A Conversation with Dario Amodei and Marc Benioff | Dreamforce 2025

All right. Good afternoon, everybody. Are you all having a good time? Oh, fantastic. Amazing. Are you ready for Metallica? Are you ready for Benson Boone? Raise your hand if you're going to Metallica. Awesome. Who's looking forward to Benson Boone? All right, so we got a. It's kind of a mix. I don't know. Please welcome Dario. Hey, Mark. Thanks for having me. All right, great to have you, Dario. Welcome.

Thank you.

We're really excited to have you, Dario, and introduce you to the audience and everybody here. And we've had such a great partnership. We've had an opportunity to speak so many times and work with some customers together and also build some technology together. It's been exciting to use your model inside Slack with our new Slack bot. But as I've talked to so many of my friends about you, we've gotten to know each other, but a lot of people don't know you. So maybe you could tell a little folks a little about yourself, kind of your beginnings, where you're from, you know, and also kind of. I'll ask you a couple of core questions along the way, but take it from the start.

Yeah, absolutely. So I grew up right here. I grew up right here in San Francisco. I was originally interested in science, not in tech per se. So I did my graduate work in neuroscience as a postdoc in computational biology at Stanford. And, you know, as I was working hard on solving the problems of biology, you know, trying to understand the diseases we face, like, like cancer and to understand biology, one of the things I noticed is that the complexity of biology felt like it was beyond human scale. You know, and I was starting to despair a little bit that, that we would be able to make progress at any speed just because of, you know, if you've ever seen of these diagrams of human metabolism, it's just. It's just beyond human scale. But almost exactly at the time that I was wondering about this, out of Google, out of University of Toronto, came the first work on deep neural networks. And I looked at it, you know, I. I replicated some of the results myself. I said, my God, this stuff really works. This stuff's really going to go there. So I went into AI. I ended up working at Google. I joined OpenAI in the early days. I led research there for, for, for several years and, you know, was one of the people who kind of documented this trend of exponential scaling of the technology. You know, myself and the folks who eventually became the anthropic co founders were among the first to study the scaling of the technology and so you know, around 2019 or 2020 had really two realizations. One was, wow, AI is, is really going to, you know, it's going to, it's going to scale to the moon and, and it's really going to be, you know, this whole thing is going to reshape the economy. It'll take a few years, but it's going to reshape the economy. And you know, we're there, almost there now. And the second was, look, there's, you know, these systems are different from ordinary software. You don't write every line of code right. Building ordinary software, it's like building a skyscraper or something. You make the blueprint, you make everything design. This is ironically a bit more like biological. It's organic or something. You're growing these models, you set a high level recipe. You know, it's like growing a plant or baking a cake or something like that. And that unpredictability combined with the incredible economic impact means that safety and responsibility are going to be very important to make sure that the models behave predictably as well as, as we deploy them. And they have enormous impact on the world. We, you know, we need to make sure that we deploy them responsibly. And so those were kind of the two pillars on which myself and the seven co founders who left OpenAI to, to found Anthropic. You know, generally, generally based, are generally based our view and you know, the rest is history. Anthropic has been, you know, around for four and a half years. We're growing very quickly. You know, I think we're at this point by revenue have at least a plurality, perhaps the majority of, you know, enterprise, enterprise customers and enterprise revenue, while most of the other players are focused on the consumer side. And we find that, you know, as you know, I think you would know from running an enterprise, enterprise focused company, these values of trust and responsibility are really, are synergistic with serving the enterprises. Right. These values help us to serve the enterprise and having a business model that serves the enterprise helps to reinforce our values.

One of the things, and you went pretty fast into kind of starting the company and kind of shot through Google and API to Anthropic. But you did, didn't mention your sister. And that was like one of the things that first really shocked me when we started to get to know you was you and your sister together are working on this company. And can you kind of tell us the origin story?

Yeah, yeah. So, you know, there are seven co founders and me and Daniela are two of them. As I mentioned. We both, you know, both grew up in San Francisco. We went to Lowell High School and, you know, even very early on, we really figured that we wanted to do.

My mom went to Lowell and she's sitting right there, so she's going to be very happy about that.

Amazing. No, no, it was a great place. I actually didn't over. Daniela was four years younger than me, so we were never there the same year, but I was there for four. And then the next four years she was there. So, yeah, very early on we had these ambitions to do something good for the world, to have some kind of grand project and to work together. I think many, many people, you know, kind of, or many siblings, I guess, have these, have these ambitions. But, you know, some, somehow things found their way to us actually, actually being able to do it. So, you know, she joined me at, she joined me at OpenAI in 2018. And then the two of us, along with some folks we worked with, many of whom had been longtime friends, decided to start Anthropic. So me and Daniela essentially, essentially run the company together. You know, she's responsible for the day running of the company. You know, kind of operate, you know, how, how the company is put, put together, how the org runs, what its processes are, does, you know, kind of, kind of a fair amount of the, the, the, you know, kind of the business on the, on the, the business on the structural side. You know, I, I, I, I, I just, you know, couldn't, couldn't run the company without her and without her, without her doing all those things. You know, I tend to think about the strategic side of the, where the research is going, what our kind of plans are as a company, strategically, how we think about safety, how we think about the company's role in the world and the vision. And I find that's a very good split. That's a very good separation because both of us can do both pieces, but it's really hard to switch between a kind of operational mode and a vision mode. And I think both people can be more effective if you split.

Thanks for allowing me to come back to that because I think it's such a great part of the company and the culture and how you're working. And last year she was here, and now this year you're here. And we've known you all from the beginning. I mean, I think Salesforce owns like some little point or something of Anthropic. So we're very happy to be an investor.

Very early investor. Very early investor.

Very early investor. Believe in you.

I Think you invested when the valuation was 4 billion. Is that correct?

I don't know, but it was too high, whatever it was.

I haven't. Before you say it's too high, you should calculate how many times it's gone up since then.

We've made money so far. Well, no, I. I'm thrilled, obviously, that we're part of your journey. And, you know, we love working with you. We love working with Daniela so much. And, you know, I think, kind of getting back to this story of you and your sister and your friends, you know, starting this company here in San Francisco, and then you came to us, we were starting to consolidate some of our real estate here, and then you, you know, we had a conversation about you taking over the Slack building. We had just bought Slack, which we have done, and now you're in the Slack building. So you' on the park with us, looking at the tower from your window.

I believe my office is. I was told it was formerly Stewart's office.

Formerly Stewart's office. Very good. And I would just, you know, it just occurred to me, like, here you are. You're the San Franciscan. You're right on the park with us. You're right in the tower with us. We're an investor in the company. It's obviously a critical part of Slack today, right, in terms of how you couldn't use Slack without it, but you're at the very bleeding edge of these models. It's really incredible. And you've really been visionary right from the very beginning. Now, when I didn't know you, when you were at OpenAI, I only met you after you started the company. So just now, kind of take us through your expectation. What has been your biggest surprise since it's only been, what, four and a half, five years since you kind of walked out the door, said goodbye to Sam, right? And then you just said, okay, Daniela, we're going to. We're going to get this going. What was your biggest shock right there?

Yeah, I mean, you know, there were. There were so many moments because I think the, you know, the last four and a half years were just so surprising overall. You know, I think. I think at a high level, there's a dichotomy where on one hand, everything we've seen over the last four and a half years was in some sense predicted by those straight lines on graphs that I made with the scaling laws. Right. These enormous data center buildouts, the way that our revenue is going up, you know, 10x per year, the use across the economy, you could, you know, you can see it all in these scribbles on the page, but actually seeing it play out is wild. And a lot of the individual things that I'm seeing happening I just never would have, I never would have expected, I guess if I were to pick one most surprising thing it is, you know, we left one company and started another company. And, and something you see over time is that as you build a company, you really do see through the people you hire, through the choices that the company makes, you kind of see the paths diverge and you see differentiation. Right. OpenAI has gone very much in the consumer direction. We've gone very much in the enterprise direction. We've taken. Just the way we approach things is very different. And so you have this market structure where there are these two companies that are like, it's sort of a natural experiment. They're, they're just taking a very different approach. Both exist in the ecosystem but, but the approach they take is very different.

And then you have to reflect back at your time at Google as well. They're also very much with Gemini and you know, very much a top model as well. How do you look at them? Do you look at them as a blend then?

Yeah, I mean, you know, Google's a partner to us, they're an investor in us. Obviously their models compete with us and compete with us at the same time. But I think this co optition dynamic is becoming more and more common. Right. As we have all these layers, we have like the chip layer, the cloud layer, the kind of model layer, the go to market and distribution layer and various companies are involved in more than one of these layers. And so I think it's not uncommon for companies to cooperate in one area and compete in another area. It's fairly normal. I think it's also been helped by, by, you know, I know quite well some of the folks behind Google. I've been friends with Demis Hassabis who leads research at Google for something like 15 years. Right. I think he was maybe one of the only people who was earlier than me in seeing the potential of the technology. He also has a neuroscience background like me. So I think that helps us to get along even as there are some competitive aspects between the companies.

What was your reaction when he won the Nobel Prize this year?

I was very happy for Demis, actually. I was writing this essay about the potential of AI in biology called Machines of Loving Grace. And two days before I was about to publish it, of course, Demis and his colleagues Alphafold won the Nobel Prize. So I ended up Dedicating the essay to the 2024 winners of the Nobel Prize in Chemistry for AlphaFold, because it was an example of the future that I was talking.

Very exciting. So just getting back to you and Daniela and you're working on and growing the company and up it goes. And you're doing it right here in San Francisco. You know, what are some of the characteristics of the models that you're looking at now that are most surprising? What's really exciting about as you see the next generations of these models unfold?

Yeah, I think broadly it's the jump to being able to do agentic abilities, being able to do things.

What was it? I didn't hear the word.

Oh, sorry. Agentic abilities, turning the models into agents that. Oh, oh, I see you've been.

Don't worry about it, Dario. Don't worry. I wasn't. I just wanted. I'm a little weak in my right ear.

I genuinely thought you didn't hear.

I see, I understand. Yeah, it was very funny, wasn't it?

Yeah, definitely a buzzword. And you could, you know, perhaps contributed more than most to making it a buzzword. But, you know, I think it's a real thing, which is being able to do tasks end to end in a way that was not possible before. And I think we see it more, you know, in code than, you know, we see it in any other area. But I think code is just a leading indicator, honestly. In other tasks, in tasks like financial services, in tasks like, you know, health and medical, in tasks like, you know, insurance or manufacturing, Many of the regulated industries that we're working together on our, on our, on our partnership, agents are. Agents are just as, you know, valuable. You just as much want to do things end to end. Right. You think of the, you know, the sales and go to market and, you know, customer management workflows. Do you want to string a bunch of things together and you want the models to pull in a bunch of things in the context and learn this, do that, access this information. So it's just as valuable. It's just the coders are early adopters, so they've kind of given us a glimpse of the future because they adopt things so quickly. But I think we're going to see a similar spike in other areas.

So I want to ask you, I have a few questions in that area. So when we first started getting in, probably think one of our very first conversations, we were starting to talk about this theory and also AGI especially, and the potential in the future and AGI at that time and this is kind of now we go back several years, was kind of being defined as. Yeah, the model is almost kind of recursive. It's building itself, it's growing, it's evolving, it knows it is coding itself, it can code and then it's enhancing and it's often going off. And you know, we had Brett Adcock on the stage earlier today and he was talking about how kind of like at a scene out of We Are Bob, We Are Legion that the robot had taken off and was now self replicating into the future and an infinite number of robots, it was incredibly compelling, would go off and that was that. To that point a few years ago when we were talking, it was like, well now the model is kind of building itself, it's growing itself. It didn't turn out exactly like that though. Or it's not. We're not at that point yet, are we?

Yeah. So, you know, I would separate two things. There, there's, I think inevitably, as we, you know, talk about AI in the future, this kind of sci fi sheen on, on, on, on, you know, the, on the whole thing. And that's, you know, in a way it's helpful as a metaphor, but also it can be distracting because, you know, it just, it just kind of conjures everyone's mind into this, into this kind of, you know, into this kind of, you know, future dreamy mode. But you know, I think there are some things that are real. One thing that's real is that, you know, every three months we make a model that's smarter than the month than model we made before. And indeed the models are slowly getting to where they're, you know, better than most humans at most things, maybe at some point better than all humans than all things. The other thing I think is real is, you know, the model is not building itself in the sense that like I have a specific copy of the model that's like making a specific copy of the next model. We don't have our self replicating nanobots or robots or whatever. But what absolutely is happening is that within Anthropic and elsewhere, we are using Claude to help build products on top of Claude and to help train the next Claude to help serve Claude faster. Six months ago I made this prediction that in six months 90% of code would be written by AI models. Some people think that prediction is wrong, but within Anthropic and within a number of companies that we work with, that is absolutely true. Now 90, you know, so you're saying.

That 90% of all code at Anthropic is being written by the, by the.

Model today on, on many teams, you know, not uniformly that's something that you.

Kind of provided something kind of auditable around that or like shown how that's happened.

Yeah, the teams play an editing and supervisory role, but Claude can both. You can say, write this feature and Claude will do it. And the human kind of looks over the code or asks for help from another model or another copy of Claude. The other thing is sometimes we have these long running bugs. When we were training the most recently released Claude, it was like the cluster was broken. There was this bug, engineers were spending a few days on it, and we just went to Claude and we said, I don't know, just play around with the cluster and see if you can figure out what's wrong. And, you know, Claude found this just super obscure bug that the engineers had missed. So this, this was one area where it was like, Claude is like having a very talented teammate.

So if Claude is now running, writing 90% of all of the code in Anthropic, do you need less engineers? If 90% of all the code, when will it be 95% or 99%?

Yeah. So, you know, I think people can misinterpret it as, you know, you know, like Claude writing 90% of the code means we're firing 90% of the software engineers. But, you know, if you're familiar with.

The principle of, well, you have made some very aggressive comments around firing executives. So I just want to put two and two together to get four. Okay.

So, you know, if one thing I've.

Learned in knowing you for so long as I have to come back and kind of like get that qualifying question again, because you're going, you know, you, you're going fast. So I'm trying to keep up with you, and it's amazing. But I want to just make sure I understand exactly what you're saying.

Yes, absolutely. Let me take them one by one. So under comparative advantage, if Claude is writing 90% of the code, what that means usually is you need just as many software engineers. You might need more because they can then be more leveraged. They can focus on the 10% that's editing the code or writing the 10% that's the hardest or supervising a group of AI models. And so what happens is you can just end up being 10 times more productive because the remaining 10% is now a complement to the AI system that's getting much better at what it's producing.

It's a rebalancing of the work is what you're really saying. It's not a replacement.

It's not a replacement, it's a rebalancing. Now, as that 90% goes to 95, 99, totally automated, end to end, there may be some point at which all bets are off.

What's your time frame for that? It's a pretty big jump there.

It's very hard to say. But you know, we're looking at it, we're looking at it closely. We're always looking for ways to encourage complementarity.

See that in the short term or the long term. Is that a five year goal or a one year goal?

Optimistic about ten year go. I'm optimistic about complementarity in the short run. I, you know, I do worry as we get out to two years, five years, that across the economy, not that there won't be anything for humans to do, but that there will be enough disruption that, that will have a, you know, a kind of labor disruption that is, you know, not fundamentally different from what we've seen with previous technologies, but that operates faster, that is broader across the economy because it relates to all.

You think that's in the next two to five years?

Two to five years. I would.

Okay. And at what point, Just coming back to my previous question, at what point is anthropic the model writing 100% of the code?

Yeah, again, it's a tricky question because maybe even if the model is writing 100% of the code, the humans are still supervising the model or prompting it or clarifying.

There's still something for us to do.

Model supervisors, comparative advantage is, it's a surprisingly clever thing. I don't, you know, I, I'm, I, I, I am concerned particularly about people's ability to adapt as the jobs are changing fast. The jobs are changing so fast. But comparative comparative advantage is powerful. That's going to be one of our tools in this.

Well, you've mentioned healthcare several times, but we've seen these kind of very conflicting studies recently in healthcare around accuracy, especially because these are word models. There seems to be some level of accuracy that's not 100% and were, you know, and then when applied to some of these kind of other things, like radiology is one where we saw these very interesting, you know, studies where we just didn't see the accuracy of a radiologist reflected in what the model was doing. Is that how you see it going forward or how did you reflect back on that?

Yeah, I would say where we are now is, I think of the models and I think a lot of people who use the models in practice.

You know where I'm going, right, where I'm trying to like understand where are.

We really in the evolution, where's the arc? So I think a lot of, I think, and a lot of the people who use the models in practice, I think, think of the models as maybe a bit like a second opinion, right. Where you don't necessarily trust them alone. Just like if a doctor says something to me, I don't necessarily take that as fact. They're an expert and they're often right, but they're not always right. So, you know, I'll tell you, especially.

In this one category, radiology, it seems strange, like it just can't get its. I was going to say can't get its head around it, but that's not really the right metaphor.

But doesn't have.

There's something about. Either it doesn't have enough data or it's not getting. It's too nuanced. What is it?

Yeah. So let me, let me actually tell you a story about one case where it did work. So, you know, we were talking about, you know, founding the company with my sister. She recently had her second child while she was pregnant. You know, pregnancy can suppress your immune system. Was. Was viral. For some reason, it didn't occur to, to any of them that it could be a bacterial infection. She took all her data, she uploaded it to Claude and you know, Claude, you know, systematically listed the possibilities in the way that the doctors had somehow missed.

That is amazing, right? How many of us have already used the models in some way to look at our health care? Raise your hand. Yeah, I think we're all having exactly that.

That's, that's a lot of people.

Yeah, it's amazing, right? Where you can kind of say, especially how many of you are the hypercom contracts that are doing that. Raise your hand. Yeah, I got a few. Okay. I know I'm.

But here's the point, which is Claude listed possibilities. If she had just blindly taken the advice of Claude, that would not have gone well. Claude was good for saying, hey, have you considered this? Have you considered that? So it has this assistive augmentative ability.

Especially in regards to words, Right? It's very good on words.

Words. Yeah. Yeah. I mean, this specialized stuff of reading images, like, I think that's more about specialized models. Like if you don't have the right data, maybe it's not doing well. Again, it's less reflective of the overall.

Is that the crux of the issue, like in radiology? Because like when we're saying it's going to improve health care. But we also saw this weird study where doctors who were using the models, some, in some cases were relying too much of the models and then ending up with bad outcomes on the other side. Then we saw models that were like, trying to read radiology but then are not able to achieve accuracy. There was like this kind of strange bifurcation between the two things.

Yeah. You know, I think there's a lot still left to work out in what the role of the human is and what the role of the model is. And of course that's changing as the models improve rapidly. But, you know, I think it's something you often see in new technologies, where there's this great technology and then these practical issues of exactly how do you use it, exactly how do you interact with it, they take surprisingly long to work out. And, you know, if you look back at, you know, the early days when people, you know, kind of, you know, deployed, deployed new technologies, I don't know, it's. It's just, it's often.

So you think this is going to be about humans and agents working together?

Yeah, no, no, no, exactly. Just. Just the collaboration model needs to be worked out a bit more than it has in these practical areas. Code is good to point to. Like, code happens so fast, Right. Developers are early adopters. And so that cycle, we've gone through it in like, months. Right. You can see people talking in blog posts about, oh, you know, what's the best way to use Claude code? Do I want it to suggest and I make edits or I write code and it makes edits and these people riff off each other and, you know, over the course of three or four months, they work out what the best way to use Claude code is. I think that same thing is happening in healthcare and radiology. But because there are a lot of barriers in these industries because they're high stakes, that process is working itself out much slower.

So that's really kind of getting down to it, right? That it's a collaborative thing and like, here's the best way to use Claude code. Claude code is writing the code. But here's really the best way as a developer now to work on Claude code. It's not writing 100% of the code, it's writing code. Now I'm like, checking, and now I'm going to have to maintain the code. I'm going to have to provide all the capabilities, the whole infrastructure around the code. Is this the right way to think about it?

I Think that's the right way to think about it right now. As the models get stronger, then the collaboration may change to more distant supervision by the human. So it's very hard to say what will happen even six months from now. But, but I think what you've said is a very accurate description of where we are now as of, you know, our, you know, as of Sonnet 4.5, which was the latest model released at.

Which is probably one of the best coders that we've seen. Right.

You know, every, every, you know, we have these benchmarks that are like, you know, what fraction of, you know, a random sample of GitHub prs can this thing write from a description? And that's over the last year and a half, that's gone from like 5%, which was basically the thing can't code at all, to something like, you know, 77%. So it's, it's, it's really.

And it's also yielding also this whole other ecosystem of things on top of it as well. Like even we showed Agent Force Fives yesterday that I think our customers will benefit from or other, you know, other tools. Lovable. These things have been built on these things. Right? Has that been a surprise to you to see a whole ecosystem on top of the model?

I mean, that's been, that's been great to see. There are, there are many different models, right. If you look at the GitHub copilots of the world, you know, that's kind of complementary to GitHub. If you look at the cursors of the world, that's kind of an IDE model. If you look at the Lovables and repl, it's. That's more of a consumer model that allows people who are not traditionally software developers to write code. You know, there's like a, you know, Vercel is like a different, a different way of looking at it. Augment code, Windsurf. There's a whole ecosystem and then there's the command line tools like Claude code. So I think it's too early to say what's going to be the winner there or even if there is one winner. It's just possible. There's just different things that work for different people or different.

There's a whole civilization, there's a whole ecosystem capability. Right? It's a whole civilization. Like it's broader. Like it's really coming across the whole Spanish. It's really impressive. You know, it's so fundamental that it's able to yield all this incredible innovation from others.

Yeah. No, this. I mean, which one of those is.

The most, like, the one you did not expect?

Which one is the one that I most did not expect? I think the model of kind of allowing people to write code end to end, like allowing non coders to write code, it totally makes sense once I see it, and it's doing really well as a business model. But I just. When I was conceptualizing.

Which product do you love the best?

Yeah, this is one of those questions that your customers are like your children. You really shouldn't give an answer. So I'm not going to give an answer.

All right. So are you getting a sense for Dario, by the way? Are you getting a sense for Dario? Okay, so I'm trying to, like, you know, help them to understand you and how you're thinking about this and where we're going. So now here you are, you know, you're an entrepreneur. You've left OpenAI. This thing is taking off. It's you and Daniella, you know, you're in the slack building. You're getting. Where is it going to be in five years? What does it look like we've seen? Zero to five years now. You don't have to tell me as an investor, and there's nobody going to hold you accountable for it, but just if you just fantasized out and now it's not 2025, but it's 2030 and where's anthropic? What does it look like? Forget about all the other companies and all the other competitors, whatever, Nobody cares. Where's Anthropic five years from now? What's your dream?

Big.

What's your big dream?

So, you know, I think maybe one of the best ways to think about this is, you know, I wrote this essay called Machines of Loving Grace where it's really. It was written from a societal perspective, but, you know, of course, the company is trying to enact that and contribute to it, and so you can back out from that where we hope the company will be. So, you know, I really thought of it as like, the upside of AI, Right. There's lots of concerns with AI you know, I'm one of the loudest in voicing them, but. But I am, you know, I am. I am. I am incredible believer in the upside of A.I. you know, I think. I think more so than people who code or describe themselves as more optimistic. I have just a really expansive view. I am perhaps most excited about what it can do for curing diseases. You know, going back to my background in biology. Like, you know, we look at these tough diseases like cancer, Alzheimer's, various kinds of metabolic disease, I just think AI is the right thing to cure them. And so I think of all the work we can do, working with pharmaceutical companies, working with scientific research institutions, and working with the healthcare system to kind of enact it out. And again, our partnership is about regulated industries. And one of the regulated industries, I think especially is healthcare. But that is one of the most positive applications. But I think that we will see applications across the industry. I'm very excited about financial services. I'm very excited about insurance. I'm very excited about taking basically every enterprise and learning to serve every enterprise with smarter and smarter models. And the skill ceiling is very high. One way to say it is what is an enterprise? It's kind of a superintelligence, right? It is an entity that acts in the world in a way with a level of strategy and knowledge and power in the world that goes far beyond what any individual human can do. So even as these models get smarter than any individual human, there's a ceiling substantially higher than that in terms of doing the things that a company tries to do in the world, most of which are good for the world, most of which create economic value. And so then my vision of Anthropic is a company that serves the enterprise of the world that transforms every. Every enterprise puts AI at the center. At the center of every enterprise. You know, and, you know, I think I won't give specific numbers, but like, if you, if you think about the actual market potential of that is very, very high. Right? Our revenue has been going up 10x per year. It's currently, I think it was reported this morning, which I'm happy to confirm is correct, is approaching approaching 7 billion run rates. That's gone up 10x a year for, for the last three years. It can't continue like that for much longer, of course, but, you know, as.

An investor, I'm looking Forward to the $70 billion year next year.

I was like, not feel bad, all right? Investing at 4, Bill, you should not feel bad. So, you know, but I think the potential is very large. But, but I think even, even more important than that, or perhaps even necessary to that success. We want to be known for, you know, the company that is trustworthy, the company that behaves responsibly, the company that has a, that has a reputation for, you know, doing what it says, saying what it does and building products that are trustworthy, that, you know, if you, if you, if you build on us, you won't get any surprises. You won't get anything crazy. You won't get the model, you know, saying it's Hitler. You won't, you won't get, you know, all kinds of, you won't get all kinds of crazy stuff. And, and I think that's very important to us being good stewards of this.

Are there certain companies that you're trying to imply that that might be.

None whatsoever.

There's an entrepreneur down in Palo Alto.

None at all. I don't know what you're talking about.

Okay, I see. That's a certain letter in the Alphabet.

I don't, I don't, I, I have no idea what you're talking about.

Okay, that's before the letter.

Yes, sorry. I still don't know what you mean.

Okay, one more thing. I'm going to take a couple, I'm going to use moderator privileger and take a couple extra minutes with you. So, number one, we see right now a tremendous fever on building data centers. And we've seen a tremendous fever around the chips. The financing, the size and scale of the data centers, the energy of the data centers. You haven't really addressed it in your comments per se, besides the scaling laws, but let's talk about it. Just part and parcel to Anthropic. And when you think about, obviously one of your core partners has been Amazon. It's been very important for your growth, very important company, even though you do have obviously relationship with others as well. But that one has been especially important to you. Just give us a vision of what's happening with these data centers right now. What are we going to see in the next few years with these data centers? And how do you think about it as an entrepreneur who's going to need data centers to grow? Who will you go to? How will you do it? Will you build your own? Will you just continue to partner? What is your plan?

Yeah. So my view is that there is going to be an enormous need for compute for all the reasons that we've laid out. The scaling laws suggest that we're going to need more and more compute to train smarter and smarter models. And then as the revenue goes up, we're going to need to serve, we're going to need to serve more of our models. So when that revenue is not 7 billion, but 100 billion or more, and when model training requires millions of chips, we're going to need many gigawatts of power to train the model. So we're trying to secure that power basically wherever we can. And I'm confident in the company's I'm confident in the company's ability to do so. I do get a bit worried when the press focuses too much on the building of the data centers and deals to build data centers. Some of these deals seem a little bit fishy. They seem like there may be double counting. And I also worry, again, deals.

Are you talking about.

I don't know what you're talking about. I have no idea.

Who is double counting. These are all public companies. We're going to want to know who's double counting.

I don't, I don't. I don't know. I don't know anything more than.

What about the ones who are doing triple counting?

I don't. There might be some of that, too. Oh, wow.

All right.

I don't know. But, but, but look, look, look, look. At the end of the day, all this data center stuff is spending money, right? I think the thing that's most important is making money. And, and so this is why I focus on, you know, the revenue and the actual work that we're doing with businesses. Anthropic is growing faster than any other company in this space. The revenue is growing faster. And I think basically, if the demand is there, if customers want the money and they're willing to pay you, then you are willing to pay your compute providers and you'll get the compute you need. At the end of the day, Daria.

We could not be more grateful for you to be here, to be in San Francisco, to be a native son, and to Daniella as well, and for everything that you're doing with Anthropic. Congratulations. Please give Dario a round of applause.