Claude Code Head Boris Cherny: Insane Growth, Tokenmaxxing, AI Agents' Next Frontier

20:08Boris Cherny:Yeah, this is, I don't know how many companies are doing this token maxing thing. I've heard of it as a trend, you know, a little bit. If you look at QuadCode's customers, we have just many, many, many customers. So it's not like, you know, there's like one company driving the usage. It's not like that. I do want to kind of step back a little bit and just think about like, how does this kind of change happen? Because I think the goal of what these companies are trying to do, I don't want to speak for them and I would recommend just talking to them. But the goal of what they're trying to do, I think, is probably like organizational change and business process change.

20:41Boris Cherny:How do you make it to your company benefits from AI? And this is often unclear. It's very dependent on the company because every company has a different business, a different culture, a different org, a different way of doing things. There was this old Harvard Business Review article from the 90s, which I just love. And I forget the title, but it was something like, computers are here. Why is no one seeing the productivity impact? And this was a big question, right? It's like, to us, it's obvious computers make us more productive. This is just incredibly obvious today. But in the 90s, this was not obvious.

21:14Boris Cherny:And what was happening is personal computers were being adopted. They were replacing mainframes. And now they're affordable. So the average company, the average startup can buy one. You don't have to spend millions of dollars on a mainframe anymore. But there was this challenge and there was this paradox. Companies were adopting it, but they were not seeing productivity improvement. What's going on? And so this Harvard Business Review article, it made the case that in order to get a benefit from computers, you have to restructure your whole business process around computers. They have to be at the center of the way that you do things.

21:49Boris Cherny:And if you still have like paper, you know, filing cabinets and you have a bunch of drawers full of stuff and it's still a paper and pen kind of physical process. And there's a computer somewhere on the periphery. You're really not going to benefit. But if you throw away your filing cabinets, you throw away your desk drawers full of papers and you put a computer at the center of it. And that's the way that you do all your business process. Then you benefit. And there was the split between companies. Some are doing this and they were doing this fairly painful change. and they benefited from it and then others didn't.

22:22Boris Cherny:And I think it's kind of the same thing now. A lot of companies are trying to figure out how to benefit from the productivity impacts of AI. And there's just a lot of experimentation and everyone is trying different approaches to figure out how to benefit from it. I don't think there's one right approach.

22:39Big Technology Podcast Host:Okay. And look, I think that when we see something grow as fast as Cloud Code has grown and as fast as Anthropic has grown. It's good to just kind of talk this stuff through and it's good to hear your perspective. So, okay, that's token maxing. Now, tokens, of course, are the output of the model, like the words or portions of words that the model outputs and the words and portions of words that go into it, right? And that is how these companies charge. And the more you have, the more data centers you need, etc., etc. You know, as these models get better, they haven't – well, let me put it to you this way.

23:21Big Technology Podcast Host:Sometimes I wonder whether they're as efficient as they can be. These big models can sometimes do a lot of work, use a lot of tokens, even if the output is great. People wonder, well, is this sort of just driving up token demand where it could have been a really easy process? And the models are expending many, many tokens and not getting there as efficiently as they could. Let me give you an example. I've been using Claude Cowork to make PowerPoint presentations. It's really good at it. And I've been using the Opus 4.7 model. And a couple of times I've said, all right, you're working on this. Ship it as a PDF.

24:04Big Technology Podcast Host:And it just starts to lose its mind. And it cycles and it uses as many tools as it possibly can. And, you know, it just seems unable to ship the PDF. And eventually I kept telling it, no, you're making this PowerPoint. You know where it is. Ship it. And it goes, I owe you an apology. I went down a rabbit hole worrying about a constraint that wasn't actually blocking us the files there. And then it shipped it. I mean, talk a little bit about the efficiency of these models. and whether that is a legitimate worry that, you know, as we've seen the growth, part of it is these like loops that a model like Opus 4.7 might find itself in to do basic tasks.

24:47Yeah.

24:48Boris Cherny:Generally, when we think about models, there's a few different aspects of it. One is just how intelligent is it. Another one is how fast it is. And another one is how efficient it is. And we generally try to move all of these together. Between these, I think we should probably optimized for intelligence. That's the most important thing. So even if it's like a little bit less efficient, but it's more intelligent and it lets you do more things, that's really useful because the efficiency optimization comes after. After we make it more intelligent, then we can make it more efficient. So it's sort of kind of, we do one, then we do the other.

25:20Boris Cherny:We've been experimenting a lot with like how exactly we give people control over this because we don't always know the right default. Sometimes like when you're using it, you know better. you know better. And so one mechanism that we had for this is picking a model. So you can pick, you know, opus or sonnet or haiku. Another mechanism that we've been experimenting with is effort. I don't know if this is like the biggest sonnet middle haiku smallest. That's right. That's right. That's right. And this is just like the size of the model. Right. And then there's effort. And effort is essentially how, you know, I think the word is actually really descriptive.

25:54Boris Cherny:It's how much effort do you want to put into it? And you can set this. we have a recommended effort. So, you know, for example, to maximize intelligence for Opus 4.7, you want to use extra high or maximum effort. But if you want it to use less tokens, you can pick like medium or low effort. And this is a control that you have.

26:12Big Technology Podcast Host:Yeah, I talked about this on the show recently and we had a commenter that came in and I was of the opinion that this will, these, you know, bigger models will find a way to become more efficient on like the export, the PDF thing. We had a commenter come in that wrote, Alex, they can't fix things like that PDF problem. It's inherent to LM technology, and it's the biggest barrier to useful widespread dissemination and usage of agentic AI. I think I'm going to try to translate that. What they were trying to say is we talked about predictions earlier that this is all probabilistic. It's sort of predicting the next word.

26:45Big Technology Podcast Host:You don't get the same answer from an AI agent twice. And so, therefore, this type of thing is a feature of the way that they work and not fixable. What do you think?

26:57Boris Cherny:No, I don't think that's right. When you think about, like, okay, let's zoom out a little bit. Yeah. So engineers are the first adopters, right? Like, engineers started using Cloud Code, like, a year and a half ago. And, you know, this is before non-engineers were using agents in a meaningful way. This is, you know, before co-work and so on. If I think back to what Cloud Code was a year and a half ago, it wasn't very good. I could use it to write a little bit of code, but if I really trust it to build an entire feature or entire product, it wouldn't turn out well. It did the same thing. It would go in spirals and the quality wasn't good or it built it and either the code was bad or it didn't work.

27:35Boris Cherny:And at some point it just started to get better. And as the model improved and as quad code improved, the result just got better and better and better. And so you fast forward to today, quad code is 100 % written by quad code. Cowork is 100 % written by quad code. An increasing number of features are fully written by quad code across Anthropic and products. And this is something that we hear from customers also. I did a talk at Y Combinator, the startup incubator, yesterday. And I asked people to raise their hands. Everyone's using quad code. And I asked them, raise your hand if 100 % of your code is written using quad code today.

28:14Boris Cherny:About half the hands went up. And then, you know, I ask people, raise your hand if 0 % of your code is, you know, written with AI. There's like one hand that went up. And this will remove like a few hundred people. Power to that person. That's right, that's right. And, you know, there's still room for this, obviously. And then everyone else was somewhere in the middle. You know, it's like most of their code is written with quad code, but not all of it. But that's kind of the place where the model is at today. It was not there a year ago. A year ago, it was not good enough for this. And so this is exactly what you're saying play out with co-work right now.

28:42Boris Cherny:It's still early. You know, we released it, well, like a few months ago. It's going to keep improving. It's going to keep getting better as the product gets better, as the model gets better. But this is early days. I think still everyone using Co-Work today is an early adopter. Everyone even using AI today is an early adopter. There are so many people in the world, and most people have not tried AI in a meaningful sense. So there's just like, there's a lot more room to improve this.

29:08Big Technology Podcast Host:Yeah, we're hosting an event here in San Francisco on June 18th. and a lot of the marketing material I've turned out with co-work. Now, I go back and forth. I don't let it one-shot it, so I'm looking at the copy. But I do things like upload our download statistics to sort of show the growth of the podcast. And I give it the names of the speakers. And it is amazing at saying, building a prospectus. Here is what the event is going to be. Here is who's going to be in the audience. Here's who's speaking. Here's where you should be there. Here's how to get in touch. Insane. It's so good.

29:42Boris Cherny:What was your feeling like the first time that you used it and the first time that you saw like the agents use your tools?

29:48Big Technology Podcast Host:Well, I mean, obviously I've sort of enabled everything. So, and I think this is kind of an experience that many people have had where there's a browser extension for Claude. And you realize that you can only get the benefit of this or you'll get most benefit by letting Claude take over your browser and do things for you. and the experience is kind of it's almost the same as i had with the waymo where um those first couple turns i was like white knuckling and like watching and like should i approve reading everything and then you start to trust it a little bit and you just hit approve approve approve right and the waymo the same thing you're like okay this looks like it's not going to kill me and then five minutes later you're on your phone as the ai does the work and that was my experience

30:30Boris Cherny:with code and co-work yeah does that sort of track i mean this is like my experience too it's like i I think it's like any technology. I was watching someone that's, it's like a friend that's been learning to use Co-Work over time. And, you know, she's not an engineer. And there's this use case the other day, like her, there was like a language input on the computer where you can kind of choose between languages on the laptop. And there was some issue with it. And she couldn't figure out how to fix it. And so before what she would have done is go to Google and ask like, hey, how do I fix this, you know, this issue that I'm having with my computer?

31:01Boris Cherny:And this time she just like asked Co-Work. And Co-Work was like, cool, let me take a look. can I use your computer? And she said yes, and it took over the computer. And it gets this kind of like orange glow, and you get to watch as co-work open settings, and it sees what's going on with the language picker, and it diagnoses it, and it fixes it. And you're still in the driver's seat, so you can see this happening, you can monitor it. It's not happening in the background or anything, but it's just magical. My instinct was to open Google. So it's funny that for her, she went to using Co-Work for this.

31:36Boris Cherny:And this is actually something I feel all the time. I think for people that have kind of grown up with these products and they've seen previous versions, they might not be as ambitious as they could. But for people that are new to the products, I often see them using QuadCode and Co-Work for things that I wouldn't have even thought of. And it's just like amazing. It's so creative. And I learn a lot every time I see it.

31:57Big Technology Podcast Host:Yeah. Now, the biggest drawback right now, I would say, and I've seen you reply to people on X about this is the rate limits. Like when I see people say, I've given Cloud Code a shot, but I'm kind of done with it. It's typically because they've hit their token allotment and it only works for like an hour for them. And then they have to wait four to use it again. And they look for alternatives. What do you think the rate limits have done to the ability for your product to grow? And what is the plan, if there is one, to make people be able to use this without those rate limits?

32:39Boris Cherny:This is something we're actively working on. The reality is a very small percent of people actually hit their rate limits, which is surprising. For pro users, it's a little bit higher. For max, it's actually quite low. And I think the thing that you're saying when people talk about it is there's a couple of things happening. One is that we actually reduced the peak rate limits. And that's now rolled back. And we've actually doubled rate limits. So we're giving people more rate limits. But there was a brief period where we reduced them. And so people were running into that. The second thing that's happening is cloud code is actually quite extensible.

33:17Boris Cherny:And so people can use plugins, they can use all sorts of integrations. And Some of these use tokens in a pretty inefficient way. And so the thing that we've been working on is surfacing this to you so users can decide, do you want to use this plugin or do you not? So you can see kind of what percentage of your tokens goes to it. And then I think the third thing is there's a lot of people that have just increasingly become power users. Like first, when we released quad code, you know, you ran one quad at a time. Nowadays, I'm running, you know, like on my computer, I run maybe five at a time. And then every night I run like, you know, not every night, but most nights I run like hundreds of quads at a time.

33:53Boris Cherny:Hundreds. Yeah, hundreds, sometimes thousands. And this is something that I just like wouldn't have imagined a year ago. And obviously this uses a lot of tokens. And there's a lot of people that are figuring out these new workflows that are using a lot more tokens. And this is sort of like at the edge of what you can do with a max plan. And, you know, this is why you can just like pay using API also. So if you just want to have as many tokens as you need, you can do this too. And this is what a lot of enterprises do. Right.

34:19Big Technology Podcast Host:Now, it wasn't long ago where I'm pretty sure Dario, Anthropics CEO, was referring to OpenAI and talking about the spending on the build out. And he's talked about this afterwards. He said, I'm trying to be disciplined in the way I spend, which is still spending many billions of dollars on data centers to enable this stuff like you're talking about. And others, which we think is OpenAI, are YOLOing. Right. But now OpenAI is doing this, too, with codex. And you could call it YOLOing, but they have a lot of data center capacity that they've built. How do you think about that? Because, you know, when people do hit these rate limits, they may just go over to codex.

35:03Big Technology Podcast Host:It's pretty intense competition. So how do you think about that? How does Anthropic think about that internally? That, you know, at least from the outside perception is that this added discipline on data center build outs might end up losing users in the most important product battle that your two companies are engaged in. Yeah.

35:24Boris Cherny:So, you know, first of all, our growth has never been faster than it is today. So, you know, for quad code, the growth is accelerating. and I think because most people don't actually hit rate limits very often, it's actually not a huge issue. For the people that are, we are laser focused on improving the experience. And so we doubled the 5-hour rate limits. We are announcing today that we're increasing the weekly rate limits. And of course, we announced the new Colossus capacity, which we brought online to serve all these new users. Via Elon Musk. Via Elon Musk. Yeah, because this growth is just no one would have predicted this.

36:05Boris Cherny:This was just beyond our wildest forecasts. And so I think for us what matters the most is we need to serve our users. We want to make sure our users are really happy. And we're doing everything we can to make that happen. Are you surprised by Codex? How do you view them as a competitor? I think there's always copycats. There's always competitors. For me, it's flattering. and I think it just forces everyone to do better. So, you know, for me, the thing that I care about the most is just doing the best job that we can to serve our users and we encourage everyone on the team to, you know, talk to users every day and, you know, just keep making the product a little bit better every day.

36:49Boris Cherny:So this is what I care about the most.

36:52Big Technology Podcast Host:Okay. I want to take a break, but we have so much more to cover. I want to talk about how this extends beyond code, the future of the chat bot, and then maybe talk a little bit about, I mean, I could go through our agenda. We really need two hours. So why don't we take a break and come back and get to as much as we can right after this. This episode is brought to you by True Diagnostic. I've been trying to get more intentional about my health lately, not just how I feel day to day, but what's actually going on under the hood. That's why I checked out True Diagnostic. They offer at-home tests that measure your biological age, not just how old you are, but how your body is aging on a cellular level.

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40:01Big Technology Podcast Host:That's S-E-R-V-A-L dot com slash bigtech. Serval.com slash bigtech. And we're back here on Big Technology Podcast with Boris Churney, the head of Claude Code at Anthropic. Boris, it's great having you here. Like I said, I'm in your product daily, so it's really fun to speak with you about it. We talked a little bit about this, but I think one thing we should highlight is that this is really going to extend beyond the chatbot. We talked about booking flights. I talked about it with marketing presentations. and the week that we're talking, you have a new use case out where Claude Cowork can be used for small businesses, including taking over QuickBooks and doing some bookkeeping.

40:49Big Technology Podcast Host:Where does this go? I mean, what do you think the broad roadmap, where does the broad roadmap take you?

40:56Boris Cherny:We're thinking about a few things for QuadCode and for Cowork. There's a few big themes. One is improving intelligence. And, you know, I think almost all of this is just the model. As the model improves, we can do more and more ambitious work. For coding, it used to be writing a line of code at a time. Now it's building entire features or entire products. For co-work, it used to be, you know, like, you know, it started pretty recently, but it was like, you know, making a document. And now it's things like booking flights, combining many tools, doing your QuickBooks. So this frontier is improving and moving just very, very quickly.

41:29Boris Cherny:We're also thinking about how to do longer running tasks. For Cloud Code, we recently shipped this thing called Auto Mode. And Auto Mode is essentially a replacement for permission prompts. Before, what we used to do is whenever the model uses a tool, Cloud would ask you, is it okay if I use this tool? And, you know, usually you just say yes. And you get kind of tired of saying yes, kind of over and over. Always allow. That's the button to hit. That's right. That's right. But it's actually very important for security that you're very thoughtful about this. And the thing that we were realizing is actually instead of being thoughtful about every prompt, because we're showing people so many of these dialogues, they just kind of got fatigued.

42:08Boris Cherny:And they would just say yes or always allow. And so auto mode is the answer. And this is a new way of routing these tool calls. And the way that it works is whenever Claude wants to use a tool, it asks another Claude, is it safe to use this tool? Quad has some of the context. It doesn't have all the context. And there's also a number of layers of safety checks. And we spent months iterating on this to make it really safe. There's thousands of different benchmarks and evals that we use to make sure that this is safe. And essentially, we found both in the laboratory setting and now we're finding in the wild, this is safer than what we had before.

42:45Boris Cherny:So as a user, it's a really nice benefit because you don't have to sit there and say yes over and over. and actually the result is better because if there's one unsafe command buried somewhere in this big list of things that Quad asks you to do, you might have accidentally said yes, but actually if you ask a second Quad using auto mode, it's not going to say yes. So this is kind of one big investment. Maybe the third big one is just running more Quads in parallel. One of the cool things about Quad, and this is something that we started to see pretty early with Quad code users, is actually very few people nowadays run one quad code at a time.

43:21Boris Cherny:Most people run many, many quad codes, you know, ranging from, you know, a few to thousands. And with co-work, we're starting to see the same exact thing. As you get more comfortable letting co-work run, you start a task and then you start a second task and you move on and just do more in parallel. And I think there's just a lot of opportunity to make this experience very nice and to make it more obvious for people. How do you do this? When do you do it?

43:45Big Technology Podcast Host:Right. And it probably extends to the way that you use a chatbot, right? And it's interesting because Anthropix had this kind of interesting relationship with the chatbot. Started out as technology first, decided to build the chatbot, ship Claude, and then just kind of moved more towards enterprise. Like you looked at all the charts and Claude was always at the bottom. But now you're seeing Claude's usage rise. And I have a thought, and I'd love to check this by you, that the future of the chatbot is not like I'm going to give you a question and you'll give me an answer. It's I will give you a question or talk to you about a problem.

44:26Big Technology Podcast Host:And the chatbot will then suggest some sort of action you can take on my behalf. Like right now I'm talking a lot about a trip to India. And what I think I'm going to get back in the future is this thing being like what you said, not having this like secondary step between having to go there and book the flights. A more proactive chatbot that's going to say, okay, let me take care of this for you. Is that the right direction? Like am I thinking about that? I could see that. I could see that, yeah.

44:55Boris Cherny:Are you working on it? Agents are the future. And, you know, we're trying all these different experiments. Okay. There's some stuff that we're trying that's like this. Yeah. Okay.

45:04Big Technology Podcast Host:But there is a limit here, right, to what this can do. A funny way people have talked about the limits of the thousands of clubs that you can run in parallel is kind of looking at who Anthropic is hiring. My favorite job listing on the Anthropic site is that you're hiring Salesforce administrators. You're also hiring consultants to help enterprises deploy this technology. And many are viewing that as like a sort of tacit admission that this stuff can only take you so far. Here's Wharton professor Ethan Mollick on it. He says, you will know that the AI labs believe in artificial superintelligence when they disband their newly formed consulting, sorry, forward deployed engineering groups.

45:50Big Technology Podcast Host:As long as people are required to figure out how AI is useful and do organizational change and systems integrations, jobs seem pretty safe. What do you think about that?

46:01Boris Cherny:Yeah. When you look at the kind of engineering that I do, I don't write code. I prompt Claude. And actually nowadays, mostly what I'm doing is I have a Claude that prompts other Claude's. So I don't even talk to Claude. I have a Claude that's talking to my Claude's. And I think in engineering, you've seen just this explosion in the amount of leverage that a single person has. It's about how big of a business can a person build? How many products can one person support? The leverage that one engineer has now at Anthropic is just insane. And I think we're starting to see this across other disciplines too.

46:41Boris Cherny:So we're starting to see this with marketers that are using Cloud to do things. We're starting to see this also for forward deployed engineers that are using Cloud Code to build implementations. We're seeing this for our sales team because, you know, actually at Anthropic, I think like half the go-to-market team uses Cloud Code. and the other half uses core. I think everyone's using all these products. And so the thing that we're seeing is the amount of leverage an individual has goes up. And we are still bottlenecked on the number of good people. And so even if the leverage per person goes up, you still just can't hire enough good people because the demand is so insane and there's so much more to build.

47:18Boris Cherny:So that's still the bottleneck for us.

47:21Big Technology Podcast Host:But I would say like, if people would argue that if this stuff was so powerful, you could say, take a look at the way my sales organization operates and then configure Salesforce that way with a prompt. Is this, and another example people give is, I'll believe that Anthropic has very powerful AI if they let it handle the IPO paperwork and don't hire an investment bank. Are these unfair tests?

47:51Boris Cherny:Well, we're starting to see – there's one person on the table that was using Claude to do their taxes. I would not necessarily recommend this, but –

47:59Big Technology Podcast Host:I'll admit, I've run my taxes through Claude and compared it against my accountant, and it was pretty close. Yeah, I did the same thing. Folks, not saying you should do that, but it is – it's an interesting use case.

48:11Boris Cherny:That's right. But I think fundamentally what people are missing in this conversation is in the end, it's a person that has to talk to Claude to ask Claude to do this thing. So even if Salesforce is automatically configured and, you know, it's not a person pressing all the buttons, it's Claude doing it, someone has to ask Claude to do that. And if you have to configure Salesforce in, you know, a bunch of different ways, it could actually be a full-time job to ask Claude to do this. And at some point, Claude is going to become really good at asking Claude to do this. And that person is going to be asking Claude that asked Claude to do this.

48:41Boris Cherny:And this chain will just keep getting deeper. But in the end, you still need people that are piloting this. But maybe their job is just asking one question then in the future. Yeah, but imagine how much leverage that has asking the right question.

48:53Big Technology Podcast Host:That's true. That's a good point. So, you know, we talked about Salesforce, so we have to talk about the SaaSpocalypse. You have some interesting views on the type of software companies that will be safe as we get more automated programming and those that might be in trouble. And you've talked previously about the different moats that exist and which moats are more important and which moats are less important. Can you just share that briefly while we're talking about it?

49:23Boris Cherny:There's this really good framework called the seven powers for talking about moats in business. There's so many of these frameworks for this, but this is my favorite. I actually studied economics in school. I didn't study computer science. So this is still kind of the way that I think is in terms of these kind of frameworks. And there's a lot of these different moats in business. And some companies have one mode, some have a few modes. They have like a portfolio of modes. There's a bunch of these modes. So like one is scale economies. So as you scale up your production, then there's increasing returns to scale.

49:55Boris Cherny:Another one is network effects. So this is like a messaging app or something like that. The more people that are on it, the more valuable it is for any person. Another one is switching costs. There's another one that's process power. I think most of these modes are still going to matter. And relatively, some are going to increase in importance over the next year, and some are going to decrease in importance. One that I think will increase in importance is something like network effects. Because it doesn't matter who's writing the code. It doesn't matter if it's an agent at the core of your product or something else, or if there's intelligence in your product.

50:28Boris Cherny:If there's a network effect in your product, that's still going to matter. Some modes get less important. And this is, for example, switching costs. because if you want to switch from vendor A to vendor B, you can just ask Quad to do that. And Quad is going to get better and better over time at it. And so I think as a company, a thing that you should be thinking about is what are your modes? And I think a lot of the largest companies just have many, many modes. It's not just one thing because the way you get to a scale and the way you build a defensible business over time is you accumulate these modes.

50:56Boris Cherny:You need a number of them. But yeah, I would just think what's going to be more valuable in a year and what's less valuable.

51:03Big Technology Podcast Host:I think that when you think about these different software companies, though, if you're using a cloud code, do the most almost kind of blend away because you could potentially be in this like one app that is interfacing with all software, which means therefore there's really only one software company.

51:23Boris Cherny:yeah i mean there's just like a lot of ways that this could play out i think something like this is possible but it seems a little far-fetched to me because if i think about for example like let's say i'm using a messaging app how do i decide which app to use i use the app that my friends are on that i can that i can reach so it doesn't matter if i can build a really awesome app for myself which i can do today like i can build a great messaging app with quad code in like a few hours it's still not useful because they can't talk to my friends but this is the

51:50Big Technology Podcast Host:example exactly you'll have you can you can fact check me on this you're gonna have an agent in your messaging apps that's going to let you know when your friends have messaged you i know you use cloud code on your iphone a lot right so then you will just see the notification and you'll speak back to people all your communication could potentially be centralized in these as long as

52:14Boris Cherny:the companies play ball yeah i mean connect it could be kind of the agent in the end but how does the communication actually happen? So like, you know, for example, if you look at a messaging app like Signal, there's a protocol that it uses to communicate. And, you know, I can build an app, it can maybe use that same protocol, but I think it actually can't message other people that are on Signal. But yeah, like I can have an agent that uses my app to do that messaging using an existing app that supports this. So yeah, it's not obvious how it's going to play out. I think today people use a mix of, you know, apps and agents.

52:50Boris Cherny:But, you know, I do fundamentally think that a lot of these modes are actually still going to increase in value over time. You can think of another example, let's say, you know, like a TSMC or some kind of like chip manufacturer. If you think about the amount of work that they put into making a process and in making a process where the costs go down with scale, this is a fundamental economic force. And there's a lot of companies that do this kind of thing where, you know, especially in manufacturing, where with scale, the cost goes down. With tech companies, this is the case for infrastructure.

53:22Boris Cherny:So if you build a really great infrastructure, you can support more users and the marginal cost per user goes down over time. So if you have this kind of effect, it doesn't matter if you or I can build apps. That's still a really powerful mode. But I do think for sure both things are in play.

53:38Big Technology Podcast Host:Okay. I got three more in 10 minutes. Let's see if we can get to them all. Jack Clark, one of the Anthropic founders, recently said, I think that he believes there's like a 60 % chance that these models will start improving themselves by 2028. It could be off by a percentage or a year. But ballpark, that's accurate. You're in the app where coding happens autonomously. You're running this app. Do you agree with Jack? Seems right. Yeah.

54:08Boris Cherny:When I look at the way that quad code is written, 100 % of quad code is written using quad code. This has been the case since, I think, November of last year, since Opus 4.5. It's like a fast takeoff scenario then.

54:22Big Technology Podcast Host:Do you anticipate that?

54:23Boris Cherny:I mean, it's possible. And this is why Anthropic exists. If you ask any engineer or any researcher why they joined Anthropic, they're going to tell you it's for AI safety. and it's because for us when we think about the future you know years from now the thing that's the most important and the thing that we want to get right you know for our kids is we want to make sure this thing is safe and we want to make sure it goes well because yeah like that is one of the possible outcomes i think that's not yet what we're saying um you know right now quad code is writing itself but it's still a person that's doing the prompting quad is starting to generate its own ideas for what to build next for quad code but it's you know it's not always good ideas And I still generate most of the ideas.

55:04Boris Cherny:And at some point, it's going to change. The model is going to improve. And it's going to become more of a self-reinforcing loop.

55:11Big Technology Podcast Host:Okay. I definitely want to get your thoughts on the world model argument here, where people who are pro-world models say that a large language model has no understanding of the consequences. And you need to build a world model into it to have effective agents. Here's something from Jan LeCun. He says, you cannot build a reliable agentic system without a world model. LLMs don't have world models. They can't predict the consequences of their actions before taking them. According to Jan, they just act. And whatever happens next is someone else's problem. I was speaking with Greg Brockman from OpenAI recently.

55:48Big Technology Podcast Host:And he said, basically, he doesn't accept that argument. And he thinks LLMs are the way directly, these text models are the way to AGI. Which side are you on? Are you a believer that that world model intelligence needs to be baked in? Or do you think that LLMs alone are good enough?

56:06Boris Cherny:I would put out an offer to Jan. If he wants to sit down and quad code together for an hour, I'd love to show him. You guys should do that on this show. Yeah, and then I'm curious to hear what he thinks. Maybe he'll change his mind. Maybe he doesn't. Right. But your perspective, though? You know, I'm pretty firmly on the product side. So, you know, I don't really have a perspective on it.

56:26Big Technology Podcast Host:Okay, let me drill down a tiny bit deeper, if you don't mind. You know, you're on the product side, but I've heard multiple people bring out this idea that without a conception of the way the world works, like in a world model, a LLM just doesn't have an understanding of the way that the world works and consequences and stuff. You use co-work to book how many flights? Eight flights in hotels? Like, you must think that it has some understanding of consequences, otherwise you wouldn't have given it. your credit card, which I presume you did. So what do you think about that argument in particular?

57:01Boris Cherny:I think from what I've read from folks working on research at Anthropic, it is surprising the degree to which these models are intelligent. Because like you said at the beginning, the thing that they fundamentally do is they predict the next token. And so you think, like, this is kind of like a stupid thing. Like, how can this possibly lead to intelligence? But, you know, we've actually published a lot of work about how the models are able to plan. They're able to actually reason. There was all these very surprising behaviors that you actually wouldn't expect from a model that just predicts the next token.

57:32Boris Cherny:So I don't know. I wouldn't discount that.

57:34Big Technology Podcast Host:I mean, I think my favorite is when they write poetry. As they're writing the first line, you can see in the model, this is anthropic research, that they're already thinking about the next line. That's right. Which is like, how is that even possible?

57:48Boris Cherny:That's right. I mean, and that's kind of how I think about it. Like if I were a poetry, that's how I would do it too. And it's crazy. Like you teach this thing to predict the next word and somehow if the next word is hard enough, it has to learn to really plan ahead and it has to learn how to do all of this.

58:03Big Technology Podcast Host:Okay, last one for you. Sometimes I wonder when I see big tech changes underway and in my career covering this stuff, some have worked out and some haven't. I always have to ask myself, how are we sure that this is the future and this is not a fever dream? and I think the data indicates that this is a real thing but I also wonder you have to sort of you have to question how much you can extrapolate towards the future in terms of how will this continue to progress the argument that this is a fever dream is that maybe people just want simple interfaces and they don't mind tapping through things and you know speaking in a cloud code feels a little bit too techie and it just won't appeal to the everyday user as much as it's really taken off with developers.

58:54Big Technology Podcast Host:I mean, how would you answer that?

58:57Boris Cherny:We had this hackathon for Opus 4.7 recently and one of the winners was a doctor that built an app. There was an electrician, there was a carpenter and a lot of these people didn't have coding experience but they used quad code to build something useful. There's one person that built and sold a startup. as a result of one of these hackathons that we put on. And undoubtedly, when we first built CloudCode, it was for engineers, and engineers kind of figured out how to use it. But very quickly, people that were not engineers figured out how to use this to build economically useful things. And actually, if you look at a lot of the usage today, it's like it's not engineers.

59:39Boris Cherny:And it's just so useful for people that they're going out of their way. They're jumping through hoops. Even before co-work, people were like installing quad code in a terminal for a lot of people this was their first time using a terminal and of course now you know for quad code we have a desktop app we have ios app we have a slack app you know there's many ways to interact with it but people were jumping through hoops to use it because it was so useful and so for me as a product person this is the ultimate market test of is this thing useful is are there a lot of people that use this every day and that keep using it every day.

1:00:12Boris Cherny:And yeah, it's a lot of people and it just keeps growing. And I'm just constantly surprised by the way that people use this.

1:00:19Big Technology Podcast Host:Yeah, I will say I've been surprised by the way that I found myself using the tools. And I don't know, well, we'll see what comes next. So excited to keep using it and thrilled to have a chance to speak with you. I hope we can do it again. Yeah, thanks for having me on. All right, thank you, Boris. Great speaking with you. All right, everybody, thank you so much for listening and watching and we'll see you next time on Big Technology Podcast.

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