A Conversation with Dario Amodei and Marc Benioff | Dreamforce 2025

Welcome and introductions

00:07All 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 Boon? >> All right. Well, so we got a It's kind of a mix. I don't know. Um, please welcome Daario.

00:39>> Hey, Mark. Thanks for having me. Great to have you, Dario. Welcome. >> Thank you. >> Uh, we're really excited to have you, Daario, and introduce you to the audience and everybody here. And, you know, we've had such a great partnership. We've had an opportunity to speak so many times and um you know work with some customers together and uh also build some technology together. It's been exciting to use your model inside Slack with our new Slackbot. But you know, 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 about a little about yourself, kind of your beginnings, where you're from, you know, and also kind of

Amodei's background and path to AI

01:28uh I'll ask you a couple of the core questions along the way, but to take it from the start. Yeah, absolutely. So I grew up right here in uh in I grew up right here in San Francisco. I was uh originally interested in science, not in uh not in tech per se. So you know I did my did my uh uh graduate work in uh neuroscience as a posttock in computational biology at at Stanford and you know as I was working hard on solving the problems of biology you know trying to understand the diseases we we we face like uh 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

02:12be able to make progress at any speed just because of you know if you've ever seen one 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 uh 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 ended up working at um uh Google I joined open AI I in the early days I led research there for for for several years um uh and and and you know was one of the people who kind of documented this trend of exponential scaling of the technology um you know

02:56myself and the folks who eventually became the anthropic co-founders were among the first to uh 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 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 they are almost there now. Um uh and 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

03:36to 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. 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 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 um who left OpenAI to to found Enthropic um you know generally generally based our generally based our

04:17view uh and you know the the the rest is history. Enthropic has been um you know around for 4 and 1/2 years. Um we're growing very quickly. um you know I think we're we're at this point um by revenue have at least a plurality perhaps the majority of uh um you know enterprise enterprise customers and enterprise revenue while most of the other players are are focused on the consumer side and we find that you know as as you know I think you would know from running an enterprise focused company these these values of trust and responsibility you know 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

Founding Anthropic with sister Daniela

04:58reinforce our values. >> One of the one of the things um and you went pretty fast into kind of starting the company and kind of shot out of uh uh uh shot through uh Google and API to Anthropic, but you 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 are two of them. As I mentioned, we both, you know, we both grew up in San Francisco. We went to to LOL High School and, you know, even even very early on, we really figured that we wanted to do

05:38>> My mom went to LOL and she's sitting right there. So, she's going to be very happy about that. >> Amazing. Um, uh, no, no, it was it was it was a great place. I actually didn't over Daniela was four years younger than me, so we were never there at the same year, but I was there for four and then, you know, the next four years she was she was there. So um uh yeah you know very early on you know we we we had these you know these ambitions to you know to to do something to do something good for the world to have some kind of grand project and and to work together. I think 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

06:15to us actually actually being able to do it. So um uh you know she joined me at at she joined me at OpenAI in uh uh 2018 um and then the two of us along with some folks we worked with many of whom had been longtime uh uh friends decided to start Anthropic. So me and Daniela essentially essentially run the company together. You know she's responsible for the dayto-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. Um, uh, you know, I I I I just, you know, couldn't

06:59couldn't run the company without her and without her without her doing all those things. Um you know I tend to think about the strategic side where the where the research is going what our kind of you know plans are as a company strategically how we think about safety how we think about the company's uh uh role in the world and and the vision and and I find that's a very good um that's a very good split that's a very good separation um because both of us can do both pieces but it's 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 if you split. >> Thanks for allowing me to come back to that because I think it's such a great

07:38part of the company and the culture and how you're working and last year she was here and now this year you're here. Um and we've known you all from the beginning. I mean I think Salesforce owns like a some little point or something of anthropics. So we're very happy to be an investor. >> Very early investor. Very >> very early investor. Believe in you. >> I think you I think you invested when the valuation was 4 billion. Is that correct? >> I 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. >> Okay. We made money so far. >> Well, um, no, I I'm thrilled obviously that we're part of your journey and, uh,

08:15you know, we love working with you. We love working with Daniela, um, 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're right on the park with us looking at the tower from your window. >> I I believe my office is what I I was told it was formerly Steuart's office. >> Formerly Steuart's office. Very good. And I would just, you know, it just just

Salesforce partnership and Slack building

08:52occurred to me like here you are. You're the San Franciscan. Um, you're right on you're right on the park with us. You're right on the tower with us. We're an investor in the company. Um, it's, you know, 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 uh, right from the very beginning. And 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, you know, since it's only been what, four and a half, 5 years since you kind

Biggest surprises since leaving OpenAI

09:30of 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 what was your biggest shock right there? Yeah, I mean, you know, there were there were so many moments because I think that, you know, the last four and a half years were were just so surprising overall. Um, 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 4 and 1/2 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 the use across the economy. You could

10:11you you know, you could see it all in these scribbles on the page. But actually seeing it play out is is wild. And and a lot of the individual things that that I'm seeing happening, I just 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 uh something you see over time is that 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, you know, just

10:56just the way we approach things is 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, compete with us at the same time. But I think this co-optition

11:31dynamic is becoming more and more common, right? As we have all these layers, we have like the chip layer, the cloud layer, the the kind of model layer, the the like the like go to market and distribution layer, and various companies are involved in more than one of these layers. and and so I think it's not uncommon for companies to cooperate in one area and and compete in another area. It's fairly normal. I think it's also been helped by, you know, I I I know quite well some of the folks behind Google. I've been, you know, friends with with Demis for uh who, you know, who leads uh 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

12:09me in in seeing the potential of the technology. He also has a neuroscience background like me. So I you know 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? >> Uh I I was I was very happy for uh Demis. Uh uh actually you know 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 Deis and his colleagues Alphafold won the won the Nobel Prize. So um uh you know I I I ended up dedicating the essay to the 2024 you know w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w

12:47w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w w of the Nobel Prize in chemistry for for AlphaFold because it was an example of the future that I was talking about. >> 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 and the models that you're looking at now that are most surprising? What what's really exciting about, you know, as you as you see the next next generations of these models unfold? >> Yeah. You know, I think broadly it's the jump to being able to do agentic abilities. Being able to do things.

Agentic abilities and AI writing code

13:20>> What was it? I didn't hear the word. >> Oh, sorry. Agentic abilities. Um, uh, uh, uh, turning the models into agents that Oh. Oh. Oh. Oh, I see. You've been I >> don't worry about it, Dario. Don't worry. I I wasn't. I just wanted I'm a little weak in my right ear. genuinely thought you didn't hear. >> I see. I understand. >> Um, >> uh, yeah, it was very funny, wasn't it? >> Definitely a buzz word and you've, you know, perhaps contributed more more to mo more than most to to making it to making it a buzz word. But I, you know, I think I think it's 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 uh you know in code than uh you

14:07know we see it in 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 uh you know insurance or manufacturing many of the regulated industries that were 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. 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. Um, so it's

14:47it's just as valuable. It's just the coders are early adopters. So, you know, they've they've kind of given us a glimpse of the future because they they adopt things so quickly. But I think we're going to see a similar spike in in other areas. So I want to ask you I have a few a few questions in that area. So the f you know when we first started getting in probably I think one of our very first conversations we were starting to talk about this theory and also AGI especially and what 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

15:28can code and then it's it's enhancing and it's off and going off and you know we had Brett Adcock on the stage earlier today and he was talking about how kind of like had a scene out of you know we are Bob we are Legion that the robot had taken off and was now you know self-replicating into the future and you know an infinite number of uh you know uh robots it was it was incredibly compelling would go off and that was that you know to this 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's develop. It didn't turn out exactly like that though, right? Or it's not we're not at that point yet, are we? >> Yeah. So, you know, I would separate two

16:11things. 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, 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 uh, you know, into this kind of uh, you know, future dreamy mode. Um, uh, but, you know, I think there are some things that are real. One thing that's real is that, you know, every 3 months we make a model that's smarter than the month than the 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

16:48point better 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 the you know the the next model you know we don't have our self-replicating you know nanobots or robots or or whatever but 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 serve quad faster. Um, you know, 6 months ago I I made this prediction that, you know, in in 6 months 90% of code would be written by by by AI models. Some people think that prediction is wrong, but within

17:32Anthropic and within a number of companies that we work with, that is absolutely true. Um now 90 you know >> so you're saying that 90% of all code adenthropics being written by the by the model today >> on on many teams you know not uniformly every >> is that something that you've kind of provided something kind of >> something auditable around that or like shown how that's happened. Yeah, the teams play an editing and supervisory role, but you know, 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 another model, another copy of Claude. Um uh the other thing is sometimes we have these long running bugs. you know, when we were training

18:14our our the most recently released Claude, it was like, you know, the cluster was broken. There was this bug. Engineers were spending a few days on it. And, you know, we just we just went to Claude and we said, I don't know, just kind of 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 enthropic do you need less engineers if 90% of all the code when will it be 95% or 99%. >> Yeah. So you know I you know I think people can misinterpret it as you know

18:53you 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 >> but you have made some very aggressive comments around firing executives >> that that so um >> I just want to put two and two together to get four. Okay. >> Um uh so uh um you know if >> one thing I've learned in knowing you for so long is I have to come back and kind of like get that qualifying question again. Yes. Because you're go you know you you're you're going fast. So trying to 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 let me let me take them one by one. So um uh you know

19:36under 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 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 a of AI models. And so what happens is, you know, you can just end up being 10 times more productive because the remaining 10% is is now a complement to the to the AI system that's that's getting much better at at getting much better at what it's producing. So So >> it's a rebalancing of the work is what you're really saying. >> It's it's not a replacement. >> It's not a replacement. It's a

20:19rebalancing. Oh, okay. >> 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? You It's a pretty big jump there. >> It's very hard to say. Um, but you know, we're looking at it. We're looking at it closely. We're always looking for ways to encourage complimentarity. >> You see that in the short term or the long term? >> Um, I you know, >> is that a 5year goal or a one-year goal? >> I'm optimistic about 10 year goal. >> I'm optimistic about complimentarity in the short run. I you know I I I I I do worry as we get out to two years 5 years that across the economy not that there won't be anything for humans to do but

20:59that there will be enough disruption that that we'll 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 kinds. I think that's in the next two to five years. >> Two to five years I would say. >> Okay. And at what point just coming back to my previous question, at what point is Enthropic the model writing 100% of the code? >> Yeah. Again, it's it's a tricky question because maybe even if the model's writing 100% of the code, the humans are still supervising the model or prompting it or clarif

21:44it's it's it's 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 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 we're you know 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

Models in healthcare and radiology

22:24radiologist 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 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 I'm trying to like understand where are we really in the evolution of the world. >> Where's the arc? So, so I think a lot of I 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 you know, I don't I don't necessarily take

23:02that as fact. They're an expert, but and they're often right, but they're not always right. Um uh so, you know, I'll tell >> 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 it's not really the right metaphor but doesn't have >> it does 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 about one case where it where it where it did work. Um so you know we're talking about you know founding the company with with my sister. She recently had her second child while she was pregnant. um you know pregnancy can suppress your immune

23:40system 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 healthcare? 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 hyperchondriacs that are doing that raise your hand. Yeah, I got a few. Okay, I know I'm >> but but but here's the point which is

24:16Claude listed possibilities. If she had just blindly taken the advice of Claude, that would not have gone well. Um Claude was good for saying, "Hey, have you considered this? Have you considered that?" So, so it has this assistive augmentative ability, >> especially in regards to words, right? is very good on words. >> Words. Yeah. Yeah. I mean, you know, 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. It's less reflective of the >> is that the crux of the issue like in radiology cuz like when we're saying it's going to improve healthare but we also saw this weird study where doctors who were using the models some in some

24:59cases 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 were not able to achieve accuracy. 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

25:38out. 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 >> 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 cloud code do I want it to suggest and I make edits or I write code and it makes edits and these

26:20people riff off each other and you know over the course of three or four months they work out what the best way to use quad 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 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 cloud code. Cloud code is writing the code, but here's really the best way as a developer now to work on cloud code. It's not writing 100% of the code. It's writing code now I'm like checking here's here's and now now I'm going to have to maintain the code. I'm gonna

The AI coding ecosystem

27:00have to provide all the capabilities, the whole infrastructure around the code. Is this the right way to think about it? >> I I think that's the right way to think about it right now. Um uh you know, as as the models get stronger, then the collaboration may change to more distant supervision by the human. Um so it's it's very hard to say what will happen even 6 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 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 >> it it you know every every we you know we have these benchmarks that are like you know what fraction of you know a

27:40random 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%. Uh 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 vibes 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

28:20co-pilots of the world you know that's kind of complimentary 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 replets, that's more of a consumer model. It allows people who are not traditionally software developers to write code. Um, you know, there's like a, you know, Versell is like a different a different way of looking at it. Augment code, wind surf, you know, there's a whole ecosystem. And then there's the command line tools like like claude code. Uh, so I think it's too early to say, you know, what's going to be the winner there or or even if there is one winner, it's just possible there's just different things that work for different people or or different

28:58>> there's a whole civilization. There's a whole EOS capability, right? It's a whole civilization like it's an EO it's even it's broader like it's really coming across the whole span. It it's really impressive, you know? It's it's 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? Um 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 but I I I just when I was conceptualizing all the

29:41>> which product do you love the best? Uh yeah, this is this is one of those questions that you know you know c your customers are like your children. You you you you really shouldn't give an answer. So I'm not going to give an answer.

Vision for Anthropic in five years

30:05All right. So are you getting a sense for Daario, by the way? Are you getting a sense for Daario? Okay. So that's 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 entrepreneur you've left open AI this thing is taking off. It's you and Daniela you know um you're in the Slack building you're get where is it going to be in 5 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 hold 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 you know where's Enthropic? What does it

30:52look like? Forget about all the other companies and all the other competitors whatever. Nobody cares. Where's Enthropic 5 years from now? What's your dream big what's your big dream? So, you know, I I think I think maybe maybe one of the best ways to think about this is, you know, I wrote this essay called Machines of Loving Grace where where it's really it was written from a societal perspective, but you know, of course, the company is trying to enact that and and and contribute to it and so you can back out from that where we where we hope the the company will be. So, you know, I I really thought of it as like the upside of AI, right? There's lots of concerns with AI. I I you know I'm one of the loudest in voicing them

31:29but but I am you know I am I am I am incredible believer in the upside of AI 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 um you know going back to my background in biology like you know we look at these tough diseases like cancer like Alzheimer's um you know like like you know various kinds of metabolic disease like I I 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 health care system to kind of enact it out and you know you

32:12again our partnership is about regulated industries and one of the regulated industries I think especially is is healthcare but you know 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 um 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 super intelligence, right? It it is an entity that acts in the world in in in in in in a way with a level of strategy and and knowledge and power in the world that

32:55goes far beyond what any individual human can do. So, you know, even as these models get smarter than than than any 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. Um, you know, most of which are good for the world, most of which create economic value. Um and and and so then my my vision of Enthropic 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. Um uh you know and and and and 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

33:37revenue 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 rate. Um, that's gone up 10x a year for the for the last 3 years. It can't continue like that for much longer, of course, but uh you know, >> as an investor, I'm looking forward to the $70 billion year next year. You should not feel bad investing at 4 bill. You should not feel bad. All right. >> Um uh uh uh so you know but I I I think the potential is is is very large. But I 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

34:19responsibly, 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 um you won't get all kinds of crazy stuff. Um 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

Data centers and compute

34:59the alphabet. >> I don't I don't I I have no idea what you're talking about. Um >> Okay, that's before the letter Y. I 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 privilege here and take a couple extra minutes with you. So number one, we see right now a tremendous fever on building data centers and you know 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 in part and parcel to enthropic and when you think about obviously one of

35:42your core partners has been Amazon it's been very important for your growth very important company um even though you do have you know obviously relationship with others as well but that one has been especially important to you know 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 what what is your plan? >> Yeah. So, you know, my my my view is is that there is going to be an enormous need for compute for for all the reasons

36:21that uh you know that that we've laid out, right? The 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 gonna we're, you know, we're going to need to serve more. We're going to need to serve more of our models. So when that revenue is not seven billion, but a hundred billion or more um uh and you know, when when model training requires millions of chips, you know, we're we're going to need many many gigawatts of power to to train the models. So we're trying to secure that power basically wherever we can. Um you know, and I'm confident in the company's uh uh I'm confident in the company's ability to do so. I I I do get a bit

36:59worried 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 they're maybe double counting. And I I also worry again >> deals are you ask talking about? >> I don't know what you're talking about. I have no idea. Um uh uh uh >> who is double counting? These are all public companies. I I >> 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 you. >> What about the ones who are doing triple counting? >> I I don't There might be some of that, too. Wow. All right. >> I don't know. Um uh uh uh but but but look look look at the end of the day,

37:38all this data center stuff is spending money, right? Um I think the thing that's most important is making money. Um and and so this is why I focus on, you know, the revenue and the actual work that we're doing with businesses. Andropic is growing faster than any other company in the space. The revenue is growing faster. And you know, I think I think basically if if you know, if the if the demand is there, if customers want the money and they're willing to pay you, then you are willing to pay your comput 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 Daniela as well, and for everything that you're

38:20doing with Enthropic. Congratulations. Please give Dario a round of applause.