Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Micron Will Be More Valuable Than Meta | How Export Controls Helped Not Hurt China | Power is the Bottleneck to AI | Why Dario Has Done a Disservice to AI with his Labour Replacement Messaging with Aravind Srinivas, Founder @ Perplexity About this episode Aravind Srinivas is the Founder and CEO of Perplexity, one of the fastest-growing AI companies in the world. Since the start of the year, Perplexity has tripled revenue to well over $500M in ARR. Aravind has raised over $1BN for the company with reported valuations reaching $20BN. AGENDA: 05:40 – "Perplexity Changed Google More Than Any PM Ever Has" 10:15 – Why Search Is Not the Future of AI 13:05 – The Most Important Insight in AI: The Model Is NOT The Product 16:10 – Why AI Agents Will Become Bigger Than Google Search 22:00 – AI Will Design Chips, Discover Drugs & Cure Diseases 24:15 – The Secret to Building a 24/7 AI Agent 32:40 – Aravind's Wild Prediction: Micron Could Become More Valuable Than Meta 41:00 – Why Power Will Be The Biggest Bottleneck In AI For The Next Decade 45:00 – Have U. S. Export Controls Accidentally Made China Stronger? 49:00 – Why Dario Amodei's AI Doom Narrative Is Wrong 55:20 – Why Token Budgets are Total BS and Useless 58:00 – When Agent Traffic Surpasses Human Traffic, What Happens To The Internet? 01:08:00 – SpaceX, OpenAI & Anthropic IPOs: Is There Enough Capital For All Three? 01:14:00 – What Elon Musk Is Really Like Behind Closed Doors
Transcript 16,236 words · 4 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 I have nothing to lose. I came from nothing. I never even imagined myself to be doing all this.
0:06 Speaker 01 A twenty billion dollar company.
0:08 Speaker 00 Forty-five million users.
0:09 Speaker 01 Over a billion searches a month.
0:11 Speaker 00 Built in three years by four hundred people. These numbers like doesn't motivate me. It's hard to get motivated by value. You want to get motivated by impact. This is perplexity with co-founder and CEO Aravind Shrinivas. No one's ever in a comfortable position. No one can relax.
0:26 Speaker 01 They forced Google to redesign their homepage. Then bid thirty-four billion dollars to buy Chrome.
0:31 Speaker 00 More than their own valuation. Perplexity changed Google.com more than any product manager at 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. That's my motto. Go all in and try your best. Be on the offense all the time.
0:49 Harry Stebbings? Aravind at perplexity has done many shows. This is the single best podcast he has ever done. 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 Micron will be more valuable than Meta. He says that the resistance today's 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. Aravind was on stellar form today, and this was such a joy to do. But before we dive into the show today, if you're a finance professional, you know the month and nightmare chasing down missing receipts and fighting with outdated tools that your employees hate using.
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4:41 Harry Stebbings? You have now arrived at your destination.
4:44 Harry Stebbings? 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.
4:53 Speaker 00 Thanks a lot, Harry.
4:54 Harry Stebbings? 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?
5:03 Speaker 00 Thrill of winning.
5:04 Harry Stebbings? Why?
5:04 Speaker 00 Because I have nothing to lose. I came from nothing, like I, 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, like doing my undergrad and, you know, just, just training neural nets with graphics cards that people in the labs were using for playing video games. It was all for fun, you know, my path led me all the way here. For my mom, just getting a job was success because we were not, we were financially lower middle class in India, which is not even like lower middle class in UK or the US. And so from there, all we wanted to do was get a job in Google. Being an engineer at Google was considered a win. And so I'm, I'm already doing remarkably well compared to that ambition we had as a family.
5:49 Speaker 00 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 like, that's the stupidest thing to do. It's better, go all in and try your best, be on the offense all the time, attack, attack, attack. That's, that's, that's my motto.
6:06 Harry Stebbings? When you review then, what are you not being aggressive enough on today?
6:11 Speaker 00 Well, I think today, maybe in the early days, we'd be very, very loud on social media, talking about perplexity with Google and I, I used to do that myself a lot. And some people don't like, like me for having done that. Today I'm a lot more measured in how I talk about our products, our competitors and stuff like that. But it's not a lack of aggression or anything. It's just that like it's, that, that is boring. People already heard that enough from me.
6:34 Harry Stebbings? Do you regret the being so bold in your messaging?
6:37 Speaker 00 No.
6:37 Harry Stebbings? So it's not a nuance and maturation of message. It's a that's stale and I need something new.
6:45 Speaker 00 Not just that, I kind of don't think it's a relevant framing anymore. We worked on search, Perplexity started out as search. We built the first answer engine in the world that people know Perplexity even today. If you mention the name Perplexity, people will think, oh, that's an answer engine. We built a lot more things after that. We built a lot of agents, browser agents, deep research, computer. We built so many products after that, but we're still known for that first product. 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.
7:19 Harry Stebbings? Make that argument for me.
7:20 Speaker 00 Well, nobody ever wanted to ship an answer engine at Google. 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, like 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.
7:46 Harry Stebbings? Is that bad or good for you that they learn from you and adapt?
7:50 Speaker 00 It's both good and bad in the sense, I knew this like around end of 2024, this was gonna 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. 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. You know, like 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. It's not for getting answers in the traditional way. They're asking for sophisticated research reports.
8:33 Speaker 00 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 lead gen for the frontier products we built. You need something, right? Like, think about it, every company needs to have one successful product to build the next set of products. In AI, nobody can sit comfortably thinking they have it all sorted out, including Anthropic. If Anthropic thinks Claude Code is already a win, in six or 12 months from now, they won't even be around. It's an uncomfortable fact about the whole field. Would you argue today, you just told me, if you don't mind me quoting you here before we started, that you think OpenAI isn't ready for an IPO?
9:16 Speaker 00 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? Think about it, 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. That's the state of the field. It's less about perplexity or Anthropic or OpenAI not having modes or having modes.
9:39 Harry Stebbings? Can I push back on you there?
9:40 Speaker 00 Yeah.
9:41 Harry Stebbings? I would stand by, two years ago, even when they were dominant 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.
9:51 Speaker 00 Okay, maybe, maybe I'll decouple that.
9:52 Harry Stebbings? Do you see what I'm saying?
9:53 Speaker 00 I'll decouple that. Let's decouple that, being like financial readiness for an IPO versus perception of a dominant leader.
10:00 Harry Stebbings? Yeah.
10:00 Speaker 00 Do you perceive them as a dominant leader right now?
10:02 Harry Stebbings? Yes.
10:03 Speaker 00 In what?
10:04 Harry Stebbings? Consumer search.
10:05 Speaker 00 Well, except there's no money there, right? It's been commoditized. Like, for example, why, why are they going all in on Codex? Because that's where the money is. We're doing the same on computer. Anthropic's doing the same on Claude 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?
10:24 Harry Stebbings? But there has to be more money than just code, Codex, Claude Code.
10:29 Speaker 00 It's not about code, 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. Today, the frontier is about going out there and doing things for you.
10:45 Harry Stebbings? Do you not think then that there will be a 100 to 200 billion dollar advertising business for OpenAI?
10:52 Speaker 00 Yet to be proven. 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. So how much do you think Booking.com spends on Google? Sixteen billion, something like that, some crazy amount like that. How do you book your hotels or flights today? Do you book it on ChatGPT or do you book it on Google?
11:11 Harry Stebbings? Google.
11:12 Speaker 00 Why is that?
11:12 Harry Stebbings? Discovery. I would like to see the options.
11:14 Speaker 00 Exactly, right? So the interface, the interface is less about conversations and more about exploration. So when 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 to 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 like 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. It also fundamentally corrupts the trust that people have when they go into a product and they want the accurate answer, which is what, you know, perplexity is known for.
12:00 Speaker 00 And then you're like, hey, by the way, you asked for the best protein shake, but by the way, these are good protein shakes you can check out. It kind of like hurts the trust that people have in your platform and your product. That's another reason why, i- if you think about it, like, like Meta or like, 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 it works out in China in WeChat because there is no other way for them to fund the whole thing. So the the whole economy and and user behavior has been optimized around gamifying. It's not how things work in America. So I I'm bearish on advertising to really take off in in the chat interface.
12:44 Speaker 00 I'm happy to be proven wrong there, but I'm bearish on that.
12:46 Harry Stebbings? There are two areas that I want to unpack there. The first one, just taking them kind of chronologically and how you said them, money's in the frontier. The more I hear this, kind of the more I question it, because I think that we dramatically overestimate how important frontier models are to do quite basic work.
13:02 Speaker 00 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." 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. And so the reason Greg is right is because if you take Codex or Perplexity Computer or Claude Code, what is that? It's an orchestration system. 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 like rules for how the agent loop should run.
13:44 Speaker 00 What are all the skills and subagents and connectors and tools it accesses? 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 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,
14:27 Speaker 00 whether it's personal connectors or business connectors, and provide the experience to people in one single unified system. 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. You would find both these models inside Perplexity Computer. 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.
15:12 Speaker 00 That nobody else can subsidize other than the government. You know that 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 most important metric in AI is token value per watt per user.
15:32 Harry Stebbings? 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.
15:41 Speaker 00 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 like unlearn for most founders. And 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. If you look at like all these crazy stories of how there's this one engineer
16:23 Speaker 00 who got Amazon to spend like half a billion dollars in a month because of some stupid way they set up like agent loop inside Claude code. Okay, maybe that's a mistake, but there are real engineers in Meta and other companies spending like 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 like $10,000 a month, something like that. Crazy and not like wasting it. They're not wasting money. 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've set up this kind of like multi-agent hierarchy and agent loops that looks like its own software architecture.
17:08 Speaker 00 And I often just ask these guys to come explain to the rest of the company, hey, like, what are you doing with these tools? Like, you clearly are consuming it way over, you know, 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 AIs as one-off tasks, you just delegate a task and then it gets done. That's like kind of using it for like deep research or like whatever, right? Like one single task versus the AIs like continuously monitoring something for you. The AIs continuously like triggering based on certain events and going and doing certain things. Giving you alerts, you set up workflows that keep running for all the time. Every time you get an inbound email like or every time there's a latency spike.
17:50 Speaker 00 It has to identify which part of the codebase caused that. It has to go and do the root cause analysis and then identify the right engineer. All these things, th- this is where the frontier is. And so going back to my main point, these products are not going to be used by, you know, a hundred million people. But they will generate revenue that's going to be higher than the advertising revenue of Google or Meta. It's gonna happen.
18:12 Harry Stebbings? I do just want to focus in on a specific element there when you were saying like the power users. Because I think one of the core numbers is actually Mark Benioff said they spend 300 million on Anthropic, which works out to be about.
18:22 Speaker 00 Yeah, it'll be interesting to know from him if that 300 million came from, you know, what is the distribution across employees?
18:29 Harry Stebbings? So it works out to be, that was on developers within sales forces, 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. If it stays at 3.8%, they will not be $5 trillion companies. But if it's 100% like Brandon at McColl said it will be in a year, they will be $10 trillion companies.
18:54 Speaker 00 Well, I think they can certainly be $10 trillion companies, whether it's gonna be a full percent of the developer payroll today or not. Because there's a lot of non-developer work that'll also be done with agents. And that's actually what we focus on for perplexity computer. We're not going after the developer market. We're going after anything that non-developers do, basically. Your finance department or your corp dev or your like sales reps or your data science teams. That's actually even bigger market. Like think of it as like Claude Code multiplied by 10, that's the size of that market.
19:26 Harry Stebbings? 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?
19:34 Speaker 00 It's hard to say. I think the costs are gonna go down. That's why it's hard to say.
19:38 Harry Stebbings? You think the costs will go down? Because this is the kind of the challenge that we've had. We thought when we went from chat to agent that costs would go down and token costs would go down. They've gone up.
19:46 Speaker 00 Yeah, for now.
19:47 Harry Stebbings? Help me understand that and how that changes.
19:49 Speaker 00 I think in software, you kind of wanna pay for the frontier. It's kind of like, if you know some engineer is awesome, if you know you have like the next Jeff Dean, would you rather hire that person and not hire people who are medium engineers but not Jeff Dean level with the same amount of budget you have? Yes. Let's say you had a million dollars, you could hire five people worth two hundred K, or you could hire one Jeff Dean and pay them a million. What would you do?
20:13 Harry Stebbings? The one Jeff Dean?
20:14 Speaker 00 Yeah. So I think you would pay for the frontier, but what stays frontier keeps changing. In twelve months from now, let's say, thought experiment, there is an open source model as good as Opus four eight. You still have to pay for inference. Nothing is truly free, but it's gonna be like, let's say, ten times cheaper than Opus four eight. 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 gonna be still high? It's not gonna be for the same things you're doing today. It's not gonna 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 like completely autonomous software engineers.
20:54 Speaker 00 Today, I think we're all using tools like Claude Code or Codex to write code, but not as literal software engineers.
21:02 Harry Stebbings? There is a large way that people that is now bearish on your frontier models who are opening eyes in your anthropics because they're realizing that you can actually do a lot with open models for a fraction of the price. What you're saying is actually, that is true, but we will still pay for the frontier and so they will still accrue great value.
21:18 Speaker 00 That's right. And I think this distinction, it 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 gonna be a thing that very few individuals might even want. Like, you could argue that after a point, like, it's not even interesting that AI can write software. You've normalized it, right? Let's say that's gonna be the case. Instead of companies being built with like 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. That's actually good for the world. We'll be seeing a lot of different businesses. We'll be seeing allocation of software labor
22:02 Speaker 00 in places that was never even possible. 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 like 10 million users. It's like a few companies, but the effect of that work will touch a lot of human lives. I think to me that 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 like wet lab experiments. But imagine taking all those tokens and putting it in the mid-training instead of just tokens from GitHub. So then that's going to produce something interesting.
22:46 Harry Stebbings? Don't laugh. Is there an asymptote to frontier problems to be solved? I know that sounds ridiculous, but if you are continuously on the chase for the next frontier problem, you get to cancer, you get to climate change, and my word, I hope they solve both in like heaven. That's a huge amount to solve. But if you're on the treadmill of continuously solving, is there an asymptote to that?
23:06 Speaker 00 Well, 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, Elon has a good argument for this, like, like he already 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, well, 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 creating, like, like, why did people even want to understand the universe? Like, like, why did we want to understand subatomic particles, quantum physics, black hole theory,
23:50 Speaker 00 you know, the origins of the universe, like, what, what, what is the purpose? But we still went ahead. And did it because that's kind of what the purpose of humanity has always been to understand the unknown. You know, David Deutsch is famous for saying this, right? Like, we are the only species capable of being curious about what is already familiar. Like, you can stare at a fruit and you know that it's a mango and like, you know exactly like how it tastes, you know how it looks, you know the shape. You know what seasons it grows and and stuff, 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 gonna ignore it. It's not, it's not interesting to them.
24:30 Harry Stebbings? 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 Sam said it's, we're gonna have twenty four seven AI and, you know, they've talked about a hardware product that's gonna come out. Do you think we will have continuous agents running?
24:45 Speaker 00 Yeah, I think so. And I think that's kind of why I believe the orchestration problem I talked about, maximizing the token value.
24:53 Harry Stebbings? Can you just help me on, sorry, when you say the orchestration problem?
24:55 Speaker 00 Yeah, so, so, okay, so there are like four objectives, intelligence and accuracy, and then privacy and cost. You know, these are all competing with each other. So you can, you could argue that you could max out on intelligence and accuracy by building giant, giant data centers and spending a lot of power to, you know, run them. And you could miss out on privacy and costs because everything will be centralized and and you're gonna 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. The solution is to figure out a sweet spot, you know, 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.
25:38 Speaker 00 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. So build a world-class harness that can even make an okay-ish model appear great and be able to use the right model for the right task and right part of the task, subagents, and even like utilize the compute we all have in our own devices all with that, that, you know, doesn't need to be always on a server. That is an orchestration problem. An awesome router, a master orchestrator router. Now, if you do that, you can realize the vision of a 24/7 AI. We thought 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.
26:21 Speaker 00 Imagine you turn it on and you could never switch it off unless something crazy happened. The thing that most people worry about those AIs is like, oh, what if it does something crazy? But the real concern actually is the cost. Nobody's going to be able to afford a crone job at the fidelity of few seconds that that runs all the time. 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 like 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
27:05 Speaker 00 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 like spying on you or looking at all your tokens, very valuable personal tokens. Imagine you have like 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 like write a memo. Imagine somebody could hack into that server and steal your deal from you. You wouldn't want that, right?
27:34 Harry Stebbings? I'm gonna be honest, I think there's much more valuable things for people to steal from a London-based VC. But yes, I can be.
27:42 Speaker 00 You're not just yet another London VC, you have like a $400 million fund last time Reddit. So imagine like, you know, you're already making your moves for the $4 billion fund. Everyone has certain levels of like, you know, sensitive stuff. And so I think that's where I believe that 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. And I think that's what that's what we want to do. Computers has been positioned explicitly as the agent orchestrator, the musicians. Orchestra are these subagents 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 subagents.
28:26 Speaker 00 And the symphony is the work and the system is the orchestra and 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, you know, models to files to tools, to chips, to devices. But it doesn't even matter like you don't care as long as it orchestrates things correctly and and maximizes the token value for what per user. If you can solve this problem, you will capture the most economic value in AI long term. Short term, it might look like, oh, like this other lab's revenue is growing, you know, 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.
29:09 Speaker 00 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 this beginning of the year and a lot of thanks to model progress made by Anthropic. And we also brought our burn down thanks to OpenAI competing with them and bringing down the cost for 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. 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.
29:54 Speaker 00 They spend more and so our business grows. 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. If Jensen produces a better chip, it's great for us. If Dario produces a better model, it's great for us. If Apple produces a better device, it's great for us. And I I love the fact that we are able to be a very positivism player at every layer of the stack and not have to rely on any one person to win.
30:26 Harry Stebbings? When we look at the different providers that we said kind of server side versus on device. When we look at server side, a lot of people talk about an AI infrastructure bubble, which I think is funny, stupid and moronic. To what extent do we have a data center supply problem today from what you see?
30:42 Speaker 00 I think the biggest problem is actually in power. So let's break down, what is a data center? Is it like that you just buy like a bunch of chips from Dell or Super Micro? No, that's just one part of it. You actually have to go secure land or you have to lease something, lease a property. You have to buy a bunch of turbines to generate power or you have to work with like power suppliers, grid suppliers. You also have to work on cooling. There's a lot of other work you got to put in that is far, far slower. You got 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 with the hopper generation. So the Blackwell generation model, I think the first model that's Blackwell generation category is Mythos.
31:27 Speaker 00 And it's already scary, like people are already like freaking out about it. So imagine that everyone pre-trains a model on like a million or like, you know, hundreds of thousands of Blackwells. And 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, like, like all the data centers of Vera Rubens will be in next, you know, 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 like orchestrating all this software layer on top and is able to convert that into frontier output tokens.
32:11 Speaker 00 That vertical integration has a lot of value. So that's why the markets are pricing infrastructure companies with a higher PE ratio than companies like Meta, for example, even though Meta builds a lot of infra, it's valued as a software company.
32:24 Harry Stebbings? But when we see like, you know, 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 Anthropos, because they aren't making money from their AI products. For Meta, the capex spend correlates to increasing accuracy on ads, which is like a six to eight percent bump in revenue. I get it, but for the capex spend, it doesn't make sense.
32:48 Speaker 00 Yeah, well, I believe like they are, they are, they are understanding what the market's saying, you know, I don't think they're dumb, do not see what what's being said. I think they're introducing a lot of subscription products. Basically, 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 like agents, subscription-based products, and maybe even a Meta Cloud that that rents out servers like what Elon's doing at SpaceX. And and maybe once they do that, the the narrative might change. But to go back to my point, it might not be inconceivable that Micron, the supplier of HPMs, might be more valuable than Meta in the next six to 12 months. It's already at like a trillion and Meta is like 1.3 to 1.4 trillion.
33:33 Harry Stebbings? Can you help me understand that? Because memory is already a massive bottleneck. It's increased 5X in price in terms of the cogs.
33:40 Speaker 00 Right.
33:41 Harry Stebbings? But people are going, wow, Micron is fully priced at this point. Why is it not fully priced?
33:46 Speaker 00 Because it's still the bottleneck. Whatever's the bottleneck will command the price. AMD is doing really well because CPUs became a bottleneck again. Agent loops, agent harnesses are all running on CPUs. The tokens are produced by the frontier models on GPUs, but whatever work, let's say like Claude generates a coding script that decides to download 500 files from different websites and then, you know, 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. All that computer 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.
34:28 Speaker 00 So then they get to be the bottleneck, like whoever's gonna be the bottleneck will win. And and so Infra 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 like interesting, like they're more important than companies that are just building data centers and not knowing how to turn that into a valuable outputs.
34:53 Harry Stebbings? Do you believe your nebius and your core weaves will be a sustainable, multi-hundred-billion-dollar company in the future? Or is it solving a short-term supply problem?
35:02 Speaker 00 I certainly think they can be sustainable.
35:04 Harry Stebbings? Yeah.
35:04 Speaker 00 I think there are some, I don't like, look, I don't know particularly which of those is gonna win. And there's also other players like Crusoe and Firebird and like a bunch of companies. It's all about being resourceful. You gotta take power from areas where there's a lot of natural resources. 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, like 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, plan well. And I think some companies are even innovating at the power layer. Generating their own power is one way to bring down the margins. And so I think there's certainly like value in that layer because it's hard to replicate work. That's how I see it.
35:49 Speaker 00 You could argue that OpenAI can do all the work that Core V was doing. And that's kind of what they wanted to do with Stargate. But why is Core V more successful at building data centers than OpenAI?
35:59 Harry Stebbings? It's hard to do and it's operationally intensive.
36:01 Speaker 00 Yeah, operationally intensive, you gotta focus, you gotta like spend most of your time securing permits, like figuring out power, figuring out like bottlenecks in the supply chain here and there, and constantly plan ahead and like test all these systems carefully, deal with like random physical issues that, you know, arise in like, you know, running a data center. There's something called TCO, you know, cost of operations. You gotta factor that in. So that said, I, I, I don't think there's value if you're just like a server renter. 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. You have to actually build some software on top, kind of like how AWS did.
36:44 Speaker 00 It's called Amazon Web Services, not Amazon servers, right? You have to have some software orchestration on top that allows you to get software margins on top of what you're doing. I think that's why you're seeing moves like NBS, like going for the AI model inference, you know, taking open source models or hosting your models. That's a business model of certain other companies like Fireworks and, you know, Base10 and all that. But you could imagine NeoCloud just going for that business.
37:09 Harry Stebbings? That was exactly my question. So I just had the co-founder of Nebius on the show 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 with the awareness that he needs to build a full stack product if he wants to have a long-term sustainable business.
37:23 Speaker 00 Yeah.
37:23 Harry Stebbings? That was the core realization for me. When I look at the inference layer, like you said, fireworks or base 10, how do you think that plays out? Do we have standalone hundred billion dollar companies in inference alone? Or do we see that?
37:36 Speaker 00 Possible. It's all about working backwards. Like, what does it take to build a hundred billion dollar company?
37:40 Harry Stebbings? 10 billion in revenue.
37:42 Speaker 00 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 that inconceivable for a company that can both do AI hosted inference and server capacity and data center build outs very operationally well. You know, there are some factors beyond their control, like open source models continuing to be awesome. If open source models stop to actually be good, where the gap between them, the frontier is like more than 12 months, like 15 months, 18 months. Then I don't think these companies really have a business model because they're only going to be able to rent capacity to OpenAI or Anthropic.
38:19 Harry Stebbings? That's exactly what Roman at 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.
38:26 Speaker 00 That's correct. Yeah. But you gotta make a leap of faith assumption that the models from China or Nvidia is making good progress on their models in Lema Tron. You got, so there's going to be enough factors in the market to keep consolidation as an outcome from like stopping from happening. But you don't control your own destiny if you're those companies. That's basically the problem.
38:48 Harry Stebbings? Okay, so we can have standalone companies that are a hundred billion dollars in inference alone. So I'm just pillaging you for your knowledge. When we look at the model selection companies, like an OpenRouter or like Factory AI just released that kind of model selection or model routing product.
39:02 Speaker 00 Yeah.
39:02 Harry Stebbings? Which did very well on launch. Is that a hundred billion dollar companies in the model selection and routing business?
39:08 Speaker 00 Probably not. I think you can't just be a provider of a router. You have to use the router to produce something meaningful. Actually, most of the business value of open routers lives in 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. Okay, so maybe let's ask this question. If you wanted to use Claude, OPIS, or I don't know, like GPT five five'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,
39:48 Speaker 00 or even if you have the rate limits, there might be an error on OpenAI servers that, you know, don't guarantee you the response time you need to run your application. And Open Router would go and they would pay for a capacity for like one year ahead with the funding they have, and secure the rate limits and multiple endpoints across multiple different providers of OpenAI models, be it Bedrock or Azure or OpenAI themselves. And so that routing is valuable. It's essentially an infra problem they're solving, which is reliable token supply. It's not actually, oh, like they're lowering the cost by deciding if this prompt should go to like GPT or Claude or something like that. That's not what they're actually selling to the developer. That's not actually the business model.
40:28 Speaker 00 And then for a lot of these Chinese open source models, you probably don't want your API tokens going to like, let's say you don't want your API tokens going to China. And let's say you don't have the bandwidth to work with like different inference providers or verify who's good and, you know, who's not. You're just trusting Open Router to take care of all that and then, you know, they're gonna like supply the tokens to you. So it's routing, not at the level of like, oh, like deciding which model is cheaper for a task. It's more like a reliable token supply. 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 like, you know, high gross margins business. It's the way the business model works with them is actually,
41:09 Speaker 00 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.
41:20 Harry Stebbings? 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?
41:30 Speaker 00 I think power will remain the bottleneck. It feels like that to me unless something dramatically changes in the way data center build outs happen. I actually believe that there'll 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 is in both are untrue. Satya even made the statement that it's like a can of water or something in terms of how efficient these companies are.
41:57 Harry Stebbings? Do you think that's why they're putting up resistance to them? I don't. I think it's because it's a symbol of job losses, increasing wealth inequality.
42:03 Speaker 00 It's a lot of things. It's a lot of things. It's a lot of apprehensions, fear about like what's going to happen, channelizing in so many different ways. Sometimes it's channelizing through hatred for wealth inequality and like wanting to tax people. Sometimes it's channeling through like concerns for the environment and like climate change. Sometimes it's channelizing in a way where you're all like, oh, like the price of the grid is going up because you guys are building all these data centers and then. Or like 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 like, like a pretty bad sentiment about AI.
42:43 Harry Stebbings? Do you think it will be meaningful to the development of those data centers? I think right now forty out of a hundred are not being developed because of public resistance.
42:52 Speaker 00 Yeah, so that's where the power bottleneck is. You could see maybe certain countries seize the opportunity for this and allow these model builders could 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. There's a lot of natural resources in other countries. Regulations might be more friendly. So we're still going to see data center build outs. It might not happen in the US. But the fact that you have to solve physical problems, like you actually have to deal with the supply chain, the permits, securing power, like making sure like things work. And getting the lead times lower and lower, you're not solving problems like cloning some SaaS apps here, right?
43:35 Speaker 00 You're or like you're building a go to market team or like doing better marketing against the competitors. Products. Yes, those are also hard problems, but these are like much harder problems where like you're not in full control of your destiny and you need a lot of capital and connections and like the right people. Sometimes even like 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 sea 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.
44:15 Speaker 00 There's something far more efficient that can run on on on people's local devices, their MacBooks, their Windows PCs. Yeah, like you're probably freaking out then.
44:24 Harry Stebbings? How likely do you think that is, though?
44:26 Speaker 00 It's probably like twenty percent, thirty percent chance. The reason I think there are some possibilities is that because of the export controls. So the deep sea is not building with the 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 Sea is building are far more like memory efficient. They've made innovations on the KV cache to be really small enough that you can host it on the SSDs. And you don't need high bandwidth memory for inference time. And they're going to have a completely different architecture for inference, completely different architecture for storage, because they're not allowed to use the 3D NANS. So their architecture, it's not just the model architecture. The model architecture is already pretty different.
45:09 Speaker 00 They've made innovations on the attention layer. They made innovations on like the training algorithms so that it doesn't consume a lot of interconnect capacity. So they basically 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.
45:28 Harry Stebbings? Do you think the export controls have helped or hurt us?
45:31 Speaker 00 Jury's still out. Short term is helping because my belief, the only reason where why there's even like a twelve month gap. We've been open source and frontier is export controls. And so it's definitely helped and and and definitely like 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. Power is not a problem, permits are not a problem, people are not a problem, 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 to a far more like potent competitor.
46:12 Harry Stebbings? Do you think we still dramatically underestimate China's capabilities?
46:15 Speaker 00 I think so. If AI is like not just digital, there's also physical AI. You've got to build fabs, robots, chips and harness the energy really well, package it into local devices. I think they have a lot more advantages than America.
46:30 Harry Stebbings? How important is it that we have our own TSMC in the US?
46:33 Speaker 00 TSMC is actually, there is a fab of TSMC in Arizona, like not a lot of people talk about this, but TSMC is investing like hundred fifty billion dollars into building American fabs. They've already invested forty billion dollars or something like that, sixty 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 ten percent of Intel, Nvidia and SoftBank own five percent 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 TerraFab, like I think people have woken up to the importance of building fabs. But this is also why China is particularly very, very competent.
47:16 Harry Stebbings? Given the capabilities of China that we just mentioned there really articulately, I know it's a ridiculous question, but sort of 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?
47:34 Speaker 00 I think take physical infrastructure a lot more seriously and continue funding it and not like have all these. 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 like they're sucking up all the water and actually be fact driven. And so, you know, I hope our product helps there, like you can, you can go to perplexity and ask any question and get fact checked on your assumptions. But yeah, like it's very important that we educate the public about what's actually going on in in a language they easily understand. And not fear monger, okay, like not be like, oh, like all their jobs are gonna go away like this, that like. There's gonna be lots of amazing companies that are gonna get built with far fewer people.
48:18 Speaker 00 Getting multi billion dollar, multi hundred million dollar valuations with like twenty thirty people. And propelling like trillions of dollars of new GDP. Like, 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, like ninety percent of the jobs are gonna be gone, like you're all gonna get screwed over by our models and like. It's it's our it's our moral duty to tell you all this like, well, like that doesn't make any sense to me. Like you can't win by saying that and also like complaining about not being able to build data centers fast.
48:51 Harry Stebbings? 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?
48:59 Speaker 00 Yeah, I think so. I mean, I think, you know, they have contradictory messages in their own like. Different social engagements so far where the most recent one I heard was there is no evidence that AI is taking over jobs. There there needs to be a consistent communication around this. And I also think that very little is being spoken about how. AIs can help you build companies in a very, very different way, like the current AIs, where agentic AI. So many things you would hire people for, you can do it with agents. But one way of looking at it is like, oh, like what happens to all the jobs? But the other way of looking at it is like, hey, like, I never had the chance to go. Build out a company on this idea that I've been having all this while. And maybe me and a group of friends can come together and build this and.
49:44 Speaker 00 Can you guys figure out a way to give us compute credits? Or, you know, Amazon gave a lot of compute credits to a lot of startups, like when we started. Perplexity, we had like around two hundred thousand dollars worth of Amazon credits and GCP credits and Azure credits. That almost like together cumulatively, this was worth like a million dollars in compute credits. Now, in in today's world, it's gonna be like a million dollars of computer credits and we're doing that, like we we're funding this thing called the billion dollar build where we. Giving a million dollars of computer credits to any group of people who have a credible path to building a billion dollar company. And I want like thousands such companies to be built.
50:18 Harry Stebbings? What did you think of Sam Altman giving two million dollars of tokens to Y C companies?
50:22 Speaker 00 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, it's good. If there are thousands of them, like, that's a lot of new GDP.
50:37 Harry Stebbings? I spoke to Anne Bordatsky before the show and she said how A I pilled the team is for you. How big is the team today?
50:44 Speaker 00 It's like four hundred people.
50:45 Harry Stebbings? Four hundred people. How big will it be in two years' time?
50:48 Speaker 00 I don't know. It's hard to say. Maybe eight hundred or a thousand.
50:51 Harry Stebbings? 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?
51:01 Speaker 00 Definitely, they'll be dramatically more efficient. And that's why I am a believer in building a lot more efficient companies. And being an example for all these companies ourselves. Like, people should look at perplexity and be like, oh, like, with four hundred people, you can build like a multi, I don't know, like twenty billion, twenty billion dollar company. And so that means with like forty people, I could probably build a billion dollar, two billion dollar company. And that's totally doable, totally doable. And so for us, maybe that means that with four thousand people, we could be worth two hundred billion. We could be worth two trillion dollars with like ten thousand people. That doesn't mean it's bad for all the hundred thousand people we did not hire for a typical two trillion dollar company.
51:43 Speaker 00 I would rather have those hundred thousand people be split into groups of like hundred thousand groups like that. And each of those thousand 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 just like difficult to work with. They don't listen to like instructions. So like they don't follow like roadmaps or not. They're not like easy to collaborate with. But maybe the flip side of that is those are the kind of qualities that founders typically have.
52:12 Harry Stebbings? Arvind, there is a population and a very large population that are not A I native people, that are not using A I to improve workflows, improve efficiency. What would you advise them?
52:22 Speaker 00 Get started, first steps, get started and channelize your curiosity. 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.
52:34 Harry Stebbings? You got a lot of heat for saying people don't like their jobs, so.
52:38 Speaker 00 I didn't say, if you actually listened to my interview, I did not say that. So people want clickbait articles and they take something I said in one sentence and out of context and make it into a headline.
52:49 Harry Stebbings? What did you say?
52:50 Speaker 00 I specifically said this, hey, like, there are a lot of people who don't enjoy their jobs. By the way, the fact 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 or standing in society. You might even be like really wealthy, but doing a job that you completely don't enjoy and like destroying like the peak years of your adult life, working on something that is horrible or like depressing. My point is that if that's you and if the reason you could never leave your job is because you were always worried if you, how would you build a company from scratch? Like there are all these things to figure out, how you have to hire a lot of people, you have to like set up an office, this, that, like, that's changed.
53:35 Speaker 00 For the first time in history, you can get started on an idea with like one or two other friends and maybe have a real genuine shot at building a billion dollar company.
53:45 Harry Stebbings? 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 and service. Everything is like up and to the right. Seeing some cracks in an 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?
54:19 Speaker 00 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 agentic inference. You need some amount of inference compute to run locally that you're not paying for tokens on, on metered intelligence, essentially.
54:35 Harry Stebbings? How will the best companies of the future structure token budgets?
54:38 Speaker 00 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 like, 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 like, like, how do you even understand like which models are good at each of those things? And like, how much do you spend on each of these divisions? You're not going to be able to keep track.
55:00 Harry Stebbings? I I had a friend on the show the other day say that Google will be the token king. They can produce the lowest cost tokens out of anyone. They own full stack, TPUs, data centers, networking, power procurement. Do you think that's true that they will be the lowest cost token producer?
55:15 Speaker 00 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.
55:29 Harry Stebbings? I was shocked the other day. I saw the Cloudflare announcement that now agent traffic has overtaken human traffic for them.
55:38 Speaker 00 Why are you shocked?
55:39 Harry Stebbings? It was quicker than I thought.
55:40 Speaker 00 Okay.
55:41 Harry Stebbings? Personally, I thought that would happen, but in two years, maybe not now. How does the world change when agent traffic far exceeds human traffic?
55:50 Speaker 00 I think people are just going to have a lot more agency.
55:52 Harry Stebbings? But do websites go away? Does design not matter? Does the advertising model of the internet die completely?
55:59 Speaker 00 My belief is that the advertising model around like travel or shopping or like 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, like the decisions are more subjective, like, like, what is the best piece of furniture inside this this this box? Like, why this particular table? Or like those kind 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 kind of how I I feel the world will split and subjective things will still be ad based. Objective things will be agent based.
56:42 Harry Stebbings? 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.
56:52 Speaker 00 Yeah.
56:53 Harry Stebbings? What question is no one asking today that maybe everyone should be asking?
56:58 Speaker 00 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 like I gave you a headcount of like hundred thousand people or 10,000 people and you know enough compute credits to run those agents. What would you do? Like, let's say I ask you, Harry, like, you know, let's say you have suddenly like 10,000 agents at your disposal. What would you do? I I remember you telling me, or not me, but but in some episode of yours where you said you're only you only did this podcasting because you felt like you didn't have an arbitrage to go and deals.
57:31 Harry Stebbings? A hundred percent. Why I still do it. I mean, I love what I do, but yeah.
57:35 Speaker 00 Okay, so you've gotten some amount of distribution. So now assuming that you have, let's say you could spend a hundred million dollars on agentic inference and grounded with all the connectors and stuff and it's all working. What would you do to with that capability to further your goals? Like, what what should your goals even be then? I I think that's the question I would ask. Assuming that in the next three to five years, you're going to be able to like delegate whatever digital task you want and and with the right harness and agents and like be able to delegate that.
58:04 Harry Stebbings? 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. That is intensely difficult to do and would be the holy grail to investing, but like that would power my end goal ambition is.
58:24 Speaker 00 Yeah, so your goal is to be the run like a 10 to 100x larger fund, right? That that's basically what I'm hearing from you. So let's let's assume it's like a 40 billion dollar fund from 400 million. Then all you got to ask is like, assuming I have all the headcount I need to do this, like, how much faster can I do it? I I think that's how I would frame this question. I think Elon has like 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 it's going to take 10 years.
59:05 Harry Stebbings? All right, interviewer, we put it on you. What's your 10 year and how does that look in a 10 month timeframe?
59:11 Speaker 00 A very interesting question. I think our mission beyond any level of capitalism is to make the planet more curious. The product is always intended to helping people ask the next question. My goal is to truly realize that like that level of agency that needs to exist in this world is quite not there.
59:30 Harry Stebbings? I think that needs to be grounded in numbers, dude, to make it like possible. It's like me saying, oh, I want the best investments. Which is why a $40 billion fund is helpful.
59:40 Speaker 00 Sure, I can say the same thing, like 2 trillion. You know, it doesn't matter, right? Like 100x, 10x, 1000x. These are all like motivational milestones.
59:47 Harry Stebbings? Do you think Pobletzy will be a trillion dollar company?
59:50 Speaker 00 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? They started selling dried fish. The SK Group started off as a textiles company. So, anyone can be worth a trillion dollar company. Like you just have to work your way towards that. I mean, the exact same logic for you that you laid out for how can a company be worth $100 billion. Okay, you said you need to make a $10 billion in revenue. Isn't that the same for a trillion? Like you need to make $100 billion in revenue?
1:00:22 Harry Stebbings? And there was actually some very interesting data that Kotu revealed. I don't know if you saw it recently, which basically says about the probability of reaching the next level of value.
1:00:31 Speaker 00 Yeah, it's higher.
1:00:31 Harry Stebbings? It's much higher. So when you're at a billion, it's like much more likely to reach 10 billion, 10 billion, much more likely.
1:00:36 Speaker 00 Yeah, that's true actually for even people. Like it's way more likely for a person with $100 million in liquid net worth to become a billionaire than someone with $10 million.
1:00:46 Harry Stebbings? Are you not worried about the wealth inequality? Being blunt, we both are very lucky now to live in kind of 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.
1:01:01 Speaker 00 I think the way to like ensure that that doesn't remain the case is to distribute the benefits more widely. By the way, the people who are using our tools, like I had an Uber driver, I'm not even like making this thing up. An Uber driver in San Francisco once told me that he watched one of my YouTube interviews where he explained how you can build a Product or a web app with an AI from scratch, went on to do it and use AIs to add like 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 vibe coding new apps. That that already tells you that for the person with agency and a positive outlook for the future, anything is possible.
1:01:45 Speaker 00 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, like you, you didn't, you started this podcasting circuit like when you had nothing, right?
1:02:13 Harry Stebbings? So Nothing.
1:02:14 Speaker 00 Exactly. So it's possible. So you gotta, you gotta talk more about that than be like, oh, I feel so guilty that I made it. And now I'm like, you know, what about all these people who haven't made it? Like you can also make it.
1:02:24 Harry Stebbings? I think I have a more pessimistic view of actual general public, which is I don't think that many people have agency. I think a lot of people have victim mentality.
1:02:32 Speaker 00 You gotta help them. Like, I think that's, that's the most important thing.
1:02:34 Harry Stebbings? I think they gotta help themselves.
1:02:35 Speaker 00 People will help themselves once they see that, okay, like I kind of want to be like this guy. Let me work hard. You need an example, right? It's not like nobody can become, get in shape, like it takes discipline. You gotta get rid of bad habits.
1:02:49 Harry Stebbings? And now is the best time ever to change your life in 12 months. The ability to go from nothing to, to actually billionaire in 12 months is now possible in some respects.
1:02:58 Speaker 00 Yes. Look, I'm not saying everyone's gonna make it and everyone's gonna be worth a billion dollars.
1:03:02 Harry Stebbings? Isn't that the caption from this, this show? Aravind, everyone's gonna make it.
1:03:06 Speaker 00 Anyone has the potential to make it. It's, it's as likely for perplexity to become worth $2 trillion as, as a founder who's yet to secure your funding to where it'd be worth a billion dollars. Equally hard. You just have to give yourself enough shots at the goal, be curious. That's, that's the message from the commencement speech, be curious.
1:03:25 Harry Stebbings? We have SpaceX, we have Anthropic, we have OpenAI going public. It feels like someone's kind of shot the gun and the race is on. Is there enough money to fund three such large IPOs?
1:03:36 Speaker 00 There will be some reallocation for sure. There might be some holders of like SaaS stocks who would put it into Anthropic or something. Let's say you believe that enterprise AI is gonna take off. You might wanna hedge between having a lot of Microsoft stock and Salesforce stock versus like putting some of that into Anthropic. So let's say like Vanguard or BlackRock own like, you know, cumulatively, they own like two hundred billion dollars of Microsoft and Salesforce. They might be like, okay, I'm gonna take thirty, forty billion of that and put it into Anthropic. Fine, you know, not a bad bit to make.
1:04:05 Harry Stebbings? What happens to all the enterprise SaaS companies that are public?
1:04:09 Speaker 00 Growing, yeah, fine. They have to weather the storm.
1:04:12 Harry Stebbings? Is it a storm or is it a continuous precipitation?
1:04:16 Speaker 00 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're just selling the same software, you're probably not gonna be around. IBM is still around because they went and bought Red Hat and HashiCarb and now they're buying Confluence. So there are ways for these companies to stay alive and extend their lifespans and stuff. It's obviously gonna be hard to preserve a brand that's as relevant. I don't think the IBM brand is that relevant anymore in terms of like evoking an emotion and people to go use their products. But as a business, it's gonna be awesome, you know, it's gonna be fine.
1:04:50 Harry Stebbings? I have to finish on, you said IPO in twenty twenty eight. I had to ask this, I woke up to this in my like, you know, group, we have a team WhatsApp and it's like, oh Gavin, IPO twenty twenty eight.
1:05:00 Speaker 00 I hope, I hope it can be sooner than that.
1:05:02 Harry Stebbings? When do you know when you're ready? Like, is there like a billion an hour, you're at five hundred million an hour now?
1:05:08 Speaker 00 More than that, far, far more than that, actually.
1:05:10 Harry Stebbings? Really?
1:05:11 Speaker 00 We're, we're, we're not ready to share it, but growing really fast.
1:05:14 Harry Stebbings? Revenue growth matters much more to you than profitability?
1:05:17 Speaker 00 Today, I think in general, but you can look at public markets. People want top line growth more than bottom line efficiency right now because it's very hard, it's, it's rare.
1:05:28 Harry Stebbings? Well, you definitely need one.
1:05:29 Speaker 00 You need to have a model in place to get the bottom line efficiency when that becomes the objective. And, and you need to also like have a path to getting there.
1:05:38 Harry Stebbings? Where are you cost inefficient today, where you expect to be significantly better in two to three years?
1:05:42 Speaker 00 We're training our own models, post-training it 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 like models we own and serve ourselves. And that's going to be the best way to bring down the costs and increase our margins.
1:06:11 Harry Stebbings? Will the largest enterprises in the world all be fine tuning open models to have tailored models that are much more specific to them?
1:06:17 Speaker 00 Absolutely, because it's in your incentives to bring down the cost.
1:06:20 Harry Stebbings? Does that not provide another bad case for the large frontier model providers?
1:06:24 Speaker 00 Frontier model providers will only remain relevant if they remain a different tier. 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. You, no one's ever in a comfortable position. Like I said in the start, no one's, no one can relax. This is.
1:06:42 Harry Stebbings? Fucking harsh. It's got harder.
1:06:46 Speaker 00 It's gonna get even harder. That's the nature. It's just the, the price is too big. Like you've never seen, like, like take Anthropic. I think it's um worth like one to one and a half trillion, something in that range. That's basically the valuation of Meta. And this all was created in like six years. Meta took like 20 years to build. So the price is so big and so no one can, no one can be comfortable. And anyone who's winning today can lose tomorrow, including, including the model providers.
1:07:13 Harry Stebbings? Pre this year, there was like a three month period where people were like, oh, perplexity. What's happening with perplexity? Do you pay attention? Do you give a shit?
1:07:20 Speaker 00 Of course I pay attention to all of that.
1:07:22 Harry Stebbings? Do you care? There was one in particular in San Francisco, do you remember where they were like, oh, what's the company you had short?
1:07:26 Speaker 00 Yeah, we were voted the most likely to fail. Cursor was voted the second most likely to fail. OpenAI was voted the third or something.
1:07:33 Harry Stebbings? You don't give a shit?
1:07:34 Speaker 00 I, I feel like we're all doing well. Cursor, I think is getting sold. SpaceX, OpenAI is.
1:07:41 Harry Stebbings? Going public, baby.
1:07:42 Speaker 00 Going public soon. We tripled our revenue since that judgment was made, so brought down the burn by more than 50%. I also feel most of those people who sit on these like meetups and what, don't actually build anything useful.
1:07:59 Harry Stebbings? I agree.
1:08:01 Speaker 00 Well, yeah.
1:08:02 Harry Stebbings? Okay, we're gonna do a quick fire round because I could talk to you all day. First one, what's one widely held belief that you think is completely wrong?
1:08:12 Speaker 00 I think a lot of people are obsessed about like, you know, identifying a mode in the first year or two of their company. I think like the only shot you have is move fast. Like we're lost in my mind, like 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.
1:08:29 Harry Stebbings? Where are you still moving too slow internally today?
1:08:32 Speaker 00 I think we can be even more AI-pilled. 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. And this is despite us being extremely agent-pilled internally and trying to delegate as much to agents. And so, yeah, that's where, that's like a big area for. 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.
1:09:16 Speaker 00 It's just gonna feel like the 10x engineer is running certain aspects of the company.
1:09:20 Harry Stebbings? If I gave you unlimited money, what would you do today that you're not doing?
1:09:24 Speaker 00 I'll build data centers.
1:09:26 Harry Stebbings? You would?
1:09:26 Speaker 00 Yeah.
1:09:27 Harry Stebbings? In space?
1:09:28 Speaker 00 I don't have expertise to do that, but I would start with land on earth. You know, 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. You know, like I said, like I think physical infrastructure build-outs is like the return of the industrial age again. Like the forefathers who built the industrial revolution, oil pipelines, steel bridges, factories producing cars. All these things that we take for granted today were built by people who, you know, spend a lot of time thinking about how to scale these things in a cost-efficient way. And so we need to do that a lot for AI. Yeah, that's what I would do. Of course, you cannot just be building infra.
1:10:09 Speaker 00 You need to be able to utilize all that infra to producing valuable output tokens to the user. But we're already good at doing that, so infra is the thing I would focus on.
1:10:17 Harry Stebbings? You can buy and hold for ten years SpaceX, Anthropic, or OpenAI. The three IPOs coming in the next few months, which you buy and hold for ten years and why?
1:10:28 Speaker 00 SpaceX.
1:10:29 Harry Stebbings? Why?
1:10:30 Speaker 00 It's an N of one company. 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?
1:10:42 Harry Stebbings? No.
1:10:42 Speaker 00 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. It's just one aspect.
1:10:55 Harry Stebbings? And small aspect of the business.
1:10:56 Speaker 00 Yeah, like there's a lot of, I'm excited about possibilities to travel from Australia to like San Francisco in like thirty minutes. You know, all this feels like sci fi, but I'm excited about all these possibilities.
1:11:08 Harry Stebbings? What job does not exist today that will be incredibly common in five years' time?
1:11:12 Speaker 00 I think it already exists. So if the forward deployed engineer is definitely on the rise. I guess like people with a really good sense of like quality control. Maybe a better way to answer this is like most jobs that exist, like valuable jobs that exist are usually like reincarnations of something that already existed. Like, so I don't think we're going to see completely new things. It's just going to reincarnate in different ways.
1:11:33 Harry Stebbings? You can advise your little sibling who's finishing university today and just done a computer science degree. One thing, what would you advise them?
1:11:42 Speaker 00 Stay curious. Don't give into like 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 like you're, everything feels hopeless to you or something. There is so much more to build. Like we are just getting started. The application layer era or like infrastructure build outs, there's like a lot of opportunities.
1:12:05 Harry Stebbings? We are seeing more spin outs from OpenAI, Anthropic, you name it, every single day. Do we have hundreds of these Neo Labs and vertical models?
1:12:13 Speaker 00 No, not a big believer in too many of them. I think you've got to produce some differentiation. That's the most important thing. Like, would you call DeepSeek a neolab?
1:12:22 Harry Stebbings? No.
1:12:23 Speaker 00 Why?
1:12:23 Harry Stebbings? I think very stupidly for me, I don't call it a neolab because I attribute neolabs like spinouts from larger labs.
1:12:30 Speaker 00 I see.
1:12:31 Harry Stebbings? And kind of verticalized, which is probably wrong on both axes, but it's horizontal and it's not a spinout.
1:12:36 Speaker 00 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 like that, foundational bets makes, or somebody goes out and builds for robotics models. I think that's somewhat uncorrelated and different, and that makes sense for a lab. But I feel like there are just labs for the sake of being lab, and I don't think they're going to make it.
1:13:01 Harry Stebbings? Can you paint for me, what was the most plausible story where Poplatti becomes a trillion dollar company? What do you do then? The orchestration layer?
1:13:10 Speaker 00 I mean, accuracy and orchestration is like two goals that have been consistently true since the beginning of our company. So I think we'll continue to do that. 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.
1:13:28 Harry Stebbings? Are you happy now? Are you enjoying this?
1:13:31 Speaker 00 Of course. Like, there are so many things I could be doing if not for this. I think the process is what motivates you. So you asked me, I think somewhere in between, you need to give me a number of where you want. I don't, I don't work like that, actually. Like, for example, like these numbers, like getting to 2 trillion or 20 trillion are exciting, but like that doesn't motivate me. It's hard to get motivated by wealth. You want to get motivated by impact.
1:13:54 Harry Stebbings? 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. Who's the smartest?
1:14:04 Speaker 00 People are smart in their own ways. It's hard to compare. Like, I've met Jensen, Elon, all these guys and like Bezos.
1:14:10 Harry Stebbings? What was it like meeting Elon?
1:14:12 Speaker 00 Amazing. Elon's like a very focused person. He might not appear that way on Twitter, but you know, with a lot of like random tweets, but he's extremely laser sharp focused on whatever he's doing at that moment in time. Actually, the one skill that as an entrepreneur that I would really like to take, like build from somebody like him, like take from somebody like him and have it for myself is that ability to just zone out of all the other things that's happening in your business or other businesses and just focus on that limiting problem right now, like the bottleneck problem and ignore everything else. It's very hard to do. Like, even within perplexity, I cannot just focus on like one part of the business alone. It's very difficult. Like, I'm always looking at other things simultaneously. And his style
1:14:56 Speaker 00 is to just always look at the limiting problem and just ignore everything else. 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.
1:15:10 Harry Stebbings? Was Jensen Huang what you thought he'd be?
1:15:12 Speaker 00 Far better. 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 like, he's so intense that he tells everybody around him that they're thirty days away from going out of business. Think about it. Five trillion dollars guaranteed to make five hundred billion dollars in revenue in the next two years, has the most advanced chips in the world, and he operates with that mentality that he could be thirty 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. There's one aspect of like, you know, being comfortable where you are, thinking you've made it, that that feels
1:15:54 Speaker 00 good to get here so far. But these guys are not stopping. Like, I don't think Elon wants to stop at, if you look at his pay package for SpaceX, it's structured around creating a colony in Mars with a million inhabitants, building enough compute in space. It's not like motivating to be worth a ten trillion in net worth or something. If he does these things, I'm sure he's gonna 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. A lot of people view this like entrepreneurship as like, oh, if it wins, if I win and I have a great outcome and I sell my company, I would have like generational money. I don't have to work ever
1:16:39 Speaker 00 again. And then what? You end up just staying at home and like your kids will obviously have like trust funds and they're not gonna get inspired watching their dad.
1:16:49 Harry Stebbings? Play paddle?
1:16:50 Speaker 00 Yeah, you're not gonna set the right example for them. They're not gonna 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. 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 got you. You need to work forever.
1:17:12 Harry Stebbings? 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 interview. I was like, oh.
1:17:21 Speaker 00 Yeah, I think it's pretty hard, but you do it despite that. I think I think that's how it works. You do it despite that.
1:17:27 Harry Stebbings? 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.
1:17:32 Speaker 00 Appreciate it.
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Previous 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory Next 20VC: SpaceX Soars to $2.7TRN | Anthropic's Fable Banned by US Government | Wix and Adobe Hit All-Time Lows | Mistral Raising at $20BN and The Case for Sovereign Models | Fin Acquired by Salesforce for $3.6BN Page updated September 29, 2026 · transcript and summary are machine-generated
