Anthropic's Boris Cherny on AI-Driven Software Engineering and Beyond

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Transcript

100% of my code is written by Quadcode.

I have not edited a single line by hand since November.

Every day I ship 10, 20, 30, 4 requests.

So like at the moment, I have like 5 agents running.

While recording this.

Yeah, you miss writing code.

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

Because I don't have to deal with all the minutiae.

Productivity per engineer has increased 200%.

There's always this question.

Should I learn to go in a year or two is 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.

This 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 over all 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 born in the same city in Ukraine.

That is so funny, I had no idea.

A huge thank you to Ben Mann, Jenny Wend and Mike Krieger for suggesting topics for this conversation.

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Let's get into it after a short word from our wonderful sponsors.

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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 think I've ever heard the actual story.

It was the fastest job change that I've ever had.

I joined cursor because I'm a big fan of the product, and honestly, I met the team, and I was just really impressed.

They're an awesome team, I still think they're awesome, and they're just building really cool stuff.

And they saw where AI coding was going, I think, before a lot of people did.

So the idea of building could product was just very exciting for me.

I think as soon as I got there, what I started to realize is what I really missed about ant was the mission.

That's actually 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.

The thing that drew me to anthropic was the mission, and it was, you know, it's all about safety.

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 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 threat of just coming back to anthropic in the work you've done there.

This plot 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, are sharing the fact that you don't write code anymore, that it's all AI generated, and many aren't even looking at code anymore is how far we've gotten.

In large part, thanks to this little project that you started, and that your team is scaled over the past year, I'm curious just to hear your reflections on this past year and impact that your work has had.

These numbers are just totally crazy, right?

Like, 4% of all commits in the world is just way more than I imagined, and like you said, it still feels like the starting point.

These are also just public commits.

So we actually think if you look at private repositories, it's quite a bit higher than that.

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 quad 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 quad code, it was just going to be, it was just supposed to be a little hack.

We broadly knew it on Thropic that we wanted to ship some kind of coding product, and for one Thropic for a long time, we were building the models in this way that kind of fit our mental model of the way that we build safe 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 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 Inthropic Labs team, and actually my Krieger, and 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 co-work we're starting to see the transition for non-technical folks also, for a lot of people that use conversational AI, this might be the first time that they're using the thing that actually acts, it can actually use your Gmail, it can use your Slack, you can do all these things for you, and it's quite good at it.

And it's only 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 Anth, I spent one month kind of hacking and built a bunch of weird prototypes, most of them didn't ship and weren't even close to shipping.

It was just kind of understanding the boundaries of what the model can do.

Then I spent a month doing post-training, so to understand kind of the research side of it.

And I think honestly, that's just for me as an engineer.

I find that to do good work, you really have to understand the layer under the layer at which you work.

And with traditional engineering work, if you're working on product, you want to understand the infrastructure, the runtime, the virtual machine, the language, kind of whatever that is, the system that you're building on 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 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.

And so I started prototyping this a little bit more, and made a post about it, and I announced it internally, and it got two likes.

That's the, that was like, those like, sense of the reaction at that 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 of all 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?

It's like, you want to under-resource things a little bit at the start.

Then we started thinking about what other form factors we should build, and we actually decided to stick with the terminal for a while, and the biggest reason was the model is improving so quickly.

We felt that there wasn't really another form factor that could keep up with it.

And honestly, this was just me kind of like struggling with kind of like, what should we build?

You know, like, for the last year, quadcode has just been all I think about.

And so just like, late at night, this is just something I was thinking about.

Like, 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 the daily act of users just went vertical.

And really early on, actually, before I launched it, then man nudged me to make a DAU chart.

And I was like, you know, it's kind of early, maybe, you know, should we really do it right now?

And he was like, yeah.

And so the chart just went vertical pretty immediately.

And then in February, we released it externally.

Actually, something that people don't really remember is quadcode was not initially a hit when we released it.

It got a bunch of users.

There was a lot of early adopters that got it immediately.

But it actually took many months for everyone to really understand what this thing is.

Just, again, it's like, it's just so different.

And when I think about it, kind of part of the reason quadcode works is this idea of latent demand, where we bring the tool to where people are and it makes existing workflows a little bit easier.

But also because it's in 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 quadcode is available in the iOS and Android quad app, it's available in the desktop app, it's available on the website, it's available as IDE extensions.

And Slack and GitHub, you know, all these places where engineers are, it's a little more familiar.

But that wasn't the starting point.

So yeah, I mean, at the beginning, it was kind of a surprise that this thing was even useful.

And as the team grew, as the product grew, as it started to become more and more useful to people.

Just people around the world from, you know, small startups to the biggest thing companies started using it and they started getting feedback.

And I think just reflecting back is, it's been such a humbling experience because we just we keep learning from our users and just the most exciting thing is like, you know, none of us really know what we're doing.

And we're just trying to figure it out along with everyone else.

And the single best signal for that is just feedback from users.

So that's just been the best.

I've been surprised so many times.

It's incredible how fast something can change in today's world.

You launched this a year ago.

And it wasn't the first time people could use AI to code.

But in a year, the entire profession of software engineering has dramatically changed.

Like there's all these predictions.

Oh, AI's going to be written 100% AI's of course going to be written by AI.

Everyone's like, no.

That's crazy.

What are you talking about now?

I'm like, of course, that's happening exactly as they said.

It's just things move so fast and change so fast now.

Yeah, it's really fast back at 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.

But I think like I didn't throw up like this is just the way the way we think about things is exponentials.

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

So we really just think in exponentials.

And if you kind of look at the exponential of the percent of code that was written by Quad at that point, if you just trace the line, it's pretty obvious we're going to cross 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 AI people just sitting around trying stuff to pushing the models further than most other people.

I mean, this is the thing about innovation, right?

Like you can't, you can't force it.

There's no roadmap for innovation.

You just have to give people space.

You have to give them, maybe the word is like safety.

So it's like psychological safety that it's okay to fail.

It's okay if 80% of the ideas are bad.

You also have to hold them accountable a bit.

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, hacking on this and luckily my, you know, my wife was very supportive.

But it just felt like it was onto something.

It wasn't obvious what.

And then sometimes, you know, you find a threat.

You just have to pull on it.

So at this point, 100% of your code is written by CloudCode.

Is that, is that kind of the current state of your coding?

Yeah, so 100% of my code is written by CloudCode.

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 are donethropic.

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 shoot like 10, 20, 30, 4 requests, something like that.

Every day.

A hunt, every day.

Yeah.

Good guy.

100% written by CloudCode.

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 it ends up a 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's already operating and or you think we'll head towards.

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

So Cloud is looking through feedback.

It's looking at bug reports, it's looking at, you know, like telemetry and then things like this.

And it's starting to come up with ideas for bug fixes and things to ship.

So it's just starting to get a little more, you know, like a little more like a co-worker 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 virtually 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, a general task, you know, like an I use co-worker every day now to do all sorts of things that are just not related to coding at all and just to do it automatically.

For example, I had to pay a parking ticket the other day, I just had to co-work do it.

All of my project management for the team, co-work does all of it.

It's like syncing stuff between spreadsheets and messaging people on Slack and email and all of those kind of stuff.

So I think the frontier is something like this.

And I don't think it's coding because I think coding is, you know, it's pretty much solved.

And over the next few months, I think what we're going to see is just across the industry.

It's going to become increasingly solved, you know, for every kind of codebase, every text talk that people work on.

This idea of helping you come up with what to work on is so interesting.

A lot of people listening to this or product managers and 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 CloudCode or Core can point it at a Slack thread.

For us, we have this channel that's all the internal feedback about CloudCode.

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 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 within a minute, within five minutes or whatever.

And it's just really fast feedback cycle.

It encourages people to give more and more feedback.

It's just so important because it makes them feel heard.

Because, you know, like, usually when you use a product, you get feedback, it just goes into a black hole somewhere and then you don't get feedback again.

So if you make people feel heard, then they want to contribute and they want to help make the thing better.

And so now I kind of do the same thing, but Cloud Honestly does a lot of the work.

So I pointed at the channel and it's like, okay, here's a few things that I can do.

I just put up a couple of PRs.

I want to take a look at them.

I'm like, yeah.

Have you noticed that it is getting much better at this?

Because this is kind of the holy grail right now.

It's like, cool building solved.

Code review became kind of the next bottleneck, the least PRs who's going to review them all.

The next big open question is just like, okay, now we need it.

Now humans are necessary for figuring out what to build, what to prioritize.

And you're saying that that's where Cloud Code is starting to help you.

Has it gotten a lot better with, like, to say, what was for six 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 Y.

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, like, all of our code bases, those, 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 this.

And so now we're using these gains of just hundreds of percentage points.

It's, it's just absolutely insane.

What's also insane is just how normalized this has all been.

Like, we hear these numbers, like, of course.

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 is just the, the world of tech.

It's just like so easy to get used to it.

But it's important to recognize this is crazy.

This is something like I have to remind myself once in a while.

There's sort of like a downside of this because the model changes.

So there's actually like, there's many kind of downsize that that we could talk about.

But I think one of the amount of personal levels, 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 debug a thousand times.

In traditionally the way that you do it is you take a heap snapshot, you put it into a specialty bugger, 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, quad, 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-in-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 sonnet 3.5 anymore.

The new models are just completely completely different.

And just this mindset shift is very different.

I hear you have these very specific principles that you've codified for your team that when people join you, you kind of walk them through them.

I believe one of them is what's better than doing something, you're having cloud do it.

And it feels like that's exactly what you describe with this memory leak is just like, you almost forgot that principle of like, okay, let me see if cloud can solve this for me.

There's this interesting thing that happens also when you, when you underfund everything a little bit, because then people are kind of forced to 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, really encourage on the team.

Early on, it was really important because it was just me.

And so our only advantage was speed.

That's the only way that we could ship a product that would compete in this very crowded coding market.

But now it is, it's still very much a principle we help on the team.

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

So it just very much encourages that.

This idea of underfunding, it's so interesting because in general, there's this feeling like AI is going to allow you to not have as many employees, not have as many engineers.

And so it's not only you can do more productive, which you're saying is that you will actually do better if you're underfunding.

It's not just that AI can make you faster.

It's you will get more out of the AI tooling if you have fewer people working on something.

Yeah, if you hire great engineers, they'll figure out how to do it.

And especially if you empower them to do it.

This is something I actually talk a lot about with you know, with wakesyTOs 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, philanthropic, we have, you know, everyone can use a lot of tokens.

We're starting to see this come up as like a perk at some companies.

Right, if you join, you get unlimited tokens.

This is a thing I very much encourage because it makes people free to try these ideas that would have been too crazy.

And then if there's an idea that works, then you can figure out how to scale it.

And that's the point to kind of optimize and to cost cut figure out like, you know, maybe you can do it with high cool or with sauna instead of all this or whatever.

But at the beginning, you just want to throw a lot of tokens at it and see if they deal 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 and drop like you want us to use as many tokens as possible.

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

Yeah.

And I think the reality is like, at small scale, like, you know, you're not going to get like a giant bill or anything like this, like, if it's an individual engineering experimenting, the token costs are still probably relatively low relative to their salary or other costs of running the business.

So it's actually like not not a huge cost as the things scales up.

So like, let's say, you know, they build something awesome and then it takes a huge amount of tokens.

And then the cost becomes pretty big.

That's the point out which you want to optimize it.

But don't don't do that too early.

Have you seen companies where their token cost is higher than their salary?

Is that a trend you think we're going to find and see, you know, I don't think we're starting to see some engineers that are spending, you know, like hundreds of thousands a month in tokens.

So we're starting to see this a little bit.

There's some companies that are we're starting to see some more things.

Yeah.

Going back to coding.

Do you miss writing code?

Is it something you're kind of sad about that there's no longer thing you'll do as a software engineer?

It's funny.

For me, you know, like when when I learned engineering for me, it was very practical.

I've earned engineering so I could build stuff.

And for me, I was I was self-taught, you know, like I studied economics in school, but I didn't study CS, but I taught myself engineering kind of early on.

I was programming in 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.

So it was like the first thing we had these like graphing calculators and the, you know, I just programmed the answer into 383.

383 plus yeah, yeah, exactly.

Plus yeah, it's like I programmed the answer is in and then the next like math test, whatever, like the next year, it was just like too hard.

Like I couldn't program all the answers and 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 A's, but then we all got caught in the teacher told us to not get off.

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

We're programming is a way to build a thing.

It's not the end in itself.

At some point, I personally fell into the rabbit hole of kind of like the beauty of programming.

So like I wrote a book about TypeScript.

I started out the actually at the time it was the world's biggest TypeScript meet up 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 um, 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 a really complicated math problem, it's kind of similar when you kind of balance the types or you know, the program is just like really beautiful.

But it's really not the end of it.

I think for me coding is very much a tool 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 Lena who you know, was still writing C++ on the weekends by because, you know, for her, she just really enjoys writing C++ by hand.

And so everyone is different.

And I think even as this field changes, even as everything changes, there's always space to do this.

There's always space to enjoy the art and to kind of do do things by hand if you want.

Do you worry about your skills aturfing as an engineer?

Is that something you can worry about or is it just like, you know, this is just how it's going to go?

I think it's just the way that it happens.

I don't worry about it too much personally.

I think for me, like programming is on a continuum and, you know, like way back in the day, you know, like software actually is like relatively new, right?

Like if you look at the way programs are written today, like using software that's running on a virtual machine or something, this has been the way that we've been writing programs since probably the 1960s.

So, you know, it's been, you know, like 60 years or something like that.

Before that it was punch cards, before that it was switches, before that it was hardware, and before that it was just, you know, like literally pen and paper.

It was like a room full of people that were doing math on paper.

And so, you know, programming has always changed in this way.

In some ways, you still want to understand the layer under the layer because it helps you be a better engineer.

And I think this will be the case maybe for the next year or so.

But I think pretty soon, I just won't really matter.

It is just going to be kind of like the assembly code running under the program or something like this.

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 after-fear or something like this.

And I don't know if you saw this, but you know, I'm 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't 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?

Well, what I heard from you is, their take is in like a year or two, you don't really need to.

My take is, I think for for people that are using, um, 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, um, what is the right, like historical analog for this?

Because like, like somehow we have to situate this thing in history and kind of figure out, when have we gone through some more transitions, what's the right kind of mental model for this?

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

And so, you know, if you look at Europe in, you know, like in the mid, the mid, the mid 1400s, literacy was actually very low.

There was sub 1% of the population.

It was scribes that, you know, they were the ones that did all the writing, they, they were the ones that did all the reading.

They were employed by like words 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 thousand years before.

And so, the volume of printed material just went way up.

The cost went way down, it went down something like 100x over the next 50 years.

And if you look at literacy, you know, it actually took a while because of learning to read and write as, you know, it's quite hard.

It takes the education system.

It takes free time.

It takes like not having to work on a farm all day.

So, you actually have time for education and things like this.

But over the next 200 years, it went up to like 70% globally.

So, I think this is the kind of thing that we might see is a similar kind of transition.

And there was actually this interesting historical document where there was an interview with some like scribe in the 1400s about like how do you feel about the printing press?

And they were actually very excited because they were actually the thing that I don't like doing this copying between books.

The thing that I do like doing is drawing the art in books and then doing the book binding.

And I'm really glad that now my time is freed up.

And it's interesting, like as an engineer, I sort of felt like a peril with us like this is sort of how I feel where I don't have to do the tedious work anymore of coding because this is 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 coming up with this.

It's talking to users.

It's thinking about these big systems.

It's thinking about the future.

It's collaborating with other people on the team and that's what I get to do more of now.

And what's amazing is that the tool you're building allows anybody to do this.

People that have no technical experience can do exactly what you're describing.

Like I've been doing a bunch of random little projects and it's just like any time you get stuck.

Just like help me figure this out.

And you got on block.

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

And I just remember spending so much time on libraries and dependencies and things and just like, oh my god, what do I do?

And then look against deck 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 it's been like a month already and they built up the service.

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 design, it 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 co-work product is kind of the first way to get at this.

But it's just the first one.

And it's the thing that I think brings AI to a agentic AI to people that haven't really used it before.

And people are starting to just to get a sense of it for the first time.

When I think back to engineering a year ago, no one really knew what an agent was, no one really used it.

But nowadays it's just the way that, you know, we do our work.

And then when I look at non-technical work today.

So you know like, or maybe semi-technical like product work and you know like data science and things like this.

When you look at the kinds of AI that people are using, it's always these like conversational AI.

It's like a chatbot or whatever.

But no one really has used an agent before.

And this word agent just gets thrown around all the time and it's just like so misused.

It's like 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 in urgent to do this work at Anthropic.

Because I think we take this very, very seriously.

And so now 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 looting to his 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-coating this 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-coat code because it just makes coding delightful again.

And that's just, that's just so fun for them.

But it's hard to know where this thing is going to go.

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

And I think the printing press is just such a good one.

Because what happened is this technology that was locked away to a small set of people, like knowing how to read and write, became accessible to everyone.

It was just inherently democratizing.

Everyone started to be able to do this.

And if that wasn't the case, then something like the Renaissance just could never have happened.

Because a lot of the Renaissance it was about like knowledge spreading.

It was about like written records that people used to communicate.

You know, because there were no phones or anything like this.

There's no internet at the time.

So it's about like, what does this enable next?

And I think that's the very optimistic version of it for me.

And that's the part that I'm really excited about.

It's just unimaginable.

You know, like we couldn't be talking today if the printing press hadn't been invented.

Like our 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?

Anyone can just build software anytime.

And I have no idea.

It's just the same way that, you know, in the 1400s, no one could have predicted this.

I think it's the same way.

But I do think in the meantime, it's 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.

Experiment with the tools, get to know them.

Don't be scared of them.

Just, you know, dive in, try them beyond the bleeding edge, beyond 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 quadcode 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 will get 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 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, there won't just be AI native and they don't just know how to use these tools really well but also their curious and their generalists and they cross over multiple disciplines and can think about the broader problem they're solving rather than just 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's ability, 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 where a lot of people are actually just doing the same thing and some people have specialties.

For example, I code a little bit more versus CADRPM does a little bit more, you know, coordination or planning or forecasting or things like this.

Take older alignment.

Take older alignment is 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 in 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.

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You talked about how you're enjoying coding more.

Actually, did this little informal survey and Twitter?

I don't know if you saw this where I just asked.

And did three different polls?

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.

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 really interesting.

That's super interesting.

I'd love to talk to these people.

Both in the more bucket and the West bucket just understands.

Did you get to follow up with any of them?

There's a few people replied and were actually doing a follow-up poll that will link to in the show notes of going deeper into some of the stuff.

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

The designers, they didn't share a lot, actually, just like the people that are actually asked.

Just like, why are you enjoying your job less?

And in here, a lot.

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, you know, when people join, we have, there's a lot of technical interviews that people go through even for non-technical functions.

And, you know, our designers 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 like go in and code.

And even some designers that didn't code before have just started to do it.

And for them, it's great because they can unblock themselves.

But I'd be really interested just to hear more people's experiences because I bet it's not, you know, for my thought.

Yeah, so maybe if you're listening to this, leave a comment if you're finding your job's less fun and you're enjoying your job less.

Because what you're saying and what I'm hearing from most people, 70% of PM's and 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, you know, for many, many months.

And so you can use this to code in a way that you don't have to open a bunch of terminals.

But you still get the power of quad code and the biggest thing is you can just run as many, you know, quad sessions in parallel as you want.

We can, you know, we call this multi-clotting.

So this is, 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 waiting 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.

Waiting 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 2016 or something like this, that 40% of posts in Facebook groups are buying and selling stuff.

So this is crazy.

It's like people are abusing the Facebook groups product to buy and sell.

And it's not, it's not abuse and kind of like a security sense.

It's abuse and that no one designed the product for this, but they're kind of figuring it out because it's 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 for 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.

And so this is kind of like traditional dating setup, but people are just like creeping on each other.

So maybe if you can build a product for this, it might work.

And so this idea of 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.

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 shocked.

I was like, Brendan, what are you doing?

You figured out how to open the terminal, which is, it's a very engineering product.

Even a lot of engineers don't want to use a terminal.

It's just like the lowest level way to do your work.

It's really, really in the weeds of the computer.

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 wait in 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 there are here's this application that I want to build.

Here's the thing that I wanted to do.

The 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 invert 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 concepts, but applied to the model.

You talked about something that I saw you talk about when you launched that initially as you your team built that in 10 days.

That's insane.

I think it came out.

I think it was like, you know, used by millions of people pretty quickly.

Something like that being built in 10 days.

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

That's it.

Yeah, it's funny.

Quadcode, like I said, when we released it, it was not immediately a hit.

It became a hit over time.

And there was a few inflection points.

So one was, you know, like, Opus 4.

It just really, really inflected.

And then in November, it inflected.

And it just keeps inflecting.

The growth just keeps getting steeper and steeper and steeper every day.

But, you know, for the first few months, it wasn't a hit.

People used it, but a lot of people couldn't figure out how to use it.

They didn't know what it was for.

The model still like wasn't very good.

Co-work when we released it, it was just immediately a hit.

Much more so than Quadcode 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 us.

He's just an incredibly strong team.

And again, the the place cover came from is just the sweet and demand.

Like we saw people using Quadcode 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 Quadcode and put it in the desktop app?

And that's essentially the thing that worked.

And so over 10 days, they just completely used Quadcode to build it.

And, you know, Quadwork 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 Quadcode 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 Quadcode.

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.

Really, certainly learn from users, get feedback, iterate.

The fact that it's hard to even know what the AI's capable of and how people will try to use it is like, is a unique reason to start releasing things early.

That'll help you as you exactly describe this idea of what is a 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 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 alignment.

This is mechanistic interpretability.

It's like the lowest wear.

The second wear is evils.

And this is essentially a laboratory setting.

The model was in a petri dish and you study it.

And you put in this synthetic situation and just say, okay, like model, what do you do?

And are you doing the right thing?

Is it aligned?

Is it safe?

And then the third wear is seeing how the model behaves in the wild.

And as the model gets more sophisticated, this becomes so important because it might look very good on these first two layers, but not great on the third one.

We released Cloud Code really early because we wanted to study safety.

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 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 Evel's.

We try to enter an Evel looks good.

We try to do it with a few customers.

It looks good.

Now we have to make sure it's safe in the real world.

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

But yeah, it's just, it's constantly improving.

And this is really the only way to to make sure that over the long term, the model is aligned and it's doing the right things.

It's such a wild space that you work in where there's this insane competition and pace at the same time.

There's this fear that if 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 tests, evolves that tell you is is doing bad things and then releasing it early.

I haven't actually heard a ton of about that first piece.

That is so cool.

So you guys can, there's an observed ability tool that can let you peek inside the models brain and see how it's thinking towards 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?

What is it?

It's a bunch of neurons that are connected.

And so what you can do is like, in a human brain or an animal brain, you can study it at this kind of mechanistic level to understand what the neurons are doing.

It turns out surprisingly, a lot of this does translate to models also.

So model neurons are not the same as animal neurons, but they behave similarly in a lot of ways.

And so we've been able to learn just a ton about the way these neurons work, about, 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, 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 way 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 this more sophisticated concept.

And it is just something we're learning about all the time, you know, in a, for anthropic as we think about the way this space evolves, doing this in a way that is safe and good for the world is just, this is the reason that we exist.

And this is the reason that everyone is at Anthropic.

Everyone that is here, this is the reason why they're here.

So a lot of this work, we actually open source.

We publish it a lot.

And, you know, we publish very freely to talk about this.

Just so we can inspire other labs that are working on similar things to do it in a way that's safe.

And this is something that we've been doing for 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, it just makes sure that there's 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, we want to make sure this thing goes well.

And this is just the, this is the we are 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, the product managers, others that work with agents is there's this kind of anxiety people feel when their agents aren't working.

There's a sense that like, oh, man, these 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.

I can't, I 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 problem you do track and think about?

I always have a bunch of agents running.

So I got the moment.

I have like five agents running.

And at any moment, like, you know, like, I wake up and I straight up on the agents.

Like the first thing I did when I woke up is like, oh, man, I want everyone to check this thing.

So like, I opened up my phone, quad iOS app, code tab, you know, like agent do do popular.

Because I wrote some code yesterday and I was like, wait, did I do this right?

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 like, this is now coding.

Coding now is describing what you want, not writing actual code.

I kind of wonder if the people that used to code using punch cards or whatever, if you show them software, what they would have said.

And I remember reading something, this was maybe like very early versions of like ACM, like magazine or something.

Where people are 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, some of 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 he told my mom growing up told these stories of like, or she told these stories when she was growing up, he would bring these punch cards home.

And there's these like big stacks of punch cards.

And for her, she would like draw all over them with crayons.

And that was a courage-held 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, yes, with Tom from 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 in your family leave?

We came in 95.

Okay.

We left in 88 little earlier.

Oh, yeah.

What are different life that would have been to not, to not leave?

Yeah.

I just, I feel, I feel so lucky every day.

But get, get to corp here.

Yeah.

My family, anytime there's like a toaster or meal, they're just like, so America, it's like, okay, enough about that.

But you get it.

You know, once you start really thinking about what life could have been.

Yeah.

Yeah.

Yeah.

Yeah.

We do the same post, but it's still vodka.

And it's still vodka.

So, 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 don't try to box the model in.

I think a lot of people who's instinct when 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 actually needed a lot of this scaffolding, but nowadays you don't really need it.

So I don't know what to call this principle, but it's like ask not what the model can do for you.

Maybe it's something like this.

Just think about how do you give the model the tools to do things?

Don't try to overture it.

Don't try to put it into a box.

Don't try to give it a bunch of context up front.

Give it a tool so that it can get the context it needs.

You're just going to get better results.

Think a second one is maybe actually like a more even more general version of this principle is just a bitter lesson.

And actually for the quote, quote, you know, hopefully hopefully listeners have read this, but we're certain how this blog post may be 10 years ago called the bitter lesson.

And it's actually a really simple idea.

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

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

But actually there's just so many core areas to the bitter lesson.

And for me the biggest one is just always bet on the more general model.

And you know over the long term like don't don't try to use tiny models for stuff.

Don't try to like 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.

And so these workflows are essentially a way that, you know, it's not, it's not a general model.

It's putting this scaffolding around it.

And in general, what we see is maybe scaffolding can improve performance.

Maybe 10, 20 percent 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 ASL 3 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 winner growth really went exponential.

And like I said, it's kind of, it's stayed there.

So I think this is something this advice that I actually give to a lot of folks, especially people building startups, it's going to be uncomfortable because your product market will be very good for the first six months.

But if you build for the model six months out, when that model comes out, you're just going to hit the ground running, and the product is going to click and start to work.

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

Is it just generally it will get better at things?

Is it just like, okay, it's like almost good enough.

And that's sign that it'll probably get better at that thing.

Is there any advice there?

I think that's a good way to do it.

Like, you know, obviously within an AI lab, we get to see the specific ways that it gets better.

So it's a little unfair, but we also, we try to talk about this.

So, you know, like one of the ways that it's going to get better is it's going to get better and better at 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 this.

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 we started going off the rails.

And you 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 I'll just 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 of 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 are all different.

Developers have different preferences.

They have different environments.

So there's just so many ways to use these tools.

There's no one right way.

You sort of have to find your own path.

Luckily, you can ask cloud code.

It's able to make recommendations.

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 in less token intensive.

If you use the most capable model, because it can just do the same thing much faster with a less correction, less handholding on Sonnet.

So the first tip is just use the best model.

The second one is use plan mode.

I start almost all of my tasks in plan mode, maybe like 80%.

And plan mode is actually really simple.

All it is is we inject one sentence into the model's prompt to say, please don't write any code yet.

There's actually nothing fancy going on.

It's just the simplest thing.

And so for people that are in the terminal, it's just shift tab twice, and that gets into plan mode.

For people in the desktop app, there's a little button on web.

There's a little button.

It's coming pretty soon to mobile also.

And we just 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.

Once the plan looks good, then you let the model execute.

I auto accept edits after that.

Because if the plan looks good, it's just going to one shot it.

It'll get it right the first time, almost every time with 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, you know, like whatever terminal you might use it works perfectly.

But we actually support a lot of other form factors too.

Like, you know, we have like iOS and Android apps.

We have a desktop app.

There's, uh, you know, 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.

Everyone 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 round 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 quadcode to me.

So that was kind of flattering.

It's, I think it's actually good, you know, to have more competition because people should get to choose and hopefully it forces all of us to like do a even better job.

Honestly, for our team, though, we're just focused on solving the problems that users have.

So for us, you know, we don't spend a lot of time looking at competing products.

We don't really try the other products.

I, you know, you kind of, you want to be aware of them.

You want to know they exist.

But for me, I just, I love talking to users.

I love making the product better.

I love just acting on feedback.

So it's really just about building a, building a good product.

Maybe a last question.

So I talked to a Ben Mann, co-founder of anthropic, what to talk you about it, 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 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 times a week, I would like bike to the farmers market.

And you know, you like bike by like 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 that, 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.

Meese was 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, you know, like a batch of white meese will 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 the thing that I love about is just thinking in these long time skills.

And yeah, I think post-AGI, or if I wasn't at 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 it really good at getting making me so amazing.

Boris, this is incredible.

I feel like we're brothers now from Ukraine.

Before we get to a very exciting ladyground, is there anything else that you wanted to share?

Is there anything you want to leave listeners with?

Anything you want, you want to double down on?

Yeah, I think I would just like underscore, you know, like for, for anthropics 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 this is the way that we know the models are going to develop, or the way that we want to build our models.

And it's also the way that we get to learn about safety, study it, and improve it the most.

So, you know, everything that's happening right now around, you know, just like quad code becoming this huge, you know, multi-billion-dollar business.

And, you know, like now all my friends use quad code and they just text me about it all the time.

So just like, you know, this thing getting kind of big.

In some ways, it's a total surprise.

Because this isn't kind of the, we didn't know that it would be this product.

We didn't know that it would start in a terminal or anything like this.

But in some ways, it's just totally unsurprising.

Because this has been our belief as a company for a long time.

At the same time, it just feels still very early.

You know, like most of the world still does not use quad code, most of the world still does not use AI.

So it just feels like this is 1% on and there's 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 cloud code alone is making $2 billion revenue.

You think anthropic.

I think the number you guys put out, you're making $15 billion in revenue.

It's insane to just think this is how early it still is.

And just the numbers we're seeing.

Yeah, yeah.

It's crazy.

And I mean, like the way that quad code has got growing is honestly just the users.

Like we, so many people use it.

They're so passionate about it.

They fall in love with the product.

And then they tell us about stuff that doesn't work, stuff that they want.

And so like the only reason that it keeps improving is because everyone is using it.

Everyone is talking about it.

Everyone keeps getting feedback.

And this is just the single most important thing.

And you know, for me, this is the way that I love to spend my days just talking to users and making it better for them.

And making me so.

And making me so, you know, the me so is like not super involved.

I just, you just got to wait.

Yeah, just kidding me.

Well, Boris with that, we've reached our very exciting lightning round.

I've got five questions for you.

Are you ready?

Let's do it.

First question.

What are two or three books that you find yourself recommending most to other people?

I am a big reader.

I would start with a technical book.

One 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 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 big genre is sci-fi.

Like probably sci-fi and fiction.

Excel Rondo is just this incredible book.

And it's just so fast pace.

The pace gets faster and faster and faster.

And I just feel like it captures the essence of this moment that we're in more than any other book that I've read.

Just the speed of it.

And it starts as a 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 like this panel of few decades or something.

So, the, the pace is just incredible.

I really love it.

Maybe I'll, I'll do one more book.

The wandering Earth, wandering Earth by Session Lou.

So, he's the guy that did three body problem.

I think a lot of people known for that.

Actually, I think three body problems was 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, um, I was he as a writer thinks about it.

So, it's just really, really interesting to read and just beautifully written.

It's so interesting how sci-fi has prepared us to think about where things are going.

Just like, it creates these walk-mounted models of, like, okay, I see.

I've read about this sort of world.

Yeah, I think, I think for me, this was like the reason that I joined in the topic, actually, because, uh, you know, like, like I said, I was looking in this rural place.

I was thinking these long time skills, because everything is just so slow out there, at least compared to us half.

And just like all the things that you do are based around the seasons.

And it's based around this food that takes many, many months.

That's the way that kind of, like, social events are organized.

That's the way you kind of organize your time.

You like, you go to the farmer's market and it's like, it's persimmon season.

And you know that because there's like 20 persimmon vendors.

And then the next week, the season is done and 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 pan in the journey of Boris through Japan, to 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?

This is vintage, right?

Yes.

Okay.

That one's like, it's like so interesting from an AI, AI perspective.

So a few people have read that.

So I love that myself.

Yeah.

It's like I really want to do a lot.

Yeah.

Yeah.

Yeah.

I like a deepness in the sky.

Also, I think those approaches the sequel later.

Yeah.

Yeah.

I think so.

Yeah.

Very long and like complex to get into, but so good.

Okay.

We'll keep going through a lighting around.

Do you have a favorite recent movie or TV show?

Really enjoyed.

So I actually don't really watch TV or movies.

I just don't really have time these days.

I did watch, I, I'm going to bring up another session loop with the three body problem series on Netflix.

I, I really loved.

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 movies which I completely understand.

Is there a favorite product?

You've recently discovered that you really love.

I'm going to like chill a little bit and just say coerke because I just, this is what you don't really, the, the one product has been pretty life-changing for me.

Just because I, I have a running all the time and the, the Chrome integration in particular is just really excellent.

So it's been like, you paid a traffic fine for me.

It like canceled a couple of subscriptions for me.

Just like the amount of like tedious work.

It gets out of the way is awesome.

And I also don't know if it's a product, but maybe I'll, uh, also another podcast that I really love.

Obviously besides, besides money is, yeah, it's, uh, it's the acquired podcast by then Ben and David.

It's, it's just like super, it's super awesome.

I feel like the way that they get into like business history and bring it alive is really, really good.

And I would start with an Nintendo episode if, if you haven't listened to it.

Great tip.

With coerke, just so people understand if they haven't tried this, like basically you type something you want to get done and it can launch Chrome and just do things for you.

I saw one of the someone went on Pat leave from anthropic and you had it fill out these like medical forms for them.

These are like really annoying PDFs or it just like loads up the browser and logs in, fills about some bits of.

Yeah, exactly exactly.

And it actually just kind of works.

Like we tried this experiment like a year ago and it didn't really work as the model wasn't ready, but now, 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 a agent before and it just feels very, very similar to me to the quad code a year ago.

But like I said, it's just growing much faster than quad code did in the early days.

So I think it's starting to, it's starting to break through a bit.

And there's also this Chrome extension that you mentioned that you could just leave stand alone.

That's it's in Chrome and you could just talk to Claude.

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, exactly exactly.

For people that are like just learning to use co-work, the thing I recommend is so you download the quad desktop app, you go to the co-work tab, it's right next to the code tab.

The thing that I recommend doing is like start by having it use a tool.

So like clean up your desktop or like summarized or email or something like this or, you know, like respond to the top three emails.

Like it actually just 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 send Slack 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, co-work just goes through and it messages every engineer on Slack that hasn't filled out their status.

And so I don't have to do this anymore.

And this is just one problem to it'll do everything.

And then the third thing is just run a bunch of Claude's in parallel.

So it can co-work.

You can have as many tasks running as you want.

So as I 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'll link to that shares a bunch of ways people use.

What was previously Claude code or now just you could do through the work.

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 I where people need to hear have 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, you had this post about, uh, it was like 50 non-technical use cases for co-accult or something like this.

So we actually, one of our PMs used that as a way to evaluate co-work before we released it.

And I think at the point where we hit work work was able to do like 48 out of the 50 that were like, okay, it's pretty good.

Wow, I did not know that.

That is also.

I've become an evil.

Yeah.

How does that go?

Amazing.

I feel like I'm valuable to the future.

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 as people just failing to use common sense.

Like, they follow a process without thinking about it.

They just do a thing without thinking about it or they're working on a product that's like not a good product or not a good idea.

And they're just following the momentum and not thinking about it.

I think the best results that I see are people thinking from first principles 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 in good ways that I give, you know, to co-workers more than anything, too.

And I feel like that alone could be some podcast conversation.

What is common sense?

How do you build it?

But we'll keep this short.

Final question.

So you've been got more active on Twitter slash X.

Here's just Y.

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 use Threads X, because I actually helped they build Threads a little bit back in the day.

And also just like the design, it's like a very clean product.

I just really like that.

I started using Threads because actually I was bored.

So in December, I was in Eurovision.

Oh, yeah, yeah, yeah, I started using Twitter because I was bored.

So my wife and I were, we were traveling around in Europe for December.

We're just kind of no-mitting around.

We went to like Copenhagen, went to like a few different countries.

And for me, it was just like a coding vacation.

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

It's 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.

I saw some people like tweeting about quadcode and then I just started responding.

And then I was like, okay, maybe actually a thing I should do is just like look for people look for bugs that people have.

Maybe people have like bugs or kind of feedback they have.

And so kind of introduced myself as for people 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 quad.

And as long as the description was good, it would just go and do it.

And then I'll all go do something else in the 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 hearing 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 bridging 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.

All at your co.

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, send feature requests.

What's missing?

What can we do to make the products better or 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.

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