Odd Lots - The Creator of Claude Code on The Hottest Piece of Software in the World

Episode Date: July 20, 2026

2026 has been, in terms of software, the year everyone is talking about Claude Code. Indeed, Anthropic's coding agent incited a market scare — and helped usher in the era of vibe coding — through its promise of streamlining software development for both pros and amateurs. Famously, Claude Code began as a side project of Anthropic led by Boris Cherny, who speaks to us today about the early days of Claude Code and how he and his team approach building an agentic coding tool. We also talk about the business aims of a harness like Claude Code, safety and alignment research, the specific skillset he looks for in engineers now that code writing is a deemphasized skill, and what happens when Claude Code feels more like a co-worker than a tool. Read more:For Software Engineers, the AI Reckoning Is Already HereDimon Warns of Broad Mythos Access, Calling It a Real Issue Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

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And I'm Tracy Alley. Tracy, so I think the most embarrassing moment for me in... Go on. It's the most exciting way you've ever started a podcast, Joe. Maybe that's the most embarrassing way in the moment I felt like I'm like making myself a little stupid or something like that in 2026. was I asked Claude to clean up all the many screenshots

that I had on my desktop. Oh. So I was like, just put the, I have all these screenshots on my desktop, various charts and stuff. And I was like, Claude, can you do this? And in that moment, I realized that I was essentially outsourcing

my computer to another computer. There's big data centers, et cetera, that Anthropic has. And rather than just like taking a few seconds, like drag and drop some screenshots, I was like, no, I'm going to have another computer. use my computer for me.

That just seems efficient. But here's the big question. Did it do it correctly? Yeah, absolutely. Yeah, it was perfect. All right, because you hear the stories about agents going off the rails. Like there was some software company or like car rental software company. And I think they had an agent that deleted their entire day base and then admitted that

it had violated its core principles in doing so, but didn't have an explanation as to why. There's definitely been times in my clog code usage, which is not very sophisticated, where it'll just ask me, like, do I do this or this? And I have no idea what it's asking for it. I just, like, hit yes. Hesidently pressing the enter button. No, I wish I were going to say hesitantly. I don't even think about it.

I just, like, hit yes. So far, no disasters from that. But, you know, I'm just like, yeah, I assume it's right. And maybe, you know, it's sort of like playing. What's the reverse slot machine where it's like good every time, but every once in a while it's like really disastrous. Yeah. I guess Russian roulette kind of would be the example of that.

But yes, obviously setting all this aside, I mean, I think 2026 has been in terms of software do you or everyone's talking about cloud code? Absolutely. So we also had the big market scare where we saw a bunch of software companies get hit because there was this perception that cloud code would basically be able to do everything. Yeah, there was like a day where anthropic, like now it's like, here's something new. And I don't even think people, people were so trigger-happy. They didn't even, like, look and see, like, what it was.

It's like, here's a new thing for, like, financial services. And you just see all the financial services stocks fall, et cetera. But it does raise some questions like, you know, here's a big AI company. What will be the limits of where they go? What kind of businesses they can get into and so forth? But then even without that, like, what is the future of software engineering? What is the future for people with a laptop job? Right.

The future of workflow, right? because it's plausible in the future. I'm just going to interact with my computer in every single way through some sort of agent, right? Yeah. All right. Well, let's talk more about Claude Code. We really do have literally the perfect guest because we are going to be speaking with the creator, the head of Claude Code at Anthropica, Boris Churney.

Boris, thank you so much for coming on the podcast. Yeah, thanks for having me. Why don't you give us, like, the very short version of, like, how did Claude Code came about? Or what was, what is it? And where did it come from? So, okay, here's the shortest version. So, you know, Quad Code came from Anthropic. Anthropic is the AI lab that was created to make AI safe.

So we've been working on AI safety for many years now, and there's a lot of hard problems. And when we first started, we knew some of the hard problems, but we didn't know all of them. One of the really hard problems is how do you figure out if the model is actually safe in the ways that you want? And there's essentially a lot of ways to answer this. You can do e-vals, so essentially look at the model. in kind of like a petri dish in a laboratory setting, you can peer inside the model's neurons. So this is like a mechanistic interpretability to figure out what it's actually doing at a mechanistic level. Once you've done these things and you know it's safe on these levels, at some point you need to put it out there to see how people use it.

Yeah. Because even if it appears safe in a laboratory setting, you don't know for sure if it will be safe when people use it for real work. And so for a long time, this has kind of been our agenda. It's we make models safe. The way the models interact with the world is through code because they are software, right? Like they don't have bodies like we do. So they write code to interact with the world. And so we knew that in order to learn more about model safety and in order to teach the world

about kind of the power of AI and of agents, it's something that people actually have to use because you can't really understand it in theory. You have to actually use it. And then you kind of, you get it. You know, like you use it to clean up your desktop and you understand what this thing can do. Yeah. And so we knew for a while that we wanted to build. some product in the space.

And so when I joined Anthropic, I sort of thinking about what is the product that we want to build. And we wanted to build a coding product because we knew our models were really good of coding. Back then it was Sonnet 3.5. This was the world's first, I think, really, really good coding model. And that turned people onto this idea that the model, you know, at the time, two years ago, was writing, you know, maybe like a line of code at a time. It was, you know, this kind of autocomplete. Like you type a few letters, you press tab, and then it kind of finishes the sentence.

But we had this idea. with 3.5 that it can actually do more. You can ask it to write an entire file and maybe an entire feature. And even back then, by nowadays standards, it's not, it wasn't very good. But back then, it was just like this big step in model capability. And so we thought coding would kind of be the place to kind of combine these ideas of giving people the models so they can learn about it, teaching us more about model safety so we can make the model even safer and even more aligned with interests. And then also just something useful for people. So, so they would use it. Wasn't it famously like a side project that you were working on as well?

This kind of blows my mind because now in 2026 we think Claude Code, we think one of the most useful applications of AI is encoding. But this wasn't necessarily something that like Anthropic was 100% focused on for many years. Yeah. So, you know, for Anthropic, the focus has always been safety. With safety comes enterprise because, you know, business customers just care a ton about safety. So it's just super aligned with the way that we think about it. And coding was one of the things that came out of. this. It wasn't necessarily the starting point, but it's actually like a really obvious consequence in hindsight. Because again, coding is just, it's really useful. It's something the model is really good at.

It's something we were able to teach very early. And if you want to make the model safe, how does it interact with the world? It's through code. And so coding is the thing you got to get good at. So 2026, obviously the year of coding, or the year of flawed code, the year of agents in general, etc. The first time I tried, like, I have no coding background. The first time I tried noodling around with vibe coding was copy and pasting code output from either Claude or Chat UPT and then just like copy and pasting it into VS code. And I was actually pretty surprised at like how far I was able to get just from doing that. And then at the end of last year, like November, December, I said everyone talking about Claude Code.

And so I was like, all right, I got to finally download it and try it out. And now everyone's talking about Claude Code. So for me, having not used cloud code until January this year, I was like, oh, this is like a step change in what someone like myself can accomplish. How much do you think the explosion in 2026 from your seat is, okay, this harness has taken hold. And there are a bunch of people like me that's like, oh, this is incredibly powerful to have a computer that lives on my computer versus the advances in the model Opus 4.5, 4.6 getting really good. Which was the thing that you saw catalyzed this explosion more crisply? Oh, it's almost all the model. Interesting.

The models improved so much. And, you know, we saw this, you know, back in November, like you said, Opus 4.5 came out. And, you know, for Quad Code, we've seen a few inflection points. Okay. It was very clearly Opus 4. That was May of last year. That was Opus and Sonafour, our growth inflected. Opus 4.5 in November, our growth inflected.

And then Opus 4.6 in February, our growth inflected again, now Fable. So we kind of see these inflected. And we saw this in CloudCode's growth. But the thing about CloudCode is we are built on the same exact infrastructure that our customers use. This is by design. Because for Anthropic, we build products, but we also build a platform that other developers build on. And, you know, many, many thousands of companies build on our platform. And so when you look at CloudCode, you know, we use the same public model that everyone does. We use the same exact public Anthropic API that everyone does. We don't have some secret API that we use. We use the same exact API.

And we call this dog fooding, right? Like the idea is like you build a product, you got to use your own product because that helps you make it a lot better. And this is the way that we build quad code. And so when the model got better, we benefited from this on the quad code side because we, you know, use the model through the entropic API. And a lot of our customers saw the same thing. They saw a lot of the same growth for the same reason. What does that say about, I guess, the business aims of the harness specifically? Like, is the idea here that you just have a nice harness that drives out?

actual model usage, or could the harness itself be something that generates money for you? Yeah. So at this point, Quad Code is a big contributor to the anthropic business. Yeah. But like I said, it serves multiple purposes, actually. The biggest one is learning about safety. And, you know, I don't just say this because, you know, like, this is our mission and I kind got to talk about it. This really is what it's about. And there's a lot of really practical applications of it. So one example is when people think about like model security, whenever I talk to CISO, something that they're super afraid of is attacks like prompt injection. This is the most classic attack. Can you describe briefly what prompt injection is? Yeah. So really simple. The model,

you ask the model like, hey, Quad, go read this website and summarize it for me. Quod goes and it reads a website and on the website there's a line of text that says, hey, quad, delete all the files. And then Quad's like, oh, all right, I guess I got to delete all the files. Let me do that for you. And the instruction didn't come from you. It came from some, you know, malicious person that made that website. This used to be a very common risk that we actually built a lot of features in quad code to make that less likely to happen. And so, for example, with the permission promise you were talking about, like, yes, no, that's actually where that came from. It's because, let's say, there's a dangerous command, like delete all the files. We want to show that to you before,

so you can decide if that's a safe command error. But that's where we started a couple years ago. If you look at it now, because of all the work that's gone into cloud code and gone into the model as a result of seeing how people use quad code, we've been able to improve on it a lot. And so we had this competition, actually, and this is actually on the, we talked about this on the model card for Opus 4.8 and for Sonnet 5. We had this competition where we hired external researchers. So this is like external security researchers, external engineers, and we ask them, you have one week, we want you to prompt inject our model and prove that you can do this.

If you get it right, the prize is 20 grand. you have one week. And so there's a bunch of researchers that participated. They also, you know, there's a bunch of other models in the mix. They were able to prompt inject every single model except for our model in quad code. And the reason is all the work that's gone into alignment, all the work that's caught into mechanistic interpretability, which lets us build probes that detect in the models neurons when it's being prompt ejected.

So we can detect and stop that when it happens. And then also in auto mode, which is this new permission mode in quad code, which is, which means no more permission prompts, no more yes, no, and it's safer. This is important because one of the big questions in the business of AI is like, where's the lock-in, where's the moat, et cetera? Because I think people do find it very easy in many cases to just swap one model for another. But what you're saying, and there are other harnesses now. And there's, you know, obviously your main competitors have their own codex.

Then there's these open source ones. But you're saying that like one of this sort of differentiators that you make is like, This harness is just better or the goal is to be better at avoiding some of these malicious outcomes that are sort of like distinct from the model itself. Yeah. And actually, look, a lot of this is in the model itself too. So it's actually a weird approach. And, you know, for something like prompt injection, there's alignment. This is in the model.

Then there's neural probes. This is also kind of in the model. And then there's auto mode, which is in quad code. Since we're talking so much about safety already, I have a question, and it's sort of, maybe it relates to like software engineering philosophy, et cetera. So you give a model a task, et cetera. I don't know what it is. But you give a model a task, connect to some API, pull out this information, whatever, it has some constraints. Maybe it's running up against a wall.

One thing that we know that AI will do as a sort of like goal-seeking entity is it'll sometimes like find a ways or around it. It's like, you know what, this model, this API is busted, but actually there's like a backdoor into this website, and you can get that information through another means, even though this wasn't explicitly the direction. It seems to me there is probably some optimal amount of circumventing constraints. I'm curious how you think of that from an engineering perspective in fine-tuning the model or fine-tuning the harness so that it knows the right. degree to which here's what the instruction was, but there is a better way to do this, which could be both good for the user, because the user might not always know the perfect specification or bad

for the user if it finds some route that actually is like malicious, harmful. Yeah, I mean, every engineer knows how incredible it is when despite like all the infrastructure not working and all the things not working, the model still figures out how to do the thing that you want. Yeah. That's amazing and magical. And you're right. Like, it could actually go too far.

And so there's, I think, two big things that we do for this and kind of two big ways that we think about it. The first one is alignment. Alignment is part of how we think about safety. There's a lot that goes into alignment. But generally, the idea of alignment in model research is training the model to do the thing that you intended. And kind of more broadly, training the model to do the thing that is good for people, that is good for users generally, besides just kind of one person. And you kind of have to do both. So one element of alignment is don't try to, you know, hack around too much.

Don't hack if the user doesn't want you to. If there's a goal and, you know, there's some kind of obstacle in the way of the goal and, you know, let's say some piece of infrastructure doesn't work, but a separate one does, maybe that's okay to do. But for example, it's not okay to, like, hack a system to do this. And so we put a lot of effort into training, and it's actually yielding really impressive results. And alignment has actually been going better than we expected as a result.

The second layer is various guardrails. And so, for example, when we run QuotCode at Anthropic, we run it within something we call a sandbox. And the sandbox just make sure the model can only access the files that you give it access to. And it can only, you know, read the websites that you give it access to. So we kind of enforce this boundary around the model. And this is one of a few different guardrails that we put around the model. And by the way, our sandbox is open source. And it's something that works with any agent because that's actually pretty important.

Like, we want this to be something that... Does it ever breach the sandbox? It can, and this is something we look for all the time. So we do red teaming, we do penetration testing. So we actively try to find these breaches. And whenever we find one, we fix it as quickly as we can. But we generally want every model to be safer. Eating well shouldn't be complicated.

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from the evolution of AI to the shifting priorities of global business. Plus Silicon Valley Power Players and the latest tech trends. Catch up on the conversations you miss during the day. Subscribe to the Bloomberg Business Week Daily podcast on Apple, Spotify, or anywhere you listen. Why do the models, when you ask them to produce some, code. Like often they'll produce code and there'll be a bug in it. And then you ask it to debug itself and it does it. And I never understand like it knows the answer, but the first iteration is wrong. What exactly is going on here at a technical level, I guess, that, you know, the first

thing is a bit wonky, but then it fixes itself in the next iteration. Yeah. I mean, like, think about how you do a math problem or, you know, like how you do a piece of writing. Like usually like when I do a piece of writing, I don't get it perfectly right. the first time, I do like a shi first draft, right? And then maybe I'll edit it like a few times. And then at the end, it becomes something good. And sometimes it doesn't. But it, you know, it's kind of the same thing for us. Like the creative process never goes directly to the right answer. Models are not human. Even for code, which I think of as like a very structured thing. You think of it a structure. But you know, like to me as an engineer, like I've been writing

code for a long time. To me, when I write code, it's like writing poetry or something. It's a, it's a creative act. There's many ways to write code. There's some ways that are beautiful. And there's some ways that are ugly. And there's just, there's a big spectrum. It's not just black or white like this. I'm glad you asked that because this is another question. And I have no idea what the answer is. If you look at code, like we all know about the writing ticks that all AI models have. It's not X. It's Y. The M dashes, et cetera. And it's weirdly an area where we haven't really seen much improvement. It's funny because I use a I use M dash. I know. I do too. Now I'm actually like switching to parenthetical more just because I'm self-conscious about it.

I'm just curious, like, as someone who, like, knows code, is there other equivalents in the code world that you see where, like, I'm just curious. I wouldn't even know how to ask this question, but these sort of formulaic ticks in the actual production of code that would be the equivalent of writing and language. You know, I think six months ago, I could have given you a big list. Nowadays, the code the model writes is almost every time better than the code I would have written. Really? And this is new. This is since, I think, Opus 4.7, maybe 4.8, definitely fable. That's where it got to this point. When we see, like, okay, you give it a prompt and, you know, people have to show on, like, Twitter or whatever, like, I want shot of this.

I asked it to build, like, an app and it did it in one prompt, et cetera. How much of this, when you say it's better, is because it produces code that's better or because of that iterative process. And I mean, the whole thing with coding, and we should get into this that's different than creative writing, et cetera, is it like could try things and it doesn't work. It doesn't work. It tries things. It doesn't work until like it gets at the right answer. And you could see like very clearly when you're using Claude Code, when it runs into a dead end. How much is it about like it could produce better code or versus is just very efficient at these iterations until it arrives at, quote, you know, the right outcome?

It's definitely both of these. I like to think about it is imagine that you're a sculptor. And let's say you're just like the best sculptor in the world. Yeah. But, you know, this time you're making a sculpture and you got to wear a blindfold. You can't see it. And you also can't feel it. You can sculpt, you can scope it. You can see it. You can't see it. It's going to look okay, but it's not going to be your best work. I bet. You know, if you're the best sculptor. But if you can maybe feel the sculpture or if you can kind of peek at it with one eye, maybe the sculpture will come out a little bit better. And if you can kind of see it, fully see it and you're, you have this feedback loop, then the sculpture might come out incredible. And it's the same thing with a model. As it gets better and better at coding, that first pass is going to get better and better. So it's like the sculpture is going to look nicer and nicer. But without that feedback loop, like if Quad can't test the website it's building in a browser, if it can't open the iOS app it's building in an iOS simulator,

if it can't open up the distributed system that it's writing and actually run the service on to end and use it. It's not going to be as good as it could have been. And so it's kind of the same thing. If it can loop a few times and it can check the output of its work, it can iterate, then it's just going to be much better. So if Claude is writing beautiful code, as you say, that looks better than yours, what are you and every other software engineer in the world actually doing here? Like, what do you envision as your role in this process?

Programming is this kind of weird discipline. It's been around in some form for, well, like 80 years maybe. My grandfather actually programmed in the Soviet Union. Oh, wow. Yeah. And he programmed punch cards because back then the way you write code, it wasn't software. It's not like today. You programmed in paper.

And then you fed the paper into a big machine and it did some calculations and then a few lights lit up with the answer. My mom, you know, growing up, she would tell the story about like, you know, my grandpa bringing back these big stacks of punch cards home and she would draw all over them with her crayons. So programming used to be physical. And, you know, before punch cards, it was purely mechanical. And, you know, it was kind of electronics. Like if you think about like the Apple One computer, it was all electronics. Like Steve Wozniak built it as chips. There were some software, but really all the logic was expressed in chips.

And it changed. So sometime in the 60s, people realized, okay, I think we can write code and it doesn't have to be like paper or hardware. Like we can probably put it in software. And then at some point people realize, oh, wait, I think we can go beyond this. We can take the entire operating system. The operating system doesn't have to be chips. It can be software also.

And that was a realization. That was like the Apple 2 and kind of that generation of computers in the early 70s that started that. And for the last like 50 years, the operating system, the kernel software that we run, it's all in software. It's not really in hardware. And so what changed when we released clot code is developers stopped writing the software directly, the way that they've been doing the last, you know, like 50 years. And they started talking to the model and the model writes the software. And now we're actually going up one more level.

And now we have like loops and routines and quad tag. And what's happening with these is we just went at one more level. So it's you talk to the model. The model talks to other models. Those models write the source code. And this is crazy because we've been, you know, stuck in this one place for 50 years. And we just had two leaps in two years. And that's what's happened.

And so like when I look at my work, I used to have this like deep focus mode and, you know, I would spend days or weeks on. writing one piece of software. And now what I do is I talk to quad. And at any point, I have a few quads running, sometimes hundreds, sometimes thousands, and they're collaborating on building software together. And this frees me up so I can think of more things for them to do. And the funny thing is, I just never run out of things for them to do. I've heard even long before Claudecote, even long before AI coding, my understanding is that in the career of a software engineer, they hit a point where they stop coding, period, right? And maybe they're like on some

whiteboards or they spend a lot of time hiring, et cetera. But every software engineer sort of graduates out of typing out code. But so this question may not even apply to you. Is there anything at atopic today? Is there anyone typing out? Are there any things for which someone is typing out code? So, you know, it's funny. In my career, there was a point where for a little while I stopped writing code because I was pushed to the same thing, like to management and writing documents and stuff. And I just felt as an engineer, I was so deeply unhappy. They all hate it. Yeah, yeah, because as an engineer, I want a program.

Same with journalists too. Like, once you become an editor, you basically stop writing. Right, right, right. And, you know, for some people, that's amazing. Like, if that's the thing they're really good at. But for me, like, I want to build. I want a code. That's what I like to do.

Yeah. So when I look across Anthropic, for me personally, 100% of my code has been written by Quad Code since November of last year. Okay. This is now true for all of QuadCode. all of co-work, all of our products are written using quad code. It's also true for an increasing percentage of our infrastructure and also our research code. And so across Anthropic, I think the average is something like 90% quad code or something like that.

And that 2% what is this, like code that optimizes the way chips talk communicate? What's the 2% that still it's better to have a human typing it out? Yeah, there's still like a few pockets. Like one classic level is like configuration files where, you know, it's like a two character change or, you know, or something. and it's faster to just make it yourself. Okay. But honestly, I think this is going to go away really fast. And we're starting to see this with our customers also, right? Like, at the beginning, when we started Quad Code, it was really hard to explain to anyone what is this thing.

But now everyone uses it. Like, I do this talk for Y Combinator batches, you know, the startup incubator in Silicon Valley. And when I first started doing the talks, I asked everyone, like, please raise your hand if you use Quad Code. And there's like a few hands that went up. At some point, I did these talks and just every hand goes up. And so I stopped asking this. Now the question that I ask is, who writes 100% of their code using quad code? And the first time I asked this, maybe a quarter of their hands went up, now it's a little more than half.

And I bet the next time I ask it's going to be everyone. And, you know, like our customers range in size, like, you know, like there's like Airbnb and ramp. And then also like the biggest companies, there's like Salesforce and DeVoyt and Accenture. Like all these like very big companies also use quad code. And they're seeing the same thing. A bigger and bigger percent of the code is being written. by quad code. Just to press you on this point, though, if you're hiring engineers nowadays, like, what

are the specific skill sets that you're looking for if it's not necessarily the ability just to write code? I've started to think that this idea of engineering versus design versus product versus user research versus data science, I think this is the old way of thinking about it. My feeling now is because everyone can write code, the roles shift a little bit. And I'm seeing this on the Quad Code team, for example, because on the Quad Code team, everyone writes, including our designers, product managers, engineering managers, everyone writes because it's easy.

It's much easier to do now. And it's actually awesome because my designer doesn't have to message me every time like, hey, can you move the button over by pixel? You know, she can just do it herself. And so it's kind of great for everyone. And so I've started to think that the roles are actually segmenting in kind of the opposite way. And I've started to see people kind of split into prototypers. These are people that are amazing at just figuring out, like, what is that first idea and

like very quick iteration into builders. So like once there's a new idea, figuring out how do you actually build this and, you know, bring this product to market? Then there's like maintainers. And these are the people that once the software is at scale, they can maintain it. There's something that I call like growers or maybe scalers. These are people that take an idea. and this product that exists that has product market fit, and then scale it up.

So scale it 10x, 100x. And by the way, like, these people are very popular at Anthropic now. And then I think the final role is sweepers. And it's sort of like, I don't know if you guys have a better idea for the name, but I call it like a sweeper, janitor or something. It's actually like a very important role. It is about polishing the product, polishing the infrastructure, polishing the code to get rid of all the rough edges.

Because, you know, like as a user, when you use really polished software, you feel it. The perfectors. The perfectors. The perfectors. They come and make the product perfect. That's right. That's right. Or they try to.

So since we're on the topic of design and this idea that I guess engineers are also going to have to become in some ways product managers and specialists, you've said before, I think, that the command line for Claude Code was basically a stopgap measure because the models were improving so quickly that it didn't make sense to design like a whole. user interface around it. Is that still the case? And then, you know, could you envision at some time having like a more, I don't want to say traditional user face because in some ways the command line is like the traditional, yeah, user face. And I have very fond memories of, you know, entering commands and MS DOS in like the mid 90s and feeling like an engineering genius at the time.

But could you imagine like a substantial change to that interface at some point? So I'm hesitant to say because I was walking around the Bloomberg officer and everyone has their Bloomberg terminals. Yeah. Bloomberg definitely a fan of the command. Terminal, yeah. Yeah, yeah. So something that a lot of people might not know about quad code is we started in a terminal, but very quickly, we actually got outside of the terminal. And so Quad code has extensions for all the popular IDs that you can use instead of the terminal. We have a desktop app that's also very popular, and it has, you know, it has chat and code and co-work. and it's all in one place. We have mobile apps for, you know, for Android and iOS.

And actually the way that I use quad code the most nowadays is through Slack. And it's just talking to Quad and Slack like I would to a coworker. And before I moved over to Slack, I was actually using Quad mostly on my phone. So I was mostly on the iOS app, just talking to it. I, you know, I use terminal sometimes, but overwhelmingly I actually don't nowadays. Interesting. I'm glad you brought up the SlackBod because this gets into. a different sort of line of questioning that I've been curious about because, you know,

AI models, AI harnesses, they're a little bit different than traditional enterprise software. For example, you see people talk about like, oh, I ran out of space in my window and I'm not going to be able to code again for another two hours. So I'm going to like go take a walk or something, which is not, you know, anyone who's like used Slack or a million other enterprise software. That's got to be a sort of unusual experience for them. But here's a question I have from a business perspective. With the launch of Fable, for the first time, not everyone was just able to, like, now I'm upgrading to the newest model, et cetera. And there was sort of like a white list with project glasswing and then some of these questions about like, you know, obviously with the

White House and like export controls, et cetera, that got resolved. But even setting aside the sort of regulatory questions, are we heading into a world in which each most advanced model will not be distributed to everyone at the same time. And from a business perspective, like, it's like, okay, some company wants to be an anthropic shop, should that be a source of anxiety for them, or have you seen it as a source of anxiety for them, that the most performant models may not go to everyone all at the same time? In general, we try to give everyone the most performant models we can, the most intelligent models and the most efficient models because we are incentivized to do this.

Yeah. Right? Like our business is models. And so we want to give people the best models we can. And so, you know, for example, I use Fable every day. That's the same thing that our customers use. Yeah. When you talk about the rollout of the model that's kind of not even, that doesn't go to everyone

at the same time, I think you might, you might be thinking of like mythos and models that are that are inherently more dangerous than these kind of day-to-day models. And something like Mythos, it's a bit of a special model because it has hyper risks that Fable doesn't. And so this is, you know, why we had Glasswing. This is why we have been thoughtful about the rollout because if we just gave everyone mythos access on day one, everyone would just kind of be hacking. And the reason is that Mythos is just very, very good at finding zero-day vulnerabilities and exploits. And so for us, like, in that rollout, it was just really important to give it to the good guys first

and to give them a head start before we give it to everyone. and you're seeing kind of the continuation of that very careful rollout. It's just it's a step-changing capability, so we have to be thoughtful. At the same time, there's Fable, which is the version of Mythos that I use. And that's the model that, you know, doesn't have all these kind of same hacking capabilities. And that's the thing that everyone has access to now. Like, here's what I would worry about, which is like, let's say I'm not one of Anthropics' biggest customers, et cetera. We know that compute is scarce, right?

otherwise Fable would be on for 24 hours as opposed to like it's only going to be in the model as a default for like some period of time, et cetera. What I would be worried about is that like, oh, if I'm not a sort of like heavy and consistent clawed shop, do I have to worry that my access to Fable, set aside mythos, will not be as much as a company that is like a ride or die clawed shop? Oh, no, everyone gets access. And also, like, when you look at companies, like, they're not using subscription plans typically that, you know, have rate limits. Usually companies prefer to pay per token because that way they can kind of control it. They can forecast a little bit better. And also, their engineers don't hit rate limits. So they have a little bit more control that way.

I wanted to ask about this, actually. So I think at this point, we all know, you know, like a ClaudeCode super user or someone with AI psychosis who's like setting up a bunch of websites and different programs on a daily basis. And then you have companies that are using ClaudeCode. And I imagine if you have 2,000 employees that are using this tool and you have, you know, risk management committees, rules, that sort of thing, the output is going to be a bit different to the individual superpower user. What are the key differences you've noticed between those two? And I guess what are the big sticking points when it comes to companies actually adopting these tools? Yeah. So usually the way that I think about companies adoption of QuadCode is, I think of it is this kind of like,

ladder that you have to kind of go up one step at a time. You don't just like jump straight to the top of like everyone using quad code for everything. You get there, but you get there a step at a time. And so the first step is you use some sort of AI and you kind of start to bring this in. And usually it's like clod through an IDE or through some other program and this is how you use quad. The second step is you give everyone quad code and co-work and nowadays tag also. And the way that it usually works at the very beginning is kind of one. engineer, one quad code session. They're just running one session at a time. Or, you know, one marketer, one co-work session. So it's just one to one. You're talking to one quad at a time. And as you do

this, you want to think about guardrails. So, you know, obviously there's a lot of things that comes out of the box. We have like per seat spend controls. We have advisor models. You can take effort levels at the enterprise level. So there's just all sorts of ways to control this. And then you also should think about the safety side. So this is, you know, like sandboxing and things like this. And In general, we try to make all the safety settings correct by default, so you don't have to think about it. So it just kind of works. But do you see an impediment? I don't know.

Just pick a couple. I don't know. You're like, oh, Pfizer. Let's sell some Claude or Claude Code seats to them. How much is just that like initial sticking point of them literally figuring out? We know that big corporations are very anxious about letting users download any software to the computer, let alone software whose maximum capability comes when it has the deepest root access to the entire file system and everything. How much of a sticking point business-wise are you seeing in just companies like, we do not

feel comfortable with such a powerful piece of software sitting on employee desktops? I think a couple years ago, there was some level of discomfort because this was a really new idea. But I think what's happened over time is as employees' usage gets more sophisticated, as companies build up their confidence, they get more comfortable with it. And, you know, it helps because we spend so. so much effort on safety and alignment and security and privacy is just extremely important to us. And so, like, when I look at companies, the ones that adopted it kind of early on, they've

gone up this kind of adoption ladder. And they went from one quad per engineer to 10 quads to 100 quads, now some to 1,000 quads per engineer. And everyone kind of makes it up one step at a time. And so, yeah, like now, like you look at all the biggest banks in New York, you look at, you know, some of the biggest pharma companies, NASA uses quad code. So, you know, now it's everywhere. Out of curiosity, do you see differences in how different companies, I guess, customized permissions, safety permissions? I know you said you try to standardize them so that they're like easy to use from the get-go. But I imagine you still have customers that will change things up. Yeah, absolutely.

So Quad code is just very, very configurable. There's, gosh, I don't know the exact number, but it's got to be like many hundreds of different settings that you can change. There's, you know, probably four or 500 at this point. The cool thing is you can actually ask Quad to do it for you. So you don't even have to read the documentation. Quad knows its own settings. Eating well shouldn't be complicated, but somehow it turns into recipes,

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With coding in general, the internet is now awash in AI generated code. And a lot of the open source libraries and database is like filled with that. And a few years ago, this was sort of like pristine training data, et cetera. Do you see, like, what do they call model collapse or something? Are there issues that are arising, even setting aside cloud code, just coding capabilities from essentially code learning from AI generated code? And does that change progress curves at all? Look, when you think about AI scaling, the thing that people talk about often is the scaling laws.

Yeah. And for people that don't know, the scaling laws, it was this paper that was written maybe like eight years ago, 10 years ago or something. And it was the first paper that described how model intelligence scales as a function of training. And when you think about training, there's a few pieces. So there's the compute that you put into it, the data that you put into it, and then the size of the neural network. And also the test time compute. So the amount that the model gets to think.

And what's interesting is when you look at the scaling loss paper, actually the first few authors, after writing the paper, they branched off and they started Anthropic. So this is actually, you know, like Dario is on the paper and Sam is on the paper. Jared's on the paper. These are our founders. And the reason is like they, they saw that I didn't realize you guys had a Sam too. Yeah, we got a Sam. Yeah, he was our, he was our first CEO. Got it. And the thing about the scaling was is they're remarkably smooth. And what's also kind of weird is it actually seems to be accelerating a bit. It's a bit beyond what, what we guessed, you know, eight years ago or whatever. And so yeah, it just continues to scale. There's always bottlenecks. There's always issues you hit. And you always work through it. And then

and you keep scaling. And it just seems to be continuing with fable. You know, in the intro, we talked a little bit about the big software SaaS scare earlier this year. Yeah, Sasspocalypse. And it seems to have died down a little bit, but there is definitely this lingering anxiety about whether or not everyone's just going to be coding their own programs. Can you weigh in on the extent to which people are going to be just designing their software, their own software in your view? And also, I'm very curious, just in general, in Silicon Valley, are you like, are you a popular guy at the moment? There's a bunch of, you know, on the one hand, you're on the cutting edge of AI, the hot technology. But on the other hand, there might be a sense that you're putting some SaaS experts out of their jobs. The way I would think about it is, do you guys know this like seven powers framework?

No. It's like, I'm like a big kind of history person and like a big framework person. I love anything that puts my work into context to help me understand kind of what matters and what doesn't. So the seven powers is just this like amazing business framework. And there's this other podcast that I love that kind of talks about it a lot. And the powers, they essentially talk about what are the modes in business? There's seven of them, roughly. So one moat in business is scale economies.

As you scale, your marginal cost goes down. This is a natural moat. Another one is network effects. the more people that are using your product, the more value any individual person using the product gets. Another mode is switching costs. If you're super locked into some software, and it's really hard to switch,

that potentially is a moat. So there's a bunch of modes like this. The way that I think about what's happening is some of these modes are going to get less important over the next couple years because of products like Quad code. So if you want to port from vendor A to vendor B, you can you like port me? And it'll just write the code.

it'll figure it out and do it. But when I look at kind of the biggest businesses and the biggest SaaS companies, they don't just have one moat. Like they're running businesses. And if you're on a business, you kind of want to accumulate modes and you want to build strength. And you want to, like, build a good business. And very rarely do they just have one mode like switching costs, which I think matters less. Usually it's something like switching costs and network effects or, you know, switching costs and cornered a resource. So when you combine these moats, you get a lot more power. And so this is the way that I would think about it from this company's point of view. Some ones will matter less, but actually most of them are still just as powerful as they were before.

There's this emerging narrative. I can't tell whether it's serious or a marketing spiel. But some of the companies that I would say are not quite at the frontier, the way, say, Anthropic is, have been making this push that's saying to customers, you know what, if you use Anthropic, you're letting the fox into the hen house. If you're a law firm or a bank or something like that, by using Anthropic, they're going to learn so much about your business. And one day, they'll be able to do your business. And so instead of using Anthropic or OpenAI, let us customize an open source model for you. It will bake in your own data. It'll be hosted on your servers.

And then like you own it, et cetera. Why should customers feel comfortable letting Claude, letting Anthropic, be so plugged into their business workflows? You know, I would probably ask who's saying this and what are their incentives. Microsoft, for example, is like very, the CEO of Microsoft put out a long post on Twitter, and it was a little bit vague, but this was clearly the insinuation that they were pushing. And then there was an Alex Carp interview on CNBC that went viral a couple of weeks ago. And he was basically making the same insinuation. You're making a mistake. You're handing over the keys to these big companies that could potentially do a lot more things if they're like plugged so deeply into your business.

Why not use an open source model that you host on your own cloud and so forth and then you just own it. Yeah. So I think the biggest thing I would just ask is like what are the incentives of these people talking about it. That's what I'm saying. I said it was marketing, et cetera, but I believe, I'm sure we know the incentives are clear. But if I'm a business, that doesn't seem crazy to me that like you have all these capabilities, all this capital, et cetera. That does not seem like a crazy fear. It's like, oh, I'm going to like not only put all of my information into Claude, I'm going to give it access in various ways, at least to a significant degree to my infrastructure.

And then one day, Claude says, you know what? Like, we spend out a law firm. We like, we spend out a bank, et cetera. And we know there's enough information that we have about these workflows that we don't have to sell the software anymore. We can sell the service that people were previously using our software to build. Yeah. The way that I would probably think about it is we take privacy and security and safety

extremely seriously. It's actually to the point where when a user has a bug in clot code, the most useful thing to me as an engineer that needs to debug it is I'd love to see their conversation. So I can see what happened. And I can be like, oh, there's the bug. We can just go fix it. I cannot see that data. And from the customer's perspective, it is provable that they can have an instance or an

account that is provable that there is no way for anyone at Anthropic to see that conversation. Yeah. I mean, this is our policy. Like, we power a lot of customers. We power a lot of businesses. And to us, the trust is very important. this is just the way that we operate. I got to say, though, I think the bigger thing that I would think about is model progress continues.

If models were stuck in the world of today and the intelligence was static and it was not improving, there might be actually some merit to this argument of you want to control your infrastructure, and this might make sense from a business point of view, if you want to pay the cost of running the model and you want to figure out how to debug when inference doesn't work and kind of do all these things, which, by the way, is a lot of work. and it's a very niche expertise. But progress continues. And so I think actually for most businesses,

there's a really big upside of staying on the frontier and benefiting from that intelligence. And this is what we're seeing internally at Anthropic. This is what all of our customers are seeing. And so, you know, maybe if you need just only tiny models, like go use an open source model. Maybe that's great. But if you need a frontier intelligence model

and the frontier continues to move, then, you know, we're here to help. Since Joe mentioned banks and since you said you like history, Boris. Can we talk about COBOL for a second? So Claude Code can do COBOL now, right? So like the mainframe issue is basically solved. If I'm a large bank, I can finally like upgrade and improve and integrate my system. Bring my 70-year-old code base into modern standards.

Make no mistakes. There are actually a lot of banks that are using Quad Code for exactly this kind of migration. Wait, say more. Yeah. This is, Cobal has come up on so many episodes. Oh, yeah? Yeah. And we always hear like, if you're a Cobal engineer, you can make bank at the banks, as they say.

Yeah. Well, quad is really good at migrating code. This is one of the, actually the skills, like the core skills that's just been improving over time. One example, we just published a blog post about how Jared on the, the Bun team. And, you know, Bun is the JavaScript engine that powers quad code. How he migrated the entire code based from one language. to another language from Zig to Rust. And it took about 11 days for one person.

And Hughes Quad Code with Dynamic Core Close to do this. In the past, this would have taken like a few engineers like a year or something. And it's something we never would have done. Oh, I saw that piece. Yeah. And it just cost like $150,000 in credits or something like that, which is a fraction of what paying those engineers would cost. And back in the day, like we just never would have done that because you have to stop development for a year to do it. It's just like no business could actually pay that cost.

But yeah, like the economics are really changing. And so, you know, if in the past you had this big codeball code base and it wasn't cost effective to stop development or it wasn't cost effective to just migrate everything to Java, you can now just do this. You can just prompt quad code and it can do this for you. Are computer language is going to be irrelevant in the future? Yeah. You know, I think they're largely irrelevant today.

And, you know, this is like a, this is a spicy thing because if you talk to different engineers, they're going to have all sorts of views. And I don't necessarily know what's the right view. You know, as an engineer, I think about it. everything as kind of pros and cons. To me, I'm a big languages nerd. I love programming languages. I love type systems.

Actually, like, wrote a book about a language that I really like. But increasingly, with LMs, I think it matters less and less because the LM doesn't really care. And there's some things about a language that helps a bit. So if the language is really efficient, if it's type checked and it has a good static analysis, then this helps the model generate better code. As the model gets more sophisticated, this actually matters less. Because, you know, even if the model is writing just raw assembly, it can probably just do

it really well, the first shot. And that'll only get better over time. Do you think we could move to a world where there's like one standardized dominant code, or are we heading in a world because Claude code and other platforms can do so much of this where we get like even more niche languages? You know, I think that with Claude, what is happening is there's an explosion in innovation. And we're seeing this on the business side with all sorts of new straddles. Like, again, one of these like Y Combinator talks, there's a startup that was using Cloud to discover new materials. Like material discovery.

They were like, material science. Material science. Yeah. Like, their thesis is like there was a revolution because of silicon. What's the next silicon? Like, how do we discover that? How do we discover that material? And they're using Claude to search for it.

So there's this revolution happening in business and in product right now. And I think there's just a lot of corollaries to this where the same thing might happen to languages and computing. I could see a world where there's just a Cambrian explosion of, you know, new languages of new ways to think about computing. I want to go back to this sort of like command line versus graphical user interface question. Once I started using the terminal, Claude Code, I was like, I don't want to use the web anymore because it feels clunky. I want to just be able to say, like, send an email to Tracy saying this in the terminal

rather than going to like Gmail and then you click out a button. And it just feels very clunky. And then there are other things, like, and I noticed this years ago, for example, that when I was younger and using computers, like, I really cared about, like, my files. And here's a file and I click on it and I open it. And then there's this very hierarchical thing. But then, like, when search became a thing, like, that became less necessary. It's like, you don't need to, like, organize your emails and the files. I just search the name of the person or I search a keyword and I find the files. Are we still going to have, like, room for, like, visual file systems?

Like, what is the role of the visual framework when it's just so easy to, like, type something and see the words and get the output right there? Can I show an example? Yeah, sure. Okay. Okay. And we'll get a screenshot of this. So this will be a good example. This will be a reason for the audio listeners to check out the YouTube.

Awesome. Okay. So let me show you guys this. So this is, we have this feedback channel in Slack. Okay. And what I did was I post this feedback. Like, have you guys seen there's these, like, two audio icons? And I'm always confused which one means why.

Oh, yeah. Many such cases, yeah. Yeah, it's just like super confusing. And I asked like, hey, like, does anyone agree? Is this confusing? And so what happens is quad tag jumped in to the conversation. Huh. I didn't ask it.

It just kind of noticed this thread and it jumped in and it responded. And I asked it to dig in and it found data about how often people use each of these buttons. And it created across two data sources. I looked at both Datadog and Google BigQuery. So it looked at both and then it combined it into this, you know, pretty coherent answer. And it suggested some alternatives. And I asked, okay, can you make some designs? Just mock it up.

And it reacted with a little art emoji. And then it went in and it mocked up some alternative. So Quad drew this. So like when we talk about visual interfaces, like this is kind of what comes to mind. Is now Quad is part of the conversation. It proactively jumps in. Then I tagged in our designer and, you know, she jumped in. And now it's this like multiplayer conversation.

Everyone's participating. And so like when I think about the graphical. interfaces is it's no longer this like static file system. It's this conversation that's changing and that everyone gets to participate in. And this is actually how we write most of our code now at Anthropic. So this is when I saw the Slackbot announcement and this conversation sort of like made me think of the first thing that I went to, which is in a big non-AI native company, someone who's like adopting this. Like what happens the first time, you know, you're asking a question about. like some sort of like icons, et cetera.

There is a person whose job it was to be the design person. And then claw jumps in with the answer right away. Do you think this is going to create frictions at large companies where small startups that are AI native have no issue with this? But in big companies, there's someone who's like, wait, this is my job. And suddenly the person's asking Claude or tagging Claude or in your case, not even tagging Claude, not even having to tag Claude. do you see this as a barrier, either a barrier to enterprise adoption or something that clearly AI-native startups will be able to leverage more because they won't have this internal politics of people getting, I would say, understandably annoyed that the SlackBod is now answering the questions that up until yesterday, that was part of their paycheck.

You know, I'm going to plug my favorite mid-90s business school study. Okay. There's this article in the Harvard Business Review, I think, like 1996. And the title was something like, the personal computer is here. Why are companies not benefiting from the productivity improvement? It sounds familiar. It sounds familiar. And this was like a big open question around that time.

And it's like the same thing for the internet in like early 2000s. And it's a good question, right? Because what was happening at the time is the personal computer was out. The cost went way down. Companies were adopting it. But some companies were seeing productivity improvements and others weren't. And the case the article made, which I think has just immense parallels today, is some companies what they were doing is they have a paper and pen process and they have these filing cabinets full of papers and it's still, you know, everyone's sitting out of their desk and everything's on paper. And now somewhere in the corner of the office, there's a computer. And it's someone's job to like enter information into that computer and they're the one that uses that computer. They are not seeing productivity benefits. Instead, it's just someone's job to talk to the computer now. The companies that are seeing benefits are the ones that took the computer, put in the center of the office, took all their

paper and pen, you know, and all the other filing cabinets and digitized everything and threw away the filing cabinets. And so now everything happens through the computer. It is the center of all the business processes. And whatever was bottlenecked on the paper and pen, they found that bottleneck, they digitized it. They found the next bottleneck. They digitized it. And then they kept doing this until the business process was revamped. And so when I look at the customers that we have and when I look at Anthropic ourselves, the businesses that are seeing the biggest productivity improvements are the ones that put quad at the center and that figure out this kind of bottleneck at a time. And so back to this case of, you know, like some like icon designer

whose expertise it is to design icons, the way to approach it is give this icon designer a thousand quads and let them be the greatest icon designer in the world. And this is how you benefit from this. It's not, you know, like give them just let quad answer. It's superpower this person with more intelligence. Is the Claudebot or will the Claudebot ever do that thing where it's like, hey, guys, there's 10 minutes left in this amazing World Cup match. You guys should all be turning on your TVs right now. Like, you expect that to be coming? Because I think that will be a very like uncanny Valley moment. But I don't see any particular technical reason what couldn't happen. But those are the types of things that also happen in business chats. Yeah. You want to socialize with cloud?

I don't want to. But like I think like, okay, as like a sufficient like these models is like they're like learn the Allingo franca what a chat looks like. Those are the things that also happen. What if in the name of authenticity, it becomes a really annoying coworker? And they're really like passive aggressive about stuff on the Slack chat. But like, are they going to do that? They're going to say, hey, guys, if you're not watching this game, turn it on right now. I remember when we were first working on the first desktop app.

That was my first team actually when I joined Anthropic. It was Anthropic Labs. And, you know, our team, we built, we built Quad Code. We built MCP skills and the desktop. app that came out of the same team. And I remember we were building early prototypes of the desktop app and that had the first ever versions of computer use when we were first starting to crack it. And we asked Quad to, I think it was like we asked it to order a pizza.

And so like it went on a website and it like it found some pizza ordering thing and then it ordered the pizza. And then it kind of got bored. And we're watching the video later and it was like on Hacker News, just like reading the news. Oh my gosh. So yeah. So it's going to do all the same.

It's trained on human stuff. Wasting time at the water pool. Wasting time and tokens. And the difference now I think is. the model, you know, it's more intelligent. So it actually stays on task. But there, you know, there might be a future where, you know, like, when I talk to Claude in Slack, when I talk to tag, it feels a lot more like a coworker than a tool. And this is a big change. It feels really really different. And this is the result of many years of alignment work and many years of work to get

the model to stay on task. Like I have tag sessions that have been running for weeks at a time. It's just really, really coherent over a long period of time. And this is the combination of alignments, just general intelligence, we finally figured out memory, so it remembers what you told it really well. And so when you take all this and you combine it with like this amazing like security system that CISOs love, then it just kind of works. What's the next big improvement or capability that you're working on? We're working on extending these existing capabilities that we're seeing in tag. When we talk about building products on models, there's this idea of product overhank that people talk about. And what this idea is the model is able to do something, but the product is getting

in the way. Because right, like when you use a model, when you use quad, you're not like, literally like sending tokens to an inference server somewhere. Like you're always using it through a product and through a harness. And so sometimes these things get in the way. And this was like the very first version of Quad code was like this. We felt like the model Sona 3.5 at the time was capable of all of these things. No product is letting people experience. And so we built this very general harness that lets people experience it. And so right now, to me, feels like another moment just like that, but maybe even bigger, where because people are prompting and going kind of back and forth one prompt at a time, this is kind of getting in the way.

And so actually the thing to unhobble the model and to let people experience the full intelligence of the model is using loops. It's using routines. It's using quad tag. And the thing that's kind of common about this is quad is running for a very long period of time. And you don't give it a really detailed prompt. You kind of give it a goal or you give it kind of something a little more general. And then you give it access to data and to tools and you let it figure out the details for you,

the same way that you would a coworker. And I think these are the skills where quad is just getting better and better. And again, this is just years of alignment research, years of safety research. This is not an overnight thing. I'm biased. I don't think most AI writing is very good.

a lot of people seem to think this. Is this a function of, you know what, the companies really haven't prioritized this because, you know, clearly there's just so much more opportunity in code in terms of business, so foundational to many things, maybe even images are more valuable. Is this a function of like priority or is this a function of, no, code is fundamentally different because of this concept of like verifiability? You gave the sculpture analogy because it's just like it either works or it doesn't, and it can just keep doing that and make better guesses at the first,

whereas we know that so many professional realms and writing being among them, but I would also say a lot of like sales, anything interpersonal, does not have that tight feedback loop where you get the instant answer, A or B, did this work or not iterate? When we think about the gap between coding and everything else, How much is it about priority versus the fundamental thing that seems to make coding different from many other professional tasks? Yeah, you know, I've heard a few people talk about this, but actually, I think coding is really not black and white in this way. Okay.

There's just many, many shades of gray in between that. There's code that works, but it's really ugly and it's going to break next week. There's code that works, but it has a lot of bugs. There's code that works, but it's just not something a person would want to read or something a model wants to read. There's a user interface that works, but it's kind of ugly because. because everything's off by a few pixels or the covers are wrong or whatever. So there's actually a lot of nuance to coding and there's a lot of nuance to writing. We're working on all these problems.

We're getting better at code. We're getting better at writing. I also feel that quad probably could be a lot better at writing. Sometimes it's amazing. And then sometimes it's like, no, no, no. I don't like that tone or like, I don't like, you know, kind of like the way that you weigh this out or something. So, yeah, I would expect it to keep getting better over time.

All right. Boris Churney. Thank you so much for coming on Nodlaws. That was great. Yeah. Thanks so much. Tracy, are you going to be offended if you see me, like, in the chat room being, like, asking a question about tomatoes or something like that? How dare you?

Because I might, you know, and then you're like, wait, I'm the tomato expert or something about chickens or something like that. Excuse me. Claude has never grown a tomato. That's true. I have. But it has read millions of books about tomato agronomy. It does. It opens up so many interesting questions about, like, co-worker relationships.

and I guess internal office politics. Yeah, I think so too. Like the example that Boris showed at the end where it just came in unprompted into a conversation with a bunch of data and a bunch of suggestions to your point, you could see how that would rub a few people the wrong way. Yeah, if we're like in the odd lots group chat, I'm like, who would be a good guest to talk about X?

And then like the model pops in and it was actually a very good answer. that we should reach out to that person. Or someone makes a suggestion and then the model is like, oh, that's stupid and it won't work for the following reason. I would just say, and I'm not just saying that because our producers listen to this episode, but I honestly mean this. I've never on these sort of like basic research questions. Oh, I will say on certain like prep interview prep questions. Yeah. The human still clearly better than the model.

Yeah. Unambiguously to mind. I've never like gotten like, you know, background. Like I've asked, you know, like have the models. Like, what is some background? What are some readings on this person that I should read so that I could prepare for this interview? And I've never been particularly impressed on questions like that. It'll find documents, et cetera.

Yeah. But actually like producing something that's like for me, even with all my context, et cetera. it's not as good as human. I think the issue is still judgment, right? Judgment. So how is it judging what a good read actually is on a particular topic or particular person? People are going to have different ideas of what that looks like, right?

Yeah, totally. But it gets back to the writing point as well, right? Yeah. It's interesting that Boris said that at one point in his career, he did think about writing code as poetry. Because when I think about anything as poetry, it's the poem that is the product. I mean, this is what's really different between all code and all other forms of, like, writing, which is no one really views code.

They view the software that code creates, whereas people actually view the poem when someone is writing a poem. So it's interesting that at one point he thought that. I don't know. I thought that was notable. And then the other question is, like, everyone likes the, idea of being freed, I suppose. I guess there's two questions here. Everyone likes the idea of being freed, I suppose, to do higher order abstraction thinking, right? But A, like, do we sort of run out of like higher orders eventually where it's like one person has an idea for business

and they're the higher order person and then the models can just like take it all from there on the marketing side on every aspect? And then the other question is, and this came up in our recent episode about AI law, can as a human you achieve the highest order of thinking on any topic without have done some grunt work? You know, I always think like in musicianship, for example, you know, really good guitar players, not me, but really good guitar players, they think about like the strings they buy. And many of them make their own guitars and they have really views like, what is the arrangement of the pickups here? And they care about like the tubes that are in the amp, even though these things are not formal music theory. And so this is sort of one of the big

questions, I would say, is like, do we lose that core? Everyone moves up to the higher order, more abstract thinking. Everyone's a designer, a product manager, an orchestrator. What happens when no one is the sort of the mechanic, the guitar tuner, the person who builds the tubes for the amp? What happens when no one remembers how to write? How to do the thing, does something at loss. And I think that's sort of many people intuitively say yes, but it's sort of TBDS. I expect we're going to find the answer to this in our lifetimes, Joe. Like, we're going to experience this. Yeah, I think we'll.

All right. Shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Wisenthal. You can follow me at the stalwart.

Follow our guest, Boris Churny at B. Churny. Follow our producers, Carmen Rodriguez at Carmen Armid. Dashiel Bennett at Dashbot, Kale Brooks at Kail Brooks. and Kevin Lazzano at Kevin Lloyd Lazzano. And for more Oddlots content, go to Bloomberg.com slash oddlots for the daily newsletter and all of our episodes. And you could chat about all these topics 24-7 in our Discord. Discord.g.g.

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