四位 CEO 谈 AI 未来：CoreWeave、Perplexity、Mistral 与 IREN

Jason Calacanis (00:00:00): I'm here at NVIDIA's annual GTC conference, and I'm gonna interview four amazing AI CEOs. Stick with the NYSE. The NYSE is a modern marketplace 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. 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, 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 wound up getting. You were very early to this trend. How did you get to it so early and how did you build out this first, I guess at the time, Neo Cloud? Michael Intrator (00:01:07): Yeah. So we didn't 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 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 and, 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 could 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 three years mining crypto, went through a couple of crypto winters because we had come from a hedge fund. Our, 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 because crypto was pretty volatile. Jason Calacanis (00:02:48): 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. Michael Intrator (00:03:01): 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 would allow folks that were trying to animate and render images, you know, kind of what makes the movies cool, right? 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 2020, 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 group that was working on Luther AI. They were working on an 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. And that worked out really well because Jason Calacanis (00:04:17): they can't complain about the SLA. Michael Intrator (00:04:19): 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 researchers are going to want to use it. And that launched our our business. It was an amazing story. Jason Calacanis (00:05:00): And Yeah. 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? Michael Intrator (00:05:09): 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 2020, 2021, before ChatGPT moment occurred. And we began to understand that computing decommoditizes at scale. 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, there's a component of this that is going to lean into 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. Yeah. 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 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. Yeah. And everything in there, all the software, the integration of software and operations and observability and all the things that you need to be able to build a cloud that's purpose built for this one specific use case. Right. So we don't we don't do everything. Really focus on one use case, which allows us Jason Calacanis (00:06:46): do web servers. You got AWS. Michael Intrator (00:06:49): You know what? They do a great job. It's like 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. Jason Calacanis (00:07:03): And when did the language model start dialing and calling you for, you know, capacity? Michael Intrator (00:07:09): Yeah. So our first, well, our first language model was really a Luther. Yes. But first like large commercial was inflection. And so, you know, we work with Mustafa and inflection, and then we really diversified from there into the hyperscalers, into OpenAI across the model, the foundation models across, and just kept scaling and scaling with the belief that, you know, once again, the the decommoditization of compute, the ability 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 moment where we've moved from research into the productization of this. It's beginning to work its way in from the fringe of organizations into the core of what they do. And you can see that every day in the amount of inference compute that is being driven through our infrastructure layer, which is just massive, which is just like one mean, of the Jason Calacanis (00:08:29): challenge I'm sure people are consuming it, not just building models, but they're deploying them and utilizing them. Michael Intrator (00:08:35): I always think of inference as the monetization of the investment in artificial intelligence. So 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 you're you're 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. Jason Calacanis (00:09:19): What chips are So Michael Intrator (00:09:22): really, we are the tip of the spear in bringing the new architecture out of NVIDIA into commercial production at scale. And 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 GB200s, 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. They continue to use them in inference for a very, very long time. Jason Calacanis (00:10:11): What is the shelf life of a 100 right now? Michael Intrator (00:10:14): Yeah. Jason Calacanis (00:10:14): That's been a big debate is, I think, for your company, for Microsoft, and I guess Michael Burry, 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? Michael Intrator (00:10:36): So my take on 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, 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 sixteen months or whatever nonsense you'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. 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 approaching it. We use a six year depreciation. We believe that the GPUs will last in excess of six years, but we felt like that was a fair and reasonable approach to a technology cycle that's moving at this velocity. The A100s, the Amperes, this year, the price has appreciated through the year. Jason Calacanis (00:11:58): And why is that? Michael Intrator (00:11:59): 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 Jason Calacanis (00:12:20): that. I mean, to make a very simple example for the audience, like, when you trade in your iPhone after three or four years, you're like, who's gonna 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 seven and it costs $50 that's still got great life left in it. Absolutely. You know? Michael Intrator (00:12:43): And so, look, you know, we 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 Amperes, 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 sixteen more, eighteen months, or two years Yeah. Jason Calacanis (00:13:10): That's farcical. Michael Intrator (00:13:11): It just it just doesn't make any farcical. Jason Calacanis (00:13:13): Yeah. I think sometimes people get caught up in Moore's Law or in just how fast our industry is growing Speaker 3 (00:13:19): Yeah. Jason Calacanis (00:13:19): 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. Michael Intrator (00:13:33): Yeah. One of the things is is, like, you know, the industry has gotten so much attention for the unprecedented scale of capital that is coming to bear on this. Yeah. 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, 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. Jason Calacanis (00:14:14): 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. Michael Intrator (00:14:27): My expectation 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 fully expect this infrastructure to last in excess of six 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 six years. That's that seems like the right schedule. I'm not making it up. Jason Calacanis (00:15:04): That's what everybody's using. Yeah. And the energy cost is the opportunity because, hey, it's just we need that space. There's a better reward here, and that might get resold that hardware to somebody else who wants it, a hobbyist or something. It's Aravind Srinivas (00:15:20): yeah, I Michael Intrator (00:15:20): mean, or it could be sent someplace else where they have more capacity when they can repurpose it there. But 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 accretive 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 five years, you know, as it becomes available, I am still able to sell it at a higher price than it was at a year ago. Jason Calacanis (00:15:52): 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 thirty days or less. Nowadays, what's the wait like even for you, a loyal old customer? And is there a bit of a battle? Is there politics to who gets the servers? Like, you see some, like, very big names talking about they gotta 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, Michael Intrator (00:16:23): I think of it as an affirmation of the business that we're in, right? Like the fact that we are attracting competitors, 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. Yeah. 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. Jason Calacanis (00:17:08): The one what's the wait time like? And is it just really competitive or not? Because I talked to Jensen about it. He 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. Does it really like that? It really is. Right? Michael Intrator (00:17:28): Like, you know, he doesn't wanna be in the position of playing favorites or like that. That just seems like a bad place to be with your clients. Jason Calacanis (00:17:37): Or auctioning them off. Speaker 3 (00:17:39): Yeah. Jason Calacanis (00:17:39): So Can you imagine? Michael Intrator (00:17:40): Yeah. That would that that Jason Calacanis (00:17:41): That'd be crazy. Michael Intrator (00:17:42): Yeah. Don't I'm not sure that Jason Calacanis (00:17:44): would be good Michael Intrator (00:17:45): for the long term business. Jason Calacanis (00:17:46): No. Yeah. Michael Intrator (00:17:46): So so our our our approach is Jason Calacanis (00:17:49): You might get some sovereigns coming in and saying, I'll pay double. Yeah. They do that with Ferraris too sometimes. These are Michael Intrator (00:17:55): the Ferraris of computing, right? Jason Calacanis (00:17:56): In a way, they are. I mean, our Michael Intrator (00:18:00): approach is to work with clients across the entire space to find opportunities that are really interesting companies that can fit into our contracting requirements, the way we're gonna be able to go out and structure the debt that we require in order to go out and build infrastructure at this scale. Jason Calacanis (00:18:20): And how does all that debt work? That is something that you guys specialize in corporate debt. I mean, 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 interest rate people are paying on, you know, a billion dollars in infrastructure. What do they pay on that? Michael Intrator (00:18:44): Yeah, so CoreWave has really been the innovator around a lot of the financing engines that have come to bear on this. We did the first GPU based loans and like, I think it's important or I'm gonna 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 And before, Microsoft comes to us and says, we'd like to buy some compute for you. 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 $35,000,000,000 in eighteen months to build infrastructure at scale. But what's important to understand is the economics in this box are such that within two and a half years of a five year deal, we have paid for everything. Jason Calacanis (00:20:38): The principal's been paid off. Well, Michael Intrator (00:20:40): 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, which gives the most sophisticated lenders in the world, whether it's banks or private equity funds or, 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. Jason Calacanis (00:21:10): Works better when that happens. Michael Intrator (00:21:12): So they look at this box and they're like, wow, we're really confident we're gonna get our money back. Jason Calacanis (00:21:16): And maybe they want 10 boxes. Michael Intrator (00:21:17): That's Jason Calacanis (00:21:17): correct. If any one box goes upside down, you can deal with it and it's not as acute. Michael Intrator (00:21:25): That's correct. And they don't cross pollinate. They don't cause contagion across the boxes. They're all independent and discreet, 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 two 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 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. Jason Calacanis (00:22:26): It means you have to say no to maybe some people who wanna be in the box? Michael Intrator (00:22:30): Yeah. So Some customers. We we look at some deals and we're just like, you know, they wanna 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 Yes. The expenses. Or and so I won't do that. Jason Calacanis (00:22:45): Right? Like once again. They can go to another provider who maybe wants to take that risk on who has extra capacity. Michael Intrator (00:22:50): 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. And they're feeding information back into us that is letting us know what the next product we need to build is or where they need help scaling or what type of compute they need. And all of that information flow is incredibly What valuable to Jason Calacanis (00:23:44): can you tell us about demand? There's been reports of, Hey, maybe the Oracle Starbase thing with OpenAI's been downsized or maybe not. And then, you know, other folks, Microsoft is going big and Google's going big, Meta's going big, and those people obviously have massive cash flow. Apple seems to be MIA. They don't seem to wanna play. Michael Intrator (00:24:07): 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 have been truly steadfast in this for years now. For four 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. Jason Calacanis (00:24:42): Have, like, Knicks tickets during the Patrick Ewing era. Yeah. Like, they got up to 50,000 So people on the wait if magically, the wait list went away if the if the constraint went away and we just had a 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 or would be deployed, I should say. Michael Intrator (00:25:08): So remember how we build our business through this box and it's a five 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 or 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, entering into contracts with counterparties that have large balance sheets, you are or we are protecting ourselves and our lenders 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, you know, if But just in terms of Jason Calacanis (00:26:03): the capacity, if you were unconstrained and Nvidia Jensen says, Hey, order as many as you want, what would happen? Michael Intrator (00:26:09): So, it's also important to understand the constraints aren't just GPUs. Jason Calacanis (00:26:15): Right. Right. Electricity. Michael Intrator (00:26:16): It's power Data shells. It's memory. It's storage. It's networking. It's optics, all of the things. And there's various throttles that will limit Memory's a throttle right now, right? Oh, yeah, it is. Oh, yeah, Jason Calacanis (00:26:29): it is. How did memory become the throttle? Michael Intrator (00:26:32): If 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 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 Jason Calacanis (00:26:55): the fabs, Michael Intrator (00:26:56): 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. Got it. That would have brought on the necessary fab capacity to be able to serve. Jason Calacanis (00:27:35): Impossible to predict what just Yeah. 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 in is there some wind? Michael Intrator (00:27:48): And anytime you you have a a very cap no. Not every any time, but many times when you have a capital intensive business like, you know, building fabs, you will get this boom and bust cycle just like an energy they overbuild. Yeah. And then, you know, fiber. Yeah. I mean, there's there's there's a lot of examples of that. Our approach in Jason Calacanis (00:28:09): 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 like 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. Michael Intrator (00:28:27): Listen, the the you know, it it does it does a lot of things having a boom bust cycle. It clears out the underbrush. Yeah. 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. Jason Calacanis (00:29:03): Yeah, people don't recognize this fact. 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 gonna have their minds blown, but your server would turn off and it would say, this person, you know, needs to pay their bill. Yes. Because they were getting charged for carriage by the megabit going Yes. Michael Intrator (00:29:41): I mean, look, and the business models change and evolve, and like you said, Moore's Law, and certainly Jensen will talk about the fact that what is going on within accelerated compute space dwarfs 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. Jason Calacanis (00:30:09): Yeah. I mean, the concept that, I don't know if it was like a million hours being uploaded every hour or minute, but at some point, Susan Wojciechie, rest in peace, told me just like how much was being uploaded every minute? And it made no logical sense until you realized, well, there's 3,000,000,000 people, two or 3,000,000,000 people in the service and 1% upload or 10 bps upload. It's like, okay, one in a thousand people upload. It's it's a big denominator. Michael Intrator (00:30:40): I was sitting on a panel with Sarah Fryer, CFO. Yes. OpenAI. And she every once in a while, she she really puts out like interesting information. And so she was talking about the cost of a million tokens when ChatGP three came out and it was $32 in change. And now a million tokens cost $09 Yeah. Right. And so you just see like like the incredible power of how the capital markets, how capitalism is fueling engineering and fueling computation. Jason Calacanis (00:31:23): It's become recursive now too. I mean, these models, if you say to the model, hey, make yourself more efficient, spend less money, and lower the cost of tokens, you'd be like, okay, captain. Yeah. I don't know if you saw Carpathi's recursive thing last weekend, but it's now civilians who've never worked in a language model during computer science are like, I'm gonna try to do something recursive this weekend. Michael Intrator (00:31:43): You know, one of the things that 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. 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. Yeah. Like, you're bringing the minds of 8,000,000,000 people a tool that allows them to overcome what was insurmountable for forever. Jason Calacanis (00:32:53): For humanity. Speaker 3 (00:32:54): Yeah. Jason Calacanis (00:32:54): 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. Aravind Srinivas (00:33:05): Thank you for having me here, Jason. Jason Calacanis (00:33:07): It's so great. I wanna 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 wanna choose OpenAI, if I wanna choose Claude, whatever it was. That was like a real unlock for me. And on the sidebar sidebar, I noticed you had done essentially like what Yahoo did in the early days, finance, sports. And when I pulled my nickname 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 is great. And I kind of made you my front door to different models, and it made it easier 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 are the first out of the gate with that. And then just the last couple of weeks, I had been clawpilled and using OpenClaw, 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 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, OpenAI 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. Aravind Srinivas (00:34:57): Thank you. Jason Calacanis (00:34:57): So tell me. Aravind Srinivas (00:34:58): First of all, thank you. Thank you so much. Perfectly 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. One common theme for us 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 wanna give somebody answers, accuracy is very essential for building trust because only then the user is gonna 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. Comet. 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'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 they're 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 Computers. The AI is itself is the computer now. Jason Calacanis (00:36:50): Still lives inside of a browser. Have you considered giving it desktop root access? Yes. 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 Yes. How do you think about root access to my Windows machine? Correct. 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? Aravind Srinivas (00:37:20): Yes. So we announced something called personal computer, Perplexity personal computer. That's essentially gonna 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 gonna 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. Jason Calacanis (00:37:52): Not on your servers, not on Anthropics. Aravind Srinivas (00:37:55): Exactly. Yeah. 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 stats 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 gonna bring this perfect hybrid of trustworthy hybrid between local and server side, Jason Calacanis (00:38:28): and you can And you'll make it easy to do it. Just be abstracted. You install one executable. Boom. Aravind Srinivas (00:38:33): Exactly. It's It's it's like OpenCLOF for dummies. Nobody needs to learn how to use it. Nobody needs to manage API keys. Nobody needs to manage separate billing across like a 100 different services, figure out what you can give access to and not access to. We take care of that. So it's a Steve Jobs way of doing it, you know, end to end integration. Jason Calacanis (00:38:51): And how do you think about local models? I have started running Kimi 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 Aravind Srinivas (00:39:04): Yeah. Jason Calacanis (00:39:05): Essentially. Yeah. 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. Aravind Srinivas (00:39:19): Yeah. Jason Calacanis (00:39:20): Or now, I don't know if you saw Dell and NVIDIA announced a giant workstation. Yeah. Arthur Mensch (00:39:25): Is it Jason Calacanis (00:39:26): a 3,800? Aravind Srinivas (00:39:27): Something like that. Jason Calacanis (00:39:28): Something like that. Yeah. It's 750 gigs of RAM. So what do you think about the desktop going back to workstation slash server Aravind Srinivas (00:39:37): Yeah. Status? I think it's very promising. My prediction is it initially start off as a sub agent, so whatever you need to go, like your tax returns, your personal photos, your emails, your calendar, all that stuff, those local apps, personal notes, very personal notes, you could 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, and more complicated stuff that accesses your data that's already on the server side, example, your Google Calendar Yeah. Your Gmail. This is personal data still, but an 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. Yeah. 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. It's all about choice, and anyway, when you're on your phone, you wanna You don't care actually which server that workload's running from because it's not gonna 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. Jason Calacanis (00:40:56): Or this 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 Aravind Srinivas (00:41:07): Right. Jason Calacanis (00:41:07): $500 a month Aravind Srinivas (00:41:08): Yes. Jason Calacanis (00:41:09): Fraud bill. Aravind Srinivas (00:41:10): Yes. Jason Calacanis (00:41:10): This is an incredible savings. Plus, you get the benefit Aravind Srinivas (00:41:13): Yes. Jason Calacanis (00:41:13): Of privacy and not educating the language models on your personal data. Aravind Srinivas (00:41:18): Yes. And it's gonna be it's gonna 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 gonna feel like you're wasting your money. Every a every home has a lot of other sensors Yeah. That runs your home that'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. Jason Calacanis (00:41:54): And I'm curious what you think of the operating system. What's eventually going to be the operating system of this workstation? Aravind Srinivas (00:42:03): 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 chart it for the max seven. Yeah. And and chart it over time. We can so that's the objective. But individually, it's running a file system, a code sandbox, access to the internet. It's having its own HTML tools and like Yeah. So I think that's basically where models, systems, and files, and connectors are all coming together. You would think of that as an OS, except you're operating at an abstraction above that where you are thinking in terms of objectives. Jason Calacanis (00:42:52): Yeah. And does it need to eventually become its own operating system in your mind? Aravind Srinivas (00:42:58): It could be. Like, people could think about us as like, yeah, I have my Perplexity computer running all the time. Whether it essentially, it runs on Linux machines right now. Every server side computer is a Linux machine. Yeah. So I think Mark in recent tweeted this right after our release that turns out Linux computers was the right idea. Desktop desktop Linux computers are finally gonna work. Yeah. Mean, they're stable. Yeah. Jason Calacanis (00:43:22): They're customizable. Exactly. And you're not at the mercy of Apple's desire to contain the experience. Aravind Srinivas (00:43:29): Exactly. Jason Calacanis (00:43:30): Or Microsoft Surface area as Exactly. For You build something rock solid, and it does feel like Linux might actually become Aravind Srinivas (00:43:38): the Correct. Jason Calacanis (00:43:39): The eventual It Aravind Srinivas (00:43:40): may not need to have a front end. That's the thing. You could you could access the Linux machine on your phone. Right. This could be running iOS or Android. Doesn't matter. Right. The actual valuable runtime is running on Linux on the server. Jason Calacanis (00:43:55): You've done great as a consumer company. Lot of love there. Now I'm starting to see corporations with computer starting engaging. And in fact, you'll be happy to know this. Last week, I took two people in my back office, and I said, stop working on OpenClaw. Your job is to do the back office automation at our venture firm only using Perplexity. And they were Perplexity computer. And they were like, Oh, okay. It doesn't talk well in Slack. It doesn't have an agent in Slack. I was like, It will. I'm gonna see Arvind. I'll talk to him about that. So we need a really strong Slack connector. Aravind Srinivas (00:44:34): It's already out. Jason Calacanis (00:44:34): It is. Okay. Great. Aravind Srinivas (00:44:35): Computer exists as a Slack bot right now Okay. That you can add to your Slack workspace on enterprise plan, and our entire company works like that. People are talking more to computer on Slack than than other Jason Calacanis (00:44:46): people. In our 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? Aravind Srinivas (00:45:01): Several tens of millions. Jason Calacanis (00:45:02): 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 Aravind Srinivas (00:45:12): Right, of it's fastest growing business for us. It's growing faster than the consumer and revenue. And things like computer unlock entirely new possibilities. For example, we've saved more than a $100,000,000 for our enterprise max customers who are on the highest tier of enterprise. Explain what Jason Calacanis (00:45:29): that is. What does it cost? 200 a month per person? Aravind Srinivas (00:45:32): 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 a computer, after you run out of your credits, you would pay for the tokens. You pay for the usage. Jason Calacanis (00:45:48): Are you making money on the $400 a month, $5,000 a year one, or at this point in time, are people Aravind Srinivas (00:45:53): going Our so one thing that Perplexity has is every revenue we make, unlike certain other wrapper companies, every revenue Perplexity makes has positive gross margins. Jason Calacanis (00:46:03): Got it. Aravind Srinivas (00:46:04): Because 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 are 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. Single penny we make, we make profits on that. But overall, the company is still yet to be profitable, but we're working towards that. Jason Calacanis (00:46:36): 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? Aravind Srinivas (00:46:44): About 400. Jason Calacanis (00:46:45): Yeah. You've 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 hypercompetitive here. How do you keep up as a 400 person organization when you got Sam Altman over here raising a $100,000,000,000, 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, 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 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. Arthur Mensch (00:47:46): Yeah. Michael Intrator (00:47:46): So how Jason Calacanis (00:47:46): do you think about it? Long term and independent company, do you think you'll need to join forces at some point? Well And why didn't you take the deal? These deals were incredible that you got offered. Aravind Srinivas (00:47:55): So one advantage we have that all these companies you mentioned don't have is the multimodal orchestration. We're like Switzerland. We don't have to have one horse in the race. If GPT wins, Gemini wins, Quad wins, Llama wins, it doesn't matter to us, or even open source models can win. No problem. Jason Calacanis (00:48:16): And you have them on the service. Yes. You have DeepSeek and Kimi? Aravind Srinivas (00:48:19): We have Kimi. We have Nematron, and we have a lot of usage of Quinn, Alibaba Quinn 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 it makes no sense for them. It would be an admission that all the data centers and CAPEX they've built out still couldn't produce them the best model. And Dario, CEO of Anthropics, said recently in an interview that models are specializing. Towards the beginning of last year, people thought models are gonna commoditize, but towards the end of last year, people models started specializing. Even within coding, Claude code and codecs have very different capabilities. Our iOS engineers love using codecs. Our backend engineers love using quad 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 the 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. Jason Calacanis (00:49:42): 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 You're Aravind Srinivas (00:49:50): gonna take care of all their orchestration. Yeah. So you don't have to manage tokens across different models. Jason Calacanis (00:49:55): Because I authenticate a couple of my different accounts, my pro accounts, interperplexity. But does it I I I don't have enough knowledge to know if you are abstracting that and people can just search across them and it's part of their Perplexity subscription? Aravind Srinivas (00:50:09): No. We're not bundling subscriptions from into other AIs. Yeah. We just ping the models directly. Got it. What you get in us is the perplexity or orchestration. Got it. The harness. Right. So the when when when models are kinda specializing, 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. Jason Calacanis (00:50:30): Does it auto route today, or do you still have the drop down somebody's gotta pick? Aravind Srinivas (00:50:34): It it definitely auto routes the best model for each prompt, but we also give users the flexibility to pick whatever model they want. Jason Calacanis (00:50:41): What do you think of, I've seen a bunch of startups hack this together, but doing the same query across multiple Aravind Srinivas (00:50:47): We built this thing called model council. Model council, yeah. That's one of the modes and perplexity where I saw Jensen say in one of his interviews that he puts the same prompt in five different AIs and sees what each of them says. Jason Calacanis (00:51:00): Yes. Everybody does that, Aravind Srinivas (00:51:02): yeah. But then you still have to apply a biological compute to read every answer and then figure out where they differ. Jason Calacanis (00:51:08): Like talking to five lawyers about your trust or your Five different doctors. Five different doctors and trying to figure it out. Aravind Srinivas (00:51:15): Exactly. It's dumb. The model council is a feature we built where it will not just give you the answers of each model, but it will tell you exactly where they agree, where they disagree, and where the nuances are. Jason Calacanis (00:51:25): And that's in the interface? Yeah. Final counsel? Aravind Srinivas (00:51:26): I didn't Jason Calacanis (00:51:27): know it was there. Aravind Srinivas (00:51:27): It's there. Jason Calacanis (00:51:28): I mean, you you release product at a pretty great cadence. How did Aravind Srinivas (00:51:32): where did you learn that, and what's your philosophy of shipping product? Our philosophy is, like, speed is our mode. Like, again, one of the things that big companies cannot do is move at the speed we do, serve customers at the speed and 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 because they're very worried about people not trusting them. Yep. 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. Jason Calacanis (00:52:05): Yeah. They get abandoned. Aravind Srinivas (00:52:06): Exactly. So Perplexity has those advantages of being very small, and towards the end of last year, we found that AI coding tools have made it much faster for us to ship things, 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. Jason Calacanis (00:52:29): The the moment I had where I became clawpilled was when I was working with it and I was like, hey. I wanna build my network. I know these 20 people in Japan. I had dinner with them during my recent trip. I wanna 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. And they said, Where do you want me to put them? And I was like, Well, where can you put them? And it said, Well, I can put it in a Google Sheet. I can put it in Notion tab. I can put it here. I could give you a PDF. 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 means you're a system. And I think that becomes I think maybe one out of a thousand people working with AI have had that experience. Yeah. Maybe it's one in 10,000 where your agent says, I'll make you bespoke software. Aravind Srinivas (00:53:27): Yeah. Jason Calacanis (00:53:28): 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? Aravind Srinivas (00:53:40): Yeah. Well, we have a board meeting tomorrow. Okay. I'll come. And and Jason Calacanis (00:53:44): I'll pitch it to the board. Aravind Srinivas (00:53:45): 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. Yeah. Computer just one shotted it. I had a press briefing with a bunch of journalists. My comms person would usually that. Oof. Brutal. And then my comms person would usually give me a memo Jason Calacanis (00:54:10): A dossier. Yeah. Aravind Srinivas (00:54:11): What to say. Yeah. Computer one shot at him. Jason Calacanis (00:54:13): So It's crazy. And it's It's crazy. The context is so good because the memory's getting better. Yeah? Yeah. So it's like, I know that journalist from the last time. Aravind Srinivas (00:54:23): Yeah. Jason Calacanis (00:54:23): I know the board meeting. Yeah. Have all the previous decks. Aravind Srinivas (00:54:27): Yes. Jason Calacanis (00:54:28): When did that happen? Aravind Srinivas (00:54:30): I think it it happened with Opus 4.5. Uh-huh. The the improper Opus four four point That was an inflection point when models were started being amazingly good at orchestration and reasoning and tool calls, and Cloud 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 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 them away when it wanted to. Yeah. And that made it, like, suddenly so good at doing very long orchestration tasks. Jason Calacanis (00:55:26): Yeah. It's it's pretty crazy. I have every episode of This Week in Startups, all the transcripts, and then all of All That was Aravind Srinivas (00:55:32): one of the tasks I did, by the way. I can send it to you. Yeah. I asked it, I want you to download every All In podcast Yeah. 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. Jason Calacanis (00:55:55): And the sentiment of what we said. Aravind Srinivas (00:55:57): Exactly. And it did. Like, it clearly said Jason Calacanis (00:55:59): Are we moving stocks? Aravind Srinivas (00:56:00): Around Google's stock going up. Yes. Prior to that, you guys were talking a lot about Google. Jason Calacanis (00:56:05): Yes. And it clearly I said, I made a bet publicly on the bank. 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. Aravind Srinivas (00:56:18): Yeah. Jason Calacanis (00:56:18): I was like, that's that company. They need to release more. Aravind Srinivas (00:56:21): Yeah. Jason Calacanis (00:56:21): And I told Sergei, I was like, like, give us the good stuff. Aravind Srinivas (00:56:26): Yeah. He's already Jason Calacanis (00:56:27): giving us the good stuff. Aravind Srinivas (00:56:28): It literally gives you the timestamps of every single, and then I can go click on it and actually hear exactly That moment. Yeah. Sweet. Yeah. So that's when that's when I was like, damn. Like, this I would've had somebody do this as a week long project. Jason Calacanis (00:56:42): It would've been ten hours a week of researcher. I'm experiencing the same thing. When I do research notes, I've created my own mega prompt. Yeah. And it will go and tell me where you worked before and who's in your circle, who your competitors are, who your friends are, blah, blah, blah. Then go I try to find old podcasts. It's one of my secrets if you're an interviewer watching. I try to find what was the person talking about five years ago, ten years ago, and then over ten years ago. And I've gone into interviews now with Michael Dell and talked about things he was talking about in the nineties. Yep. And it finds me some ancient stuff. Like, you would pay a researcher or 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. Aravind Srinivas (00:57:32): Yeah. Jason Calacanis (00:57:33): It's really gotten weird just in the last six months. What do think the next six months looks like? Aravind Srinivas (00:57:38): I think the the dream that what we are gonna try to do is help businesses run as autonomously as possible. You know, everybody talks about this. AI is gonna create this one person, $1,000,000,000 company. Some people say it's already happened because people pay researchers like 1,000,000,000, but it's not truly moving the GDP by 1,000,000,000. 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 computers, run their business on that, or, like, run it on the server. It doesn't matter. And actually make real money. Yeah. Hundreds of thousands or even millions a year and grow it. Computer go and run your ad campaigns on Instagram or Google. Jason Calacanis (00:58:27): I mean Aravind Srinivas (00:58:28): Integrate it with SEM and SEO tools, find new users, and 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, so 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 wanna do. Jason Calacanis (00:58:52): Yeah. It's a great vision because when I watched startups twenty years ago, there were so many check boxes they had to do. I have to find an office space. I gotta put up a bunch of servers. I gotta hire an HR firm. I gotta 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 1,000,000. Who are you hiring? And they're like, I don't know if we need to hire anybody. I'm like, If you could hire somebody, will you hire? They're like, Well, I do my own HR. I have this partner. 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, ask them a bunch of questions, and then I meet with the last two. And I'm like, that's what a recruiter did. Like, the entire recruiting job has been abstracted. Like a a tool like computer is gonna make that Aravind Srinivas (00:59:51): even work to do. A lot of connectors, a lot of specific workflows. People don't wanna, 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. Jason Calacanis (01:00:09): Yeah. It's an incredible future and it feels like it's right here. How do you think about job displacement? Because you're actually making the tool that enables people to be a solo entrepreneur and get to a million in 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. Aravind Srinivas (01:00:35): Yeah. A lot Jason Calacanis (01:00:35): of people are gonna lose their jobs really fast. Aravind Srinivas (01:00:37): Yeah. Jason Calacanis (01:00:38): And then, oh my God, you can learn any skill you want and all the things that were hard are now easy. Aravind Srinivas (01:00:44): Yeah. Jason Calacanis (01:00:45): I go back and forth. I'm 80% super positive about this, but I do worry about like 20% of the time, I'm a little worried. Aravind Srinivas (01:00:52): Yeah. Jason Calacanis (01:00:52): Where do you sit? Aravind Srinivas (01:00:53): I mean, America has always been about like entrepreneur entrepreneurship, right? Yeah. We've we've been about, like, trying to build new things, discover new things, go explore. 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 I think the reality is people most people don't enjoy their jobs. They're doing it No. They 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 your 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. Jason Calacanis (01:01:45): I think you're exactly right. There will be some displacement, but then there's also gonna be so many opportunities to open up, and it requires the individual to not be passive. Aravind Srinivas (01:01:55): Exactly. Jason Calacanis (01:01:55): They have to be rugged individualists. They have to be resilient. Yep. And they have to be resourceful. And I think once you start playing with these tools, that's what happens. Aravind Srinivas (01:02:04): Exactly. Jason Calacanis (01:02:05): You you all of a sudden feel like Aravind Srinivas (01:02:06): things are the best in you if you truly are in a good space. Yeah. Yeah. I I Jason Calacanis (01:02:11): and then today, Comet for iOS is out. Aravind Srinivas (01:02:14): Yeah. Jason Calacanis (01:02:14): I'm a Comet super fan. I required everybody. You were nice enough when I I emailed you. I was like, can you send me some licenses you said? You don't may not remember. You sent me a bunch of licenses. I said, everybody put this on because it was $300 a month when you first came out with the common browser. Now it's free, I think, for all users. Highly recommend it. Highly recommend getting a pro account. It's only $20 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? Because that's another thing I'm having. Yeah. Clawed code computer. They there's not a good enough integration with Aravind Srinivas (01:02:52): this mobile device Yeah. Computer is already on the Perplexity app, so you can just toggle the computer and start using this. Comet's uniqueness and perplexity for the company and the strategy is the fact that you can control the browser. So the browser also becomes a tool for a computer. Yep. Just like your Google Workspace and all these other things. 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, fill up forms, click on things, upload stuff. All that stuff, if you wanna automate, you need a browser. You need Yeah. 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 OpenClaw or, like, ClawCowork Jason Calacanis (01:03:42): Yeah. Aravind Srinivas (01:03:43): Executing tasks on a browser on the server side along with all the other things is something uniquely Perplexity can do. Jason Calacanis (01:03:50): Yeah. My dream is that you'll create an Android app that roots my Android phone Aravind Srinivas (01:03:56): Yeah. Jason Calacanis (01:03:56): And that you just take over and see everything because one of the blockers I have now is some of the websites have gotten a little persnickety. Aravind Srinivas (01:04:05): Yeah. Jason Calacanis (01:04:06): I don't wanna mention too many, but Reddit, LinkedIn. Aravind Srinivas (01:04:09): Yeah. Jason Calacanis (01:04:10): And like, they're just I am a great Reddit user. I'm a great LinkedIn supporter, but sometimes like I need to get my in mail from my LinkedIn and I just need to, you know, find seven people a company. Is there gonna be a solution between the LinkedIn and Reddits of the world and the claws and perplexities? How is that negotiation going? Aravind Srinivas (01:04:34): Don't have Jason Calacanis (01:04:34): to speak about any specific ones unless you want to. But it feels like there's gotta be a solution and I'm willing to pay for it as a user. I'm willing to play Reddit to allow my bot to show up and behave properly. Aravind Srinivas (01:04:45): Yeah. Well, I cannot speak about any particular company, but we are happy to work with anyone, right? So I think with with Comet, our idea is to give people the flexibility to set things up on their own. Yeah. And any official APIs that anyone's willing to offer, we're always happy to put that as part of computer. Here's what Jason Calacanis (01:05:07): I think should happen. Let me see if you agree. And this is for Steve Huffman at Reddit. I go on Reddit. I do a pro account for $20 a month. And when I do that, I can authenticate whatever tool I want to do a series of well behaved things a certain number of times a day. Aravind Srinivas (01:05:30): Yeah. Jason Calacanis (01:05:30): So it's not unlimited. I'm not going 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 the This Week in Startups and All In subreddit? Summarize it for me so I get the customer feedback. And I would literally name my agent and I would say, it won't post on my behalf. It won't vote on my behalf. Just needed to do a couple of little read only things. This would be an easy solution or LinkedIn. I would like if you I already pay LinkedIn like $50 a month. Like they should just let the $50 a month one work with computer. Aravind Srinivas (01:06:04): Yeah, absolutely. I mean Okay. Jason Calacanis (01:06:07): This is for Satya Nadella. Let LinkedIn work with Perplexity and the other players and we'll pay you extra. Aravind Srinivas (01:06:13): Perfect. Jason Calacanis (01:06:14): It's a revenue stream. Don't you think API access for customers is a revenue Aravind Srinivas (01:06:18): I think so. 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. And I would say the same thing applies to any website in the world. Like, if want an AI to use it on your behalf, it should be okay for it because that's what the user wants. Jason Calacanis (01:06:37): I mean, I have a paid New York Times subscription. Like, let me go in there and do whatever, a 100 searches a day, a week, a month, whatever they choose, But that would make the subscription that much more sticky. Aravind Srinivas (01:06:51): Exactly. Jason Calacanis (01:06:51): All right. Aravind, 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 two years. Thank you, Jason. 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. Aravind Srinivas (01:07:12): Yes. Jason Calacanis (01:07:13): All right. Great seeing. Thank you. We have an amazing guest, Arthur Manchus here, the CEO of Mistral AI. How are you doing, sir? Great. Arthur Mensch (01:07:21): Thank you for having me. Jason Calacanis (01:07:22): And so you're here at NVIDIA's big conference. Big announcement. You're going to be working with NVIDIA to build models, to open source them. What is the big announcement here? Arthur Mensch (01:07:37): Well, we are announcing that we are going to be training the next generation of frontier models with NVIDIA. It's something that we've been doing before with NVIDIA with Mr. Almevo, something we did like eighteen months ago. And 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, in making them better in certain languages when we work with governments, etcetera. Jason Calacanis (01:08:10): And Mistral, obviously based in 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? What's the pros and the cons? I'd say, first, Arthur Mensch (01:08:36): we have 25% of our business in The US, and 25% of our researchers are actually here. So I actually spend a lot of time here as well as in France, as well as in The UK and Singapore, where we are. So of course, different markets. It's markets where you have language, which is a topic where there's much more manufacturing. Manufacturing is a bigger piece of the cake than it is here. And I'd say our strength has been to also work with European companies that are a bit lagging behind and that wants to adopt the technology 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 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 models 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. Jason Calacanis (01:09:35): And so your belief is specialized verticalized models, healthcare, finance, engineering, different verticals will win the day or a global model will win the day that does everything? Well, you Arthur Mensch (01:09:49): need general purpose models to do the orchestration parts, etcetera. But at some point, enterprises sits on a lot of intellectual property, on a lot of signals coming from physical systems, from factories, from tools. And 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, 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, etcetera. So we believe that eventually building an open source technology is a way to save cost, is a way to have better control, cause 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 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 data and we build models that are specifically good for their Jason Calacanis (01:10:58): And own 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 wanna send their data, I guess, over to Meta. We're investors in a company called Micro One 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. Arthur Mensch (01:11:43): I can use 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 because everything stays there. Now, the way we then use that technology that has been deployed is that we're going to be working with the teams that is doing 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 can explain how an image is being detected, how do you detect defaults, etcetera. 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 itself. And so we send the technology, typically we send a little bit of scientists 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, etcetera. So that combination of data segregation, expertise transfer, knowledge transfer is the one thing that makes us quite unique and allows us to sell 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. Jason Calacanis (01:13:16): Yeah, this seems to be once the entire open web, what was available, legally, gray market, etcetera. I wouldn't have you comment on that controversy, but we 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 Arthur Mensch (01:13:41): 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 wanna train a small model, you will use your large model to process and to produce a lot of synthetic data at the beginning. But eventually, you do need to have human signal. So the human signal is something that's 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 further compress the models. At the end, you do need to go and get data that is produced by humans. So yeah, it's way to have, it's mostly an efficient way of training models to have bigger models that are used as teachers for smaller models, but it's not enough. Jason Calacanis (01:14:26): And so you also need human signal. Arthur, we've seen an incredible explosion. We're sitting here on AO 52 after OpenClaw, the year of our lord, fifty two days. When you first saw OpenClaw and saw the reaction of hackers, founders, startups, 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 does that open claw moment mean? Well, it resonated a lot with Arthur Mensch (01:15:05): what we were doing with our customers because pretty quickly, enterprises realized that if they wanted to make some gains with artificial intelligence, generative AI, they would need to automate full processes. And to automate the full process as an enterprise, well, you can use OpenCLO, but it's going to be, it's actually not really enough because you have data problems, you have governance problems, you can't observe the process that is running, and you can't control it. 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 CIO that it's always going to go through these gates. And that's not something that OpenFlow is providing because it doesn't have 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 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 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 because otherwise you will not be at ease when you're sleeping. Jason Calacanis (01:16:24): 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 g 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 performance improvement plan, 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 Arthur Mensch (01:17:21): 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 everyone. And so you need to have this layer that actually understands what is in the data. You need to have a semantic of what can actually be proposed to HR or what can be proposed to engineering. And typically compensation is one of these things. You wanna make sure that the compensation data does not flow back to all of the enterprise, because you're going to have a lot of problems if that's the case. And so what you actually need, which is hard to do, is what we call context engine. So a mapping of where the data sits 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 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 information flow is completely different today 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 So top, there's some IT problems to solve and you need the right primitives, you need sandboxes, you need airbag, oil based access control and these kind of things. And you have change to do. You need to rethink your entire customer service department because suddenly you actually don't need that much transfer of information operated by humans. Jason Calacanis (01:18:55): Alright. You have to go. You got a flight to catch. I have. It is so great to see you, Arthur. Continued success with MISTRYL. Arthur Mensch (01:19:00): Thank you very much. Jason Calacanis (01:19:01): Cheers. I'm really lucky to have Daniel Roberts here. He's the co CEO and co founder along with his brother of Eyen. They are a publicly traded company. They started in BTC. Welcome to the all in interview program. Speaker 3 (01:19:15): Thanks, Jason. Pleasure to be here. Jason Calacanis (01:19:16): Yeah. And so you started in Sydney. You and your brother, it was seven, eight years ago, and you got in early on Bitcoin, and all these Bitcoin monitor miners wanted to have data centers. Yeah. Speaker 3 (01:19:31): That 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. Jason Calacanis (01:19:58): When did you first start seeing the demand in the company shift from, hey, Bitcoin miners, we need some h one hundreds, whatever it is, to, hey, we're this nonprofit OpenAI. Hey, we're this research lab. We need some AI compute. When did that start hitting? Speaker 3 (01:20:17): 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. But I would say about two years ago and month by month, the demand just continues to escalate. Jason Calacanis (01:20:34): 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? Speaker 3 (01:20:49): 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 seven fifty megawatts is our flagship Texas site four 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're saying, we believe in the future digitization, high performance computing, and obviously now today it's paying dividends. Jason Calacanis (01:21:17): Yeah. I don't think anybody could have predicted when ChatGPT came out, OpenClaw recently as a turning point, And then, you know, Microsoft, Google, and everybody embracing this. And that's your big partner, Microsoft. Speaker 3 (01:21:33): Yes. Microsoft's one of our early partners. We signed a $9,700,000,000 contract with them late last year. But as I was explaining to you before the show, that's 5% of our capacity. So Wow. Things are busy at the moment. Yeah. Jason Calacanis (01:21:45): 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 constraint today. Yeah? Speaker 3 (01:21:59): Look. For many of the industry, it is. But for us, because we started eight years ago tying up all this land and power, it's not. So we've got four and a half gigawatts. 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. Jason Calacanis (01:22:23): And time to compute means trades people coming to West Texas, living in 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? Speaker 3 (01:22:42): Exactly. And this is the whole real world challenge to respond to these digital exponential demand curves that are 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. Jason Calacanis (01:23:07): And you get to spend time there. What's it like when you set up a town or you bring a thousand people or 2,000 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? It sounds to me like something out of, like, the gold mining era when people first, you know, went and and were prospectors. Yeah. Prospecting town. Speaker 3 (01:23:37): Pretty pretty much. I mean, the barbecue's great. Jason Calacanis (01:23:39): That was the Speaker 3 (01:23:39): draw card. But apart from that, look. We've always had a policy of hiring locals, supporting the local community. This year, we're hitting a million dollars in community grants cumulatively. Yeah. 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. Jason Calacanis (01:24:00): That's very thoughtful. Yeah. And and these folks are coming, say an electrician or a construction worker, they're coming having built houses or, you know, maybe building corporate offices, and now they come for a tour of duty here and the salaries go up massively, but they gotta leave their family for a three month tour or something? Speaker 3 (01:24:24): 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. So we go in, leverage that sunk CapEx, rehire, retrain local workforces, and bring a new industry to town in these data centers. Jason Calacanis (01:24:45): 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? Speaker 3 (01:24:57): A 100%. We're partnering with universities, trade colleges. Absolutely. Jason Calacanis (01:25:01): 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 50 ks a year in debt, 200 ks a year in debt. What's the starting salary for a trades person working on a data center, doing electrical or construction or HVAC? What's the ballpark range? Speaker 3 (01:25:22): 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 a good rate. Jason Calacanis (01:25:30): 150 to, like, 300 k. Yeah. Do you Am Speaker 3 (01:25:32): I in the ballpark? The lower end, directionally, you're up. Aravind Srinivas (01:25:35): Yeah. Jason Calacanis (01:25:35): 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. President Trump, Chris Wright, the administration that 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 source of solar installations. Yeah? Yes. Talk to us about energy. Speaker 3 (01:26:15): So so our our philosophy has been sustainability from day one. We have used 100% renewable energy since inception. What? 100 Wait. Jason Calacanis (01:26:23): How is that possible? Aravind Srinivas (01:26:24): It's Speaker 3 (01:26:25): We use hydro in British Columbia. We use wind and solar 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 source of low cost excess renewable energy, monetize it into this digital commodity, export it at the speed of light as Jason Calacanis (01:26:48): token Great arbitrage. And the wind is producing a lot, but it they 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 and you're coming from Australia. We're also on the West Side. People don't understand exactly how much just pure nature land there is. Yeah. Undeveloped. Speaker 3 (01:27:11): So much land, and the issue is distance. You've gotta 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? Yeah. So the whole opportunity for our industry is to go to the source of that power and monetize it. Jason Calacanis (01:27:30): 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 gonna 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 gonna have fifty days where the sun's not beating down. So how do you deal with the demand and and and softening that duck curve? Speaker 3 (01:27:55): We don't need to. 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 twenty four seven reliable power. Jason Calacanis (01:28:11): Got it. So on their side, they're figuring it out. Yeah. Something goes down, and they could fall back even though you're a 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 OpenAI project, maybe downscaling a little bit. Is OpenAI a partner as well or? Speaker 3 (01:28:39): Can't comment. Jason Calacanis (01:28:40): Can't comment. Okay. So we'll we'll read into that whatever we want. But are there pockets where people are saying, hey. Let's slow down, or is it still gangbusters? Speaker 3 (01:28:51): It's right up the end of the spectrum. It's gangbusters. Jason Calacanis (01:28:54): We Speaker 3 (01:28:54): 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. Jason Calacanis (01:29:02): Yeah. And what's your take on when software makes and this is a big discussion from Jensen himself during his two and a half hour keynote yesterday. We're sitting here Wednesday. Think he did his keynote on Tuesday. He was talking about, hey, software is gonna make it 50 times more, you know, lower the cost of tokens, 50 x. And then you have 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? Speaker 3 (01:29:38): 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. You know, I better get this prompt right. Yeah. Finally, two minutes later, comes. Now I'll give you an example. If we 10 x the amount of compute available, which is an enormous task from where we are today, and those images take five to ten seconds, are we gonna generate more or less images? Jason Calacanis (01:30:05): Oh, many more. This is Jevan's 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. Speaker 3 (01:30:20): Love the analogy. Jason Calacanis (01:30:21): Yeah. So what do you think about or or what are you seeing? I mean, we're here at NVIDIA. Obviously, they make the leading edge chips. They just walk 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? Speaker 3 (01:30:56): 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 road maps, but, absolutely, over time, we are seeing these chips emerge. Jason Calacanis (01:31:20): And in terms of desktop computing, I don't know if you saw the announcement that Dell and NVIDIA are making a really powerful desktop, 750 gigs of RAM, lot of power. You're gonna be able to run some local models, open source with OpenClaw and open source coming from Kimi 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 ten or twenty thousand dollar desktop setup and running this local. What do you think of that trend? I'm curious. Speaker 3 (01:32:00): 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 OpenCLR, the generation of demand and appetite for compute at a 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 gonna compound. Jason Calacanis (01:32:26): And what about nuclear? The Trump administration really seemed to flip the switch on a growing belief that, hey. Wait. Nuclear is pretty great. It's clean. It's the original renewable in a way, And these new modular reactors have nothing to do with Chernobyl, Fukushima, or 3 Mile Island. They're much safer. They're a completely different architecture. Have the 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? Speaker 3 (01:33:03): 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. Yeah. But now is the time to start that conversation, put in place policies, mobilize capital, and start that ball rolling. Jason Calacanis (01:33:17): Yeah. Have you do you have a data center going up near nuclear? Speaker 3 (01:33:21): No. Not at the moment. Jason Calacanis (01:33:22): Not at the moment. But you're actively tracking that activity? Because, Yeah. This seems pretty inevitable. Yeah? Speaker 3 (01:33:30): Feels like it. Jason Calacanis (01:33:31): And if that happens, what impact does it have on your industry? If if you could because, 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 think this has if you could actually have small modular reactors next to data centers? Speaker 3 (01:33:52): Well, I I think it just opens up the market and enhances The US' 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 Jevan's paradox and demand. Jason Calacanis (01:34:25): 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? Speaker 3 (01:34:39): 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 build in is essentially the old desktop PC we had under our desk at home. Yeah. 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. Jason Calacanis (01:35:07): Yeah. And where do you think or or what do you think of Elon's 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. Speaker 3 (01:35:22): 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. Yeah. I'm not gonna Jason Calacanis (01:35:41): get 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 dinner party. He might show up at dessert, but generally, 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? Speaker 3 (01:36:12): So this was one of the big myths that we had to bust when we started this business because everyone said, data centers must be located close to population centers, metropolitan areas. Latency is really important. And we say, 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. Six millisecond round trip latency. What's six milliseconds? There's a thousand seconds milliseconds in a second. Yeah. We're talking six. Yeah. It's it's adjacent. Jason Calacanis (01:36:49): Yeah. It's it's not even yeah. It's definitely not material. Listen. Continued success, and you're hiring a lot of people. Speaker 3 (01:37:00): Yep. Yep. I think we got a 129 job advertisements up at the moment. Jason Calacanis (01:37:04): Alright. So everybody go to the iRand website. And listen. Company's doing fantastic. Thanks for spending some time with us here at all in at GTC. Thanks, Jason. Appreciate it.