook, Instagram, WhatsApp, well, this stuff.

And a lot of that was about productivity because if you make the code higher quality, then engineers are more productive.

And things that we saw is, you know, in a year with hundreds of engineers working on it, you would see a gain of like a few percentage points of productivity.

So I'm going to make that. And so now we're using these gains of just hundreds of percentage points. It's just absolutely insane. What's also insane is just how normalize this has all been. Like, we hear these numbers, like, of course. Yeah, I was doing this to us. It's just, it's so unprecedented. The amount of change that is happening to software development, to building products, it's just the world of tech.

It's just, like, so easy to get used to it. But it's important to recognize this is crazy. This is something like, I have to remind myself once in a while. So there's sort of like a downside of this because the model changes. So there's actually like, there's many kind of downsize 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 months ago where there was a memory leak.

And so like, what this is is, you know, like, quadcode the memory usage is going up and at some point it crashes. This is like a very common kind of engineering problem that, you know, every engineer has debugged a thousand times. And traditionally the way that you do it is you take a heap snapshot, you put it into a special debugger. You kind of figure out what's going on. You know, use these special tools to see what's happening. And I was doing this and I was kind of like looking through these traces and trying to figure out what was going on.

And the engineer that was newer on the team just had quadcode do it. And it was like, hey, quiet, it seems like there's a leak and you figure it out. And so like quadcode 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 and time program. And it found the issue and put up a poor quest 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 sunny at 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 quad do it.

It feels like that's exactly what you describe with as memory league. You almost forgot that principle of like, okay, let me see if cloud can solve this for me. There's this interesting thing that happens also when you, when you underfund everything a little bit, because then people are kind of forced to quantify. 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 a 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 a cloud, you can just use that to automate a lot of work. And that's kind of what we see over and over. So I think that's kind of like one principle is underfunding things a little bit. I think another principle is just encouraging people to go faster.

“So if you can do something today, you should just do it today.”

And this is something we really, really 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.

And if you want to go faster, a really good way to do that is to just have cloud do more stuff.

So it just very much encourages that. This idea of underfunding.

“It's so interesting because in general, there's this feeling like AI is going to allow you to not have as many employees,”

not have as many engineers. And so it's not only you can be more productive, which you're saying is that you will actually do better if you're 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,

you know, with Lake 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, you know, at Anthropic, we have, you know, everyone can use a lot of tokens. We're starting to see this come up as like a perk at some companies.

Or if you join, you get unlimited tokens. This is a thing I very much encourage because it makes people free to try these ideas that would have been too crazy. And then if there's an idea that works, then you can figure out how to scale it. And that's the point to kind of optimize and to cost cut figure out, like,

you know, maybe you can do it with high cool or with sauna instead of open or whatever. But at the beginning, you just want to throw a lot of tokens at it and see if they do it works. And give engineers the freedom to do that. So the advice here is just be loose with your tokens with the cost on using these models. People hearing this may be like, of course, he works at Anthropic.

You want us to use as many tokens as possible. But what you're saying here is that the most interesting innovative ideas will come out of someone just kind of taking it to the max and seeing what's possible.

“Yeah, and I think the reality is like, at small scale, like, you know, you're not going to get like a giant bill or anything like this.”

Like, if it's an individual engineer experimenting, the token cost is still probably relatively low relative to their salary or other cost of running the business. So it's actually like not not a huge cost. As the thing scales up, so like, let's say, you know, they build something awesome and then it takes a huge amount of tokens. And then the cost becomes pretty big. That's the point out what you want to optimize it. But don't don't do that too early.

Have you seen companies where their token cost is higher than their salary? Is that a trend you think are going to find them and see? You know, Anthropic, we're starting to see some engineers that are spending, you know, like hundreds of thousands a month in tokens. So we're starting to see this a little bit. There's some companies that are starting to see some more things. Yeah. Going back to coding, do you mess writing code?

Is it something you're kind of sad about that there's no longer a thing you'll do as a software engineer? It's funny for me, you know, like when I learned engineering for me, it was very practical. I've learned engineering so I could build stuff. And for me, I was also taught, you know, like I studied economics in school, but I didn't study CS. But I taught myself engineering kind of early on.

I was programming in like middle school. And from the very beginning, it was very practical. So I actually, like, I've learned to code so that I can cheat on a math test. It was like the first thing we had these like graphing calculators and the, you know, I just program the answer. Yeah, 83, 83 plus. Yeah, yeah, exactly.

But yeah, it's like I program the answers in and then the next like math test, whatever, like the next year, it was just like too hard like I couldn't program all the answers in because I didn't know what the questions were. And so I had to write like a little solver so that it was a program that would just like solve these like, you know, these algebra questions or whatever. And then I figured out you can get a little cable.

You can give the program to the rest of the class and then the whole class gets is. But then we all got caught in the teacher told us to knock it off.

But from the very beginning, it's always just been very practical for me.

Where programming is a way to build the thing. It's not the end in itself. At some point, I personally fell into the rapid hole of kind of like the the beauty of of programming. So like I wrote a book about TypeScript. I sort of the actually at the time it was the world's biggest TypeScript, you know,

just because I fell in love with the language itself. And I kind of got a deep into like functional programming and all this stuff.

“I think a lot of coders they get distracted by this.”

For me, it was always sort of, there is a beauty to programming and especially to functional programming. There's a beauty to type systems. There's a certain kind of like this like buzz that you get, like when you solve like a really, really complicated math problem.

It's kind of similar when you kind of balance the types or you know, the program is just like really beautiful. But it's really not the end of it. I think for me coding is very much a tool and it's a way to do things. That said, not everyone feels this way.

So for example, you know, like there's one engineer on the team Lina, who, you know, was still writing C++ on the weekends by hand. Because, you know, for her, she just really enjoys writing C++ by hand. And so everyone is different.

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 atrfing as an engineer is that something you worry about or is it just like, you know, this is just how it's going to 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 the continuum and, you know, like way back in the day, you know, 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 the 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 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 is just going to be kind of like the assembly code running under the program

or something like this. I didn't emotion a 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 work way comfortable within the field. But at the same time, I, you know, this isn't true for everyone.

“And I think for some people, they're going to feel a greater sense of,”

I don't know, maybe like loss or nostalgia or out of here or something like this. And I don't know if you saw this, but Elon was saying that why isn't the, I just writing binary straight to binary because what's the point of all this, you know, programming the abstraction in the end. Yeah, it's a good question.

I mean, totally can do that if you wanted to. Oh, man.

So what I'm hearing here is in terms, there's always a question.

Should I learn to code? Should people on the school learn to code? What I heard from you is their take is in like a year to you don't really need to. My take is I think for for people that are using. They're 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 going to 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 somewhere transitions? What's the right kind of mental model for this?

“I think the thing that's come closest for me is the printing press.”

And so, you know, if you look at Europe in, you know, like in the mid, the mid, the mid, 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, 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, in the 1000 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 the education system. It takes free time.

You, it takes like not having to work on a farm all day. So you actually have time for education and things like this. But over the next 200 years, it went up to like 70% globally. So, I think this is the kind of thing that we might see is a similar kind of transition. And there is, there was actually this interesting historical document where

there was an interview with some like scribe in the 1400s about like, how do you feel about the printing press? And they were actually very excited because they were actually the thing that I don't like doing this copying between books. The thing that I do like doing is drawing the art in books and then doing the book binding. And I'm really glad that now my time is freed up. And it's interesting, like, as an engineer,

I sort of felt like a peril with us, like, this is sort of how I feel, where I don't have to do the tedious work anymore of coding. Because this has always been sort of the detail of it. It's always been the tedious part of it and kind of like messing with a kid and kind of using all these different tools. That those not the fun part, the fun part is figuring out what to build and kind of coming up with us.

It's talking to users. It's thinking about these big systems. It's thinking about the future. It's collaborating with, you know, other people on the team. And what's amazing is that the tool you're building allows anybody to do this.

People that have no technical experience can do exactly what you're describing. Like, I've been doing a bunch of random little projects and it's just like any time you get stuck, just like, help me figure this out.

Like, I used to, yeah, I was an engineer for early in my career for 10 years.

And I just remember spending so much time on libraries and dependencies and things and just like, Oh, I got it. What do I do? And I'm looking at stack overflow. And now it's just like, help me figure this out. And here's step by step on 234. Okay, we got this. Yeah, exactly. I was talking to an engineer earlier today. They're like, they're writing some service and go.

And, you know, it's been like a month already. And they built up the service. Like, it's working quite well. And then I was like, okay, so like, how do you feel writing and he was like, you know, like, I still don't really know go.

“But, and I think we're going to start to see more and more of this.”

It's like, if you know that it works correctly and efficiently, then you don't 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 check.

Just like, what do you think? Where do you think AI is going next? I think this is going to be a lot of the roles that are adjacent to engineering. So, yeah, it could be like product managers. It could be designed. Could be data science. It is going to expand to pretty much any kind of work that you can do on a computer because the model is just going to get better and better at this. And, you know, like, this is the core product is kind of the first way to get at this.

But it's just the first one. And it's the thing that I think brings AI to a gigantic AI to people that haven't really used it before. And people are starting just to get a sense of it for the first time. When they think back to engineering a year ago, no one really knew what an agent was, no one really used it. But nowadays it's just the way that, you know, we do our work.

And then when I look at non-technical work today, so, you know, like, you know, or 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 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 a vast all meaning. But agent actually has like a very specific technical meaning, which is it's a AI. It's an LM that's able to use tools.

So it doesn't just talk, it can actually act and it can interact with your system. And, you know, this means, like, it can use your Google Docs and it can, it can send email. It can run commands on your computer and do all this kind of stuff. So I think, like, any kind of job where you do use computer tools in this way, I think this is going to be next.

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 is you're kind of alluding to is jobs and job loss and things like that. There's this concept of Jevon's paradox of just as we can do more. We hire more and it's not actually as scary as it looks. What did you experience so far, I guess, with AI becoming a big part of the engineering job, just are you hiring more than if you didn't have AI and just thoughts on jobs?

Yeah, I mean, for our timber, we're hiring. So quad-coding 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 quad-code because it just makes coding delightful. And that's just, that's just so fun for them. But it's hard to know where this thing is going to go.

And again, I just, like, I have to reach for these historical analogs.

“And I think the printing process is just such a good one.”

Because what happened is this technology that was locked away to 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. Like we couldn't be talking today. If the printing press hadn't been invented, like our microphone wouldn't exist.

None of the things around us would exist. It just wouldn't be possible to coordinate such a large group of people. If that wasn't the case. And so I imagine a world, you know, a few years in the future where everyone is able to program. And what is that unlock?

And I have no idea. It's just the same way that, you know, in the 1400s, no one could have protected this. I think it's the same way. But it's going to be very disruptive. And it's going to 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. But 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 quad-code team, everyone codes, you know, our product manager codes, or engineering manager codes, or designer codes, are finance guy codes, or data scientists 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 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 their curious and their generalists. And they cross over multiple disciplines and can think about the broader problem they're solving, rather than just the engineering part of it. You find these three separate disciplines still useful as a way to think about the team.

They're, you know, engineering design product management. 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 will persist. But one thing that we're starting to see is there's maybe a 50% overlap in these roles.

We're 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 CADRPM.

Does a little bit more, you know, coordination or planning or forecasting or things like this? They call their alignment. They call their alignment. Exactly. I do think that there is a future where I think by the end of the year what we're going to start to see

is these start to get even more clear, more clear. Where I think in some places that title software engineer is going to start to go away. And it's just going to be replaced by builder or maybe it's just everyone's going to be a product manager and everyone codes or something like this. Who says hiring has to be fair? Every founder and hiring manager I've been speaking with these days is feeling the same pressure.

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That's m-e-t-a-view.au/lany. You talked about how you're enjoying coding more. Actually did this little informal survey in Twitter. I don't know if you saw that's where I just asked. And did three different polls?

Ask engineers. Are you enjoying your job more or less since adopting AI tools? And then I did a separate one for PMs and one for designers. And both engineers and PMs, 70% of people said they're enjoying their job more. And about 10% said they're enjoying their job less.

Designers, interestingly, only 55% said they're enjoying their job more. And 20% said they're enjoying their job less. That was a really interesting. That's super interesting. I'd love to talk to these people, both in the more bucket and the less bucket.

Just understand. Did you get to follow up with any of them? A few people replied and were actually doing a follow-up poll.

But a lot of, there's like the factors that make it more fun and less fun.

The designers, they didn't share a lot actually. They've just, like, the people that are actually asked. Just like, why are you enjoying your job less? And in here, lots. I'm curious what's going on there.

Yeah, I'm seeing this a little bit with, I don't think everyone is fairly technical. This is something that we screen for when people join. There's a lot of technical interviews that people go through even for non-technical functions. And, you know, our designers were actually code.

“So, I think for them, this is something that they have enjoyed from what I've seen.”

Because now instead of bugging engineers, they can just 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 from my thought. 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 PM's engineers are loving their job more. That's like if you're not on that bucket, you could something's going on. Yeah, yeah. We do see that people use also different tools.

So, for example, our designers, they use the quad desktop app a lot more to do their coding. So, you just download the desktop app, there's a code tab. It's right next to cowork. And it's actually the same as that quad code, so it's like the same agent and everything. We've had this 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 quad sessions in parallel as you want. We can, you know, we call this multi-clotting. So this is a, it's a little more native. I think for folks that are not engineers.

And really this is back to bringing the product to where the people are. You don't want to make people use a different workflow. You don't want to make them go out of their way to earn a new thing. It's whatever people are doing if you can make that a little bit easier. Then that's just going to be a much better product that people enjoy more.

“And this is just this principle of latent demand, which I think is just the single most important principle in product.”

Can you talk about that actually, because I was going to go there explain what this principle is. And, and just what happens when you unlock this lean demand. Wait and 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 it 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 in example, 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 has started based on the observation back in this must have been like 20, 20, 16 or something like this. That 40% of posts in Facebook groups are buying and selling stuff. So this is crazy. It's like people are abusing the Facebook groups product to buy and sell.

And it's not, it's not abuse and kind of like a security sense, it's abuse and that no one designed the product for this. But they're kind of figuring it out because it's just so useful for this. And so it's 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 can't each other's profile is on Facebook. 60% of profiles were people that are not friends with each other that are opposite gender. So this is kind of like traditional kind of dating setup, but you know, people are just like creeping on each other. So maybe if you can build a product for this, it might work.

And so this idea of leading demand, I think it's just so powerful.

And for example, this is also where cowork came from. We saw that for the last six months or so, a lot of people using quadcode were not using it to code. There was someone on Twitter that was using it to grow tomato plants, there was someone else using it to analyze their genome. Someone was using it to recover photos from a corrupted hard drive, those like 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 to the office in our data scientist Brendan was had a quadcode on his computer. He just had a terminal up.

And I was like, I was shocked. It was like Brendan, what are you doing? Like, you figured out how to open the terminal, which is, you know, it's a very engineering product. Even a lot of engineers don't want to use a terminal. It's just like a, it's like just like the lowest level way to do your work.

And so he figured out how to use the terminal. He downloaded Node.js. He downloaded quadcode. 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, do 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 where people are doing, make that a little bit easier and power 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 quadcode, 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 the right here is this application that I want to build. Here's the thing that I wanted to do model. You're going to do this one component of it.

Here's the way that you're going to interact with these tools and APIs and whatever. And for quadcode 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 you can do the things. They can decide which tool store. I 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 what the model is trying to do. In product terms, latent demand is just the same exact concept, but applied to the model. You talked about co-work. Something that I saw you talk about when you launched that initially is you've your team built that in 10 days. That's insane.

“Yeah, I think it came out. I think it was like, you know, used by millions of people pretty quickly, something like that being built in 10 days.”

Anything there, any stories there other than just it was just, you know, we used cloud code to build it. 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, open four.

It just really, really inflected. And then in November it inflected. And it just keeps inflecting. The growth just keeps getting steeper and steeper and steeper every day.

But, you know, for the first few months, it wasn't a hit. 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. The core 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 the, and Jenny and the team that built this.

It's just an incredibly strong team. And again, the, the place core came from is just the sweet 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, you know, the core is actually there's this very sophisticated security system that's built in. And essentially these guard rails to make sure that the model kind of does the right thing. It doesn't go off the rails.

So for example, we ship an entire virtual machine with it. And Cloud code just wrote all of this code. So we just had to think about, all right, how do we make this a little bit safer? A little more self-guided for people that are not engineers. It was fully implemented with Cloud code. Took about 10 days. We launched it early. You know, 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 will shape where the product goes in the future. Yeah, I think that point is so interesting.

And it's so unique. There's always been this idea.

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 like, is a unique reason to start releasing things early. That'll help you as you exactly describe this idea of what is the latent demand and this thing that we didn't really know. Let's put it out there and see what people do with it. Yeah, and in front of the safety of the other dimension of that is safety.

Because when you think about model safety, there's a bunch of different ways to study it. Sort of the lowest level is alignment and mechanistic interpretability. So this is when we train the model, we want to make sure that it's safe. We at this point have like pretty sophisticated technology to understand what's happening in the neurons to trace it. And so for example, 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.

And so this is just the assignment, this is mechanistic interpretability, it's like the lowest layer. The second layer is Evelos and this is essentially a laboratory setting the model is in a petri dish and you study it. And you put in this synthetic situation and just say, okay, like model, what do you do? And are you doing the right thing? Is it aligned? Is it safe? And then the third layer is seeing how the model behaves in the wild.

And as the model gets more sophisticated, this becomes so important because it might look very good on these first two layers,

We released Cloud Code really early because we wanted to study safety. And we actually used it within anthropic for I think four or five months or something before we released it.

Because we weren't really sure like this is the first agent that, you know, the first big agent that I think folks had released at that point.

It was definitely the first, you know, coding agent that became brought we used. And so we weren't sure if it was safe. And so we actually had to study it internally for a long time before we felt good about that. And even since, you know, there's a lot that we've learned about alignment, there's a lot that we've learned about safety. That we've been able to put back into the model back into the product.

And for co-work, it's pretty similar. The models in this new setting, it's, you know, doing these tasks that are not engineering tasks. It's an agent that's acting on your behalf. He looks good on alignment. It looks good on emails. We try to enter an oil looks good. We tried it with a few customers of looks good. Now we have to make sure it's safe in the real world.

“And so that's why we released a little early, that's why we call it a research preview.”

But yeah, it's just, it's constantly improving. And this is really the only way to make sure that over the long term of the models aligned and it's doing the right things. It's such a wild space that you work in where there's this insane competition and pace at the same time. There's this fear that if you get the, you know, the God 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. It's how you all think about the safety piece. But just what I'm hearing is there's these three layers you work with. There's kind of like observing the model thinking and operating.

There's e-tests, e-vails that tell you is doing bad things and then releasing it early.

I haven't actually heard a ton about that first piece. That is so cool.

So you guys can, there's an observability tool that can let you peek inside the models brain and see how it's thinking and where it's heading. Yeah, you should at some point have Chris Ola on the podcast because he, he's the industry expert on this. He invented this field of, we call it mechanistic interpretability.

“And the idea is, you know, like, at its core, like, what is your brain?”

Like, whatever, what is it? It's like, it's a bunch of neurons that are connected. And so what you can do is like, in a human brain or animal brain, you can study it at this kind of mechanistic level to understand what the neurons are doing. It turns out surprisingly a lot of this does translate to models also. So, model neurons are not the same as animal neurons, but they behave similarly in a lot of ways.

And so we've been able to learn just a ton about the way these neurons work, about, you know, this layer or this neuron maps to this concept. How particular concepts are encoded, how the model is planning, how it thinks ahead. You know, like, a long time ago, we weren't sure if the model is just predicting the next token or is doing something a little bit deeper. Now I think there's actually quite strong evidence that it is doing something a little bit deeper. And then the structures that we had to do this are pre-sophisticated now, where, as the models get bigger,

it's not just like a single neuron that corresponds to a concept, a single neuron might correspond to a dozen concepts. And if it's activated together with other neurons, this is called superposition. And together it represents a more sophisticated concept. And it is just something we're learning about all the time. You know, for anthropicas, 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 quadcode, also, we call this the race to the top internally. And so, for quadcode, for example, we release the open source sandbox. And this is a sandbox, they 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 quadcode. Because we wanted to make it really easy for others to do the same thing. So this is just the same principle of race to the top.

We want to make sure this thing goes well. And this is just the, this is the leader that we have. Incredible. Okay, I definitely want to spend more time on that. I will follow up with this suggestion. Something else that I've been noticing in the, in the field across engineers,

product managers, others that work with agents, is there's this kind of anxiety. People feel when their agents aren't working. There's a sense that like, oh, me. And these look has a question in the answer, or it's like blocked on something. Or it's, or I just like, I'm like, there's all this productivity.

I'm losing it. Yeah, like, I need to wake up and get it going again.

“Is that something you feel that something your team feels?”

Do you feel like this is a, a problem we need to track and think about.

I always have a bunch of agents running.

So like at the moment, I have like five agents running. And at any moment, like, you know, like, I wake up and I start a bunch of agents. Like the first thing I did when I woke up is like, oh, man, I want, I really want to check this thing. So like, I opened up my phone, quad iOS app, code tab, you know, like agent to do,

I was like, kind of double, double guessing something and it was correct. But it's just like so easy to do this. So I don't know, there is this little bit of anxiety. Maybe I personally haven't really felt it just because I have agents running all the time. And I'm also just like not locked into a terminal anymore.

Maybe a third of my code now is in the terminal, but also a third is using the desktop app. And then a third is the iOS app, which is just so surprising, because I did not think that this would be the way that I code in even in 2026. I love this is described as coding still, which is just talking to the cloud code to code for you, essentially.

And it's interesting that this is not like this is now coding. Coding now is describing what you want, not writing actual code. I kind of wonder if the people that used to code using punch cards or whatever, if he showed them software, what they would have said. And I remember reading something, this was maybe like very early versions of like ACM,

like magazine or something, where people were saying, "No, it's not the same thing." This isn't really coding. They call it a programming. I think coding is kind of a new word. But I kind of think about those.

In the back in the, my family's from the Soviet Union, I was born in Ukraine. And my grandpa was actually one of the first programmers in the Soviet Union. And he programmed using punch cards. And you know, like he told my mom growing up told these stories of like, or she told these stories a when she was growing up, he would bring these punch cards home.

And there's these like big stacks of punch cards. And for her, she was like draw all over them with crayons. And that was like her childhood memory. But for him, that was like his experience of programming.

And he actually never saw the software transition.

But at some point, it did transition to software.

“And I think there's probably this older generation of programmers.”

That just didn't take software very seriously. And they would have been like, well, you know, it's not really coding. But I think this is a field that just has always been changing in this way. I don't think you know this, but I was born in Ukraine also. Oh, I don't know. Yeah, I'm from Odessa.

Oh, me too. Yeah, that's crazy. Wow, incredible. What a moment. Maybe we're related in some small way. What year did your, did you leave and your family leave?

We came in 95. Okay. We left in 88 little earlier. Oh, yeah. What are different life that would have been to not leave? Yeah, I just, I feel, I feel so lucky every day.

But, uh, get, get to corp here. Yeah, my family, anytime there's like a toaster or meal. They're just like, to America. Yeah. It's like, okay, enough about that, but you get it.

You know, once you start really thinking about what life could have been. Yeah, yeah, exactly. Yeah, we do the same post, but 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. I had a build on AI, I had a build great products on AI.

One tip you shared is give your team as many tokens as they want, just like let them experiment. You also shared just advice generally of just build towards the model. Or the model is going, not to where it is today. What other advice do you have for folks that are trying to build AI products?

I probably share a few more things. So one is, one is, one is, I don't know. They build on the model is, they try to make it behave a very particular way. This is a component of a bigger system. I think some examples of this are people layering like very strict workflows on the model, for example.

“You know, to say like, you must do step one, then step two, then step three,”

and you have this like very fancy orchestrator doing this.

But actually, almost always you get better results if you just give the model tools,

you give it a goal and you let it figure it out. I think a year ago, you know, if you have a lot of things like that, the way is you get better results if you just give the model tools, you give it a goal and you let it figure it out. I think a year ago, you actually needed a lot of this scaffolding, but nowadays you don't really need it. So, you know, I don't know what to call this principle, but it's like, you know, like, ask not what the model can do for you.

Maybe it's something like this. Just think about, how do you give the model the tools to do things? Don't try to overture it, don't try to put it into a box. Don't try to give it a bunch of context upfront. Give it a tool so that it can get the context it needs.

I think the second one is maybe actually like a more even more general version of this principle is just a bit of a lesson. And actually for the quality, we have a, you know, hopefully, hopefully listeners have read this, but we're certain how this blog post may be 10 years ago, called the bit of lesson. And it's actually a really simple idea.

His idea was that the more general model will always outperform the more specific model.

“And I think for him, he was talking about, like, self-driving cars and other domains like this.”

But actually, there's just so many core areas to the better lesson. And for me, the biggest one is just always bet on the more general model. And, you know, over the long term, like, don't 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.

It's putting this 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 Claude 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 Son of 3.5, then it was like 3.6 or forget 3.5 new, whatever, whatever, whatever day we give 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 did, you used to get for me, it automated some things, but it really wasn't doing a huge amount of my coding. And so the bet with Claude Code was at some point the model gets good enough that it can just write a lot of the code.

And this is a thing that we first started to sing with Opus 4 and Son of 4.

And Opus 4 was our first kind of ASL3 class model that we really speck in May. And we just saw this in Function because everyone started to use Claude Code for the first time. And that was kind of when our growth really went exponential. And like I said, it's kind of, it's stayed there. So I think this is something, this is advice that I actually give to a lot of folks, especially people building startups.

It's going to be uncomfortable because your product market, if it 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 going to hit the ground running. And the product is going to click and start to work.

“And when you say, go for the model six months out, what is, what is it that you think people can assume will happen?”

Is it just generally, it will get better at things? Is it just like, okay, it's like almost good enough. And that's 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 a AI lab, we get to see the specific ways that it gets better.

So it's a little unfair, but we also, we try to talk about this. So, you know, like, one of the ways that it's going to get better is it's going to get better and better using tools and using computers. This is a bet that I would make. Another one is it's going to get better and better for running for a long periods of time. And this is a place you know, like, there's also some studies about those.

But if you just trace that trajectory or, you know, maybe even like, for my own experience, when I used Sonic 3.5 back, you know, a year ago, it could run for baby 15 or 30 seconds before, or it started going off the rails and just really had to hold the tan 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 all just like start another quad and have a do something else.

And, you know, like I said, it always have a bunch of quad running.

And they can also run for hours or even days at a time.

“I think there are some examples where they ran for many weeks.”

And so I think over time, this is going to become more and more normal. Where the models are running for a very, very long period of time and you don't have to sit there and babysit them anymore. So you just talked about tips for building AI products. And he tips for someone just using cloud code for say for the first time, or just someone already using cloud code that wants to get better. What are like a couple prototypes that you could share?

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 or 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. They can edit your settings. It kind of knows about itself. So it can help. You can help with that. A few tips that generally I find pretty useful.

So number one is just use the most capable model.

Currently, that's open 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 on to on. So the first step is just use the best model. The second one is use plan mode. I start almost all of my tasks in plan mode, maybe like 80%. And plan mode is actually really simple.

All it is is we inject one sentence into the model's prompt to say, "Please don't write any code yet." And so there's actually nothing fancy going on. It's just the simplest thing. And so for people that are in the terminal, it's just shift tab twice. That gets you into plan mode. For people in the desktop app, there's a little button on web.

There's the little button. It's coming pretty soon to mobile also. And we just want you to for this walk integration too. So plan mode is the second one. And essentially the model would just go back and forth with you.

I auto accept edits after that. Because if the plan looks good, it's just going to one shot.

It'll get it right the first time, almost every time.

With the open 4.6. And then maybe the third tip is just play around with different interfaces.

“I think a lot of people when they think about quadcode, they think about a terminal.”

And of course we support every terminal we support like Mac, Windows, whatever terminal you might use it works perfectly. But we actually support a lot of other form factors too. We have iOS and Android apps. We have a desktop app. There's the Slack integration.

There's all sorts of things that we support. So I just like play around with these. And again, it's like every engineer is different. That's building is different. Just find the thing that feels right to you and use that. You don't have to use a terminal.

It's the same quad agent running everywhere. Amazing. Okay. Just a couple more questions to stick around things out. What's your take on code X? 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 I came out.”

It looked a lot like quad code to me, so that was kind of flattering. I think it's actually good to have more competition because people should get to choose. And hopefully it forces all of us to do a even better job. Honestly, for our team, though, we're just focused on solving the problems that users have. So for us, we don't spend a lot of time looking and competing products.

We don't really try other products. 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 a bandman, co-founder of Anthropic. What to talk to you about here about just suggestions, which I've integrated throughout our chat. One question you had for you is, what's your plan post, EGI? What do you think you're going to be doing with your 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 farmers market.

And you know, you like bike bike race patty isn't stuff. It's just like a totally different speed than it just complete opposite of San Francisco. One of the things that I really liked is a way that we got to know our neighbors. And we kind of built friendships by trading like pickles. So in the town where we lived, it was actually like everyone made like me so everyone made the goals.

And so I actually got like decently good at making me so. And you know, I made a bunch of batches and this is something that I still make. Me so is this interesting thing where it teaches you to think on these a long time skills. It's just very different than engineering. Because like a batch of white me so it takes like at least three months to make.

And I read me so it's like, you know, two, three, four years. You have to be very patient. Kind of mix it up and then you just like let it sit. You have to be very, very patient.

“So I think that I love about it is just thinking in these long time skills.”

And yeah, I think postage I or if I wasn't an anthropic, I'd probably be making me so. I love this answer. Ben asked me to ask you about what's the deal with you and me so. And so I love the answer. Okay, so the future the future might be just going deep into me.

So getting really good at making me so. Amazing.

Boris, this is incredible.

I feel like we're brothers now from Ukraine. Before we get to a very exciting lady ground, is there anything else that you wanted to share? Is there anything you want to leave listeners with? Anything you want. And you want to double down on.

Yeah, I think I would just like underscore. For, you know, like for philanthropic since the beginning, this idea of like starting at coding, then getting to to use, then getting to computer use has just been the way that we think about things. And we, this is the way that we know the models are going to develop or the, you know, the way that we want to build our models. And it's also the way that we get to learn about safety, study it and improve it the most.

So, you know, everything that's happening right now around, you know, just like quadcode becoming this huge, you know, multi-billion dollar business. And, you know, like now all my friends use quadcode and they just text me about it all the time. So just like, you know, this thing getting kind of big, in some ways it's a total surprise. Because this isn't kind of the, we didn't know that it would be this product. We didn't know that it would start in a terminal or anything like this.

But in some ways, it's just totally unsurprising because this has been our belief as a company for a long time. At the same time, it just feels still very early, you know, like most of the world still does not use quadcode, most of the world still does not use AI. So it just feels like this is one percent on and there is so much miracle. Yeah, man. That's insane to think seeing the numbers that are coming out, you guys just raised the bazillion dollars.

I think quadcode alone is making $2 billion revenue.

It's insane to just think this is how early it still is and just the numbers we're seeing. Yeah, yeah, yeah, it's crazy.

“And I mean, like the way that quadcode has got growing is honestly just the users.”

Like we so many people use it, they're so passionate about it. They fall in love with the product. And then they tell us about stuff that doesn't work, stuff that they want. And so like the only reason that it keeps improving is because everyone is using it. Everyone is talking about it, everyone keeps getting feedback.

And this is just the single most important thing.

And you know, for me, this is the way that I love to spend my days just talking to users and making it better for them. And making me so. You know, the me so's like, not super involved. I just, you just got to wait. Just kidding me.

Well, Boris with that, we've reached our very exciting lighting round. I've got five questions for you. Are you ready? Let's do it. First question, what are two or three books that you find yourself recommending most to other people? I am a big reader.

I would start with the 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 going to use Scala. And I don't know how much this matters in the future now.

But there's this just elegance to functional programming and thinking 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 on. I'd love this.

I've never before mentioned book.

My favorite. Oh, amazing. Amazing. Okay. Second one is Excel Rondo by Strauss. This is probably, you know, like my my dig genre is sci-fi. Like probably sci-fi and fiction.

Excel Rondo is just this incredible book. And it's just so fast-paced that 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 lift off is starting to happen and, you know, starting to approach the singularity.

And it ends with, like, this, like, collective lobster consciousness orbiting Jupiter. And, you know, this happens over the span of a few decades or something. So, the, the pace is just incredible. I really love it. Maybe I'll do one more book. The wandering earth.

Wondering earth by C. Shinlu. So, he's the guy that did three body problems. I think a lot of people know for that.

“I actually, I think three body problems are awesome.”

But I actually like to 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 a Chinese sci-fi. Because it has a very different perspective than Western sci-fi.

And kind of the way that always he has 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 is 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 in the topic actually.

Because, you know, like I said, I was living in this rural place. I was thinking these long time skills because everything is just so slow out there. At least compared to us half. And just like all the things that you do are based around the seasons. And it's based around this food that takes many, many months.

That's the way that kind of, like, social events are organized. That's the way you kind of organize your time. You go to the farmer's market and it's, like, its persimmon season. And, you know that because there's, like, 20 persimmon vendors. And then the next week, the season is done, and then it's like, grapes season.

And then you kind of see this. So it's like, these kind of long time skills. And those also reading a bunch of sci-fi at the time. And just, like, being in this moment, I was like, you know, just thinking about these long time skills. I know how this thinking 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 in Anton. Ben Manos was a big part of that too. I feel like I want to do a whole podcast just talking about your timeage pattern. 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 the fire upon the deep? Uh, this is Vinge, right? Yeah.

Yeah. Yeah. Okay. That one's like, it's like so interesting from a AI-AGI perspective. Uh, so a few people have read that.

So, um, I was like, I was like, yeah, it's like, I really want to. Yeah.

“Yeah. Yeah. Yeah. I like a deepness in the sky also. I think those approaches the sequel later.”

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. Uh, do you have a favorite recent movie or TV show you really enjoyed? So, I actually don't really watch TV or movies.

I just don't really have time these days. Um, I did watch, I, I, I, I'm going to bring up another session loop, but the three body problem series on Netflix. I, I really loved, um, I thought those like a great rendition of the book series. So, the common pattern across AI leaders is no time to watch TV.

Or maybe switch, I completely understand. Uh, 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, "Corek." Because I just, this is, this is what you don't really, the, the one product has been pretty life-changing for me.

Uh, just because I, I have a running all the time.

Uh, so it's been like, you paid a traffic fine for me. It, like, canceled a couple of subscriptions for me. Uh, just like the amount of, like, tedious work. It gets out of the way is awesome. Um, and I also don't know if it's a product, but maybe I'll, uh, also another podcast that I really love.

Obviously, besides, uh, besides money. Obviously. Yeah, it's, uh, it's the acquired podcast that I've been, been in David. Uh, it's, it's just like super, it's super awesome. Um, I feel like the way that they get into like business history and bring it alive is, is really, really good.

And I would start with an Nintendo episode if, uh, if you haven't listened to it. Great tip. Uh, with cohort, just so people understand if they haven't tried this. Like, basically, you type something you want to get done and it can launch Chrome and just do things for you. I saw one of the someone went on Pat leave from anthropic and you had it fill out these, like, medical forms for him.

These, like, really annoying PDFs or just, like, loads up the browser and logs in and fills about some bits in. 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, now it actually just works and it's amazing.

“I think a lot of people just don't really understand what this is.”

Because they haven't used to agent before. And it just feels very, very similar to me to the quad code a year ago. Um, 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, is starting to break through a bit. And there's also this Chrome extension that you mentioned that you could just use standalone.

That's it's in Chrome and you could just talk to Claude. Uh, uh, looking at your screen at your browser and have it do stuff. Have it tell you about what you're looking at, summarized, which you're looking at things like that. Exactly. Exactly. For people that are like just learning to use cowork, the thing I recommend is so you download the quad desktop app. You go to the cowork tab. It's right next to the code tab.

Um, the thing that I recommend doing is like start by having it use a tool. So like clean up your desktop or like summarize or email or something like this or, you know, like respond to the top three emails. Like it actually just responds to emails for me now too.

The second thing is connect tools.

So like if you connect like if you say look at my top emails and then sense fact messages or, you know, like put them in a spreadsheet or something. Or for example, like I use it for all my project management. So we have a single spreadsheet for the whole team. There's like a row per engineer every week. Everyone fills out a status. And every Monday, cowork just goes through and it messages every engineer on swag that hasn't filled out their status. And so I don't have to do this anymore.

And this is just one problem to do everything. And then the third thing is just run a bunch of Claude's in parallel. So it can cowork. You can have as many tasks running as you want. So as they start one task, you know, I have this project management thing running. Then I'll have to do something else. Then something else. And then I'll kick these off.

And then I just go get a coffee while it runs. There's a post I like to that shares a bunch of ways people use. What was previously Claude code or now just you could do through cowork. Because a lot of this is just like, oh, I hadn't thought I could use it for that.

“And once you see like these examples, I think it were people who need to hear.”

I've just like, oh, wow, I didn't know I could do that. Yeah, I think a lot of this was also some of this was also inspired by you, when you. You had this post about it. It was like 50 non technical use cases for Quack load or something like this. So we actually, one of our PMs used that as a way to evaluate cowork before we released it. And I think at the point where we hit work work was able to do like 48 out of the 50 that Rick, okay, it's pretty good. Wow, I did not know that.

That is also. It's I've become an evil. Yeah. How does that go? That amazing.

I feel like I'm valuable to the future. Yeah. This is like reverse breaking through. Wow, that is so cool. Wow, okay, I wonder if it does last you 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 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 in just developing their own common sense.

Like if something smells weird, then, you know, it's probably not a good idea. So I think I think just this, this is this single advice that I give, you know, to coworkers more than anything to. Like that alone could be some podcast conversation. What is common sense? How do you build, but we'll keep this short final question. So you've been got more active on Twitter slash X. I'm curious just why and just what's your experience been with with Twitter, the world of Twitter, because you get a lot of engagement on Twitter slash X.

So for one time, I used Threads X, which was really because I actually helped they build Threads a little bit back in the day. And also just like the design that'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 Eurovision. Oh 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're just kind of nominating around. We went to like Copenhagen, went to like a few different countries.

So every day I was coding and that's like my favorite kind of vacation.

Just like cold all day. It's the best. And at some point I just kind of got bored and like I ran out of ideas for you know, like a few hours. I was like, okay, what do I want to do next? And so I open Twitter and saw some people like tweeting about quadcode and then I just started responding. And then I was like, okay, maybe actually a thing I should do is just like look for people look for bugs that people have.

Maybe people have like bugs or kind of feedback they have. And so kind of introduced myself as far as people had a bunch of bugs and feedback.

“And I think they were kind of surprised by like the pace at which we were able to address feedback nowadays.”

For me it's just like so normal like if someone has a bug like I can probably fix it within a few minutes. Because I just sort of clawed and as long as the description was good, it would just go and do it. And then I'll all go do something else and answer the next thing. But I think for a lot of people is 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 feedback. I'm about bugs, hearing about features.

“I say complaints in the key to beer the other day on Twitter, just you're like posting many threads and it was breaking and just like oh man, let's come on here.”

Yeah, 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. Work in folks, find you online. How can listeners be useful to you? Yeah, find me on threads or on Twitter. That's the that's the easiest place. And please just tag me on stuff.

“Send bugs and feature requests. What's missing? What can we do to make the products better?”

What do you like? What do you want? I love love hearing it.

Amazing. Boris, thank you so much for being here.

Cool. Thanks, funny. 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 review as that really helps other listeners find the podcast.

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