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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 program. You're the original hyperscaler. You guys got in very early and secured your I don't know which GPUs you end up getting but you were very early to this trend. How did you get to it so early and how did you build out this, you know, first I guess at the time neocloud?
Yeah. So we didn't really start it as a neocloud. I was running an algorithmic hedge fund focused on natural gas. When 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. And you know, we're pretty nerdy, 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 there's some brilliant engineer that built the ASIC and they're probably going to be better at running it than we are. So we really began to focus on the GPUs mostly because the GPUs were you can mine Ethereum with them. But you could also do all these other things and really so right from the start we looked at the compute as an option to be able to deploy our computing power to different use cases. And so, you know, began the company in 2017, spent the first kind of three years mining crypto, went through a couple of crypto winters. Because we had come from a hedge fund, we have real chops in risk management and how we think about capital and risk exposure and allocation and all of that. And so we were really careful around that right from the start. So we weathered crypto winter really well and began to scale the company and immediately started to look for other use cases that you could use this compute for because crypto was pretty volatile.
Yeah. And crypto was a question mark at that time.
Yeah. I mean Bitcoin was speculative and there were many other speculative projects. The only other people using this type of hardware quants
Medical researchers. So a good way to think about it is like the progression of products that we kind of started to work on. You know, first was crypto, but we immediately moved from crypto to CGI rendering, and we built projects that allow folks that were trying to animate and render images, you know, kind of what makes the movies cool, right? And we started to work on that and then we moved to batch computing and started to look at medical research and different ways of using the computer science. And we just kind of kept moving up the stack in terms of complexity on how GPUs could be used. And ultimately in like call it like 2020, 2021 we started to really try to figure out how you can go ahead and use GPUs for neural networks and that was not something that we knew how to do. And so we actually went out and bought a bunch of A100s and donated them to a group that was working on Luther AI. They were working on an open-source project with the thought that these guys are taking the GPU compute because we're donating it. They can't really get pissed at us if we're not very good at it initially. And that worked out really well because
They can't complain about the SLA.
They kept telling us like we need more of this, you got to work on this. And that began to really give us an understanding of what was necessary to run scale parallelized computing. And you know, we went through it. I kind of feel like buying those initial GPUs was the tuition we paid to learn how to run this business. And then one of the interesting things is all of those guys went back to their day jobs because they were all volunteers working on this. They were like-minded scientists. And when they got to their day jobs, they were all like, I want that infrastructure. It's built the right way. That's the way that researchers are going to want to use it. And that launched our business. It was an amazing story. And
So you went from crypto to these researchers into academia and deep research. What's the next card to turn over in the poker game?
Yeah. So what became very clear to us 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 understand that computing decommoditizes at scale, right? Like when you know anybody can run a GPU but can you run a cluster that's large enough to train a model that can change the world and that's a different question. And so we really began to think about like how do you go about scaling up your delivery of this computing to clients, larger and larger clients. And that was the next card to turn is to think about it from a okay, you know, there's a component of this that is going to lean into our ability to access the capital to be able to deliver our solution to the broadest possible audience to the most sophisticated consumers of this compute. Yeah. And that was really the next card is thinking about it as a business rather than as an engineering project to be able to deliver the infrastructure and the software and really everything between. You know, when you're thinking about what we do, we kind of live above the NVIDIA GPUs but below the models. Yeah. And everything in there, all the software, the integration of software and operations and observability and all the things that you need to be able to build a cloud that's purpose-built for this one specific use case, right? So we don't 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 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 deliver compute that solves that problem.
And when did the language model start dialing and calling you for capacity?
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 Inflection and then we really diversified from there into the hyperscalers, into OpenAI, across the foundation models, and just kept scaling and scaling with 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 now the world has gone through this moment where we've moved from research into the productization of this. It's beginning to work its way in from the fringe of organizations into the core of what they do. And you can see that every day in the amount of inference compute that is being driven through our infrastructure layer which is just massive, which is just like one of the
People are consuming it not just building models but they're deploying them and utilizing them.
I always think of inference as the monetization of the investment in artificial intelligence. So when we see our compute being used to stand up the massive scale of inference that's hitting our compute every day, and like you know inference is when people ask the model a question it comes back with an answer that's an inference, or when you ask the model a question and then to go do something that's inference, right? And that's actually where you have the opportunity to really drive value outside of the model itself but into the real world, and that's really exciting for us. That's what we like to watch. That's what I like to watch in terms of gauging the health.
So really we are the tip of the spear in bringing the new architecture out of Nvidia into commercial production at scale. Yeah. And so when we were the first ones to bring the H100s at scale, we were the first ones to bring the H200s at scale, first ones with the GB200s, and now you've got the GB300s. One of the things that's amazing and really fascinating for us is people are using the bleeding edge GPUs to train models as the new architectures come out and then they take those GPUs and they move them into different experiments and then over time they move them into inference and they continue to use them in inference for a very long time.
What is the shelf life of a 100 right now? That's been a big debate is I think for your company for Microsoft and I guess Michael Burry, you know who you must have known when you were a quant, you know saying oh my god the whole industry is the sky's falling and then we all know in the industry that people don't just throw this hardware away that they find uses for it. The street finds its own use for technology. So what's the reality of the lifespan of these things?
So my take on the GPU depreciation debate is that it's nonsense. Right. It's a debate that is being brought to the forefront by some traders that have a short position in the stock and they're trying to talk down. Look, here's what we know. When we buy infrastructure, we're a success-based company, right? We're a small company on a relative basis compared to the enormous companies that we're competing with. And so our clients come into us and they buy compute for five years, for six years. Our average contract is five years. So any commentary by anyone either inside or outside of the industry that this stuff becomes obsolete in 16 months or whatever nonsense they're spewing, it doesn't in any way match up with the facts on the ground. The facts on the ground is they're buying it for five years, right? If my approach to this has always been if people are willing to pay me for it,
Correct. Pretty simple way of approaching it. We use a six-year depreciation. We believe that the GPUs will last in excess of six years, but we felt like that was a fair and reasonable approach to a technology cycle that's moving at this velocity. The A100s, the Ampere this year, the price has appreciated through the year.
Yeah. Why is that? I think it's because one of the things that happens is as more installed capacity becomes available, you have new companies that come into existence that have new use cases that have different size models that are trying to build new commercial ventures that maybe have been blocked out of the H100s and never had an opportunity to run on. I mean to make a very simple example for the audience, like when you trade in your iPhone after three or four years you're like who's going to use an iPhone 12 and it's like have you been to South America or Africa where you go to the store and you buy an iPhone 12 or you buy the Pixel 7 and it costs $50 that's still got great life left in it.
And so look, we find these amazing use cases, new companies that have come into existence or existing companies that have integrated new models into their workflow that are able to use the Ampere and so they keep buying any GPUs that we have available. And once again, the concept that a GPU is no longer relevant or commercially viable after 16, 18 months or two years, it just doesn't make sense.
It's obviously farcical. I think sometimes people get caught up in Moore's law or in just how fast our industry is growing and that there's so much at stake that big companies are demanding the most recent products. That doesn't mean that the lifespan has gotten shorter. It means the opportunity and the surface area of the opportunity has gotten much larger.
Yeah. One of the things is 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 an incredible focus on the companies that are building on these most advanced chipsets. And the truth of the matter is even within those companies they have a long tail of useful life
To provide inference horsepower to work on other experiments to do less bleeding edge activity but still needs to be done
And yeah, I mean rendering comes to mind as well. We're making images on nano banana. There will be a use for it. There is a moment in time where maybe the compute to power ratio doesn't make sense. My expectation is obsolescence will be defined by the moment in time where the power in the data center for me will be able to be repurposed for a higher margin than the existing infrastructure provides. And you know, like I said, I fully expect this infrastructure to last in excess of six years, but the standard in the space has really been used with one exception, which is Amazon, which is six years. That seems like the right schedule. I'm not making it up. That's what everybody's using.
Yeah. And the energy cost is the opportunity because, hey, it's just we need that space. There's a better reward here. And that might get resold that hardware to somebody else who wants it, a hobbyist or something. It's
Or it could be sent someplace else where they have more capacity when they can repurpose it there. But I kind of feel like we'll deal with that part of the business when we get there. What I know right now is it is extraordinarily profitable. It's very accretive to my company to continue to keep the infrastructure that's been up and running, that's been on these long-term contracts, and as it rolls off, as it's been in use for five years, 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 these from Jensen back in the day, you could buy them and have them shipped within 30 days or less. Nowadays what's the wait like even for you a loyal old customer? And is there a bit of a battle, is there politics to who gets the servers? I see some very big names talking about they got to get an allocation. Is it still a little bit crazy? What's it like to be in that category having to buy something everybody wants? Look, I think of it as an affirmation of the business that we're in, right? The fact that we are attracting competitors means that the business is healthy and there's a lot of people trying to deliver this service because the need for this infrastructure, the need to integrate the infrastructure into the software layers to deliver it to artificial intelligence either at the model level or the inference level or the application level or whatever level of the five layer cake that Jensen's focused on. The fact that there are more people coming into this doesn't discourage me. As far as getting access to the GPU, we show up like everybody else with a PO and we're ready to pay. What's the wait time like and is it just really competitive or not? Because I talked to Jensen about it. I said how do you manage all these big egos and names and companies trying to buy stuff and he said well they order it and we give it to them in the order in which they order it.
It really is, right? 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.
Yeah, that would that that
That would be crazy. Yeah. I'm not sure that would be good for the long-term business. No. So our approach is
You might get some sovereigns coming in and saying I'll pay double. They do that with Ferraris too sometimes. I
Guess these are the Ferraris of computing, right?
In a way they are. Yeah. Bugattis.
Our approach is to work with clients across the entire space to find opportunities that are really interesting companies that can fit into our contracting requirements where we're going to be able to go out and structure the debt that we require in order to go out and build infrastructure at this scale. And um
How does all that debt work? That is something that you guys specialize in. Corporate debt. I'm in the venture business. People are like, why should I be in venture when corporate debt pays so well? Corporate paper's so huge. I'm curious how this fits in and like what interest rate people are paying on a billion dollars in infrastructure. What do they pay on that?
Yeah. So CoreWeave has really been the innovator around a lot of the financing engines that have come to bear on this. We did the first GPU-based loans. I think it's important to try to explain this in a way people can understand. So what we do is we go out and we find a client. Let's use Microsoft. You brought them up before, right? And Microsoft comes to us and says, we'd like to buy some compute from you. And we say, okay, great. We're going to sign a contract. Once I have a contract in hand, then what I do is I create something. It's not a particularly creative name. It's called the box. Right. And what I do with the box is I take my contract with Microsoft and I put it in the box. I go to Jensen and I buy the GPUs, I put it in the box. I take my data center contract, I put it in the box. And now the box governs cash flow. And it has a waterfall of cash flow that comes into it and goes out of it. And so the way it works is then I build the compute and then I deliver the compute to Microsoft and they pay the box. They don't pay me, right? It goes into the box and the first thing it does is it pays the data center. It pays the power bill. It pays the interest and the principal and then whatever's left flows back to us, right? And so it is an incredibly well-structured, time-tested, pressure-tested vehicle to be able to borrow money against client paper and all of the other collateral around the deal. Which is why CoreWeave, which is a company that many people haven't ever heard of, was able to go out and raise $35 billion in 18 months to build infrastructure at scale. But what's important to understand is the economics in this box are such that within 2.5 years of a five-year deal, we have paid for everything.
The principal's been paid off.
The principal's been paid off. The interest has been paid off. The return into the box is such that we are able to generate returns to our company at the box level which gives the most sophisticated lenders in the world, whether it's banks or private equity funds, confidence that they're going to
Be able to achieve the one rule of lending which is give me my money back.
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.
And if any one box goes upside down, you can deal with it and it's not as acute.
That's correct. And they don't cross-pollinate. They don't cause contagion across the boxes. They're all independent and discrete. And number two is as you do this and as you show the lenders how this financing tool and how this financing mechanism works, what they do is they continue to lend you money at progressively lower rates. And so when you think about our cost of capital over the last two years, we have dropped our cost of capital by 600 basis points.
It is enormous, right? And so you're seeing a company that is driving its cost of capital down towards where the hyperscalers borrow, which will enable us to be able to be competitive with them over time. And we have been extremely militant and diligent about feeding, watering, and caring for those boxes so that we continue to have access to the capital markets in a way that allows us to build and drive our business.
Means you have to say no. You have to say no to maybe some people who want to be in the box.
Yeah. So we 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, that's not a deal that I can do because it's too short for me to amortize the expenses, 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.
Absolutely. But our business is really built around the risk management of being able to get to scale. Because in my mind, during this period of disequilibrium, during this period where there were not enough GPUs in the world to provide the compute for all of the different use cases in artificial intelligence, the part that's important for me and for my company is to get enormously large so we can drive down our cost of capital so that we have information flow coming in from all different parts of the market. Large language models, high-speed trading, search, all of these things. And they're feeding information back into us that is letting us know what the next product we need to build is or where they need help scaling or what type of compute they need. And all of that information flow is incredibly valuable to us.
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, other folks, Microsoft is going big, and Google's going big, Meta's going big, and those people obviously have massive cash flow. Apple seems to be MIA. They don't seem to want to play. You've named a lot of really big companies with really big balance sheets that have the capacity to drive a lot of demand. Look, I have been truly steadfast in this for years now. For four years, the depth of the demand for the service we provide has been relentless and overwhelms the global capacity of the world to deliver enough compute to enable all of the demand for artificial intelligence to be sated. And that has been we have been relentless about that. Sounds like Knicks tickets during the Patrick Ewing era. Like they got up to 50,000 people on the wait list. So if magically the wait list went away, if the constraint went away and we just had a large amount of GPUs available, a lot of energy available, a lot of data center available, how much capacity would just all of a sudden come out of the system?
Or would be deployed I should say.
So remember how we build our business through this box and it's a five-year box. So if we had an air pocket, if demand were suddenly to disappear because of a technology breakthrough, because of a war, anything, right? The why from a risk management perspective does not matter. You have to prepare your company for the what happens if it happens. Yeah. And so by entering into these long-term contracts, by entering into contracts with counterparties that have large balance sheets, we are protecting ourselves and our lenders. So that we are confident and they are confident because you can see how confident they are by the rate that they're charging us continuing to decline that they're ultimately going to get their money back. And that is the one rule of lending. And so, 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 it's also important to understand the constraints aren't just GPUs, right? Electricity, power shells, memory, storage, networking, optics, all of the things. And there's various throttles that will limit the
Memory is a throttle right now, right?
Oh yeah it is, oh yeah it is.
Why how did memory become the throttle?
Memory has historically been a cyclical business, right? We have seen these waves of demand driving up the cost for memory and then it collapses and then it drives it up. It's a very boom and bust business. It's cyclical in its nature because the fabs are so capital intensive that people invest in the fabs, build a ton of capacity and then overbuild if there's any type of turndown. And we've seen that cycle again and again. What's happening right now is the confluence of two things. One is with all the demand for artificial intelligence and the corresponding demand for compute and the ancillary services around the GPU, the demand is through the roof. That's number one. Number two is that
There was probably an investment cycle that needed to happen back in 2023. Got it. That would have brought on the necessary fab capacity to be able to serve.
Impossible to predict what just happened. Just with energy, it's impossible to predict what just happened. And now people are chasing energy. The data centers are going where the energy is. It's not based on real estate. It's based on
And where's there some wind. And anytime you have a very capital intensive business, like building fabs, you will get this boom and bust cycle just like in energy they overbuild and you know
Yeah, I mean 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 bust cycle that 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.
Listen, it does a lot of things having a boom cycle. It clears out the underbrush. The strongest companies will be able to survive and take advantage of that and it sows the seeds of future business. The other thing that it does is you put that infrastructure into the ground. You put the fiber into the ground which became the backbone of how we watch movies every day and how we communicate and how we hop on a Zoom and all of these things were based on that infrastructure that was available to be consumed.
Yeah. People don't recognize this fact. The premise of YouTube from the founders who I knew, Chad Hurley and his other partner, they basically had the realization that storage is coming down so quickly we could offer free unlimited uploads and bandwidth is coming down so I guess we don't have to charge people for sharing a video online. Before that, if your video went viral, people are going to have their minds blown. But your server would turn off and it would say this person needs to pay their bill. Yes.
Because they were getting charged for carriage by the megabit going out.
Yes. I mean, look, the business models change and evolve. And like you said, Moore's law and certainly Jensen will talk about the fact that what is going on within the accelerated compute space dwarfs
Moore's law right and all of that is going to lead to
More opportunity to build more companies that are going to do things like you two did which is really change the world.
Yeah. I mean the concept that I don't know if it was like a million hours being uploaded every hour or minute but at some point Susan Wojcicki, rest in peace, had told me
Just 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 bits per second upload and it's like okay one in a thousand people upload, it's a big denominator.
I was sitting on a panel with Sarah Friar, CFO of OpenAI, and she every once in a while puts out interesting information. So she was talking about the cost of a million tokens when ChatGPT-3 came out and it was $32 and change and now a million tokens cost nine cents.
Right. And so you just see the incredible power of how the capital markets, how capitalism is
Fueling engineering and fueling competition.
It's become recursive now too. I mean these models if you say to the model, hey, make yourself more efficient and spend less money and lower the cost of tokens, it'll be like okay captain.
Yeah. I don't know if you saw Andrej Karpathy's recursive thing last weekend, but it's like now civilians who've never worked in a language model computer science are like, I'm going to try to do something recursive this weekend. You know, it's one of the things that I talked to the other founders about. And it's like when you think about some of the things that AI does, right? It's lowering the barrier to operations. So if you have a good idea or a great idea, you can open up your model and you can tell your model, you can vibe code it, you can do all kinds of different things and create things that never existed before. That's amazing, right? Like that's bringing down this incredible barrier that kept human creativity contained. And now all of a sudden this whole new vector of medical research or different approaches to 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 build new things and to create new things. And I just think that's incredibly exciting, right? Like you're bringing the minds of 8 billion people a tool that allows them to overcome what was insurmountable for forever
Yeah, it's a bright new future. Michael, appreciate you sharing the information with us and the vision. I am really delighted to have Aran Shas on the program.
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 want to choose OpenAI, if I want to choose Claude, whatever it was, that was like a real unlock for me. And on the sidebar, I noticed you had done essentially like what Yahoo did in the early days, finance, sports, and when I pulled my nickname up, it gave me a live version of that. When I pulled my stocks up, it summarized the news in real time. And I was like, wow, this execution's great. And I kind of made you my front door to different models and it made it easier for me to check it. Then you came out with the Comet browser and I was like, holy cow, I can give this a series of instructions. Go to my LinkedIn, find everybody from this company, put them into a Google sheet and boom, you were the first out of the gate with that. And then just the last couple of weeks I had been Claude-pilled in using Claude, but you came out with Computer and I started using Computer and boy it's good. It's a really strong start, allowing me to do repetitive tasks very similar in some ways to co-work from Claude or basically an engineer or developer using it. So are these the evolution of the company and I should think about it that way? But how do you look at Perplexity now? You have a very loyal fan base. You're making a lot of money. I don't know if you disclose it, but I think it's hundreds of millions to billions. You can tell us. But what is Perplexity in the face of wow, Claude's having a great run, OpenAI still doing strong, Grok doing very well, Gemini coming on strong. There's like six or seven of you and you just happen to be one of my top twos right now.
Thank you. First of all, thank you. Thank you so much. Perplexity has always been built for people who are always looking for the extra edge, the curious people. So it's very natural that you are one of our power users. One common theme for us for the last three and a half years is accuracy. Perplexity wants to be the company that's building the most accurate AI. So when you want to give somebody answers, accuracy is very
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 it's a great idea for AI to have full access to a browser so that it can be accurate when you task it to go do something that you would do yourself on a browser. Agentic browsing comment. Now the last phase is it turns out it's a great idea for AI to be given full access to a computer so that it can do whatever you do on a computer on its own, essentially becoming the computer itself, an orchestra of everything AI can do today, every single capability each individual AI model has, be it GPT or Claude or Gemini or anything else, an orchestra of all those capabilities. That's what Perplexity the computer is, and all these subagents that are running inside computer are the musicians. The models are essentially the instruments, and there are hundreds of models out there each having their own specialization. Some are good at coding, some are good at writing, some are good at multimodal, visual synthesis, image generation, video generation, audio, but what matters is the end output, the music you play. That's the work AI gets done for you. And that's what Perplexity computer is. The AI itself is the computer now.
Still lives inside of a browser. Have you considered giving it desktop root access? That feels like the next place this is going. But that comes with a lot of security issues, a lot of trust issues. As you mentioned, trust is paramount. Getting the right answer is what builds it, but also not getting hacked and not having to delete your files. So, how do you think about root access to my Windows machine? Correct.
Obviously iOS they won't let you, but with an Android phone it would let you.
Yes. So do you have that in the works?
Yes. So we announced something called personal computer. Perplexity personal computer that's essentially going to take all the trust and reliability and the server side execution of Perplexity computer but synchronize it with your local computer so that you can use it from your phone. And we're going to do this with the Mac Mini where you synchronize your computer with the Mac Mini. So that becomes your local server. All the agent orchestration that has to do with your local private data will run on that local orchestration loop, that runtime with the Mac Mini.
Not on your servers, not on Anthropic's.
It could still ping Frontier models if it needs to with your permission, but it will be orchestrating everything on your local hardware.
But it will be orchestrating everything on your local hardware.
Yeah. And if it needs to run on the server side hardware, if you don't want very complicated long running tasks to be running on your local hardware, yeah, you can delegate it to run on your server side computer, which is again only accessible to you and you alone. So that way we're going to bring this perfect hybrid of trustworthy hybrid between local and server side and you...
And you'll make it easy to do. It just be abstracted. You install one executable, boom, it's done. It's like OpenClaw for dummies. Nobody needs to learn how to use it. Nobody needs to manage API keys. Nobody needs to manage separate billing across like 100 different services. Figure out what you can give access to and not access to. We take care of that. So it's a Steve Jobs way of doing it, you know, end to end integration. And how do you think about local models? I have started running Kimmy 2.5 on a Mac studio. It's not as good as Claude or Gemini or Grok, but you can probably do about 80% there for free.
Yeah. Yeah. Essentially. Yeah.
Uh and so that's quite compelling considering some of my other bills, Claude and stuff are getting expensive. So, do you have one of those? You started testing on your local Mac Studio. I assume you have a Mac Studio and you're doing this yourself. Yeah. Or now, I don't know if you saw Dell and Nvidia announced a giant workstation. Is it a 3,800? Something like that with 750 gigs of RAM. So, what do you think about the desktop going back to workstation/server status?
I think it's very promising. My prediction is it'll initially start off as a subagent. So whatever you need to go like your tax returns, your personal photos, your emails, your calendar, all that stuff, those local apps, your personal notes, very personal notes. You can make sure that the models that access those tokens will be running on your local hardware if you want to, if you're that privacy conscious. And more complicated stuff that accesses your data that's already on the server side. Example, your Google calendar, your Gmail. This is personal data still, but an AI runtime can access that through your connector, your Google calendar connector, your Google Workspace connector, and that could run on the server side because anyway, the data is on the servers. It's not even lying on your device. 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. It's all about choice. And anyway, when you're on your phone, you don't care actually which server that workload's 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 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 language models on your personal data.
Yes. And it's going to be like you're buying a refrigerator, your internet modem. The cost for these will eventually go down. Yeah. But it's not going to feel like you're wasting your money. Every home has a lot of other sensors that runs your home that'll also be part of this orchestration loop. Yeah. So that's where it gets exciting because now you can just dictate something to your phone and that can control your entire home. So that's the dream that everybody has and all that orchestration loop can run on your local hardware, no problem. And I'm curious what you think of the operating system. What's eventually going to be the operating system of this workstation?
AI is the operating system. Like earlier in the traditional operating system, you execute programmatically. Now you start with objectives, not specific instructions, right? You come up with a high-level objective: go build this website for me that takes all the transcripts of all in podcast and tracks the stock price just before the podcast and after. Yeah. And chart it for the max 7. And chart it 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. So I think that's basically where models, systems, and files and connectors are all coming together. You would think of that as an OS except you're operating at an abstraction above that where you're thinking in terms of objectives.
Yeah. And does it need to eventually become its own operating system in your mind?
It could be like people could think about 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. So, I think Mark and Reason tweeted this right after our release that turns out Linux computers was the right idea. 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 for hackers.
You build something rock solid and it does feel like Linux might actually become the correct eventual winner. It may not need to have a front end.
That's the thing. You could access the Linux machine on your phone. This could be running iOS or Android. It doesn't matter, right? The actual valuable runtime is running on Linux on the server.
You've done great as a consumer company. Lot of love there. Now I'm starting to see 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 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. It doesn't talk well in Slack. It doesn't have an agent in Slack.' I was like, 'It will. I'm going to see AR and I'll talk to him about that.' So, we need a really strong Slack connector.
It is. Okay, great. Computer exists as a Slack bot right now. That you can add to your Slack workspace on the enterprise plan and our entire company works like that. People are talking more to computer on Slack than to other people. In our first volley, we were sending reports in, but it wasn't interactive. That's perfect. So now you've got your company going in two different directions. This incredible consumer run you have, how many people are using the product every month?
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. The fastest growing business for us. It's growing faster than the consumer in revenue. And things like computer unlock entirely new possibilities. For example, we've saved more than $100 million for our enterprise max customers who are on the highest tier of enterprise.
Explain what that is. What does it cost? $200 a month per person?
So there are two tiers. One is the enterprise pro which is $40 a month and there's the enterprise max which is $400 a month. And on a computer after you run out of your credits you would pay for the tokens you pay for the usage.
Are you making money on the $400 a month $5,000 a year one or at this point in time are people going so crazy? Our one thing that Perplexity has is every revenue we make, unlike certain other wrapper companies, every revenue Perplexity makes has positive gross margins.
Got it. Because we're not just selling tokens, right? Most of our revenue is recurring because people are paying a subscription fee. And because we route through multiple different models, we're very efficient in terms of how we spend on the tokens. Because we have all this advantage with RAG and orchestration and search. We don't actually need to blow up the context window of the models. 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 overall, the company is still yet to be profitable, but we're working towards that.
You've had the opportunity to exit. A lot of rumors. Apple, other people were like, 'Hey, this is a great team.' How many people on the team now?
Yeah. You've got a very coveted team. You obviously understand consumer. You obviously understand business. It's a product driven organization. Reports are you declined, but the world's getting hyper competitive here. How do you keep up as a 400 person organization when you got Sam Altman over here 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. Amazon getting in the game and obviously Gemini very strong product and Google really good at consumer. I think we'd all agree Facebook and Meta haven't figured it out yet except maybe for serving us better ads, but they haven't figured out the consumer case yet, but they'll copy it. They always do. How do you look at the playing field? Because the degree of difficulty, this isn't playing checkers, this is like playing against the 10 best chess players in the world. That's what you have to do every day. So, how do you think about it? Long-term and independent company. Do you think you'll need to join forces at some point? And why didn't you take the deal? These deals were incredible that you got offered.
