Four CEOs on the Future of AI: CoreWeave, Perplexity, Mistral, and IREN

I'm here at Nvidia's annual GTC. conference and I'm going to interview. four amazing AI CEOs. Stick with us. I'm. going all in. Our episode is sponsored by the New York. Stock Exchange. Are you looking to. change the world and raise capital? Do. it at the NYSE. The NYSE is a modern. marketplace [music] and a massive. platform built for scale and long-term. impact. So, if you're building for the.

future, the NYSE is where it happens. I'm going all in. One of the great companies of the AI era. is, of course, CoreWeave. They're. building massive infrastructure for. these hyperscalers and in some ways, Michael, uh in trader, welcome to the. program. You're. the original hyperscaler. You guys got. in very early and secured your. I don't know which. GPUs you wind up getting, but you were. very early to this trend. And how did.

you. get to it so early and how did you build. out this, you know, first, I guess at. the time, neo cloud? Yeah, so we we. didn't really start it as a neo cloud. and I I I was. running an algorithmic hedge fund. focused on natural gas and. uh. when when you build an algorithmic hedge. fund, once the algorithms are built, you're. really just monitoring it and testing. different pieces and doing all that, but. there's also a lot of downtime and we. got super interested in crypto. Um and.

you know, we're pretty nerdy. We kind of. dig under the hood and we started to get. interested in the security layer. We looked at Bitcoin and the mining for. Bitcoin and we didn't like it. We just. thought that like there's some brilliant. engineer that built the ASIC and they're. probably going to be better at running. it than we are. So, we really began to. focus on the GPUs mostly because the. GPUs were you can mine Ethereum with. them, but you can also do all these other. things. And really so right from the.

start, we looked at the compute as an. option to be able to deploy our. computing power to different use cases. And so, you know, began the company in. 2017, you know, spent the first kind of 3. years mining crypto, went through a. couple of crypto winters. because we had come from hedge fund our. you know, we we have real chops in risk. management and how we think about.

capital and risk exposure and allocation. and all of that. And so we were really. careful around that right from the. start. So we weathered crypto winter. really well. and began to scale the company and. immediately started to look for other. use cases that you could use this. compute for cuz crypto was pretty. volatile. Yeah, and crypto was a. question mark at that time. Absolutely. Yeah, I mean Bitcoin was speculative and. there were many other speculative. projects. The only other people using. this type of hardware, quants, yeah, medical researchers. So a good way.

to think about it is like the. progression of products that we kind of. started to work on. You know, first was. crypto, but we immediately moved from. crypto to CGI rendering and we built. projects that will let it allow. folks that were trying to animate and. render images, you know, kind of what makes the movies. cool, right? And and we started to work. on that and then we moved to batch. computing and started to look at medical. research and different ways of using the.

compute to be able to drive science. And we just kind of kept moving up the. stack in terms of complexity. on how GPUs could be used and ultimately. in like call it like 20 20 2021, we. started to really try to figure out how. you can go ahead and use GPUs for neural. networks and that was not something that. we knew how to do. And so we actually. went out and bought a bunch of A100s and. donated them to a a group that was.

working on Luther AI. They were working. on open-source project with the thought. that. these guys are. taking the GPU compute because we're. donating it, they can't really get. pissed at us if we're not very good at. it initially. And that worked out really. well because. >> They can't complain about the SLA. >> They kept telling us like we need more. of this, you got to work on this. And. that began to really. give us an understanding of what was. necessary to run. scale parallelized computing. And you.

know, that that. we went through it. I kind of feel like. buying those initial GPUs was the. tuition we paid to learn how to run this. business. And then one of the. interesting things is all of those guys. went back to their day jobs because they. were all volunteers working on this. They were like-minded scientists. And. when they got to their day jobs, they. were all like, I want that. infrastructure. It's built the right. way. That's the way that research is. going to want to use it. And that. launched our our business. It was an. amazing story. And So you went from.

crypto to these researchers into. academia and deep research. What's the. next card to turn over in the poker. game? >> Yeah, so so what became very clear to us. very very early on was that the scaling. laws were going to drive and remember. this is really back in the you know, 2020, 2021 before. chat GBT moment occurred. And we began. to understand that like computing. decommoditizes at scale. Right? Like.

when when you know, anybody can run a. GPU, but can you run a cluster that's. large enough to train a model that can. change the world? And that's a different. question. And so we really began to. think about like how do you go about. scaling up your delivery of this. computing to clients, larger and larger. clients. And that was the next card to. turn is to think about it from a okay, you know, there's a component of this. that is going to lean into. uh our ability to access the capital to.

be able to deliver our solution to the. broadest possible audience, to the most. sophisticated consumers of this compute. And and that was really the next card is. thinking about it as a business rather. than as a engineering project to be able. to deliver the the uh. the infrastructure and the software and. really everything between, you know, when you when you're thinking about what. we do, we kind of live. above the NVIDIA GPUs but below the. models. And everything in there, all the. software, the integration of software.

and operations, and uh observability, and all the things that you need to be. able to build uh a cloud that's. purpose-built for this one specific use. case, right? So, we don't we don't do. everything. We really focus on one use. case, which allows us to. >> web servers? It's different. You got. AWS. You know what? They do a great job. It's like it's a it's a great solution. It was a brilliant solution to solve a. problem. We just looked at it and said, "There's a new problem. And let's go. about Let's go about looking at this. problem and try and come up with a.

solution to deliver compute that solves. that problem." And when did the language. model start dialing and calling you for, you know, capacity? Yeah, so uh our our. our first uh. um well, our our first language model. was really Eleuther. Yes. Um but uh our. our first like large commercial. uh was Inflection. Uh. and so, you know, we work with Mustafa. and and and Inflection, and then we we. really diversified from there.

uh into the hyperscalers, into, you. know, OpenAI across the the the the model. uh the foundation models across. um you know, and and just kept scaling. scaling with the belief that, you know, once again, the the the. decommoditization. of compute, the ability to to deliver a. solution, and the solution is building. supercomputers that can change the. world. And that's really what we began. to focus on. That was the lead into.

training, and now the world has gone. through, you know, this this moment. where we've moved from research into the. productization of this. It's it's it's. beginning to work its way in from the. the the the fringe of organizations into. the core of what they do, and you can. see that every day in the. in the amount of inference compute that. is being driven through, you know, our. infrastructural layer, which is just. massive, which is just like one of the. massive.

>> are consuming it, not just building. models, but they're deploying them and. and utilizing them. >> I always think of inference as the. monetization Yeah. of the investment in. artificial intelligence. So, when when. when we when we see our compute being. used. to stand up the massive scale of. inference that's hitting our compute. every day, and like, you know, inference. is when people ask the model a question, it comes back with an answer. That's an. inference. Or when you ask the model a.

question and then to go do something, that's inference, right? And that's. actually where your your you have the. opportunity to really drive value. outside of the model itself, but into. the real world. And. that's really exciting for us. That's. what we like to watch. That's what I. like to watch in terms of gauging the. health What chips are those? Um, so so. really, uh, you know, we are we are the tip of the spear in. bringing, um, the new architecture, uh,

out of Nvidia, uh, into uh, into commercial production at scale. Yeah. uh, so when when, you know, we were the. first ones to bring the H100s at scale. We were the first ones to bring the. H200s at scale. First ones with the GB. 200s and now you've got the GB300s. And. one of the things that's that's that's. amazing and really fascinating for us is. is you know people are using the. bleeding edge GPUs to train models. as the new architectures come out and. then they take those GPUs and they move.

them into different experiments and then. over time they move them into inference. and they continue to use them in. inference for a very very long time. What is the shelf life of a 100 right. now? That's been a big debate is I think. for your company for Microsoft and I. guess Michael Berry you're you know who. you must have known when you were a. quant you know saying oh my god the. whole industry is the sky's falling and. then we all know in the industry that. people don't just throw this hardware. away that they find uses for it the.

street finds its own use for technology. So what's the reality of the lifespan of. these things? >> So so my my take on the the. the GPU depreciation debate is that it's. nonsense right? It's a debate that is. being. brought to the forefront by some traders. that have a short position in the stock. and they're trying to talk down. Look. here's what we know right? Um. when when we buy infrastructure we're a. success based company right? We're a.

small company on a relative basis. compared to the enormous companies that. we're competing with. And so they come. our clients come into us and they buy. compute for five years for six years. Our average contract is five years. So. any commentary by anyone either inside. or outside of the industry that this. stuff becomes obsolete in 16 months or. whatever nonsense they're spewing it's. it doesn't it doesn't in any way match. up with the facts on the ground. The. facts on the ground is they're buying it. for five years right? If and my approach.

to this has always been if people are. willing to pay me for it, it still has value. Correct. >> Pretty simple way of. of approaching it. We use a 6-year. depreciation. Um we believe that the GPUs will last in. excess of 6 years, but we felt like that. was a fair and reasonable approach to a. technology cycle that's moving at this. velocity. Um. the A100s, the Ampere's, this year the. price has appreciated through the year. Now, why's that? I I think it's because.

one of the things that happens is as. more installed capacity becomes. available, you have new companies that. come into existence that have new use. cases, that have different size models. that are trying to. build new commercial ventures that maybe. have been blocked out of the H100s and. never had an opportunity to run on that. >> to make a very simple example for the. audience, like you when you trade in. your iPhone after three or four years, you're like, "Who's going to use an. iPhone 12?" And it's like, "Have you.

been to South America or Africa where. you go to the store and you buy an. iPhone 12 or you buy the Pixel 7 and it. costs $50. That's still got great life. left in it.". >> Absolutely. Yeah, you know. Well, and so. look, you know, we we find these amazing. use cases, new companies that have come. into existence or existing companies. that have integrated new models into. their workflow that are able to use. the Ampere's. And so they keep buying. any GPUs that we have available. And.

once again, you know, the concept that a. GPU. is no longer relevant or commercially. viable after 16 more 18 months or 2. years. >> Yeah, that's far as it goes. It's just. It just doesn't make any sense. >> it goes, far as it goes. I think. sometimes people get caught up in. Moore's law or in just how fast our. industry is growing and that there's so. much at stake that big companies are. demanding the most recent products. That doesn't mean that the lifespan has. gotten shorter, it means the opportunity.

and the surface area of the opportunity. has gotten much larger. >> Yeah. One of the things is is like, you know, the the. the the industry has gotten so much. attention for the. unprecedented scale of capital that is. coming to bear on this. And. because of that, there tends to be a. incredible focus on. the companies that are building on these. most advanced chipsets. And the truth of.

the matter is is, you know, even within. those companies, they have a long tail. of useful life. to provide inference horsepower, to work. on other experiments, to do. less bleeding edge activity, but still. needs to be done. And yeah, I mean, rendering comes to mind as well. Or. yeah, we're making images on nano. banana. Like there there there will be a. use for it. There is a moment in time. where maybe the compute to power ratio. doesn't make sense. My My expectation is. is obsolescence will be.

defined by the moment in time where the. power. in the data center. for me will be able to be repurposed for. a higher margin than the existing. infrastructure provides. And, you know, like I said, I I fully expect this. infrastructure to last in excess of 6. years, but the the the standard in the. in in in the space has really been used. with one exception, which is Amazon,

which is yeah, it's 6 years. That's that. seems like the right schedule. I'm not. making it up. That's what everybody's. using. Yeah. And. the energy cost is the opportunity. because hey, it's just a we need that. space. There's a better uh reward here, and that might get resold that hardware. to somebody else who wants it, a. hobbyist or something. It's Yeah. I. mean, or it could be sent someplace else. where they have more capacity when they. can repurpose it there. But I I I. I kind of feel like, you know, we'll. we'll deal with that part of the.

business when we get there. What I know. right now is it is extraordinarily. profitable. It's very creative to my. company to continue to keep the. infrastructure that's been up and. running, that's been on these long-term. contracts. And as it rolls off, as it's. been in use for 5 years, you know, as it. becomes available, I'm still able to. sell it at a higher price than it was at. a year ago. There's competition now. When you were buying these from Jensen. back in the day, yeah, you could buy. them and have them shipped I would. assume within 30 days or less.

Nowadays, what's the wait like even for. you, a a loyal old customer? And is. there a bit of a battle? Is there. politics to who gets the servers? Like. I've you see some like very big names. talking about they got to get an. allocation. Is it still a little bit. crazy? What's it like to be in that. category having to buy something. everybody wants? Look, you know, I. I think of it as an affirmation of the. business that we're in, right? Like the. fact that we are attracting competitors.

The means that the business is healthy. and there's a lot of people trying to. deliver this service because. the need for this infrastructure, the. need to integrate the infrastructure, you know, into the software layers to. deliver it to artificial intelligence. either at the model level or the. inference level or the application level. or whatever you know, level of the. five-layer cake that Jensen's you know, focused on. The fact that there are more people. coming into this it doesn't discourage. me. As far as getting access to the GPUs, we.

show up like everybody else with a. you know, we'd like to buy here's a PO. and we're ready to pay. The one. What's the wait time like and is it just. really competitive or not? Because I. talked to Jensen about it, said I said, "How do you manage all these like big. egos and names and companies trying to. buy stuff?" And he said, "Well, they. order it and we give it to them in the. order in which they order it." Is it. really like that? >> It really is. Right? Like, you know,

he doesn't want to be in the position of. playing favorites or like that that just. seems like a bad place to be with your. clients. Or auctioning them off. >> Yeah, so yeah, that would that that. That'd be crazy. Yeah, I don't I'm not sure that would be good. for the long-term business. >> No. Yeah, so So our our our approach is. >> get some sovereigns coming in and. saying, "I'll pay double." They do that. with Ferraris, too, sometimes. >> [laughter]. >> Cuz these are the Ferraris of computing, right? >> way, they are. Yeah. They're the. Bugattis. Our our approach is to work.

with. clients across the entire space to find. opportunities that are really. interesting companies that can fit into. our contraction. contracting requirements where we're. going to be able to go out and structure. the debt that we require in order to go. out and and build infrastructure at this. scale. And um. >> How does all that debt work? I That is. something that you guys specialize in. Um corporate debt. I'm in the venture business. People are. like, "Why should I be in venture when.

corporate debt pays so well?". Corporate paper is so huge. I'm curious. how this fits in and like what. uh interest rate people are paying on, you know, a billion dollars in. infrastructure. What do they pay on. that? >> Yeah, so So Coreweave is. really been the innovator around a lot. of the financing engines that have come. to bear on this. We did the first. GPU-based. uh loans. Um and like I I think it's important or.

I'm going to try to explain this in a. way people can understand. So, what we. do is we go out and we find a client. Let's use Microsoft. You brought them up. before, right? And Microsoft comes to us. and says, "We'd like to buy some thing.". And we say, "Okay, great. We're going to. sign a contract." Once I have a contract. in hand, then what I do is I create something. It's not a particularly creative name. It's called the box, yeah, right? And. what I do with the box is I take my. contract with Microsoft and I put it in. the box. I go to Jensen and I buy the. GPUs. I put it in the box. I take my.

data center contract, I put it in the. box. And now the box governs cash flow. And it has a waterfall of cash flow that. comes into it and goes out of it. And so. the way it works is then I build the. compute and then I deliver the compute. to Microsoft and they pay the box. They. don't pay me. Right. It goes into the. box and the first thing it does is it. pays the data center. It pays the power. bill. It pays the interest and the. principal. And then whatever's left flows back to.

us, right? And so it is an incredibly. well-structured, time-tested, pressure-tested vehicle to be able to. borrow money against client paper and. all of the other. collateral around the deal, which is why. CoreWeave, which is a company that many. people haven't ever heard of, was able. to go out and raise $35 billion in 18. months to build infrastructure at scale. But what's important to understand is. the economics in this box are such that.

within 2 and 1/2 years of a 5-year deal, we have paid for everything. The. principal's been paid off. Well, the principal's been paid off. The. interest has been paid off. The return. into the box is such that we are able to. generate returns to our company at the. box level, Right. >> which gives the most sophisticated. lenders in the world, whether it's banks or private equity. funds or.

um you know, whoever, confidence that. they're going to. be able to achieve the one rule of. lending, which is give me my money back. Yes, and so it's better when that. happens. >> So they look at this box and they're. like, "Wow, we're really confident we're. going to get our money back.". >> And maybe they want 10 boxes. That's. correct. >> And and if anyone box. um goes upside down, you can uh deal. with it and it's not as acute. >> That's correct. And they don't. cross-pollinate, they don't cause. uh a contagion across the boxes. They're.

all independent and discrete. One, and number two is as you do this. and as you show the lenders how this. financing tool and how this financing. mechanism works, what they do is they. continue to lend you money at. progressively lower rates. And so when. you think about our cost of capital over. the last 2 years, we have dropped our cost of capital by. 600 basis points. Wow. It is enormous, right? And so you're seeing a company.

that is driving its cost of capital down. towards where the hyperscalers borrow, which will enable us to be able to be. competitive with them. over time. And we have been extremely. uh militant and diligent about feeding, watering, and caring for those boxes so. that we continue to have access to the. capital markets in a way that allows us. to build and drive our business. >> Means you have to say no you have to say. no to maybe some people who want to be. in the box? Yeah, so.

we we we look at some deals and we're. just like, you know, they want to buy. GPUs for a year and I look at it and. say, "I I that's not a deal that I can. do because it's too short for me to. amortize the expenses." Or and so I. won't do that, right? Like once again. >> can go to another provider who maybe. wants to take that risk on who has extra. capacity. >> Absolutely, but our business is really. built about around the risk management. of being able to get to scale because in. my mind. during this period of disequilibrium,

during this period where there were not. enough GPUs in the world to provide the. compute for all of the different use. cases in artificial intelligence. The. part that's important for me and for my. company is to get enormously large so we. can drive down our cost of capital so. that we have information flow coming in. from all different parts of the market. The large language models, high-speed. trading, search, all of these things. They're. feeding They're feeding information back. in to us that is letting us know what.

the next product we need to build is or. where, you know, they need help scaling. or what type of compute they need. And. all of that information flow is. incredibly valuable to us. What what can. you tell us about demand? There's been. reports of, "Hey, maybe the Oracle star. base thing with Open AI's been downsized. or maybe not." And then, you know, other folks, Microsoft is going big and. Google's going big, Meta is going big,

and those people obviously have massive. cash flow. Apple seems to be MIA. They. don't seem to want to play. You you you. you've you've named a lot of really big. companies with really big balance sheets. that have the capacity to drive a lot of. demand. Look, I I have been. truly steadfast in this. for years now. For for for 4 years, the depth of the demand for the service. we provide. has been relentless and overwhelms the. global capacity of the world to deliver.

enough compute to enable all of the. demand for artificial intelligence to be. stated. And that has been We have been. relentless about that. We have. >> Sounds like Knicks tickets during the. Patrick Ewing era. Like they got up to. 50,000 people on the waitlist. So, if. magically the waitlist. went away if the constraint went away. and we just had a large amount of GPUs. available, a lot of energy available, a. lot of data center available.

How much. capacity would just all of a sudden come. out of the system? >> So so Or would would be deployed, I. should say. >> So. Remember how we build our our business. through this box. And it's a 5-year box. So if we had an. air pocket. if if demand were suddenly to disappear. because of a technology breakthrough, because of a war. a war, anything, right? Like like the. why from a risk management perspective. does not matter. You have to prepare.

your company for the what happens if it. happens. >> Yeah. And so by entering into these. long-term contracts, into entering into. contracts with counterparties that have. large balance sheets, you are or we are protecting ourselves. and our lenders. >> Yeah. so that we are confident and they. are confident because you can see how. confident they are by the rate that. they're charging us continuing to. decline that they're ultimately going to. get their money back and that is the one. rule of lending. Yeah. And so um you.

know. if. >> in terms of the capacity, if you were. unconstrained and Nvidia Jensen says, "Hey, order as many as you want." What. would happen? So um the the it's also. important to understand the constraints. aren't just GPUs. >> Right. Right. Electricity. >> It's it's power shells, it's memory, it's storage, it's it's networking, it's. optics, all of the things. And there's. there's various throttles that will. limit the. >> Memory is the throttle right now, right? >> Oh, yeah, it is. Oh, yeah, it is. >> Why How did memory become the throttle?

If um. memory and. it has historically been a cyclical. business, right? We have seen these. waves of demand driving up the cost for. memory and then it collapses and then it. drives it up. It's a very boom and bust. business. It's cyclical in its nature. because the fabs are so capital. intensive that people invest in the. fabs, build a ton of capacity, and then. overbuild.

if there's any type of turn down. And. that we've seen that cycle again and. again. What's happening right now is the. confluence of two things, right? One is. is. with all the demand for artificial. intelligence and the corresponding. demand for compute and the ancillary. services around the GPU, the demand is through the roof. That's. number one. Number two is is that. there was probably an investment cycle. that needed to happen back in 2023.

that would have brought on the necessary. fab capacity to be able to serve. Impossible to predict what just. happened. Just with energy, it's. impossible to predict what just. happened, and now people are chasing. energy. The data centers are going where. the energy is. It's not based on real. estate, it's based on It's And where. there's some wind. And anytime you you. have a uh very cap Not not every any. time, but many times when you have a. uh a capital intensive business like, you know, building fabs, you will get. this boom and bust cycle. Just like in.

energy, they overbuild. And then, you. know, Fiber. Yeah, I mean, there's. there's there's a lot of examples of. that. Our approach. >> Some ways when you look at that, it's a. beautiful aspect of capitalism that. we're able to have a boom bust cycle, that we're able to weather it, right? If. you think just at capitalism from first. principles, something like that happens and we have. too much fiber, it creates an. opportunity for Google to buy it all up. or the next person. Listen, the the the. um um.

you know, it it does a it does a lot of. things having a boom bust cycle. It. clears out the underbrush. The strongest. companies will be able to survive and. take advantage of that. And it sows the. seeds of future business. The other. thing that it does is you put that. infrastructure into the ground. You put. the fiber into the ground, which became. the backbone of how, you know, we watch. movies every day and how we, you know, communicate and how we hop on a zoom. and, you know, COVID and all of these. things were based on that infrastructure.

that was available to be consumed. Yeah, people don't recognize this fact. If you. the the premise of YouTube from the. founders who I knew, Chad Hurley and his. other partner, they basically had the. realization at this curve, storage is. coming down so quickly, we could offer. free unlimited uploads and bandwidth is. coming down, so I guess we don't have to. charge people for sharing a video. online. Before that, if your video went. viral, people are going to have their minds.

blown, but your server would turn off. and it would say, "This person, you. know, needs to pay their bill." Yes. Cuz. they were getting charged for carriage. by the megabit going out. Yes. I mean, it look and and, you know, these these. the business models change and evolve. and, you know, like you said, Moore's. law and and and certainly Jensen will. talk about the fact that like what what. is going on within the the the. accelerated compute says dwarfs Yeah. Moore's law, right? And all of that is.

going to lead to. more opportunity to build more companies. that are going to do things like YouTube. did, which has really changed the world. Yeah. Yeah. I mean, the the concept that. I I don't know if it was like a million. hours being. loaded every hour or minute, but at some. point Susan Wojcicki, rest in peace, said to me, just like. how much was being uploaded every minute. and it made no logical sense until she. realized, well, there's.

3 billion people out 2 or 3 billion. people in the service and 1% upload or. 10 bi- bips upload. It's like, okay, 1. in a thousand people upload. It's a big. It's a big denominator, like I I was uh. sitting on a a panel uh with Sarah Fryer. CFO of. open AI and uh. um. she every once in a while. she really puts out like interesting. information and so she was talking about. the cost of a million tokens when chat.

GP3 came out and it was $32 and change. And now a million tokens cost 9 cents. Yeah. >> Right and so you just see like like the. incredible power of. how the capital markets, how capitalism. is. uh. fueling engineering and fueling. competition and. >> become recursive now too. I mean these. models if you say to the model, "Hey, make yourself more efficient and spend. less money and lower the cost of.

tokens." It'll be like, "Okay, Captain.". Yeah. I don't know if you saw Karpathy's. recursive thing last weekend but it's. like now civilians who've never worked. in a language model or done computer. science are like, "I'm going to try to. do something recursive this weekend.". >> You know it's. one of the things that I. that uh. uh I talked to you know. the other founders about you know and. it's like. when you think about some of the things. that AI does right? It's lowering the.

barrier to operations. So if you have a. good idea or a great idea, you can. open up your model and you can tell your. model, you can vibe code it, you can do. all kinds of different things and create. things that never existed before. That's. amazing right? Like that's bringing down. this incredible barrier that kept human. creativity contained and now all of a. sudden this whole new vector of. you know medical research or different. approaches to you know baseball cards or.

whatever you want. If you've got a great. idea, if you've got a new creative idea, that's the valuable kernel right now. that allows you to to build new things. and to create new things. And I just. think that's incredibly exciting. Like. you're bringing the minds of 8 billion. people a tool that allows them to. overcome what was insurmountable for. forever. For humanity. Yeah. It's a. bright new future, Michael. Appreciate. you sharing the. information with us and the vision. I am.

really delighted to have Aravind. Srinivas on the program. Thank you for. having me, your Jason. It's so great. I. want to go through three stages in which. I fell in love with your product. The. first phase was. I could go in, pick my language model if. I wanted to use Open AI, if I wanted to. use Claude, whatever it was. That was. like a real unlock for me. And on the side bar side bar, I noticed. you had done. essentially like.

what Yahoo did in the early days. Finance, sports. And when I pulled my. Nick game up, it gave me a live version. of that. When I pulled my stocks up, it. summarized the news in real time and I. was like, "Wow, this execution's great.". And I kind of made you my front door to. different models and it made it easier. for me to check it. Then you came out. with the Comet browser. And I was like, "Holy cow, I can give. this a series of instructions. Go to my. LinkedIn, find everybody from this company, put.

them into a Google Sheet." And boom, you. were the first out of the gate with. that. And then just the last couple of. weeks, I had been Claude pilled and. using Open Claude, but you came out with. computer. And I started using computer and boy, it's good. It's a really strong start. allowing me to do repetitive tasks very. similar in some ways to co-work from. Claude or. basically an engineer or developer using. it. So, are these the evolution of the company.

and I should think about it that way, but how do you look at Perplexity now? You have a very loyal fan base. You're. making a lot of money. I don't know if. you disclose it, but I think it's. hundreds of millions to billions. You. can tell us. But what is Perplexity in the face of. Wow, Claude's having a great run, opening eyes still doing strong, Grok. doing very well, Gemini coming on. strong. There's like six or seven of you. and you just happen to be one of my top. twos right now. Thank you. So tell me. >> all, first of all, thank you. Thank you. so much. Perplexity has always been.

built for people who are always looking. for the extra edge, the curious people. So it's very natural that you are one of. our power users. Uh. one common theme for us uh for the last. three and a half years is accuracy. Perplexity wants to be the company. that's building the most accurate AI. So when you want to give somebody. answers, accuracy is very essential for. building trust cuz only then the user is. going to ask the next set of questions.

It turns out it was a great idea to give. AI access to the internet to be. accurate. So that's the Perplexity Ask. product. It turns out it's a great idea. for AI to have full access to a browser. so that it can be accurate when you task. it to go do something that you would do. yourself on a browser. Agentic browsing, comment. Now, the last phase is it turns out it's. a great idea for AI to give it be given. a full access to a computer. So that it can do whatever you do on a. computer on its own.

Essentially becoming the computer. itself. An orchestra of everything AI can do. today, every single capability each. individual AI model has, be it GPT or. Claude or Gemini or anything else. An orchestra of all those capabilities. That what That's what Perplexity. computer is. And all these sub-agents that are. running inside computer are the. musicians. The models are essentially the. instruments. And there are like hundreds of models.

out there, each having their own. specialization. Some are good at coding, some are good at writing, some are good. at multimodal visual synthesis, image. generation, video generation, audio. But what matters is the end output, the. music you play. That's the work AI gets. done for you, and that's what Perplexity. computer is. The AI itself is the. computer now. Still lives inside of a browser. Have you considered giving it desktop. root access? That feels like the next. place this is going, but. that comes with a lot of security.

issues, a lot of trust issues. As you. mentioned, trust is paramount, getting. the right answer is what builds it, but. also not getting hacked and not having. it delete your files. So, how do you. think about root access to my Windows. machine? Obviously, iOS, they won't let. you, but with an Android phone, it would. let you. Yes. So, do you have that in. the works? Yes, so we announced. something called personal computer. Perplexity personal computer. That's essentially going to take all the. trust and reliability and the. server-side execution of Perplexity.

computer, but synchronize it with your. local computer, so that you can use it. from your phone. And we're going to do. this with the Mac mini, where you. synchronize your computer with the Mac. mini, so that becomes your local server. All the agent orchestration that has to. do with your local private data will run. on that local orchestration loop, that. runtime, with the Mac mini. >> Not on your servers, not on Anthropic's. Exactly. It could still ping frontier models if. it needs to, with your permission,

but it will be orchestrating everything. on your local hardware. Yeah. And if it. needs to run on the server-side. hardware, if you don't want very. complicated, long-running tasks to be. running on your local hardware, Yeah. you can delegate it to run on your. server-side computer, which is again. only accessible to you and you alone. So, that way we're going to bring this. perfect hybrid of trustworthy uh. hybrid between local and server side. And you. >> And you'll make it easy to do. It'll.

just be abstracted. You install one. executable, boom, it's done. >> It's It's like open claw for dummies. Nobody needs to learn how to use it. Nobody needs to manage API keys. Nobody. needs to manage separate billing across. like 100 different services, figure out. what you can give access to and not. access to. We take care of that. So, it's the Steve Jobs way of doing it, you. know, end-to-end integration. And And. how do you think about local models? I have started running Kimmy 2.5 on a. Mac Studio. It's not as good as Claude or Gemini or.

Grok, but you can probably do about 80%. there for free. >> Yeah. Essentially. Yeah. >> Uh and so, that's quite compelling. considering some of my other bills, Claude and and stuff were getting. expensive. So, do you have one of those you started. testing on your local Mac Studio? I. assume you have a Mac Studio and you're. doing this yourself? >> Yeah. So, or now, I don't know if you. saw. uh Dell and Nvidia announced a giant. workstation. Um is it a 3800? Something like that. >> Something like that with 750 gigs of.

RAM. So, what do you think about the desktop. going back to workstation {slash} server. Yeah. status? I think it's very. promising. Um my my prediction is it'll. initially start off as a sub-agent. So, whatever you need to go uh like your. tax returns, your personal photos, your. emails, your your calendar, all that. stuff, those local apps, your personal. notes, very personal notes, you can make. sure that the models that access those.

tokens will be running on your local. hardware if you want to, if you're that. privacy-conscious. Uh and more complicated stuff that. accesses your data is already on the. server side. Example, your Google. Calendar, your. >> Gmail. This is personal data still. But. uh AI runtime can access that through. your connector, your Google Calendar. connector, your Google Workspace. connector. And that could run on the server side. because anyway the data's on the.

servers, it's not even lying on your. device. >> So that sort of hybrid orchestration is. where we are headed to. I don't think. it's a dichotomy between fully local. versus fully server. Uh it's all about. choice. And anyway when you're on your. phone, uh you want to you don't care. actually which server that workload's. running from because it's it's not going. to be able to run on your phone anyway. The chips need to exist on a Mac Studio. or a Mac mini. and or on the server. >> new Dell that's coming out and I I. really think the idea of.

spending $10,000 on a powerful desktop. will appeal to people if it lowers their. $500 a month Yes. cloud bill. Yes. This. is an incredible savings plus you get. the benefit. >> Yes. of privacy and not educating the. language models on your personal data. Yes. And it's going to be it's going to. be like you're buying a refrigerator. Your your your your internet modem. Like. the cost for these will eventually go. down. Yeah. But it's not going to feel. like you're wasting your money. Uh every.

every home has a lot of other sensors. Yeah. that runs your home. They'll also be part of this. orchestration loop. Yeah. So so that's where it gets. exciting because now you can just. dictate something to your phone and that. can control your entire home. So that's the dream that everybody has. and all that orchestration loop can run. on your local hardware, no problem. And. I'm curious what you think of the. operating system. What's eventually. going to be the operating system of this.

workstation? AI is the operating system. Like earlier in the traditional. operating system you execute. programmatically. Now you start with objectives, not. specific instructions. Right. You come. up with a high-level objective. Go build. this website for me that you know, takes. all the transcripts of all-in podcast. and tracks the stock price just before. the podcast and after. Yeah. >> And charted for the max 7. Yeah. And and. charted over time. You can So, that's. the objective. But, individually, it's.

running a file system, a code sandbox, access to the internet. It's having like. its own HTML tools and like. >> Yeah. So, I think that's basically. where, you know, models, systems, and. files, and connectors are all coming. together. You would think of that as an. OS. Mhm. Except you're operating at an. abstraction above that where you're. thinking in terms of objectives. Yeah. And does it need to eventually become. its own operating system in your mind? It could be. Like, people could think. about it as like, "Yeah, I have a my.

Perplexity computer running all the. time." Whether it Essentially, it runs. on Linux machines right now. Every. server-side computer is a Linux Linux. machine. Yeah. So, I think Mark. Zuckerberg recently tweeted this right. after our release that "Turns out Linux. computers was the right idea. Desktop. Desktop Linux computers are finally. going to work." Yeah, I mean, they're. stable. Yeah. They're customizable, and. you're not at the mercy of Apple's. desire to contain the experience. >> Exactly. or Microsoft's surface area as.

for hackers. Exactly. You build. something rock solid, and it does feel. like Linux might actually become the. Correct. the eventual winner. It may not. need to have a front end. Right. >> thing. You could You could access the. Linux machine on your phone. Right. >> running iOS or Android. It doesn't. matter. Right. The actual valuable. runtime is running on Linux on the. server. You've done great as a consumer company. A lot of love there. Now, I'm starting.

to see uh corporations with computer ink. starting engaging. it. fact, you'll be. happy to know this. last week I took two. people in my back office and I said, "Stop working on Open Claw. Your job is to do the back office. automation at our venture firm only. using Perplexity.". And they were like Perplexity computer. and they were like, "Oh, okay. Um it. doesn't talk well in Slack. It doesn't. have an agent in Slack." I was like, "It. will.". I'm going to see Aravind. [laughter].

I'll talk to you about that. So, we need. a really strong Slack connector. >> It's already out. It is. Okay, great. >> Our computer exists as a Slack bot right. now. Okay. >> That you can add to your Slack workspace. on the enterprise plan. And our entire. company works like that. People are. talking more to computer on Slack than. other people. >> first volley, we were sending reports in. but it wasn't interactive. That's. perfect. So, now you've got your company going in. two different directions. This. incredible consumer run you have. How. many people are using the product every.

month? Several tens of millions. So, tens of millions of people. That's very. much similar to the trajectory of the. Google and Yahoo consumer business. Now, you've got corporate. How are you doing. on the corporate side? Thousands of. companies? >> growing. business for us. Ah. It's growing faster. than the consumer in revenue. And things. like computer unlock entirely new. possibilities. For example, we've saved. more than $100 million for our. enterprise max customers who are on the. highest tier of enterprise. Explain what. that is. What does it cost? 200 a month.

per person? >> So, there are two tiers. One is the. enterprise pro which is $40 a month. And. there's the enterprise max which is $400. a month. And that that and and and and. on computer, after you run out of your. credits, you would pay for the tokens. You pay for the usage. Are you making money on the $400 a. month, $5,000 a year one or at this. point in time are people going so crazy? Every. One thing that Perplexity has is every. revenue we make, unlike certain other. wrapper companies, every revenue.

Perplexity makes has positive gross. margins. Got it. Because uh we're not. just selling tokens. Right. Most of our. revenue is recurring because people are. paying a subscription fee. And because. we route through multiple different. models, we're very efficient in terms of. how we spend on the tokens. Because we. have all this advantage with rag and. orchestration and search, we don't. actually need to blow up the context. window of the models. >> Yeah. As a result of that, we have. positive gross margins on all the. revenue we make. Every single penny we.

make, we make profits on that. The Overall, the company is still yet to be. profitable, but we're working towards. that. You've had the opportunity to. exit. A lot of rumors, Apple, other people. were like, "Hey, this is a great team.". How many people on the team now? About. 400. Yeah, you you've got a very coveted. team. You obviously understand consumer. You obviously understand business. It's. a product-driven organization. Reports. are you declined. But the world's getting. hyper-competitive here. How do you keep. up as a 400-person organization when you.

got Sam Altman over here racing a. hundred billion dollars, you know, and. then you have Elon putting data centers. in space and merging with SpaceX and. Twitter. You have Google with unlimited. resources, Amazon getting in the game, and obviously Gemini, uh very strong product, and Google. really good at consumer. I think we'd all agree Facebook and Meta. haven't figured it out yet except maybe. for. serving us better ads, but they they. haven't figured out the consumer case. yet, but they'll copy it. They always.

do. How do you look at the playing field. because. the degree of difficulty, this isn't. playing checkers or This is like playing. against the 10 best. chess players in the world. That's what. you have to do every day. Yeah. So, how. do you think about it? Long-term an. independent company? Do you think you'll. need to join forces at some point? Well, And why didn't you take the deal? This. deals were incredible that you got. offered. So, one advantage we have that all these. companies you mentioned don't have is.

the multi-model orchestration. We're like Switzerland. We don't have to. have one horse in the race. If GPT wins, Gemini wins, Claude wins, Llama wins, it doesn't matter to us. Uh or even open-source models can win, no problem. >> And you have them on the service. You. have Deep Seek and Kimmy? We have Kimmy, we have Nematron, and we have uh a lot. of usage of Quen. Ali Baba Quen? Yeah. Silently under the hood. So, for us like that advantage of being able.

to take the best in each model and give. the user the orchestra of everything. they can do, I don't think any of the companies you. mentioned can do that. Right, nor would. they. Nor would they. It makes no sense. for them. It would be an admission that. all the data centers and capex they've. built out mean they still couldn't. produce them the best model. And uh. Dario, uh CEO of Anthropic, said. recently in an interview uh that models. are specializing. Towards the beginning of last year, people thought models are going to.

commoditize, but towards the end of last year, people. models started specializing. Even within. coding, uh. Claude Code and Codex have very. different capabilities. Our iOS. engineers love using Codex. Our back-end. engineers love using Claude Code. Yeah. So, even within a specialization like. coding, models have their own unique. specialties, and there are many other. use cases outside coding where different. models are good at different things, which means the orchestra conductor.

that has no one model to horse in the. race can win by providing a very unique. value and service to the customer that. each of these. amazing names that you mentioned cannot. And so, you're buying tokens wholesale. from them, and then you'll charge. customers to do it, or do you think it's. all We're going to take care of all that. orchestration. Yeah. So you don't have. to manage tokens across different. models. Cuz I authenticate a a couple of. my different accounts, my pro accounts. into Perplexity, but does it I I I don't.

have enough knowledge to know if you're. abstracting that and people can just. search across them and it's part of. their Perplexity subscription? >> No, we're not bundling subscriptions. from into other AIs. Yeah. We just ping. the models directly. >> Got it. Uh what you get in us is the. Perplexity or orchestration. Got it. >> Right. >> when when when when when models are kind. of specializing, the there's a bigger. value in the one who knows how to build. a great harness. Right. >> That can take the best in each model.

Does it auto route today or do you still. have the drop down? Somebody's got to. pick. It definitely auto routes the best. model for each prompt. But we also give. users the flexibility to pick whatever. model they want. What do you think of. I've seen a bunch of startups hack this. together, but doing the same query. across multiple We built a thing called. model council. Model council, yeah. Yeah, so that's one of the one of the. modes in Perplexity where I saw Jensen. say in one of his interviews that he he. puts the same prompt in five different. AIs and sees what each of them says.

Yes. Everybody does that, yeah. >> But then. you still have to apply your biological. compute to read every answer Yeah. >> and then figure out where they differ. >> to five lawyers about your trust or your. >> Five different doctors. >> Five different doctors and [laughter]. trying to figure it out. >> Exactly. It's dumb. So the model council. is a feature we built where it will not. just give you the answers of each model, but it'll tell you exactly where they. agree, where they disagree, and where. the nuances are. >> And that's in the interface? Model. council? I didn't know it was there. It's there. I mean you you release. product at a pretty great cadence, huh?

How do. >> Yes. Where did you learn that and what's. your philosophy of shipping product? Our. philosophy is like speed is our mode. Like, you know, again, one of the things. that big companies cannot do is move at. the speed we do, serve customers at the. speed and quality it's it's very hard to. maintain quality, speed, and trust at. the same time. Yeah. Like Apple takes a. long time to ship anything. Right. >> Cuz they're very worried about people. not trusting them. Yeah. And so some companies are bureaucratic. and they just take forever to ship. something. They don't maintain what they.

ship. They may make a big deal about an. event, but nobody even knows how to go. and use that feature. Yeah, they get. abandoned. >> Exactly. So, Perplexity has those. advantages of being very small. And. towards the end of last year, we found. that like AI coding tools have made it. much faster for us to ship things. Mhm. Which is honestly one of the reasons why. we built computer because now even. non-engineers are shipping code here by. just pinging a Slack bot and asking it. to fix bugs. Yeah. >> So, this the the iteration has just been. like exponential. The moment I had where I became.

claw-pilled was when I was working with. it and I was like, "Hey, I want to build. my network. I know these 20 people in. Japan. I had dinner with them during my. recent trip. I want to know who they. know. So, check out LinkedIn and other. things and who they're associated with. and make me like a mind map of it. And. then the next trip, I want to meet with. the next circle of you know, those. connections. So, I started asking It's. like, "Okay, I got the results." I was. like, "Great.". Um and it said, "Where do you want me to. put them?" And uh I was like, "Well,

where can you put them?" And it said, "Well, I could put it in a Google Sheet. I could put it in a Notion table. I. could put it here. I could give you a. PDF. I could give you a CSV file or. I could write you a CRM." And I was. like, "Yeah, sure. Make me a CRM. system." And it made a CRM system. >> Yeah. And I think that becomes And I think. maybe one out of a thousand people. working with AI have had that. experience. Yeah. Maybe it's one in. 10,000. Yeah. Where your agent says, "I'll make you bespoke software." Yeah. Have you had that yet? And And do you.

see that as a part of computer that when. a person needs a spreadsheet, you don't. launch Excel or Google Sheets, you just. pop up a spreadsheet? Yeah. Well, we. have a board meeting tomorrow. Okay, I'll come. And and. >> [laughter]. >> So, pitch it to the board. >> Sure. Our computer computer made the memo. Oh, wow. Yeah. And. we had a partner meeting to pitch a. partnership idea and earlier we would. have a design team do the whole deck. Computer just one shot at it.

I had a press briefing with a bunch of. journalists. My comms person would be. like. >> that. Brutal. And then my comms person would. usually. give me a memo what to say. Computer one. shot it. So It's crazy and it's the. context is so good because the memory is. getting better, yeah? So it's like I know that journalist from. the last time. I know the board meeting. I have all the previous decks. Yep. When did that happen? I think it it.

happened with Opus or Phi. Uh-huh. The the intro. or Phi. That was the inflection point. when. models were. started being amazingly good at. orchestration and reasoning and tool. calls and quad code brought in this new. idea in AI that everything can happen. inside a sandbox, a console, a terminal. with access to tools where tools are. just command line tools. Yeah. They. don't even need to have graphical user. interface. So when you did that and when.

you. organize around files and sub agents and. skills and CLIs. the model started making becoming very. good at handling the context. So the. context window no longer became a. problem. It just put whatever necessary. into the context whenever it wanted to. and dumped dumped them away when it. wanted to. And that made it like suddenly so good. at doing very. long orchestration tasks. Yeah, it's. it's pretty crazy. I have every episode. of This Week in Startups, all the.

transcripts and then all of All-In. >> one of the tasks I did by the way. I can. send it to you. I asked it I want you to. download every All-In podcast. >> Yeah. Uh. since the beginning. And I want you to. take a mention of all the public. companies they mentioned during the. episode. >> Yes. I want you to have a histogram of. the counts. And I also want you to chart it across. time. And then I want you to analyze the. impact on the stock price. And the. sentiment of what we said. >> Exactly. And it did. Like it clearly. said. >> moving stocks? around Google's stock.

going up. Yes. Prior to that, you guys. were talking a lot about Google. Yes. And it clearly. >> And I said I made a bet publicly on the. thing. I said, "I am buying a bunch of. Google because I believe, even though. they're behind, it's because they're too precious." You. were kind of mentioning a company that. might be too precious at times and. doesn't release. I was like, "That's. that company. They need to release. more." And uh I told Sergey, I was like, "Like. give us the good stuff.". >> Yeah. He started giving us the good. stuff. >> It literally gives you the timestamps of. every single And then I can go click on.

it and actually hear exactly. >> Yeah. Sweet. Yeah. So, that's when. that's when I was like, "Damn. Like. this I would have had somebody do this. as a week-long project.". >> It would have been 10 hours a week of. researcher. I I'm experiencing the same. thing. When I do research notes, I've. created my own. like mega. prompt. Yeah. And it will go and like. tell me where you worked before and. who's in your circle, who your.

competitors are, who your friends are, blah blah blah. And then go find I try. to find old podcasts is one of my. secrets if you're an interviewer. watching. I try to find what was the. person talking about 5 years ago, 10. years ago, and then over 10 years ago. And I've gone into interviews now with. Michael Dell and talked about things he. was talking about in the '90s. Yeah. And. it finds me some ancient stuff. Like you. would pay a researcher, a producer, you know, $70,000 a year, $80,000 a year. to do this, and they would have done a. third of the job in 10 times longer.

>> Yeah. It's really gotten weird just in. the last 6 months. What do you think the. next 6 months looks like? I think the. the dream what we are going to try to do. is help businesses run as autonomously. as possible. You know, everybody talks about this AI. is going to create this one person one. billion dollar company. Some people say it's already happened. because people pay researchers like one. billion. But it's not truly moving the. GDP by one billion. It's not truly. creating new value. So, the best way to.

do that is to actually help a small. business, people who would otherwise. drive Ubers for like Yes. extra passive. income to like buy like a Mac Mini, set. up Perplexity personal computer, and run. their business on that or like run it on. the server, it doesn't matter. Uh. and actually make real money. Yeah. Hundreds of thousands or even millions a. year. and uh grow it. Have computer go and run. your ad campaigns on Instagram or. Google. I mean Integrate with SEM and. SEO tools, find new users, and.

uh integrate with Stripe, charge them, ship new features, have your own like. Intercom integration for customer. support, and like have this all working. while you can be sipping wine in Napa. That's the dream that you know, it's uh. it feels awesome to say. Everybody. thinks AI is already there. It's not. there yet. Someone has to do that hard. work. Yeah. That's what we want to do. Yeah, it it's a great vision because. when I watched startups 20 years ago, there were so many checkboxes they had.

to do. I have to find an office space. I. got to put up a bunch of servers. I got. to hi- hire an HR firm. I got to hire a. PR person. All this stuff. And now I talk to young founders, they. got a three-person team, they've come. out of A16Z, my program launch. accelerator, whatever it is, Y. Combinator. And I'm like, "Okay, you raised a half. million, you raised a million. Who are. you hiring?" And they're like, "Um I. don't know if we need to hire anybody.". I'm like, "If you could hire somebody, who would you hire?" They're like, well, I do my own HR. I have this partner and.

They're I'm like, how are you doing. hiring anyway? And they're like, well, I. put out an ad and then it sorts and. ranks. the candidates and then it emails the. top 10, asks them a bunch of questions. and then I meet with the last two and. I'm like, that's what a recruiter did. Yeah. >> Like the entire recruiting job has been. abstracted and like a. tool like computer is going to make that. even faster. >> work to do. A lot of connectors. A lot of specific. workflows. People don't want to like. learn how to write like, you know, essay.

long prompts. You know, it needs to be. so quick and fast and autonomous. You. just set it up and done. And you have an. idea, you can turn it into a business. and start making money. Yeah, it's it's. an incredible future and it feels like. it's right here. Do you. How do you think about job displacement. cuz you're actually making the tool that. enables people to be a solo entrepreneur. and get to a million revenue, but it's. also the same tool that doesn't require. them to hire and we've had this debate a. million times on the podcast. Do you. I'm wondering if you like me, you have.

moments where you're like, oh my god, this is really terrifying. >> Yeah. A lot of people are going to lose. their jobs really fast. >> Yeah. And then, oh my god, you can learn. any skill you want and all the things. that were hard are now easy. >> Yeah. I I go back and forth. I'm 70-80%. super positive about this, but I do. worry about like 20% of the time I'm a. little worried. Where do you sit? >> I mean, America has always been about. like entrepreneur entrepreneurship, right? Like we've been about like trying. to build new things, discover new.

things, go explore. Uh, I think this. whole like Henry Ford came and built. factories and brought in jobs and things. like that and like put people into a. box. But, uh, I think the reality is. people most people don't enjoy their. jobs. They're doing it for. >> hate them. Exactly. So, there is. suddenly a new possibility, a new. opportunity to go use these tools, learn. them, and start your own mini business. And if it pays for your. needs for a year or multiple years, and lets you have a high-quality life.

and good work-life balance, and true. feeling of agency and ownership and. passion to like get your ideas out. there, I think that is. even if there is temporary job. displacement to deal with, that sort of. glorious future is what we should look. forward to. I I I think you're exactly. right. If there will be some. displacement, but then there's also. going to be so many opportunities. opening up, and it requires the. individual to not be passive. Exactly. >> They have to be rugged individualists. They have to be resilient. Yeah. And. they have to be resourceful. And I think.

once you start playing with these tools, that's what happens. >> Exactly. You You all of a sudden feel It. brings out the best in you if you truly. are in a good space. Yeah. Yeah. I I And. then today uh Comet for iOS is out. >> Yeah. I'm a Comet super fan. I required. everybody You were nice enough, but I I. emailed you. I was like, "Can you send. me some licenses?" You sent You don't. may not remember. You sent me a bunch of. licenses. I said, "Everybody put this. on." Cuz it was $300 a month when you. first came out with the Comet browser. Now it's free, I think, for all users.

Highly recommend it. Highly recommend. getting a Pro account. It's only 20. bucks a month to get into Perplexity, which is a joke. So, you can get on. board for nothing, less than a dollar a. day. But what does iOS allow me to do? And. And how does it connect to computer? Cuz. that's another thing I'm having. Yeah. Cloud code uh computer There There's not. a good enough integration with this. mobile device yet. Yeah. So, computer is already on the. Perplexity app, so you can just toggle. to computer and start using it. >> Yes. Uh.

Comet's uniqueness in Perplexity for the. company uh and and and the strategy is. the fact that you can control the. browser. So, the browser also becomes a. tool. for computer, yeah, just like your. Google Workspace and all these other. things. Uh until the whole world is. organized around CLIs and tools, Yeah. there's still a lot of tasks we have to. do manually on the web, on the browser, open tabs full of forms, click on. things, upload stuff. All that stuff, if you want to automate,

you need a browser. You need an AI that. can natively control the browser. So, that is Comet. And that's why, no matter. how many other tools in the market exist. like Open Claw or like Claw Co-work, >> Yeah. executing tasks on a browser on the. server side, along with all the other. things, is something uniquely Perplexity. can do. Yeah, my dream is that you'll. create an Android app that roots my. Android phone. Yeah. And that you just. take over and see everything. Because. one of the blockers I have now is a some.

of the websites have gotten a little. persnickety. Yeah. I don't want to mention too many, but. Reddit, LinkedIn, Yeah. and like they're just. I I am a great Reddit user. I'm a great. LinkedIn supporter. But sometimes like I. need to get my in mail Yeah. from my. LinkedIn. And I just need to, you know, find seven people at a company. I Is. there going to be a solution. between the LinkedIn and Reddits of the. world and the Claws and Perplexities? Is.

How is that [clears throat]. I mean, the negotiation going? Yeah. You. don't have to speak about any specific. ones unless you want to. >> Yeah. But it feels like there's got to. be a solution. And I'm willing to pay. for it as a user. I'm willing to pay. Reddit to allow my bot to show up and. behave properly. >> Yeah. Well, I I I cannot speak about any. particular company, but uh, we are happy. to work with anyone, right? So, uh, I think with Comet, our idea is to. give people the flexibility to set. things up on their own. Yeah. And uh,

any um, official APIs that anyone's. willing to offer, we're always happy to. put that as part of computer. Here's what I think should happen. Let. me see if you agree. Um, and this is for Steve Huffman at. Reddit. I go on Reddit. I do a pro account for 20 bucks a month. And when I do that, I can authenticate. whatever tool I want. um, to do a series of well-behaved. things a certain number of times a day.

Yeah. So, it's not unlimited. I'm not. going to scrape the whole site, but I. would like it to just let Perplexity or. computer go and just tell me, "Hey, what are people saying on this week in. startups and all in subreddit? Summarize. it for me so I get the customer. feedback." And I would literally name my. uh, agent. And I would say, "I It won't. post on my behalf. It won't vote on my. behalf. Just need it to do a couple of. little read-only things." This would be. an easy solution. Or LinkedIn, I would. like if you I have I already pay. LinkedIn like 50 bucks a month. Like,

they should just let the $50 a month one. work with computer. Yeah. Absolutely. I. mean, Okay. This is for Satya Nadella. Let LinkedIn work with Perplexity and. the other players, and we'll pay you. extra. Perfect. >> It's a revenue stream. Don't you think. API access for our customers is a. revenue stream? >> so. I think so. Let I think I think. fundamentally giving users a choice. and setting it up as a win-win for both. the business and the user. is where the world should head to. Yeah. And and I I would say the same thing.

applies to any any website in the world. Like, if if you want an AI to use it on. your behalf, it should be okay for cuz. that's what the user wants. I mean, I. have a paid New York Times subscription. Like, let me go in there and do, you. know, whatever 100 searches a day, a. week, a month, whatever you they choose. But, that would make the subscription. that much more sticky. Exactly. Uh, all. right. Arvind, love the product. Anybody at home, it's just tremendous. Go learn computer. and get the Comet browser. It has.

changed my business for the last 2. years. Love the product and we'll have. you back soon when you launch your. operating system and come up with your. own server and desktop server, but. business is the focus. Yeah. Yes. All. right. Great seeing you. We have an amazing. guest, Arthur Mensch is here, the CEO of. Mistral AI. How are you doing, sir? Great. Thank you for having me. And so, you're here. at uh Nvidia's big conference. Big announcement. You're going to be.

working with Nvidia to build models, uh to open source them. What is the uh. big announcement here? Well, we're. announcing that we are going to be. training the next generation of frontier. models with uh with Nvidia. Um. it's something that we've been doing. before with Nvidia with Mistral Nemo, something we did like 18 months ago. And. [snorts] the point for us is really to. be able to produce the best open-source. models out there so that we can actually. use those assets to specialize them. through products that we do for our. customers like Forge, that helps us.

customize the models for the enterprise. we work with in engineering, in physics, in science, uh in making them better at. certain languages when we work with. governments, etc. And and Mistral obviously based in uh. France, you're the leading AI company there. What's it like running the company and. building a large language model in. Europe? Obviously, there's regulations. and. all kinds of considerations, privacy. The French are known for protecting. privacy. In the United States, we're. known for taking it away. How is the landscape there and what do.

you have to deal with there that maybe. you wouldn't have to deal with in. America? And what's the pros and the. cons? Let's say first uh we have 25% of. our business in the US and 25% of our. researchers are actually here. So, I. actually spend a a lot of time here as. well as in France, as well as in the UK, in Singapore, where we are. So, of. course, it's it's different markets. Uh. it's markets where you have language, which is a topic, uh where there's much. more manufacturing manufacturing is a. bigger piece of the cake than it is. here. Uh and I'd say the our strength has been.

to also work with European companies. that are a bit lagging behind and that. wants to adopt the technology to to leap. forward and we've been able to do that. through a forward deployment engineering. engagement through our forge product for. our studio product that allows you to. deploy agents that do end-to-end. automation. But on top of that, the. thing that we have announced today like. forge is something that is actually. being used today with customers in the. US because they come to us with needs. for post training for making more and. specifically good at financial services.

and what's happening is that we have. this product and we can bring the models. to specialize them as well. And so your. belief is specialized verticalized. models, health care, finance, engineering, different verticals will. win the day or a a global model will win. the day that does everything? Well, you. need general purpose models to do the. orchestration part etc. But at some. point you enterprises sit on a lot of. intellectual property, on a lot of. signals coming from physical systems,

from factories, from tools and the it's. actually not trivial to connect those. systems, to connect those data to models. that are closed source. If you have open. models, you can actually add new. parameters, you can make a lot of deeper. things that you cannot do with closed. models. You can also and that's what. something that we do, we don't we not. only do we work at the model side but. also at the orchestration side. We sit. with subject matter experts to. understand their needs and we build. business applications that are fully. bespoke to their needs by modifying the. models but also modifying the harness on.

top etc. So we believe that eventually. building an open source technology is a. way to save cost, is a way to have. better control because you can see the. thing on every cloud that you want, on. your hardware if you want, you can. deploy it on the edge if you want and. eventually. from a from a customization perspective. and from leveraging your decades of IP. that you've been accruing in financial. services, in heavy manufacturing like. companies like ASML for instance, they. do benefit from working with us because. we take their that and we build models. that are specifically good for them for. their purposes. >> um.

this training data using experts to come. in and refine a model. Most people don't. know this business that well, but this. has become a very large part of the. industry. Obviously, Scale AI was doing. it. They went to Facebook, lost a lot of. the customer base who didn't want to. uh send their data, I guess, over to. Meta. Uh we're investors in a company. called MicroOne that's doing pretty well. in this space. There's other folks doing. it. Explain to the audience. what you're doing specifically for. companies and how this training works in.

a verticalized way, and then how you. silo that data because if you're working. with one customer. in aerospace or fintech, they might have. a need set, but they may not want that. training to go to a competitor. I can. [snorts] use you a few examples. I think. overall, the data segregation is super. important. And the way we have solved. that is through a portable platform. So, our technology is a set of services, a. set of training tools, a set of data. processing tools that I can take and. that I can put on the infrastructure of. my customers. So, suddenly, from an IT.

perspective, and when we talk to the. CIOs, they realize that from security. perspective, the flow of data doesn't go. There's no data flow coming back to. Mistral cuz everything stays there. Now, uh. the way we we then use that technology. that has been deployed is that we're. going to be working with uh the teams. that is doing. uh image scanning and default detection. with ISML, for instance. And we're going. to be sending forward deployment. engineers, scientists. They're all PhDs. They know how to train models. And they. spend some time with the subject matter.

experts that can explain how an image is. being detected, what How do you detect. defaults, etc. And based on that, we're. going to work out what kind of data. needs to be used to train the models. that it's going to solve the task in in. itself. And so, the. we we send the technology. Typically, we. send a little bit of scientists cuz. uh you do need that expertise transfer. and that knowledge transfer in between. our teams and the vertical experts. And. then we make sure that eventually our. team no longer needs to be there to. retrain the models to get more data. access, etc. So, that combination of.

data segregation, expertise transfer, knowledge transfer is the one thing that. makes us quite unique and allows us to. serve the most critical use cases, the. most critical processes in industries. that actually need to take their data. and put it into models for it to work. Yeah, this seems to be once the entire. open web, what was available legally, gray market, etc. I wouldn't have you. comment on that controversy, but we kind. of exhausted what's in the open crawl,

yeah? We have. And and it's time to. actually. either make synthetic data or actually. use experts. Do you believe in synthetic. data and where does that work and where. does it fail? We use synthetic data as a way to warm. up the models. It's a way to actually be. quite efficient at the beginning. If you. have a large model and you want to train. a small model, you would you will use. your large model to. to process and to produce a lot of. synthetic data at the beginning, and. then but eventually you do need to have. human signal. So, the human signal is.

something that is always a bit costly to. acquire because you need to talk to the. experts. They need to give feedback to. the machines. And so, at the beginning. synthetic data allows you to do the. compression to to further compress the. models. At the end you do need to go and. get data that is produced by humans. So, yeah, it's a it's a way to have. it's it's mostly an efficient way of. training models to have big bigger. models that are used as as teachers for. smaller models, but it's not enough. And. so, you also need human signal. Arthur, we've seen an incredible explosion.

We're sitting here on AO 52, after open. claw, the year of our Lord 52 days. When you first saw open claw and saw the. reaction of hackers, founders, startup CEOs, just the amount. of energy and it racing to the top of. GitHub with the most number of stars and. likes. And and all these contributors, what did that say to you as an executive. in the space who's been grinding on this.

for many years? What what is that open. claw moment mean? Well, it resonates a. lot with what we are doing with our. customers because pretty quickly. enterprises realize that if they wanted. to make some gains with artificial. intelligence, generative AI, they would. need to automate full processes. And to. automate a full process as an. enterprise, well, you can use open claw, but it's going to be it's actually not. really enough cuz you you have data. problems, you have governance problems, you can't observe the process that is. running, and you can't can't control it. in.

in many cases when you run a KYC. process, if you're HSBC for instance, one of our customers, you will want to. have deterministic gates that are going. to always do the same thing in a way. that is observable and that you can. guarantee the CEO that it's always going. to go through these gates. And that's. not something that open claw is. providing cuz it doesn't have its the. kind of primitives that you need to work. on collective productivity, observable. productivity, and to work on. mission-critical systems. On the other. hand, the autonomy it gives and the. autonomy it brings to to people that are.

just individuals that are hacking. together things is a way to also show to. enterprises that if you set up the right. control plane, if you set up the right. sandboxes, if you connect to the right. data sources, if you make sure that you. your access controls are well respected, then you can actually unleash the power. of agents doing things for your. employees, and that's going to work work. on the platform cuz otherwise you will. not be at ease when you're sleeping. It. is. definitely something you have to be. thoughtful about. When I installed it, I. gave it just for my agent root access to.

my Google Docs and my G Suite, my. Notion, my Zoom, and my Notion and the Calendar, everything. And then I realized, "Wow, I can with my. enterprise edition of Gmail, essentially, I can just summarize for my entire. 21-person investment company every. conversation going on in Gmail, and then. correlate it with every conversation in. Slack.". And then I realized, "Oh my gosh, there's compensation discussions going.

on. There is a person on a PIP who we. put them on a preferred performance. improvement plan, perhaps, or something. like that.". Oh, I have to make sure nobody else can. access this because the power comes from. giving it access to data, but with great. power comes great responsibility, and I. think people are learning that in real. time. Yeah, it's a big problem because the. enterprise data is not a single thing. that you want to put into a single. system that is going to be accessible by. by everyone. And so, you need to have. this layer that actually understands.

what is the. what is in the data. You need to have a. semantic of what can actually be. proposed to. uh HR or what can be proposed to uh. engineering. And typically, compensation. is one of these things. You want to make. sure that the compensation data does not. flow back to all of the all of the. enterprise cuz you're going to have a. lot of problems uh if if that's the. case. And so, what you actually need, and which is hard to do, is what we call. a context engine. So, a mapping of where. the data sits uh that comes with a. certain number of metadata that is.

telling you that this data is actually. not accessible to this part of the. company. And if you actually have. someone in engineering that is asking. for something related to comp, the thing. is actually going to tell you, "Look, you actually can't access that data.". So, that's. uh that's hard. It's actually hard. You. need to rethink entirely the way your IT. systems are being connected. And at some. point, you also need to think about your. management because your. your information flow is completely. different today uh if you're connecting. agents together with your data sources. than it used to be. And suddenly, maybe.

you don't need that manager whose only. purpose was to take information from the. bottom and put the information on the. top, etc. So, there's so many problems. to solve and you need the right. primitives, you need sandboxes, you need. role-based access control and these kind. of things. And uh you have change to do. You you need to rethink your entire. customer service uh department cuz. suddenly you actually don't need that. much transfer of information operated by. humans. All right, uh you have to go, you got a flight to catch. It is so. great to see you, Arthur. Continue. success with Mistral. Thank you very.

much. Cheers. I'm really lucky to have. Daniel Roberts here. He's the co-CEO and. co-founder, along with his brother, of. Iren. They uh are a publicly traded. company. They started in BTC. Welcome to the All-In interview program. Thanks, Jason. Pleasure to be here. Yeah, and so you started in Sydney. You and your brother um. it was 7-8 years ago and you got in. early on Bitcoin and all these Bitcoin. monitor uh miners wanted to have data. centers, huh? Yeah, that's directionally.

right. So, the thesis we saw was this. explosion of the digital world, the. growth in the online, and at some point. the real world was going to struggle. So, we set about to build out. large-scale data centers. Yes, the first. use case was Bitcoin mining, but as we. said to our seed investors, use that to. bootstrap the platform, generate cash. flow, layer in higher and better use. cases over time as they emerge. Here we. are today with AI. We are swapping out. all the Bitcoin for AI chips. When did. you first start seeing the demand in the.

company shift from, "Hey, [snorts]. Bitcoin miners, well, we need some. H100s, whatever it is." Uh to, "Hey, we're this nonprofit Open AI. Hey, we're. this research lab. We need some AI. compute." When did that start hitting? Look, we had a bit of a false dawn, I. would say, back in 2020. We signed an. MOU with Dell to start bringing out. customers and compute, but in hindsight. it was too early. So, we went back to. Bitcoin, kept bootstrapping the. platform. Look, I would say about 2.

years ago, and month by month the demand. just continues to escalate. And you were. in so early that when you were looking. at data center space in the United. States, you were one of one looking at the. space, one of two or three people. looking at the space. They They were. trying to sell you on space, yeah? Yeah, so we actually developed the data. centers ourselves. So, we go and find. the land, we go and get the permits, we. go and apply for grid connections, and. we were doing it at a scale that just. amazed people at the time. Like, 750 MW.

is our flagship Texas site. 4 years ago. was unheard of. In the middle of the. desert, we're building these big data. centers. The traditional data center. industry going, "What are you guys. doing?" We say, "We believe in the. future digitization, high-performance. computing, and obviously now today it's. paying dividends." Yeah, I don't think. anybody could have predicted when. ChatGPT came out, Open Claw recently as a turning point, um and then, you know, Microsoft, Google, and everybody embracing this. Uh.

and that's your big partner, Microsoft. Yes, Microsoft's one of our early. partners. We signed a $9.7 billion. contract with them late last year. But, as I was explaining to you before the. show, that's 5% of our capacity. So, things are busy at the moment. Yeah. And. when you do these build-outs, the big conversation today is not is no. longer the number of GPUs putting in, it's just power. Power is. the uh constraint today, yeah? For many of the industry, it is. But,

for us, because we started 8 years ago. tying up all this land and power, it's. not. So, we've got 4 and 1/2 GW. For. context, that's almost as much power. annually as the Bay Area uses in its. entirety each year. It's huge. So, for us, the hurdle or the. constraint is really time to compute, and that's emerging across the industry. as well. And time to compute means. trades people. coming to West Texas. living in a a trailer that you set up to.

then break ground on a data center, build foundations, build water cooling. systems. Like this is hard manual labor. going on, yeah? Exactly. And this is the. whole real world challenge to respond to. these digital exponential demand curves. They're unconstrained by the real world. in terms of their appetite. And it just. compounds. You need thousands of people. out in these locations that haven't. supported it. You put stress on supply. chains. We're seeing what's happening. with the memory. Every aspect of it. So.

it's just permanent whack-a-mole, permanent solving fires to try and be. bring online this compute. And you get. to spend time there. What's it like when you set up a town or. you bring a thousand people or two. thousand people to what's a pretty much. remote small town? You I'm assuming that. like when you bring a thousand there. might only be. 500 living there right now. So what are. those towns like? I'm It sounds to me. like something out of like the gold.

mining era when people first, you know, went and and were prospectors. Yeah, yeah. It's a prospecting town? Pretty. pretty much. I mean, the barbecue's. great. That was the draw card. Then part. from that, look, we've always had a. policy of hiring local, supporting the. local community. This year we're hitting. a million dollars in community grants. cumulatively. That's things like local. playgrounds, supporting the fire. departments. But we will hire locally. Once we can't find that trade locally, we will expand the radius by 20 miles. and hire out of that and so on and so. on. For us That's very thoughtful. Yeah,

it's and and these folks are coming, say. an electrician or a construction worker, they're coming having built houses or, you know, maybe building um. corporate offices. And now they come for. a tour of duty here and the salaries go. up massively, but they got to leave. their family for a 3-month tour or. something? Yeah, yes and no, because. typically where we locate is where. there's heavy electrical infrastructure.

Where there's heavy electrical. infrastructure is typically where old. manufacturing and industry has closed. down. Ah. So, we go down in, leverage. that sunk CapEx, rehire, retrain local. workforces, and bring a new industry to. town in these data centers. Ha Ha Has. that workforce. now been completely depleted and we need. to train another generation, a younger. generation, to be generation two-belt. and really embrace the trades? 100%. We're partnering with universities, trade colleges, absolutely.

And you go to a trade school, you got. You go to a college, people are getting degrees in philosophy. and English literature. They're going 50K a year in debt, 200K a. year [laughter] in debt. What's the. starting salary for a tradesperson. working on a data center doing. electrical or construction or Exactly. What's the ballpark range? Ah, look, I. won't talk specifics, but they they are. going up. The price is going up. Depends. on the level, but yes, there is a rush. for good.

>> I'm hearing 150 to like 300K. Am I in. the ballpark? The lower end. directionally, you're right. Yeah. Yeah. I mean, it's incredible when you think. about it. There's concern. about, "Hey, AI taking jobs." And then. on this other side of the ledger, can't find enough talent to to to. service it. Talk to me about energy. sources and how you think about that. Uh. President Trump, Chris Wright, the. administration, they kind of started. with, "Hey, clean beautiful coal." Year. two, they're like, "All sources matter.

Nuclear.". Obviously, nat gas is plentiful in that. area. We obviously got a lot of oil. People don't know this about Texas. In. the United States, the number one. uh source of solar installations, yeah? Yes. >> Talk to us about energy. >> So, our our philosophy has been. sustainability from day one. We have. used 100% renewable energy since. inception. What? 100%. >> Wait, how is that possible? It's We use. hydro in British Columbia. We use wind. and solar in West Texas. In West Texas, where we're located, there's around 45.

to 50 gigawatts of wind and solar. Yeah. The transmission line to export that. down to the load centers in Dallas and. Houston is 12 gigawatts. Oh. So, you go. and locate to the close source of. low-cost excess renewable energy, monetize it into this digital commodity, export at the speed of light as token. >> Great arbitrage. And the wind is. producing a lot, but it it's harder to. get from those areas where people are. willing to put up. I mean, people don't. understand how big West Texas is. It is.

an incredible amount of land. And you're. coming from Australia, where also on the west side, people. don't understand exactly how much just. pure nature land there is. Yeah, undeveloped. >> So much land. And the issue is distance. You've got to spend billions of dollars. on this transmission connection. infrastructure to move that power to. where people actually want it. You can. build wind farms, you can build solar. farms, but if you build it in the desert. and no one can use it, then what's the. point? So, the whole opportunity for our. industry is to go to the source of that. power and monetize it.

So, the data centers follow the wind. turbines, the solar installations. How do you think about batteries? And. are you able to put those online because. obviously you're going to have periods. where hey, it's not a windy day. In. Texas, we have very few days when it's. overcast. So, that problem's pretty much. solved, but you're going to have 50 days. where the sun's not beating down. So, how do you deal with the demand and and. and softening that duck curve? We don't. need to. Huh. The utility does that on. our behalf. So, this is why these grid. connections are so scarce, so hard to.

get, and so highly valued because once. you get that grid connection, the. utility underwrites all of that. variability. They guarantee you 24/7. reliable power. >> Got it. So, on their side, they're. figuring it out something goes down and. they could fall back even though you're. 100% committed to renewables, if they. needed to fall back to to gas or. whatever, they have that ability out. there. So, you have that as a backup. A lot of talk about or a debate, are we. getting ahead of our skis?

Are people slowing down? There was some. talk about the Open AI project maybe. downscaling a little bit. Is Open AI a. partner as well or Uh I can't comment. Can't comment. Okay, so we'll we'll read. into that whatever we want. But, [snorts]. are there pockets where people are. saying, "Hey, let's slow down." or is it. still gangbusters? It's right up the end of the spectrum. It's gangbusters. We we cannot meet. demand. That's why the whole industry. now is around time to compute. There are. no idle GPUs in the world sitting in a.

data center. Yeah. And what's your take. on. when software. makes and this is a big. discussion from Jensen himself during. his 2 and 1/2 hour keynote yesterday. Uh. we're sitting here Wednesday. I think he. did his keynote on Tuesday. He was. talking about, "Hey, software is going. to make it 50 times more. uh you know, lower the cost of tokens. 50x." And then you have um. transport also contributing to that. When do you think the curve goes from.

parabolic to simply growing at a. ridiculous level? Is Is there a slowdown. coming or how are you planning for the. future? Look, I think it's actually the. opposite. I think it feeds on itself. So, I'll give you one example. You go. into ChatGPT today and you generate an. image. You ain't to the prompt. It's. like the dial-up internet days. It is. Right? It takes minutes and you're like, "I better get this prompt right." Yeah. Finally, 2 minutes later it comes. Now, I'll give you an example. If we 10x the. amount of compute available, which is an. enormous task from where we are today,

and those images take 5 to 10 seconds, are we going to generate more or less. images? Oh, many more. Uh this is Jevons. paradox. This is the theory of induced. traffic. You know, you build a couple. more lanes, people start to think, well, maybe the distance from Bondi Beach to. to the central business district in. Sydney terms would be an acceptable. commute. >> Love the analogy. Yeah. So, what do you think about or what are you. seeing? I mean, we're here at NVIDIA. Obviously, they make the leading edge. chips. They just bought Rocks, so now.

you've got, you know, two of the leading. edge chips coming out of the same. company. But custom silicon becoming a big. discussion. Has that started to land in. the data centers yet? Obviously, Google, don't know if they're a customer you can. tell us, but they're making custom. silicon. Amazon is making custom. silicon. Meta is making custom silicon. Talk to. me about that revolution and is it. actually making it to the data centers. yet? Look, it to various degrees it is. They're promoting their products. They're trying to tie up data center.

capacity. So, yes, there's multiple. silicon looking for homes. I think I. think it's fair to say NVIDIA has a. massive head start. The ecosystem. they've incubated, the standards that. they're setting. So, I would say the. safest pathway to build out at scale. early is to follow the NVIDIA roadmaps, but absolutely over time we are seeing. these chips emerge. And. in terms of. desktop computing. I know there was a survey announcement. that. Dell. and NVIDIA are making a really powerful.

desktop, 750 gigs of RAM, a lot of. power. You're going to be able to run. some local models. Open source and with. open claw and open source coming from. Kimmy and a bunch of the models out in. China. Has the hacker group, which I think you. started in like I did, probably in. similar time periods, people are starting to get really. obsessed with having a 10 or 20,000. dollar desktop set up and running this. local. What do you think of that trend?

I'm curious. >> Yeah, I mean, the breakthroughs we're. seeing in software, the way it's. distributing power to every man and. every woman in every house, and their. ability to code and use products like. Open Claw, the generation of demand and. appetite for computer to local level all. the way through to these mega data. centers, it's absolutely real. And as we. see the emergence of agents using more. and more as we see autonomous vehicles. and other automation, robotics, it's. absolutely going to come compound. And. what about nuclear? Uh the Trump. administration.

really seemed to flip the switch on a. growing. uh belief that, hey wait, nuclear's. pretty great. It's clean. It's the. original renewable in a way. Uh and. these new modular reactors have nothing. to do with Chernobyl, Fukushima, or. Three Mile Island. They're much safer. They're a completely different. architecture. Have they have those. started to land yet? And are you, since. you followed correctly in in the great. state of Texas where I'm from, you. followed correctly that time, are you.

following nuclear? I I think you have. to. I think the reality is it's going to. take a decade, a bit longer by the time. big projects can come into. commissioning. But now is the time to. start that conversation, put in place. policies, mobilize capital, and start. that ball rolling. Yeah, have you do you have a data center. going up near nuclear? No, not at the. moment. Not at the moment, but you're. actively tracking that activity cuz. >> Yes. Yeah, this seems. uh. pretty inevitable.

>> Feels like it. And if that happens, what. impact does it have on your industry? If. if you could cuz obviously it's. happening in China and people always put. the Bitcoin miners, they were like the. canary in the coal mine, near the hydro. dams and near the nuclear where there. was excess capacity. What impact do you think this has if you. could actually have small modular. reactors next to data centers? Well, I. think it just opens up the market and. enhances the US's competitive advantage. in this space. Like, AI is inevitable, robotics is inevitable. The reality is.

the correlation between human progress. and energy consumption is really, really. high over a very long time period. So, if we can find a way to unlock new. generation, clean generation as nuclear, and locate that more at the source and. enable more compute on a distributed. basis, all those use cases we just. discussed become easier, more fluid, faster, and then you get that positive. flywheel around Jevons paradox and. demand. Talk to me about the architecture today. of. Ethernet and data moving between data.

centers, within data centers. That. backbone is. going through a paradigm shift as well, yeah? Yeah, yeah, it is. And Jensen. coins coins the term the data center is. the new computer. Yeah. So, you need to. step back and you say, "Right, this big. building is essentially the old desktop. PC we had under our desk at home." You. go, "Right, how does that work?" So, all. the cabling, the latency, the number of. hops between each GPU, how they talk to. each other, the fabric around.

InfiniBand, Ethernet, it's absolutely. critical because every millisecond. matters in terms of performance of that. cluster. Yeah. And where do you think. uh or or what do you think of Elon's uh. vision? It's obviously a a longer term vision of. putting data centers in space, and. there's a couple other people working on. it as well. Yeah, I mean. it's very hard to argue with Elon. He's. been very right on a number of things. for a very long time. I think sitting. here today, it feels exceptionally.

difficult given the cost of moving. things to space, the challenges around. radiation. There's a huge amount of. engineering challenges, but that's never. scared Elon before, so He's uniquely. qualified, and he's he he's inevitably. right, but sometimes he's late. He might be. late to the party, he might be late to. the a party, he might show up at. dessert, but generally uh he nails it. How much of an issue is getting the data. out of the data center. to consumers today? Is that not.

something people are worried about when. you're building something out in West. Texas? All that data, fiber, all that's. been taken care of, or does that become. a gating issue at some point? >> So, this was one of the big myths that. we had to bust when we started this. business, cuz everyone said, "Data. centers must be located close to. population centers, metropolitan areas. Latency is really important." And we. said, "Yeah, that's right. Latency is. important." But, the reality is, in the. US, Texas especially, there is fiber. everywhere underneath the ground. Lots.

and lots and lots of it. And when you. look at latency from our site, in the. middle of the desert, in West Texas, down to Dallas, the big carrier hotel, >> Yeah. 6 ms round trip latency. What's 6. ms? There's a thousand seconds. milliseconds in a second. >> Yeah. We're talking six. Yeah. It's fit. It's adjacent. Yeah, it's it's It's not. even. uh yeah, it's definitely not material. Uh listen, continued success. uh and uh. you're hiring. >> [snorts]. >> a lot of people. Yeah. Yeah, I think.

we've got 129 job advertisements up at. the moment. All right, so everybody go. to the I ran website uh and listen, company's doing fantastic. Thanks for. spending some time with us here at All. In at GTC. Thanks, Jason. Appreciate it. I'm going. all in. >> [music]. [music].

>> I'm going all in.