Anthropic C.E.O.: Massive A.I. Spending Could Haunt Some Companies

[00:00:00] Speaker ?: Субтитры создавал DimaTorzok [00:00:30] Speaker 1: Please welcome Andrew Ross Sorkin and his guest, CEO and co-founder of Anthropic, Dario Amadei. [00:00:43] Speaker 2: Good afternoon, everybody. I hope you guys all had a great lunch. We have a huge afternoon, starting with Dario Amadei here. He is one of the most consequential people here in the world of artificial intelligence. He's the co-founder and CEO of Anthropic. Anthropic, of course, known for its clawed model. It's one of the fastest growing technology companies in history and uniquely backed now by all three giants, tech giants, Amazon, Microsoft and Google. That's new, at least one of them. And by the way, he has been at this really longer than most. He worked at Baidu, then Google, was an early employee at OpenAI, where he led the development of ChatGPT 2 and 3. And the reason that we wanted to speak with him this year more than anything else is because he has singularly been perhaps the most outspoken and candid person about AI when it comes to the way he's been thinking about jobs and job losses and selling chips to China and politics on our country and where all of this goes. So welcome to you. Thank you for being here. Thank you for having me. We got a lot to talk about, including, by the way, are we in an AI bubble? But I promise you we will get there. I'll start here, though, which is I mentioned you were a research scientist back at Baidu, 2014. And if I had sat with you then and said, we're going to sit together in 2025 talking about AI, you would have told me what? What would have been your expectation for what would have happened? [00:02:15] Speaker 3: So I'll tell you what I am surprised by and what I'm not. I'm not surprised by the economic impacts of the technology, the value that it's creating, you know, the fact that, you know, I walk by any billboard in New York and, you know, it's kind of everything about AI. [00:02:34] Speaker 2: You thought in 2014 that this would be real by now? [00:02:38] Speaker 3: In some form that this would be real, that it would be central to the economy, that it would be central to national security, that it would be central to scientific research. You know, I don't think I imagined that I would be leading one of the companies in the space, right? I think that would have been, you know, very surprising to me. I didn't think of that as kind of my role at the time. And the exact way in which things happened, all of the, you know, all the kind of strange lingo we've developed around language models, all of the kind of financialization of it. You know, if you think about it, if you think about the implications of the models becoming as smart and as powerful as they are and scaling in the way that they are, in the way that me and some of my colleagues and predicted, it all makes sense. But I don't think I would have derived it from first principles. [00:03:27] Speaker 2: Okay. Well, then let's go straight to the question I said I'd asked at the beginning, because maybe this is the place, thinking about where this goes and the fact that I didn't think you would say, by the way, that you thought that this is where we were going to be in 2025. Because I think even people back then thought this would be a much longer road. But if you're right, do you look at the amount of economic muscle that's being put into this industry right now? I mean, it really does represent potentially almost all of the growth in the United States GDP right now, literally. Yes. That we are in some form of a bubble. Are we overspending? Does the math of all of this make sense? [00:04:04] Speaker 3: So this is really complicated. And I want to separate out the technological side of it from the economic side of it. On the technological side of it, I feel really solid. I think I'm one of the most bullish people around. And I think it pencils out. On the economic side, you know, I have my concerns where even if the technology is, you know, really powerful and fulfills all its promises, I think there may be players in the ecosystem who if they just make a timing error, if they just get it off by a little bit, bad things could happen. So let me go through both of them on the technological side. The reason that I'm not in honestly by the pure technology, not that surprise is myself and some of the people who eventually became my my my co-founders. We were the first to document the scaling laws of AI, which is you put more compute, you put more data into AI with small modifications. We've seen these things like reasoning models and test time compute. They're all tiny little tweaks. And I've been watching that trend for the last 12 years or so since I joined the field. And the thing that is most striking about all of it is as you train these models in this very simple way, you know, with a few simple modifications, they get better and better at every task under the sun. They get better at coding. They get better at doing science. They get better at biomedicine. They get better at the law. They get better at finance. They get better at, you know, materials and manufacturing. And that's just that's just a listing of all the sources of value, of value in our economy. If I just take Anthropic itself, which because we work so much in the enterprise side, I think we're a good barometer, maybe a purer barometer than the others, which kind of filter through consumers, which kind of have their habits and their use cases. We look at our revenue. It's grown 10x a year every year for the last three years. Zero to 100 million in 2023, 100 million to a billion in 2024, 1 billion to it's going to land somewhere between 8 and 10 at the end of this year. Will it continue? I don't know. But the technology is driving there and the economic value is coming with it. It will you know, that that trend is going to slow down for sure, but it's still going to be really fast. And so I have this confidence that the the ultimate eventually the economic value is the eventually the economic value is going to be there. [00:06:31] Speaker 2: But when you let's just go to this, because there are companies that are spending 100 billion dollars a year more. You're going to be spending 50, you look at what Sam Altman, who was here last year, plans to be spending. These are extraordinary numbers. And this is all a bet, a big bet that this is going to scale in this way. And my question is, is there a real way to pencil this out or is this more of a gut feeling at this point? [00:06:58] Speaker 3: So let me let yeah, so that really gets to the second part of it. And I will describe as transparently as I can, I think there's a real dilemma deriving from uncertainty and how quickly the economic value is going to grow and the lag times on building the data centers that that drives it. So I think there's genuine uncertainty, there's genuine dilemma, which we as a company try to manage as responsibly as we can. And then I think there are some players who, you know, who who are YOLOing, who who pull the wrist dial too far. And I'm very concerned. Who is YOLOing? So that's a question I'm not going to answer. So on the first one, put yourself. We'll come back to that. Put yourself, we won't put, put yourself in, in the position of, of anthropic, put yourself in my position. You, you, you've seen this revenue curve that goes up 10 X a year for three years. You're like, okay, what's going to happen next year? If I'm really dumb and I extrapolate the pattern 10 to a hundred billion, I don't believe that just to be clear. I don't, I don't believe that at all, even though it's happened in the last three years, just at this scale, I don't believe it. But that's one of the outer bounds of, you know, the, the outer limits of possibility. If I go in and I'm like this enterprise and that enterprise and this use case, and this is our go to market motion. If I try and do it, you know, bottom up, then, you know, maybe it's, maybe it's 20 or 30 or something like that. So there is what, what I've been calling internally, this cone of uncertainty, where I don't know if a year from now, it's going to be 20 billion or, you know, it's, it's going to be 50 or, or like, you know, it's, it's, it's very uncertain. I try to plan in a conservative way. So I plan for the lower side of it, but, but that is very disconcerting. And you add to that, the idea that building the data centers has a long lag time. It's like a year or two. So I have to decide now, literally now, or in some cases, a few months ago, how, how, how much compute I need to buy in, you know, early 20, for, to serve the models in early 2027, when I get to that revenue amount. And there's two couple dangers, one is that if I, if I don't buy enough compute, I won't be able to serve all the customers I want. I'll have to turn them away and send them to, to, to my competitors. If I buy too much compute, of course, I might not get enough revenue to pay for that compute. And, and, and, you know, and, and, you know, in the extreme case, there's kind of the risk of going bankrupt. And how, how much buffer there is in that, in that cone, um, it's basically determined by my margins. If I have 80% margins, I can buy $20 billion of compute and it could serve a hundred billion dollars, a hundred billion dollars of revenue. But because the cone is so wide, it's hard to avoid, you know, making a mistake on, on, on, on one side, on one side or the other. Now we've been a relatively responsible company. Um, and I think because we focus on enterprise, I think we have a better business model. I think we have better margins. I think we're being responsible about it. But again, let's say you have a different business model. Let's say you have a consumer business model, you know, you're, you're, you're, you're, you're, you're, you're kind of the source of your revenue isn't as good. Your margins aren't certain. And let's say you're a person who just kind of like constitutionally just wants to YOLO things or just likes big numbers. Um, uh, then that, then you may turn that dial pretty far. And so I think there's a real underlying risk. Whenever there's uncertainty, there's a risk of overextension. We all face it. I face it. All the other companies face it. There's, there's an inherent risk when, when the timing of the economic value is, is uncertain. There's an inherent risk of, of underreacting or overextension. And because the companies are competing with each other, and frankly, we genuinely need to compete with, you know, our authoritarian adversaries. Um, uh, there, there's, there's kind of a lot of pressure to push things. So I think there's some amount of irreducible risk here, and I absolutely don't want to deny this. But at the same time is that I think there are some players who are not managing that risk well, who are taking unwise risk. [00:11:08] Speaker 2: So I think, let me ask you about that with, maybe you'll mention who that is. I think we all know who that is. Um, you have said that you're going to break even, uh, this is privately, at least to your investors by 2028, even with the spending plans. I think, uh, Sam Altman, who you worked with and for, uh, says that he's going to do it by 2030. Uh, I'll use his math, not yours. He would have to go from a $74 billion loss in the course of two years to being profitable two years later. Does that make sense to you? [00:11:41] Speaker 3: So, look, I don't, I don't know the internal financials of any other company. I can't, I can't say anything about what the economics of any other, of, of, of, of, of, of any other company is. I will just go back to our own calculation and the cone of uncertainty where, where, where, where, where, where, where we basically say, we want to buy enough compute that we're confident, you know, even in the 10th percentile, you know, scenario, like might be in a bad position, but like we think we can pay for it. There's, there's some end of the curve where, you know, things go so badly that, you know, we can't pay that, that, that there's all, there's always a tail risk. There's not zero, but we're trying to manage that risk well, while also buying an amount, an amount of compute that allows us to be competitive with the other players. We're very efficient in training. We're very efficient in inference. We have good margins. I think, I, you know, I think, I think we can manage it. I think the odds are on our side. [00:12:33] Speaker 2: What are we supposed to think of what I think people now describe as circular deals? Yes. Back in the day, we called this vendor financing. Yes. But in this context, you have a situation where NVIDIA in particular, but others as well, effectively have been taking stakes in companies. And invariably, those companies are using some of that money one way or the other, considering money is fungible, and going to buy NVIDIA chips. [00:12:56] Speaker 3: Yes. So, I mean, we've done some of these deals, not, not at the same scale as, as, as some other players. But we, we've done some of these deals and I can just, I can just, I can just, I can just, I can just kind of walk you through not a specific deal, because I'm not going to go into details, but, but, but kind of like a stylized what these deals often look like and kind of why they can make sense. So if you want to buy a gigawatt of compute, right, that buying the chips and building the chips, building everything that costs, you know, roughly, let's say $50 billion of, of capital expense to, to kind of, to kind of fund that. And you can think of that as a useful lifetime as people argue about it, but maybe it's five years. So that's 10, $10 billion for basic, basically for five years. And, and so if you're a company, you know, you're, you're, you know, you're a company that's making, you know, eight, $10 billion of revenue, you think that's growing, you don't know how fast it's growing, you have to make the decision right now and you don't have $50 billion, you don't have $50 billion on you. So, you know, a thing you can do, a deal you can make with a large player who has an incentive to, to, to, to, to do this because they're the one selling the chips or providing the cloud is they'll say, okay, you know, I'll give you, I don't know, 20% of it. I'll invest $10 billion. So that lets you pay for the first year. And then for, and then for the other years, you can kind of pay as you go, because I know you don't have $50 billion now. But, but, you know, looking at how things are, are, are, are, are growing, that isn't a crazy bet. We're already almost, we're already almost at, at, at, at, we're already almost at the $10 billion of revenue. So it, it kind of, it, you know, takes a year to build a data center, it's financed for a year. So you're, you're, you know, you're basically saying I need to get $10 billion of, of, of revenue per year, two years from now. Right. So I don't think there's anything wrong with that. One player has capital and has an interest because they're selling the, because they're selling the, you know, they're selling the chips. And the other player is pretty confident they'll have, they'll have the revenue at the right time, but they don't have $50 billion at hand. So I don't think there's anything inappropriate about that in principle. Now, if you start stacking these, where they get to huge amounts of money, and you're saying, you know, by, by 2027 or 2028, I need to make $200 billion a year, then yeah, you can, you can, you can overextend yourself, of course. Let me ask you. [00:15:09] Speaker 2: It's all a matter of the size. But I think is one of the key questions behind the math of this entire industry is what's called the depreciation schedule, if you will, for these chips. And there seems to be a big debate about it, meaning, when you buy a new chip, is that chip going to work for you, effectively, for three or four years, or is it going to work for you for six or seven or eight years or even 10 years? And depending on where you think the math really lands, all of this pencils out or really doesn't? What do you think the schedule is? [00:15:39] Speaker 3: So, look, from, from, from, from our point of view, we make very conservative assumptions here. Um, I don't think there's a particular depreciation schedule, right? When new chips come out, the issue isn't the lifetime of the chips. Chips, chips keep working for a long time. The issue is new chips come out that are faster and cheaper. And you may need them if your competitors have them. Yeah, yeah. And so, and so kind of the value of old chips goes down somewhat. In fact, that, that can happen, you know, a year after you buy the chips, because, you know, now there are multiple companies, TPUs, GPUs, coming out with, coming out with new chips. So, I think the way we think about it is we take into account that, you know, the old chips are going to be less valuable as, as, as time goes on. And, you know, we assume very aggressive, you know, a kind of continuation of the, of the chip efficiency curve. And again, I can only speak for Anthropic. Like we make conservative assumptions here and, and we think we're going to be, we think we're going to be okay in basically almost all worlds. Can't be literally all worlds, but we think we're going to be okay in almost all worlds. I can't speak for other companies. Again, I can imagine that there may be other players out there who are, you know, who are deluding themselves and, and, you know, making, making assumptions that are very far inflected on the optimistic side. [00:16:50] Speaker 2: Just so we're clear, there's only two of you who are, who are not attached to one of the -- [00:16:54] Speaker 3: Look, I don't know who you're talking about, okay? I, I just have no idea. [00:16:58] Speaker 2: Let me ask you this about the models themselves and how you see the competition. So one of the things that's happened literally in the last week and there has been a complete sort of meltdown in the valley over what's happening here. And Sundar Pichai was also here last year. And it appears at least that his new model has gotten a lot of people excited about what he's doing. And that Google, which I used to think from the beginning, given all of the data they have, should have been sort of the, the winner by default. And you have a memo that went out from Sam Altman saying there's code red. Everyone's got to get back to their desk to figure out what the, what the next thing is to break the, to get to the next place. How do you stack rank right now where these models are and how important do you think it is in any given moment? [00:17:43] Speaker 3: Yeah. So this is one of the cases where I'm just very grateful that Anthropic is taking a different path, right? On one hand -- The path being the enterprise. The, the, the path being the enterprise, right? Both of the players that you, that you mentioned, right? Both of these other two players are, you know, primarily focused on the consumer. They try and do some enterprise work, but they're, they're, they're, and they're fighting consumer. That is, you know, that is, that is the reason for kind of the code red, the intense fighting, right? It's, you know, Google has a search monopoly that they're trying to defend and the center of what OpenAI is doing is in, is in consumer. So those, those two are fighting it out. For both of them, serving businesses is secondary. And so what we found is over time, we've optimized our models more and more for the needs of businesses. The one that's gone the fastest has been coding. You know, I think that has really, you know, moved forward the quickest, but we're starting to go beyond that to finance, biomedical, retail, you know, energy, manufacturing, kind of all of that. And so, and so, and so what we find is that these model wars, as much as our models are like really good, like, you know, the one we released last week, Opus 4.5, is hands down, I think almost everyone thinks the best model for coding. So I think that's, it's very important that we continue to have this, this model superiority, but there's a way in which we're kind of going in a different direction or on a different dimension. And so we, we kind of have to worry less about this back and forth. We have a little bit of a privileged position where we can just keep growing and just keep developing our models and we don't have to do any code reds. [00:19:13] Speaker 2: But what is the, the moat around any of these businesses? And, and when I say that, I assume if Google is success, is as successful as it wants to be, or OpenAI or any, or Meta or anybody else who's involved in this, that they think that one day, if we ever get to AGI, that all these models effectively will be able to do what either, you know, any of them do. And whether it's, you know, is there a mode, is it the persistent memory? So I'm a, I use chat GPT for certain things. It knows me now because it's, I've been, you know, asking it different questions. Is, or do you think people just switch back and forth, whoever's got the, the latest thing? [00:19:51] Speaker 3: Yeah, so I, look, I can only speak to the enterprise side. What I will say is, is it is surprising how different the personality and capabilities of the models are if you're building for businesses versus if you're building for, if you're building for consumers. You just focus on different things. You focus less on engagement. You focus more on coding, high intellectual activities, scientific ability. And, you know, I don't think it's true that if we got AGI, they would all converge to the same place. Is everyone in this audience converge to the same place? Is everyone in this audience a copy of everyone else because we're all agent? No, we're, we're all specialized. Specialization exists, you know, in, in, in, it exists alongside general intelligence. And then I think there's all the standard enterprise stuff as well, which is that, you know, companies build relationships with you. They get used to using certain models. And, and we're starting to see that, like even our, even our API business, which is basically just selling the raw model. You wouldn't think that would be very sticky. Companies have great difficulty switching from one model to another because they have downstream customers who use the model. And they like the current model. And you prompt and interact with the models in different ways. And they have different personalities. It's actually quite hard to switch. So, so I think there really is a durable business here. [00:21:05] Speaker 2: One quick AGI question. It's a science question, which is, do you think just the way transformers work today and just compute power alone from a scalability sense that that is what will get to AGI? Or do you think there's some other ingredient? And maybe there's a technical question, but I'm trying to keep it very, very easy, that has to be included in this that gets you to someplace where this stuff is actually going to really think on its own? [00:21:28] Speaker 3: No, I think, I think scaling is going to get us there. Again, with small -- every once in a while, there will be a small modification, you know, so small you may not even read about it. It's just something going on in the lab. I have been watching these scaling laws for 10 years. So what's your -- what's your timeline now? There's no one particular point, right? That this is what I've said over -- I've never liked these terms, AGI, artificial superintelligence. I don't know what it means. There's just an exponential. Just like we had an exponential with Moore's law, chips getting faster and faster until they could, you know, do any, you know, simple calculation, you know, faster than -- faster, faster than any human. I think the models are just going to get more and more capable of everything. Every few months, we release a new model. It gets better at coding. It gets better at science. You know, now models are routinely winning, you know, high school math olympiads are moving on to college math olympiads. They're starting to do new mathematics. For the first time, I've had internal people at Anthropic say, I don't write any code anymore. I don't write -- I don't open up an editor and write code. I just let Claude Code write the first draft and all I do is edit it. We had never reached that point before. And the drumbeat is just going to continue. And I don't think there's any privileged point around -- there's no point at which the models start to do something different. What we're going to see in the future is just like we've seen in the past, except more so. The models are just going to get more and more intellectually capable. And, you know, the revenue is going to keep adding zeros. Let me ask a couple of policy questions. [00:22:57] Speaker 2: Yes. You have been outspoken -- we spoke to the president of Taiwan earlier today. Yes. You have been outspoken about the idea that we should not be selling NVIDIA chips, for example, the most advanced chips, to China. By the way, it's interesting that you now have a partnership with NVIDIA. Jensen Wang, who's also been here, was not so happy with you when you made those comments. Do you have a new view on that? [00:23:19] Speaker 3: Jensen Wang: My view hasn't changed. So, I definitely will say -- and this has always been the case -- you know, I have an enormous amount of respect for Jensen and for NVIDIA. Jensen is an immigrant who came to the U.S. with nothing. He built the most -- you know, the most valuable company in the world. This isn't personal. This is a policy question. This is a question of how best to defend our national security. And there, my view hasn't changed. It's the following, which is if we go back to my picture of the models getting smarter and smarter as we continue to improve them. A phrase I've used in an essay I wrote a year and a half ago was eventually the models are going to get to the point where they look like a country of geniuses in the data center. Jensen Wang: And so, once we get to that point, think about what that country of geniuses in the data center can do and which existing country on earth it's plopped down in. If it's plopped down in an authoritarian country, I feel like they can outsmart us in every way -- intelligence, defense, economic value, R&D. Jensen Wang: And I worry that they'll be able to oppress their own people, that they'll be able to have a perfect surveillance state. And so, I have always felt that we need to have the advantage here. And this is a national security issue, right? Some people are saying this is an -- they say this is an economic issue, that it's an analogy to, like, the Internet or 5G. We need to defuse our stack, like we needed to beat Huawei in, you know, in telecommunications. You know, I don't -- I don't see it that way. I think we're building a growing and singular capability that has singular national security implications. And democracies need to get there first. It is absolutely -- it is absolutely an imperative. Jensen Wang: Do you think it could happen? Jensen Wang: And if we -- if we sell these chips to China, that just makes it more likely they will get there first. It's common sense. [00:25:15] Speaker 2: Jensen Wang: Okay, but do you think that could happen here? So, we had Alex Karp here earlier. And there has been lots of worries about surveillance here. Talk about it in a democracy, right? Jensen Wang: Yes. Jensen Wang: What is your concern there? I should say, by the way, there was a period of time where you called the president -- this was before he was the president -- a feudal warlord at one point. So, how do you think about the president today, America today, and this idea that AI and surveillance could come together? Jensen Wang: Yes. [00:25:45] Speaker 3: Jensen Wang: Yes. So, look, I want to -- I want to say over and over again that, you know, I think that the tendency to drag this down into being about specific personalities and, you know, and specific fights, I think, is not helpful here. Jensen Wang: We should really think -- we should really think at a policy level here. Jensen Wang: And it's not about one administration. Jensen Wang: It's not about another administration. Jensen Wang: We should have principles here. Jensen Wang: And I think that the principle I would give that I think is very important is actually it can happen anywhere, right? Jensen Wang: You know, a -- we should -- we should worry about concentration of power in democracies, not as much as we worry about it in authoritarian states. Jensen Wang: But, you know, we need to make sure that the technology is governed in, you know, in a way that allows people to participate, that gives people basic rights. Jensen Wang: And so the formulation I have always given when I think about how to apply these models for national security is I think we should aggressively use them in every possible way except in the ways that would make us more like our authoritarian adversaries, right? Jensen Wang: We need to beat them, but we need to not do the things that would cause us to become them. Jensen Wang: That is the one constraint we should observe. Jensen Wang: OK, let me ask you a separate question. [00:26:59] Speaker 2: Jensen Wang: And maybe you will say it's a fight, but you can take it out of the person if you want. Jensen Wang: Which is, you have been very vocal about your concerns about the chip issue, but also what could happen to jobs, regulating -- regulating this technology so it doesn't do bad things or other things like that. Jensen Wang: David Sachs, who works as the AIs are at the White House, said this about you. Jensen Wang: He says that "Anthropic is running a sophisticated regulatory capture strategy based on fear mongering. Jensen Wang: It is principally responsible for the state regulatory frenzy that is damaging the startup ecosystem." [00:27:37] Speaker 3: Jensen Wang: Again, I don't think this should be about specific individuals, right? Jensen Wang: Right. Jensen Wang: This isn't about any particular administration. Jensen Wang: This is about -- this is about policy questions. Jensen Wang: I mean, you know, going all the way back to 2016, I have written papers about AI, right, before I even had a company, right? Jensen Wang: You know, could have been a plan around anything like regulatory capture. Jensen Wang: And by the way, almost all the AI regulation that we've supported has exemptions for small players, right? Jensen Wang: Right. Jensen Wang: The main AI bill we supported, SB 53, you know, doesn't even apply at all to startups with under $500 million in revenue. Jensen Wang: Right. Jensen Wang: So we've been very careful about this. Jensen Wang: And, you know, if there's one point -- again, I want to say again, I think people should focus on the policy. Jensen Wang: You can throw out these accusations, and they don't match reality at all. Jensen Wang: They don't match the reality of the bills we've actually supported. Jensen Wang: They don't match the reality of -- [00:28:29] Speaker 2: Jensen Wang: There are these two worlds right now, which is, by the way, you know, Andreessen Horowitz and others have one super PAC. Jensen Wang: You guys are building another super PAC to approach regulation of this industry completely differently. Jensen Wang: And the question is why. Jensen Wang: What do you see that they don't? [00:28:44] Speaker 3: Jensen Wang: So, you know, again, I want to keep this at the policy level. Jensen Wang: How I see this technology, I am concerned, and I can understand where folks are coming from, Jensen Wang: but I am concerned that there are some who see this technology as analogous to previous technological revolutions, Jensen Wang: as being like the Internet, as being like telecommunications, where, yes, there are some issues, Jensen Wang: but, you know, the market will figure it out, which I think was maybe a more reasonable view in these previous technological revolutions. Jensen Wang: I think those who are closest to AI don't feel this way. Jensen Wang: If you pull the actual researchers who work on AI, not investors who invest in some AI application companies, Jensen Wang: not general tech commentators who, you know, think they know something about AI, Jensen Wang: but the actual people who are building the technology, they're excited about the potential, but they're also worried. Jensen Wang: They're worried about the national security risks, they're worried about alignment of the models, Jensen Wang: they're worried about the economic impacts of the models. Jensen Wang: And for example, the idea that we would put a moratorium on all kind of regulation or all state regulation without a federal, Jensen Wang: you know, without a federal framework for 10 years, which, you know, was attempted in the summer. Jensen Wang: And I think it was just attempted this last week again, and it failed because it was very unpopular, Jensen Wang: because even the average person understands that this is a new and powerful technology. Jensen Wang: So, you know, I think we, I am more than any, you know, I am maybe the most optimistic about the upsides, right? Jensen Wang: I wrote this whole essay, Machines of Loving Grace, where I said, AI is going to, you know, extend, Jensen Wang: it might even extend the human lifespan to 150, 10 years after we get the country of geniuses in the data center, Jensen Wang: because we'll have a virtual biologist that can make discoveries much faster than we can. Jensen Wang: That, you know, it could drive, you know, economic growth to, you know, five or 10%. Jensen Wang: I'm incredibly optimistic about the technology, frankly, much more optimistic than some of people Jensen Wang: who describe themselves as boosters of the technology. Jensen Wang: But, but nothing that powerful doesn't have a significant number of downsides. Jensen Wang: And, and we as a society, as a polity, need to think ahead about those downsides. Jensen Wang: Saying that for 10 years, we won't regulate that technology, it's like, it's like saying I'm driving a car, Jensen Wang: I'm going to rip out the steering wheel because I don't need to steer for 10 years. [00:31:11] Speaker 2: Jensen Wang: Okay, so here's the question about the downside then. Jensen Wang: One of the downsides beyond hacking and everything else that I know you worry about is jobs. Jensen Wang: You spoke about it in 60 Minutes recently. Jensen Wang: But I want to know not just that you, you think that there's a good chance, Jensen Wang: and I don't want to put words in your mouth, that it could be, you know, Jensen Wang: half of all entry level jobs get lost. Jensen Wang: I want to know what you think should be done about it. [00:31:31] Speaker 3: Jensen Wang: Absolutely. Jensen Wang: So, you know, I think at the end of the day, I warn about these things not to be a prophet of doom, Jensen Wang: but because warning about them is the first step towards solving them. Jensen Wang: And if we don't warn about them, then we'll just, you know, we'll just kind of blindly walk into the landmine Jensen Wang: and blow us up. Jensen Wang: If we warn about them, if we see the landmine, we can walk around it and we can avoid it. Jensen Wang: So I have been thinking a lot about these ideas. Jensen Wang: I've been thinking about them inside Anthropic, where, you know, Claude is starting to write a lot of our code Jensen Wang: and we're thinking about how the jobs change. Jensen Wang: So I think there's several levels of it that maybe go from short term to long term or just kind of requiring Jensen Wang: more and more of the resources of society to happen. Jensen Wang: I think some of it can happen in the private sector and even in our relationships with customers. Jensen Wang: Every customer we work with has a following trade off, and it's not either or. Jensen Wang: They can, you know, they can increase efficiency by basically having AIs do what humans used to do. Jensen Wang: And there's plenty of that. Jensen Wang: Things like, you know, insurance claims processing or know your customer. Jensen Wang: Whole workflows that can just be done end to end via AI and I think will need a lot less humans for them. Jensen Wang: It will increase efficiency. Jensen Wang: It will save costs and do the same thing for lower cost and much less people needed. Jensen Wang: But you can also do things where you can create a lot of new value. Jensen Wang: And even in cases where AI does 90% of the job, not 100% but 90%, the humans can be 10 times more leveraged. Jensen Wang: And sometimes you need 10 times more of them to do 100 times what you did before because it's so efficient and valuable. Jensen Wang: And so encouraging companies to do as much of the second relative to the first. Jensen Wang: We know they're going to do the first. Jensen Wang: We're not trying to stop them from doing the first. Jensen Wang: But if they can do more of the second than the first, maybe more jobs can be created than -- Jensen Wang: Does that mean we need government incentives? Jensen Wang: So, again, that's level one. Jensen Wang: Level two is the involvement of the government. Jensen Wang: I don't see retraining programs as a panacea, but I think we're going to need to do some form of that. Jensen Wang: Companies are going to do it. Jensen Wang: Companies are going to have to work with governments to do it. Jensen Wang: And I -- but I do think fiscally at some point the government is going to need to step in. Jensen Wang: I don't know if that's tax policy, but this world of fast growth, right? Jensen Wang: We did this report where we said even current models, it looks like they will increase productivity by 1.6% a year. Jensen Wang: That's almost, you know, almost a doubling of productivity. Jensen Wang: And the models are getting better and better. Jensen Wang: So I think we're going to get up to 5% a year, maybe 10% a year. Jensen Wang: That's a big pie. Jensen Wang: That's a big pie that we can give out to the people who are not such fortunate beneficiaries, right? Jensen Wang: If the wealth concentrates, there really is a big pie here. Jensen Wang: So I think the government is going to need to have some role here. Jensen Wang: That's level two. Jensen Wang: And I think level three is, over the long run, the structure of a society that has built powerful AI is just going to have to be different. Jensen Wang: If we go back to John Maynard Keynes, right, economic possibilities for our grandchildren, right? Jensen Wang: He invented this idea of technological unemployment. Jensen Wang: He suggested that maybe his grandchildren would only have to work 15 or 20 hours a week, right? Jensen Wang: That's like a different way of structuring the society. Jensen Wang: Some people will always want to work as hard as possible. Jensen Wang: There will always be segments of society who want to do that. Jensen Wang: But can we have a world where work doesn't, for many people, doesn't need to have the centrality that it does, Jensen Wang: That people find their locus of meaning elsewhere or work is about different things. Jensen Wang: It's more about fulfillment than it is about economic survival. Jensen Wang: There are so many possibilities here. Jensen Wang: I think society is flexible and society can -- I'm not suggesting anything top-down. Jensen Wang: I think society needs to restructure itself. Jensen Wang: We all need to figure out how to operate in the post-AGI age. Jensen Wang: So I think those three levels will go from fast and easy for individual companies to do to requiring a lot of consensus and very slow to do. Jensen Wang: But over the years, we're going to need to do all three of these things. [00:35:32] Speaker 2: Jensen Wang: Dario, I hope you come back so we can have a conversation as we have to do all of those things to figure out what comes next. Редактор субтитров А.Семкин Корректор А.Егорова