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

17:37Tracy Alloway:Now a message from Meta, our sponsor. People are building things in America again. And America's Workforce Academy, built by Meta, is training the next generation of builders. This unprecedented program offers paid training for in-demand jobs like electricians, plumbers, crew leaders, fiber installers, and more. When participants finish the program, they are offered a job. Because the future is for everyone. Learn more at Meta.com slash America's Workforce Academy. Support for this show comes from Public.com. If you're actively involved in your portfolio, you probably catch yourself repeating the same actions.

18:09Boris Cherny:Buying the dip, manually sweeping idle cash, putting on a hedge. On public, you can now create AI agents that handle all these tasks on your behalf. Just describe what you want to do in plain English. Like, if the VIX hits 25, buy a put option on the S &P 500. Or, if my cash balance goes above$20 ,000, move the excess into my direct index. You approve of the workflow, and your agent handles the rest. Monitoring the market, watching for your conditions, and executing your strategies exactly as defined. An investing platform driven by your intent, not just your clicks. You can also get full read and write access to your account via the public API.

18:49Go to public.com slash market and fund your account in five minutes or less. That's public.com slash market. Paid for by Public Investing. Brokered services by Open to the Public Investing, Inc., Member FINRA and SIPC. Advisory services by Public Advisors, LLC, SEC Registered Advisor. Complete disclosures available at public.com slash disclosures. Ask yourself, what are your best people spending their time on right now? Expense reports, receipt chasing, month-end close that takes weeks. You become what you spend on, and that's not what you're building toward. Brex is the intelligent finance platform that eliminates that work before it starts.

19:29AI agents that handle the manual stuff automatically. so your team can spend their time on what actually compounds. It's time to get Brex AF. Learn more at brex.com slash AF. 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.

20:07Boris Cherny: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 shitty 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, 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 as structured, but you know, like to me as an engineer, like I've been writing code for a long time.

20:38Boris Cherny:To me, when I write code, it's like writing poetry or something. 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 a big spectrum. It's not just black or white like this.

20:49Tracy Alloway: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 is weirdly an area where we haven't really seen much. It's funny because I use M dashes. I know, I do too. Now I'm actually like switching to parentheticals 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 a language.

21:34Boris Cherny: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.

21:42Tracy Alloway:Really?

21:43Boris Cherny: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.

21:50Tracy Alloway:when we see like, okay, you give it a prompt and, you know, people have to show on like Twitter or whatever, like I one 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 Like could try things and it doesn't work, then it tries things and it doesn't work and tries things and doesn't work until it gets at the right answer.

22:22Tracy Alloway:And you could see like very clearly when you're using cloud code, when it runs into a dead end, how much is it about like it can produce better code or versus is just very efficient at these iterations until it arrives

22:37Boris Cherny:at, quote, you know, the right outcome? It's definitely both of these. The way 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.

22:46Tracy Alloway:Yeah.

22:46Boris Cherny: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, but you can't see it. It's going to look OK, 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 have this feedback loop, then the sculpture might come out incredible. And it's the same thing with the model. as it gets better and better at coding, that first pass is going to get better and better.

23:18Boris Cherny: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 an iOS simulator. If it can't open up the distributed system that it's writing and actually run the service end-to-end and use it, it's just 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 gonna be much better. So if Claude Code 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?

23:54Like, what do you envision as your role in this process?

23:58Boris Cherny: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.

24:30Boris Cherny: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 was 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. We can probably put it in software. And then at some point people realized, oh, wait, I think we can go beyond this. We can take the entire operating system.

25:03Boris Cherny:The operating system doesn't have to be chips. It can be software also. And that was a realization. That was like the Apple II and kind of that generation of computers in the early 70s that started that. And for the last 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 Quad Code is developers stopped writing the software directly the way that they've been doing the last 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.

25:39Boris Cherny: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, you know, at any point I have a few Quads running, sometimes hundreds, sometimes thousands, and they're collaborating on building software together.

26:16Boris Cherny: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. So I've heard even long before cloud code, 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.

26:37Tracy Alloway: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 anathropic today? Is there anyone typing out? Are there any things for which someone is typing out code?

26:58Boris Cherny: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.

27:10Tracy Alloway:Yeah, yeah. Because as an engineer, I want to program.

27:11Boris Cherny:Same with journalists, too. 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 to code. That's what I like to do. So when I look across Anthropic, for me personally, 100 % of my code has been written by QuadCode since November of last year. Okay. This is now true for all of QuadCode, all of Cowork. All of our products are written using QuadCode. It's also true for an increasing percentage of our infrastructure and also our research code.

27:41Boris Cherny:And so across Anthropic, I think the average is something like 90 % QuadCode or something like that.

27:45Tracy Alloway:And that 2%, what is this? Like code that optimizes the way chips talk, communicate? Like what's the 2 % that still it's better to have a human typing it out?

27:55Boris Cherny:Yeah, there's still like a few pockets. Like one classic example 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 QuadCode.

28:28Boris Cherny: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 QuadCode? 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 Deloitte and Accenture, like all these like very big companies also use quad code and they're saying the same thing.

28:59Boris Cherny: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 QuadCode team, for example, because on the QuadCode team, everyone writes code, including our designers, product managers, engineering managers, everyone writes because it's easy.

29:44Boris Cherny: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 a 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.

30:16Boris Cherny: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, you know, 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 or janitor or something. It's actually like a very important role.

30:46Boris Cherny: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. Call it the perfectors. The perfectors. The perfectors. The perfectors, yeah. And they come and make the product perfect. That's right, that's right. Or they try to. Yeah. 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 Cloud 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.

31:29is 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 in 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?

31:56Boris Cherny:So I'm hesitant to say, because I was walking around the Bloomberg office and everyone has their Bloomberg terminals. Yeah, Bloomberg definitely a fan of the command prompt. Yeah, yeah, yeah. So something that a lot of people might not know about QuadCode is we started in a terminal, but very quickly we actually got outside of the terminal. And so QuadCode has extensions for all the popular IDEs that you can use instead of the terminal. We have a desktop app that's also very popular and 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.

32:31Boris Cherny: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.

32:53Tracy Alloway:Interesting. I'm glad you brought up the Slack Bob, 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.

33:23Tracy Alloway: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 whitelist 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.

34:06Tracy Alloway: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

34:19Boris Cherny: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.

34:35Tracy Alloway:Yeah.

34:35Boris Cherny: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 be thinking of like Mythos. and models 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 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.

35:12Boris Cherny: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.

35:36Tracy Alloway:Like, here's what I would worry about, which is like, let's say I'm not one of Anthropik's biggest customers, et cetera. And 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 Claude 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 Claude shop?

36:13Boris Cherny:Oh, no, everyone gets access. And also when you look at companies, they're not using subscription plans typically that have rate limits. Usually companies prefer to pay per token because that way they can 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 Claude code 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 Claude code.

36:48And I imagine if you have 2000 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 tool 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?

37:09Boris Cherny:Yeah. So usually the way that I think about companies' adoption of quad code is, I think of it as 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 Claude through an IDE or through some other program. And this is how you use Claude. The second step is you give everyone Claude 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 Claude code session.

37:46Boris Cherny: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 Claude 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 proceed spend controls. We have advisor models. You can pick 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.

38:17Boris Cherny:So you don't have to think about it. So it just kind of works.

38:19Tracy Alloway:But do you see an impediment? I don't know. 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 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?

38:59Boris Cherny:I think a couple of 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 it helps because we spend so much effort on safety and alignment and security and privacy. It's just extremely important to us. And so 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.

39:30Boris Cherny:And everyone kind of makes it up one step at a time. And so, yeah, now you look at all the biggest banks in New York. You look at some of the biggest pharma companies. NASA uses quad code. So now it's everywhere. Out of curiosity, do you see differences in how different companies, I guess, customize permissions, safety permissions? I know you said you try to standardize them so that they're easy to use from the get-go, but I imagine you still have customers that will change things up. Yeah, absolutely. So QuadCode is just very, very configurable. Gosh, I don't know the exact number, but it's got to be like many hundreds of different settings that you can change.

40:07Boris Cherny:There's probably four or five hundred 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.

40:30Support for the show comes from public.com. If you're actively involved in your portfolio,

40:35Boris Cherny:you probably catch yourself repeating the same actions, buying the dip, manually sweeping idle cash, putting on a hedge. On public, you can now create AI agents that handle all these tasks on your behalf. Just describe what you want to do in plain English. Like, if the VIX hits 25, buy a put option on the S &P 500. or if my cash balance goes above$20 ,000, move the excess into my direct index. You approve the workflow and your agent handles the rest. Monitoring the market, watching for your conditions and executing your strategies exactly as defined. An investing platform driven by your intent, not just your clicks.

41:13You can also get full read and write access to your account via the public API. Go to public.com slash market and fund your account in five minutes or less. That's public.com slash market. Paid for by Public Investing. Brokered services by Open to the Public Investing, Inc., Member FINRA, and SIPC. Advisory services by Public Advisors, LLC, SEC Registered Advisor. Complete disclosures available at public.com slash disclosures. Ask yourself, what are your best people spending their time on right now? Expense reports, receipt chasing, month-end close that takes weeks. You become what you spend on, and that's not what you're building toward.

41:53Brex is the intelligent finance platform that eliminates that work before it starts. AI agents that handle the manual stuff automatically, so your team can spend their time on what actually compounds. It's time to get Brex AF. Learn more at brex.com slash AF. Discover a spectacular island destination with crystal blue seas, endless sunshine, and the cool Bahamian breeze. Baja Mar, located in Nassau, Bahamas, offers your choice of three luxury hotels, over 45 fine dining and nightlife venues, John Batiste's all-new jazz club, the Caribbean's most luxurious casino, and one-of-a-kind experiences for the entire family, like our 15-acre tropical water park, wildlife sanctuary, world-class golf course, and so much more.

42:38Visit BajaMar.com today.

42:41Tracy Alloway:With coding in general, the internet is now awash in AI-generated code, and a lot of the open source libraries and databases 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?

43:15Boris Cherny:Look, when you think about AI scaling, The thing that people talk about often is the scaling laws. 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 laws paper, actually the first few authors after writing the paper, they branched off and they started Anthropic.

43:53Boris Cherny: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 saw that.

44:00Tracy Alloway:Oh, I didn't realize you guys had a Sam too. Yeah, we got a Sam.

44:05Boris Cherny:Yeah, he was our first CTO. Got it. And the thing about the scaling laws 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 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 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, SaaSpocalypse. Yeah, SaaSpocalypse.

44:37And 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.

45:12Boris Cherny: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 just like 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 mode in business is scale economies. As you scale, your marginal cost goes down.

45:46Boris Cherny:This is a natural mode. 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 mode. 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 of years because of products like QuadCode. So if you want to port from vendor A to vendor B, you can ask Quad, hey, can you like port me? And it'll just write the code.

46:19Boris Cherny: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 in a business, you kind of want to accumulate moats and you want to build strength and you want to like build a good business. And very rarely do they just have one moat 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 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.

46:51Boris Cherny:Some of it will matter less, but actually most of them are still just as powerful as they were before.

46:56Tracy Alloway: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 henhouse. 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.

47:36Tracy Alloway: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?

47:51Boris Cherny:You know, I would probably ask who's saying this and what are their incentives?

47:55Tracy Alloway:I'll just say Microsoft, for example, is like very – the CEO of Microsoft put out a long post on Twitter. And it was a little bit like vague, but this was clearly the insinuation that they were pushing. And then there was an Alex Karp 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?

48:34Boris Cherny: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.

48:39Tracy Alloway: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, let's spin out a law firm. We spin 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.

49:20Tracy Alloway:We can sell the service that people were previously using our software to build.

49:24Boris Cherny: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 Claude 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.

49:46Tracy Alloway: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.

49:59Boris Cherny:Yeah. I mean, this is our policy. We power a lot of customers. We power a lot of businesses. And 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.

50:34Boris Cherny: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 maybe if you need just only tiny models, like, oh, use an open source model. Maybe that's great. But if you need a frontier intelligence model and the frontier continues to move, then 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?

51:10So Claude Code can do COBOL now, right? So the mainframe issue is basically solved. If I'm a large bank, I can finally upgrade and improve and integrate my

51:23Tracy Alloway:Bring my 70-year-old code base into modern standards.

51:27Boris Cherny:Make no mistakes. There are actually a lot of banks that are using quad code for exactly this kind of migration. Wait, say more. COBOL has come up on so many episodes. Oh, yeah? Yeah, and we always hear, like, if you're a COBOL 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 BUN team, and BUN is the JavaScript engine that powers quad code, how he migrated the entire code base from one language to another language, from ZIG to Rust.

52:04Boris Cherny:And it took about 11 days for one person. And he used quad code with dynamic workflows 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.

52:16Tracy Alloway:Oh, I saw that piece. Yeah, and it just cost them like$150 ,000 in credits or something like that. Something like that, yeah. Which is a fraction of what paying those engineers would cost.

52:25Boris Cherny: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 can actually pay that cost. But, yeah, like, the economics are really changing. And so, you know, if in the past you had this big cold ball 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.

52:46Tracy Alloway:Are computer languages going to be irrelevant in the future? Yeah.

52:49Boris Cherny:You know, I think they're largely irrelevant today. And, you know, 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 everything as kind of pros and cons. To me, I'm a big languages nerd. I love programming languages. I love type systems. I actually wrote a book about a language that I really like. But increasingly with LLMs, 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.

53:17Boris Cherny: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 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 one standardized dominant code? Or are we heading in a world because CloudCode 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.

53:55Boris Cherny:And we're seeing this on the business side with all sorts of new startups. Like, again, one of these like Y Combinator talks, there's a startup that was using Claude to discover new materials, like material discovery. They were like -

54:07Tracy Alloway:Like material science.

54:08Boris Cherny: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 new languages, of new ways to think about computing.

54:31Tracy Alloway:I want to go back to this sort of like command line versus graphical user interface question. And once I started using the terminal at Cloud 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.

55:08Tracy Alloway: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 into 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.

55:36Boris Cherny:Can I show you an example?

55:37Tracy Alloway:Yeah, sure. Okay. And we'll get a screenshot of this. So this will be a good, this will be a reason for the audio listeners to check out the YouTube.

55:45Boris Cherny:Awesome. Awesome. Okay. So let me show you guys this. So this is, we have this feedback channel in Slack.

55:50Tracy Alloway:Okay.

55:51Boris Cherny:And what I did was I posted this feedback. Like, have you guys seen there's these like two audio icons and I'm always confused which one means what. Oh yeah.

55:56Tracy Alloway:Many such cases. Yeah.

55:58Boris Cherny:Yeah. It's just like super confusing. And I ask like, hey, like, does anyone agree? Is this confusing? And so what happens is quad tag jumped in to the conversation. 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 I looked at both and then I combined it into this, you know, pretty coherent answer. And it suggested some alternatives. And I asked, okay, can you make some designs?

56:29Boris Cherny:Just mock it up. And it reacted with a little like art emoji. And then it went in and it mocked up some alternatives. 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 a multiplayer conversation, everyone's participating. And so when I think about the graphical interfaces, it's no longer this 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.

57:05Tracy Alloway:So when I saw the Slack bot announcement and this conversation sort of 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 ask 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 at big companies, there's someone saying, wait, this is my job.

57:44Tracy Alloway: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 Slack bot is now answering the questions that up until yesterday, that was part of their paycheck?

58:12Boris Cherny:You know, I'm going to plug my favorite mid-90s business school study. 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, you know, 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.

58:47Boris Cherny: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 at 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 it in the center of the office, took all their paper and pen and all their filing cabinets and digitized everything and then threw away the filing cabinets.

59:25Boris Cherny:And so now everything happens to 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 icon designer whose expertise it is to design icons.

1:00:01Boris Cherny: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 Claude answer. It's superpower this person with more intelligence.

1:00:15Tracy Alloway:Is the Claude bot or will the Claude bot 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.

1:00:38Boris Cherny:Yeah. You want to socialize with Cloud Code? I don't want to.

1:00:41Tracy Alloway:But I think, OK, as a sufficient—these models, they learn the lingua franca of 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? Yeah. And they're really passive-aggressive about stuff on the Slack chat. Are they going to do that? Are they going to say, hey, guys, if you're not watching this game, turn it on right now?

1:01:02Boris Cherny: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 Cloud 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 Cloud to, I think it was like we asked it to order a pizza. And so, like, it went on a website, and it found some pizza ordering thing, and then it ordered the pizza.

1:01:31Boris Cherny: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.

1:01:37Tracy Alloway:Oh, wow.

1:01:37Boris Cherny:So, yeah.

1:01:38Tracy Alloway:So it's going to do all the same. It's trained on human stuff, right? Wasting time at the waterfall. Wasting time and tokens.

1:01:43Boris Cherny:And the difference now, I think, is the model, you know, it's more intelligent. So it actually stays on task. But, 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 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.

1:02:15Boris Cherny:We finally figured out memory so it remembers what you told it like 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 tech. When we talk about building products on models, there's this idea of product overhang 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 when you use a model, when you use Cloud, you're not literally sending tokens to an inference server somewhere.

1:02:54Boris Cherny: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, Sonnet 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, it feels like another moment just like that, but maybe even bigger, where because people are prompting quad and going kind of back and forth one prompt at a time, this is kind of getting in the way.

1:03:26Boris Cherny: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.

1:03:56Boris Cherny:And again, this is just years of alignment research, years of safety research. This is not an overnight thing.

1:04:02Tracy Alloway: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 first.

1:04:44Tracy Alloway: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?

1:05:11Boris Cherny: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. There's just many, many shades of gray in between that. There's code that works but is 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 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.

1:05:42Boris Cherny: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. like I don't 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

1:05:59Tracy Alloway:right Boris Cherney thank you so much for coming on odd loss that was great yeah thanks so much

1:06:16Tracy Alloway:Tracy are you going to be offended if you see me like in the 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. 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. And 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.

1:06:59To your point, you could see how that would rub a few people the wrong way. Yeah.

1:07:04Tracy Alloway:For 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. It was actually a very good answer that we should reach out to that person. 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 listened 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's still clearly better than the model.

1:07:39Yeah.

1:07:40Tracy Alloway: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. 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, yeah. So how is it judging what a good read actually is on a particular topic or a particular person?

1:08:20People are going to have different ideas of what that looks like, right?

1:08:23Tracy Alloway:Yeah, totally. This kind of 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 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.

1:09:03Tracy Alloway: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 a 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 a 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?

1:09:44Tracy Alloway: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.

1:10:30Tracy Alloway: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 TBD. 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 will. 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 Allaway. You can follow me at Tracy Alloway. And I'm Jill Weisenthal. You can follow me at The Stalwart. Follow our guest, Boris Cherney, at B Cherney. Follow our producers, Carmen Rodriguez, at Carmen Armand, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks, and Kevin Lozano at Kevin Lloyd Lozano.

1:11:06Tracy Alloway:And for more OddLots content, go to Bloomberg.com slash OddLots. We have a daily newsletter and all of our episodes. And you can chat about all these topics 24-7 in our Discord, discord.gg slash OddLots. And if you enjoy Odd Lots. If you like it when we talk about Claude Code, then please have your agent leave a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

1:11:53Thank you.

1:12:19or spending time on month-end clothes? It's time to get Brex AF, a gentic finance that eliminates that work before it starts. Learn more at brex.com slash AF. Discover a spectacular island destination with crystal blue seas, endless sunshine, and the cool Bahamian breeze. Bahamar, located in Nassau, Bahamas, offers your choice of three luxury hotels, the richly refined Rosewood, the playfully hip SLS, and the stylishly modern Grand Hyatt. With over 45 restaurants, bars, and lounges, Baja Mar serves up delicious dining from world-renowned chefs like Daniel Bouloud and Marcus Samuelson, nightlife venues like the new Jean-Baptiste Jazz Club, and the Caribbean's most luxurious casino.

1:13:04At Baja Mar, you'll find every pleasure under the sun and one-of-a-kind experiences for the entire family, like Baja Bay, our 15-acre lush tropical water park, interactive wildlife experiences, including our daily flamingo parade, world-class golf, tennis, spa, and so much more. Visit Bahamar.com today and discover a vacation destination where memories are made for a lifetime. Bahamar. Life spectacular.

1:13:30Boris Cherny:The Hulu original series Furious is now streaming on Hulu and Hulu on Disney+. Starring Emmy Rossum as Alice Black, Furious follows a rookie FBI agent on the hunt for Catherine, played by Lola Petticrew, a mysterious and calculating female serial killer. While Alice upholds justice, Catherine kills for it, terrorizing the rich and powerful men of New York in her pursuit of vengeance. With secrets that change everything, Alice discovers there's a thin line between hunter and prey. Watch the Hulu original series, Furious, on July 27th. Streaming on Hulu and Hulu on Disney Plus for bundle subscribers.

1:14:08Boris Cherny:Terms apply.