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Alex KantrowitzAlex Kantrowitz

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

He joins Big Technology Podcast for a raw, unfiltered conversation about AI's potential, its stakes, and the source of his outspokenness. The interview is part of a major Big Technology profile about Amodei, ft.

Dario AmodeiguestAlex Kantrowitzhost
Jul 30, 20251h 8mWatch on YouTube

Summaries, chapters, transcripts, and FAQs on this page are made by uListen, not YouTube.

Transcript

  1. 0:00 – 3:33

    Intro

    1. DA

      I get very angry when people call me a doomer. When, you know, when, when someone's like, "This guy's a doomer, he wants to slow things down." You, you heard what I just said. Like, you know, my, my father died because of, you know, cures that, you know, could have, could have happened a few years later. I understand the benefit of this technology.

    2. AK

      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." What do you think about that?

    3. DA

      I've never said, I've never said anything like that. That's an outrageous lie. That's the most outrageous lie I've ever heard.

    4. AK

      Anthropic CEO Dario Amodei joins us to talk about the path forward for artificial intelligence, whether generative AI is a good business, and to fire back at those who call him a doomer. And he's here with us in studio at Anthropic headquarters in San Francisco. Dario, it's great to see you again. Welcome to the show.

    5. DA

      Thank you for having me.

    6. AK

      So let's recap the past couple months for you. Uh, you said AI could wipe out half of entry-level white collar jobs. You cut off Windsurf's access to Anthropic's top tier models when you learned that OpenAI was going to acquire them. You asked the government for export controls and annoyed NVIDIA CEO Jensen Huang. Uh, what's gotten into you?

    7. DA

      Um, uh, you know, I think, I think Anthropic, myself and Anthropic are always focused on kind of trying to do and say the things that we believe. Um, uh, and I think as we've gotten, uh, more close to AI systems that are more powerful, um, you know, I think I've wanted to say those things, um, you know, more, uh, more forcefully, more publicly to make the point clearer. Um, you know, I've been saying for many years that, you know, we have these, we can talk in detail about them, but, you know, we have these scaling laws. AI systems are getting more powerful. They're going from the level of, you know, a f- a few years ago they were barely coherent. Now, you know, 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, they're starting to apply across the economy. And so I think all the issues related to AI, ranging from kind of the national security issues to the economic issues, um, you know, are, are, are starting to become quite near to where, to where we, um, you know, to where we're actually going to face them. And so, and so I think as these problems have come closer, I've, y- you know, even though in, you know, in some form Anthropic has been saying these things for a while, I think the urgency of these things has gone up. And, and, and, and, and, you know, I, I, I wanna make sure that we, uh, you know, I wanna make sure that we say what we believe, and that we warn the world about the possible downsides. Even though, you know, no one can say what's gonna happen, we're, you know, we're saying what we f- you know, what we think might happen, what we think is likely to happen. You know, we, we back it up as, as best we can, although it's often, you know, extrapolations about the future where, where no one, where no one can be sure. Um, but, you know, I think we, we see ourselves as kind of, as kind of having a duty to, you know, to kind of warn the world about what's gonna happen. A- and that's not to say, you know, I think there's an incredible number of like positive applications of AI, right? I've, I've kind of continued to talk about that. I read this, I wrote this essay, Machines of Loving Grace. Um, I, I, I feel in fact 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.

  2. 3:33 – 9:49

    AGI timelines and the exponential case

    1. DA

      Um, so I think we probably appreciate the benefits more than, more than anyone. Um, 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.

    2. AK

      So all of this is coming from your timeline. Basically, you-- It seems like you have a shorter timeline than most, and so you are feeling a sense of urgency to get out there because you think that this is imminent.

    3. DA

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

    4. AK

      And so I should say that this is part-- our conversation is part of a profile I'm writing about you. I've spoken with more than two dozen people who've worked with you, who know you, who've competed with you, and I'm gonna link that in the show notes if anybody wants to read it. It's free to read. Uh, but one of the themes that has, uh, come through across everybody I've spoken with is that you have about the shortest timeline of any of the major lab leaders, and you just referenced it, uh, just, just now. So why do you have such a short timeline, and why should we believe in yours?

    5. DA

      Yeah. It, it really depends what you mean by timeline. Um, so one thing, and, you know, I've, I've, I've been consistent on this over the years is, you know, there are these terms in the AI world like AGI and superintelligence. Like you'll hear leaders of companies say, "We've achieved AGI, we're moving on to superintelligence," or like, "It's really exciting that someone stopped working on AGI and started working on superintelligence." So I think these terms are totally meaningless. I don't know what AGI is. I don't know what superintelligence is. It, it sounds like a, it sounds like a marketing term. It's, it-

    6. AK

      It is marketing

    7. DA

      ... yeah, it, it sounds like, you know, something, something designed to activate people's, people's dopamine. So you'll see in public I never use those terms, and, uh, I, um, you know, I'm, I'm, I'm actually, you know, careful to criticize the use of those terms. Um, but I think, I think despite that, I am, I am indeed one of the most bullish about, 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 that we, 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 wanna call it. I, 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, from other companies, and I don't see anything blocking that, the further scaling of that. There's some stuff about, you know, how do we broaden the tasks. On the RL side of it, we've seen Uh, more progress on, say, math and code, where, where the models are, you know, getting pretty close to, like, a high professional level and less on more subjective tasks. But I think that is very much a temporary obstacle. Um, uh, so when I look at it, I see this exponential, and I say, "Look, people aren't very good at making sense of exponentials," right? Like, you know, if, if something is doubling every six months, then, uh, you know, two years before it happens, it looks like it's only one-sixteenth of the way there. And, and, and so we are sitting here in the middle of, of twenty twenty-five, um, and the models are really starting to explode in ter- in, in terms of the economy, right? If you look at the capabilities of the model, they're starting to saturate all the benchmarks. If you look at revenue and, you know, Anthropic's revenue every year has grown ten X. Um, uh, every year we're, we're kind of conservative and we say, you know, it can't grow ten X this time. You know, I, I, I, you know, I never, I never assume anything and, and actually always am very conservative in saying, "Ah, I think it's gonna slow down on the business side." But we went from zero to a hundred million in twenty twenty-three. We went from a hundred million to a billion in twenty twenty-four. And, you know, this year, in this first half of the year, we've gone from one billion to, you know, I think as of, as of speaking today, it's, it's well above four, it might be four point five. Um, uh, and so if you think about it, you know, suppose that exponential continued for two years. I'm not saying it will, but suppose it continued for two years. You know, you're, you're like 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, it, you know, it looks like it's just starting to be a thing. Um, and so that's the fundamental dynamic. You know, we saw that with the internet in, i- we saw that with the internet in the nineties, right? Where it was like, you know, networking speeds and the underlying speed of the computers were getting fast. And over a few years, it became possible to have to basically build a digital global communications network on top of all this, when it wasn't possible just a few years ago. And, and almost no one except for a few people really saw the implications of that and how fast it would happen. And so that's, that's where I'm coming from. That w- that's what I think. Now, I don't know, like if a bunch of satellites crashed, maybe the internet would have taken longer. If there was an economic crash, maybe it would've

  3. 9:49 – 16:47

    Scaling update: diminishing returns, continual learning, new techniques

    1. DA

      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, and not realizing how fast it, it might be, how fast I think it probably will, although I'm not sure.

    2. AK

      But so many folks in the AI industry are talking about diminishing returns from scaling now. That really doesn't fit with the vision you just laid out. Are they wrong?

    3. DA

      Yeah. Uh, I, I-- from what we've seen, I can only speak in terms of the models at Anthropic. Um, but what I th- seen in terms of the models at Anthropic, if we look at... You know, let's take coding. Coding is one area where, you know, I think 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, if you look at, if you look at coding, um, you know, every m- you know, we released three point five Sonnet, a model we call three point five Sonnet V2, um, uh, which I, you know, let, let's call it three point six Sonnet now, um, three point seven Sonnet, uh, and then four point oh Sonnet and four point oh Opus. And, you know, that series of four or five models, each one got substantially better at coding than, than the last. If you wanna look at benchmarks, you can look at, you know, SWE-bench growing from, uh, you know, I think eighteen months ago, it was at like three percent or something, um, growing all the way to, you know, seventy-two to eighty percent, depending on how you, how you measure it. And, and the real usage has grown ex- grown exponentially as well, where we're heading more and more towards autonomously you can just use these models. I think the actual majority of code, um, uh, uh, that's written, written at Anthropic, uh, is, you know, at this, at this point, uh, written by or at least with the involvement of one, you know, one of the Claude models. Um, and various, uh, various other companies have, have said, you know, have said, you know, similar, similar statements to, uh, similar statements to that. So we see the progress as being very fast and the exponential is continuing and, and we don't see any diminishing returns.

    4. AK

      But there are some liabilities, it seems like, with large language models. For instance, continual learning. We had Dwarkesh on a couple weeks ago. Here's how he put it, and he wrote about it in his Substack. "The lack of continual learning is a huge, huge problems. The LLM baseline at many tasks might be higher than an average human, but you're stuck with the abilities you get out of the box." So you just make the model and that's it. It doesn't learn. That seems like a glaring liability. What do you think about that?

    5. DA

      So first of all, I would say even if we never solved continual learning, um, [clears throat] even if we never solve continual learning and memory, um, I think that the potential for the LLMs to do, you know, incredibly, uh, incredibly well to, you know, affect things at the scale of the economy will be very high, right? If I think of the field I used to be in, biology and medicine, like, you know, let's say I had a very smart Nobel Prize winner and, you know, I, I, I said, "Okay, you know, you're, you know, you've, you've, you've discovered all these things. You have this incredibly smart mind, but, you know, you can't, you can't, you know, you can't like read new textbooks or absorb any new information." I mean, that would be difficult, but like still, if you had like ten million of those, like they're, they're still gonna make a lot of biology breakthroughs. Like, they're gonna be limited. They're gonna be able to do some things humans can't, and there are some things humans can do that they can't. But, but, but even that, even if we impose that as a ceiling like Man, that's pretty damned impressive and transformative, and even if I said you never solve that, like, I think, you know, I think people are underestimating the impact. But, but look, context windows are getting longer, and models actually do learn during the context window, right? So, so as, as I, you know, 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, but, you know, just, just like I'm talking to you here and we're having a conversation and I listen to the things you say and I, I, you know, I think and, and, and I, like, respond to them, the models are able to do that. And, and from a, from an, 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, right? Which is roughly what a human hears, what, what, what a human hears in their lifetime. Um, uh, there's no reason that we can't, uh, do that. It's, it's really inference support. Um, uh, and so again, even that fills in many of the gaps. Not all the gaps-

    6. AK

      Right

    7. DA

      ... 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. So, um, you know, there, there, there are a number of things around, you know, types of reinforcement learning, learning training. You know, you know, we used to many years ago talk about inner loops and outer loops, right? The inner loop is like 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, and kind of the outer loop is, is, is the agents learning over episodes. Um, and so I think maybe that inner loop, outer loop structure is a way to learn the continual learning. One thing we've 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, uh, around reasoning. Turned out just to be, just to be RL. You just train with RL and you let the model write some stuff down. You know, you, you let the model write things down to try and figure out objective math problems. Um, without being too specific, um, you know, I think and we already have maybe some, some, you know, some, some evidence to suggest that this is another of those problems that is, is not as difficult as it seems, that will fall to scale plus a slightly different way of thinking about things.

    8. AK

      Do you think your obsession with scale might blind you to some of the new techniques like Demis Hassabis says? You know, to get to AGI or you might call it super powerful AGI, whatever. Human-level intelligence is what we're all talking about. We might need a couple new techniques for that to happen.

    9. DA

      Yeah.

    10. AK

      If you're so-

    11. DA

      So, you know-

    12. AK

      ... focused on scale-

    13. DA

      We're developing new techniques-

    14. AK

      Can you get there?

    15. DA

      We're developing new techniques every day.

    16. AK

      Okay.

    17. DA

      Um, uh, you know, Claude is very good at code and we, you know, we don't really talk externally that much about why, why Claude is so good at code.

    18. AK

      Why is it so good at code?

    19. DA

      Um, it-- Li-like I said, we don't talk externally about it.

    20. AK

      [laughs] I have to ask.

    21. DA

      Um, uh, uh, uh, so, you know, every new version of Claude that we make has, you know, 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. Um, new techniques are a part of every, you know, model that we build and, you know, that's why we... You know, I've said these things about, like, you know, we're trying to optimize for, like, talent density as much as possible. Like, you need that talent density in order, in order to invent the new techniques.

    22. AK

      You know, there's one thing that's been hanging over this conversation, uh, which is that maybe Anthropic is the company with the right idea, uh, but the wrong resources. Because you look at what's happening with, uh, xAI and, um, and inside Meta where, uh, Elon's built his massive cluster. Mark Zuckerberg is building this five-gigawatt,

  4. 16:47 – 20:10

    Scale & competition: resources, talent wars

    1. AK

      um, data center, and they are putting so much resources towards scaling up. Um, is it possible-- And I mean, Anthropic, obviously you have raised billions of dollars, but these are trillion-dollar companies. Um-

    2. DA

      Yeah. So we-

    3. AK

      So is it possible-

    4. DA

      We've raised, I think at this point, uh, a little short of $20 billion.

    5. AK

      It's not bad.

    6. DA

      So that's, that's not-

    7. AK

      Right

    8. DA

      ... 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, um, I, I don't think our data center scaling is substantially smaller than that of any of the other companies in the space. Um, you know, in many cases these things are limited by energy, they're limited by capitalization. Um, you know, when, when, when, when people talk about, you know, these large amounts of money, they're, they're talking about it over, over, over several years, right? And when you hear some of, some of these announcements, sometimes they're not funded yet. They're, you know... We've, we've seen the size of the, of the, of the data centers that folks are building and we're, we're actually, we're actually pretty confident that, you know, we're, w- we, we will be within, we will be within a r- rough range of the size of the data centers they built.

    9. AK

      You talked about talent density. What do you think about what Mark Zuckerberg is doing on the talent density front? I mean, combining that with these massive data centers, it seems like he's gonna be able to compete.

    10. DA

      Yeah. So, uh, this is, this is actually very interesting because, um, you know, one thing we noticed is that relative to other, uh, companies, um, you know, I think, I think, I think, uh, very, you know, a lot fewer people from Anthropic, uh, have been, have been caught by these, and it, it's not for lack of trying. I've talked to plenty of people, uh, you know, who, who got these offers at Anthropic and, and who just turned them down, um, who wouldn't even talk to Mark Zuckerberg, who said, um, you know, uh, uh, "No, I'm, I'm, I'm staying at Anthropic." And, and our, our general response to this was, you know, I posted something to the, to the whole company Slack where, where, where, where I said, "Look, um, you know, we are not willing to compromise our, you know, 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, um, uh, uh, because, because we think it's unfair. We wanna have a systematic way. If, you know, if Mark Zuckerberg, you know, throws a dart at a dartboard and hits your name- That doesn't mean that you should be paid 10 times more than the guy next to you who's, who's, you know, who's, who's just as skilled, who's just as talented. Um, uh, and, and, and, and my view of the situation is that, you know, 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, to defend the company. Um, and I think actually this was a unifying moment for the company where, um, you know, we, we, we didn't give in. We refused to compromise our principles because we had the confidence that people are anthropic because they truly believe in the mission. Um, uh, and, and, you know, I think, um, I, I th- that, that gets to kind of how I see this. I think that what they are doing is trying to buy something that cannot be bought, um, and, and that is alignment with the mission. Um, uh, and you know, I, I, you know, I, you know, I think there's selection effects here. Like I, you know, I, I, you know... Are they getting the people who are most enthusiastic,

  5. 20:10 – 27:26

    Business model and revenue ramp

    1. DA

      who are most mis- mission aligned, who are most excited to-

    2. AK

      But they have talent and GPUs. You're not underestimating them?

    3. DA

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

    4. AK

      So let's talk a little bit about your business because a lot of people have been wondering, is the business of generative AI a real thing? And I'm also curious. I have questions all the time. You talked about how much money you've raised, close to 20 billion. Um, you've raised three billion, three billion from Google, eight billion from Amazon, three and a half billion, uh, from a new round led by Lightspeed, who I've spoken with. Um, what, what is your pitch? Because you are not part of like a big tech company. You're out there on your own. Do you just bring the scaling laws and say, "Can I have some money?"

    5. DA

      So my, my view of this has, my view of this has always been, um, that, uh, talent is the most important thing. Um, so you know, if you go back three years ago, um, you know, we were in a position where we had raised mere hundreds of, hundreds of millions. OpenAI had already raised, you know, 13 billion from, from Microsoft and of course, you know, the, the large hyper cap tech companies were sitting on $100 billion, $200 billion. And, and basically the pitch we made then is we know how to make these models better than others do, right? There may be a curve, there may be a curve of scaling laws, but, but look, if we are in a position where we can do for 100 million what others can do for a billion, and we could do for 10 billion what they can do for 100 billion, then it's 10 times more capital efficient to invest in Anthropic than it is to invest in, 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, the, you know, the, the, the, the, the money is a temporary defect that, that, that, 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, you know, investors aren't, aren't idiots, or at least they aren't always idiots. Um-

    6. AK

      It depends which one you go to. [laughs]

    7. DA

      I'm not gonna name any names. Um, uh, uh, uh, but um, uh, uh, you know, they, they basically understand the concept of capital efficiency. Um, and so we've been in a position, you know, three years ago where, you know, these differences were like 1000x, and now you're saying, you know, with $20 billion, can you compete, ca- can you compete with $100 billion? Um, and, and, and my answer is basically yes because of the talent density. You know, I've said this before, but uh, you know, the, the, the Anthropic is actually the fastest growing software company in history at the scale that it's at. Um, so we grew from zero to 100 million in 2023, 100 million to a billion in 2024, and this year we've, we've grown from one billion to, I think I said this before, 4.5.

    8. AK

      Right.

    9. DA

      So that, that 10x a year, I mean, you know, every, every e- you know, every year I like suspect that we'll grow at that scale and, and every year I'm almost afraid to say it publicly because I'm like, "No, it couldn't, it couldn't possibly happen again." So, you know, I think, I think the growth at that scale like kind of speaks for itself in terms of our ability to compete with the big players.

    10. AK

      Okay. So CNBC says 60 to 75% of Anthropic sales are through the API. That was th- according to internal documents. Is that still accurate?

    11. DA

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

    12. AK

      So you're making the most pure bet on this technology. Like s- you know, OpenAI might be betting on ChatGPT and Google might be betting on the fact that no matter where the technology goes, it can, you know, integrate into Gmail and Calendar. So why have you made this bet on this, the pure bet on the tech itself?

    13. DA

      Yeah, I mean, I would say I wouldn't quite put it that way. I think we've... I would describe it more as we've bet on business use cases of the model, um, 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. So you know, as you mentioned, OpenAI is very focused on the consumer side. Google is very focused on kind of 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, right? I should say the business use because it's enterprise, it's startups, it's developers, and it's kind of, you know, power users using the model, model for productivity. Um, I, I also think that- Uh, being a company that's focused on the business use cases actually gives us better incentives to make the models better. Um, a, a thought, a thought experiment that I think is worth running is, you know, suppose I have this model and it's, uh, it's, um, you know, it's, it's as good as an undergrad at biochemistry. Um, and then I improve it and it's as good as a PhD student at, at biochemistry. If I go to a consumer, right, if I give them the chatbot and I say, "Great news, I've improved the model from, you know, undergrad to graduate level in, in biochemistry," um, you know, maybe, I don't know, 1% of consumers care about that at all, right? 99% are just gonna be like, "I don't understand it either way." Um, but now suppose I go to Pfizer and, and I say, "You know, I've improved this from undergraduate biochemistry to, to graduate biochemistry," like this is gonna be the biggest deal in the world, right? They might pay 10 times more for something like that. It might have 10 times more value to them. And so the general aim of making the models solve the problems of the world to make them smarter and smarter, but also able to s- to bring many of the positive applications, right? The things I wrote about in like Machines of Loving Grace of like solving the problems of biomedicine, solving the problems of geopolitics, solving the problems of, you know, of economic development, um, you know, as well as more prosaic things like finance or legal or productivity or insurance. Um, I think it gives a better incentive to kind of develop the models, um, as, as far as, uh, as, as, uh, as, as, as far as possible. And I think in many ways it's like it may even be a more, a more, a more positive business.

    14. AK

      Right.

    15. DA

      So I would say we're making a bet on the business use of AI because it's most aligned with kind of the exponential.

    16. AK

      Okay, then briefly, how did you decide to go with the coding use case?

    17. DA

      Yes. So, um, you know, uh, originally, as happens with most things, we're trying to optimize for making the model better at a bunch of stuff and, you know, coding particularly stood out in, in terms of how, how valuable it was. You know, I've worked with thousands of engineers, and there was a point about a year, a year and a half ago, where one of the best I'd ever worked with said, um, uh, you know, "Every previous coding model has been useless to me, and, 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, you know, a lot of the coding companies like Cursor, Windsurf, GitHub, Augment Code started, started exploding in, in popularity. And then when we saw how popular it was, we kind of doubled, doubled down on it. Um, my view is that coding is particularly

  6. 27:26 – 36:29

    Economics: pricing changes, inference costs & profitability

    1. DA

      interesting because, A, the adoption is fast, um, and, and, B, getting better at coding with the models actually helps you to develop the next model. Um, so it has, it has a number of, uh, you know, uh, uh, you know, I would say advantages.

    2. AK

      And now you're selling, uh, your AI coding through Claude Code. Um, but it's very interesting, the pricing model, um, has been confounding to some. You can spend $200 a month and get the equivalent... I spoke to one developer, they got the equivalent of $6,000 a month, uh, from your API. Um, Ed Zitron has pointed out the more popular that your models get, the more money you're going to lose if people are super users of this technology. So how does that make sense?

    3. DA

      So, um, uh, uh, so actually pricing schemes and rate limits are surprisingly complicated. Um, so, so some of this is basically the result of when we released our, um, uh, when we, when we released Claude Code and the Max tier, which we eventually tied together, actually not fully understanding the implications of, you know, 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, uh, a- 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, um, uh, uh, with a, with a, with a $200, uh, uh, subscription. And, you know, it's possible more changes will, more changes will come in the future. But we're always gonna have a distribution of users who use a lot and, and, and users who lose-- who use some amount. And it, it doesn't necessarily mean we're losing money, that there are some, some users who get more, um, uh, you know, who i- if you were to measure via API credits, spend, you know, get, get a better deal on the consumer, uh, on, on the consumer subscription than they would on the API products, right? There's a l- there's a lot of assumptions there. Um, uh, and I can tell you that, that some, that some of them, that some of them are wrong. Um, uh, we are not, in fact, uh, losing money.

    4. AK

      But I guess there's another question about whether you can continue to serve these use cases, um, and not raise prices. So, uh, just to give you a couple stats, there are some developers that are upset because, uh, using Anthropic's newer models in Cursor is costing them more than it ever has. Um, startups that I've spoken with say Anthropic is down a much-- down a bunch because they can't get access to the GPUs. At least that's what they, um, what they imagine is happening. And I was just with Amjad Masad at Replit, um, in an interview that we're gonna air next week, who said there was a period of time where the price per token, price per to use these models, was coming down, and it stopped coming down. So is it the... Is what's happening that these models are just so expensive for Anthropic to run that it's hitting its wall of its own?

    5. DA

      Again, I think you're, I think you're making assumptions here. Um-

    6. AK

      That's why I'm asking the CEO.

    7. DA

      Uh, yeah. Um, uh, you know, uh, you know- The, the, the way I think about it is we think about the models in terms of how much value are they creating, right? Um, so 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, and, you know, those who make, those who make the, the, the, um, the, the, the, the, the underlying applications. So again, without being too specific, like, I think there are some assumptions in your question that are not necessarily correct. Um, uh, you know, I-

    8. AK

      Can you tell me which ones?

    9. DA

      I, I... So I'll say this. I do expect the, um, I, I, I expect the price of providing a given level of intelligence to go down. Um, I expect the price of providing the frontier of intelligence, which will, which will provide a kind of 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, you know, goes way up. So two year, two, 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 th- than they are capable of today.

    10. AK

      One of the things that Amjad mentioned was he thinks that the bigger models are not as intensive to run or more intensive to run given their size because of the architecture and some of these techniques that we talked about, that they're lighting up only certain sections of the model. So his idea, I'm hopefully conveying this truthfully, is that, um, Anthropic can run these models without too much bulk on the back end, but is still keeping those prices, uh, where they are. And I think the, the line that I'm gonna draw there is maybe that, um, to get to software margins, there were some reports that Anthropic is slightly below software gross margins. You're gonna have to charge a little bit more for these models.

    11. DA

      So, um, yeah. A- a- again, um, I think larger models cost more to run than smaller models.

    12. AK

      Okay.

    13. DA

      Um, uh, uh, you know, I think the technique you're referring to is maybe mixture of experts or something like that.

    14. AK

      That's right. Yeah.

    15. DA

      So whether your models are mixture of experts or not, like mixture of experts is, is like a way to run models more cheaply. They 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 m- smaller models that don't use that technique. And if you're using that technique, larger models that use that technique cost, you know, more to run than, than smaller models that, that, that are using that technique. So I think, I think that's sort of a distortion of the... I think that's sort of a distortion of the situation. Um, you know-

    16. AK

      Basically, I'm, I'm just guessing and I'm trying to find out what the truth is from you, so.

    17. DA

      Yeah. Look, so I, um, you know, in, in, in, in terms of like the cost of the models, like one thing you'd be surprised by, people, you know, people i- kind of impute this thing to like, "Oh man, it's gonna be really hard to get the margins from like X percent to Y percent." We make improvements all the time that make the models like fifty percent more efficient than they are before. We're just at the beginning of optimizing inference. Um, uh, inference has improved a huge amount where, from, from where it was a couple, a couple years ago to where it is now. That's why the prices are coming down.

    18. AK

      And then how long is it gonna take to be profitable? Because I think the loss is gonna be like three billion this year, or that's what they predict.

    19. DA

      So I would distinguish different things.

    20. AK

      Okay.

    21. DA

      Um, there's the cost of running... There's the cost of running the model, right? So, so for every dollar the model makes, it costs a certain amount. Um, that is actually already fairly profitable. Um, there are separate things. There's, you know, the cost of paying people, um, and like buildings. That is actually not that, that large in the scheme of things. The big cost is the cost of training the next model.

    22. AK

      Mm-hmm.

    23. DA

      Um, and I think this idea of like the company's losing money and not being profitable is, it's a little bit misleading. Um, and you start to understand it better when you, when you look at the scaling laws. So as a thought exercise, these n- numbers are not exact or even close for Anthropic. Let's imagine that in 2023, you train a model that costs $100 million. Um, and then in 2024, you deploy the 2023 model and it makes $200 million in revenue, but you spend a billion dollars to train, um, you know, to train a new model in 2024. So a- and then, you know, and then in 2025, the billion-dollar model makes two billion in revenue and you spend 10 billion to train the next model. Um, so the company every year is unprofitable. It lost 800 million in 2024, and then in 20- 2025 it lost $8 billion. Um, so you know, this looks like a hugely unprofitable enterprise. But if instead I think in terms of is each model profitable, right? Think of each model as a venture.

    24. AK

      Mm-hmm.

    25. DA

      Um, I invested 100 million in the model, and then I got, then I got a hund- then I got 200 million out of the model the next year. So that model had fifty percent margins and, and, you know, and like, and like made me 100, $100 million. The next year, um, you know, the, the, the company invested a billion dollars and made, and made $2 billion in, in... Or sorry, the next model-

    26. AK

      Right

    27. DA

      ... the company invested a billion dollars and made-

    28. AK

      So are your models profitable?

    29. DA

      So every model is profitable-

    30. AK

      They're all profitable

  7. 36:29 – 40:10

    Open source vs. hosted frontier models

    1. DA

      the, you know, the, the companies, the industry will kind of return to this, you know, to this plateau, to this level of profitability, or the exponential can keep going. Um, so I think, I think that's a long-winded way to say I don't think it's really the right way to think about things.

    2. AK

      Right. But what about open source? Because if you stopped, let's say you stopped investing in the models and open source caught up, then people could swap in open source. Now, I'd love to hear your perspective on this because one of the things people have talked to me about when it comes to the Anthropic business is there is that risk eventually that open source gets good enough that you can take Anthropic out and put open source in.

    3. DA

      Yeah. So, you know, people have... I, you know, I think one of the things that's been true of this industry is that... And, you know, I saw it early in, I saw it early in the history of AI. Every community that a- that AI has gone through, it has this set of heuristics about how things work. Like back when I was in, you know, AI back in 2014, there was an existing kind of AI and machine learning research community that, like, thought about things in a certain way and were like, "This is just a fad. This is a new thing. This can't work. This can't scale." And then because of the exponential, all of those things turned out to be false. Then a similar thing happened with kind of like people deploying AI within companies to various applications. Then there was the same thought in the startup ecosystem. And I think now we're at the phase where kind of the world's business leaders, like the investors and the busi- They have this whole lexicon of commoditization, um, you know, moats, which layer is the value gonna, gonna, g- gonna get... Which layer is the value gonna accrue to? And open source is this idea that you can kind of see everything that's going on, you know, that, that it has a significance, that it kind of undermines, um, uh, uh, the, the fact that it, you know, the idea that it undermines business. And I actually find, as someone who didn't come from that world at all, who never thought in terms of that lexicon, this is one of these situations where not knowing anything often leads you to make better predictions than kind of the people who have their way of thinking about things from the last generation of tech. Um, and I, you know, this is all, I think, a long-winded way of saying I, I don't think open source works the same way in, in AI that it has worked in other areas. Primarily because with open source you can, you can see the, you know, you can see the source code of the model. Here, we can't see inside the model. Um, you know, it's often called open weights instead of open source-

    4. AK

      Right

    5. DA

      ... to kind of distinguish that. But a lot of the benefits, which is that many people can work on it, that it's kind of additive, it doesn't quite work in the same way. Um, so, you know, I've, I've actually always seen it as a red herring. When I see a, when I see a new model come out, I don't care whether it's open source or not. Like if we talk about DeepSeek, I don't think it mattered that DeepSeek is open source. I think I ask, "Is it a good model?"

    6. AK

      Right.

    7. DA

      "Is it better than us at, at, you know, the things that we..." Th- that's the only thing that I care about. It, it actually, it actually doesn't, doesn't matter either way. Um, because ultimately you have to, you have to host it on the cloud. The people who host it on the cloud do inference. These are big models. They're hard to do inference on. And conversely, many of the things that you can do when you see the weights, um, uh, uh, you know, we're increasingly offering on clouds where you can fine-tune the model, you can, you know, um, you know... We're even looking at, at ways to, you know, to, to kind of, you know, investigate the activations of the model as part of like an interpretability interface. We did some little things around steering last time. Um, so I think it's the wrong axis to think in terms of. When I think about competition, I think about,

  8. 40:10 – 54:15

    Personal background: SF, family, father’s illness

    1. DA

      like, which models are good at the tasks that we do. Um, I think open source is actually a red herring.

    2. AK

      But if it's free and cheap to run, I think-

    3. DA

      It's not free. You have to, you have to, you have to-

    4. AK

      Okay

    5. DA

      ... you have to run it on inference, and someone, someone has to make it fast on inference.

    6. AK

      All right. So I wanna learn a little bit more about Dario the person.

    7. DA

      Yeah.

    8. AK

      So we have a little bit of time left. Um, so I have some questions for you about early life and then how you became who you are.

    9. DA

      Yes.

    10. AK

      So what was it like growing up in San Francisco?

    11. DA

      Yeah. Um, I... You know, the city when I first grew up here had not really, had not really gentrified, uh, uh, that much. You know, when I grew up, the tech boom hadn't, hadn't happened, uh, hadn't happened yet. Um, you know, it happened as, as I was going through high school, and actually, I had no interest in it. Um, it was totally, it was totally boring to me. Um, you know, I was interested in being like a scientist. I was interested in physics and math and, you know, the idea of like, you know, you know, like writing some website actually had no interest to me, to me whatsoever. Like founding a company, like those weren't things that I was, uh, that I was interested in at all.

    12. AK

      Mm-hmm.

    13. DA

      Um, you know, I was interested in discovering fundamental scientific truth, and I was interested in like, you know, how can I, how can I do something that like makes the world better? Um, uh, so, so, you know, that was, that was kinda more... And, you know, I watched the tech boom happen around me, but I, I feel like, you know, there was all kinds of things I probably could've learned from it that would've been helpful now, but I just actually wasn't paying attention and had no interest in it, even though I was like right at the center of it.

    14. AK

      So you're the son of a Jewish mother-

    15. DA

      Uh, yes

    16. AK

      ... and an Italian father.

    17. DA

      That is true.

    18. AK

      From where I'm from in Long Island, we call that a pizza bagel.

    19. DA

      A pizza bagel. I've never, I've never heard that term before.

    20. AK

      [laughs] So what was your relationship with your parents like?

    21. DA

      Yeah, I mean, you know, I was, I was always, uh, I was always, I was always pretty close with them. You know, I feel like they gave me a sense- Of, you know, of, of kind of right and wrong and what was important in the world. I feel like, you know, kind of imbuing a strong sense of responsibility is, is maybe the thing that I remember most. You know, they were always people who felt that sense of responsibility and, you know, wanted to, wanted to make the world, um, uh, wanted to make the world better. And I, and I feel like, you know, that's one of the, one of the main things that I, that I learned from them. You know, it was always a very, a very, um, a very loving family, a very caring family. Uh, I was very close with my sister Daniella-

    22. AK

      Mm-hmm

    23. DA

      ... who of course became my, became my, became my co-founder. And, you know, I think we decided very early that we wanted to work together in some, in some capacity. I don't know if we imagined that it would happen, you know, at, at quite the scale that, that, that, that, that it has happened. Um, uh, but it, um, you know, I think, I think it really, um, you know, that was, that was something we kinda decided early that we wanted to do.

    24. AK

      The people that I've spoken with that have known you through the years have told me that your father's illness had a big impact on you. Can you share a little bit about that?

    25. DA

      Um, yes. Yes. He was, um, yeah, you know, he was ill for a long time, um, and, uh, eventually, uh, uh, died in, uh, eventually died in, uh, in, in 2006. Um, uh, so that, you know, that was actually one of the things that drove me to, you know, I, I don't think we mentioned it yet in this interview, but before, um, you know, before I went into AI, um, you know, I went into biology. So, you know, I'd gone to, uh, I, you know, I'd shown up at, I'd shown up at Princeton, um, uh, wanting to be a theoretical physicist and, you know, I did some, did some work in, in cosmology for the first few ti- few months of my time there. Um, you know, and, and, you know, that was, that was around the time that, that my father died. And, you know, that did have an influence on me and kind of was one of the things that convinced me, um, you know, to, to go into biology, you know, to try and address, um, uh, uh, uh, you know, human illnesses and biological problems. And so I started talking to some of the folks who worked on biophysics and computational neuroscience in the department that I was at at Princeton, and that was what led to the switch to biology and computational neuroscience. And then, you know, of course, after that I eventually, I eventually went into AI. And the reason I went into AI was actually a continuation of that motivation, which is that, um, you know, as I spent many years in biology, I realized that the complexity of the underlying problems in biology felt like it was beyond human scale. You know, y- in order to understand it all, you needed hundreds, thousands of, you know, human researchers and, you know, they often had a hard time collaborating or sharing their, you know, combining their, their knowledge. And AI, which was, I was just starting to see the discoveries in it, felt to me like the only technology that could kind of bridge that gap, could bring us beyond human scale to, you know, to, to fully understand and solve the problems of biology. So yeah, there is a through line there.

    26. AK

      Right. And I could have this wrong, uh, but one thing I heard was that his illness was, um, largely uncurable when he had it-

    27. DA

      Yes

    28. AK

      ... and that there have been advances that have been... Can you share a little bit more?

    29. DA

      Yes.

    30. AK

      There are advances that have made it much more manageable today.

  9. 54:15 – 1:08:49

    Governance & safety: “race to the top” and the control debate

    1. AK

      and therefore," speaking of that word control, "wants to control the entire industry." What do you think about that?

    2. DA

      I've never said, I've never said anything like that. That's an outrageous lie. That's the most outrageous lie I've ever heard. Um-

    3. AK

      By the way, I'm sorry if I got Jensen's words wrong, but-

    4. DA

      But no, no, no. The words were correct.

    5. AK

      Okay.

    6. DA

      But, but, but, but-

    7. AK

      What he's saying is-

    8. DA

      ... but the words are, the words are outrageous. In fact, I've said multiple times, and I think Anthropic's actions have shown it, that, um- You know, we're aiming for something we call a race to the top. Um, you know, I've said this on podcasts over the years, and I think Anthropic's actions have shown it, where, you know, w-with a race to the bottom, right, everyone is competing to like, you know, get things out as fast as possible. And so I say when you have a race to the bottom, it doesn't matter who wins, everyone loses, right? Because you make the unsafe system that, you know, helps your adversary or causes economic problems or, uh, you know, is, is unsafe from an alignment perspective. The way I think about the race to the top is that, um, it doesn't matter, it doesn't matter who-- it doesn't matter who wins, everyone wins, right? So the way the race to the top works is you set an example for how the field works. You say, um, uh, uh, you know, "We're gonna engage in this practice." So a key example of this is responsible scaling policies. We were the first to put out a responsible scaling policy. And, you know, we didn't say, "Everyone else should do this," or, "You're bad guys." We didn't, you know, we didn't, you know, kind of try to use it as an advantage. We put it out, and then we encouraged everyone else to do it. Um, and many-- And, and then we discovered in the months after that that, you know, there were people within the other companies who were trying to put out responsible scaling policies, but the fact that we had done it allowed, you know, gave those people permission, right? Kind of, kind of enabled those people to, um, you know, to make the argument to leadership, "Hey, Anthropic is doing this, so we should do it as well." The same has been true of investing in interpretability. We release our interpretability research to everyone, um, uh, and allow other companies to copy it, even though we've seen that it sometimes has commercial advantages. Same with things like constitutional AI. Same with the, the, the measure-- the, the, you know, the measurement of the da-dangers of our system, dangerous capabilities evals. So we're trying to set an example for the field. But there's an interplay where it helps to be a powerful commercial competitor. I've said nothing that, that any-- that anywhere near resembles the idea that this company should be the only one to build the technology. I don't know how anyone could ever derive that from anything that I've said. Um, it's, it's, it's, it's, it's just... Yeah, yeah, it's just, it's just, it's just a, it's just an incredible and bad faith distortion.

    9. AK

      All right, let's see if we can lightning round like one or two before I ask you the last one, which we'll have five minutes for. Um, what happened with SBF? Like-

    10. DA

      What happened with SBF?

    11. AK

      I mean, he was one of the-

    12. DA

      I couldn't-

    13. AK

      Go ahead.

    14. DA

      I couldn't tell you. I probably-

    15. AK

      But wasn't-- Was-- What was the-- Uh, yeah, why don't you answer?

    16. DA

      I, I probably met the guy four or five times.

    17. AK

      Okay.

    18. DA

      Um, uh, so I have no great insight, um, into the, you know, what w-- i-in, you know, into the psychology of SBF or, or, you know, why, why [chuckles] he did things as stupid as-- stupid or immoral as, as, as, as, as, as he did. I think the only, uh, the only, you know, the, the only thing I had ever seen ahead of time with, uh, SBF was, uh, you know, a couple people mentioned to me that he was, like, hard to work with, that, you know, he was, he was like a bit of a move fast and break things guy.

    19. AK

      Hmm.

    20. DA

      Um, and I was like, okay, you know, there's like plenty of people-

    21. AK

      Welcome to Silicon Valley. [laughs]

    22. DA

      Yeah, like welcome, welcome to Silicon Valley. Um, and so I remember saying, "Okay, I'm gonna give this guy non-voting shares. I'm not gonna put him on the board. He sounds like a bit, you know, he sounds like a bad person to deal with every day. Um, but, you know, he's excited about AI, he's excited about AI safety. He's a, he's, you know, he's a bull on, on, on AI, and he's interested in AI safety. So, you know, seems like a, seems like a sensible, seems like a sensible thing to do." You know-

    23. AK

      Right

    24. DA

      ... in, in, uh, in, in, you know, in, in, like in, in, in, in, in retrospect, um, you know, the, that, you know, move fast and, and, and break things, you know, was turned out to be much, much, much more extreme and bad than, than, you know, than, than, than I ever imagined.

    25. AK

      Okay, so let's end here. So you found your impact. I mean, you're, you're working the dream pretty much right now. I mean, think about all the ways that, uh, AI can be used for biology, uh, just to start. You also say that this is a dangerous technology, and I'm curious if your desire for impact, um, could be pushing you to accelerate this technology, um, while, you know, potentially devaluing the possibility that it could-- that controlling it might not be feasible.

    26. DA

      So, you know, I think I have more than anyone else in the industry warned about the dangers of the technology, right? We just spent ten, twenty minutes talking about, you know, the, the frightening, the, you know, the large array of, you know, people who run, you know, trillion-dollar companies criticizing me for, you know, for talking about the, the, the dangers of these technologies, right? You know, I have US government officials, I have people who run four trillion dollar companies criticizing me for talking about the dangers of the technology, right? Imputing all these bizarre motives that bear no relationship to, you know, to anything I've ever said, not supported in anything I've ever done, and yet I'm going to continue to do it. Um, I actually think that, you know, as the revenues, as the economic business of AI ramps up, and it's ramping up exponentially. You know, if, if I'm right, in a couple years it'll be the biggest source of revenue in the world, right? It'll be the biggest industry in the world, and people who run companies already think it. So we actually have this terrifying situation where, uh, you know, hundreds of billions to trillions to, I would say maybe twenty trillion of capital's on the side of accelerate AI as fast as possible. We have this, you know, company that's very valuable in absolute terms but, you know, looks very small compared to that, right? Sixty, sixty, sixty billion dollars. And I keep speaking up, even if, you know, it makes folks in... You know, there have been these articles fo- you know, some folks in the US government are upset at us, for example, for opposing the moratorium on AI regulation, for being in favor of export controls for chips on China, for talking about the economic impacts of AI. Every time I do that, I get attacked by many of my peers.

    27. AK

      Right, but you're still assuming that we can control it. That's what I'm pointing out.

    28. DA

      But I'm, I'm just, I'm just telling you how much, how much effort, how much persistence, how much despite everything that's stacked up, despite all the dangers, despite the risk that it has to the company of being willing to speak up, I'm willing to do it. And, and, and that's, that's what-- that's, that's why I'm saying that, look, if, if I thought that there was no way to control the technology, right? If I thought-- even, even if I thought this is just a gamble, right? Some people are like, "Oh, you think there's a five or ten percent chance that AI could go wrong, you're just rolling the dice." That's not the way I think about it. This is a multi-step game, right? You take one step, you build the next step of most powerful models, you have a more intensive testing regime. As we get closer and closer to the more powerful models, I'm speaking up more and more, and I'm taking more and more drastic actions because I'm concerned that the risks of AI are getting closer and closer. We're working to address them. We've made a certain amount of progress, but when I worry that the progress that we've made on the risks does not, you know, is not fully aligned with the, um, uh, you know-- i-i-is not going as fast as we need to go for the speed of the technology, then I speed up-- then I, then I speak up louder. Um, and so, you know, you're asking why am I, why am I ta-- you know, what, you know-- y-you started this interview by saying-

    29. AK

      What's up with you? [laughs]

    30. DA

      ... "What's gotten into you? Why are you talking about this?" It's because the exponential is getting to the point that, that I worry that we may have a situation that our ability to handle the risks is not keeping up with the speed of the technology, and that's how I'm responding to it. If I believed that there was no way to control the technology, which I, I, I, I, I see absolutely no evidence for that proposition. We've gotten better at controlling models with every model, with every model that we release, right? All these things go wrong, but, like, you really, you really have to stress test the models pretty hard. That doesn't mean you can't have emergent bad behavior. And I think, you know, if we got to much more powerful models with only the alignment techniques we have now, then I'd be very concerned. Then I'd be out there saying everyone should stop building these things. Even China should stop building these. I don't think they'd listen to me, which is one reason I think export controls is a better-- is, is, is, is a better measure. But if, if we got a few years ahead in models and had only the alignment and steering techniques we had today, then, you know, I would definitely be advocating for us to, to, you know, to, to, to, to, to slow down a lot. The reason I'm warning about the risk is so that we don't have to slow down, so that we can invest in safety techniques and can continue the progress, continue the progress of the field. It would be a huge economic effort. Even if one company was willing to slow down the technology, you know, that doesn't stop all the other companies. That doesn't stop our geopolitical adversaries to whom this is a existential fight, fight, fight, fight for survival. So, you know, there-- there's, there's, there's very little, you know, there's very little latitude here, right? We're stuck between all the benefits of the technology, the race to, the race to accelerate it, and the fact that that is a multi-party race. And so I am doing the best thing I can do, which is to invest in safety technology to speed, to, to speed up the progress of safety. I've written essays on the importance of interpretability-

Episode duration: 1:08:49

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