Dario Amodei

Big enterprises, big financial companies, big pharmaceutical companies, all of them are adopting Claude Code much faster than enterprises typically adopt new technology.

They're moving much faster than when we tried to sell them just the ordinary API, which many of them use. Claude Code is a more compelling product, but it's not an infinitely compelling product.

Anthropic has predicted that by late '26 or early '27 we will have AI systems that "have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world".

I said the AI model will be writing 90% of the lines of code in three to six months.

I said we'll get that in 2026, maybe 2027. Again, that is my hunch.

we've done some experiments. A couple years ago, we did an experiment with the Collective Intelligence Project to basically poll people and ask them what should be in our AI constitution. At the time, we incorporated some of those changes.

Broadly speaking, the exponential of the underlying technology has gone about as I expected it to go. There's plus or minus a year or two here and there.

What has been the most surprising thing is the lack of public recognition of how close we are to the end of the exponential.

Now we have RL scaling and there's no publicly known scaling law for it. It's not even clear what the story is.

I think AI will diffuse much faster than previous technologies have, but not infinitely fast.

Big enterprises, big financial companies, big pharmaceutical companies, all of them are adopting Claude Code much faster than enterprises typically adopt new technology.

They're moving much faster than when we tried to sell them just the ordinary API, which many of them use. Claude Code is a more compelling product, but it's not an infinitely compelling product.

I don't know for sure, but I think they're going to get you a large fraction of it. There may be gaps, but I certainly think that just as things are, this is enough to generate trillions of dollars of revenue.

We have the pre-training and RL stage where you throw a bunch of data and tasks into the models and then they generalize. So it's like learning, but it's like learning from more data and not learning over one human or one model's lifetime.

I would describe it as kind of like human on-the-job learning, but a little weaker and a little short term.

Anthropic has predicted that by late '26 or early '27 we will have AI systems that "have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world".

My guess for that is there's a lot of problems where basically we can do this when we have the "country of geniuses in a data center". My picture for that, if you made me guess, is one to two years, maybe one to three years.

One question is: How many years after that do the trillions in revenue start rolling in? I don't think it's guaranteed that it's going to be immediate. It could be one year, it could be two years, I could even stretch it to five years although I'm skeptical of that.

The fifth is that you need an objective function that can scale to the moon. The pre-training objective function is one such objective function. Another is the RL objective function that says you have a goal, you're going to go out and reach the goal. Within that, there's objective rewards like you see in math and coding, and there's more subjective rewards like you see in RLHF or higher-order versions of that.

That was the hypothesis, and it's a hypothesis I still hold. I don't think I've seen very much that is not in line with it. The pre-training scaling laws were one example of what we see there. Those have continued going.

It’s continuing to give us gains. What has changed is that now we're also seeing the same thing for RL. We're seeing a pre-training phase and then an RL phase on top of that.

Let me take the RL out of it for a second, because I actually think it's a red herring to say that RL is any different from pre-training in this matter.

You had very standard language modeling benchmarks. GPT-1 itself was trained on a bunch of fanfiction, I think actually. It was literary text, which is a very small fraction of the text you can get. In those days it was like a billion words or something, so small datasets representing a pretty narrow distribution of what you can see in the world.

It was only when you trained over all the tasks on the internet — when you did a general internet scrape from something like Common Crawl or scraping links in Reddit, which is what we did for GPT-2 — that you started to get generalization.

The language models are much more like blank slates. They literally start as random weights, whereas the human brain starts with all these regions connected to all these inputs and outputs. Maybe we should think of pre-training — and for that matter, RL as well — as something that exists in the middle space between human evolution and human on-the-spot learning.

We're starting first with simple RL tasks like training on math competitions, then moving to broader training that involves things like code. Now we're moving to many other tasks. I think then we're going to increasingly get generalization.

I think the framework you're laying down obviously makes sense. We're making progress toward AGI. Nobody at this point disagrees we're going to achieve AGI this century.

Maybe the irreducible uncertainty puts us at 95%, where you get to things like multiple companies having internal turmoil, Taiwan gets invaded, all the fabs get blown up by missiles. Now you've jinxed us, Dario. You could construct a 5% world where things get delayed for ten years. There's another 5% which is that I'm very confident on tasks that can be verified.

The goal is not to teach the model every possible skill within RL, just as we don't do that within pre-training. Within pre-training, we're not trying to expose the model to every possible way that words could be put together. Rather, the model trains on a lot of things and then reaches generalization across pre-training. That was the transition from GPT-1 to GPT-2 that I saw up close.

I said the AI model will be writing 90% of the lines of code in three to six months.

With coding, except for that irreducible uncertainty, I think we'll be there in one or two years. There's no way we will not be there in ten years in terms of being able to do end-to-end coding.

Do we see in the world out there a renaissance of software, all these new features that wouldn't exist otherwise? At least so far, it doesn't seem like we see that.

I think diffusion is very real and doesn't exclusively have to do with limitations on the AI models. Again, there are people who use diffusion as kind of a buzzword to say this isn't a big deal. I'm not talking about that. I'm not talking about how AI will diffuse at the speed of previous technologies. I think AI will diffuse much faster than previous technologies have, but not infinitely fast.

We've seen this climb in benchmarks, and benchmarks are always imperfect measures. But I think when we first released computer use a year and a quarter ago, OSWorld was at maybe 15%. I don't remember exactly, but we've climbed from that to 65-70%.

I think what I'm saying is that we're kind of taking a different path. Don't you think with coding that's because there is an external scaffold of memory which exists instantiated in the codebase? I don't know how many other jobs have that. Coding made fast progress precisely because it has this unique advantage that other economic activity doesn't.

But in fact, if you look at their output and how much was actually merged back in, there was a 20% downlift. They were less productive as a result of using these models.

if you look at their output and how much was actually merged back in, there was a 20% downlift. They were less productive as a result of using these models.

Within Anthropic, this is just really unambiguous. We're under an incredible amount of commercial pressure and make it even harder for ourselves because we have all this safety stuff we do that I think we do more than other companies. The pressure to survive economically while also keeping our values is just incredible.

I think my model of the situation is that there's an advantage that's gradually growing. I would say right now the coding models give maybe, I don't know, a 15-20% total factor speed up. That's my view. Six months ago, it was maybe 5%. So it didn't matter. 5% doesn't register. It's now just getting to the point where it's one of several factors that kind of matters. That's going to keep speeding up.

What I see instead—if I look at you, OpenAI, DeepMind—is that people are just shifting around the podium every few months. Maybe you think that stops because you've won or whatever.

I think we're working on that too. There's a good chance that in the next year or two, we also solve that. Again, I think you get most of the way there without it.

If you train at a small context length and then try to serve at a long context length, maybe you get these degradations.

I said we'll get that in 2026, maybe 2027. Again, that is my hunch.

You could use many more AI researchers. You also think there are these self-reinforcing gains from smart people working on AI tech. You can have the data center working on AI progress.

I get the impression that some of the other companies have not written down the spreadsheet, that they don't really understand the risks they're taking. They're just doing stuff because it sounds cool. We've thought carefully about it. We're an enterprise business. Therefore, we can rely more on revenue. It's less fickle than consumer. We have better margins, which is the buffer between buying too much and buying too little.

I would happily buy $5 trillion worth of compute to run an actual country of human geniuses in a data center. Let's say JPMorgan or Moderna or whatever doesn't want to use them. I've got a country of geniuses. They'll start their own company.

But then you're saying the TAM by 2028 is $200 billion. Again, I don't want to give exact numbers for Anthropic, but these numbers are too small.

Let’s say you pay $100 billion a year for compute. On $50 billion a year you support $150 billion of revenue. The other $50 billion is used for training. Basically you’re profitable and you make $50 billion of profit.

We could be profitable in 2026 if the revenue grows fast enough. If we overestimate or underestimate the next year, that could swing wildly.

The point is it doesn't equilibrate to perfect competition with zero margins. If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't equilibrate to zero. Help me understand that, because right now we do have three leading firms and they're not making profit. So what is changing? Again, the gross margins right now are very positive. What's happening is a combination of two things. One is that we're still in the exponential scale-up phase of compute. A model gets trained. Let's say a model got trained that costs $1 billion last year. Then this year it produced $4 billion of revenue and cost $1 billion to inference from. Again, I'm using stylized numbers here, but that would be 75% gross margins and this 25% tax. So that model as a whole makes $2 billion. But at the same time, we're spending $10 billion to train the next model because there's an exponential scale-up. So the company loses money. Each model makes money, but the company loses money.

The world where frontier labs are making money is one where they continue to make fast progress. Because fundamentally your margin is limited by how good the alternative is. So you are able to make money because you have a frontier model. If you didn't have a frontier model you wouldn't be making money.

the whole economy will kind of go at the same pace. I am worried geographically, though. I'm a little worried that just proximity to AI, having heard about AI, may be one differentiator. So when I said the 10-20% growth rate, a worry I have is that the growth rate could be like 50% in Silicon Valley and parts of the world that are socially connected to Silicon Valley, and not that much faster than its current pace elsewhere. I think that'd be a pretty messed up world.

So will robotics be revolutionized? Yeah, maybe tack on another year or two. That's the way I think about these things. Makes sense. There's a general skepticism about extremely fast progress.

I don't think it's dependent on learning like a human. It could happen in different ways. Again, we could have trained the model on many different video games, which are like robotic controls, or many different simulated robotics environments, or just train them to control computer screens, and they learn to generalize.

I think continual learning, as I've said before, might not be a barrier at all. I think we may just get there by pre-training generalization and RL generalization. I think there just might not be such a thing at all.

I think we may get to the point in a year or two where the models can just do SWE end-to-end. That's a whole task. That's a whole sphere of human activity that we're just saying models can do now.

I feel like that is AGI-complete, which maybe is internally consistent. But it's not like saying 90% of code or 100% of code. No, I gave this spectrum: 90% of code, 100% of code, 90% of end-to-end SWE, 100% of end-to-end SWE. New tasks are created for SWEs. Eventually those get done as well. It's a long spectrum there, but we're traversing the spectrum very quickly.

I don't know what will turn out to be the right thing. I take your point that people will have to try things to figure out what is the best way to use this blob of intelligence.

We might live in an offense-dominant world where one person or one AI model is smart enough to do something that causes damage for everything else.

If checks and balances were going to work, they would work with humans as well. If they aren't going to work, they wouldn't work with AIs as well. So maybe this just dooms human checks and balances as well.

I don’t want to say this is so far ahead in time, but it’s so far ahead in technological ability that may happen over a short period of time, that it's hard for us to anticipate it in advance.

I think reform of the regulatory process should bias more towards the fact that we have a lot of things coming where the safety and efficacy is actually going to be really crisp and clear, a beautiful thing, and really effective. Maybe we don't need all this superstructure around it that was designed around an era of drugs that barely work and often have serious side effects.

I think the last six months and maybe the next few months are going to be about transparency. Then, if these risks emerge when we're more certain of them—which I think we might be as soon as later this year—then I think we need to act very fast in the areas where we've actually seen the risk.

I don't worry as much about the chatbot laws. I actually worry more about the drug approval process, where I think AI models are going to greatly accelerate the rate at which we discover drugs, and the pipeline will get jammed up.

Cloud is like this. I think cloud is a good example of this. There are three, maybe four, players within cloud. I think that's the same for AI, three, maybe four. The reason is that it's so expensive. It requires so much expertise and so much capital to run a cloud company.

I think that's the same for AI, three, maybe four. The reason is that it's so expensive. It requires so much expertise and so much capital to run a cloud company.

Models are more differentiated than cloud. Everyone knows Claude is good at different things than GPT is good at, than Gemini is good at. It's not just that Claude's good at coding, GPT is good at math and reasoning. It's more subtle than that. Models are good at different types of coding. Models have different styles.

it seems like AI research is especially loaded on raw intellectual power, which will be especially abundant in the world of AGI.

there are very few technologies that seem to be diffusing as fast as AI algorithmic progress.

I think coding is going fast, but I think AI research is a superset of coding and there are aspects of it that are not going fast.

I actually predict that it's going to exist alongside other models, but we're always going to have the API business model because there's always going to be a need for a thousand different people to try experimenting with the model in a different way.

I actually do think that the API model is more durable than many people think.

I think the exponential of the underlying technology will continue as it has before. The models get smarter and smarter, even when they get to a "country of geniuses in a data center."

I do think the exponential will continue, but there will be certain distinguished points on the exponential. Companies, individuals, and countries will reach those points at different times.

There are points where if you reach a certain level, maybe you have offensive cyber dominance, and every computer system is transparent to you after that unless the other side has an equivalent defense.

My interest is in making that negotiation be one in which classical liberal democracy has a strong hand.

authoritarian countries with AI are these self-fulfilling cycles that are very hard to displace

Today, the view, my view, in most of the Western world is that democracy is a better form of government than authoritarianism

the technology and the market will deliver all the fundamental benefits, this is my fundamental belief, almost faster than we can take them

What will not come easily is distribution of benefits, distribution of wealth, political freedom. These are the things that are going to be hard to achieve

in a world where labor is no longer the constraining factor, this mechanism no longer works

I think it'd be great to build data centers in Africa. As long as they're not owned by China, we should build data centers in Africa

we will not sell data centers, or chips, and the ability to make chips to China

it seems like a document that people at Anthropic write, that can be changed at any time, that guides the behavior of systems that are going to be the basis of a lot of economic activity

One is we iterate within Anthropic. We train the model, we're not happy with it, and we change the constitution. I think that's good to do. Putting out public updates to the constitution every once in a while is good because people can comment on it.

we've done some experiments. A couple years ago, we did an experiment with the Collective Intelligence Project to basically poll people and ask them what should be in our AI constitution. At the time, we incorporated some of those changes.

But that is a thing you could try to do. Is there some much less heavy-handed version of that? Maybe.

It's an interesting, in some ways compelling, vision, but things will go wrong that you hadn't imagined.

This is a bias that's often present in history. Anything that actually happened looks inevitable in retrospect.

I think the weirdness of it, unfortunately the insularity of it... If we're one year or two years away from it happening, the average person on the street has no idea.

how absolutely fast it was happening, how everything was happening all at once. Decisions that you might think were carefully calculated, well actually you have to make that decision, and then you have to make 30 other decisions on the same day because it's all happening so fast.

You don't even know which decisions are going to turn out to be consequential.

some very critical decision will be some decision where someone just comes into my office and is like, "Dario, you have two minutes. Should we do thing A or thing B on this?" Someone gives me this random half-page memo and asks, "Should we do A or B?"

I probably spend a third, maybe 40%, of my time making sure the culture of Anthropic is good.

one thing that's very leveraged is making sure Anthropic is a good place to work, people like working there, everyone thinks of themselves as team members, and everyone works together instead of against each other.

We've seen as some of the other AI companies have grown—without naming any names—we're starting to see decoherence and people fighting each other.

I think we've done an extraordinarily good job, even if not perfect, of holding the company together, making everyone feel the mission, that we're sincere about the mission, and that everyone has faith that everyone else there is working for the right reason.

I think an important thing in the culture is that the other leaders as well, but especially me, have to articulate what the company is about, why it's doing what it's doing, what its strategy is, what its values are, what its mission is, and what it stands for.

As Anthropic has gotten larger, it's gotten harder to get directly involved in the training of the models, the launch of the models, the building of the products. It's 2,500 people.

This is why I get up in front of the whole company every two weeks and speak for an hour.

I get up in front of the whole company every two weeks and speak for an hour.

That direct connection has a lot of value that is hard to achieve when you're passing things down the chain six levels deep.

I have a channel in Slack where I just write a bunch of things and comment a lot.

It makes it a better place to work, it makes people more than the sum of their parts, and increases the likelihood that we accomplish the mission because everyone is on the same page about the mission, and everyone is debating and discussing how best to accomplish the mission.