Dario Amodei

Their words

What I would say is that you know we are we are about to be in a world where growth and economic value will come very easily. If right if we're able to build these powerful AI models, growth and economic value will come very easily. What will not come easily is distribution of benefits, distribution of wealth, political freedom, um you know, these are the things that are going to be hard to achieve.

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The counterarguments against it are I'll politely call them fishy. Um, uh and yet it doesn't happen and we sell the chips because there's there's so much money. There's so much money riding on it.

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given the serious dangers that I lay out in adolescence of technology around things like the you know, kind of biological weapons and bioterrorism, autonomy risk and the timelines we've been talking about, like 10 years is an eternity. Like that's that's a that's a I I think that's a crazy thing to do.

Their words

so so I think we're definitely going to see business models that that recognize that, you know, at some point we're going to see, you know, pay for results or you you know, in some in some form or we may see forms of compensation that are like labor. Um, uh you know, that that kind of work by the hour.

Their words

The the chatbot is already running into limitations of, you know, making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that, you know, the models are are the models are good enough and they're they're, you know, them getting better doesn't matter to the economy.

Their words

In fact, I would point to the history in in ML of people coming up with things that are barriers that end up kind of dissolving within the big blob of compute, right?

Their words

does that mean the robotics industry will also be generating trillions of dollars of revenue? My answer there is yes, but there will be the same extremely fast but not infinitely fast diffusion. So, will robotics be be revolutionized? Yeah, maybe tack on another year or two.

Their words

a worry I have is that the growth rate could be like 50% in Silicon Valley and, you know, parts of the world that are kind of socially connected to Silicon Valley and, you know, not that much faster than its current pace elsewhere. And I think that'd be a pretty messed up world.

Their words

Like models are good at different types of coding. Models have different styles. Like I think I think these things are actually, you know, quite different from each other. And so expect more differentiation than you see in in um cloud.

Their words

The way you get industries in which there are small number of players are very high costs of entry, right? Um so, you know, uh cloud is like this. I think cloud is a good example of this. You have three, maybe four players within cloud. I think I think that's the same for AI, three maybe four.

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but at the same time, we're spending $10 billion to train the next model because there's an exponential scale up. And so the company loses money. Each model makes money, but the company loses money.

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I actually think profitability happens when you underestimated the amount of demand you were going to get and loss happens when you overestimated the amount of demand you were going to get because you're buying the data centers ahead of time.

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I kind of get the impression that, you know, 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 kind of doing stuff cuz it sounds cool.

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And if my if my revenue is not a trillion dollars, if it's even 800 billion, there's no force on Earth. There's there's no hedge on Earth that could stop me from going bankrupt if I if I buy that much compute.

Their words

There's There's nothing preventing longer context from working. You just have to train at longer context and then learn to to serve them at inference. And both of those are engineering problems that we are working on and that I would assume others are working on as well.

Their words

I think the trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in adolescence of technology can happen without it, but I I I also think we, and I imagine others, are working on it. And I think there's a good chance that that, you know, that we get there within the next year or two.

Their words

Like I would say right now the coding models give maybe I don't know a a like 15 maybe 20% total factor speedup. Like that's my view. Um and 6 months ago it was maybe 5% and so and so it didn't matter.

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Like we see the end productivity every few months in the form of model launches. Like there's no kidding yourself about this. Like the models make you more productive.

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but it's not an infinitely compelling product, and I don't think even AGI or powerful AI or country of geniuses in a data center will be an infinitely compelling product. It will be a compelling product enough maybe to get three or five or 10x a year growth even when you're in the hundreds of billions of dollars, which is extremely hard to do and has never been done in history before, but not infinitely fast.

Their words

But but that's actually a very weak criterion, right? People thought I was saying like we won't need 90% of the software engineers. Those things are worlds apart, right?

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We already see substantial generalization from things that that verify to things that don't verify. We're already seeing that.

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My one little bit, the one little bit of of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable. Like, planning a mission to Mars, like, uh you know, doing some fundamental scientific discovery like like CRISPR, like, you know, writing a writing a novel. Hard to hard to verify those tasks.

Their words

on the basic hypothesis of you know, as you put it, within 10 years we'll get to, you know, you know, what I call kind of country of geniuses in a data center. I'm at like 90% on that. Um and it's hard to go much higher than 90% cuz the world is so unpredictable.

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the goal is not to teach the model every possible skill within RL just as we don't do that within pre-training, right? Within pre-training, we're not trying to expose the model to, you know, every every possible you know, way that words could be put together, right? You know, we're it's it's rather that the model trains on a lot of things and then and then it reaches generalization across pre-training, right?

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I think there's something going on that pre-training it's it's not like the process of humans learning. It's somewhere between the process of humans learning and the process of human evolution.

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I will tell you though what the most surprising thing has been. The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential.

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This is a topic that I warned about very publicly in 2025 , where I predicted that AI could displace half of all entry-level white collar jobs in the next 1–5 years, even as it accelerates economic growth and scientific progress.

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It could be as simple as a large universal basic income for everyone, although I suspect that will only be a small part of a solution.