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

26:12I think it gives a better incentive to kind of develop the models as far as possible. And I think in many ways, it's like it may even be a more positive business. 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. Okay, then briefly, how did you decide to go with the coding use case? Yes. So, you know, 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 terms of 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, you know, every previous coding model has been useless to me.

27:02And 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 exploding in popularity. And then when we saw how popular it was, we kind of doubled down on it. My view is that coding is particularly interesting because, A, the adoption is fast. And, B, getting better at coding with the models actually helps you to develop the next model. So it has a number of, I would say, advantages. And now you're selling your AI coding through CloudCode.

27:44But it's very interesting. The pricing model 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 from your API. 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? So actually, pricing schemes and rate limits are surprisingly complicated. So some of this is basically the result of when we released our, when we released Claude Code in 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.

28:30So over the last few days, 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 with a$200 subscription. And, you know, it's possible more changes will come in the future. But we're always going to have a distribution of users who use a lot and users who use some amount. And it doesn't necessarily mean we're losing money, that there are some users who get more, you know, who, if you were to measure via API credits, spend, you know, get a better deal on the consumer subscription than they would on the API products, right?

29:15There's a lot of assumptions there. And I can tell you that some of them are wrong. We are not, in fact, losing money. But I guess there's another question about whether you can continue to serve these use cases and not raise prices. So just to give you a couple stats, there are some developers that are upset because using Anthropik's newer models and cursors is costing them more than it ever has. Startups that I've spoken with say Anthropik is down a bunch because they can't get access to the GPUs. At least that's what they imagine is happening. And I was just with Amjad Massad at Replit in an interview that we're going to air next week, who said there was a period of time where the price per token, price to use these models was coming down and it stopped coming down.

30:03So is it the is what's happening that these models are just so expensive for Anthropic to run that it's hitting a wall of its own? Again, I think you're I think you're making assumptions here. That's why I'm asking the CEO. Yeah. You know, you know, the way I think about it is we think about the models in terms of how much value are they creating. Right. 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, you know, those who make those who make the the the the the the the underlying applications.

30:40So again, without being too specific, I think there are some assumptions in your question that are not necessarily correct. Can you tell me which ones? So I'll say this. I do expect the price of providing a given level of intelligence to go down. I expect the price of providing the frontier of intelligence, which will provide 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 goes way up. So 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 than they are capable of today.

31:30One 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 Anthropic can run these models without too much bulk on the back end, but is still keeping those prices where they are. And I think the line that I'm going to draw there is maybe that to get to software margins, There were some reports that Anthropica is slightly below software gross margins.

32:07You're going to have to charge a little bit more for these models. So, yeah, again, I think larger models cost more to run than smaller models. You know, I think the technique you're referring to is maybe mixture of experts or something like that. So whether your models are mixture of experts or not, like mixture of experts 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 smaller models that don't use that technique. And if you're using that technique, larger models that use that technique cost more to run than smaller models that are using that technique.

32:48So I think that's sort of a distortion of the situation. Basically, I'm just guessing, and I'm trying to find out what the truth is from you. Yeah, look, so I, you know, in terms of like the cost of the models, like one thing you'd be surprised by, people, you know, people kind of impute this thing to like, oh, man, it's going to 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 50 percent more efficient than they are before. We are just the beginning of optimizing inference. Inference has improved a huge amount from where it was a couple years ago to where it is now.

33:29That's why the prices are coming down. And then how long is it going to take to be profitable? Because I think the loss is going to be like$3 billion this year. That's what they predict. So I would distinguish different things. There's the cost of running the model, right? So for every dollar the model makes, it costs a certain amount. that is actually already fairly profitable. There are separate things. There's the cost of paying people and buildings. That is actually not that large in the scheme of things. The big cost is the cost of training the next model. And I think this idea of the companies losing money and not being profitable, it's a little bit misleading.

34:13And you start to understand it better when you look at the scaling laws. So as a thought exercise, these numbers are not exact or even close for anthropic. Let's imagine that in 2023, you train a model that costs$100 million. And then in 2024, you deploy the 2023 model, and it makes$200 million in revenue, but you spend a billion dollars to train a new model in 2024. And then in 2025, the billion dollar model makes$2 billion in revenue and you spend$10 billion to train the next model. So the company every year is unprofitable. It lost$800 million in 2024. And then 2025, it lost$8 billion. So this looks like a hugely unprofitable enterprise.

34:57But if instead, I think in terms of is each model profitable, right? Think of each model as a venture. I invested$100 million in the model and then I got$200 million out of the model in the next year. So that model had 50 % margins and made me$100 million. The next year, the company invested a billion dollars and made$2 billion in, or sorry, the next model, the company invested a billion dollars and made. So every model is profitable, but the company is unprofitable every year. I'm not, this is a style. I'm not like claiming these numbers for Anthropic are claiming these facts. Is it true for Anthropic?

35:38This general dynamic is, in general terms, the explanation for what is going on. And so at any time, if the models stopped getting better or if a company stopped investing in the next model, you would have probably a viable business with the existing models, but everyone is investing in the next model. And so eventually it'll get to some scale. But the fact that we're spending more to invest in the next model suggests that the scale of the business is going to be larger the next year than it was the year before. Now, of course, what could happen is like the model stopped getting better and there's this kind of one-time cost that's like a boondoggle and we spend a bunch of money.

36:27But then the companies, the industry will kind of return to this plateau, to this level of profitability, or the exponential can keep going. So I think that's a long-winded way to say I don't think it's really the right way to think about things. 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.

37:04Yeah. So, you know, people have, 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 the history of AI. Every community 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 we're 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 those things turned out to be false.

37:44Then 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 business, they have this whole lexicon of commoditization, you know, modes, which layer is the value going to accrue to. and open source is this idea that you can kind of see everything that's going on, you know, that it has a significance, that it kind of undermines the fact that, you know, the idea that it undermines business.

38:22And 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. And this is all, I think, a long-winded way of saying, I don't think open source works the same way in AI that it has worked in other areas. Primarily because with open source, you can see the source code of the model. Here, we can't see inside the model. It's often called open weights instead of open source to kind of distinguish that.

39:03But 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. So, you know, I've actually always seen it as a red herring. 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? Is it better than us at, you know, the things that we – that's the only thing that I care about. It actually doesn't matter either way. because ultimately you have to host it on the cloud. The people who host it on the cloud do inference.

39:39These 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, we're increasingly offering on clouds where you can fine tune the model. We're even looking at ways to kind of investigate the activations of the model as part of like an interpretability interface. We did some little things around steering. last time. So I think it's the wrong axis to think in terms of when I think about competition, I think about like, which models are good at the tasks that we do. I think open source is actually a red herring. But if it's free and cheap to run, it's not free.

40:18You have to, you have to, you have to run it on inference and someone, someone has to make it fast on inference. All right. So I want to learn a little bit more about Dario the person. Yes. So we have a little bit of time left. So I have some questions for you about early life and then how you became who you are. Yes. So what was it like growing up in San Francisco? Yeah. I, you know, the city, when I first grew up here, I had not really, had not really gentrified that much. You know, when I grew up, the tech boom hadn't, hadn't happened, hadn't happened yet. You know, it happened as, as I was going through high school and actually I had no interest in it.

40:53It was totally, it was totally boring to me. 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, that I was interested in at all. 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? So, so, you know, that was, that was kind of more. And, you know, I watched the tech boom happen around me, but I feel like, you know, there was all kinds of things I probably could have learned from it that would have 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.

41:37So you're the son of a Jewish mother and an Italian father. That is true. Where I'm from in Long Island, we call that a pizza bagel. A pizza bagel. I've never heard that term before. So what was your relationship with your parents like? Yeah, I mean, I was always pretty close with them. I feel like they gave me a sense of kind of right and wrong and what was important in the world. I feel like kind of imbuing a strong sense of responsibility is maybe the thing that I remember most. They were always people who felt that sense of responsibility and wanted to make the world better. And I feel like that's one of the main things that I learned from them.

42:24It was always a very loving family, a very caring family. I was very close with my sister, Daniela, who, of course, became my co-founder. And I think we decided very early that we wanted to work together in some capacity. I don't know if we imagined that it would happen, you know, at quite the scale that it has happened. But it, you know, I think it really, you know, that was something we kind of decided early that we wanted to do. 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?

43:01Yes, yes. He was, yeah, you know, he was ill for a long time and eventually died in 2006. So that, you know, that was actually one of the things that drove me to, you know, I don't think we mentioned it yet in this interview, but before, you know, before I went into AI, you know, I went into biology. So, you know, I'd gone to, you know, I'd shown up at Princeton wanting to be a theoretical physicist. And, you know, I did some work in cosmology for the first few months of my time there. You know, and, you know, that was around the time 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, you know, to go into biology.

43:52to try and address 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, of course, after that, I eventually went into AI. And the reason I went into AI was actually a continuation of that motivation, which is that, 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, in order to understand it all, you needed hundreds, thousands of, you know, human researchers.

44:36And, you know, they often had a hard time collaborating or sharing their, you know, combining their knowledge. And AI, which 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 fully understand and solve the problems of biology. So, yeah, there is a through line there. Right. And I could have this wrong. But one thing I heard was that his illness was largely uncurable when he had it. And there have been advances that have been. Can you share a little bit more? Yes. There are advances that have made it much more manageable today.

45:10Yes, yes. That is true. Actually, only in the maybe three or four years after he died, the cure rate for the disease that he had went from 50 % to roughly 95%. Yeah. I mean, it has to have felt so unjust to have your father taken away by something that could have been cured. Of course, of course. But it also tells you of the urgency of solving the relevant problems, right? That, you know, there was someone who worked on the cure to this disease that, you know, managed to cure it and save a bunch of people's lives, but, you know, could have saved even more people's lives if, you know, they had managed to find that cure, you know, a few years earlier than they did.

46:02And I think that's one of the tensions here, right? That I think AI has all of these benefits. And I want everyone to get those benefits as soon as possible. I probably understand better than almost anyone how urgent those benefits are. And so I really understand the stakes. When I speak out about AI has these risks and I'm worried about these risks, I get very angry when people call me a doomer. I got really angry when someone's like, this guy's a doomer. He wants to slow things down. You heard what I just said. Like, you know, my father died because of cures that could have happened a few years later.

46:43I understand the benefit of this technology. When I sat down to write Machines of Loving Grace, I wrote out all the ways that billions of people's lives could be better with this technology. Some of these people, some of these people who on Twitter cheer for acceleration. I don't think they have a humanistic sense of the benefit of the technology. Their brain's just full of adrenaline. And they're like, they want to cheer for something. They want to accelerate. I don't get the sense they care. And so when these people call me a doomer, I think they just completely, completely lack any moral credibility in doing that.

47:18You know, it really makes me lose respect for them. And I've been wondering what this word impact has been because it's come up so often that those who have been around you have said you've been singularly obsessed with having impact. In fact, I spoke with someone who knew you well who said you wouldn't watch Game of Thrones because it wasn't tied to impact, that it was a waste of time and you wanted to be focused on impact. Actually, that's not quite right. I wouldn't watch it because it was so negative sum. People were playing such negative. It was like these people start off and they're partly the situation and partly because they're just horrible people.

47:56They like create the situation where at the end of it, everyone is like worse off than everyone was before. I'm really excited about like creating positive some situations. I recommend you watch it. It's a great show. But I hear your complaints. I actually have watched some parts of it. I was just very reluctant and didn't watch it for a long time. Let's get back to the impact. Okay. Let's get back to the impact. So that's what impact is, is effectively your career has been this quest to have that impact, to be able, tell me if I'm going too far, to prevent other people from being in similar situations.

48:29You know, I think that's a piece of it. I mean, you know, I have looked at, you know, many attempts to help people. And, you know, some of them are more effective than others. and you know I think I think I've always tried to you know there should be strategy behind it there should be brains behind you know trying to trying to help people you know which often means that there's a long path to it right it can run through a company and you know many activities that are technical and not immediately tied to the the kind of impact impact that you're trying to have but you know the the the arc is I'm always trying to bend the arc towards that I think I I think that's my picture of it.

49:13That's really why I got into this, right? I think similar to the reason to get into AI was that I saw the problems of biology as almost intractable without it, or at least too slow moving. You know, I think my reason to start a company was that I had worked at other companies and I just didn't feel like the way those companies were run was really oriented towards, you know, trying to have that impact. There was a story around it that was often used for recruiting, but it became clear to me over the years that that story was not sincere. I'm going to circle around a little bit because it's clear that you're referring to OpenAI here.

49:56from what I understand you had 50 % of open AI's compute I mean you ran the GPT-3 project so if anyone was going to be focused on impact and safety wouldn't have been you yes I I was you know there was a period during which uh during which that was uh that was true that wasn't true the entire time that was for example when we were scaling up GPT-3 um yeah So, you know, when I was at OpenAI, I and a lot of my colleagues, including the people who, you know, eventually founded Anthropic. The pandas. The pandas. That's the name you gave them. That isn't a name I gave them. The name they took. That isn't a name they took.

50:39That's the name other people called them? Maybe it's a name other people called them. That's not a name I ever used for my team. Okay. Sorry. Go ahead. That's a good clarification. Thank you. So, yeah, you know, we were involved in scaling up these models. Actually, the original reason for building GPT-2 and GPT-3, it was an outgrowth of the kind of AI alignment work that we were doing, right? Where myself and Paul Cristiano and some of the Anthropic co-founders had invented this technique called RL from human feedback. and that was designed to help steer models in a direction to follow human intent.

51:21It was actually a precursor to, you know, we were trying to scale up another method called scalable supervision, which I think is just starting to work many years later to help models follow more kind of scalable human intent. But what we found is even with the more primitive technique, RL from human feedback, it wasn't working with the small language models with, you know, GPT-1 that we applied it to and that had been built by other people at OpenAI. And so the scaling up of GPT-2 and GPT-3 was done in order to kind of study these techniques in order to apply RL from human feedback at scale. You know, this goes to one thing, which is that I think in this field, the alignment of AI systems and the capability of AI systems is intertwined in this way that always ends up being kind of more tied and more intertwined than we think.

52:11Actually, what this made me realize is that it's very hard to work on the safety of AI systems and the capability of AI systems separately. It's very hard to work on one and not the other. I actually think the value and the way to inflect the field in a more positive way comes from organizational level decisions. When to release things, when to study things internally, what kind of work to do on systems. And that was one of the things that kind of motivated me and some of the other to be anthropic founders to kind of go off and do it our own way. But again, like if you were driving, if you think language, if you think capabilities and safety are interlinked, and you were the guy driving the cutting edge models within OpenAI, if you left, you knew they were going to be a company that was still doing this stuff.

53:03That's right. It seems like if you're driving the capabilities, you'd be the one in the driver's seat to help it be safe the way that you want it to. Again, I will say, you know, if there's a decision on releasing a model, if there's a decision on the governance of the company, if there's a decision on, you know, how the personnel of the company works, you know, how the company represents itself externally, the decisions that the company makes with respect to deployment, the claims it makes about how it operates with respect to society. You know, many of those things are not things that you control just by training the model.

53:42And, you know, I think trust is really important. I think the leaders of a company, they have to be trustworthy people. They have to be people whose motivations are sincere. No matter how much you're driving forward the company technically, if you're working for someone whose motivations are not sincere, who's not an honest person, who does not truly want to make the world better, it's not going to work. You're just contributing to something bad. So, and 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, speaking of that word control, wants to control the entire industry.

54:19I'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. By the way, I'm sorry if I got Jensen's words wrong, but... No, no, no. The words were correct. Okay. But the words are outrageous. In fact, I've said multiple times, and I think Anthropics' actions have shown it, that we're aiming for something we call a race to the top. I've said this on podcasts over the years, and I think Anthropik's actions have shown it, where, you know, with a race to the bottom, right, everyone is competing to like, you know, get things out as fast as possible.

54:58And 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, you know, is unsafe from an alignment perspective. The way I think about the race to the top is that 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, you know, we're going to 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.

55:37And, 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. And many 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, you know, to make the argument to leadership. Hey, Anthropic is doing this, so we should do it as well.

56:11The same has been true of investing in interpretability. We release our interpretability research to everyone 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 measurement of the 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 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.

56:53It's just an incredible and bad faith distortion. All right. Let's see if we can lightning around like one or two before I ask you the last one, which we'll have five minutes for. What happened with SBF? What happened with SBF? I mean, he was one of the – go ahead. I couldn't tell you. What was the – what didn't you answer? I probably met the guy four or five times. Okay. So I have no great insight into the psychology of SBF or why he did things as stupid or immoral as he did. I think the only thing I had ever seen ahead of time with SBF was a couple people mentioned to me that he was hard to work with, that he was a bit of a move fast and break things guy.

57:52And I was like, OK, there's plenty of people. Welcome to Silicon Valley. Yeah, like welcome to Silicon Valley. And so I remember saying, OK, I'm going to give this guy non-voting shares. I'm not going to put him on the board. He sounds like a bad person to deal with every day. But he's excited about AI. He's excited about AI safety. He's a bull on AI, and he's interested in AI safety. So seems like a sensible thing to do. In retrospect, that move fast and break things turned out to be much, much, much more extreme and bad than I ever imagined. Okay, so let's end here. So you found your impact.

58:38I mean, you're working the dream pretty much right now. I mean, think about all the ways that AI can be used for biology just to start. You also say that this is a dangerous technology. And I'm curious if your desire for impact could be pushing you to accelerate this technology while, you know, potentially devaluing the possibility that it could, that controlling it might not be feasible. 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 10, 20 minutes talking about, you know, the frightening, 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 dangers of these technologies, right?

59:32You know, I have U.S. government officials. I have people who run$4 trillion 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. 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 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.

1:00:09And people who run companies already think it. So we actually have this terrifying situation where, you know, hundreds of billions to trillions to, I would say, maybe 20 trillion of capitals on the side of Accelerate AI as fast as possible. We have this company that's very valuable in absolute terms, but looks very small compared to that, right? $60 billion. And I keep speaking up, even if it makes folks—there have been these articles. Some folks in the U.S. 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.

1:00:53Every time I do that, I get attacked by many of my peers. Right, but you're still assuming that we can control it. That's what I'm pointing out. But I'm just telling you 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 that's why I'm saying that, look, if I thought that there was no way to control the technology, right? If I thought, even if I thought this is just a gamble, right? Some people are like, oh, you think there's a 5 % or 10 % chance that AI could go wrong.

1:01:31You'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 is not fully aligned with the – is not going as fast as we need to go for the speed of the technology, then I speak up louder.

1:02:15And so you're asking, why am I – you started this interview by saying, 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 believe that there was no way to control the technology which 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 have to stress test the models pretty hard.

1:02:54That 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 measure. But 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, you know, to slow down a lot.

1:03:27The 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 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 an existential fight, fight, fight, fight for survival. So, you know, 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 accelerate it, and the fact that that is a multi-party race.

1:04:07And so I am doing the best thing I can do, which is to invest in safety technology to speed up the progress of safety. I've written essays on the importance of interpretability, on how important various directions in safety are. We release all of our safety work openly because we think that's the thing that's a public good. That's the thing that everyone needs to share. So if you have a better strategy for balancing the benefits, the inevitability of the technology, and the risks that it face, I am very open to hear it. Because I go to sleep every night thinking about it. Because I have such an incredible understanding of the stakes in terms of the benefits, in terms of what it can do, the lives that it can save.

1:04:54I've seen that personally. I also have seen the risks personally. We've already seen things go wrong with the models. We have an example of that with Grok, and people dismiss this, but they're not going to laugh anymore when the models are taking actions, when they're manufacturing, and when they're in charge of medical interventions. People can laugh at the risks when the models are just talking, but I think it's very serious. And so I think what this situation demands is a very serious understanding of both the risks and the benefits. These are high-stakes decisions. they need to be made with a seriousness.

1:05:33And I think something that makes me very concerned is that on one hand, we have a cadre of people who are just doomers. People call me a doomer, I'm not, but there are doomers out there. People who say they know there's no way to build this safely. I've looked at their arguments. They're a bunch of gobbledygook. The idea that these models have dangers associated with them, including dangers to humanity as a whole, that makes sense to me. The idea that we can kind of logically prove that there's no way to make them safe, that seems like nonsense to me. So I think that is an intellectually and morally unserious way to respond to the situation we're in.

1:06:12I also think it is intellectually and morally unserious for people who are sitting on$20 trillion of capital, who all work together because their incentives are all in the same way, there are dollar signs in all of their eyes, to sit there and say we shouldn't regulate this technology for 10 years. Anyone who says that we should worry about the safety of these models is someone who just wants to control the technology themselves. That's an outrageous claim and it's a morally unserious claim. We've sat here and we've done every possible piece of research. We speak up when we believe it's appropriate to do so.

1:06:48We've tried to back up, you know, when we make claims about the economic impact of AI, we have an economic research council. We have an economic index that we use to track the model in real time. And we're giving grants for people to understand the economic impact of the technology. I think for people who are far more financially invested in the success of the technology than than I am to just, you know, breezily lob ad hominem attacks. You know, I think that is just as intellectually and morally unserious as the Doomer's position. I think what we need here is we need more thoughtfulness. We need more honesty.

1:07:30We need more people willing to go against their interest, willing to not have, you know, breezy Twitter fights, hot takes. We need people to actually invest in understanding the situation, actually do the work, actually put out the research, and actually add some light and some insight to the situation that we're in. I am trying to do that. I don't think I'm doing that perfectly, as no human can. I'm trying to do it as well as I can. It would be very helpful if there were others who would try to do the same thing. Well, Dario, I said this off camera, but I want to make sure to say it on as we're wrapping up.

1:08:08I appreciate how much Anthropic publishes. We have learned a ton from the experiments, everything from red teaming the models to vending machine Claude, which we didn't have a chance to speak about today. But I think the world is better off just to hear everything going on here. And to that note, thank you for sitting down with me and spending so much time together. Thanks for having me. Thanks everybody for listening and watching. And we'll see you next time on Big Technology Podcast.

1:08:47We'll see you next time.