A Cheeky Pint with Anthropic CEO Dario Amodei

I'm excited to finally learn what it's like to start a company with your sibling? I don't know why you're asking me that question, because you know.

Yes, right, in addition to having this learning impulse, the models have this capitalistic impulse.

No, no, exactly, exactly. I think we're gonna be in a world where the models will make mistakes much less often than humans, but they'll be stranger mistakes. So we need to invent slurring for LLMs.

Cheers.

Yes.

Yes. And we'll get into the Anthropic business, but I'm excited to finally learn what it's like to start a company with your sibling? I could ask the same question of you, but you know, it's almost like there's two things you need to do when you're running a company. You need to operationally execute and you need to have a good strategy and kind of see the most important thing or the thing that no one else sees. And so my job is the second, and Daniela's job is the first. And we're both good at the things that we do. And so I think it's allowed us each to spend most of our time on the thing that we're best at.

Yeah, yeah, where you have just total and complete trust. I mean, I think even beyond that, Anthropic has seven co-founders. When we founded it, basically, the advice from pretty much everyone was like, 'Seven co-founders is a disaster. The company will fall apart before you know it. Everyone will be fighting with each other.' There was even more negativity on my decision to give everyone the same amount of equity. But what we found, and I think it was because, obviously, me and Daniela are siblings, but then all seven of us, some of us knew each other for a long time or had a history of working, not just knowing each other, but working together in the past, and I think that really allowed us to always be on the same page. And I think especially as the company grows, the idea that you have seven people who really carry the values of the company and project them to a wide set of people, it allows you to scale the company to a much larger size while holding onto the values and the unity that we have.

Yeah, there's a wide range of things, and it's kind of changed over time. I would say definitely the application that has grown the fastest, although it's not very far from the only application, we have a wide range of them, is definitely coding. And my theory on why it's grown so fast, other than that we focused on coding and the models are good at coding, it's actually really a statement about kind of societal diffusion. Which is that if we look at today's AI models, I think in every area, there's a huge overhang in terms of what they could do compared to how they're actually being deployed today. There's some friction, people at large enterprises are not familiar with the technology. I look at what a bank does or what an insurance company does, and there's huge potential, even if the model stopped getting better, right? Even if we stopped building products on top of the model, there's still huge, billion-dollar potential in an individual enterprise. And often, the CEOs of companies that I talk to understand that perfectly well. But if the company is a 10,000 or a 100,000 person company, companies that size, they're set up to operationally do a certain thing a certain way, and it takes time to change them. But in code, the people who write code are very socially and technically adjacent to the folks who develop AI models. And so the diffusion is very fast. They're also the kind of people who are early adopters, who are used to new technology. And so I think the big growth in code, I would say the biggest cause of that is just that the people doing it and the startups devoted to it are fast adopters who understand the technology super well. But it's by no means limited to code at all. If you look at, there are a bunch of companies that do things like tool use. There are, as you mentioned, customer service. You know, we work closely with companies like Intercom. We're starting to see some things on the biology side. We're working both with pharmaceutical and healthcare companies, and we're working on the side of kind of basic scientific research. We work with companies like Benchling, for example. But we also work with some of the very large pharma companies. There was something done a while back where we worked with Novo Nordisk to write clinical study reports. So clinical study reports are like, you've done a clinical trial, and then you would kind of write up the results, and it's like, these are the adverse events, these are the statistics. And the clinical study report normally takes like nine weeks. Well, Claude could do it in like five minutes, and then it took a human a few days to check it. And so you can really see the opportunity for acceleration, and as the models get better, you know, they'll reach into the deep research as well. So, I guess a way to summarize it would be to say that kind of code is out in the lead, but we see a long tail of quite a lot of other stuff, including some very, very significant use cases. I think code is maybe an early indicator, like a premonition of what's gonna happen everywhere else. It's the same exponential. It's just faster. It's happening faster.

How do you decide which verticals to do yourself versus which to allow platform... Like, you have Claude Code, and obviously, there's also platform companies like Windsurf and Cursor and everyone like that. You launch Claude for Financial Services. Presumably there are other verticals where you say, 'Well, we're not building a tool there.' We have things like Claude for Enterprise, which is not a vertical, but a general play to go with enterprise. I think the way we like to think about it, I think we think of ourselves as a platform company first. The analogy here would be maybe clouds or something. If you think of a really large platform business, of the size we're trying to get to in hopefully a small number of years, there are a number of reasons why you would also want to have things that are first-party and some verticals end up being more first-party heavy. One is when you want to have direct exposure to the users. The end user gives you some sense of how exactly are they using it, what are they most looking for? If you're a pure platform and you don't have that direct connection, you can be disadvantaged in various ways. It's hard to build the best products. Yeah, it's hard to build the best products. It may even be hard to know where the model really, really needs to go, right? People say things like coding, but like, there are many models that seem to be good at coding, but they aren't good in the way that's actually relevant, right? We've actually managed to make Claude good in a way that's relevant to what people actually use. I think that's one reason. Another reason goes back to the large enterprises, where building on an API, sometimes it's more challenging for a more traditional company to do that. And you need to give them something that's a little bit easier to use, either a kit to help them build things, or you need to give them an app. Enterprises have also liked Claude Code, and we're gradually developing Claude for Enterprise into, what we call a virtual coworker.

Yeah, or I mean, maybe in fact it's the next launch, but... Yeah, we're not currently working on Claude for oil and gas exploration. I would draw a distinction between things we just don't allow, right? Things that are illegal, or things like that. And there are a number of use cases that it's like, okay we're a platform, people are gonna do a bunch of things. We're just not passionate about it. But like, we're not passionate about it. We're not gonna go out and make this happen before the other use cases. I think there is a component of that where, probably we work on things like science and biomedical out of proportion to its immediate profitability. Because you think it's worthwhile. We feel the same way about things in the developing world. One I'll give you that's controversial, people think about it the opposite way. So the work we do on defense and intelligence, people are often like, 'Oh, these guys are selling out.' I think about it the opposite way, right? So, you know, there was this contract with a ceiling of $200 million with the DOD and intelligence community. People are like, 'Oh, man, Anthropic's selling out.' It's exactly the opposite. Getting another $200 million from some coding startup would take an order of magnitude less effort than getting that contract. But you think defense is very worthwhile. And we're doing it because we want to defend democracies. And we do it within bounds. There are some things we're concerned about. I'm deeply concerned about abuse of government authority on the domestic side. We think more on the outward directed side. But that's an example of the things we prioritize are things that we think are good, not necessarily things that feel good or that we think the external buzz will be positive. We actually have conviction around some things, and we do them regardless.

AI is strange in a number of ways. I think one of the ways it's strange is that because it's an exponential, we have a hard time calibrating exactly how big the business will be. We had the following experience. In 2023 I'd never raised money from institutional investors before, and so our revenue was zero at the beginning of 2023 because we had not released a product. So I was putting together something, and I'm like, 'Oh, I think we can probably get $100 million of revenue in the first year.' This caused some investors to say, 'This is crazy. This has never happened in the history of capitalism. You've lost all credibility.' 'You're just making up numbers.' 'Goodbye, goodbye.' And then we actually did it. And so then the next year, I was like, 'I think we can go from $100 million to a billion.' And actually, having done it the first time, people were like, it was a little bit less dismissed as crazy, but still often dismissed as crazy, and then we did it again. This year, we're halfway through the year. We're, as you mentioned, well past $4 billion revenue. In kind of logarithmic space, to add another order of magnitude. So, there's a bunch of different futures. There's one where once things get to a certain size, the curve slows down, but there's a provocative world where the exponential continues and in two or three years, these are the biggest businesses in the world. And I think one of the fundamental experiences and uncertainties of working at or running something like Anthropic is you kind of don't know. You make this exponential projection. It sounds crazy. It might be crazy, but also, it might not be crazy, because that trend line has followed before. And I've said much the same thing in the context of training AI models, in the context of the cognitive capabilities of AI models on the technological side, but now we're seeing the same kind of continuous lines on the business side.

Yeah, yeah, yeah, there's something like, there's some curve where you spend 5x or 10x more to train a model, or you have 5x or 10x more data or whatever the scaling laws say, and there's some transfer curve for revenue, right, where I spend 10 times more on the model, and the model goes from being a smart undergrad to a smart PhD student. Then I go to a pharmaceutical company, and I'm like, 'Well, how much more is that worth?' Often, they end up saying 'That's worth about 10x,' where these kind of power law distributions occur in a bunch of contexts. Going on the technical side, when you train the model, there's a longer and longer tail of correlations that you're capturing as you train the model, right? Correlations in the structure of language, in the world, in patterns. And that correlation is what's thought to lead to the scaling laws, because there's this kind of logarithmic distribution. And then as you think of the model getting more and more capable in terms of cognitive tasks, there must be, or we're seeing empirically so far, if you think of the uses of the model in the economy, right? If I think of the way that companies are organized, right? There's a kind of power law, there's a power law structure of, like, the org charts of companies. And it almost feels like you're climbing that power law distribution of value. And then I guess the way I think about product and go-to-market is that the model wants to be on that exponential of revenue, and product and go-to-market, they're kind of a way to clean the window and let the light shine through. A way to open the aperture and let the exponential happen.

Yes, right, in addition to having this learning impulse, the models have this capitalistic impulse that they want to embody, unless they're given a bad product or bad sales to go with them. Because they're really useful, that intelligence is really useful to people, and so it kind of gets pulled out of you.

It's very hard to tell for sure, and I think there was quite a lot of uncertainty two or three years ago. But I think we might be relatively close to the final set of players, if not necessarily the final market structure or the roles of the players. I would say there's probably somewhere between three and six players, depending on how you count, and those are the players that are capable of building frontier models and have enough capital to plausibly bootstrap themselves.

There's kind of two different ways you could describe what's happening in the model business right now. So let's say in 2023, you train a model that costs $100 million, and then you deploy it in 2024 and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs $1 billion. And then in 2025, you get $2 billion of revenue from that $1 billion, and you spend $10 billion to train the model. So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year, so it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example, profitable. What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's much more expensive and requires much more upfront R&D investment. And so the way that it's gonna shake out is this will keep going up until the numbers go very large and the models can't get larger, and then it'll be a large, very profitable business, or at some point the models will stop getting better, right? The march to AGI will be halted for some reason, and then perhaps it'll be some overhang, so there'll be a one-time, 'Oh man, we spent a lot of money and we didn't get anything for it,' and then the business returns to whatever scale it was at. Maybe another way to describe it is, the usual pattern of venture-backed investment, which is that things cost a lot and then you start making it, is kind of happening over and over again in this field within the same companies. And so we're on the exponential now. At some point, we'll reach equilibrium. The only relevant questions are, at how large a scale do we reach equilibrium, and is there ever an overshoot?

Yeah, yeah, it's like the F-16 or something, or it might be a little bit like drug development.

Yes, and when do you actually go to the effort of training a model? Yeah, yeah, it's almost like a drug company, where it's like you develop one drug, and then like if that works, you develop 10 drugs, and then if that works, you develop 100 drugs. The drug development market does not work like that numerically, but it is as if it did.

I don't wanna make any specific claims— Sure, yeah, yeah.

Yes, because the ever-growing CapEx is masking the underlying quality of the model businesses.

Yeah, so I don't know, people talk about things in public, and sometimes they're rumors or suppositions or whatever. I wouldn't even necessarily assume that there's a data wall. One thing I will say is that the idea of using RL has been around for a while, right? If we go all the way back to when Google DeepMind beat the world Go champion with AlphaGo, it was RL first. And then we built these language models, and now we're kind of uniting the two together by putting RL on top of the language models. That's all chain of thought or reasoning is, it's just a fancy way of saying RL, where the RL environment is that the model writes a bunch of things and then gives an answer. There's nothing more to it than that. It just kind of has a fancy name. And so I think of these as kind of the two key ways of learning, right? I think of base LLM training as learning by imitating, and RL as learning by trial and error. I think those are the two styles of learning. If I'm like a child, there's two ways for me to learn. I look at my parents and I'm like, 'Oh, they do something,' and I try and learn what they do, or I can just kind of experiment with the world and learn things, and it's very clear in developmental psychology that people use both. And so we're now seeing that recapitulated in the language models, and so we have a stage where we do the imitative learning, and we have a stage where we learn by trial and error. So it seems very natural to me.

Of course. And so how does one... Like, in the pharma industry, they protect their secrets with patents. In Wall Street, where also they have $100 million secrets that are just a very simple idea, Renaissance Technologies, the hedge fund, just very successfully locked up its employees. How do you make keeping a commercial lead work in kind of the current AI environment? One thing I will say is that there are some things that are like that, but I think more and more as the field matures, it starts to be more about know-how and ability to build complex objects. Some of the ideas we work with are simple, but I would say the simple ideas, the ones that are like, oh, yeah, twiddle this element of the transformer or something, those tend to be independently discovered or anyone knows them before too long. But there are things like, oh man, this thing is actually really hard to implement from an engineering sense, and we have it implemented, or this thing is just kind of a pain to do, or there's a know-how to doing it. And those tend to be more collective things that are more difficult to leak. And so I think those things are substantially more defensible. That said, there's still leakage, and we still don't want it to happen again, both for commercial competitive reasons and for national security reasons. Both are problems. Yes. And so a few things we do. One is, you know, we tend to compartmentalize information. So if you talk to any intelligence agency, that's how they operate. You're only told what you need to know, and I think everyone within Anthropic understands that.

Yes, we actually do that at the same time as we have a very open culture. I say things to the company that maybe another person would put in PR speak, but when there is a secret, then I think that actually leads to people trusting that it's something that you actually need to know. And then finally, having better retention rates and losing less people is one of the most important things here. We have the highest retention rate of all the AI companies. I think the differences are even starker, because everyone has a non-regretted attrition rate that's maybe constant, so if you subtract that off, then the difference is even larger. Sometimes when people leave, they come back.

If you look at the list of people who went to the Meta Superintelligence lab, even if you normalize for our size, it's not many, and many turned them down.

I think relative to other companies, we've done well. We even have been relatively advantaged. It's like a mixture of true belief in the mission and belief in the upside of the equity. Anthropic has developed a reputation for doing what it says it will do, for in some cases, making fewer promises, but keeping those promises that we make. And being very clear on what we stand for, and being consistent over the years and standing for it. That creates a unity around the company, and I think it's a good guard against cynicism.

Often I'll talk about the platform and the importance of the models. For some reason, sometimes people think of the API business and they say, 'Oh, you know, it's not very sticky,' or, 'It's gonna be commoditized.' I run an API business. I love API businesses. There are even bigger ones than both of ours. I would point to the clouds again. Those are $100 billion API businesses, and when the cost of capital is high and there are only a few players... and relative to cloud, the thing we make is much more differentiated. These models have different personalities. They're talking to different people. A joke I often make is, if I'm sitting in a room with 10 people, does that mean I've been commoditized? There's like nine other people in the room who have a similar brain to me, they're about the same height, so who needs me? But we all know that human labor doesn't work that way. And so I feel the same way about this. I think the API business is a great business, but we wanna go broader than that. The way I think about it is other players such as OpenAI and existing incumbents such as Google are very focused on the consumer side. The idea of providing AI to businesses is something that we are trying to get better and better at. I think we're out to an early lead in that. I'm not sure, because I don't know for sure what the revenues of the other players are, but I think we probably at this point have the plurality of the API market, most likely, and the AI for business market, perhaps.

Yeah, yeah, yeah, exactly. All these products work differently. I mean, we're one of the biggest customers of the clouds. And we use more than one of them. I can tell you, the clouds are much less differentiated than the AI models.

No, no, no, exactly. You don't know why. It's a little like baking a cake, right? You put in the ingredients— It just works. It just kind of comes out a certain way. One chef makes it this way, and the other chef makes it this way. And if you're like, 'Make it exactly like that chef makes it,' you can't, right? You just can't.

Will be a huge deal. And will be a big source of stickiness for the... Like, 'cause you won't want to switch products. And I don't know exactly what that looks like, but given the amount of... For both the consumer and the business use case. Absolutely, absolutely. You know, I think we've just started to scratch the surface in terms of models that are customized in various ways for working with a particular business or particular person within the business. So I think we're just seeing the beginning of the API business. But I don't think AI for business is just about API. With things like Claude Code, we're selling that to not just individual developers, but enterprises as well. And they find it useful. Claude for Enterprise, that is selling to a lot of enterprises. I actually see it, and you see this with some of the clouds, where they have a bunch of different services, right? Some of them are apps, some of it is the underlying cloud itself, and what they are is the way that AWS or GCP or Azure will present themselves, and the way that we are starting to present ourselves is, hey, we wanna be your one-stop shop for AI or for cloud, and you can buy all of these things, and you can talk to us about which to use for what. And so I think that starts to create the outlines of a more durable business.