Anthropic’s Amodei on AI: Power and Risk

Yeah. Thanks for having me. I mean, you know, I've never liked the artificial general intelligence or superintelligence. Not because I don't think it's very powerful. I'm not a skeptic. I'm actually extreme in terms of my views of how powerful the technology is going to be. But it's the wrong model for thinking about it, that there will be some one point where we build something completely different. What we see here, actually, what we've seen for the last ten years, maybe even 15, and you know, me and Anthropic's co-founders were among the first to document it, is this very smooth, exponential process. Just like in the nineties, you saw Moore's Law that the amount of computing power would double every 12 months or every 18 months. We have a Moore's Law-like law, except it's for intelligence itself, for the cognitive ability of the model across many tasks. And so what we are seeing is that that cognitive ability is, depending on how you measure it, doubling every four to 12 months. And so, you know, we're just climbing the ladder of cognitive ability. And where we are now is, to take coding as one example that's been exploding really fast over the last year or two. I have engineers. In fact, the team that leads one of our lead products called Code, which is the way you use our models for coding, he says he hasn't written any code in the last two months. He's edited it, he's looked at it, but it's all been written by Claude. Indeed, we recently released this thing called Claude Code, which was bringing code to non-coding tasks. It really seems to have taken off. We wrote it in like a week and a half, almost entirely with Claude Code. So I think we're entering the world where, you know, the junior level software engineers, maybe many of the tasks of the more senior level software engineers are starting to be done most of the way by AI systems. Now that's going to go further, can be more end-to-end, the project can be more. But I think we're going to be surprised at how the exponential turns upward. The whole thing about exponentials is, you know, it looks like it's going very, very slowly. It speeds up a little bit and then it just zooms past you. And I think we're on the precipice. I think we're a year or two away from it really zooming past us.

Yeah. I mean, you know, so I think as the field has developed, you know, I think it's no longer appropriate to think of it as like a scalar, like runners in a race who are at one position. The players have gone in different directions. So some of the other players have gone into very consumer-oriented direction. And that leads you to make your models super humanly engaging or super humanly good at recommending shopping or ads or various things like that. Anthropic has focused, I think, first and foremost on enterprises, developers. And to the extent we do consumer, we're very focused on productivity and the kind of high-value end of the consumer work. And so that gives very different incentives. If you want to get things done. You know, we talked about AGI and superintelligence before. One of the things I would say is that, you know, there are superintelligences today and they're basically large corporations, right? They're smarter than any human can be, solving the problem of shipping commerce at the lowest possible cost or making solar panels the lowest possible cost or launching rockets at the lowest possible cost. And so there's really high returns to intelligence in this area, which I think incentivizes us to kind of build the right thing. It's also, you know, I think it's a business that is more stable than consumer. We don't need ads. We don't need large numbers of free users. We can just very directly create value. There aren't all these weird externalities of, like you're prioritizing engagement, you're prioritizing these other things, you're generating all this slop. We just make stuff that people can use.

You know, I think they never really caught up that much. So, you know, of course, there was this huge excitement around DeepSeek. But, you know, the truth was a couple of things. One, you know, those models are very optimized for the benchmarks. It's actually very easy to optimize a model for a finite list of benchmarks. When we go out into the world, when we're competing against other companies for enterprise contracts, we see just honestly and candidly, we see Google and we see OpenAI. Every once in a while, we see a couple other U.S. players. I have almost never lost a deal, lost a contract to a Chinese model.

That's right. That's right. The thing that is holding them back, and they've said it themselves. The CEOs of these companies say it's the embargo on chips that's holding us back. They explicitly say this. And now, indeed, there are some policies, and I hope they change their mind, to explicitly send not quite our latest generation of chips, although it was reported that even that was being considered. But, you know, the generation of chips that's just one back, that's still extremely powerful. And we are many years ahead of China in terms of our ability to make chips. So I think it would be a big mistake to ship these chips. You know, the analogy I thought of, if you think about the incredible national security implications of building models that are essentially cognition, that are essentially intelligence. I've called where we're going with this a country of geniuses in the data center. So imagine 100,000, 100 million people smarter than any Nobel Prize winner. And it's going to be under the control of one country or another. So I think this is crazy. It's a bit like selling nuclear weapons to North Korea and bragging. Oh yeah, we made that.

I wouldn't refer to any particular people, but I would just say that this particular policy, I think, is akin to not well-advised.

Yeah. So I would separate out the two things. One is the basic exponential, and that's what we've been talking about. That's been kind of the technological direction. And there, you know, just like with Moore's Law, you can never be sure that the technology is sort of fully going to advance. It's going to keep going. It's a fundamentally inductive problem. But now that I've seen well over a decade of it, I would say I am pretty confident that it's going to continue, or at least I'm more confident than I've ever been at any time in the past that it's going to continue to where the models are basically smarter than humans at almost everything. And I think there's a good chance that even happens in the next year or two. Again, it seems far away. But this property of exponentials is that they catch you by surprise. And if it's not a year or two, I think it's pretty likely that it's at least less than five years, within the 2020s. I think this moment will come in the 2020s. So, you know, I'll get into that economic angle. I'll get to the economic diffusion, which I think is complicated, but I don't want anyone to forget that as long as we have that fundamental technological gain, there's going to be many trillions of revenue, maybe many trillions per company in this area, because the economic potential is so great. Now there's a separate thing, which is that we don't know how fast enterprises and companies are actually going to be able to use this technology. The uses of this, what the technology is capable of today is probably ten times what the enterprise of the world are able to deploy. And I see it with our customers every day where I'll talk to a CEO and they'll understand and their executive team will understand the power of this technology for automating customer service, for coding, for many other things. But they have a company of tens of thousands of people who are perfectly brilliant people but expert in something that is not AI and have to learn to use AI. And that, you know, different names for it, change management, enterprise transformation, whatever it's called, the need to do this is very slow. It can take years. And so we have this powerful technology that I have an incredible amount of confidence will generate trillions in revenue. But we don't know exactly when, plus or minus a few years. We don't know exactly when. And in the meantime, companies have to buy compute to serve all that revenue. And you don't want to buy too much because you could financially overextend yourself and you don't want to buy too little because then you can't serve all the revenue. And so that's where the kind of economic and financial problems.

So we have some advantages in this. There is a stability to enterprise business that there is not to consumer. Consumers are very fickle. Enterprise purchasing, enterprise predictions are predictable. Also, enterprise tends to have better margins. The margin is basically how much buffer you have between buying too little and buying too much, between what you have to pay for and what you have to support in revenue. So we have a number of advantages here. But I won't deny that there is some inherent risk in all of the companies to this process. Whenever you have an upside whose amount is uncertain or even whose timing is uncertain, and you have to make financial decisions somewhat blind to those things, you have to decide early to build out capital, to build out resources a few years in advance. There's going to be some inherent risk that's driven by that uncertainty. And I do think that some companies have probably overbought. I'm not able to look at their financials. I just know what they announce. And I'm like, whoa, I wouldn't necessarily have done that. I'm pretty happy with the decisions that we've made. But, you know, I can't be sure about others, but it is entirely consistent that this could be the most transformative technology in the history of the world, that some companies will do really well, but not every company may very quickly.

I mean, you know, we're not in that market, so I don't know about that. You know, the statistics would seem to suggest that, you know, with their kind of greater distribution, with their greater existing reach, they've been able to make a lot of progress in kind of recent times. And again, this is why, you know, consumers are fickle. This is why I would be careful if I were a consumer company in what was on my balance sheet.

Yeah. So, you know, my view of AI, there's maybe two axes in terms of which to think. There's good things happening versus bad things happening. And there's like AI is a small deal versus AI is a big deal. You know, my view is on the extreme side of AI is a big deal, but I'm kind of in both top quadrants where I think some really good things will happen. And if we don't act to prevent them, some really bad things will happen. You know, I wouldn't be building this technology if I didn't believe that the good outweighed the bad and that we could mitigate the bad. That's why I warn about the bad, so that we can.