Speaker A
I'm here at Nvidia's annual GTC conference, and I'm going to interview four amazing AI CEOs. Stick with us.
Four AI CEOs discuss the evolution and future of AI infrastructure, compute scaling, and language models at Nvidia's GTC conference.
Ask about this video. Answers come from its transcript only — with the timestamp, so you can check them.
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Key Takeaways
- AI infrastructure requires specialized, scalable GPU clusters beyond commodity compute.
- CoreWeave evolved from crypto mining to supporting major AI research and commercial language models.
- Donating GPUs to open-source projects helped CoreWeave learn operational best practices.
- The AI compute market is shifting from research to widespread productization and inference workloads.
- Access to capital and building purpose-built AI clouds are critical for scaling AI infrastructure.
What the video covers
- The video features interviews with four AI CEOs at Nvidia's annual GTC conference discussing AI infrastructure and compute evolution.
- CoreWeave's CEO Michael Intrader shares the company's journey from crypto mining to building large-scale GPU infrastructure for AI research and commercial use.
- CoreWeave initially focused on crypto, then CGI rendering, medical research, and finally neural network training and inference workloads.
- The company learned to run large GPU clusters by donating hardware to open-source AI projects, gaining operational insights.
- Scaling compute delivery to large clients and building a cloud purpose-built for AI workloads is a core business focus.
- The transition from research to productization of AI models has driven massive inference compute demand on CoreWeave's infrastructure.
- The discussion touches on the decommoditization of compute at scale and the importance of capital access to grow AI infrastructure.
- Other CEOs discuss related topics such as AI model deployment, open-source AI, data center infrastructure, and fiber connectivity.
- The video includes insights on managing GPU supply, AI cloud service differentiation, and the future role of open-source models.
- The conversation highlights the integration of AI into core business functions and the infrastructure challenges to support it.
Chapters
- 00:00Introduction and Sponsor Message
- 05:39Scaling AI Compute Delivery
- 11:38GPU Depreciation and Technology Cycles
- 17:15Managing GPU Demand and Customer Orders
- 22:27Capitalism and Infrastructure Growth
- 33:43AI Model Deployment and Browser Tools
- 38:59Open Source Models and Accessibility
- 45:38Data Center Infrastructure and Energy Use
- 53:09Future of AI Standards and Connectivity
Full Transcript — Download SRT & Markdown
Speaker A
Our episode is sponsored by the New York Stock Exchange. Are you looking to change the world and raise capital? Do it at the NYSE. The NYSE is a modern marketplace and a massive platform built for scale and long-term impact. So if
Speaker A
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 Intrader, welcome to the
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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, but you were very early to this trend. How did you get to it so early, and how did you build out
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this, you know, first, I guess at the time, Neocloud? Yeah. So we didn't really start it as a Neocloud, and I was running an algorithmic hedge fund focused on natural gas, and when
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you build an algorithmic hedge fund, once the algorithms are built, you're really just monitoring it and testing different theses and doing all that. But there's also a lot of downtime, and we got super interested in crypto.
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And, you know, we're pretty nerdy. We kind of dig under the hood, and we started to get interested in the security layer. We looked at Bitcoin and the mining for Bitcoin, and we didn't like it. We just thought that, like,
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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
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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,
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spent the first kind of three years mining crypto, went through a couple of crypto winters, because we had come from a hedge fund where, you know, we have real chops in risk management and how we think about capital and risk
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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
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use cases that you could use this compute for because crypto was pretty volatile. Yeah. And crypto was a question mark at that time.
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Absolutely. Yeah. I mean, Bitcoin was speculative, and there were many other speculative projects. The only other people using this type of hardware were quants and medical researchers.
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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
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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
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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
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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
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bought a bunch of A100s and donated them to a group that was working on a Luther AI. They were working on an open-source project with the thought that these guys are taking the GPU compute
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because we're donating it. They can't really get pissed at us if we're not very good at it initially. And that worked out really well because they can't complain about the SLA.
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They kept telling us, like, we need more of this, you got to work on this.
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And that began to really give us an understanding of what was necessary to run scale parallelized computing. And, you know, that, we went through it. I kind of feel like buying those initial GPUs was the
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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.
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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 business. It was an
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amazing story. And so you went from crypto to these researchers into academia and deep research. What's the next card to turn over in the poker game?
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Yeah. So what became very clear to us very, very early on was that the scaling laws were going to drive—and remember, this is really back in the, you know, 2020, 2021, before the ChatGPT moment occurred—and we began to
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understand that computing decommoditizes at scale, right? Like, when 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
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question. And so we really began to think about 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: to think about it from a, okay,
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you know, 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.
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And that was really the next card: thinking about it as a business rather than as an engineering project to be able to deliver the infrastructure and the software and really everything between, you know, when
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you're thinking about what we do, we kind of live above the Nvidia GPUs but below the models. And everything in there: all the software, the integration of software and operations and observability and all the things that you need to be able to
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build a cloud that's purpose-built for this one specific use case, right? So, we don't do everything. We really focus on one use case, which allows—you want to do web servers, different, you got AWS, you know what they do, a great job. It's
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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 looking at this problem and try and come up with a solution to
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deliver compute that solves that problem. And when did the language model start dialing and calling you for, you know, capacity?
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Yeah. So our first language model was really a Luther. But our first large commercial was Inflection. And so, you know, we work with Mustafa and
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Inflection, and then we really diversified from there into the hyperscalers, into, you know, OpenAI across the model, the foundation models across, you know, and just kept scaling and scaling with
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the belief that, once again, 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
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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
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do. And you can see that every day in the amount of inference compute that is being driven through, you know, our infrastructure layer, which is just massive, which is just like one of the shows you people are consuming it,
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not just building.
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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
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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
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like to watch in terms of gauging the health. What chips are those? Um so so really uh you know we are we are the tip of the spear in bringing um the new architecture uh out of Nvidia uh
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into uh um into commercial production at scale. Yeah. And uh so when when you know we were the first ones to bring the H100s at scale, we were the first ones to bring the H200s at scale, first ones
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with the GB uh 200s, and now you've got the GB300s. And one of the things that's that's that's amazing and really fascinating for us is is you know people are using the bleeding edge GPUs to train models as the new architectures
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come out and then they take those GPUs and they move them into different experiments and then over time they move them into inference and they continue to use them in inference for a very very long time.
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What is the shelf life of a 100 right now? That's been a big debate is I think for your company for Microsoft and I guess Michael Bur you know who you must have known when you were a quant you
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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
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reality of the lifespan of these things so so my my take on the the uh uh the GPU uh depreciation bait is that it's nonsense Right. It's a debate that is being uh brought to the forefront by uh
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some traders that have a short position in the stock and they're trying to uh talk down. Look, here's what we know, right? Um when when we buy infrastructure, we're a success based company, right? We're a small company on a relative basis
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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.
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Our average contract is 5 years. So any commentary by anyone either inside or outside of the industry that this stuff becomes obsolete in 16 months or whatever nonsense they're spewing, it's it doesn't it doesn't in any way match
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up with the facts on the ground. The facts on the ground is they're buying it for 5 years. Right? If and my approach to this has always been if people are willing to pay me for it, it still has value.
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Correct. Pretty simple way of of approaching it. We use a six-year depreciation. Um, 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
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this velocity. Um, the A100s, the ampers this year, the price has appreciated through the year.
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And why is that? I I think it's because one of the things that happens is as more installed capacity becomes available, you have new companies that come into existence that have new use cases that have different size models
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that are trying to uh build new commercial ventures that maybe have been blocked out of the H100s and never had an opportunity to run on that. I mean to make a very simple example for the audience like when you trade in your
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iPhone after 3 or 4 years you're like who's going to use an iPhone 12 and it's like have you been to South America or Africa where you go to the store and you buy an iPhone 12 or you buy the Pixel 7
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and it costs $50 that's still got great life left in it. Absolutely. Yeah. you know, and so look, you know, we we find these amazing use cases, new companies that have come into existence or existing companies that have integrated new
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models into their workflow that are able to use the Ampierce and so they keep buying any GPUs that we have available.
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And once again, you know, the the concept that a GPU is no longer relevant or commercially viable after 16 more 18 months or two years.
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Yeah, that's it just it just doesn't make sense. It's obviously far. I think sometimes people get caught up in Moore's law or in just how fast our industry is growing and that there's so much at stake that big companies are demanding the most
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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.
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Yeah. Uh one of the things is is like you know the the uh the the industry has gotten so much attention for the unprecedented scale of capital that is coming to bear on this. And because of that, there tends to be a
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incredible focus on the companies that are building on these most advanced chipsets. And the truth of the matter is is you know even within those companies they have a long tale of useful life to provide inference horsepower to work
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on other experiments to do less bleeding edge activity but still needs to be done and yeah I mean rendering comes to mind as well or yeah we're making images on nano banana like there there will be a use for it. There is a moment in time
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where maybe the compute to power ratio doesn't make sense. My my expectation is is obsolescence will be defined by the moment in time where the power in the data center for me will be able to be repurposed for a higher margin
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than the existing infrastructure provides. And you know, like I said, I I fully expect this infrastructure to last in excess of 6 years, but the the the standard in the in in in the space has really been used with one exception,
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which is Amazon, which is Yeah, it's 6 years. That's that seems like the right schedule. I'm not making it up. That's what everybody's using.
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Yeah. And the energy cost is the opportunity because hey, it's just a we need that space. there's a better uh reward here and that might get resold that hardware to somebody else who wants it a hobbyist or something. It's
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available and or it could be sent someplace else where they have more capacity when they can repurpose it there. But I I I um I kind of feel like, you know, we'll we'll deal with that part of the business when
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we get there. What I know right now is it is extraordinarily profitable. It's very creative to my company to continue to keep the infrastructure that's been up and running, that's been on these long-term contracts, and as it rolls
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off, as it's been in use for 5 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. There's competition now. When you were buying
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these from Jansen back in the day, yeah, you could buy them and have them shipped, I would assume, within 30 days or less. nowadays what's the weight like even for you a loyal old customer and is there a bit of a battle is there
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politics to who gets the servers like I you see some like very big names talking about they got to get an allocation is it still a little bit crazy what's it like to be in that category having to
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buy something everybody wants look uh you know I I uh I I think of it as an affirmation of the business that we're in right like the fact that we are attracting competitors the the means that the business is healthy and there's
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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 uh either at the model level or the inference level or
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the application level or whatever you know level of the five layer cake that Jensen's you know focused on the the fact that there are more people coming into this it doesn't discourage me um as far as getting access to the GP
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CPUs, we show up like everybody else with a um you know, we'd like to buy here's a PO and we're ready to pay. Um the one what's the wait time like? And is it just really competitive or not?
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Because I talked to Jensen about 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
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it." Is it really like that? It really is. Right. like you know he he doesn't want to be in the position of playing favorites or all like that that just seems like a bad place to be with your clients
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or auctioning them off. Can you imagine? Yeah, that would that that that would be crazy.
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Yeah. I don't I'm not sure that would be good for the long-term business. No. Yeah.
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So, so our our our approach is you might get some sovereigns coming in and saying I'll pay double. They do that with Ferraris too sometimes.
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I guess these are the Ferraris of computing right? In a way they are. Yeah. Bugattis. Our our our approach is to work with clients across the entire space to find opportunities that are really interesting companies that can fit into
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our contraction contracting requirements where we're going to be able to go out and structure the debt that we require in order to go out and and uh build infrastructure at this scale. And um how does all that debt work? I that is
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something that you guys specialize in. um corporate debt uh I'm in the venture business people are like why should I be in venture when corporate debt pays so well corporate paper's so huge I'm curious how this fits in and like what
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uh interest rate people are paying on you know a billion dollars in infrastructure what do they pay on that yeah so so coreweave has really been the innovator around a lot of the financing engines that have come to bear on this
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we did the first GPU based uh loans. Um, and like I I think it's important or I'm going to try to explain this in a way people can understand. So what we do is we go out and we find a client. Let's
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use Microsoft. You brought them up before, right? And Microsoft comes to us and says, "We'd like to buy some compute for you." And we say, "Okay, great.
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We're going to sign a contract." Once I have a contract in hand, then what I do is I create something.
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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
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take my data center contract, I put it in the box. And now the box governs cash flow.
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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
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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?
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And so it is an incredibly well ststructured time-tested pressure-etested vehicle to be able to borrow money against client paper and all of the other collateral around the deal. which is why Corewave, which is a company that many people haven't ever
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heard of, was able to go out and raise $35 billion in 18 months to build infrastructure at scale. But what's important to understand is the economics in this box are such that within 2.5 years of a 5-year deal, we have paid for
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everything. The principal's been paid off. The well the principal's been paid off, the interest has been paid off. The return into the box is such that we are able to generate returns to our company at the box level which gives the most
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sophisticated lenders in the world whether it's banks or private equity funds or um you know whoever. confidence that they're going to be able to achieve the one rule of lending, which is give me my money back.
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Yes. Works better when that happens. So, they look at this box and they're like, "Wow, we're really confident we're going to get our money back." And maybe they want 10 boxes.
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That's correct. And if any one box um goes upside down, you can deal with it and it's not as acute.
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That's correct. And they don't cross-pollinate. They don't cause uh contagion across the boxes. are 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,
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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.
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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
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extremely uh militant and diligent about feeding, watering, and caring for those boxes so that we continue to have access to the capital markets in a way that allows us to build and drive our business.
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Means you has to say no. You have to say no to maybe some people who want to be in the box.
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Yeah. So, we we look at some deals and we're just like, you know, they want to buy GPUs for a year and I look at it and say I I that's not a deal that I can do because it's too short for me to am
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advertise the expenses or and so I won't do that. Right. Like once and they can go to another provider who maybe wants to take that risk on who has extra capacity.
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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 are not enough GPUs in the world to uh provide
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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
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from all different parts of the market. large language models, high-speed trading, uh, uh, search, all of these things. And they're feeding they're feeding information back into us that is letting us know what the next product we need to build is or where, you know,
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they need help uh, scaling or what type of compute they need and all of that information flow is incredibly valuable to us.
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What What 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 uh other folks Microsoft is going big and Google's going big Meta's going big
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and those people obviously have massive cash flow Apple seems to be MIA they don't seem to want to play you you you've you've uh you've named a lot of really big companies with really big balance sheets that have the capacity to
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drive a lot of demand look I I have been truly steadfast in this for years now for for for four The depth of the demand for the service we provide has been relentless and overwhelms the global capacity of the
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world to deliver enough compute to enable all of the demand for artificial intelligence to be sated and that has been we have been relentless about that.
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Sounds like Nick's tickets during the Patrick Euing era like they got up to 50,000 people on the wait list. So if magically the weight list went away, if the if the constraint went away and we just had a large amount of GPUs
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available, lot of energy available, a lot of data center available, how much capacity would just all of a sudden come out of the system.
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So so or would be deployed I should say. So remember how we build our our business through this box and it's a fiveyear box. So if we had an air pocket, if if demand were suddenly to disappear because of a technology
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breakthrough, because of a uh a war, anything, right? Like like the why from a riskmanagement perspective does not matter. You have to prepare your company for the what happens if it happens.
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Yeah. And so by entering into these long-term contracts into entering into contracts with counterparties that have large balance sheets, you are or we are protecting ourselves and our lenders.
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Yeah. So that we are confident and they are confident because you can see how confident they are by the rate that they're charging us continuing to decline that they're ultimately going to get their money back. And that is the
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one rule of lending. And so um you know I if but just in terms of the capacity if you were unconstrained and Nvidia Jensen says hey order as many as you want what would happen so um the the it's also important to
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understand the constraints aren't just GPUs right electricity it's it's power shells it's memory it's storage it's it's networking it's optics all of the things and there's there's various throttles that will limit the memory is a throttle right now right
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oh yeah it Oh yeah, it is. Why? How did memory become the throttle? If um memory and uh 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
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and then it drives it up. It's a very boom and bust business. is cyclical in its nature because the fabs are so capital intensive that people invest in the fabs, build a ton of capacity and then overbuild if there's any type of
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turndown. 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
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services around the GPU, the demand is through the roof. That's number one. Number two is is that there was probably an investment cycle that needed to happen back in 2023 that would have brought on the necessary fab capacity to be able to serve.
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Impossible to predict what should happen 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.
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It's not based on real estate. It's based on it's and where's there's some wind.
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And anytime you you have a uh very cap not every any time, but many times when you have a uh a capital inensive business like you know building fabs, you will get this boom and bust cycle just like in energy they overbuild.
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Yeah. And you know fiber. Yeah. I mean there's there's there's a lot of examples of that our approach in some ways when you look at that it's a beautiful aspect of capitalism that we're able to have a boom bus cycle that
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we're able to weather it right if you think just that 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
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listen the the the um um you know it it does it does a lot of things having a boom cycle it clears out the underbrush.
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will be able to survive and take advantage of that and it sews the seeds of future business.
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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 uh communicate and how we hop on a zoom and you know co and all of these
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things were based on that infrastructure that was available to be consumed. Yeah, people don't recognize this fact if you the the premise of YouTube from the founders who I knew, Chad Hurley and his other partner. They basically had the
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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
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viral, people are going to have their minds blown. But your server would turn off and it would say this person, you know, needs to pay their bill. Yes.
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Because they were getting charged for carriage by the megabit going out. Yes. I mean it look and and you know these these the business models change and evolve and you know like you said Moore's law and and and certainly Jensen
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will talk about the fact that like what what is going on within the the the accelerated comput 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 you two
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did which has really changed the world. Yeah. I mean the the concept that I I don't know if it was like a million hours being uploaded every hour or minute but at some point Susan what Jackie rest in peace said told me just
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like how much was being uploaded every minute and it made no logical sense and she realized well there's three billion people two or three billion people in the service and 1% upload or 0.1 10 bit bips upload and
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it's like okay one in a thousand people upload it's a big it's a big denom denominator like I I was uh sitting on a a panel uh with Sarah Frier, CFO of uh Open AAI and u um she every once in a while uh um she she
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really puts out like interesting uh information and so she was talking about the cost of a million tokens when ChatG3 came out and it was $32 and change and now a million tokens cost nine cents.
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Yeah. Right. And so you you just see like like the incredible power of how the capital markets, how capitalism is uh uh fueling engineering and fueling uh uh competition.
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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.
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It'll be like okay captain. Yeah. I don't know if you saw Cararpathy's recursive thing last weekend but it's like now civilians who've never worked in a language model or done computer science are like, I'm going to try to do
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something recursive this weekend. You know, it's one of the things that I that uh uh I talked to, you know, the other founders about, you know, and it's like when you think about some of the things that AI does, right, it's lowering the
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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
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things that never existed before. That's amazing, right? like that's bringing down this incredible barrier that kept human creativity contained and now all of a sudden this whole new vector of uh uh you know medical research or different approaches to you know
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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
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incredibly exciting like you're bringing the minds of 8 billion in people a tool that allows them to overcome what was insurmountable for forever for humanity.
Speaker A
Yeah, it's a bright new future. Michael, appreciate you sharing the uh uh information with us and the vision. I am really delighted to have Arvin Shri Nas on the program.
Speaker A
Thank you for having me here Jason. It's so great. I want to go through three stages in which I fell in love with your product. The first phase was I could go in pick my language model if I
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wanted to choose open AI, if I wanted to use 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
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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.
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time and I was like, "Wow, this execution's great." And I I kind of made you my front door, two different models, and it made it easier for me to check it. Then you came out with the Comet browser and I was like, "Holy cow, I can
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give this a series of instructions. Go to my LinkedIn, find everybody from this company, put them into a Google sheet and boom, you were the first out of the gate with that." And then just the last couple of weeks I had been claw pilled
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in using openclaw but you came out with computer and I started using computer and boy it's good uh it's a really strong start uh allowing me to do repetitive tasks very similar in some ways to co-work from claude uh or
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basically an engineer or developer using it. So are are these the evolution of the company and I should think about it that way. But how do you look at perplexity now? You have a very loyal fan base.
Speaker A
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. Grock
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doing very well. Gemini coming on strong. There's like six or seven of you and uh you just happen to be one of my top twos right now.
Speaker A
Thank you. So tell me first of all, first of all, thank you. Thank you so much. Perplexity has always been built for people who are always looking for the extra edge, the curious people. So it's very natural that you are uh one of
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our power users. Uh one common theme for us uh for the last three and a half years is accuracy. Plexity wants to be the company that's building the most accurate AI. So when you want to give somebody answers, accuracy is very
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essential for building trust because only then the user is going to ask the next set of questions. It turns out it was a great idea to give AI access to the internet to be accurate. So that's the perplexity ask product. It turns out
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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. Aentic browsing comet. Now the last phase is it turns out it's a
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great idea for AI to 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
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each individual AI model has be it GPT or cloud or Gemini or anything else an orchestra of all those capabilities that what that's what perplexity computer is and all these sub agents that are running inside computer are the
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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 is image generation, video generation, audio, but
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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 itself is the computer. Now, still lives inside of a browser. Have you considered giving it desktop root
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access? That feels like the next place this is going, but that comes with a lot of security issues, a lot of trust issues. As you mentioned, trust is paramount. getting the right answer is what builds it, but also not getting
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hacked and not having it delete your files. So, how do you think about root access to my Windows machine? Obviously, iOS, they won't let you, but with an Android phone, it would let you.
Speaker A
Yes. So, do you have that in the works? Yes. So, we announced something called personal computer. Perplexity personal computer that's essentially going to take all the trust and reliability and the server side execution of perplexity computer but synchronize it with your
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local computer so that you can use it from your phone and we're going to do this with the Mac Mini where you synchronize your computer with the Mac Mini so that becomes your local server all the agent orchestration that has to
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do with your local private data will run on that local orchestration loop that runtime with the Mac Mini. Not on your servers, not on anthropics.
Speaker A
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.
Speaker A
Yeah. And if it needs to run on the server side hardware, if you don't want very complicated, longunning stask to be running on your local hardware. Yeah.
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You can delegate it to run on your server side computer, which is again only accessible to you and you alone. So that way we're going to bring this perfect hybrid of trustworthy uh hybrid between local and server side
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and you and you'll make it easy to do. It just be abstracted. You install one executable, boom, it's done.
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It's it's like open claw for dummies. Nobody needs to learn how to use it.
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Nobody needs to manage API keys. Nobody needs to manage separate billing across like 100 different services. Figure out what you can give access to and not access to. We take care of that.
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So it's a Steve Jobs way of doing it. you know, end to end integration and and how do you think about local models? I have started running Kimmy 2.5 on a Mac Studio.
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It's not as good as Claude or Gemini or Grock, but you can probably do about 80% there for free.
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Yeah. Essentially. Yeah. Uh and so that's quite compelling considering some of my other bills, Claude and and stuff were getting expensive.
Speaker A
So, do you have one of those? You started testing on your local Mac Studio. I assume you have a Mac Studio and you're doing this yourself. Yeah.
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Or now, I don't know if you saw uh Dell and Nvidia announced a giant workstation. Um is it 3,800?
Speaker A
Something like that. Something like that with 750 gigs of RAM. So, what do you think about the desktop going back to workstation/server?
Speaker A
Yeah. Status. I think it's very promising. Um my my prediction is it'll initially start off as a sub agent. So whatever you need to go uh like your tax returns, your personal photos, your emails, your your calendar, all that stuff, those local
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apps, your personal notes, very personal notes. You can make sure that the models that access those tokens will be running on your local hardware if you want to, if you're that privacy conscious.
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uh 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
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Google Workspace connector, and that could run on the server side because anyway, the data is on the servers. It's not even lying on your device.
Speaker A
So, that sort of hybrid orchestration is where we're headed to. I don't think it's a dichotomy between fully local versus fully server. Uh it's all about choice. And anyway, when you're on your phone, uh you want to you don't care
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actually which server that workloads running from because it's not going to be able to run on your phone anyway. The chips need to exist on a Mac Studio or a Mac Mini and or on the server or this new Dell that's coming out. And
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I I really think the idea of spending $10,000 on a powerful desktop will appeal to people if it lowers their $500 a month claude bill. This is an incredible savings. Plus, you get the benefit of privacy and not educating the
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language models on your personal data. Yes. And it's going to be it's going to be like you're buying a refrigerator, your your your internet modem. Like the cost for these will eventually go down.
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Yeah. But it's not going to feel like you're wasting your money. Uh every every home has a lot of other sensors.
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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.
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So that's the dream that everybody has and all that orchestration loop can run on your local hardware, no problem. And I'm curious what you think of the operating system. What's eventually going to be the operating system of this
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workstation? AI is the operating system. Like earlier in the traditional operating system, you execute programmatically.
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Now you start with objectives, not specific instructions. Right? You come up with a highlevel objective.
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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.
Speaker A
And charted for the max 7. Yeah. And and charted over time you can so that's the objective but individually it's running a file system a code sandbox access to the internet. It's having like its own HTML tools and like
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so I think that's basically where you know models systems and files and connectors are all coming together. You would think of that as an OS except you're operating at an abstraction about that where you're thinking in terms of objectives.
Speaker A
Yeah. And does it need to eventually become its own operating system in your mind?
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It could be like people could think about like yeah I have a my perplexity computer running all the time whether it essentially it runs on Linux machines right now. Every server side computer is a Linux machine. Yeah.
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So, I think Mark Anderson tweeted this right after our release that turns out Linux computers was the right idea.
Speaker A
Desktop desktop Linux computers are finally going to work. Yeah. I mean, they're stable. They're customizable and you're not at the mercy of Apple's desire to contain the experience or Microsoft surface area as for hackers.
Speaker A
Exactly. You build something rock solid and it does feel like Linux might actually become the correct the eventual winner. It may not need to have a front end.
Speaker A
That's the thing. You could you could access the Linux machine on your phone. You could be running iOS or Android. It doesn't matter.
Speaker A
The actual valuable runtime is running on Linux on the server. You've done great as a consumer company.
Speaker A
Lot of love there. Now I'm starting to see uh corporations with computer starting engaging it. 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
Speaker A
to do the back office automation at our venture firm only using Perplexity." And they were like, "Perplexity computer." And they were like, "Oh, okay. Um, it doesn't talk well in Slack. It doesn't have an agent in Slack." I was like, "It
Speaker A
will. I'm going to see AR and I'll talk to him about that." So, we need a really strong Slack connector.
Speaker A
It's already out. It is. Okay, great. computer exists as a Slackbot right now. Okay, that you can add to your Slack workspace on enterprise plan and our entire company works like that.
Speaker A
People are talking more to computer on Slack to other than to other people. In our first volley, we were sending reports in, but it wasn't interactive.
Speaker A
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?
Speaker A
Several tens of millions. So tens of millions of people that's very much similar to the trajectory of the Google and Yahoo consumer business. Now you've got corporate. How are you doing on the corporate side? Thousands of companies.
Speaker A
The fastest growing business for us. Ah it's growing faster than the consumer in revenue and things like computer unlock entirely new possibilities. For example, we've saved more than $und00 million for our uh enterprise max customers who are on the highest tier of enterprise.
Speaker A
Explain what that is. What does it cost? 200 a month per person. So there are two tiers. One is the enterprise pro which is $40 a month and there's the enterprise max which is $400 a month. And that that and and and and
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on a computer after you run out of your credits you would pay for the tokens.
Speaker A
You pay for the usage. Are you making money on the $400 a month, $5,000 a year one or at this point in time are people going so crazy?
Speaker A
Our uh one thing that Perplexity has is every revenue we make, unlike certain other rapper companies, every revenue Perplexity makes has positive gross margins.
Speaker A
Got it. Because uh we're not just selling tokens, right? Most of our revenue is recurring because people are paying a subscription fee and because we route through multiple different models, we're very efficient in terms of how we spend on the tokens.
Speaker A
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.
Speaker A
Yeah. As a result of that, we have positive gross margins on all the revenue. Every single penny we make, we make profits on that. But the overall the company is still yet to be profitable, but we're working towards that.
Speaker A
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?
Speaker A
About 400. Yeah. You you've got a very coveted team. You obviously understand consumer. You obviously understand business. It's a product driven organization. Reports are you declined, but the world's getting hyper competitive here. How do you keep up as
Speaker A
a 400 person organization when you got Sam Alman over here raising a hundred billion dollars, you know, and then you have Elon putting data centers in space and merging with SpaceX and Twitter. You have Google with unlimited resources.
Speaker A
Amazon getting in the game and obviously Gemini uh very strong product and Google really good at consumer. I think we'd all agree Facebook and Meta haven't figured it out yet except maybe for serving us better ads, but they they
Speaker A
haven't figured out the consumer case yet, but they'll copy it. They always do. How do you look at the playing field?
Speaker A
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.
Speaker A
So, how do you think about it? Long-term and independent company. Do you think you'll need to join forces at some point?
Speaker A
Well, and why didn't you take the deal? This deals were incredible that you got offered.
Speaker A
So, one advantage we have that all these companies you mentioned don't have is the multimodel orchestration. We're like Switzerland. We don't have to have one horse in the race. If GPT wins, Gemini wins, Claude wins, Llama wins, it
Speaker A
doesn't matter to us. Uh or even open source models can win, no problem. And you have them on the service. You have DeepSeek and Kimmy.
Speaker A
We have Kimmy, we have Neotron, and we have uh a lot of usage of Quen, Alibaba Quen.
Speaker A
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
Speaker A
mentioned can do that right nor would they nor would they it makes no sense for them. It would be an admission that all the data centers and capex they've built out mean still couldn't produce them the best model. And uh Daario uh CEO of
Speaker A
Anthropic said recently in an interview uh that models are specializing. Towards the beginning of last year people thought models are going to commoditize but towards the end of last year people models started specializing. Even within coding u cloud code and codeex have very
Speaker A
different capabilities. Our iOS engineers love using codeex. Our backend engineers love using cloud code.
Speaker A
Yeah. So even within a specialization like coding, models have their own unique specialtities 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
Speaker A
horse in the race can win by providing a very unique value and service to the customer that each of these amazing names that you mentioned cannot. And so you're buying tokens wholesale from them and then you'll charge customers to do
Speaker A
it or do you think it's all we're going to take care of all that orchestration?
Speaker A
Yeah. So you don't have to manage tokens across different models cuz I authenticate I a couple of my different accounts my pro accounts into perplexity. But does it I I I don't have enough knowledge to know if you're
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abstracting that and people can just search across them and it's part of their perplexity subscription. No, we're not bundling subscriptions from into other AIS.
Speaker A
We just ping the models directly. Got it. Uh what you get in us is the perplexity or orchestration.
Speaker A
Got it. The harness, right? So the when when when when models are kind of specializing the there's a bigger value in the one who knows how to build a great harness, right?
Speaker A
That can take the best in each model. Does it auto route today or do you still have the drop down somebody's got to pick it? It definitely auto routes the best model for each prompt, but we also give users the flexibility
Speaker A
to pick whatever model they want. What do you think of I've seen a bunch of startups hack this together, but doing the same query across multiple We built a thing called model council.
Speaker A
Model council. Yeah. Yeah. So that's one of the one of the modes and perplexity where I saw Jensen say in one of the interviews that he he puts the same prompt in five different AIs and sees what each of them says.
Speaker A
Yes. Like everybody does that. Yeah. But then you still have to apply your biological computers about your trust or your five different doctors.
Speaker A
Five different doctors trying to figure it out. Exactly. So it's dumb. So the model council is a feature we built where it will not just give you the answers of each model, but it will tell you exactly where they agree, where
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they disagree, and where the nuances are. And that's in the interface. Model council, I didn't know it was there.
Speaker A
It's there. I mean, you you released product at a pretty great cadence, huh? How where did you learn that and what's your philosophy of shipping product?
Speaker A
Our philosophy is like speed is our mode. Like you know again one of the things that big companies cannot do is move at the speed we do serve customers at the speed and qual it's it's very hard to maintain quality speed and trust
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at the same time. Yeah. Like Apple takes a long time to ship anything because they're very worried about people not trusting them.
Speaker A
Yeah. Uh 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.
Speaker A
Yeah. They get abandoned. Exactly. So, Perplexity has those advantages for being very small. And towards the end of last year, we found that like AI coding tools have made it much faster for us to ship things which is honestly one of the reasons why
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we built computer because now even non-engineers are shipping code here by just pinging a slack bot and asking it to fix bugs.
Speaker A
So, this the the iteration has just been like exponential. The the moment I had where I became clawilled was when I was working with it and I was like, "Hey, I want to build my network. I know these
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20 people in Japan. I had dinner with them during my recent trip. I want to know who they know. So, check out LinkedIn and other things and who they're associated with and make me like a mind map of it. And then the next trip
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I want to meet with the next circle of, you know, those connections." So, I started asking like, "Okay, I got the results." I was like, "Great." Um, and they said, "Where do you want me to put them?" And uh, I was like, "Well,
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where can you put them?" And it said, "Well, I can put it in a Google sheet. I can put it in notion table. I can put it here. I can give you a PDF. I can give you a CSV file. Or I could write you a
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CRM." And I was like, "Yeah, sure. Make me a CRM system." And it may a CRM system.
Speaker A
And I think that becomes, and I think maybe one out of a thousand people working with AI have had that experience. Maybe it's one in 10,000.
Speaker A
Where your agent says, I'll make you bespoke software. Yeah. Have you had that yet? And and do you see that as a part of computer that when a person needs a spreadsheet, you don't launch Excel or Google Sheets, you just
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pop up a spreadsheet? Yeah. Well, we have a board meeting tomorrow. Okay, I'll come.
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And and so I'll pitch it to the board. Sure. Uh our computer computer made the memo.
Speaker A
Oh, wow. Yeah. And um we had a partner meeting to pitch a partnership idea and uh earlier we would have a design team do the whole deck.
Speaker A
Yeah. Computer just oneshotted it. Uh I had a press briefing with a bunch of journalists. My comm's person would Sorry about that. Brutal.
Speaker A
And then my comms person would usually u give me a memo what to say.
Speaker A
Computer one-shoted him. So it's crazy. And it's the context is so good because the memor is getting better. Yeah. Yeah.
Speaker A
So it's like I know that journalist from the last time. I know the board meeting. I have all the previous decks.
Speaker A
When did that happen? I think it it happened with Opus 45 Opus 45. That was a 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
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happen inside a sandbox, a console, a terminal with access to tools where tools are just command line tools.
Speaker A
Yeah, they don't even need to have graphical user interface. So when you did that and when you organize around files and sub aents and skills and CLIs, the model started be becoming very good at handling the context. So the context
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window no longer became a problem. It just put whatever necessary into the context whenever it wanted to and dumped dumped them away when it wanted to.
Speaker A
Yeah. And that made it like suddenly so good at doing very long orchestration tasks.
Speaker A
Yeah. It's it's pretty crazy. I have every episode of this week in startups, all the transcripts and then all of all in that was one of the tasks I did by the way I can send it to you. I asked it I
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want you to download every all-in podcast. Yeah. U since the beginning and I want you to take a mention of all the public companies they mentioned during the episode.
Speaker A
Yes. I want you to have a histogram of the counts and I also want you to chart it across time and then I want you to analyze the impact on the stock price and the sentiment of what we said.
Speaker A
Exactly. And it did like it clearly said, "Are we moving stocks around Google's stock going up?" Yes.
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Prior to that, you guys were talking a lot about Google. Yes. And it clearly And I said I made a bet publicly on the thing. I said, "I am buying a bunch of Google because I believe even though
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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.
Speaker A
I was like, "That's that company. They need to release more." Yeah. And uh I told Sergey, I was like, like give us the good stuff. started giving us the good stuff.
Speaker A
It literally gives you the timestamps of every single and then I can go click on it and actually hear exactly that moment.
Speaker A
Yeah. Sweet. Yeah. So that's when that's when I was like damn like this I would have had somebody do this as a weekl long project.
Speaker A
It would have been 10 hours a week of researcher. I I'm experiencing the same thing when I do research notes. I've created my own uh like mega prompt.
Speaker A
Yeah. and it will go and like tell me where you worked before and who's in your circle, who your competitors are, who your friends are, blah blah blah, and then go find I try to find old podcast is one of my secrets. If you're
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an interviewer watching, I try to find what was the person talking about 5 years ago, 10 years ago, and then over 10 years ago. And I've gone into interviews now with Michael Dell and talked about things he was talking about
Speaker A
in the '9s. Yeah. And it finds me some ancient stuff. Like you would pay a research or a producer, you know, $70,000 a year, $80,000 a year to do this and they would have done a third of the job in 10 times longer.
Speaker A
It's really gotten weird just in the last 6 months. What do you think the next 6 months looks like?
Speaker A
I think the the dream that what we are going to try to do is help businesses run as autonomously as possible. You know, everybody talks about this AI is going to create this one person $1 billion company. Some people say it's
Speaker A
already happened because people pay researchers like 1 billion, but it's not truly moving the GDP by 1 billion. It's not truly creating new value. So the best way to do that is to actually help a small business people who would
Speaker A
otherwise drive Ubers for like yes extra passive income to like buy like a Mac mini set up perplexity personal computer and run their business on that or like run it on the server it doesn't matter uh and actually make real money.
Speaker A
Yeah. Hundreds of thousands or even millions a year and uh grow it. Have computer go and run your ad campaigns on Instagram or Google. I mean integrate with SEM and SEO tools, find new users and uh integrate with Stripe,
Speaker A
charge them, ship new features, have your own like intercom integration for customer support and like have this all working well. You can be sipping wine in Napa. That's the dream that you know it feels awesome to say. Everybody thinks
Speaker A
AI is already there. It's not there yet. Someone has to do that hard work.
Speaker A
Yeah, that's what we want to do. Yeah, it it's a great vision because when I watched startups 20 years ago, there were so many check boxes they had to do. I have to find an office space. I got to put up a bunch of servers. I I
Speaker A
got to hire hire an HR firm. I I got to hire a PR person. All this stuff. And now I talk to young founders. They got a three-person team. They've come out of A16Z, my program, Launch Accelerator, whatever it is, Y Combinator. And I'm
Speaker A
like, "Okay, you raised a half million, you raised a million. Who are you hiring?" And they're like, "Um, I don't know if we need to hire anybody." I'm like, "If you could hire somebody, would you hire?" They're like, "Well, I do my
Speaker A
own HR. I have this partner." And they're I'm like, "How are you doing hiring anyway?" And they're like, "Well, I put out an ad and then uh it sorts and ranks the candidates and then it emails the top 10, asks them a bunch of
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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. And like a a tool like computer is going to make that even faster.
Speaker A
Much work to do. Uh lot of connectors, a lot of specific workflows. People don't want to like learn how to write like, you know, essay long prompts. You know, it needs to be so quick and fast and autonomous. You just set it up and done.
Speaker A
And you have an idea, you can turn it into a business and start making money.
Speaker A
Yeah. It's it's an incredible future. Uh and it feels like it's right here. Do you how do you think about job displacement? is you're actually making the tool that enables people to be a solo entrepreneur and get to a
Speaker 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.
Speaker A
Do you I'm wondering if like me, you have moments where you're like, "Oh my god, this is really terrifying." Yeah.
Speaker A
A lot of people are going to lose their jobs really fast. Yeah. And then, oh my god, you can learn any skill you want and all the things that were hard are now easy.
Speaker A
Yeah. I I go back and forth. I'm 70 80% super positive about this, but I do worry about like 20% of the time I'm a little worried. Yeah. Where do you sit?
Speaker A
I mean, America has always been about like entrepreneur entrepreneurship, right? Like we we've been about like trying to build new things, discover new things, go explore.
Speaker A
Uh I think this whole like Henry Ford came and built factories and brought in jobs and things like that and like put people into a box. But u I think the reality is people most people don't enjoy their jobs. They're doing it for
Speaker A
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 year or multiple years and lets you
Speaker A
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
Speaker A
glorious future is what we should look forward to. I I I think you're exactly right. If there will be some displacement, but then there's also going to be so many opportunities open up and it requires the individual to not be passive.
Speaker A
Exactly. They have to be rugged individualists. They have to be resilient. Yeah. And they have to be resourceful. And I think once you start playing with these tools, that's what happens.
Speaker A
Exactly. you you all of a sudden feel like it brings out the best in you if you truly are in a good space.
Speaker A
Yeah. Yeah. I today uh Comet for iOS is out. Yeah. 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 sent You don't
Speaker A
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.
Speaker A
Now it's free, I think, for all users. Highly recommend it. Highly recommend getting a pro account. It's only 20 bucks a month to get into perplexity, which is a joke. So, you can get on board for nothing, less than a dollar a
Speaker A
day. But what does iOS allow me to do? And and how does it connect to computer?
Speaker A
Because that's another thing I'm having. Yeah. Cloud code. Uh computer, there's not a good enough integration with this mobile device yet.
Speaker A
Yeah. So, computer is already on the perplexity app. So, you can just toggle the computer and start using it.
Speaker A
uh comet's uniqueness and perplexity for the company uh and and and the strategy is the fact that you can control the browser. So the browser also becomes a tool for computer just like your Google workspace and all these other things. uh until the whole
Speaker A
world is organized around CLI and tools. Yeah, there's still a lot of tasks we have to do manually on the web on the browser.
Speaker A
Open tabs, fill up forms, click on things, upload stuff, all that stuff. If you want to automate, you need a browser. You need an AI that can natively control the browser. So that is comet. And that's why no matter how many
Speaker A
other tools in the market exist like open claw or like claw co-work executing tasks on a browser on the server side along with all the other things is something uniquely perplexity can do.
Speaker A
Yeah. My dream is that you'll create an Android app that roots my Android phone.
Speaker A
Yeah. 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 pnicity.
Speaker A
Yeah. I don't want to mention too many, but Reddit, LinkedIn. Yeah. And like they're just I I am a great Reddit user. I'm a great LinkedIn supporter, but sometimes like I need to get my inmail.
Speaker A
Yeah. From my LinkedIn and I just need to, you know, find seven people at company. I is there going to be a solution between the LinkedIn and Reddits of the world and the claws and perplexities? Is how is that
Speaker A
I mean negotiation going? You don't have to speak about any specific ones unless you want to, but it feels like there's got to be a solution and I'm willing to pay for it as a user.
Speaker A
I'm willing to play Reddit to allow my bot to show up and behave properly.
Speaker A
Well, I I I cannot speak about any particular company, but we are happy to work with anyone, right? So, um I think with with Comet, our idea is to give people the flexibility to set things up on their own.
Speaker A
Yeah. and uh any um official APIs that anyone's willing to offer, we're always happy to put that as part of computer.
Speaker A
Here's what I think should happen. Let me see if you agree. Um and this is for Steve Huffman at Reddit.
Speaker A
I go on Reddit. I do a pro account for 20 bucks a month. And when I do that, I can authenticate whatever tool I want um to do a series of well- behaved things a certain number of times a day.
Speaker A
Yeah. So, it's not unlimited. I'm not going to scrape the whole site, but I would like it to just let Perplexi or computer go and just tell me, hey, what are people saying on the this weekend startups and all-in subreddits?
Speaker A
Summarize it for me so I get the customer feedback. And I would literally name my uh agent and I would say I it won't post on my behalf. It won't vote on my behalf. Just needed to do a couple of
Speaker A
little readonly things. This would be an easy solution. Or LinkedIn I would like if you I have I already pay LinkedIn like 50 bucks a month. Like they should just let the $50 a month one work with computer.
Speaker A
Yeah, absolutely. I mean, okay, this is for Satia Nadella. Let LinkedIn work with Perplexity and the other players and we'll pay you extra.
Speaker A
Perfect. It's a revenue stream. Don't you think API access for our customers is a revenue stream?
Speaker A
I think so. I think so. I think I think fundamentally giving users a choice and setting it up as a win-win for both the business and the user Yeah.
Speaker A
is where the world should head to. And and I I would say the same thing applies to any any website in the world.
Speaker A
Like if if you want an AI to use it on your behalf, it should be okay for cuz that's what the user wants.
Speaker A
I mean, I have a paid New York Times subscription. like let me go in there and do you know whatever 100 searches a day, a week, a month, whatever they choose, but that would make the subscription that much more sticky.
Speaker A
Exactly. Uh all right, Arvin, 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. Love the product and we'll have you back soon when you launch your
Speaker A
operating system and come up with your own server and desktop server but business is the focus. Yeah.
Speaker A
Yes. All right. Great seeing you. We have an amazing guest Arthur Manchester here the CEO of Mistral AI.
Speaker A
How are you doing sir? Great. Thank you for having me. And so you're here at Nvidia's big conference, big announcement. You're going to be working with Nvidia to build models.
Speaker A
uh to open source them. What is the uh big announcement here? Well, we're announcing that we are going to be training the next generation of frontier models with uh with Nvidia. Um it's something that we've been doing before with Nvidia with MLMO, something
Speaker A
we did like 18 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
Speaker A
do for our customers like Forge that helps us customize the models for the enterprise we work with in engineering in physics in science uh in making them better at certain languages when we work with governments etc.
Speaker A
And and Michel obviously based in uh France you're the leading AI company there. What's it like running the company and building a large language model in Europe? Obviously, there's regulations and all kinds of considerations. Privacy, the French are
Speaker A
known for protecting privacy. In the United States, we're known for taking it away. How is the landscape there and what do you have to deal with there that maybe you wouldn't have to deal with in America? And what's the pros and the
Speaker A
cons? I'd say first, we have 25% of our business in the US. Uh, 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
Speaker A
in Singapore where we are. So of course it's it's different markets. Uh it's markets where you have language which is a topic uh where there's much more manufact manufacturing is a bigger piece of the cake than it is here. uh and I'd
Speaker A
say the our strength has been to also work with European companies that are a bit lagging behind uh and that wants to adopt the technology to to leap forward and we've been able to do that through a forward deployment engineering
Speaker A
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 uh
Speaker A
with customers in the US because they come to us with uh needs for post training for making mod specifically good at financial services and what's happening is that we have this product and we can bring the models to
Speaker A
specialize them as well. And so your belief is specialized verticalized models healthcare finance engineering different verticals will win the day or a a global model will win the day that does everything.
Speaker A
Well you need general purpose models to do the orchestration parts etc. But at some point you enterprises sits on a lot of intellectual property on a lot of signals coming from physical systems from factories from tools and the it's
Speaker A
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 uh new parameters you can make a lot of deeper things that you cannot do with closed
Speaker A
models. You can also and that's what something that we do. We don't we not only do we work at the model side but also at the orchestration side. We see it with subject matter experts to understand their needs and we build
Speaker A
business applications that are fully bespoke to their needs by modifying the models but also modifying the harness on top etc. So we believe that eventually building on open source technology is a way to save cost is a way to have better
Speaker A
control because you can sit 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 uh from a from a customization perspective and from leveraging your
Speaker A
decades of IP that you've been acrewing in financial services in heavy manufacturing like companies like SML for instance they do benefit from working with us because we take their data and we build models that are specifically good for their
Speaker A
um just 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
Speaker A
the customer base who didn't want to uh send their data, I guess, over to Meta.
Speaker A
Uh 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
Speaker A
because if you're working with one customer in aerospace or fintech they might have a need set but they may not want that training to go to a competitor. I can use a few examples. I think overall the data segregation is
Speaker A
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
Speaker A
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.
Speaker A
Now uh the way we we then use that technology that has been deployed is that we're going to be working with uh the team that is doing uh image scanning and default detection with ISML for instance and we're going to be sending
Speaker A
forward deployment engineers scientists they're all PhDs they know how to train models and they spend some time with the subject matter experts that can explain how an image is being detected what how do you def detect defaults etc and based
Speaker A
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 the we we send the technology typically we send a
Speaker A
little bit of scientists because uh you do need that expertise transfer and that knowledge transfer in between our teams and the vertical experts and then we make sure that eventually our team no longer needs to be there to retrain the
Speaker A
models to get more data access etc. So that combination of data segregation, expertise transfer, knowledge transfer is the one thing that makes us quite unique and allows us to serve the most critical use cases, the most critical processes in industries that actually
Speaker A
need to take their data and put it into models for it to work. Yeah, this seems to be once the entire open web, what was available legally, gray market, etc. I wouldn't have you comment on that controversy. Uh but we we kind of
Speaker A
exhausted what's in the open crawl. Yeah, we have. And and it's time to actually either make synthetic data or actually use experts. Do you believe in synthetic data and where does that work and where does it fail? We use synthetic data as a
Speaker A
way to warm up the models. It's a way to actually be quite efficient at the beginning. If you have a large model and you want to train a small model, you would you will use your large model to
Speaker A
pro to process and to produce a lot of synthetic data at the beginning. uh and then but eventually you do need to have human signal. Uh so the human signal is something that is always a bit costly to
Speaker A
acquire because you need to talk to the experts they need to give feedback to the machines and so at the beginning synthetic data allows you to do the compression to to further compress the models. At the end you do need to go and
Speaker A
get data that is uh produced by humans. So yeah, it's a it's a way to have uh it's it's mostly an efficient way of training models to have big bigger models that are used as as teachers for smaller models, but it's not enough. And
Speaker A
so you also need human signal. Arthur, we've seen um an incredible explosion. We're sitting here on AO52 after OpenClaw, the year of our Lord, 52 days.
Speaker A
when you first saw Open Claw 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
Speaker A
contributors. What did that say to you as an executive in the space who's been grinding on this for many years? What what does that openclaw moment mean?
Speaker A
Well, it resonated a lot with what we were doing with our customers uh because pretty quickly uh enterprises realized that if they wanted to make some gains with artificial intelligence geni, they would need to automate full processes.
Speaker A
And to automate a full process as an enterprise, well, you can use open cloud, but it's going to be uh it's actually not really enough because you you have data problems, you have governance problems, you can't observe uh the process that is running and you
Speaker A
can't can't control it in um in many cases when you run a KYC process. So if you're HSBC for instance, one of our customers, uh you will want to have deterministic gates that are going to always do the same thing in a way that
Speaker A
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 this the kind of primitives that you need to work on
Speaker A
collective productivity, observable productivity and to work on mission critical systems. On the other hand, uh the autonomy it gives and the autonomy it brings to to people that are just individuals that are hacking together things is a way to also show to
Speaker A
enterprises that if you set up the right control plane, if you set up the right sandboxes, if you connect to the right data sources, if you make sure that you your access controls are well respected, then you can actually unleash the power
Speaker A
of agents doing things for your employees and that's going to work. Work on the platform cuz otherwise you will not be at ease when you're sleeping. It is um definitely something you have to be thoughtful about. When I installed
Speaker A
it, I gave it just for my agent root access to my Google Docs and my G Suite, my notion, my Zoom and uh my notion and uh GCAL, everything. And then I realized, wow, I can with my enterprise
Speaker A
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.
Speaker A
There's a person on a PIP who we put them on a perform performance improvement plan perhaps or something like that. Oh, I have to make sure nobody else can access this because the power comes from giving it access to
Speaker A
data. But with great power comes great responsibility and I think people are learning that in real time. Yeah, it's a big problem because the enterprise data is not a single thing that you want to put into a single system that is going
Speaker A
to be accessible by by everyone and so you need to have this layer that actually understands what is the is what is in the data. you need to have a semantic of what can actually be proposed to uh HR or what can be
Speaker A
proposed to uh engineering and typically compensation is one of these things you want to make sure that the compensation data does not flow back to all of the all of the enterprise because you're going to have a lot of problems uh if if
Speaker A
that's the case and so what you actually need and 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
Speaker A
not accessible to 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 so that's uh
Speaker A
that's hard it's actually hard you need to rethink entirely the way your IT systems are being connected and uh at some point you also need to think about your management because your influ your information flow is completely different
Speaker A
today uh if you're connecting agents together with your data sources than it used to be and suddenly maybe you don't need that manager whose only purpose was to take information from the bottom and put the information on top etc. So
Speaker A
there's some IT problems to solve and you need the right primitives, you need sandboxes, you need airback based access control and these kind of things and uh you have change to do. You you need to rethink your entire customer service uh
Speaker A
department cuz suddenly you actually don't need that much transfer of information operated by humans.
Speaker A
All right. Uh you have to go. You got a flight to catch. It is so great to see you Arthur. Continued success with Mishril.
Speaker A
Thank you very much. Cheers. I'm really lucky to have Daniel Roberts here. He's the co-CEO and co-founder along with his brother of Iron. They are a publicly traded company. They started in BTC.
Speaker A
Welcome to the All-In Interview program. Thanks, Jason. Pleasure to be here. Yeah. And so you started in Sydney. You and your brother um was seven, eight years ago. And you got in early on Bitcoin and all these Bitcoin monitor uh
Speaker A
miners wanted to have data centers. Huh. Yeah, 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
Speaker A
to build out largecale 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
Speaker A
they emerge. Here we are today with AI, we are swapping out all the Bitcoin for AI chips. When did you first start seeing the demand in the company shift from hey Bitcoin miners we need some H100s whatever it is uh to hey we're
Speaker A
this nonprofit open AAI hey we're this research lab we need some AI compute when did that start hitting look we had a bit of a false dawn I would say back in 2020 we signed anou with Dell to start bringing out
Speaker A
customers and compute but in hindsight it was too early so we went back to Bitcoin kept bootstrapping in the platform. Look, I would say about 2 years ago and month by month, the demand just continues to escalate.
Speaker A
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
Speaker A
trying to sell you on space. Yeah. Yeah. So, we actually develop 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
Speaker A
amazed people at the time. Like 750 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
Speaker A
guys doing? We're saying we believe in the future digitization, high performance computing and obviously now today it's paying dividends.
Speaker A
Yeah. I don't think anybody could have predicted when chat GPT came out, Open Claw recently as a turning point. Um, and then you know, Microsoft, Google, and everybody embracing this. Uh, and that's your big partner, Microsoft.
Speaker A
Yes, Microsoft's one of our early partners. We signed a $9.7 billion contract with them late last year, but as I was explaining to you before the show, that's 5% of our capacity. So, things are busy at the moment.
Speaker A
Yeah. And when you do these buildouts, the big conversation today is not is no longer the number of GPUs putting in.
Speaker A
It's just power. Power is the uh constraint today. Yeah. Look, for many of the industry it is, but for us, because we started 8 years ago tying up all this land and power, it's not. So, we've got 4 1/2 gawatt.
Speaker A
For context, that's almost as much power annually as the Bay Area uses in its entirety each. Wow. 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.
Speaker A
And time to compute means trades people coming to West Texas living in a a trailer that you set up to then break ground on a data center, build foundations, build water cooling systems. Like this is hard manual labor
Speaker A
going on. Yeah, 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
Speaker A
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-ole, permanent solving fires to try and be bring online this compute.
Speaker A
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
Speaker A
only be 500 living there right now. So what are those towns like? I'm it sounds to me like something out of like the gold mining era when people first you know uh went and and were prospectors prospecting town
Speaker A
pretty pretty much. I mean the barbecue's great that was a draw card but apart from that uh look we've always had a policy of hiring local supporting the local community. Uh this year we're hitting a million dollars in community
Speaker A
grants cumulatively. That's things like local playgrounds, supporting the fire departments, but we will hire locally.
Speaker A
Once we can't find that trade locally, we will expand the radius by 20 mi and hire out of that and so on and so on for us.
Speaker A
That's very thoughtful. Yeah. And and these folks are coming say an electrician or a construction worker.
Speaker A
They're coming having built houses or you know uh maybe building um corporate offices and now they come for a tour of duty here and the salaries go up massively but they got to leave their family for a 3-month tour or something.
Speaker A
Yeah. Yes and no. Because typically where we locate is where there's heavy electrical infrastructure. Where there's heavy electrical infrastructure is typically where old manufacturing and industry has closed down. Ah, so we go in, leverage that sunk capex, rehire, retrain local workforces and
Speaker A
bring a new industry to town in these data centers. H has has that workforce now been completely depleted and we need to train another generation, a younger generation to be generation tool belt and really embrace the trades 100%. We're partnering with
Speaker A
universities, trade colleges. Absolutely. And you go to a trade school, you got you go to a college, people are getting degrees in philosophy and English literature, they're going 50k a year in debt, 200k a year in debt.
Speaker A
What's the starting salary for a trades person working on a data center doing electrical or construction or HVAC?
Speaker A
What's the ballpark range? Uh, look, I won't talk specifics, but they they are going up. The price is going up. Depends on the level, but yes, there is a rush for good. hearing 150 to like 300K. Am I in the ballpark?
Speaker A
The lower end directionally, you're right. Yeah. Yeah. I mean, it's incredible when you think about it. There's concern about, hey, AI taking jobs and then on this other side of the ledger can't find enough talent to to to service it. Talk
Speaker A
to me about energy sources and how you think about that. Uh, 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, nack gas is plentiful in that
Speaker A
area. We obviously got a lot of oil. People don't know this about Texas in the United States, the number one uh source of solar installations. Yeah.
Speaker A
Yeah. Talk to us about energy. So, so our our philosophy has been sustainability from day one. We have used 100% renewable energy since inception.
Speaker A
What? 100%. Wait, how is that possible? It's We use hydro in British Columbia. We use wind and solar in West Texas. In West Texas, where we're located, there's around 45 to 50 GW of wind and solar.
Speaker A
Yeah. The transmission line to export that down to the load centers in Dallas and Houston is 12 GW.
Speaker A
Oh. So you go and locate to the source of lowcost excess renewable energy, monetize it into this digital commodity, export it at the speed of light as token.
Speaker A
Great arbitrage. And the wind is producing a lot, but it it's harder to get from those areas where people are willing to put up. I mean, people don't understand how big West Texas is. It is an incredible amount of land. And you're
Speaker A
coming from Australia where also on the west side, people don't understand exactly how much just pure nature land there is. Yeah.
Speaker A
Undeveloped. So much land. And the issue is distance. You've got to spend billions of dollars on this transmission connection infrastructure to move that power to where people actually want it. You can build wind farms, you can build solar
Speaker A
farms, but if you build it in the desert and no one can use it, then what's the point? So the whole opportunity for our industry is to go to the source of that power and monetize it.
Speaker A
So the data centers follow the wind turbines, the solar installations. How do you think about batteries and are you able to put those online? Because obviously you're going to have periods where, hey, it's not a windy day. In
Speaker A
Texas, we have very few days when it's overcast, so that problem's pretty much solved. But you're going to have 50 days where the sun's not beating down. So, how do you deal with the demand and and and softening that duck curve?
Speaker A
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
Speaker A
of that variability. They guarantee you 24/7 reliable power. Got it. So, on their side, they're figuring it out. something goes down and they could fall back even though you're 100% committed to renewables if they needed to fall back to gas or whatever
Speaker A
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
Speaker A
little bit. Is OpenAI a partner as well or uh can't comment. Can't comment. Okay. So we'll we'll read into that whatever we want.
Speaker A
But are there pockets where people are saying, "Hey, let's slow down." Or is it still gang busters?
Speaker A
It's right up the end of the spectrum. It's gang busters. We we cannot meet demand. That's why the whole industry now is around time to compute. There are no idle GPUs in the world sitting in a data center.
Speaker A
Yeah. And what's your take on when software makes and this is a big uh discussion from Jensen himself during his two and a half hour keynote yesterday. Uh we're sitting here Wednesday. I think he did his keynote on
Speaker A
Tuesday. He was talking about hey software is going to make it 50 times more uh you know lower the cost of tokens 50x and then you have um transport also contributing to that.
Speaker A
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 A
Look, I think it's actually the opposite. I think it feeds on itself. So, I'll give you one example. You go into chat GPT today and you generate an image. You enter to the prompt. It's like the dialup internet days.
Speaker A
It is right. It takes minutes and you're like, I better get this prompt right.
Speaker A
Yeah. Finally, 2 minutes later, it comes. Now, I'll give you an example. If we 10x the amount of compute available, which is an enormous task from where we are today, and those images take 5 to 10 seconds, are we going to generate more or less
Speaker A
images? Oh, many more. Uh, this is Jevans paradox. This is the theory of induced traffic. You know, you build a couple more lanes, people start to think, well, maybe the uh distance from Bondai Beach to to the central business district in
Speaker A
Sydney terms would be an acceptable commute. Love the analogy. Yeah. Uh, 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 bought
Speaker A
Grock, so now you've got, you know, two of the leading edge chips uh coming out of the same company, but custom silicon becoming a big discussion. Has that started to land in the data centers yet?
Speaker A
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 A
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
Speaker A
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.
Speaker A
But absolutely over time we are seeing these chips emerge. A and in terms of desktop computing I don't know if you saw the announcement that um Dell and Nvidia are making a really powerful desktop 750 gigs of RAM
Speaker A
lot of power. You're going to be able to run some local models open source and with openclaw and open source coming from uh Kimmy and a bunch of the models out in China.
Speaker A
has the hacker group, which I think you started in like I did probably in similar time periods. People are starting to get really obsessed with having a 10 or $20,000 desktop setup and running this local. What do you think of
Speaker A
that trend? I'm curious. Yeah, I mean the breakthroughs we're seeing in software, the way it's distributing power to every man in every and woman in every house and their ability to code and use products like open core, the generation of demand and
Speaker A
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
Speaker A
absolutely going to compound. And what about nuclear? Uh the Trump administration really seemed to flip the switch on a growing uh belief that hey wait, nuclear is pretty great. It's clean. It's the original renewable in a way. Uh, and
Speaker A
these new modular reactors have nothing to do with Chernobyl, Fukushima, or Three-Mile Island. They're much safer.
Speaker A
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 A
I I think you have to. I think the reality is it's going to take a decade, a bit longer by the time big projects can come into commissioning, but now is the time to start that conversation. and put in place policies, mobilize capital,
Speaker A
and start that ball rolling. Yeah. Have you do you have a data center going up near nuclear?
Speaker A
No, not at the moment. Not at the moment. But you're actively tracking that activity cuz Yeah, this seems uh pretty inevitable.
Speaker A
Yeah, feels like it. 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
Speaker A
coal mine near the hydro dams and near the nuclear where there was excess capacity. What impact do you think this has if you could actually have small modular reactors next to data centers?
Speaker A
Well, I I think it just opens up the market and enhances the US's competitive advantage in this space. Like AI is inevitable, robotics is inevitable. The reality is the correlation between human progress and energy consumption is really really high over a very long time
Speaker A
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
Speaker A
fluid, faster and then you get that positive flywheel around Jebans's paradox and demand. Talk to me about the architecture today of Ethernet and data moving between data centers within data centers. That backbone is going through a paradigm shift as well. Yeah.
Speaker A
Yeah. Yeah, it is. And Jensen coins coins the term the data center is the new computer.
Speaker A
So you need to step back and you say right this big building is essentially the old desktop PC we had under our desk at home. You go right how does that work? So all the cabling, the latency, the number of hops between each GPU, how
Speaker A
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.
Speaker A
Yeah. And where do you think uh or or what do you think of Elon's uh vision?
Speaker A
It's obviously a a longer term vision of putting data centers in space and there's a couple other people working on it as well. Yeah, I mean it's very hard to argue with Elon. He's been very right on a number of things for a very long
Speaker A
time. I think sitting here today, it feels exceptionally difficult given the cost of moving things to space, the challenges around radiation. There's a huge amount of engineering challenges, but that's never scared Elon before. So, I'm not qualified and he's he he's inevitably
Speaker A
right, but sometimes he's late. He might be late to the party. He might be late to the dinner party. you might show up at dessert, but generally uh he nails it. How much of an issue is getting the
Speaker A
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
Speaker A
some point? 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
Speaker A
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
Speaker A
to Dallas, the big carrier hotel, six millisecond roundtrip latency. What's six milliseconds? There's a thousand seconds milliseconds in a second. Yeah, we're talking six.
Speaker A
It's it's adjacent. Yeah, it's it's not even uh Yeah, it's definitely not material. Uh listen, continued success uh and uh you're hiring a lot of people.
Speaker A
Yeah. Yeah, I think we got 129 job advertisements up at the moment. All right, so everybody go to the Iran website. Uh, and listen, company's doing fantastic. Thanks for spending some time with us here at Allin GTC.
Speaker A
Thanks, Jason. Appreciate it. I'm going all in.
Topics:AI infrastructureCoreWeaveGPU clustersNvidia GTClanguage modelsAI compute scalingcrypto miningopen-source AIAI cloudinference compute
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