How Export Controls Helped Not Hurt China & Power is the Bottleneck to AI | Perplexity CEO

Transcript

I have nothing to lose. I came from nothing. I never even imagined myself to be doing all this. A $20 billion company. 45 million users. Over a billion searches a month. Built in three years by 400 people. These numbers don't motivate me. It's hard to get motivated by wealth. You want to get motivated by impact. This is Perplexity with co-founder and CEO, Aravind Srinivas. No one's ever in a comfortable position. No one can relax. They forced Google to redesign their homepage. Then bid $34 billion to buy Chrome. More than their own valuation. Perplexity changed Google.com more than any product manager Google has ever done. Now you look at AI mode, it looks exactly like Perplexity. He doesn't do defense. He doesn't do comfortable. His words: Attack, attack, attack. [SPEAKER_02] That's my motto. Go all in and try your best. Be on the offense all the time. [SPEAKER_01] You know what I hate with podcasts? When people sit on the fence. Aravind has really strong opinions in the show today. He says that Mike Frum will be more valuable than Matter. He says that the resistance to data centers will continue and get worse. He says the biggest problem today is a lack of power. He claims that Perplexity has changed Google more than any Google PM. You want opinions? This is the show for you. Ready to go? Aravind, dude, I am so excited that we get to do this. We've done one remote and then we did one at Founders Forum last year. So thank you so much for joining me in person. [SPEAKER_02] Thanks a lot, Harry. Dude, it's a weird start, but just roll with me on it. I ask this of the best founders that I meet. Are you motivated more by the fear of failing or by the thrill of winning? [SPEAKER_02] Thrill of winning. [SPEAKER_01] Why? Because I have nothing to lose. I came from nothing. I never even imagined myself to be doing all this. So my life has already been extraordinary beyond any level of imagination. I was just in India doing my undergrad and training neural nets with graphics cards that people in the labs were using for playing video games. It was all for fun. And my path led me all the way here. It was never for my mom. Just getting a job was success because we were financially lower middle class in India, which is not even lower middle class in the UK or the US. And so from there, all we wanted to do was get a job at Google. Being an engineer at Google was considered a win. And so I'm already doing remarkably well compared to that ambition we had as a family. So there's really nothing for me to lose. That's why anytime I try to act like I'm trying to avoid failure and being on the defense, I remind myself that's the stupidest thing to do. It's better to go all in and try your best, be on the offense all the time, attack, attack, attack. [SPEAKER_01] When you review then, what are you not being aggressive enough on today? Maybe in the early days, we'd be very loud on social media talking about Perplexity and Google. And I used to do that myself a lot. And some people don't like me for having done that. Today, I'm a lot more measured in how I talk about our products, our competitors, and stuff. [SPEAKER_02] But it's not a lack of aggression or anything. It's just that is boring. People already heard that enough from me. [SPEAKER_01] Do you regret being so bold in your messaging? No. [SPEAKER_01] So it's not a nuance and maturation of message. [SPEAKER_01] It's that stale and I need something new. Not just that. I don't think it's a relevant framing anymore. We worked on search. [SPEAKER_02] Perplexity started out as search. [SPEAKER_02] We built the first answer engine in the world that people know Perplexity even today. [SPEAKER_02] If you mention the name Perplexity, people will think, oh, that's an answer engine. [SPEAKER_02] We built a lot more things after that. We built agents, browser agents, deep research, computer. We built so many products after that, but we're still known for that first product. And the mark has already been made. We changed the roadmap of Google. You could argue that I or the company Perplexity changed google.com more than any product manager at Google has ever done. [SPEAKER_01] Make that argument for me. Well, nobody ever wanted to ship an answer engine at Google. Nobody. Nobody. Nobody wanted to tinker anything on the interface that made them $250 billion a year. And then now you look at AI mode. It looks exactly like Perplexity. There's not even any difference. [SPEAKER_02] It's the font, the citations, the specific building of inline text, inline hyperlinks, suggested follow-ups. The whole experience is literally looking like Perplexity, except it's still not as good. [SPEAKER_01] And so— [SPEAKER_01] Is that bad or good for you that they learn from you and adapt? It's both good and bad in the sense that you have to obviously—I knew this around the end of 2024, this was going to happen. So it never caught me by surprise at all. It was just a matter of time. I still am surprised that the quality is still not there because I regularly test every product out there. But I'm happy that honestly they changed Google to be what it should be. And I believe that the frontier is where the money is. The frontier in AI is not about answering questions anymore. [SPEAKER_02] It's about actually going and doing work for you. [SPEAKER_02] We still have the state of the art deep research in the world. And that's actually where people subscribe to pay for our Pro or Max products—not for getting answers in the traditional way. They're asking for sophisticated research reports. They're asking for agents that go and do things for you. And so we wouldn't have been able to do all that if we were sitting in 2024 thinking we have everything settled here. We're good and comfortable. And I believe that the frontier is where the money is. The frontier in AI is not about answering questions anymore. It's about actually going and doing work for you. We still have the state of the art deep research in the world. And that's actually where people subscribe to pay for our pro or max products is not for getting answers in the traditional way. They're asking for sophisticated research reports. They're asking for agents that go and do things for you. And so we wouldn't have been able to do all that if we were sitting in 2024 thinking we have everything settled here. We're good and comfortable. No, the answer engine was always a legion for the frontier products we build. You need something, right? Every company needs to have one successful product to build the next set of products. And in AI, nobody can sit comfortably thinking they have it all sorted out, including Anthropic. If Anthropic thinks cloud code is already a win in six or 12 months from now, they won't even be around. And so that's the uncomfortable fact about the whole field. Would you argue today, you just told me, if you don't mind me quoting you here, you just told me before we started that you think OpenAI isn't ready for an IPO. Would you have believed you would be in a position to say this two years ago when nobody wanted to deal with any product other than ChatGPT? So anyone, even in such a massive advantages position, can be put in a position where they're no longer the kings. They're fighting from behind, right? So that's the state of the field. It's less about perplexity or Anthropic or OpenAI not having modes or having modes. Can I push back on you there? [SPEAKER_01] Yeah. [SPEAKER_01] I would stand by it two years ago, even when they were and they are still a dominant consumer product. But I would stand by it because I don't think they are financially ready. When you look at the balance sheet of that. [SPEAKER_02] Okay, maybe I'll decouple that. Do you see what I'm saying? I'll decouple that. [SPEAKER_02] But let's decouple that being financial readiness for an IPO versus perception of a dominant leader. Yeah. Do you perceive them as a dominant leader right now? Yes. In what? Consumer search. Well, except there's no money there, right? Because it's been commoditized. So it's always a lead. For example, why are they going all in on Codex? Because that's where the money is. And we're doing the same on computer. Anthropic's doing the same on Cloud Code. Google doesn't yet have a product in this category, but I'm sure they're going to come after that. Meta's trying to launch Hatch for $200 a month. You see what's happening, right? So nobody has- [SPEAKER_01] But there has to be more money than just Code, Codex, Cloud Code. It's not about code. [SPEAKER_02] That's the main thing. The money, at least in non-advertising. I'm not talking about advertising revenue. In non-advertising subscription or usage-based revenue, the money is in whatever is the frontier. And today the frontier is about going out there and doing things for you. [SPEAKER_01] And- [SPEAKER_01] Do you not think then that there will be a $100 to $200 billion advertising business for OpenAI? [SPEAKER_02] Yet to be proven. [SPEAKER_02] Let's work through the categories of advertising. Who's the number one advertiser on Google? Amazon. It's the number two. Booking.com. Number three or four, I think, is Expedia. Yeah. So how much do you think Booking.com spends on Google? $16 billion, something like that. Something, some crazy amount like that. And how do you book your hotels or flights today? Do you book it on ChatGPT or do you book it on Google? Google. Why is that? [SPEAKER_01] For me, I actually like discovery. [SPEAKER_01] I would like to see the options. [SPEAKER_02] Right? [SPEAKER_02] So the interface. [SPEAKER_02] The interface is less about conversations and more about exploration. So when the decision-making is more subjective and vibes-based, you don't need an objective answer engine. And you think about the other category of advertising, direct consumer products, fashion. Where is most of that advertising budget going into? It's going to meta, Instagram. Because you're just browsing. You're just doom scrolling or whatever you call it. Right? And so the chat interface doesn't capture that user intent, that user behavior right now, which is why it was never a great fit for advertising. And it also fundamentally corrupts the trust that people have when they go into a product and they want the accurate answer, which is what perplexity is known for. And then you're like, hey, by the way, you asked for the most highest, best protein shake. But by the way, these are good protein shakes you can check out. But it kind of hurts the trust that people have in your platform and your product. And so that's another reason why, if you think about it, meta or I think some other companies in the past have tried to put ads inside messaging apps and emails, and it's never really worked out. It works out in China, in WeChat, because there's no other way for them to fund the whole thing. So the whole economy and user behavior has been optimized around gamifying. It's not how things work in America. So I'm bearish on advertising to really take off in the chat interface. I'm happy to be proven wrong there, but I'm bearish on that. [SPEAKER_01] There are two areas that I want to unpack there. [SPEAKER_01] The first thing, just taking them chronologically and how you said them, money's in the frontier. [SPEAKER_01] The more I hear this, the more I question it, because I think that we dramatically overestimate how important frontier models are to do quite basic work. Yeah. So frontier doesn't mean a frontier model. Frontier just means whatever is the frontier outcome you can have right now with AI. Greg Brockman recently tweeted, the model is no longer the product, right? And it's funny because you know that as a leader of a frontier lab, he has all incentive to say the model is the product. The first thing, just taking them chronologically and how you said them, money's in the frontier. The more I hear this, the more I question it, because I think that we dramatically overestimate how important frontier models are to do quite basic work. [SPEAKER_02] Yeah. [SPEAKER_02] So frontier doesn't mean a frontier model. Frontier just means whatever is the frontier outcome you can have right now with AI. Greg Brockman recently tweeted, the model is no longer the product, right? And it's funny because you know that as a leader of a frontier lab, he has all incentive to say the model is the product. And that's what Google people tell. I think one of the Google people keeps tweeting that model is the product. I forgot who. And so the reason Greg is right is because if you take Codex or Perplexity Computer or Plot Code, what is that? It's an orchestration system, right? It takes a model, pairs it with an agent harness. And what is an agent harness? Think of it, the simplest way of describing it is rules for how the agent loop should run. What are all the skills and sub-agents and connectors and tools it accesses? And without the harness, you don't necessarily capture and convert the intrinsic intelligence in the model into valuable output tokens. The output tokens, if you're literally just a reseller of model tokens, you have no business because the model will get commoditized. So even if you're a model builder, you don't have a business. As an infra layer, you have some business on serving those output tokens. But as an application layer or a model builder, you don't really have a business if you're just a reseller of tokens that come directly out of the model. You have a business if you know how to take the model, grounded in valuable context, orchestrate it with a really good agent harness, connect it to the right set of tools and connectors, whether it's personal connectors or business connectors, and provide the experience to people in one single unified system. And the way we differentiate ourselves at Perplexity is we don't just orchestrate across tools and files and connectors. We also orchestrate across models. That is the differentiation that Anthropic and OpenAI cannot claim because you wouldn't find GPT-5-5 inside the Claude code harness. You wouldn't find Claude Opus 4, 7, or 8 inside the Codex harness. These are competing with each other, right? Whereas you would find both these models inside Perplexity Computer. And that way we can increase the token value per watt per user. If you assume that whatever decides the price, the dollars is the power watts fundamentally. That's the thing that nobody else can subsidize other than the government. Whoever provides the most valuable output tokens with the least amount of power expended to produce them generates the greatest value to the end user and has the most pricing power, has the most value. And so that is the orchestration problem to solve. The one single most important metric in AI is token value for watt per user. [SPEAKER_01] What does it mean for the value of OpenAI and Anthropic if model is not the product and it becomes a utility, something you can switch into and switch out of? [SPEAKER_01] The interface. Everyone thinks we're all building the model layer or the race. We're not actually. I would even argue that building models is a way to stay at the frontier, but you have to own an interface in which valuable AI output tokens are generated. The most valuable tokens. It doesn't have to be the product. This is the single most important thing to unlearn for most founders. I had to do it too, which is to be successful in AI product layer, whether you're a model builder or not. It's not about building something that gets a billion users. That mentality has to completely shift. There are a few power users who are propelling this token economy right now. [SPEAKER_02] If you look at all these crazy stories of how there's this one engineer who got Amazon spend half a billion dollars a month because of some stupid way they set up an agent loop inside Cloud Code. [SPEAKER_02] Okay, maybe that's a mistake. Maybe that's a mistake, but there are real engineers in Meta and other companies spending 10 million a year per engineer on these coding tools. There are users in Perplexity Computer. There's one user, I think, who spends upwards of $10,000 a month or something like that. They're not wasting it. Their business runs using agent loops that are running inside these harnesses. And they use these products in sophisticated ways that I couldn't even conceive when we were building the product ourselves. Even internally, inside our own company, there are some people who set up this kind of multi-agent hierarchy and agent loops that looks like its own software architecture. And I often just ask these guys to come explain to the rest of the company, hey, what are you doing with these tools? You clearly are consuming it way over what we thought the average person in the company would do. And the single biggest differentiation between those who use agents a lot and those who don't is whether they run repetitive cron jobs. Whether you use AI as one-off tasks, you just delegate a task and then it gets done. That's using it for deep research or whatever, right? One single task versus AI continuously monitoring something for you. AI continuously triggering based on certain events and going and doing certain things, giving you alerts. You set up workflows that keep running all the time. Every time you get an inbound email, it triages, or every time there's a latency spike, it has to identify which part of the code base caused that. It has to go and do the root cause analysis and then identify the right engineer. All these things, this is where the frontier is. And so going back to my main point, these products are not going to be used by 100 million people. But they will generate revenue that's going to be higher than the advertising revenue of Google or Meta. It's going to happen. [SPEAKER_01] Completely understand what you say there. [SPEAKER_01] I do just want to focus in on a specific element there when you were saying the power users. [SPEAKER_01] Because I think one of the core numbers is actually Mark Benioff said they spent 300 million on Anthropic, which works out to be about three. It'll be interesting to know from him if that 300 million came from, what is the distribution across employees? [SPEAKER_01] So it works out to be, that was on developers within Salesforce. [SPEAKER_01] Yeah. [SPEAKER_01] So it's about 3.8% of developer salaries. [SPEAKER_01] What percent of developer salaries do you think will be spent on tokens in 24 months' time? [SPEAKER_01] Because that fundamentally changes the value of OpenAI and Anthropic. [SPEAKER_01] I do just want to focus on a specific element there when you were saying the power users. [SPEAKER_01] Because I think one of the core numbers is actually Mark Benioff said they spent 300 million on Anthropic, which works out to be about three. It'll be interesting to know from him if that 300 million came from what is the distribution across employees? [SPEAKER_01] So it works out to be, that was on developers within Salesforce. [SPEAKER_01] Yeah. So it's about 3.8% of developer salaries. What percent of developer salaries do you think will be spent on tokens in 24 months' time? Because that fundamentally changes the value of OpenAI and Anthropic. [SPEAKER_01] If it stays at 3.8%, they will not be $5 trillion companies. [SPEAKER_01] But if it's 100% like Brandon at McCool said it will be in a year, they'll be $10 trillion companies. [SPEAKER_02] Well, I think they can certainly be $10 trillion companies, whether it's going to be a full percent of the developer payroll today or not. [SPEAKER_02] Because there's a lot of non-developer work that will also be done with agents. [SPEAKER_02] And that's actually what we focus on for Perplexity Computer. [SPEAKER_02] We're not going after the developer market. [SPEAKER_02] We're going after anything that developers don't, non-developers do. [SPEAKER_02] Your finance department or your corp dev or your sales reps or your data science teams, your research analysts, I think that's actually an even bigger market. [SPEAKER_02] That's not even like clock code multiplied by 10. That's the size of that market. [SPEAKER_01] If I push you on developer salary spend, what percent of token spend as a portion of salary do you think we'll see in 24 months? It's hard to say. I think the costs are going to go down. That's why it's hard to say. [SPEAKER_01] You think the costs will go down? [SPEAKER_01] Because this is the challenge that we've had. [SPEAKER_01] We thought when we went from chat to agent that costs would go down and token costs would go down. [SPEAKER_01] They've gone up. Yeah, for now. [SPEAKER_01] Help me understand that and how that changes. I think in software, you kind of want to pay for the frontier. It's kind of like if you know some engineer is awesome. [SPEAKER_02] If you know you have the next Jeff Dean, would you rather hire that person and not hire five people who are medium engineers but not Jeff Dean level with the same amount of budget you have? [SPEAKER_02] Yes. [SPEAKER_02] Right. [SPEAKER_02] Let's say you had a million dollars. [SPEAKER_02] You could hire five people worth 200K or you could hire one Jeff Dean and pay them a million. [SPEAKER_02] What would you do? [SPEAKER_01] One Jeff Dean. Yeah. So I think you would pay for the frontier. But what stays frontier keeps changing. In 12 months from now, let's say, thought experiment, there is an open source model as good as Opus 4.8. And you still have to pay for inference. Nothing is truly free. But it's going to be, let's say, 10 times cheaper than Opus 4.8. [SPEAKER_02] And when you pair it with the right agent harness, and all the connectors, GitHub, everything, all your developer workflows work fine, why would you assume that the token spend is going to be still high? It's not going to be for the same things you're doing today. It's not going to be. But there might be a different set of things you might do with the frontier that you're not conceiving today. My prediction would be agents that are completely autonomous software engineers. Today, I think we're all using tools like Cloud Code or Codex to write code, but not as literal software engineers. [SPEAKER_01] There is a large swathe of people that is now bearish on your frontier models who are openizing your Anthropics because they're realizing that you can actually do a lot with open models for a fraction of the price. [SPEAKER_01] What you're saying is actually that is true, but we will still pay for the frontier. [SPEAKER_01] That's right. [SPEAKER_01] And so they will still accrue great value. [SPEAKER_01] That's right. And I think this distinction feels like a contradiction. It's not, though. It feels like two things cannot be true simultaneously, but that's not quite the case. In fact, I would argue that the frontier is increasingly going to be a thing that very few individuals might even want. [SPEAKER_02] You could argue that after a point, it's not even interesting that AIs can write software. [SPEAKER_02] You've normalized it, right? [SPEAKER_02] Let's say that's going to be the case. [SPEAKER_02] Instead of companies being built with tens of thousands of software engineers, unlike the past, there'll be a lot more companies with smaller software teams, and each of us will be using a lot of AIs. [SPEAKER_02] So that's actually good for the world. [SPEAKER_02] We'll be seeing a lot of different businesses. We'll be seeing allocation of software labor in places that was never even possible. And whatever is the frontier is going to be things that AI is going and designing chips. AI is designing drugs. AI is figuring out how to build robots. AI is figuring out how to cure cancer. These are applications where you don't have 10 million users. It's a few companies, but the effect of that work will touch a lot of human lives. I think to me that's where the frontier is headed. You could also see that from the moves that frontier labs are making. Anthropic bought a wet lab. Could be for the talent. Could be for the infrastructure to run wet lab experiments. But imagine taking all those tokens and putting it in the mid-training instead of just tokens from GitHub. Right? So then that's going to produce something interesting. [SPEAKER_01] Don't laugh. [SPEAKER_01] Is there an asymptote to frontier problems to be solved? [SPEAKER_01] I know that sounds ridiculous. [SPEAKER_01] But if you are continuously on the chase for the next frontier problem, you get to cancer. [SPEAKER_01] You get to climate change. [SPEAKER_01] And my word, I hope they solve both. [SPEAKER_01] And heaven, that's a huge amount to solve. [SPEAKER_01] But if you're on the treadmill of continuously on it, is there an asymptote to that? [SPEAKER_01] Do you see what I mean? There's no mathematical argument to there being a cap on the amount of economic value one can create with AGI or ASI-like systems. And Elon has a good argument for this. [SPEAKER_02] He says money loses all meaning in a post-AGI economy. [SPEAKER_02] Because you will be producing an abundance of energy and labor. [SPEAKER_02] And fundamentally, the economy is grounded to energy and labor. [SPEAKER_02] If you can produce an abundance of them, well, what meaning does money have? And heaven, that's a huge amount to solve. But if you're on the treadmill of continuously on it, is there an asymptote to that? Do you see what I mean? [SPEAKER_02] There's no mathematical argument to there being a cap on the amount of economic value one can create with AGI or ASI-like systems. [SPEAKER_02] And Elon has a good argument for this. He says money loses all meaning in a post-AGI economy. Because you will be producing an abundance of energy and labor. And fundamentally, the economy is grounded to energy and labor. If you can produce an abundance of them, what meaning does money have? And so I don't think we run out of things to solve at the frontier. I think we're always going to be creative. Why did people even want to understand the universe? Why did we want to understand subatomic particles, quantum physics, black hole theory, the origins of the universe? What is the purpose? But we still went ahead and did it because that's what the purpose of humanity has always been, to understand the unknown. David Deutsch is famous for saying this, right? We are the only species capable of being curious about what is already familiar. You can stare at a fruit and you know that it's a mango and you know exactly how it tastes, you know how it looks, you know the shape, you know what seasons it grows. But you can still look at it and ask one more question about it that you haven't asked before. Other animal species cannot. Once they have it in their mental model, what it looks like and touches and feels like, they're going to ignore it. It's not interesting to them. [SPEAKER_01] Can I ask you, you mentioned about agent usage and you said if you do repetitive tasks versus one off, say cron jobs. I think someone said we're going to have a 24-7 AI. [SPEAKER_01] And they've talked about a hardware product that's going to come out. [SPEAKER_01] Do you think we will have continuous agents running? Yeah. And I think so. [SPEAKER_02] And I think that's why I believe the orchestration problem I talked about maximizing the token value. Can you just help me on, sorry, when you say the orchestration problem. [SPEAKER_02] Yeah. So there are four objectives. [SPEAKER_02] Accuracy, intelligence and accuracy, and then privacy and cost. These are all competing with each other. So you could argue that you could max out on intelligence and accuracy by building giant data centers and spending a lot of power to run them. And you could miss out on privacy and costs because everything will be centralized and you're going to be paying a lot. You could argue that everything can run locally. And so that'll be good for privacy and costs, but may not be frontier intelligence, may not be frontier accuracy. So the solution is to figure out a sweet spot. Use local models when necessary, use server side models when necessary. And orchestrate across local models and server side models, grounded in valuable personal context. Sometimes the intelligence might already be there, but the system might not work because the harness isn't grounded in the right set of tools, right? So build a world-class harness that can even make an okayish model appear great and be able to use the right model for the right task and the right part of the task, subagents. And even utilize the compute we all have in our own devices that doesn't need to be always on the server. That is an orchestration problem, a router, an awesome router, a master orchestrator router. Now, if you do that, you can realize the vision of a 24-7 AI without people freaking out about going bankrupt. Because no one's going to be able to afford a 24-7 AI, frontier AI, running on the server. Imagine you turned it on and you could never switch it off unless something crazy happened. Imagine you're the thing that most people worry about with AI is, what if it does something crazy? But the real concern actually is the cost. Nobody's going to be able to afford a cron job at the fidelity of a few seconds that runs all the time. And so the bottleneck there is actually orchestration and local compute. And so I believe one needs to build a continuously learning local model that can save you on compaction, context windows, and try to preserve as much compute locally and rely on the server-side frontier only when necessary. And keeps learning, keeps adapting, keeps evolving. And that model is not just a model. It's a model plus the harness, plus the local chip and the compute and the ecosystem of devices it controls. That system is going to be your own intelligence. Essentially, the data center moved to your local device. And you get to control it. You get to own it. You don't get to worry about somebody spying on you or looking at all your tokens, very valuable personal tokens. Imagine you have very sensitive deal materials. Let's say you're doing a deal. And then a frontier lab has all your tokens that you use to write a memo. Imagine somebody could hack into that server and steal your deal from you. You wouldn't want that, right? [SPEAKER_01] I'm going to be honest. [SPEAKER_01] I think there's much more valuable things for people to steal from a London-based VC. [SPEAKER_01] But yes, I can see. You're not just another London VC. You have a $400 million fund last time I read it. So imagine you're already making your moves for the $4 billion fund, right? [SPEAKER_02] So everyone has certain levels of sensitive stuff. [SPEAKER_02] And so I think that's where I believe the 24-7 always-on agent is going to be realized by the company that wants to play the role of the orchestrator, not the model builder, not the frontier model builder, but the orchestrator. [SPEAKER_02] And I think that's what we want to do. So Computers has been positioned explicitly as the agent orchestrator. The musicians in the orchestra are these sub-agents that utilize these different models. Think of them as the instruments. And the tools, the connectors, the models, these are all the instruments. And the musicians are the sub-agents. And the symphony is the work. And the system is the orchestra. And Computers is the orchestra conductor. That's how it's been positioned. So what it orchestrates keeps evolving, right? It changes. It changes from models to files to tools to chips to devices. But it doesn't even matter. You don't care as long as it orchestrates things correctly and maximizes the token value for the user. And the tools, the connectors, the models, these are all the instruments. And the musicians are the sub-agents. And the symphony is the work. And the system is the orchestra. And computer is the orchestra conductor. That's how it's been positioned. So what it orchestrates keeps evolving, right? It changes. It changes from models to files to tools to chips to devices. But it doesn't even matter. You don't care as long as it orchestrates things correctly and maximizes the token value for what? For user. If you can solve this problem, you will capture the most economic value in AI long term. Short term, it might look like this other lab's revenue is growing exponentially, this, that. But long term, this is the one objective that truly matters. Who is best positioned to do that? I believe it's us. Because you have the incentive of not token maxing. You have the incentive of delivering the most value to the user. Every time any part of the AI stack improves, our product improves. Since the beginning of the year, Anthropic's models have made tremendous progress. But what's also true is that our revenue has more than tripled since the beginning of the year. Tripled since just the beginning of the year. And a lot of thanks to model progress made by Anthropic. And we also brought our burndown thanks to OpenAI competing with them and bringing down the cost of the same capability. And now, with progress in open source and local models and local chips, we're going to move some of the inference back to the local devices and bring down the cost even more. So every time any part of the AI stack, whether it's chips, models, harnesses, any of these gets better, our system improves tremendously. And if our system improves tremendously, our users love it and they pay more. They spend more. And so our business grows. So I think to your question of who's best positioned to win in that world for that objective of being an orchestrator is the one whose product or business benefits from other people's progress at any layer of the stack. [SPEAKER_02] And so if Jensen produces a better chip, it's great for us. [SPEAKER_02] If Dario produces a better model, it's great for us. If Apple produces a better device, it's great for us. And I love the fact that we are able to be a very positive player at every layer of the stack and not have to rely on any one person to win. [SPEAKER_01] When we look at the different providers that we said server-side versus on-device, when we look at server-side, a lot of people are wearing an AI infrastructure bubble, which I think is funny, stupid and moronic. [SPEAKER_01] To what extent do we have a data center supply problem today from what you see? I think the biggest problem is actually in power. So let's break down. What is a data center? Is it that you just buy a bunch of chips from Dell or Super Micro? No, that's just one part of it. You actually have to secure land or you have to lease something, lease a property. And you have to buy a bunch of turbines to generate power. Or you have to work with power suppliers, grid suppliers. And you also have to work on cooling. So there's a lot of other work you have to put in that is far, far slower. You have to get permits to do all these things. And so usually what's happening is there's a lot of lead time doing this. And the models that are already in use today, these have been trained in the Hopper generation. So the Blackwell generation model, I think the first model that's Blackwell generation category is Mitos. And it's already scary. People are already freaking out about it. So imagine that everyone pre-trains a model on a million or hundreds of thousands of Blackwells. Now those models are going to be far more powerful than what exists today. And then the Vera Rubens are coming next year in full capacity. All the data centers of Vera Rubens will be used next year. That model will be even more powerful. So I think there is a certain physical build out time that always bottlenecks frontier capabilities. That's why there's a value in that layer. Whoever knows how to do this puts together a bunch of GPUs and chips and networking and power and cooling and actually orchestrates all this software layer on top and is able to convert that into frontier output tokens. [SPEAKER_02] That vertical integration has a lot of value. [SPEAKER_02] So that's why the markets are pricing infrastructure companies with a higher PE ratio than companies like Meta, for example. [SPEAKER_02] Even though Meta has a lot of infra, it's valued as a software company. When we see Meta's capex spend and it wanting to increase in the last few days and thinking about raising more and more money to increase capex spend, I get it with a lot of the AI providers like your OpenAI or your Anthropics because they are making money from their AI products. For Meta, the capex spend correlates to increasing accuracy on ads, which is a 6% to 8% bump in revenue. I get it. [SPEAKER_01] Yeah. [SPEAKER_01] But for the capex spend, it doesn't make sense. Well, I believe they are understanding what the market's saying. I don't think they're dumb to not see what's being said. I think they're introducing a lot of subscription products from what I'm reading. So they're definitely going to the company needs to not just be a social platform maximizing engagement and turning that into ad revenue, right? And I think that requires them to launch a lot of agents, subscription-based products, and maybe even a cloud, MetaCloud that rents out servers like what Elon's doing at SpaceX. And maybe once they do that, the narrative might change, right? [SPEAKER_02] But to go back to my point, it might not be inconceivable that Micron, the supplier of HBMs, might be more valuable than Meta in the next 6 to 12 months. [SPEAKER_02] It's already at a trillion, and Meta is 1.3 to 1.4 trillion. Can you help me understand that? Because memory is already a massive bottleneck. It's increased 5x in price in terms of the cogs. Right. [SPEAKER_01] But people are saying Micron is fully priced at this point. [SPEAKER_01] Why is it not fully priced? Because it's still the bottleneck. Whatever is the bottleneck will command the price. AMD is doing really well because CPUs became a bottleneck again. And agent loops, agent harnesses are all running on CPUs. The tokens are produced by the frontier models on GPUs. [SPEAKER_01] Can you help me understand that? [SPEAKER_01] Because memory is already a massive bottleneck. [SPEAKER_01] It's increased 5x in price in terms of the COGS. [SPEAKER_01] Right. [SPEAKER_01] But people are going, wow, Micron is fully priced at this point. [SPEAKER_01] Why is it not fully priced? Because it's still the bottleneck. Whatever is the bottleneck will command the price. [SPEAKER_02] AMD is doing really well because CPUs became a bottleneck again. And then agent loops, agent harnesses are all running on CPUs. [SPEAKER_02] The tokens are produced by the frontier models on GPUs. But whatever work, let's say Claude generates a coding script that decides to download 500 files from different websites and then munges a lot of data and transforms it in certain ways and generates a plot and then hosts it on a website that you can share with your people. So all that computing is running on CPUs. Agents are using CPUs more than humans. And so suddenly there's a rise in enterprise CPUs. And the beneficiaries of these are like Intel and AMD. So then they get to be the bottleneck. Whoever is going to be the bottleneck will win. And so infrastructure is the bottleneck right now because there's a lot of demand and we just don't have the supply. And so whoever supplies memory, SSDs for storage, CPU compute, suddenly these are all interesting. They're more important than companies that are just building data centers and not knowing how to turn that into a valuable output. [SPEAKER_01] Do you believe your Nebius and your Coreweaves will be a sustainable multi-hundred billion dollar company in the future or is it solving a short-term supply problem? I certainly think they can be sustainable. [SPEAKER_01] Yeah. I think there are some—I don't know particularly which of those is going to win. And there's also other players like Crusoe and Firebird and a bunch of companies. It's all about being resourceful. You got to take power from areas where there's a lot of natural resources. [SPEAKER_02] And the cost to bring up the data center is pretty cheap. And the time to bring up the data center is cheap. And your service is reliable. If somebody commits to buying a hundred thousand GPUs from you, the service should be pretty good. And you should be able to secure the supply ahead of time and plan well. And I think some companies are even innovating at the power layer. You know, generating their own power is one way to bring down the margins. And so I think there's certainly value in that layer because it's hard to replicate work. That's how I see it. You could argue that OpenAI can do all the work that CoreWeave was doing. And that's what they wanted to do with Stargate. But why is CoreWeave more successful at building data centers than OpenAI? It's hard to do. [SPEAKER_01] It's operationally intensive. Yeah, operationally intensive. [SPEAKER_02] You got to focus. [SPEAKER_02] You got to spend most of your time securing permits, figuring out power, figuring out bottlenecks in the supply chain here and there. [SPEAKER_02] And constantly plan ahead and test all these systems carefully, deal with random physical issues that arise in running a data center. There's something called TCO, total cost of operations. [SPEAKER_02] You got to factor that in. [SPEAKER_02] And so that said, I don't think there's value if you're just a server renter. [SPEAKER_02] If you're just a GPU server rack renter, if you're just leasing it to different companies on certain hourly pricing rates, there's not a lot of value. [SPEAKER_02] You have to actually build some software on top, how AWS did. [SPEAKER_02] It's called Amazon Web Services, not Amazon servers, right? [SPEAKER_02] So you have to have some software orchestration on top that allows you to get software margins on top of what you're doing. [SPEAKER_02] And I think that's why you're seeing moves like Nebius going for AI model inference, taking open source models or hosting your models. And that's a business model of certain other companies like Fireworks and Base 10 and all that. But you could imagine Nebius just going for that business. [SPEAKER_01] That was exactly my question. So I just had the co-founder of Nebius on the show. [SPEAKER_01] And the really clear takeaway was the challenge that he has, which is there's a huge amount of money that wants just capacity and compute. Yeah. [SPEAKER_01] With the awareness that he needs to build a full-stack product if he wants to have a long-term sustainable business. [SPEAKER_01] Yeah. [SPEAKER_01] That was the core realization for me. [SPEAKER_01] When I look at the inference layer, like you said, Fireworks or Base 10, how do you think that plays out? [SPEAKER_01] Do we have standalone $100 billion companies in inference alone? [SPEAKER_01] Or do we see that commodity? I mean, it's all about working backwards. [SPEAKER_02] What does it take to build a $100 billion company? [SPEAKER_02] Assume— 10 billion in revenue. [SPEAKER_02] Exactly. 10 billion in revenue, 30 to 40% gross margins, good amount of net income, good cash flow. Okay. 10 billion in revenue is not inconceivable for a company that can both do AI-hosted inference and server capacity and data center buildouts. It's operationally well. It's all about factors beyond their control, like open source models continuing to be awesome. [SPEAKER_02] If open source models stop being good, where the gap between them and the frontier is more than 12 months, 15 months, 18 months, then I don't think these companies really have a business model because they're not going to be able to host. [SPEAKER_02] They're only going to be able to rent capacity to OpenAI or Anthropic. And so— That's exactly what Roman and Nebius said. He said if consolidation happens and there's Anthropic and OpenAI or two or three dominant providers, that is the biggest threat to Nebius. That's correct. [SPEAKER_02] Yeah. [SPEAKER_02] And so you got to make a leap of faith assumption that models from China or NVIDIA is making good progress on their models in Nemotron. [SPEAKER_02] So there's going to be enough factors in the market to keep consolidation from happening as an outcome. But you don't control your own destiny if you're those companies. That's basically the problem. [SPEAKER_01] Totally get that. [SPEAKER_01] Okay. [SPEAKER_01] So we can have standalone companies that are $100 billion in inference alone. [SPEAKER_01] Sorry, I'm just using you for your knowledge. [SPEAKER_01] When we look at the model selection companies like OpenRouter or Factory AI just released that kind of model selection or model routing product. [SPEAKER_01] Yeah. [SPEAKER_01] Which did very well on launch. So there's going to be enough factors in the market to keep consolidation as an outcome from stopping from happening. But you don't control your own destiny if you're those companies. That's the problem. [SPEAKER_01] Totally get that. [SPEAKER_01] Okay. [SPEAKER_01] So we can have standalone companies that are $100 billion in inference alone. [SPEAKER_01] Sorry, I'm just pillaging you for your knowledge. [SPEAKER_01] When we look at the model selection companies like an open router or Factory AI just released that model selection or model routing product. [SPEAKER_01] Yeah. [SPEAKER_01] Which did very well on launch. [SPEAKER_01] Is that $100 billion companies in the model selection and routing business? Probably not. I think you can be a provider of a router. You have to use the router to produce something meaningful. Actually, most of the business value of open router is less than the router, even though the product is called open router. It's not routing across models there. It's actually just routing across different endpoints of the same model. So, maybe let's ask this question. If you wanted to use Claude Opus or GPT-55's developer, why would you not want to just use it with your own API key versus using it inside open router? Number one argument. The single simplest argument as to why you would want to do that is model fallbacks. Sometimes your API keys might not have the rate limits. Or even if you have the rate limits, there might be an error on open AI servers that don't guarantee you the response time you need to run your application. [SPEAKER_02] And open router would go and earn the, they would pay for capacity for one year ahead with the funding they have and secure the rate limits and multiple endpoints across multiple different providers of open AI models, be it Bedrock or Azure or open AI themselves. [SPEAKER_02] And so that routing is valuable. [SPEAKER_02] It's essentially an infra problem they're solving, which is reliable token supply. It's not actually lowering the cost by deciding if this prompt should go to GPT or Claude or something like that. [SPEAKER_02] That's not what they're actually selling to the developer. That's not the business model. And then for a lot of these Chinese open source models, you probably don't want your API tokens going to, let's say you don't want your API tokens going to China. Yeah. And let's say you don't have the bandwidth to work with different inference providers or verify who's good and who's not. You're just trusting open router to take care of all that. And then they're going to supply the tokens to you. [SPEAKER_02] So it's routing not at the level of deciding which model is cheaper or task. [SPEAKER_02] It's more reliable token supply. [SPEAKER_02] And I think there's some value in that layer, definitely. Otherwise, they wouldn't have these many users and these many trillions of tokens being routed a month. But it's not high gross margins business. The way the business model works for them is they would secure a discount from the model providers by guaranteeing a lot of supply. But they would still charge the user listing price on the API. And that difference is their margins. Do you understand? [SPEAKER_01] I totally get you. [SPEAKER_01] We spoke about bottlenecks and you said about HBM, high bandwidth memory and Micron and the value that they have today and what it can be. What bottleneck will we have in three years that we're not discussing today? [SPEAKER_02] I think power will remain the bottleneck. [SPEAKER_01] Yeah. Unless something dramatically changes in the way data center buildouts happen. I actually believe that there will be a lot of resistance to building data centers. It's because people incorrectly think that data centers consume a lot of water or eat up a lot of power, which both are untrue. Satya even made the statement that it's a can of water or something in terms of how efficient these companies are. [SPEAKER_01] Do you think that's why they're putting up resistance in them? I don't. I think it's because it's a symbol of job losses, increasing wealth inequality. It's a lot of things. [SPEAKER_01] It's a lot of things. [SPEAKER_01] It's a lot of apprehensions, fear about what's going to happen, channelizing in so many different ways. Sometimes it's channelizing through hatred for wealth inequality and wanting to tax people. Sometimes it's channeling through concerns for the environment and climate change. Sometimes it's channelizing in a way where you're all, oh, the price of the grid is going up because you guys are building all these data centers. And then I'm paying more for my phones and laptops now because the RAM prices have gone up because you guys went and bought all of it. So I think there's a lot of different ways in which it's getting channelized, but the common sentiment is a pretty bad sentiment about AI. [SPEAKER_01] Do you think it will be meaningful to the development of those data centers? I think right now 40 out of 100 are not being developed because of public resistance. [SPEAKER_01] Yeah. So that's where the power bottleneck is. And you could see maybe certain countries seize the opportunity for this and allow these model builders to build data centers there. Elon's going to space to do that. So that's going to be an interesting experiment because there's a lot of energy from the sun that can be harnessed there. And there's a lot of natural resources in other countries. Regulations might be more friendly. So we're still going to see data center buildouts. It might not happen in the U.S. But the fact that you have to solve physical problems, you actually have to deal with the supply chain, the permits, securing power, making sure things work and getting the lead times lower and lower, you're not solving problems by cloning some SaaS apps here. Right. Or building a go-to-market team or doing better marketing against the competitors' products. Yes, those are also hard problems, but these are much harder problems where you're not in full control of your destiny and you need a lot of capital and connections and the right people, sometimes even political help to unlock progress. And so that's why this will continue to remain the bottleneck, in my opinion. And there's a lot of risk as well, because if you do encounter another Deep Seek moment here, where there's a vastly more efficient model that's been built with a very different vertically integrated architecture, and you built out all this capacity and you're like, damn, that's, I overbuilt. That's something far more efficient that can run on people's local devices, your MacBooks, their Windows PCs. Yeah, you're probably freaking out then. And so you hope that— [SPEAKER_01] How likely do you think that is though? It's probably 20%, 30% chance. [SPEAKER_02] And so that's why this will continue to remain the bottleneck, in my opinion. And there's a lot of risk as well, because if you do encounter another deep seek moment here, where there's a vastly more efficient model that's been built with a very different vertically integrated architecture, and you built out all this capacity and you're like, damn, that's, I overbuilt. That's something far more efficient that can run on people's local devices, your MacBooks, their Windows PCs. Yeah, you're probably freaking out then. And so you hope that— How likely do you think that is though? It's probably like 20%, 30% chance. The reason I think there are some possibilities is because of the export controls. So deep seek is not building within NVIDIA stack. They're building the Huawei stack. And because there are export controls on not just NVIDIA GPUs, but also on HPMs, these architectures that deep seek is building are far more memory efficient. They've made innovations on the KV cache to be really small enough that you can host it on the SSDs. And they don't need high bandwidth memory for inference time. And they're going to have a completely different architecture for inference, [SPEAKER_02] completely different architecture for storage, because they're not allowed to use the 3D NANDs. [SPEAKER_02] So their architecture is going to look—it's not just a model architecture. The model architecture is already pretty different. They've made innovations on the attention layer. They've made innovations on the training algorithms so that it doesn't consume a lot of interconnect capacity. [SPEAKER_02] So they've made a lot—their whole stack is getting vertically integrated to their hardware and their chips and their fabs and so on. And so that's a very different bet from what America is making. Do you think the export controls have helped or hurt us? [SPEAKER_01] Hurts a lot. Short term it's helping. Because the only reason I—my belief, the only reason why there's even like a 12-month gap between open source and frontier is export controls. It's definitely helped. And definitely companies like Anthropic lobbied very hard for it. But there is a chance that because of that, they now get really good at the physical layer. And one advantage they have is they can actually build data centers a lot, lot faster. [SPEAKER_02] Power is not a problem. [SPEAKER_02] Permits are not a problem. [SPEAKER_02] People are not a problem. [SPEAKER_02] Labor is not a problem. Expertise is not a problem. And so by forcing them to go out there and build all this, you're converting them into a far more potent competitor. Do you think we still dramatically underestimate China's capabilities? [SPEAKER_01] I think so. [SPEAKER_00] Because if AI is not just digital, but also physical AI, you've got to build fabs, robots, chips, and harness the energy really well and package it into local devices. I think they have a lot more advantages than America. How important is it that we have our own TSMC in the US? [SPEAKER_01] So TSMC is actually—there is a fab of TSMC in Arizona. Not a lot of people talk about this, but TSMC is investing like $150 billion into that, into building American fabs. And they've already invested $40 billion or something like that, $60 billion last time I checked. So there is a TSMC in Arizona that's coming up. There's also Intel. And that's why, you know, American government owns 10% of Intel. Nvidia and SoftBank own 5% each. So there is a lot of investment going into an American fab as well as TSMC is investing into its American fabs. Elon's building TeraFab. I think people have woken up to the importance of building fabs. But this is also why China is particularly very, very competent. Because given the capabilities of China that we just mentioned that really articulately, [SPEAKER_01] I know it's a ridiculous question, but if I were to say to you, your job is to make sure America stays competitive, what would you do to ensure that you retained competitiveness in an increasingly strong China? I think take physical infrastructure a lot more seriously and continue funding it. And not have all these—you know, I wouldn't say meaningless. It's more like not propagate fake news around data centers about how data centers are polluting, contaminating water, or they're sucking up all the water and actually be fact-driven. And so, you know, I hope our product helps there. You can go to Perplexity and ask any question and get fact-checked on your assumptions. But it's very important that we educate the public about what's actually going on in a language they easily understand. And not fear monger. Not be like, oh, all their jobs are going to go away. There's going to be lots of amazing companies that are going to get built with far fewer people getting multi-billion dollar, multi-hundred million dollar valuations with 20, 30 people and propelling trillions of dollars of new GDP. Let's talk about how to enable that. Let's talk about how to build that and create a more positive future together. Instead of, oh, 90% of the jobs are going to be gone. They're all going to get screwed over by our models. [SPEAKER_02] And it's our moral duty to tell you all this. That doesn't make any sense to me. You can't win by saying that and also complaining about not being able to build data centers fast. Do you think we've done a complete disservice by having the marketing message that Dario has had that all jobs are going and it's all doom and gloom? [SPEAKER_01] Yeah. I think so. [SPEAKER_02] I mean, I think they have contradictory messages in their own different social engagement so far where the most reasoned one I heard was there is no evidence that AI is taking over jobs. [SPEAKER_02] But there needs to be a consistent communication around this. [SPEAKER_02] And I also think that very little is being spoken about how AI can help you build companies in a very different way. [SPEAKER_02] With the current AI, organic AI. [SPEAKER_02] It's already true that so many things you would hire people for, you can do it with agents. But one way of looking at it is, oh, what happens to all the jobs? But the other way of looking at it is, hey, I can—I never had the chance to go build out a company on this idea that I've been having all this time. And maybe me and a group of friends can come together and build this and can you guys figure out a way to give us compute credits? [SPEAKER_02] Or, you know, Amazon gave a lot of compute credits to a lot of startups. [SPEAKER_02] The current AIs, organic AI. It's already true that so many things you would hire people for, you can do it with agents. But one way of looking at it is, oh, what happens to all the jobs? But the other way of looking at it is, hey, I can never had the chance to go build out a company on this idea that I've been having all this time. And maybe me and a group of friends can come together and build this and can you guys figure out a way to give us compute credits? Or, Amazon gave a lot of compute credits to a lot of startups. When we started Perplexity, we had around $200,000 worth of Amazon credits and GCP credits and Azure credits that almost together, cumulatively, this was worth a million dollars in compute credits. Now, in today's world, it's going to be a million dollars of compute credits. And we're doing that. We're funding this thing called the billion dollar build, where we're giving a million dollars of compute credits to any group of people who have a credible path to building a billion dollar company. And I want thousands of companies to be built. What did you think of Sam Altman giving $2 million of tokens to YC companies? [SPEAKER_01] I think we should do more of that. Yeah, that's the right thing to do. We should do a lot more of this because you want new companies to be built. And even if they're worth multi-hundred million dollars, right, it's good. If there are thousands of them, that's a lot of new GDP. I spoke to Anne Bordetsky before the show, and she said how AI-pilled the team is for you. [SPEAKER_01] How big is the team today? It's 400 people. 400 people. How big will it be in two years' time? [SPEAKER_01] I don't know. It's hard to say. Maybe 800,000. So will companies follow the same headcount trajectory that they have always followed and we will just solve new problems? Or will they be dramatically more efficient with a much fewer number of people? [SPEAKER_01] Definitely they'll be dramatically more efficient. Right. And that's why I am a believer in building a lot more efficient companies and being an example for all these companies ourselves. People should look at Perplexity and be, oh, with 400 people, you can build a multi, I don't know, $20 billion company. And so that means with 40 people, I could probably build a $1 billion or $2 billion company. And that's totally doable. Totally doable. And so for us, maybe that means with 4,000 people, we could be worth $200 billion. We could be worth $2 trillion with 10,000 people. I think that doesn't mean it's bad for all the 100,000 people we did not hire for a typical $2 trillion company. I would rather have those 100,000 people be split into groups of 100,000 groups. And each of those 1,000 groups are worth a few billion dollars. That's awesome. And I think a lot more people need to be entrepreneurial. There are people who would be bad employees in any company because they're difficult to work with. They don't listen to instructions. They don't follow roadmaps or they're not easy to collaborate with. But maybe the flip side of that is those are the kind of qualities that founders typically have. Aaron, there is a population and a very large population that are not AI native people that are not using AI to improve workflows, improve efficiency. What would you advise them? [SPEAKER_01] Get started. First steps, get started. And channelize your curiosity, right? You don't need to use AIs to do your existing work. If your existing work is boring to you, you probably won't enjoy it even if you use AIs to do it. You got a lot of heat for saying people don't like their jobs. I didn't say if you actually listen to my interview, I did not say that. People want clickbait articles and they take something I said in one sentence out of context and make it into a headline. [SPEAKER_01] What did you say? I specifically said this. Hey, there are a lot of people who don't enjoy their jobs. By the way, the fact that that thing went viral is not because I was completely wrong. I think a lot of people resonated with the fact that I was actually honest in saying a lot of people don't enjoy their jobs. And that has nothing to do with your economic position of standing in society. You might even be really wealthy, but doing a job that you completely don't enjoy and destroying the peak years of your adult life, working on something that is horrible or depressing. So my point is that if that's you and the reason you could never leave your job is because you were always worried if you, how would you build a company from scratch? There are all these things to figure out. You have to hire a lot of people. You have to set up an office, this, that. That's changed for the first time in history. You can get started on an idea with one or two other friends and maybe have a real genuine shot at building a billion dollar company. [SPEAKER_01] Totally get that. Everything that we've discussed today has been on the back of unprecedented demand up and to the right. We need more memory. We need more data center supply. We need on demand service. Everything's up and to the right. Seeing some cracks in. Uber's saying, I'm not sure I'm getting the productivity gains that I thought. Microsoft lining with them, putting a $1,500 token budget. Do you think we will have a continuous up and to the right acceptance that productivity gains are unwavering? We have to do this. Or will there be falterings along the way? I mean, I'm sure there's going to be falterings along the way. And people are rightfully freaking out about token maxing, which is why I think you need some form of hybrid hygienic inference. You need some amount of inference compute to run locally that you're not paying for tokens on, unmetered intelligence, essentially. [SPEAKER_01] How will the best companies of the future structure token budgets? My hope is that they don't have to understand that. They will be able to work with an orchestrator who does it for them. It's not going to be easy for you to constantly keep track of which models are the best at what things and how do you allocate. Oh, this is the budget for coding. This is the budget for finance. How do you even understand which models are good at each of those things and how much do you spend on each of these divisions? You need some amount of inference compute to run locally that you're not paying for tokens on, unmetered intelligence, essentially. [SPEAKER_02] How will the best companies of the future structure token budgets? My hope is that they don't have to understand that. They will be able to work with an orchestrator who does it for them. It's not going to be easy for you to constantly keep track of which models are the best at what things and how do you allocate. Oh, this is the budget for coding. This is the budget for finance. So how do you even understand which models are good at each of those things and how much do you spend on each of these divisions? You're not going to be able to keep track. I had a friend on the show the other day say that Google will be the token king. [SPEAKER_01] They can produce the lowest cost tokens out of anyone. [SPEAKER_01] They own full stack TPUs, data centers, networking, power, procurement. [SPEAKER_01] Do you think that's true, that they will be the lowest cost token producer? [SPEAKER_01] They have advantages, all advantages one needs to have to be that. But they underestimated the importance of coding models. And so they're far behind the frontier right now. So, again, they could catch up. Totally capable, totally competent team. But today they're not quite at the frontier. [SPEAKER_01] I was shocked the other day. [SPEAKER_01] I saw the Cloudflare announcement that now agent traffic has overtaken human traffic for them. Why are you shocked? [SPEAKER_02] It was quicker than I thought. Okay. I thought that would happen, but in two years. Maybe not now. How does the world change when agent traffic far exceeds human traffic? I think people are just going to have a lot more agency. [SPEAKER_02] That's it. [SPEAKER_02] But do websites go away? Does design not matter? Does the advertising model of the internet die completely? [SPEAKER_02] No, it doesn't. Because my belief is that the advertising model around travel or shopping or fashion are not getting disrupted by agents because the judgment is not objective. Anything where the judgment is objective, the transaction is based on objective judgment, that's going to get disrupted by agents. Anything where the transaction is more subjective, where the decisions are more subjective—like what is the best piece of furniture inside this spot? [SPEAKER_02] Why this particular table? Or those kinds of things. Probably for the mic, you would buy an objective decision. The table, you probably are caring about the aesthetics of the room. I think that's how I feel the world will split. And subjective things will still be ad-based. Objective things will be agent-based. I watched your commencement speech on the back of speaking to Samir at Excel, and he said I had to watch it. So obviously, I watched it. And one of the points you made was the defining skill of the area is asking better questions. [SPEAKER_02] What question is no one asking today that maybe everyone should be asking? I think people need to ask more about like, okay, assuming I have a lot of agency available to me, what do I do? Imagine I gave you a headcount of 100,000 people or 10,000 people and enough compute credits to run those agents. What would you do? Let's say you have suddenly 10,000 agents at your disposal. What would you do? I remember you telling me, or not me, but in some episode of yours where you said you only did this podcasting because you felt like you didn't have an arbitrage to go win deals. [SPEAKER_01] A hundred percent. Yeah. That's why I still do it. [SPEAKER_01] I mean, I love what I do, but yeah. [SPEAKER_02] So you've gotten some amount of distribution. So now assuming that you can, you have, let's say you could spend $100 million on a generic inference and grounded with all the connectors and stuff, and it's all working. [SPEAKER_02] What would you do with that capability to further your goals? [SPEAKER_02] What should your goals even be then? [SPEAKER_02] I think that's the question I would ask. [SPEAKER_02] Assuming that in the next three to five years, you're going to be able to delegate whatever digital task you want and with the right harness and agents and be able to delegate that. [SPEAKER_01] Fundamentally, it would be to build an agentic infrastructure to be able to find, identify, outreach, set up, win great investments and have the media sit on top and power that. [SPEAKER_01] That is intensely difficult to do and would be the holy grail to investing. But that would power what my end goal ambition is. [SPEAKER_02] So your goal is to run a 10 to 100X larger fund, right? [SPEAKER_02] That's basically what I'm hearing from you. So let's assume it's a $40 billion fund from $400 million. Okay. Then all you got to ask is, assuming I have all the headcount I need to do this, how much faster can I do it? I think that's how I would frame this question. I think Elon has a similar thing he spoke about once where, okay, assume that a task, somebody tells you a task is going to take 10 years. Ask the question, what would it take to do it in 10 months? Maybe it's impossible to do it in 10 months, but you'll probably get pretty far asking those questions compared to somebody who takes it for granted that it's going to take 10 years. [SPEAKER_01] All right, interviewer, put it on you. [SPEAKER_01] What's your 10 year and how does that look in a 10 month timeframe? I think our mission beyond any level of capitalism is to make the planet more curious. The product is always intended to help people ask the next question. And my goal is to truly realize that the level of agency that needs to exist in this world is quite not there. I think that needs to be grounded in numbers to make it possible. [SPEAKER_01] It's like me saying, oh, I want the best investments. Which is why a $40 billion fund is helpful. [SPEAKER_02] I can say the same thing, $2 trillion. [SPEAKER_02] It doesn't matter, right? [SPEAKER_02] 100x, 10x, 1,000x. These are all motivational milestones. Do you think Perplexity will be a trillion dollar company? [SPEAKER_01] Yeah. [SPEAKER_01] Anyone can be a trillion dollar company. I think that needs to be grounded in numbers, dude, to make it possible. [SPEAKER_01] It's me saying, oh, I want the best investments. [SPEAKER_01] Which is why a $40 billion fund is helpful. [SPEAKER_01] Sure. I can say the same thing, $2 trillion. It doesn't matter, right? [SPEAKER_02] 100x, 10x, 1,000x. [SPEAKER_02] These are all motivational milestones. [SPEAKER_02] Do you think Poplexy will be a trillion dollar company? Yeah. [SPEAKER_01] Anyone can be a trillion dollar company. SK Hynix and Samsung are worth a trillion last couple of weeks. Did you know Samsung started off as a grocery store? Did you know that? You didn't know that? Okay. So they started selling dried fish. Seriously. Hynix was SK, the SK group started off as textiles company. [SPEAKER_02] So anyone can be worth a trillion dollar company. [SPEAKER_02] You just have to work your way towards that. [SPEAKER_02] The exact same logic for you that you laid out for how can a company be worth $100 billion. [SPEAKER_02] Okay, you said you need to make $10 billion in revenue. Isn't that the same for a trillion? [SPEAKER_02] You need to make $100 billion in revenue? And there was actually some very interesting data that KOTU revealed. [SPEAKER_01] I don't know if you saw it recently, which says about the probability of reaching the next level of value. [SPEAKER_01] Yeah, it's higher. [SPEAKER_01] It's much higher. [SPEAKER_01] So when you're at a billion, it's much more likely to reach $10 billion. [SPEAKER_01] $10 billion, much more likely. [SPEAKER_01] Yeah, that's true actually for even people. It's way more likely for a person with $100 million in liquid net worth to become a billionaire than someone with $10 million. Are you not worried about the wealth inequality? [SPEAKER_01] Aaron, if we were being blunt, we both are very lucky now to live in nice worlds and rarefied airs. Are you not worried by just how much money a very small number of people have and how hard it is for everyone else? And that gap is getting bigger. [SPEAKER_01] I think the way to ensure that doesn't remain the case is to distribute the benefits more widely. You got to let anyone... By the way, the people who are using our tools... [SPEAKER_02] I've had an Uber driver. [SPEAKER_02] So as honest as it can get, an Uber driver in San Francisco once told me that he watched one of my YouTube interviews where I explain how you can build a product or a web app with an AI from scratch. [SPEAKER_02] Went on to do it and used AIs to add billing and all that. And that makes more passive income for him than driving Ubers. And so he actually reduced the amount of time he's driving Uber because he loves live coding new apps. And that already tells you that for the person with agency and a positive outlook for the future, anything is possible. And so if you keep communicating all the negative things you can about AI and wealth inequality all the time, and that's the only thing news and press writes about, I think it'll perpetuate and people will only think the bad things. And so it's very essential that if you think you're already doing well, it's very essential that you talk about what are all the things that can go well and give hopes to people who were once upon a time like you. [SPEAKER_02] You started this podcasting circuit when you had nothing, right? [SPEAKER_02] Nothing. [SPEAKER_02] Exactly. So it's possible. So you got to talk more about that than be, oh, I feel so guilty that I made it. And now what about all these people who haven't made it? They can make it. [SPEAKER_01] I think I have a more pessimistic view of actual general public, which is I don't think that many people have agency. [SPEAKER_01] I think a lot of people have victim mentality. You got to help them. I think that's the most important thing. I think they got to help themselves. Sure. But people will help themselves once they see that, okay, I want to be like this guy. Let me work hard. You need an example, right? Nobody can become in shape without it. It takes discipline. [SPEAKER_02] It takes discipline. [SPEAKER_02] You got to get rid of bad habits. [SPEAKER_02] And so. [SPEAKER_02] And now is the best time ever to change your life in 12 months. The ability to go from nothing to actually billionaire in 12 months is now possible in some respects. Yes. And so I'm not saying everyone's going to make it and everyone's going to be worth a billion dollars. Isn't that the caption from this show? Aravind, everyone's going to make it. Anyone has the potential to make it. [SPEAKER_02] So it's as likely for Perplexity to become worth $2 trillion as a founder who's yet to secure funding to be worth a billion dollars. [SPEAKER_02] So it's equally hard, equally hard. And I think you just have to give yourself shots at the goal and be curious. That's the message from the commencement speech. Be curious. We have SpaceX. [SPEAKER_01] We have Anthropic. [SPEAKER_01] We have OpenAI going public. [SPEAKER_01] It feels like someone's shot the gun and the race is on. [SPEAKER_01] Is there enough money to fund three such large IPOs? There will be some reallocation for sure. There might be some holders of SaaS stocks who would put it into Anthropic or something. Let's say you believe that enterprise AI is going to take off. You might want to hedge between having a lot of Microsoft stock and Salesforce stock versus putting some of that into Anthropic. So let's say Vanguard or BlackRock own cumulatively they own $200 billion of Microsoft and Salesforce. They might be, okay, I'm going to take 30, 40 billion of that and put it into Anthropic. Fine. Not a bad bet to make. What happens to all the enterprise SaaS companies that are public? [SPEAKER_01] Growing. [SPEAKER_01] Yeah, fine. They have to weather the storm. [SPEAKER_02] Is it a storm or is it continuous precipitation? [SPEAKER_01] I think you have to bring down the costs and produce new value. Salesforce has done well because they always went and bought the next thing. If you were just selling the same software, you're probably not going to be around. They might be okay, I'm going to take 30, 40 billion of that and put it into Anthropic. Fine. Not a bad bit to make. What happens to all the enterprise SaaS companies that are public? [SPEAKER_01] Growing. [SPEAKER_01] Yeah, fine. [SPEAKER_01] They have to weather the storm. Is it a storm or is it continuous precipitation? [SPEAKER_01] I think you have to bring down the costs and produce new value. [SPEAKER_02] Salesforce has done well because they always went and bought the next thing. [SPEAKER_02] If you were just selling the same software, you're probably not going to be around. IBM is still around because they went and bought Red Hat and HashiCorp and now they're buying Confluent. So there are ways for these companies to stay alive and extend their lifespans. [SPEAKER_02] It's obviously going to be hard to preserve a brand that's as relevant. [SPEAKER_02] I don't think the IBM brand is that relevant anymore in terms of evoking an emotion and getting people to use their products. But as a business, it's going to be awesome. It's going to be fine. I have to finish on, you said IPO in 2028. [SPEAKER_01] I had to ask this. [SPEAKER_01] I woke up to this in my group. [SPEAKER_01] We have a team WhatsApp and it's Garvin, IPO 2028. [SPEAKER_02] I hope it can be sooner than that. [SPEAKER_02] When do you know when you're ready? Is there a billion in an hour? You're at 500 million an hour now? More than that. [SPEAKER_01] Far more than that, actually. [SPEAKER_01] Really? [SPEAKER_01] We're not ready to share it, but growing really fast. Revenue growth matters much more to you than profitability. [SPEAKER_01] Today. I think in general, by the way, you can look at public markets. People want top line growth more than bottom line efficiency right now. Because it's very hard. [SPEAKER_02] It's rare. [SPEAKER_02] Well, you definitely need one. Of course, sustainable businesses. [SPEAKER_01] You need to have a model in place to get the bottom line efficiency when that becomes the objective. [SPEAKER_02] And you need to also have a path to getting there. Where are you cost inefficient today where you expect to be significantly better in two to three years? [SPEAKER_01] We're training our own models. Post-training on top of amazing open source models. And that will bring down the cost that we currently spend on frontier model tokens. We expect to continue to use frontier models for designing new experiences and new capabilities that do not exist today in our products. But whatever exists today in our products right now, we expect it to completely rely on models we own and serve ourselves. And that's going to be the best way to bring down the costs and increase our margins. Will the largest enterprises in the world all be fine-tuning open models to have tailored models that are much more specific to them? [SPEAKER_01] Absolutely. [SPEAKER_02] Because it's in your incentives to bring down the costs. [SPEAKER_02] Does that not provide another bad case for the large frontier model providers? Frontier model providers will only remain relevant if they remain at the frontier. If for six months you're not seeing a new capability, it's bad for them. And so that's the uncomfortable nature of this field. [SPEAKER_02] No one's ever in a comfortable position. [SPEAKER_02] No one can relax. [SPEAKER_02] This is hard. [SPEAKER_02] Yeah. [SPEAKER_02] It's going to get even harder. And that's the nature. It's just the price is too big. [SPEAKER_02] Take Anthropic. I think it's worth one to one and a half trillion, something in that range. That's basically the valuation of Meta. And this all was created in six years. Meta took 20 years to build. So the price is so big. [SPEAKER_02] And so no one can be comfortable. [SPEAKER_02] And anyone who's winning today can lose tomorrow, including the model providers. Pre this year, there was a three month period where people were talking about perplexity. [SPEAKER_01] What's happening with perplexity? Do you pay attention? [SPEAKER_01] Do you give a shit? [SPEAKER_01] Of course I pay attention to all of that. [SPEAKER_01] Do you care? [SPEAKER_01] There was one in particular in San Francisco, do you remember? [SPEAKER_01] Where they were voting on which company would fail? [SPEAKER_01] Yeah, we were voted the most likely to fail. [SPEAKER_02] Cursor was voted the second most likely to fail. OpenAI was voted third or something. You don't give a shit? I feel we're all doing well. Cursor, I think, is getting sold. SpaceX. OpenAI. Going public, baby. [SPEAKER_01] Going public soon. We tripled our revenue since that judgment was made. Brought down the burn by more than 50%. [SPEAKER_02] I don't know. My sense is that most of those people who sit on these meetups and vote don't actually build anything useful. Cool. [SPEAKER_01] Yeah. [SPEAKER_01] Okay, we're going to do a quick fire round because I could talk to you all day. [SPEAKER_01] First one, what's one widely held belief that you think is completely wrong? [SPEAKER_01] I think a lot of people are obsessed about identifying a moat in the first year or two of their company. But I think the only shot you have is to move fast. In my mind, moving fast is a way of expressing humility because you're constantly making contact with the world and trying to question your assumptions all the time. Where are you still moving too slow internally today? [SPEAKER_01] I think we can be even more AI-pilled. [SPEAKER_02] It's insane I'm saying this because we are building some of the most interesting AI products and internal adoption of our own products or competitors' products can be even higher. [SPEAKER_02] And this is despite us being extremely agent-pilled internally and trying to delegate as much to agents. [SPEAKER_02] Yeah, that's a big area. [SPEAKER_02] My hope is that we can turn this company almost into an AGI. [SPEAKER_02] And that doesn't mean no humans work here, but there will be an AGI that has all the context it needs to run different divisions of the company in a semi-autonomous way with some scaffolding provided by humans here and there. [SPEAKER_02] And that's not going to feel scary at all. [SPEAKER_02] We'll normalize that feeling very fast. [SPEAKER_02] It's just going to feel like the 10x engineer is running certain aspects of the company. If I gave you unlimited money, what would you do today that you're not doing? agents. Yeah, that's a big area. [SPEAKER_02] My hope is that we can turn this company almost into an AGI. And that doesn't mean no humans work here, but there will be an AGI that has all the context it needs to run different divisions of the company in a semi-autonomous way with some scaffolding provided by humans here and there. And that's not going to feel scary at all. We'll normalize that feeling very fast. It's just going to feel like the 10x engineer is running certain aspects of the company. [SPEAKER_02] If I gave you unlimited money, what would you do today that you're not doing? [SPEAKER_01] I would build data centers. You would? [SPEAKER_01] Yeah. In space? [SPEAKER_01] I don't have expertise to do that, but I would start with land on earth. I think there's a lot of land and maybe you can be resourceful in securing permits and power in different countries, but I would start there. [SPEAKER_02] I think physical infrastructure build-outs is the return of the industrial age again. [SPEAKER_02] Like the forefathers who built the industrial revolution, oil pipelines, steel bridges, factories, producing cars. [SPEAKER_02] All these things that we take for granted today were built by people who spend a lot of time thinking about how to scale these things in a cost efficient way. [SPEAKER_02] And so we need to do that a lot for AI. And yeah, that's what I would do. Of course, you cannot just be building infra. You need to be able to utilize all that infra to produce valuable output tokens to the user. But we're already good at doing that. So infra is the thing I would focus on. [SPEAKER_02] You can buy and hold for 10 years: SpaceX, Anthropic, or OpenAI, the three IPOs coming in the next few months. Which would you buy and hold for 10 years and why? [SPEAKER_01] SpaceX. [SPEAKER_01] Why? [SPEAKER_01] It's an end of one company. Like Anthropic and OpenAI can claim they do whatever each other does. But SpaceX is the only company building space infrastructure for connectivity. Have you been on a flight with Starlink? No. [SPEAKER_01] You should. You will hate being on a flight without Starlink after that. Imagine we can record this. I can watch this podcast while flying on a plane. Starlink lets you do that. That's just one aspect of the business. That's just one aspect of the business. [SPEAKER_02] One small aspect of the business. [SPEAKER_01] There's a lot of. I'm excited about possibilities to travel from Australia to San Francisco in 30 minutes. [SPEAKER_02] You know, all this feels like sci-fi, but I'm excited about all these possibilities. What job does not exist today that will be incredibly common in five years' time? I think it already exists. So the forward deployed engineer is definitely on the rise. I guess people with a really good sense of quality control. Maybe a better way to answer this is most jobs that exist, valuable jobs that exist are usually reincarnations of something that already existed. So I don't think we're going to see completely new things. It's just going to reincarnate in different ways. You can advise your little sibling who's finishing university today and just done a computer science degree. What's the one thing you would advise them? [SPEAKER_01] Stay curious. Don't give in to FOMO and trying to max out on something here in the short term. Don't go to Twitter and feel like a loser that people on Frontier Labs are getting so rich and everything feels hopeless to you or something. There is so much more to build. We are just getting started. The application layer era or infrastructure build outs. There's a lot of opportunities. We are seeing more spin outs from OpenAI, Anthropic, you name it, every single day. [SPEAKER_01] Do we have hundreds of these NeoLabs and vertical models? [SPEAKER_01] No. [SPEAKER_02] Not a big believer in too many of them. [SPEAKER_02] I think you've got to produce some differentiation. [SPEAKER_02] That's the most important thing. [SPEAKER_02] Would you call DeepSeek a NeoLab? No. Why? I think very stupidly for me, I don't call it a NeoLab because I attribute NeoLabs to spin outs from larger labs. [SPEAKER_01] I see. And kind of verticalized, which is probably wrong on both axes, but it's horizontal and it's not a spin out. [SPEAKER_01] Yeah. I mean, I kind of like the idea of labs taking a differentiated bet. Okay. If somebody really questions the transformer architecture itself, or somebody really questions needing to build on NVIDIA GPUs or something, foundational bets makes sense. Or somebody questions building for robotics models. I think that's somewhat uncorrelated and different, and that makes sense for a lab, but I feel there are just labs for the sake of being a lab, and I don't think they're going to make it. Can you paint for me the most plausible story where Poplasty becomes a trillion dollar company? [SPEAKER_01] I do like this one. What do you do then? [SPEAKER_01] The orchestration layer? I mean, accuracy and orchestration are two goals that have been consistently true since the beginning of our company. So I think we'll continue to do that. [SPEAKER_02] We'll be orchestrating across devices, chips, models, tools, files, connectors, everything, right? So what would I do once that happens? I don't know. We'll chart our path to 10 trillion. Are you happy now? But are you enjoying this? Of course. [SPEAKER_02] I wouldn't. There are so many things I could be doing if not for this. [SPEAKER_02] And I think the process is what motivates you. [SPEAKER_02] So you asked me somewhere in between, you need to give me a number of where you want. I don't work that way, actually. Like these numbers, getting to 2 trillion or 20 trillion are exciting, but that doesn't motivate me. It's hard to get motivated by wealth. You want to get motivated by impact. Final one: who's the smartest person you've met? [SPEAKER_01] You've met Jensen Huang. [SPEAKER_01] You've met the best of the best. I've been fortunate enough to. Who's the smartest? People are smart in their own ways. It's hard to compare. I don't work that way, actually. For example, these numbers, getting to 2 trillion or 20 trillion are exciting, but that doesn't motivate me. It's hard to get motivated by wealth. You want to get motivated by impact. Who's the smartest person you've met, final one? You've met Jensen Huang. You've met the best of the best. I've been fortunate enough to. [SPEAKER_01] Who's the smartest? [SPEAKER_01] People are smart in their own ways. It's hard to compare. I've met Jensen, Elon, all these guys, and Bezos. What was it meeting Elon? [SPEAKER_02] It's amazing. [SPEAKER_02] Elon's a very focused person. [SPEAKER_02] He might not appear that way on Twitter, with a lot of random tweets, but he's extremely laser sharp focused on whatever he's doing at that moment in time. [SPEAKER_02] Actually, the one skill that as an entrepreneur I would really build from somebody like him, take from somebody like him and have it for myself is that ability to zone out of all the other things happening in your business or other businesses. [SPEAKER_02] And just focus on that limiting problem right now, the bottleneck problem and ignore everything else. It's very hard to do. Even within Perplexity, I cannot just focus on one part of the business alone. It's very difficult. I'm always looking at other things simultaneously. His style is to just always look at the limiting problem and ignore everything else. And that's very hard to do because you actually have to be really good at concentration. You have to be really good at ignoring even important things, which are distractions to your core objective right now. Was Jensen Huang what you thought he'd be? [SPEAKER_01] Far better. [SPEAKER_02] Really? [SPEAKER_01] Yeah. Yeah. Jensen is so truth seeking. It's insane. I think he or somebody else told me or read in a book that he is so intense that he wakes up every day and tells himself that he sucks. And he's so intense that he tells everybody around him that they're 30 days away from going out of business. Think about it. [SPEAKER_02] Right? [SPEAKER_02] Right? [SPEAKER_02] Five trillion dollars guaranteed to make $500 billion in revenue in the next two years. [SPEAKER_02] And has the most advanced chips in the world. [SPEAKER_02] And he operates with that mentality that he could be 30 days away from going out of business. That is what it takes to be Jensen Huang. And there's so much to learn from these guys. There's so much to learn. I think there's one aspect of being comfortable where you are, thinking you've made it. That feels good to get here so far. But these guys are not stopping. I don't think Elon wants to stop. But if you look at his pay package for SpaceX, it's structured around creating a colony in Mars with a million inhabitants and building enough compute in space. So that's why it's not motivating to be worth 10 trillion in net worth or something. If he does these things, I'm sure he's going to get there. But it's more motivated around making the impossible things happen. And having that long-term outlook. I think that has been the biggest thing to learn from maybe these two individuals in particular is a lot of people view entrepreneurship as, if it wins, if I win and have a great outcome and I sell my company, I would have generational money. I don't have to work ever again. And then what? You end up just staying at home and your kids will obviously have trust funds and they're not going to get inspired watching their dad. [SPEAKER_02] Play paddle. [SPEAKER_01] Yeah. [SPEAKER_02] You're not going to set the right example for them. They're not going to be able to take your wealth and multiply it because they didn't watch somebody who actually did that. You did it before they were adults. And so I think you always need to be doing something. Jensen said recently that he hopes to die on the job or something. That's the attitude you need to have. You need to work forever. I was so upset, though, when Jensen said if I'd known how hard it was going to be, I wouldn't have done it. [SPEAKER_01] When he did, I don't know if you saw that interview. [SPEAKER_01] I was like, oh. [SPEAKER_01] Yeah. I think it's pretty hard, but you don't do it because you do it despite that. I think that's how it works. Aaron, listen, this has been so fantastic. [SPEAKER_01] I so appreciate you taking the time while you're in London. [SPEAKER_01] So thank you so much for joining me. [SPEAKER_01] Appreciate it. Thank you. Like, I don't think Elon wants to stop it. But if you look at his pay package for SpaceX, it's structured around creating a colony in Mars with a million inhabitants and building enough compute in space. So that's why it's not motivating to be worth a 10 trillion in net worth or something. You know, if he does these things, I'm sure he's going to get there. But it's more motivated around, like, making the impossible things happen. And having, like, that long-term outlook. Like, you, I think that has been the biggest thing to learn from maybe these two individuals in particular is a lot of people view this, like, entrepreneurship as, like, oh, if it wins, if I win and have a great outcome and I sell my company, I would have, like, generational money. I don't have to work ever again. And then what? You end up, like, just staying at home and, like, your kids will obviously have, like, trust funds and they're not going to get inspired watching their dad. Play a paddle. Yeah. You know, you're not going to set the right example for them. They're not going to be able to take your wealth and multiply it because they didn't watch somebody who actually did that. You did it before they were, like, adults. And so I think you always need to be doing something. Like, Jensen said recently that he hopes to die on the job or something like that. Like, that's the attitude you need to have. Like, you need to work forever. I was so upset, though, when Jensen said, if I'd known how hard it was going to be, I wouldn't have done it. When he did, I don't know if you saw that into you. I was like, oh. Yeah. I think it's pretty hard, but you don't do it because you do it despite that. I think that's how it works. Aaron, listen, this has been so fantastic to do. I so appreciate you taking the time while you're in London. So thank you so much for joining me. Appreciate it. Thank you.