Anthropic CEO Dario Amodei: AI's Potential, OpenAI Rivalry, GenAI Business, Doomerism

I get very angry when people call me a doomer. When someone says, 'This guy's a doomer. He wants to slow things down.' You heard what I just said. My father died because of cures that could have happened a few years later. I understand the benefit of this technology. I'm sure you've heard the criticism from people like Jensen who say, 'Well, Dario thinks he's the only one who can build this safely and therefore wants to control the entire industry.' I've never said anything like that. That's an outrageous lie. That's the most outrageous lie I've ever heard.

Thank you for having me.

I think Anthropic, myself and Anthropic, are always focused on trying to do and say the things that we believe. And I think as we've gotten more close to AI systems that are more powerful, I've wanted to say those things more forcefully, more publicly, to make the point clearer. I've been saying for many years that we have these scaling laws. AI systems are getting more powerful. They're going from the level of a few years ago, they were barely coherent. Now, a couple years ago, they were at the level of a smart high school student. Now we're getting to smart college student, PhD, and they're starting to apply across the economy. So I think all the issues related to AI, ranging from the national security issues to the economic issues, are starting to become quite near to where we're actually going to face them. And so as these problems have come closer, even though in some form Anthropic has been saying these things for a while, I think the urgency has gone up. I want to make sure that we say what we believe and that we warn the world about the possible downsides, even though no one can say what's going to happen. We're saying what we think might happen, what we think is likely to happen. We back it up as best we can, although it's often extrapolations about the future where no one can be sure. But I think we see ourselves as having a duty to warn the world about what's going to happen. And that's not to say there aren't an incredible number of positive applications of AI. I've continued to talk about that. I wrote this essay, 'Machines of Loving Grace.' I feel that I and Anthropic have often been able to do a better job of articulating the benefits of AI than some of the people who call themselves optimists or accelerationists. So I think we probably appreciate the benefits more than anyone. But for exactly the same reason, because we can have such a good world if we get everything right, I feel obligated to warn about the risks.

Yes, I'm not sure. I think it's very hard to predict, particularly on the societal side. So if you say when are people going to deploy AI, or when are companies going to use X dollars of spend of AI, or when will AI be used in these applications, or when will it drive these medical cures, that's harder to say. I think the underlying technology is more predictable but still uncertain. No one knows. But on the underlying technology, I've started to become more confident. There is uncertainty about it. The exponential that we're on could totally peter out. I think there's maybe a 20 or 25% chance that sometime in the next 2 years the models just stop getting better for reasons we don't understand, or maybe reasons we do understand like data or compute availability, and then everything I'm saying just seems totally silly and everyone makes fun of me for all the warnings I've made. And I'm just totally fine with that, given the distribution that I see.

It really depends what you mean by timeline. One thing I've been consistent on over the years is there are these terms in the AI world like AGI and super intelligence. You'll hear leaders of companies say we've achieved AGI, we're moving on to super intelligence, or it's really exciting that someone stopped working on AGI and started working on super intelligence. I think these terms are totally meaningless. I don't know what AGI is. I don't know what super intelligence is. It sounds like a marketing term, something designed to activate people's dopamine. So you'll see in public I never use those terms, and I'm careful to criticize the use of those terms. But despite that, I am indeed one of the most bullish about AI capabilities improving very fast. The thing I think is real that I've said over and over again is the exponential. The idea that every few months we get an AI model that is better than the AI model we got before. And we get that by investing more compute in AI models, more data, more new types of training models. Initially, this was done by what's called pre-training, which is when you just feed a bunch of data from the internet into the model. Now we have a second stage that's reinforcement learning or test time compute or reasoning or whatever you want to call it. I think of it as a second stage that involves reinforcement learning. Now both of those things are scaling up together as we've seen with our models and as we've seen with models from other companies, and I don't see anything blocking the further scaling of that. There's some stuff about how do we broaden the tasks on the RL side of it. We've seen more progress on say math and code where the models are getting pretty close to a high professional level, and less on more subjective tasks, but I think that is very much a temporary obstacle. So when I look at it, I see this exponential and I say people aren't very good at making sense of exponentials. If something is doubling every six months, then two years before it happens it looks like it's only 1/16th of the way there. And so we are sitting here in the middle of 2025, and the models are really starting to explode in terms of the economy. If you look at the capabilities of the model, they're starting to saturate all the benchmarks. If you look at revenue, Anthropic's revenue every year has grown 10x. Every year we're kind of conservative and we say it can't grow 10x this time. I never assume anything and always am very conservative in saying I think it's going to slow down on the business side. But we went from zero to 100 million in 2023, we went from 100 million to a billion in 2024, and this year in the first half of the year we've gone from 1 billion to, as of speaking today, it's well above four, it might be 4.5. So if you think about it, suppose that exponential continued for two years. I'm not saying it will, but suppose it continued for two years, you're well into the hundred billions. I'm not saying that'll happen. I'm saying the situation is that when you're on an exponential, you can really get fooled by it. Two years away from when the exponential goes totally crazy, it looks like it's just starting to be a thing. That's the fundamental dynamic. We saw that with the internet in the '90s, where networking speeds and the underlying speed of the computers were getting fast, and over a few years it became possible to basically build a digital global communications network on top of all this when it wasn't possible just a few years ago, and almost no one except for a few people really saw the implications of that and how fast it would happen. That's where I'm coming from. That's what I think. Now, if a bunch of satellites crashed, maybe the internet would have taken longer. If there was an economic crash, maybe it would have taken a little longer. So we can't be sure of the exact timelines, but I think people are getting fooled by the exponential and not realizing how fast it might be. How fast I think it probably will, although I'm not sure.

Yeah. From what we've seen, I can only speak in terms of the models at Anthropic, but what I've seen in terms of the models at Anthropic, if we look at coding, coding is one area where Anthropic models have advanced very quickly. Adoption has been very quick. We're not just a coding company; we're planning to expand to many areas. But if you look at coding, we release 3.5 Sonnet, a model we call 3.5 Sonnet V2, which let's call it 3.6 Sonnet now, 3.7 Sonnet, and then 4.0 Sonnet and 4.0 Opus. That series of four or five models each one got substantially better at coding than the last. If you want to look at benchmarks, you can look at SweepBench growing from 18 months ago at like 3% or something, growing all the way to 72 to 80% depending on how you measure it. And the real usage has grown exponentially as well, where we're heading more and more towards autonomously using these models. I think the actual majority of code written at Anthropic is at this point written by or at least with the involvement of one of the Claude models, and various other companies have said similar statements to that. So we see the progress as being very fast and the exponential is continuing, and we don't see any diminishing returns.

First of all, I would say even if we never solved continual learning, even if we never solve continual learning and memory, I think that the potential for the LLMs to do incredibly well, to affect things at the scale of the economy, will be very high. If I think of the field I used to be in, biology and medicine, let's say I had a very smart Nobel prize winner and I said, 'Okay, you've discovered all these things, you have this incredibly smart mind, but you can't read new textbooks or absorb any new information.' I mean that would be difficult, but still if you had like 10 million of those, they're still going to make a lot of biology breakthroughs. They're going to be limited. They're going to be able to do some things humans can't and there are some things humans can do that they can't. But even if we impose that as a ceiling, that's pretty damned impressive and transformative. Even if I said you never solve that, I think people are underestimating the impact. But look, context windows are getting longer and models actually do learn during the context window. As I talk to the model during the context window, I have a conversation, it absorbs information. The underlying weights of the model may not change, but just like I'm talking to you here and we're having a conversation and I listen to the things you say and I respond to them, the models are able to do that. And from a machine learning perspective, from an AI perspective, there's no reason we can't make the context length a hundred million words today, which is roughly what a human hears in their lifetime. There's no reason that we can't do that. It's really inference support. So again, even that fills in many of the gaps, not all the gaps, but it fills in many of the gaps. And then there are a number of things like learning and memory that do allow us to update the weights. There are a number of things around types of reinforcement learning training. We used to many years ago talk about inner loops and outer loops. The inner loop is I have some episode and I learn some things in that episode and I'm trying to optimize for the lifetime of that episode, and the outer loop is the agent learning over episodes. So I think maybe that inner loop outer loop structure is a way to learn the continual learning. One thing we learned in AI is whenever it feels like there's some fundamental obstacle, like two years ago we thought there was this fundamental obstacle around reasoning. Turned out just to be RL. You just train with RL and you let the model write some stuff down to try and figure out objective math problems. I think we already have some evidence to suggest that this is another of those problems that is not as difficult as it seems, that will fall to scale plus a slightly different way of thinking about things.

If you're developing new techniques, we're developing new techniques every day. Claude is very good at code, and we don't really talk externally that much about why Claude is so good at code.

Like I said, we don't talk externally about it. Every new version of Claude that we make has improvements to the architecture, improvements to the data that we put into it, improvements to the methods that we use to train it. So we're developing new techniques all the time. New techniques are a part of every model that we build. That's why I've said these things about trying to optimize for talent density as much as possible. You need that talent density in order to invent the new techniques.

We've raised I think at this point a little short of $20 billion. It's not bad. That's not nothing. And I would also say if you look at the size of the data centers that we're building with for example Amazon, I don't think our data center scaling is substantially smaller than that of any of the other companies in the space. In many cases these things are limited by energy, they're limited by capitalization. When people talk about these large amounts of money, they're talking about it over several years. And when you hear some of these announcements, sometimes they're not funded yet. We've seen the size of the data centers that folks are building and we're actually pretty confident that we will be within a rough range of the size of data centers they build.

This is actually very interesting because one thing we noticed is that relative to other companies, very few people from Anthropic have been caught by these. And it's not for lack of trying. I've talked to plenty of people who got these offers at Anthropic and who just turned them down, who wouldn't even talk to Mark Zuckerberg, who said, 'No, I'm staying at Anthropic.' Our general response to this was I posted something to the whole company Slack where I said, 'Look, we are not willing to compromise our compensation principles, our principles of fairness, to respond individually to these offers.' The way things work at Anthropic is there's a series of levels. One candidate comes in, they get assigned a level, and we don't negotiate that level because we think it's unfair. We want to have a systematic way. If Mark Zuckerberg throws a dart at a dart board and hits your name, that doesn't mean that you should be paid 10 times more than the guy next to you who's just as skilled, who's just as talented. My view of the situation is that the only way you can really be hurt by this is if you allow it to destroy the culture of your company by panicking, by treating people unfairly in an attempt to defend the company. I think actually this was a unifying moment for the company where we didn't give in. We refused to compromise our principles because we had the confidence that people are at Anthropic because they truly believe in the mission. That gets to how I see this. I think that what they are doing is trying to buy something that cannot be bought, and that is alignment with the mission. There are selection effects here. Are they getting the people who are most enthusiastic, most mission-aligned, most excited? But they have talent and GPUs. You're not underestimating them.

We'll see how it plays out. I am pretty bearish on what they're trying to do.

My view of this has always been that talent is the most important thing. If you go back three years ago, we were in a position where we had raised mere hundreds of millions. OpenAI had already raised 13 billion from Microsoft. And of course, the large hyper cap tech companies were sitting on 100 billion, 200 billion. And basically the pitch we made then is we know how to make these models better than others do. There may be a curve of scaling laws. But if we are in a position where we can do for a hundred million what others can do for a billion, and we could do for 10 billion what they can do for a hundred billion, then it's 10 times more capital efficient to invest in Anthropic than it is to invest in these other companies. Would you rather be in a position where you can do anything for 10 times cheaper or where you start with a large pile of money? If you can do things 10 times cheaper, the money is a temporary defect that you can remedy. If you have this intrinsic ability to build things for the same price, much better than anyone else, or as good as anyone else for much lower price, investors aren't idiots, or at least they aren't always idiots. Depends which one you go to. I'm not going to name any names. But they basically understand the concept of capital efficiency. So we've been in a position 3 years ago where these differences were like a thousandx, and now you're saying with $20 billion can you compete with hundred billion? And my answer is basically yes because of the talent density. I've said this before, but Anthropic is actually the fastest growing software company in history at the scale that it's at. We grew from zero to 100 million in 2023, 100 million to billion in 2024. And this year we've grown from 1 billion to 4.5. That 10x a year, every year I suspect that we'll grow at that scale, and every year I'm almost afraid to say it publicly because I'm like no, it couldn't possibly happen again. So I think the growth at that scale kind of speaks for itself in terms of our ability to compete with the big players.

I won't give exact numbers, but the majority does come through the API, although we also do have a flourishing apps business. And more recently, the Max tier which power users use, as well as Claude Code which coders use. So we have a thriving and fast growing apps business, but yes, the majority comes through the API.

I wouldn't quite put it that way. I would describe it more as we've bet on business use cases of the model more so than we've bet on the API per se. And it's just that the first business use cases of the model come through the API. OpenAI is very focused on the consumer side. Google is very focused on the existing products that Google has. Our view is that if anything, the enterprise use of AI is going to be greater even than the consumer use of AI, or I should say the business use because it's enterprise, it's startups, it's developers, and it's power users using the model for productivity. I also think that being a company that's focused on the business use cases actually gives us better incentives to make the models better. A thought experiment that I think is worth running is: suppose I have this model and it's as good as an undergrad at biochemistry. And then I improve it and it's as good as a PhD student at biochemistry. If I go to a consumer, if I give them the chatbot and I say, 'Great news. I've improved the model from undergrad to graduate level in biochemistry,' maybe 1% of consumers care about that at all. 99% are just going to be like, 'I don't understand it either way.' But now suppose I go to Pfizer and I say, 'I've improved this from undergraduate biochemistry to graduate biochemistry.' This is going to be the biggest deal in the world. They might pay 10 times more for something like that. It might have 10 times more value to them. So the general aim of making the models solve the problems of the world, to make them smarter and smarter but also able to bring many of the positive applications, the things I wrote about in 'Machines of Loving Grace' of solving the problems of biology and medicine, solving the problems of geopolitics, solving the problems of economic development, as well as more prosaic things like finance or legal or productivity or insurance. I think it gives a better incentive to develop the models as far as possible, and I think in many ways it may even be a more positive business. So I would say we're making a bet on the business use of AI because it's most aligned with the exponential.

Originally, as happens with most things, we're trying to optimize for making the model better at a bunch of stuff, and coding particularly stood out in terms of how valuable it was. I've worked with thousands of engineers, and there was a point about a year and a half ago where one of the best I'd ever worked with said, 'Every previous coding model has been useless to me, and this one finally was able to do something I wasn't able to do.' And then after we released it, it started getting quick adoption. This was around the time that a lot of the coding companies like Cursor, Windsurf, GitHub, Augment Code started exploding in popularity. And then when we saw how popular it was, we kind of doubled down on it. My view is that coding is particularly interesting because the adoption is fast, and getting better at coding with the models actually helps you to develop the next model. So it has a number of advantages.

Pricing schemes and rate limits are surprisingly complicated. Some of this is basically the result of when we released Claude Code and the Max tier, which we eventually tied together, not fully understanding the implications of the ways in which people could use the models and how much they were actually able to get. So over the last few days, as of the time of this interview, we've adjusted that particularly on the larger models like Opus. I think it's no longer possible to spend that much with a $200 subscription. And it's possible more changes will come in the future, but we're always going to have a distribution of users who use a lot and users who use some amount. And it doesn't necessarily mean we're losing money that there are some users who get more, who if you were to measure via API credits spend, get a better deal on the consumer subscription than they would on the API products. There's a lot of assumptions there. And I can tell you that some of them are wrong. We are not in fact losing money.

I think you're making assumptions here.

The way I think about it is we think about the models in terms of how much value are they creating. As the models get better and better, I think about how much value they create, and there's a separate question about how the value is distributed between those who make the model, those who make the chips, and those who make the underlying applications. Again, without being too specific, I think there are some assumptions in your question that are not necessarily correct. I'll say this: I do expect the price of providing a given level of intelligence to go down. I expect the price of providing the frontier of intelligence, which will provide increasing economic value, that might go up or it might go down. My guess is it probably stays about where it is. But again, the value that's created goes way up. So two years from now, my guess is that we'll have models that cost of the same order of magnitude that they cost today, except they'll be much more capable of doing work much more autonomously, much more broadly than they are capable of today.

Larger models cost more to run than smaller models. The technique you're referring to is maybe mixture of experts or something like that. Whether your models are mixture of experts or not, mixture of experts is a way to run models more cheaply that have a given number of parameters. It's a way to train models. But if you're not using that technique, then larger models that don't use that technique cost more to run than smaller models that don't use that technique. And if you're using that technique, larger models that use that technique cost more to run than smaller models that are using that technique. So I think that's sort of a distortion of the situation.