Inside the Mind of Anthropic CEO Dario Amodei | The Circuit | Extended Interview

You know, I've never been someone who slept all that well. Let's just say I'm learning the art of finding ways to relax and sleep through moments of unusual pressure.

It's this feeling of like the exponential. Suppose you were to accelerate away from earth on a spaceship at relativistic speed. The way special relativity works is you go to sleep and you wake up and two days have gone by on earth. So you have to deal with two days in one day. And then you go to sleep, and because you've continued to accelerate, three days have gone by on earth, and then the next day four days have gone by. That's a little bit what it feels like.

There are enough clear and present issues that we have to deal with that I'm constantly dealing with those, while thinking about how we can prepare. But I don't think paranoia or worrying about what you'll wake up to is productive. I've looked at people in history who've dealt with these very high pressure situations, and you need to learn to respond rationally and not put dangers out of proportion to each other. This yo-yoing between 'I'm not worried' and 'oh my God, we need to panic today' — I think that's a hallmark of immature decision making. The actual mature decision making is we can't ignore this, we can't be complacent. In fact, it's getting to be a bigger and bigger risk. But we have to respond rationally, like a surgeon would deal with an operation, or a military officer would deal with a military operation, or someone making decisions that affect a lot of people has to make those decisions rationally and maintain a basic sense of calm.

The interesting thing is that the experience I've had for my whole career, and certainly the whole time at Anthropic, is that there's this kind of smooth exponential. The experience of the smooth exponential is nothing's happening, nothing's happening, nothing's happening, a little things happen, and then zoom, it goes crazy. That's the experience of the world, the scale of the company compared to the other companies and compared to the world. So I was watching this graph for a while and I said, 'Oh yeah, we'll probably become the AI company with the most revenue and the most valuation sometime around this time.' And indeed it has happened. So in one sense I'm not surprised because there's just a smooth line on the graph. But of course in another sense, when things actually happen, you see so much more detail and color to it, and it definitely is surprising. And we're just keeping in mind all the things we usually keep in mind: how do we train good models, how do we put them in good products, how do we make sure that everything's safe, how do we help people but also manage the societal risks around the technology? It's all the same questions, just under a bigger microscope as it were.

The whole first internet revolution was happening around me and I had absolutely no interest in it. I was just interested in doing math and scrawling things. I was interested in understanding the universe. I was interested in science fiction. That was the general milieu. I think I just felt a lot of curiosity about the world.

Yeah, I think the general spirit of nonconformism and individualism and it's okay to be crazy. I think a good deal of that probably did rub off on me. You hear these stories about going to countries in Europe or even other parts of this country where it's discouraged or considered weird to think about things in some different way, or have some set of crazy ideas. There's a lot of things I'm actually very critical about with Silicon Valley, but one thing that I think is good about it is this encouragement that it doesn't matter if all the experts are against you, if you have a coherent vision and a coherent worldview, you should go and pursue it. Maybe it just won't work at all, but if it does, there's this kind of long-tailedness to it where you can search certain veins and find a huge goldmine. I think that spirit is very important.

That was the time when the Open Philanthropy project was first being started up, which Holden was the lead of. And I was at that time a biological scientist. So I was helping them with some of the stuff they were doing around developing world health or biological research. I advised on that stuff — what were the areas that were promising, what were the areas that were less promising.

Look, I'll say it very simply. There are many difficult issues that you face when you're building powerful technology that Anthropic faces every day, where we don't know whether we're making the right decision or the wrong decision. So there are many valid disagreements to be had on safety. We certainly had some of those disagreements with them, but that alone is not sufficient to leave. People here have had disagreements with me, people here have disagreements with each other. But when you feel that you can't trust someone, when you feel that their values are not what they say they are, when you feel that they're not honest, when you feel that they're not in it for the reasons that they say, when you see disturbing patterns of behavior, dishonesty, that makes it very hard to continue to work with a company, to continue to trust the company. And look, at the end of the day, why argue with someone when you don't have the same vision and you don't trust them? The way to resolve it is you go off and do your thing, they go off and do their thing. And I am completely at peace with the idea that we're doing things our way and they're doing things their way. We'll see who wins in the market and we'll see who wins in the court of public opinion. I think those things speak louder than any drama about who left. We're providing an example of how to deploy this technology in what we think is a responsible way. If they disagree, they should make that argument. And I think that's really all there is to say about it.

What happened is that the summit was extremely disorganized. We all came up at the last minute and they changed the order in which we were standing, and then they took a picture of us and then they ordered us all to hold hands. If you've ever been to one of these summits — I'm not saying anything bad about India in particular — but all of these international type summits that have heads of state are super disorganized.

I don't know what to tell you. There was Narendra Modi up there suddenly telling everyone to hold hands.

Here's what I will tell you. There is a wide variance in the quality and the trustworthiness of the people building this technology. I think this meme that no one trusts each other, I don't think it's right. I've known Demis Hassabis, who builds the Gemini models — they're a competitor to Claude models — I've known him for 15 years. We've worked together on a number of issues. We buy compute from Google, we swap safety ideas all the time. So my view is that there are some players who are more trustworthy than others. And I think there are players outside Anthropic who I trust, who I see as trustworthy. What I think needs to happen is that the trustworthy actors need to get together and put the untrustworthy actors in a position where they have to adopt the same standards. With a lot of experience, I've learned that there are some folks who don't do the right thing on their own, but if there's a majority of the industry doing the right thing, then the rest of the industry is left in a position where there's not much they can do but come along. There's the positive version where you inspire other people — that's Demis and me inspiring each other. He does AlphaFold, we're trying to do something in bio as well. We do interpretability research, they start interpretability research. It's not even competition, it's just each company does something cool and the other company is like, 'That's cool, we'd like to do that too and see if there's something new within that we can do.' So that's the carrot side of the race to the top. Then there's the stick side, or the implicit stick, where you're like, 'These guys are doing the right thing, those guys will look bad if they don't do the right thing.' And often we see behaviors where they grudgingly do the right thing while trying to pretend they're doing something different. But I think that's the way we get the industry together, and that's the way we get the industry to cooperate.

When we started Anthropic, the base thing that mattered, the thing that always matters, is we want to do this right. But then you have to ask yourself, in order to fund the very expensive creation of these models, it needs to be a company, it needs to have a business model. Does the business model get in the way of the values? There's always this question. But I think one of the things I learned from being at other companies and watching other companies is that if you pick a business model that fundamentally conflicts with your values, you're going to have a hard time. Either you betray your own values or you become irrelevant. You end up in a catch-22 situation. It's far better to pick a business model that is compatible with your values. So when we thought about it, we said, we've seen the world of social media, the consumer world — it really seems to encourage engagement, even addiction. The slop we've seen with AI video models — it wants to maximize the number of minutes you're paying attention to because that's the advertising revenue driven incentive. Whereas if we look at enterprise, we want to make these models useful to people. If I think of all the positive things you can do with AI — I warn a lot about the negative things, but ultimately we think the positive things will outweigh the negative things. Many of those fall under the banner of enterprise. We want to use AI to cure diseases that we couldn't cure before — that's working with biotech, pharma, academic research groups. All of those are enterprises. We want to use AI to make energy cheaper and more efficient — that's all enterprise. We want to use AI to help with education — most of that is enterprise. We want to use AI to address health in the developing world — there are nonprofits, but those are basically enterprises. We want to increase economic growth — that is basically enterprise as well. And then I think there's another factor: enterprises care a lot about trust and long-term relationship. Consumer can have this almost gimmicky aspect to it, whereas with enterprise, what matters is you build a relationship where you work with a company for many years, you deliver on what you say, and they basically trust you. So it's very synergistic with our goal of deploying these models in a positive and safe way. And so I think it serves us well to have this business model that largely aligns with our values. Not that there aren't conflicts sometimes, not that there aren't hard choices we have to make, but I think the number of such choices is much lower than it would be otherwise.

Model quality is the most important thing. We're very far ahead right now on model quality. There is some amount of inertia, but I've never relied on that. Anthropic has never relied on 'this is sticky and people won't switch.' I think you want to have a better model, you want to have a better product. And we see the growth rates haven't inflected at all. If anything, they've gone up, at least at the time of taping this interview. So I tend to think that is the most important thing.

This is one of these questions that's very hard to predict in advance. If you could predict it perfectly, people would make a huge amount of money on the market and they'd always be right. So no one knows exactly what's going to happen. But I would note a few things. All of these traditional software companies have a number of moats. I think what's going to happen is some of these moats are going to go away, but others are going to stay around. The ability to quickly write software — I definitely think that's going away. If your moat is 'we wrote this complex software that no one else can write,' good luck, you're not going to be able to defend that. But folks have customer relationships, folks have know-how of how the field works, folks have unique domain knowledge. So my advice to all of these folks is, don't be complacent, don't ignore it. Make a list of all your moats and be very aware that some of them are going to go away, while others are going to become relatively more important. Because there are limiting factors, and there may also be new moats. And I think those that deftly respond, that lean into the list of moats that are still present as well as the new ones, will do well. I think those that are complacent, that delude themselves that what worked in the past will continue to work, they're not going to have a good time. So that is the advice I would give. And at the end of the day, I would guess — it depends what you call SaaS and what you don't — but I would guess that the software industry gets larger, not smaller. Although there will be some big losers.

I just think the pie is getting bigger. With AI, the pie is getting bigger. The existing incumbents may be smaller in relative terms. Some of them may go down in value, some may even go out of business if they don't adapt in the right way. But I think you see this often when growth is really fast. If what's possible with AI grows by 10x, it's very easy for an existing incumbent industry to go up by 1.5x — just not as much as the whole big pie is growing. So I think that may happen. That's not to say we won't have some big losers. I think those who don't adapt, who put their heads in the sand, who don't see what's coming, who don't identify the moats they have, they're going to have a really hard time.

There have been a number of cases where we've really spoken our minds about what we think. I've been very outspoken about the need for export controls on chips to China. I say this because I think it would be really bad for America, for the state of democracy in the world, for China to be ahead in AI capabilities. And some of the chip makers obviously don't agree with that view, but it hasn't stopped me from saying it. I'm saying it again now, even after we've signed more partnerships. What they know is that we always work with them, we've been good partners. We can work together. I'm sure they wish we didn't say these things, but these things are what I believe. What are you going to do? At the end of the day, they benefit from these deals as much as we do. We're all adults here. We can work together on one thing while disagreeing about another thing.

The compute is ramping up very quickly. So it can both be the case that the fundamentals of the business look good, but in a year you'll have three times as much compute, or four times — I'm not going to give exact numbers — but these compute ramps are very fast. And we have every expectation that the revenue ramp will meet and exceed those. But raising money is the buffer against this cone of uncertainty. So it's a totally rational thing to do. It's a very small dilution to the business, and it logically is not at all the same thing as there being anything wrong with the fundamentals of the business. In fact, it's compatible with the opposite.

One of these things about compute is there's a marketing compute. My view is that over a period of time, even longer than a couple months, we can get large amounts of compute. One thing that's worth saying here is I don't think we bought too little compute by any reasonable standard. We were planning for a 10x a year growth in compute. 10x a year is what we expect. That isn't what we've seen over the first quarter of 2026. We saw a greater than 3x growth in revenue quarterly — not annualized, 3x in the quarter, which of course 3 to the fourth power is 80x over the course of the year. We didn't plan for 80x annualized growth. It would not have been rational to plan for 80x annualized growth, because that means if you only get 10x, you have 8 times less. So we're in a locally extreme explosion of compute that's not going to continue. If that continued, you'd get to revenue numbers by the end of the year that no company on earth has. I don't think that's going to happen. It just can't. But you can have these short periods where it's like, 'Oh my God, this is faster growth than we ever possibly anticipated.' But you saw the compute deals with Google, you saw the compute deals with Amazon. There are more that we can and will do. The market's liquid. If you're able to use compute really well and there's the demand, you'll get your compute. It might just take a month or two.

Look, we have a lot of difficult challenges in front of us, and there's this race to the top idea that we're trying to pull other companies along with us. And I think we've seen that. We have pulled them along with us. Sometimes they don't admit that that's what they're doing. Sometimes they copy us while they're attacking us. But this pull is very valuable. So I think the value of being the preeminent company, both commercially and in terms of models, it's not about beating rivals for the sake of beating rivals. It's about having the ability to pull the ecosystem along with us. And we hope that we can do more of that in the future.

I mean, look, we're always trying to succeed. We're not trying to fail here. I'm not someone who believes we should shut this technology down. We shouldn't build it. We exist within a free enterprise system, and there's nothing wrong with this. We just have to mitigate the risks of the models. And so it's always been the balance between the two.

What I would say is that I've put a lot of time into thinking about how that's the case. As companies scale, I've been paranoid at every scale of the company. At every scale, there's some new challenge, some new way the company can lose — either its will to win commercially or the core of its values. I'm worried about both because I see them as synergistic. I actually see the fact that we've been able to make such good models as the thing that allows us to assert our values in a way that works as the company grows. There are lots of pitfalls here, lots of ways to go wrong. Not because me or the co-founders or the company's leaders' values change, but because the composition of the company changes very fast. So I spend probably half of my time just talking to the company about the culture of Anthropic and how the culture works. When you're growing this fast, you're hiring a bunch of people from big tech companies. If you don't tell them how Anthropic operates, they'll simply recapitulate the only thing they know, which is how to operate at the companies they came from. So this is a constant struggle and a constant challenge. It's me and Daniela's maybe number one top priority: figuring out how to preserve this because we recognize that this is the core of who we are in the long run.

I would say two things. The first is we have a unified company, a unified culture. I think we've grown larger while still being incredibly efficient. Everyone's still on the same page. The cultural and organizational unity — I would say that's the biggest factor. And I would say the second biggest factor is Claude itself. We're now using Claude to help develop our models, make them more efficient, and quickly develop products. There are all kinds of new practices you have to develop. We're still new at it, but it's producing a lot of acceleration and increasingly producing reliable acceleration. So those are the two factors I would point to.

I think some of the wildest stuff I've seen is around biology and medicine. I've seen a number of cases, including Daniela actually, where Claude diagnosed a medical problem that a bunch of fancy doctors had missed. And on the biology side, the models are starting to get surprisingly good at tasks like drug design or computational chemistry. And as someone who used to be a biologist, I look at it and I'm like, 'Wow, that's hard. You need a lot of training to do that.' And Claude is getting good at it. That's one area where I think we're going to get a hell of a lot of benefit. That's the positive for AI. We're going to get these huge, enormous benefits. Life is going to get better. The quality of human experience is going to get better. A century of scientific progress and a century of progress in what it's like to be human. Go back to 1900, think of all the problems we had in the 1900s — all the reasons people died prematurely, all the problems they had to suffer, all the material deprivation that we don't have to deal with today. Then think of another hundred years of that. I really believe this century of scientific and medical progress, if we can get through this — and I think we will, I'm increasingly optimistic — we're going to have a much, much better world.

I do. I have not gotten to the point where I actually allow text directly written by Claude in, because I have such a specific style that I'm a little picky about it. But I basically use Claude to help me brainstorm, to help me think through the themes, to help me with references I could use. So it plays a supportive role. I don't know how far we are from Claude being able to write better than me. We're not quite there yet. But I think certainly it's coming. I love writing too, and I feel like writing helps you struggle through ideas. There is a lot of critical thinking involved in that. Do we lose that if we let Claude do it for us? I'm a little worried about that. And in fact, that's half the reason I write myself. It certainly is for external audiences — many people read what I write — but it is just as much to clarify my own thinking so that I know what to do next and to create a common reference point across me and others. I think we're still grappling with the question of how exactly do we use AI in a way that preserves those benefits. I think the thing I'm doing now does that, where I use Claude for research and to help organize my own thoughts. I think if we just used it end to end — like 'write an essay about the risks of AI' — first of all, it wouldn't write the things that I think, but also I would exactly lose that benefit. As the models get better, I think probably we'll use them much more directly in the writing and yet still preserve those benefits. But I think it's going to be a subtle thing. It won't be all one thing. We'll have to figure it out over time.

I've always said — and if you go back to those original clips, they always get cut out of context to three seconds — but the real statement was always, 'I don't know what's going to happen, but this is an order of magnitude for how crazy things could be.' Also, I always talk about all the things we can do in response to this. I've talked about token tax and working with enterprises to adjust people. I'm a little skeptical of retraining programs, but we should throw them in the mix. Macroeconomic policy, even from the beginning, I always talked about solutions. But somehow there's this tendency in human psychology to clip the three seconds of 'doom is coming.' So my message is definitely not 'doom is coming.' My message is this is something we should see coming, that we're worried about, and that we need to actually respond to positively. I don't know exactly, but I'm still pretty concerned. I'm still the same order of concern. We are seeing right now that AI is making people more productive. But that's the usual hump. If you go back to the industrial revolution — I wrote about this in 'Adolescence of Technology' — you automate 90% of the job, great, people are 10 times more productive in the other 10% because they're 10 times more leveraged. But eventually it gets close to 100%. Now the sequel to that is, well then you have to find something else for them to do. I don't know about the long run, I'm truly uncertain about that. But I do think there are types of adaptation. One thing I'll talk about is software engineers within Anthropic. We're going through this transition right now where AI makes the software engineers more productive, even though AI writes all the code or almost all the code. But still it makes people more productive. But we're already starting to see the beginning of there may be some people that it's not making more productive, that it's better for the AI to just do the thing. So that's one side of it. The other side is what do we need more demand for? There's something we call a forward deployed engineer or applied AI solutions architect, where their job is a mix of technical work and talking to customers. There's a lot of demand for that because there's a lot of customers and we're growing very quickly. Now, does every person who is in the pure software engineering role fit into this other? No, it's not perfect, it's not one-to-one. That gives you a flavor that there's going to be a hell of a lot of disruption, but things will also adjust. Which wins out? I don't know. But the reason it's important to warn about it is that that's how we can respond. That's how we can make policy, both within Anthropic and macroeconomically for the whole world. We want to put out carefully considered thoughts. We don't want to say things that people don't believe we'll actually do. We don't want to say things that are half baked. We want to think carefully about what should actually be done about these problems.

No one knows for sure, because the economy is unpredictable. It's the same as the stock market — they're these decentralized processes that you don't really know ahead of time what are the pieces of the job that people are still going to be able to do. But what I would say broadly is that anywhere you have these entry level white collar jobs — whether it's banking, finance — there's going to be a lot of potential for AI to first make people more productive. But then there's going to be a wholesale AI can do the job, and then we're going to have to think about what it is that people can do. And I think we need to plan about that ahead of time. We're already doing it. When we talk to enterprise customers, we see choices that they face. They face the choice of 'should I save costs?' which often means hiring less people — basically do the same thing with less resources — or 'should we do more things with the same amount of resources?' And we always, when we can, try to push them to doing more with the same amount of resources, because that means hire the same number of people or maybe even more people, but just do new things. Pushing them towards the positive sum. The thing that we have going for us here is the pie is going to expand a lot. And so because the pie is going to expand a lot, there are probably going to be places where people can go. It's just a matter of finding them fast enough. It's the size of the disruption. It's going to be big. And that's what I'm warning people about. But we have to solve that matching problem.