Micron Will Be More Valuable Than Meta

Thrill of winning. Why? Because I have nothing to lose. I came from nothing. 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 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 any time 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 that’s that’s my motto.

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 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 like that. But it’s not lack of aggression or anything. It’s just that that is boring. People already heard that enough from me.

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’ve mentioned the name Perplexity, people will think, 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. Make that argument for me. Well nobody ever wanted to ship an answer engine at Google. Nobody wanted to tinker anything on the interface that made them $250,000,000,000 a year. And then now you look at AI mode, it looks exactly like Perplexity. There’s 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.

Is that bad or good for you that they

adapt? It’s both good and bad in the sense. I knew this, like, around end of twenty twenty four, this is 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. Like we still have the state of the art deep research the world, and that’s actually where people subscribe to pay for our pro or max products, it’s not for getting answers in 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 lead gen for the frontier products we build.

You need something, Like think about it, every company needs to have one successful product to build the next 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 twelve 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.

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. Can I push back on you that?

Maybe maybe I’ll decouple that. Do see what I’m saying? I’ll decouple that. Let’s decouple that being like financial readiness for an IPO versus perception of a dominant leader. Yep. Do you perceive them as a dominant leader right now? Yes. In what? Consumer search. Well, except there’s no money there. Right? It’s been commoditized. Like for example, why are they going all in on Codex? Because that’s where the money is. We’re doing the same on computer, Anthropic is doing the same on Claude code. Google doesn’t yet have a product in this category, but I’m sure they’re gonna come after that.

Meta is trying to launch Hatch for $200 a month. You see what’s happening. Right? But

coverage.

about code. That’s the main thing. The money, at least in non advertising. I’m 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.

Yet to be proven. Let’s work through the categories of advertising. Who’s the number one advertiser on Google? Amazon. The number two? Booking.com. Number three or four, I think, Expedia. So how much do you think booking.com spends on Google? 16,000,000,000, 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? Google. Why is that?

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 Perplexity is known for. 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 kinda like hurts the trust that people have in your platform and your product. That’s another reason why, if you think about it like what 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 works out in China in WeChat because there is 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 am 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.

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. 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 reading that model is the product. And so the reason Greg is right is because if you take Codex or Perplexity Computer or ClotCode, 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. What are all the skills and sub agents 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 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, 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-five-five inside the Claude code harness. You wouldn’t find Claude opus four, seven or eight inside the codecs 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 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.

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 who got Amazon spend like $500,000,000 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 10,000,000 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 set up this kind of like 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? Like you clearly are consuming way over what we thought the average person in the company would do. And 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 deep research or like whatever, like one single task, versus the AIs 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, 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 gonna be used by a 100,000,000 people, but they will generate revenue that’s gonna be higher than the advertising revenue of Google or Meta.

Gonna It’s happen.

It’ll be interesting to know from him if that 300,000,000 came from, you know, what is the distribution across employees?

Well, I think they can certainly be $10,000,000,000,000 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.

It’s hard to say. I think the costs are gonna go down. That’s why it’s hard to say.

I think in software, you kinda wanna pay for the frontier. It’s kind of like, if you know some engineers 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 200 ks, or you could hire one Jeff Dean and pay them a million. What would you do?

Dean. 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 4.8. You still have to pay for inference, nothing is truly free. But it’s gonna be like, let’s say 10 times cheaper than OPUS four point 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. Today I think we’re all using tools like Claude code or Codex to write code, but not as literal software engineers.

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 going to be a thing that very few individuals might even want. Like you could argue that after a point, it’s not even interesting that AI can write software, you’ve normalized it, right? Let’s say that’s going to 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 and labor 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. Cancer. These are applications where you don’t have like 10,000,000 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’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 gonna produce something interesting.

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, they can where he says money loses all meaning in a post AGI economy because you’ll be producing an abundance of energy 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 gonna be creative. Like why did people even wanna understand the universe? Like why did we wanna understand subatomic particles, quantum physics, black hole theory, the origins of the universe? Like 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. David Deutsch is famous for saying this, 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 you know exactly how it tastes, you know how it looks, you know the shape, you know what seasons it grows in 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 it’s not very interesting to them.

Yeah, I think so. And I think that’s kind of why I believe orchestration problem I talked about, maximizing the token value. Can you just help me answer that? Sorry, when you say the

Yeah, so okay, so there are like four objectives, 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, 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.

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. 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 the right part of the task, sub agents, and even like utilize the compute we all have in our own devices, doesn’t need to be always on a 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 20 fourseven AI, without people freaking out about going bankrupt. Because no one’s gonna be able to afford a 20 fourseven AI, frontier AI running on the server. Imagine you turned 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 is gonna be able to afford a cron job at the fidelity of few seconds 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 from here 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 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?

You’re not just yet another London VC, you you had like $400,000,000 fund last time Yeah, you read yeah. So imagine like you’re already making your moves for the $4,000,000,000 fund. Everyone has certain levels of like sensitive stuff, and so I think that’s where I believe that the $24.07 always on agent is gonna 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 we wanna do. Computers has been positioned explicitly as the agent orchestrator.

The 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. 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 from 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 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 labs revenue is growing exponentially does that, but long term, this is the one objective that truly matters. Who is

positioned

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 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 of the same capability.

And now with progress in open source and local models and local chips, we’re gonna 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, 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 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 love the fact that we are able to be a very positive some player at every layer of the stack and not have to rely on any one person to win.

I think the biggest problem is actually in power. So what 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 in the hopper generation. So the black hole generation model, I think the first model that’s black hole generation category is MITOS, And it’s already scary, people are already like freaking out about it. So imagine that everyone pre trains a model on like a million or like hundreds of thousands of black holes.

And those models are going to be far more powerful than what exists today. And then the Vera Rubins are coming next year in full capacity. Like all the data centers of Vera Rubins 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 like orchestrating all this software layer on top, and is able to convert that into frontier output tokens.

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. But

Yeah, well, I believe they are understanding what the market’s saying, know, they don’t think they’re dumb to not see 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, And I think that requires them to launch a lot of like agents, subscription based products, and maybe even a Meta Cloud that rents out service like what Elon’s doing at SpaceX. And maybe once they do that, the narrative might change.

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 six to twelve months. It’s already at like a trillion, and Meta is like 1.3 to 1,400,000,000,000.

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. 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 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 host it on a website that you can share with your people.

All that compute 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’s going to be the bottleneck will win. 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 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.

I certainly think they can be sustainable.

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 there’s 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 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 100,000 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 on 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. You could argue that OpenAI can do all the work that CoreV was doing. And that’s kind of what they wanted to do with Stargate. But why is CoreVee more successful at building data centers than OpenAI?

Yeah, operationally intensive, you got to focus, you got to 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 arise in like running a data center. There’s something called TCO, cost of operations. You got to factor that in. So that said, 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. 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 Neebs, like going for the AI model inference, taking open source models or hosting your models. That’s a business model of certain other companies like Fireworks and and all that. But you could imagine Neo Clouds just going for that business.

It’s all about working backwards. Like, what does it take to build a $100,000,000,000 company?

Exactly. 10,000,000,000 revenue, 30 to 40% gross margins, good amount of net income, good cash flow. Okay, 10,000,000,000 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. 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 twelve months, like fifteen months, eighteen 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.

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 and Neematron. So there’s gonna 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 that that’s basically the problem.

Probably not. I think you can just be a provider provider of of a a router, you have to use the router to produce something meaningful. Actually most of the business value of open router is listen to 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, Opus, or I don’t know, like GPT-five thousand developer, why would you not want to just use it with your own API key versus using it inside OpenRouter?

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 OpenAI servers that don’t guarantee you the response time you need to run your application. And OpenRouter would go and they would pay for 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. And then for a lot of these Chinese open source models, you probably don’t want your API tokens from going to 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 who’s not. You’re just trusting OpenRouter to take care of all that, and then they’re supply the tokens to you. So it’s routing not at the level of like, oh, like deciding which model is cheaper, what 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 high gross margins business. The way the business model works for them is actually, 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 differences their margins.

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 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.

things. It’s a of things. It’s a lot of apprehensions, fear about what’s gonna happen, channelizing in so many different ways. Sometimes the channelizing through hatred for wealth inequality and wanting to tax people. Sometimes it’s channeling through concerns with environment and climate change. Sometimes it’s channelizing in a way where you’re all like, oh, 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 a pretty bad sentiment about AI.

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 go 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 gonna see data center build out, 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? 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 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. There’s something far more efficient that can run on people’s local devices, their Macbooks, their Windows PCs. Yeah, like you’re probably freaking out then. How likely do you think that is though? It’s probably like 20%, 30% chance. The reason I think there are some possibilities that because of the export controls. So the DeepSea 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 DeepSeek’s building are far more like memory efficient. They’ve made innovations on KB 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 gonna have a completely different architecture for inference, completely different architecture for storage, because they’re not allowed to use the three d NANDs. So their architecture, 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 like the training algorithm, so that it doesn’t consume a lot of interconnect capacity.

So 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’s making. Do

Jury’s still out. Short term is helping because my belief, the only reason why there’s even like a twelve month gap between 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 into a far more like potent competitor.

If AI is like not just digital, that’s also physical AI, you gotta 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.

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 $150,000,000,000 into building American fabs. They’ve already invested $40,000,000,000 or something like that, 60,000,000,000 last time I checked. So there is a TSMC in Arizona that’s coming up. There’s also Intel and that’s why 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 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.

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 and contaminating water or like they’re sucking up all the water and actually be fact driven. And so I hope our product helps there. 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 a language they easily understand and not fear monger.

Okay, like not be like, oh, all their jobs are going to go away. This, that, like there’s going to be lots of amazing companies that are going to get built with far fewer people getting multi billion dollar, multi $100,000,000 evaluations 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 90% of the jobs are going be gone, like you’re all going to get screwed over by our models.

And like, it’s moral duty to tell you all this, like blah, blah, 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.

Yeah, I think so. I mean, I think they have contradictory messages in their own different social engagements so far, where the most recent one I heard was there is no evidence that AI is taking over jobs. 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, or 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, what happens to all the jobs? But the other way of looking at it is like, hey, 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 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 like around $200,000 worth of Amazon credits and GCP credits and Azure credits that together cumulatively this was worth like a million dollars in compute credits.

Now in today’s world, going be like a million dollars of computer credits. We’re doing that, we’re funding this thing called a billion dollar build, where we’re 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.

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 $100,000,000, it’s good. If there are thousands of them, like, that’s a lot of new GDP.

I don’t know. It’s hard to say. Maybe 800 or a thousand.

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. People should look at Perplexity and be like, oh, with 400 people, you can build a multi, I don’t know, $20,000,000,000 company. And so that means with 40 people, I could probably build a billion dollar or $2,000,000,000 company. And that’s totally doable, totally doable. And so for us maybe that means is with 4,000 people, we could be worth 200,000,000,000.

We could be worth $2,000,000,000,000 with like 10,000 people. That doesn’t mean it’s bad for all the 100,000 people we did not hire for a typical $2,000,000,000,000 company. I would rather have those 100,000 people be split into groups of like 100,000 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, 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.

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. You got a lot of heat for saying people don’t like their

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. What did you say? 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.

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, have to hire a lot of people, you have to like set up an office, this, that, like that’s changed.

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.

I mean, I’m sure there’s gonna 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 computer run locally that you’re not paying for tokens on, on metered intelligence essentially. How will

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 gonna 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. 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.

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.

I think people are just gonna have a lot more agency.

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 gonna get disrupted by agents. Anything where the transaction is more subjective, like the decisions are more subjective. Like what is the best piece of furniture inside this part? Like why this particular table? Or like those kind of things. Probably for the mic you would buy an objective decision. Mhmm.

The table, you probably are caring about the aesthetics of the room. I think that’s kinda how I feel the world will split and subjective things will still be ad based, objective things will be agent based.

Yeah.

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 100,000 people or 10,000 people, and enough compute credits to run those agents. What would you do? Let’s say I ask you Harry, let’s say you have suddenly like 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.

Okay, so you’ve gotten some amount of distribution. So now assuming that you have, let’s say you could spend a $100,000,000 on agentic inference and grounded with all the connectors and stuff, and it’s all working. What would you do with that capability to further your goals? What should your goals even be then? I think that’s the question I would ask. Assuming that the next three to five years, you’re gonna be able to delegate whatever digital you want and with the right harness and agents and like be able to delegate that.

Yeah. So your goal is to be to run like a 10 to 100x larger fund. Right? That’s basically what I’m hearing from you. So let’s assume it’s like a $40,000,000,000 fund from 400,000,000. Then all you got to ask is like, 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, that a task, somebody tells you a task is gonna take ten years.

Ask the question, why would it take to do it in ten months? Maybe it’s impossible to do it in ten months, but you’ll probably get pretty far asking those questions compared to somebody who takes it for granted that it’s gonna take ten years.

It’s 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.

Sure, I can say the same thing, 2,000,000,000,000. It doesn’t matter, right? Like 100x, 10x, 1000x, these are all motivational milestones.

Anyone can be a trillion dollar company. SK Hynix and Samsung are worth a trillion last 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 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 a $100,000,000,000. Okay you said you need to make a $10,000,000,000 in revenue.

Isn’t that the same for trillion? Like, you need to make a $100,000,000,000 in revenue?

Yeah, that’s true actually for even people. Like it’s way more likely for a person with $100,000,000 in liquid net worth to become a billionaire than someone with $10,000,000

I think the way to 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 making this thing up. An Uber driver in San Francisco once told me that he watched one of my YouTube interviews where I explained how you can build a product or a web app with an AI from scratch. Went on to do it and use AI style 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 white coding new apps. 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, like you, you didn’t you started this podcasting circuit, like, when you had nothing. Right? Nothing. 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.

gotta help them. Like, I think that’s the most important thing. I think they gotta help themselves. People will help themselves once they see that, okay, like, I kinda wanna 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 it takes discipline. You gotta get rid of bad habits.

Look, I’m not saying everyone’s gonna make it and everyone’s gonna be worth a billion dollars. Isn’t that the caption from this show? Aravind, everyone’s gonna make it. Anyone has the potential to make it. It’s as likely for Perplexity to become worth $2,000,000,000,000 as a founder who’s yet to secure your funding to 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.

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 putting some of that into Anthropic. So let’s say like Vanguard or BlackRock own like cumulatively they own like $200,000,000,000 of Microsoft and Salesforce. They might be like, okay, I’m gonna take $3,040,000,000,000 of that and put it into Anthropic. Fine.

You know, not a bad bit to make.

They have to weather the storm.

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 HashiCorp, 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 going to 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 in people to go use their products.

But as a business, it’s gonna be awesome. You know, it’s gonna be fine.

Far far more than that actually.

We’re not ready to share it, but growing really fast.

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, it’s rare. Well, you definitely need one. You need to have a model in place to get the bottom line efficiency when that becomes the objective. And you need to also have a path to getting there.

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 expected 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.

Absolutely. Because it’s in your incentives to bring down the cost.

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. No one’s ever in a comfortable position. Like I said in the start, no one can relax. This is fucking a harsh. Yeah. It’s got harder. 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 worth like 1 to 1,500,000,000,000, something in that range.

That’s basically the valuation of Meta, and this all was created in like six years. Meta took like twenty 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 mod providers.

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.

I feel like we’re all doing well. Cursor, I think is getting sold, SpaceX. OpenAI is Exactly, baby. 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 meetups and vote don’t actually build anything useful.

I think a lot of people are obsessed about identifying a mode in the first year or two of their company. I think the only shot you have is 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.

I think we can be even more AI built. 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, our competitors products can be even higher. And this is despite us being extremely agent build internally and trying to delegate as much to agents. And so, that’s that’s like a big area. 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 gonna feel scary at all. We’ll normalize that feeling very fast. It’s just gonna feel like the 10x engineers running certain aspects of the company.

I’d build data centers. You would?

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. Like I said, 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 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 can just be building infra. You need to be able to utilize all that infra to producing valuable output tokens to user. But we’re already good at doing that, so infra is the thing I would focus

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 base infrastructure for connectivity. Have you been on a flight with Starlink? No. 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

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, I’m excited about all these possibilities.

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 like most jobs that exist, like valuable jobs that exist are usually reincarnations of something that already existed. So I don’t think we’re gonna see completely new things. This is gonna reincarnate in different ways.

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 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 like a lot of opportunities.

No, not a big believer in too many of them. I think you gotta produce some differentiation. That’s the most important thing. Like, I I if would you call deep seq a Neolab? No. Why?

Yeah. I mean, kinda 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 or somebody goes out and builds for robotics models. I think I think that’s, like, somewhat uncorrelated and different, and that makes sense for a lab. But I feel like they’re, like, just labs for the sake of being lab, and I don’t think they’re gonna make it.

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 10,000,000,000,000. Are you happy now? Are you enjoying this? 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 work like that actually. Like, for example, like, these numbers, like, getting to 2,000,000,000,000 or 20,000,000,000,000 are exciting, but, like, that that doesn’t motivate me. It’s hard to get motivated by wealth. You you you wanna get motivated by impact.

People are smart in their own ways. It’s hard to compare. Like, I’ve met Jensen, Elon, all these guys and like Bezos. What was it like meeting Elon? Amazing. Elon’s like a very focused person. He might not appear that way on Twitter, but 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 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 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.

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, dollars 5,000,000,000,000 guaranteed to make $500,000,000,000 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 being comfortable where you are, thinking you made it, that that feels good to get here so far. But these guys are not stopping. 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 10,000,000,000,000 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 that long term outlook. I think that has been the biggest thing to learn from maybe these two individuals in particular. A lot of people view this entrepreneurship as like, oh, 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 gonna get inspired watching their dad.

Play paddle? 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 adults. And so I 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.

Oh. 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.