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

0:00 I remember when we were first working on, the first desktop app. That was that was my first time, actually, when I joined Anthropic, it was Anthropic labs. And, you know, our team, we built, we built quad code, we built the MXGp 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.

0:21 And we asked quad to, I think it was like we asked it to order a pizza. And so like, I went on a website and it like it found some pizza ordering thing and then it ordered the pizza. And then I 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 God. Oh, wow. So yeah, that's going to do all the same. It's trying to waste time, wasting time and wasting time, wasting time and choking.

0:42 And the difference now I think is the model. You know, it's more intelligent. So it actually it actually stays on task. But, you know, there might be a future where, you know, like when I talk to Clod 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 it feels really different. And this was the result of many years of alignment work and many years of work to get the model to stay on task.

1:03 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 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 seesaws above, then it just kind of works. Hello, and welcome to another episode of the Odd Lots Podcast.

1:36 I'm Joe Weisenthal and I'm Tracy Alloway. Tracy. So I think the most embarrassing moment for me and go on, this is the most exciting way you've ever started a podcast, Joe. Maybe not the most embarrassing way. The moment I felt like I'm like making myself a little stupid or something like that in 2026 was, I, clawed code to, clean up all the menu screenshots that I had on my desktop. Oh. So I was like, just put the iPhone.

2:05 These told me you have all these, like, screenshots on my desktop, various charts, various charts and stuff. And I was like, cloud code, 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, etc. 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.

2:29 For me, that just seems efficient. But here's here's the big question did it do it correct? Yeah, absolutely. Yeah. It was 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 data and then admitted that it had violated its core principles in doing so, but didn't have an explanation as to why.

2:54 There's definitely been times in my 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 yes has never Joe pressing the enter button? No. I wish you could say hesitantly, I don't even think about it. I just like yes. So far no disasters from that. But you know, just like, yeah, I assume it's right. And maybe, you know, it's sort of like playing what's like the reverse, what's the reverse slot machine?

3:23 Or it's a 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 yeah, it's you know, obviously setting all this aside, I mean, I think 2026 has been in terms of software, the year everyone's talking about cloud code. Absolutely. So we also had the big market scare where we saw software companies get hit because there was this perception that cloud code would basically be able to do everything.

3:50 Yeah, there was like a day where, Anthropic like now it's like, here's something new. And I don't even think people who 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, etc.. But it does raise some questions like, you know, here's a big AI company. What will be the limits of you know, where they go, what kind of businesses they can get into and so forth.

4:14 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 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 cloud 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 Anthropic.

4:42 Boris Cherny. 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 how or where did it come from? So okay, here's the shortest version. So I, you know, Claude code came from Anthropic. Yeah. 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.

5:11 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, if you look at the model in kind of like a petri dish in a laboratory setting, you can peer inside the model's neurons.

5:30 So this is like a mechanistic interpretability to figure out what it's actually doing at a, 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 is kind of been our agenda.

5:52 It's we make model safe. The way the models interact with the world is through code because they are they're software. Right? Like you don't have bodies like we do. So they very potent react 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, of AI and of of agents, it's something that people actually have to use because you can't really understand in theory, you have to actually use it.

6:16 And then you kind of you get it, you know, like 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, when I joined Anthropic, 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 are really good of coding.

6:37 Back then it was sonnet 3.5. This was the world's first, I think, really, really good coding model. And that turned people on to 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 you can actually do more.

6:59 You can ask it to write an entire file and maybe an entire feature. And, you know, 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 the interest, and then also do something useful for people.

7:24 So so they would use it wasn't a famously like a side project that you were working on as well. This kind of blows my mind because now in 2026, we think Cloud Code, we think one of the most useful applications of AI is in coding. But this wasn't necessarily something that, like Anthropic was 100% focused on for many years. Yeah. So, you know, for for Anthropic the focus has always been safety. With safety comes enterprise because, you know, business customers just care written about safety.

7:51 So it's just super aligned with the way that we think about it. And, you know, 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. Is something we were able to teach very early and if you want to make the model safe, how does it interact with the world?

8:13 It's through code. And so coding is the thing you got to get good at. So 2026 obviously the year of coding the or the year of code, 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, you know, copy and pasting code output from, either Claude or ChatGPT and then just like, copy and paste it into VS code. And I was actually pretty surprised at like how far I was able to get, just from doing that.

8:46 And then at the end of last year, like November, December. So everyone talked about cloud code. And so I was like, I got to finally download it and try it out. And now everyone talk about cloud code in your view. Like so for me, having never having not used cloud code until January this year, I was like, oh, this is like a step change. And what someone like myself can accomplish. How much do you think the explosion in 2026 from your seat is?

9:13 Okay, this harness is taking hold and 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 4.54.6 getting really good, which was the thing that you saw catalyze this explosion more crisply. Oh, it's almost all the model interest that the models improve so much. And, you know, we saw this, you know, back in November, like you said, opus 4.5 came out.

9:40 And, you know, for quad code we've seen a few inflection points. Okay. It was very clearly opus four. That was May of last year. That was opus on sign up for our growth inflected, opus 4.5 and in, in November, our growth inflected and then opus 4.6. In February, algorithm again, now we're able So so we kind of see these inflection points. And we saw this in cloud code growth. But the thing about cloud code is we are built on the same exact infrastructure that our customers use.

10:08 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 company is built on our platform. And so when you look at cloud code, 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 dogfood.

10:31 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 cloud 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 Anthropic API. And a lot of our customers, all the same thing. They saw a lot of the same growth for the same reason.

10:50 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 actual model usage, or could the harness itself be something that generates money for you? Yeah. So at this point, cloud code is a you know, it's a big contributor to the to the Anthropic business. Yeah. But like I said, it serves multiple purposes actually. The biggest one is learning about safety.

11:17 And, you know, I don't just say this because, you know, like, this is our mission, and I kind of 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, if whenever I talk to see so something that they're super afraid of is, attacks like prompt injection, this is the most classic attack. Can you describe briefly what prompt the prompt injection is?

11:40 Yeah. So really simple. The model. Yes. The the model like you know hey quad go or read this website. Summarize it for me. Quad goes and it reads a website. And on the website there's a line of text that says, hey, quad, delete all the files. Oh, and then quad 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 you know me that made that website.

12:03 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 this, you know, the permission promise we're talking about, like, yeah, no, that that's actually where that came from. It's it's because let's say there was 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 or not.

12:25 But that's where we started a couple of years ago. If you look at it now, because of all the work that's gone into quad code and gone into the model as a result of seeing how people use quad code, we've been able to improve on a lot. And so, we had this, competition actually, and this is actually on the on we talked about this on the model card for opus 4.8 and first on it for sonnet five, where this competition where we hired external researchers.

12:51 So this is like external security researchers, external engineers. And we and we asked them, you have one week. We want you to prompt and direct our model and, you know, prove that, that you can do this, if you 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 and every single model except for our model, in quad code.

13:16 And the reason is all the work that's gone into alignment, all the work that's called into, mechanistic interpretability, which lets us build probes that detect in the model's neurons when it's being prompt injected, so we can detect and stop that when it happens. And then also in, auto mode, which is this new permission mode and quad code, which means no more permission prompts, no more guess. No. And it's safer. So so this is important because one of the big questions in the business of AI is like, where's the lock and where's the mode, etc.

13:47 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 the 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.

14:18 Yeah. And actually look like a lot of this is in the model itself. Okay. So it's actually a weird approach in, you know, for something like prompt injection, there's a there's alignment. This is the model. Then there's neural probes. This is also kind of a 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, etc..

14:43 So you give a model a task, etc.. 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 I will do as a sort of like goal seeking entity is a little sometimes, like find a way around it. It's like, you know what this this model, this API is busted.

15:08 But actually there's like a back door into this website and we can get 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 and fine tuning the model or fine tuning the harness so that it knows the right degree to which. Here's what the obstruction was.

15:39 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, networking and all the things not working, the model still figures out how to do the thing that you want to do that's amazing and magical.

16:04 And, you're right. Like it could actually go too far. And so there's a, there's, I think two big things that, 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.

16:36 So one element of alignment is it 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.

17:02 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 when we run quad code at Anthropic, we run it within something we call a sandbox. In 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.

17:27 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's never breached the sandbox. It can. And this is something we look for all the time. So we do rent 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.

17:50 But we generally want every model to be safer. Can I ask a question since we're on the topic of, models, sort of, doing workarounds in some ways, why do the models, why aren't they 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.

18:20 What exactly is going on here? And 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 you should be like when I, when I do a piece of writing, I don't get it perfectly right the first time. I do like a *expletive* first draft, right?

18:42 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. And models are not even for code, which I think of as like a very structured thing. You think of it as structure, but, you know, like to me as an engineer, like I've been writing code for a long time.

19:01 To me, when I write code, it's like, 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 are 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, the writing ticks that all AI models have, it's not X, it's Y, the dashes, etc.

19:27 and there's weirdly an area where we haven't really seen much. It's funny because I use, I use it, I know, I use, you know, I'm actually like switching to parenthetical more, just because I'm self-conscious about it. I'm just curious, like, as as someone who, like, knows code is there are there equivalents in the code world that you see where like, there's sort of a story, I don't know, like I'm just curious. I wouldn't even know how to ask this question, but these sort of formulaic tics in the actual production of code, that would be the equivalent of writing a language, you know, I think six months ago I could have given you a big list.

20:05 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. How much? When we see, like, okay, you give it, you give it a prompt and, you know, people, to show up on, like, Twitter or whatever. Like, I want to show this. I asked it to build, like an app and it did it in one prompt, etc..

20:32 How much of this when you say it's better, is because it produces code that's better, or because of that iterative process? I mean, the whole thing with coding and we should get into this. That's different than creative writing, etc.. Is it like could try things and it doesn't work that it tries things, it doesn't work. It tries thing, it doesn't work until it gets at the right answer. 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 at, you know, the right outcome.

21:11 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. 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, but you can't see it. It's going to look okay, but it's not going to be your best work.

21:29 But, you know, if you're the best color. 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.

21:49 So it's like the sculpture is going to look nicer or nicer, but without that feedback loop. Like if the if the 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 on to and, and and use it, it's it's not going to be as good as it could have been.

22:10 And so it's kind of the same thing if you 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 quad 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? Like what? What do you envision as your role in this process programing is this kind of weird discipline?

22:37 It's been around in some form for, well, like 80 years. Maybe my grandfather actually programed, back in the, in the Soviet Union. Oh, wow. Yeah. And he, he programed, punch cards because back then, the way you write code, it wasn't software. It's not. It's not like today you programed in the paper, and then you fed the paper into a big machine. And it did some calculations, and then a few lights lit up with, with the answer.

23:01 And, my, my mom, you know, growing up, she would tell the story about, like, you know, my, my grandpa bringing back these big stacks of punch cards home. And she would draw all over them with her crayons. So, so programing used to be physical, and, you know, before punch cards, it was, purely mechanical, and, you know, it was 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, as chips.

23:27 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. 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.

23:51 And that was a realization that was like the Apple two and the kind of that generation of computers in the early 70s that started that and the for the last, like 50 years, the operating system, the kernel software, you know, that that we run it's 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, you know, like 50 years.

24:17 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 up 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.

24:41 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 on building software together.

25:02 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 called code, even long before I 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, etc., but every software engineer sort of graduates out of typing out code. But.

25:33 So this question may not even apply to you. Is there anything of Tropic today? Is there anyone typing out other anything for which someone is typing out code? So, you know, it's funny, I in my career there was a point where for for for a little while, I stopped writing code because I was pushed to the same thing, like management and writing documents and stuff. And I just felt as an engineer, I was so deeply unhappy.

25:55 They all hate it. Yeah, yeah, because I don't know what to say with journalists. Like once you become an editor, you basically start 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 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.

26:17 Okay. This is now true for all of the all of quad code, 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 is going? What what what's the 2% that still, it's better to have a human typing it out.

26:46 Yeah, there's still like a few pockets. Like one 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?

27:06 But now everyone uses it. Like I do this talk for, for Y Combinator batches, you know, the startup incubator in, in Silicon Valley. 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 do these talks and just every hand goes up. And so I stopped asking. That's now the question that I ask is, who writes 100% of their code using quad code?

27:30 And the first time I asked this, maybe a quarter, 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 and ramp and then also like the biggest companies there's like Salesforce and Avoid and Accenture like all these like very big companies also use quad code and they're seeing the same thing.

27:51 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 rules shift a little bit, and I'm seeing this on the quad core team, for example, because on the quad core team, everyone writes code, including our designers, product managers, engineering managers, everyone writes, because it's easy.

28:37 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, prototype ers. These are people that are amazing at just figuring out like, what is that first idea?

29:03 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, you know, this product that exists that has a product market fit and then scale it up.

29:26 So scale it ten x 100 x. And by the way, like these people are very popular at Anthropic now. And then I think the, the final role is, sweepers and it's sort of like, I don't know if you gets a better idea for the name, but I call it like a sweeper or a 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.

29:48 Because, you know, like as a user, when you use really polish software, you feel the factors, the polish factors that make the product perfect. That's right, that's right. They try to. Yeah. So since we're on the topic of, you know, 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 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.

30:28 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 phase, 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?

30:57 So I you know, I'm hesitant to say because I was walking around the Bloomberg office or whenever other Bloomberg terminals. Yeah, yeah, Bloomberg definitely a fan of the terminal. Yeah, yeah yeah, yeah. So so something that a lot of people might not know about quite 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 Ides that you can use instead of the terminal.

31:22 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 Spock, like, like I would tell 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.

31:49 Just talking to it. I, you know, I use terminal sometimes, but overwhelming overwhelmingly. I actually don't. Now, it is interesting. I'm glad you brought up the slack bug, because this gets into a different sort of line of questioning that I've been curious about because, you know, AI models, harnesses, they're a little bit different than traditional traditional enterprise software. For example. You see people talking 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.

32:16 So I'm going to like go take a walk or something, which is not, you know, anyone who's like you, 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 on set. And there was sort of like a white list with Project Glass waiting.

32:42 And then some of these questions about like, you know, obviously with the white House and like export controls, etc. that got resolved, are we going? But even setting aside the sort of regulatory impact or the sort of regulatory questions, are we heading into a world in which every most and 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.

33:14 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.

33:40 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 kind of the, the, the roll out of the model, that's kind of not even that that doesn't go down. Yeah. 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 is, it's a bit of a special model because it is more, it has hyper risks that that fable doesn't.

34:11 And so this is, you know, why we had West Wing. 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 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 headstart before we give it to everyone.

34:33 And you're seeing the kind of the continuation of that very careful rollout. It's just it's a step change 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, it doesn't have all these kind of same hacking capabilities. And, that's the thing that everyone has access to now. But you don't see, like, by and large, you don't see and you're like, here's what I would worry about, which is like, let's say I'm not one of Anthropic biggest customers, etc.

35:03 we know that, compute is scarce, right? Otherwise fable would be on for, you know, 24 hours as opposed to like it's only going to be in the, in the model as a default for like some period of time, etc.. What I would be worried about is that, like, oh, if I don't like, if I'm not a sort of like heavy and consistent Claude shop, do I have to worry that my access to, Fable Sunnyside mythos will not be as much as a company that is like a ride or die card shop?

35:38 Oh, no. Everyone gets access. Okay. And also, like when you work 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 hate it really much. 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 cloud code super user or someone with AI psychosis who's like setting up a bunch of websites and different programs on a daily basis.

36:12 And then you have companies that are using cloud code. And 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 super power user. What are the key differences you've noticed between those two? And I guess what are the the big sticking points when it comes to companies actually adopting these tools?

36:36 Yeah. So usually the way that I think about, companies adoption of cloud code is I think of it is there's this kind of like ladder that you have to kind of go up one step at a time. You don't you don't just, like, jump straight to the top of, like, everyone using cloud code for everything you get there, but you get through 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.

36:58 And, usually it's like cloud through an IDE or through some other program. And this is how you use cloud. The second step is you give everyone cloud 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 cloud code session. They're just running one session at a time or, you know, one marketer, one co-work session. So it's just 1 to 1. You're you're talking to one quite at a time.

37:25 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 perceived spend controls. We have, advisor models. You can pick effort levels at the enterprise level. So there's just all, 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.

37:47 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, pick a call I don't know, you know, like oh, Pfizer here. Let's sell some, you know, cloud or cloud code seats to them. How much is just it like an initial sticking point of them literally figuring out, how do we know that big corporations are very anxious about letting users download any software to the computer, let alone software whose maximum capability comes on?

38:16 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 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.

38:44 And, you know, it helps because we spent so much effort on safety and alignment and security and privacy is just extremely important to us. And so like when I work 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 cloud per engineer to ten clouds to 100 clouds, now some to a thousand clouds per engineer. And everyone kind of makes it up one step at a time.

39:05 And so yeah, like now like you look at all the, the biggest banks in New York, you look at, you know, some of the biggest pharma companies, NASA uses cloud code. So, you know, it's 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, like easy to use from the get go. But I imagine you still have customers that will change things up.

39:31 Yeah, absolutely. There's, sort of cloud 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 4 or 500 at this point. The cool thing is you can actually ask quanta to do it for you, so you don't even have to read the documentation. Quite knows its own settings. This is the remarkable thing about AI.

39:51 It's like you just run into workflows. It's like, how do I fix this? How do I do this? How do I do this? And it, it it usually works with coding in general, obviously, like the internet is now a wash 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 etc.. Do you see, like what do they call it, model collapse or something?

40:20 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. Yeah. The thing that people talk about often is the scaling laws. Yeah. And for people that don't know, the scaling was it was this paper that was written maybe like eight years ago, ten years ago or something. And, it was the first paper that described how model intelligence scales as a function of training.

40:52 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 so the test time compute. So the amount that the model gets to think. And what's interesting is when you look at the scaling was paper. Actually the first few authors after writing the paper they they branched off and they started Anthropic. Okay.

41:12 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 I was like the they saw that I didn't realize you guys had a Sam to. Yeah. We got. Yeah. He was our he was our first CTL. Got it. Yeah. And the thing about the scaling was, is the remarkably smooth and, what's also kind of weird is it actually seems to be accelerating a bit.

41:36 It's a, it's a bit beyond what, what we just, 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 people. You know, in the intro we talked a little bit about the big software SAS scare earlier this year. Apocalypse. Yes, apocalypse. And it seems to have died down a little bit.

42:01 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.

42:27 But on the other hand, there might be a sense that you're putting some SAS experts out of their out of their jobs. The way I would think about it is, do you guys know this, like seven Powers framework? No, it's just like the it's like, I'm like a big kind of history person and like a big framework person. And just like above anything that puts my work into context to help me understand kind of what matters and what doesn't.

42:51 So the Seven Powers is just this, like, amazing business framework. And there's this other podcast that that I have of that that kind of talks about it a lot in the powers. They essentially talk about what are the moats in business? There's a 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.

43:20 Another moat is, switching costs. If you're super locked into some software and it's really hard to switch that, potentially use a moat. So there's a bunch of moat 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 quad code. So if you want to port from vendor A to vendor B, you can ask quad, hey, can you like port me?

43:42 And it will. 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 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 moats and you want to build strength in, you want 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.

44:05 You should wait something like switching costs and network effects or, you know, switching costs and, corner to resource. So when you combine these modes, you get a lot more power. And so this is the way that I would think about it from this company as point of view, similar to a 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 saying to customers, you know what, if you use Anthropic, you're letting the fox into the henhouse.

44:43 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 thing, be they they'll be able to use your business, do your business. And so instead of using Anthropic or, open AI, let us customize an open source model for you. It's built on your training data or sorry, it's built on your will. Bake in your own data. It'll be hosted on your servers.

45:10 And then, like you own it, etc.. Why should customers feel comfortable letting Claude let it Anthropic be so plugged into their business workflows? You know, I would probably ask who who's who's saying this and when Microsoft 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.

45:42 And then we do is, scout carp. Alex. Sorry. 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 if you like. You know, you're handing over the keys to these big companies that could potentially do a lot more things if they're like, plug 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?

46:11 Yeah. So I think the biggest thing I would just ask is like, what are the incentives of these people talking about? So that's what I'm saying, I said with marketing etc., but I believe I'm sure the 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, etc.. This 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.

46:45 And then one day, Claude, says, you know what? Like spin out a law firm we like, we spin out a bank, etc., and we know and there's enough information that we have about these workflows that we don't have to sell to 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 quote 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.

47:21 And I can be like, oh, there's the bug, we can just go fix it. I cannot see that data. And from the customization aspect of 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 this our policy. Like we are we we power a lot of customers. We power a lot of businesses. And to us the trust is very important.

47:47 This is just the way that we operate. The I got to say though, I think the bigger thing that I would think about is model progress continuous. 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.

48:11 Yeah. And you want to, you know, figure out how to debug when inference doesn't work and to kind of do all these things, which, by the way, is a lot of work. And it's, it's a very niche expertise but progress continuous. 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 where all of our customers are seeing.

48:37 And so, you know, 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 in the frontier continues to move, then, you know, we're 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. Oh, yeah. Claude. Code can do COBOL now, right? So, like, the mainframe issue is basically solved.

49:03 If I'm 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 and make no mistakes. There are a lot of things that are using cloud code for exactly this kind of migration. Yeah, well, you say more. Yeah. This is COBOL has come up a lot. So money episode. Oh yeah. Yeah. And we always hear like if you're if you're a COBOL engineer.

49:29 Yeah. You can make bank at the banks as they say. Yeah. Well quad is really good at migrating code. This is, 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 base from one language to another language from zig to rust.

49:55 And it took about 11 days. Wow. For one person. And, 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 that's something we never would have done. Oh, I saw that piece. Yeah. And it just cost him like 50,000 in credits or something like that. Just a fraction of what being 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 to do it.

50:21 It's just like no business can actually pay that cost. But yeah, like the the economics are really changing. And so, you know, if in the past you had this big COBOL 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 pump quad code and they can do this for you. Are languages or computer languages going to be irrelevant in the future?

50:42 Yeah. You know I think they're are largely irrelevant today. And you know this is like a this is a spicy thing because if you talk to different engineers, they're gonna have all sorts of views. And yeah, I don't necessarily know what's the right view. You know, as an engineer I think about everything else kind of pros and cons. To me, I'm a big languages nerd. I love programing languages. I love type systems, actually, like wrote a book about a language that I really like.

51:05 But increasingly with l'OMS, I think it matters. Well, it must, because the alum doesn't really care. And there's something 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.

51:27 And that will 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 code and other platforms can do so much of this where we get like even more niche languages. You know, I think that with cloud, what is happening is there is an explosion in innovation. And we're seeing this on the business side with all sorts of new startups.

51:54 Like, I like going to 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 was like there was a revolution because of silicon. What's the next to looking like? How like, how do we discover that? How do we discover that material? And they're using cloud to search for it. So there's this revolution happening in business and in product right now.

52:18 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, about computing, how will to go back to this sort of like command line versus graphical user interface question. Once I started using the terminal cloud code, I was like, I don't want to use the web anymore because it feels clunky.

52:44 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 on a button and it just feels very clunky, like I'm, you know, 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.

53:11 But then like when the 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, the visual framework when it's just so easy to like, type something and see the words and get the output right there?

53:40 Can I show you an example? Yeah, sure. 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, awesome. Okay, so so let me show you guys this. So this is we have this feedback channel in slack okay. 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 oh yeah.

54:01 There's many such cases. Yeah. Yeah. It's just like super confusing. I didn't 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. You just kind of noticed this thread and it jumped in and it responded. And I asked it again and it found data about how often people use each of these buttons, and it created across two data sources. I worked at both, Datadog and, Google BigQuery.

54:27 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? Just mock it up. And, it reacted with a little like, art emoji. And then it went in and it mocked up some alternative so called true this. So like when we talk about visual interfaces like that, this is kind of what comes to mind is, now quite as part of the conversation, it proactively jumps in.

54:52 Then I talked in our designer and, you know, she jumped in and now it's just like a multiplayer conversation. Everyone's participating. And so like when I think about the graphical interfaces, 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. I don't drop it. Can I ask a question about that? So this is when I saw the slack bot 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 that was like adopting this, like what happens the first time, you know, you ask a question about like some sort of like icons etc.

55:36 there is a person whose job it was to be the design person. And then Claude 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, they're someone's like, wait, this is my job. And suddenly the person is asking Claude or tagging Claude, or in your case, not even, tagging Claude, not even having to tag Claude.

56:03 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. You know, I I'm gonna plug, my favorite mid-nineties business school study. Okay. There's this, article in, in the Harvard Business Review in the I think, like 1996.

56:34 And the title was something like the personal computer is here. Why are companies not benefiting from the productivity improvement? And sounds familiar. It sounds familiar. This was like a big open question around the time, you know, it's like the same thing for the internet in like early 2000 and the, the it's a good question, right. Because the 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 in the case.

56:59 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.

57:21 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, you know, and all the other filing cabinets and digitized everything and then threw away the filing cabinets. 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.

57:47 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 idea 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.

58:20 And this is how you benefit from this. It's not, you know, like, give them just what what quality answer. It's super power. This person with more intelligence has is when his the Claude bot or will the Claude bot ever do that thing or take. Hey guys, there's ten 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.

58:48 But I don't see any particular technical reason why it couldn't happen. But those are the types of things that also happen in business chats. Yeah. If you want to socialize with me, I don't want to. But like I think like okay as like a sufficient like these models as like they're like learn the lingua 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?

59:12 Yeah. And they're really, like passive aggressive about stuff on the slack chat, but like, are they going to do that? Like I 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 that was my first time, actually, when I joined Anthropic, it was Anthropic labs. And, you know, our team, we built the we built quad code, we built the MXGp skills and, the desktop app that came out of the same team.

59:35 And I remember we were building early prototypes of the desktop app, and I'd 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 I kind of got bored. And, we're watching the video later and it was like on Hacker News, just like reading the news.

59:55 Oh my God. Oh, wow. So yeah, that's going to do all the same. It's trying to waste wasting time and wasting time, wasting time and token. And the difference now I think is the model. You know it's more intelligence. So it actually it actually stays on task. But you know, there might be a future where, you know, like when I talk to Clod in Slack, when I talk to tag, it feels a lot more like a coworker than a tool.

60:16 And this is a big change. It feels really different. And this was 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 like really.

60:38 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 and tag, you know, when we talk about building products or models, there's this idea of product overhang, that people talk about. And what this idea is, is, the model is able to do something, but the product is getting in the way because.

61:15 Right, like when you use a model, when you use quality, you're not like literally like sending tokens to a inference server somewhere, like you're always using a third 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 on a 3.5 at the time was capable of all of these things. No product is letting people experience.

61:36 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. And so actually, the thing to unhackable the model and to let people experience the full intelligence of the model is using loops.

61:59 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 into tools, and you let it figure out the details for you the same way that you would a coworker.

62:19 And I think these are the skills were 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 just have one last question actually. But I think it's kind of core. And it came up at the very beginning. 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 company is really haven't prioritized this because, you know, clearly there's just so much more opportunity in, code in terms of business.

62:54 It's 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 being 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. Whereas we know that so many professional realms and writing being among them. But I would also say a lot of like sales or anything interpersonal does not have that tight feedback loop where you get the instant answer A or B, did this work or not?

63:33 Iterate how much? When we think about the gap between coding and everything else, how much is it about priority versus the fundamental thing that make seems to make coding different from many other professional tasks? Yeah, I 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. Code, there's just many, many shades of gray in between that there's code that works but is really ugly.

63:56 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 it. Okay. And there's a lot of nuance to writing.

64:17 You know, we're working on all these problems. We're getting better at code. We're getting better at writing. I also feel that code 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 wait this out or something. So yeah, I would expect it to keep getting better over time.

64:36 All right. Boris Cherny, thank you so much for coming on Odd Lots. That was great. Yeah. Thanks so much. 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, and then 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.

65:04 Agronomy. It does. It opens up so many interesting questions about, like, coworker 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. To your point, you could see how that would rub a few people the wrong way. Yeah, for like in the odd lots, group chat, like, who would be a good guest to talk about X and then like, the model problem was like, oh, it was actually a very good answer, that we should reach out to them or someone makes a suggestion and then the model is like, oh, that's stupid, and it won't work for the following reasons.

65:47 I would just say, and I'm not just saying that because our producers listen to this episode, but I honestly mean I mean, this I've never, 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 mine. 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?

66:24 And I've never been particularly impressed on questions like that. It'll find documents etc.. Yeah. But actually like producing something that's like for me, even with all my context etc.. It's not as 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 I just gets back to the writing point as well.

66:54 Right. Like, yeah. You know, 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, you know, one really views code, they view the software that code creates, whereas people actually view the poem, when someone is writing a poem.

67:26 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 a business and they're the higher order person, and then the the models can just like take it all from there on the marketing side, on every aspect.

68:02 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? 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?

68:32 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 or a product or an orchestrator. What happens when no one is the sort of the mechanic, the guitar tuner, the person who builds the tubes?

69:00 What happens when no one remembers how to write, how to do the thing? Does something get lost? And I think that 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 loss. Yeah, I think we will. All right. Shall we leave it there? Let's leave it there. This has been another episode of the Odd Lots podcast.

69:20 I'm Tracy Alloway. You can follow me @tracyalloway. -And I'm Joe Weisenthal. You can follow me @thestalwart. Follow our guest Boris Cherny. He's @bcherny. Follow our producers Carmen Rodriguez @carmenarmen, Dashiell Bennett @dashbot, Cale Brooks @calebrooks and Kevin Lozano @kevlloydlozano. And for more Odd Lots content, you should check out our daily newsletter. You can find that at bloomberg.com/oddlots. -And you can chat about all of these topics 24/7 in our discord, discord.gg/oddlots. And if you enjoyed this conversation, then please leave a comment or like the video. Or better yet, subscribe!

69:56 Thanks for watching.