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

23:44Aravind Srinivas:Why did we want to understand subatomic particles, quantum physics, black hole theory, the origins of the universe? What is the purpose? But we still went ahead and did it because that's 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 stuff, but you can still look at it and ask one more question about it that you haven't asked before.

24:23Aravind Srinivas:Other animal species cannot. Once they have it in their mental model, what it looks like and touches and feels like they're going to ignore it's not it's not interesting to them can actually 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 a 24 7 ai and yeah they've talked about a hardware product that's going to come out do you think we will have continuous agents running yeah i think so and i think that's kind of why i believe the orchestration problem i talked about maximizing the token value can you just help me on sorry when you say the orchestration problem Yeah.

24:56Aravind Srinivas: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.

25:29Aravind Srinivas: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. 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 OKish model appear great, and be able to use the right model for the right task and the right part of the task, subagents, and even utilize the compute we all have in our own devices that doesn't need to be always on a server.

26:02Aravind Srinivas:That is an orchestration problem, a router, an awesome router, a master orchestrator router. Now, if you do that, you can realize the vision of a 24-7 AI without people freaking out about going bankrupt because no one's going to be able to afford a 24-7 AI, frontier AI running on the server. Imagine you turned it on and you could never switch it off unless something crazy happened. 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 cron job at the fidelity of a few seconds that runs all the time.

26:38Aravind Srinivas: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 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 i'm gonna be honest i think there's much more valuable things for people to steal from London-based VC.

27:41But yes, I can be.

27:42Aravind Srinivas:You're not just yet another London VC. You have a$400 million fund last time I read it. So imagine you're already making your moves for the$4 billion fund. Everyone has certain levels of 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, but the orchestrator. And I think that's what we want to do. Computers has been positioned explicitly as the agent orchestrator. The musicians in the orchestra are these sub-agents that utilize these different models.

28:19Aravind Srinivas:Think of them as the instruments. And the tools, the connectors, the models, these are all the instruments. And the musicians are the sub-agents. And the symphony is the work. And the system is the orchestra. And 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 maximizes the token value for what? For user. If you can solve this problem, you will capture the most economic value in AI long-term.

28:53Aravind Srinivas: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. Every time any part of the AI stack improves, our product improves. Since the beginning of the year, Anthropics 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 the beginning of the year.

29:24Aravind Srinivas:And a lot of thanks to model progress made by Anthropics. And we also brought our burndown thanks to OpenAI competing with them and bringing down the cost of the same capability. And now with progress in open source and local models and local chips, we're going to move some of the inference back to the local devices and bring down the cost even more. 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.

29:56Aravind Srinivas:To your question of who's best position 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 love the fact that we are able to be a very positive player at every layer of the stack and not have to rely on any one person to win. 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 are in an AI infrastructure bubble, which I think is funny, stupid and moronic.

30:38To what extent do we have a data center supply problem today from what you see?

30:42Aravind Srinivas: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 Supermicro? 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 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 have to get permits to do all these things. And so usually what's happening is there's a lot of lead time doing this.

31:15Aravind Srinivas:And the models that are already in use today, these have been trained in the hopper generation. So the Blackwell generation model, I think the first model that's Blackwell generation category is Mitos. And it's already scary. People are already freaking out about it. So imagine that everyone pre-trains a model on a million or hundreds of thousands of Blackwells. Those models are going to be far more powerful than what exists today. And then the wearer rubens are coming next year in full capacity. All the data centers of wearer rubens will be used next year. That model will be even more powerful.

31:48Aravind Srinivas: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 p-e ratio than companies like meta for example even though meta builds a lot of infra is valued as a software company 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 open eyes or anthropos because they are making money from their ai products for meta the capex bank correlates to increasing accuracy on ads, which is like a 6 % to 8 % bump in revenue.

32:45I get it. But for the CapEx spend, it doesn't make sense.

32:48Aravind Srinivas:Yeah. Well, I believe they are understanding what the market's saying. I don't think they're dumb to not see what's being said. I think they're introducing a lot of subscription products. 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 agents, subscription-based products. and maybe even a Metacloud that rents out servers 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 HPMs, might be more valuable than Meta in the next 6 to 12 months.

33:29Aravind Srinivas:It's already at like a trillion, and Meta is like 1.3 to 1.4 trillion. Can you help me understand that? Because memory is already a massive bottleneck. it's increased 5X in price in terms of the cogs. Right. But people are going, wow, Micron is fully priced at this point. Why is it not fully priced? 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 computers 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 bottle like whoever's going to 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 do you believe your nebbius and your core weaves will be a sustainable multi-hundred billion company in the future, or is it solving a short-term supply problem?

35:02Aravind Srinivas: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 going to 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.

35:36Aravind Srinivas: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. You could argue that OpenAI can do all the work that Core was doing. And that's kind of what they wanted to do with Stargate. But why is CoreV more successful at building data centers than OpenAI? It's hard to do. It's operationally intensive. Yeah, operation intensive. You got to focus. You got to spend most of your time securing permits, figuring out power, figuring out bottlenecks in the supply chain here and there, and constantly plan ahead and test all these systems carefully, deal with random physical issues that arise in like you know running a data center there's something called tco you know cost of operations you got to factor that in so that said 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 into 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.

36:54Aravind Srinivas:I think that's why you're seeing moves like Nibius, 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 Base 10 and all that. But you could imagine NeoCloud just going for that business. That was exactly my question. So I just had the co-founder of Nibius 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.

37:23That 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$100 billion companies in inference alone, or do we see that commodity?

37:36Aravind Srinivas:It's all about working backwards. What does it take to build a$100 billion company? $10 billion in revenue. 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 buildouts 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 and 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 open AI or Anthropik.

38:19That's exactly what Roman and Nebius said. He said if consolidation happens and there's Anthropik and open AI or two or three dominant providers, that is the biggest threat to Nebius. That's correct.

38:27Aravind Srinivas:Yeah. But you got to make a leap of faith assumption that the models from China or NVIDIA is making good progress on their models in Lemotron. So there's going to be enough factors in the market to keep consolidation as an outcome from stopping from happening. But you don't control your own destiny if you're those companies. That's basically the problem. Okay, so we can have standalone companies that are$100 billion in inference alone. Sorry, I'm just pillaging you for your knowledge. When we look at the model selection companies, like an open router or like Factory AI just released that kind of model selection or model routing product, which did very well on launch.

39:04Is that$100 billion companies in the model selection and routing business?

39:08Aravind Srinivas:Probably not. I think you can 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 listen, the router, even though the product is called open router is 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-55 as developer, why would you not want and 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.

39:45Aravind Srinivas: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, you know, 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.

40:24Aravind Srinivas: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 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 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 going to supply the tokens to you. So it's routing, not at the level of like, oh, deciding which model is cheaper or task. It's more like a reliable token supply.

40:56Aravind Srinivas: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 margin 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 difference is their margins. We spoke about bottlenecks and you said about HBM, high bandwidth memory and Micron and the value that they have to say and what it can be. What bottleneck will we have in three years that we're not discussing today?

41:30Aravind Srinivas:I think power will remain the bottleneck. It feels like that to me, unless something dramatically changes in the way data center buildouts 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 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. Do you think that's why they're putting up resistance in them? I don't. I think it's because it's a symbol of job losses, increasing wealth inequality.

42:04It's a lot of things.

42:05Aravind Srinivas:It's a lot of things. It's a lot of apprehensions, fear about what's going to happen, channelizing in so many different ways. Sometimes it's channelizing through hatred for wealth inequality and wanting to tax people. Sometimes it's channeling through concerns for the environment and climate change. Sometimes it's channelizing in a way where you're all 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.

42:35Aravind Srinivas: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. Do you think it will be meaningful to the development of those data centers? I think right now, 40 out of 100 are not being developed because of public resistance. 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 to build data centers there. Elon's going to space to do that. So that's going to be an interesting experiment because there's a lot of energy from the sun that can be harnessed there.

43:11Aravind Srinivas:There's a lot of natural resources in other countries. Regulations might be more friendly. So we're still going to see data center buildouts. it might not happen in the 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.

43:55Aravind Srinivas:And so that's why this will continue to remain the bottleneck in my opinion. And there's a lot of risk as well, because if you do encounter another deep seek moment here, where there's a vastly more efficient model that's been built with a very different vertically integrated architecture, and you built out all this capacity and you're like, damn, that's, I overbuilt. That's something far more efficient that can run on people's local devices, your MacBooks, their Windows PCs. Yeah, like you're probably freaking out then. How likely do you think that is though? It's probably a 20%, 30 % chance.

44:28Aravind Srinivas:The reason I think there are some possibilities is that because of the export controls. So DeepSeq is not building with the NVIDIA stack. They're building with the Huawei stack. And because there are export controls on not just NVIDIA GPUs, but also on HPMs, these architectures that DeepSeq's 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 a model architecture the model architecture is already pretty different they've made innovations on the attention layer they made innovations on like the training algorithm 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.

45:24Aravind Srinivas:And so that's a very different bet from what America is making. Do you think the export controls have helped or hurt us? Jury's still out. Short term, it's helping because my belief, the only reason why there's even like a 12-month gap between open source and frontier is export controls. And so it's definitely helped. 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.

46:01Aravind Srinivas: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 potent competitor. Do you think we still dramatically underestimate China's capabilities? I think so. If AI is not just digital, that'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. How important is it that we have our own TSMC in the US? TSMC is actually, there is a fab of TSMC in Arizona.

46:37Aravind Srinivas:Not a lot of people talk about this, but TSMC is investing like$150 billion into building American fabs. They've already invested $40 billion or something like that,$60 billion last time I checked. So there is a TSMC in Arizona that's coming up. There's also Intel, and that's why 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. I think people have woken up to the importance of building fabs but this is also why china is particularly very very competent given the capabilities of china that we just mentioned that really articulately i know it's a ridiculous question but soda 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 any strong China?

47:34Aravind Srinivas:I think take physical infrastructure a lot more seriously and continue funding it and not 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 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, 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, not be like, oh, all their jobs are going to go away.

48:12Aravind Srinivas:Like there's that. There's going to be lots of amazing companies that are going to get built with far fewer people getting multi-billion dollar, multi-hundred million dollar valuations with like 20, 30 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 to be gone. Like, you're all going to get screwed over by our models. And like, it's our moral duty to tell you all this. 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.

48:51Do 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?

48:58Aravind Srinivas: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 reason 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.

49:40Aravind Srinivas: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. Like when we started Perplexity, we had like around$200 ,000 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 today's world, it's going to be like a million dollars of computer credits. And we're doing that. Like we're funding this thing called the 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 company and i want like thousands such companies to be built what did you think of sam altman giving two million dollars of tokens to yc companies 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 i spoke to anne bordetsky before the show and she said how ai pilled the team is for you how big is the team today it's like 400 people 400 people how big will it be in two years time i don't know it's hard to say maybe 800 or a thousand 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 definitely they'll be dramatically more efficient and and that's why i i am a believer in building a lot more efficient companies and being an example for all these companies ourselves.

51:13Aravind Srinivas:Like people should look at perplexity and be like, oh, like with 400 people, you can build like a multi, I don't know, like$20 billion company. And so that means with like 40 people, I could probably build a billion dollar or$2 billion company. And that's totally doable, totally doable. And so for us, maybe that means that with 4 ,000 people, we could be worth 200 billion. We could be worth$2 trillion 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 trillion company. I would rather have those 100 ,000 people be split into groups of like 100 ,000 groups like that.

51:48Aravind Srinivas: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 they don't follow roadmaps, or they're not easy to collaborate with. But maybe the flip side of that is those are the kind of qualities that founders typically have. Aaron, there is a population and a very large population that are not AI-native people, that are not using AI to improve workflows, improve efficiency.

52:21What would you advise them?

52:22Aravind Srinivas: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 jobs. 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.

52:58Aravind Srinivas: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 really wealthy, but doing a job that you completely don't enjoy and destroying the peak years of your adult life, working on something that is horrible or depressing. My point is that if that's you, and if the reason you could never leave your job is because you were always worried how would you build a company from scratch? There are all these things to figure out. You have to hire a lot of people.

53:32Aravind Srinivas:You have to set up an office, this, that. That's changed. For the first time in history, you can get started on an idea with one or two other friends and maybe have a real genuine shot at building a billion dollar company. 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's like up and to the right. Seeing some cracks in and 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.

54:08Do 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?

54:18Aravind Srinivas: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, unmetered intelligence, essentially. How will the best companies of the future structure token budgets? My hope is that they don't have to understand that they will be able to work with an orchestrator who does it for them. It's not going to be easy for you to constantly keep track of 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.

54:53Aravind Srinivas:How do you even understand which models are good at each of those things, and how much do you spend on each of these divisions? You're not going to be able to keep track. I had a friend on the show the other day say that Google will be the token king. 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? 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.

55:23Aravind Srinivas:So again, they could catch up, totally capable, totally competent team. But today they're not quite at the frontier. I was shocked the other day. I saw the Cloudflare announcement that now agent traffic has overtaken human traffic for them. Why are you shocked? It was quicker than I thought, 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? I think people are just going to have a lot more agency. But do websites go away? Does design not matter? Does the advertising model of the internet die completely?

55:58Aravind Srinivas:My belief is that the advertising model around travel or shopping or fashion are not getting disrupted by agents because the judgment is not objective. Anything where the judgment is objective, the transaction is based on objective judgment, that's going to get disrupted by agents. Anything where the transaction is more subjective, like the decisions are more subjective. Like what is the best piece of furniture inside this spot? Like why this particular table? Or like those kinds of things. Probably for the mic, you would buy an objective decision. The table, you probably are caring about the aesthetics of the room.

56:35Aravind Srinivas:I think that's kind of how I feel the world will split and subjective things will still be ad-based. Objective things will be agent-based. I watched your commencement speech on the back of speaking to Samir at Excel, and he said, I had to watch it. So obviously it watched it. And one of the points you made was the defining skill of the AI era is asking better questions. Yeah. What question is no one asking today that maybe everyone should be asking? I think people need to ask more about like, okay, assuming I have a lot of agency available to me, what do I do? Imagine 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?

57:13Aravind Srinivas:Let's say I ask you, Harry, let's say you have suddenly 10 ,000 agents at your disposal. What would you do? I remember you telling me, or not me, but in some episode of yours where you said you only did this podcasting because you felt like you didn't have an arbitrage to go win deals. 100%. That's why I still do it. I mean, I love what I do, but yeah. Okay. So you've gotten some amount of distribution. So now assuming that you have, let's say you could spend$100 million on a Genic inference and ground it with all the connectors and stuff, and it's all working. What would you do with that capability to further your goals?

57:50Aravind Srinivas:What should your goals even be then? 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 delegate whatever digital task you want and with the right harness and agents and be able to delegate that. Fundamentally, it would be to build a genetic 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 that would power what my end goal ambition is. Yeah, so your goal is to run like a 10 to 100x larger fund, right?

58:28Aravind Srinivas:That's basically what I'm hearing from you. So let's assume it's like a$40 billion 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 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 going to take 10 years.

59:05All right, interviewer, put it on you. What's your 10 year and how does that look in a 10 month timeframe?

59:11Aravind Srinivas: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 level of agency that needs to exist in this world is quite not there. I think that needs to be grounded in numbers, dude, to make it possible. It's like me saying, oh, I want the best investments, which is why a$40 billion fund is helpful. Sure. I can say the same thing, like$2 trillion. It doesn't matter, right? 100X, 10X, 1 ,000X, these are all motivational milestones. Do you think Poplexy will be a trillion dollar company?

59:50Aravind Srinivas: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. 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$10 billion in revenue. Isn't that the same for a trillion? You need to make$100 billion in revenue. And there was actually some very interesting data that CO2 revealed, I don't know if you saw it recently, which basically says about the probability of reaching the next level of value.

1:00:31Yeah, it's higher. It's much higher. So when you're at a billion, it's much more likely to reach 10 billion, 10 billion much more likely.

1:00:36Aravind Srinivas:Yeah, that's true actually for even people. It's way more likely for a person with$100 million in liquid net worth to become a billionaire than someone with$10 million. Are you not worried about the wealth inequality? quality. Being blunt, we both are very lucky now to live in kind of nice worlds and rarefied heirs. 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. 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 like making this thing up, An Uber driver in San Francisco once told me that he watched one of my YouTube interviews where I explain how you can build a product or a web app with an AI from scratch.

1:01:24Aravind Srinivas:Went on to do it and use AI 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 wide 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 started this podcasting circuit like when you had nothing, right?

1:02:14Aravind Srinivas:Nothing. Exactly. So it's possible. So you got to 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. 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. You got to help them. Like I think that's the most important thing. I think they got to help themselves. People will help themselves once they see that, okay, like, I kind of want to be like this guy. Let me work hard.

1:02:42Aravind Srinivas:You need an example, right? It's not like nobody can get in shape. Like, it takes discipline. You got to get rid of bad habits. And now is the best time ever to change your life in 12 months. The ability to go from nothing to actually billionaire in 12 months is now possible in some respects. Yes. Look, I'm not saying everyone's going to make it and everyone's going to be worth a billion dollars. Isn't that the caption from this show? Aravind, everyone's going to make it. Anyone has the potential to make it. It's as likely for perplexity to become worth$2 trillion as a founder who's yet to secure your funding to be worth a billion dollars.

1:03:18Aravind Srinivas:Equally hard. You just have to give yourself shots at the goal. Be curious. That's the message from the commencement speech. Be curious. We have SpaceX. We have Anthropoc. 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 IPAs? There will be some reallocation for sure. There might be some holders of SaaS stocks who would put it into Anthropic or something. Let's say you believe that enterprise AI is going to take off. You might want to hedge between having a lot of Microsoft stock and Salesforce stock versus putting some of that into Anthropic.

1:03:54Aravind Srinivas:So let's say Vanguard or BlackRock, cumulatively, they own$200 billion of Microsoft and Salesforce. They might be like, okay, I'm going to take 30, 40 billion of that and put it into Anthropic. Fine. Not a bad bit to make. What happens to all the enterprise SaaS companies that are public going, yeah, fine? They have to weather the storm. Is it a storm or is it a continuous precipitation? I think you have to bring down the costs and produce new value. Salesforce has done well because they always went and bought the next thing. If you were just selling the same software, you're probably not going to be around.

1:04:27Aravind Srinivas:IBM is still around because they went and bought Red Hat and HashiCorp and now they're buying Confluent. So there are ways for these companies to stay alive and extend their lifespans 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 evoking an emotion and people to go use their products. But as a business, it's going to be awesome. It's going to be fine. I have to finish on, you said IPO in 2028. I had to ask this. I woke up to this in my group. We have a team WhatsApp and it's like, I was going to happen, IPO 2028.

1:05:00I hope it can be sooner than that. When do you know when you're ready? Is there like a billion in an hour? You're at 500 million an hour now? More than that.

1:05:09Aravind Srinivas:Far more than that, actually. Really? We're not ready to share it, but growing really fast. Revenue growth matters much more to you than profitability. 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. Where are you cost inefficient today where you expect to be significantly better in two to three years?

1:05:42Aravind Srinivas: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 models we own and serve ourselves. And that's going to be the best way to bring down the costs and increase our margins. Will the largest enterprises in the world all be fine-tuning open models to have tailored models that are much more specific to them?

1:06:17Aravind Srinivas:Absolutely. Because it's in your incentives to bring down the costs. Does that not provide another bad case for the large frontier model providers? Frontier model providers will only remain relevant if they remain at the frontier. If for six months you're not seeing a new capability, it's bad for them. And so that's the uncomfortable nature of this field. No one's ever in a comfortable position. Like I said in the start, no one can relax. This is fucking harsh. It's got harder. It's going to get even harder. That's the nature. It's just the price is too big. Like you've never seen, like take Anthropic.

1:06:53Aravind Srinivas:I think it's 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 be comfortable. And anyone who's winning today can lose tomorrow, including the mod providers. Pre this year, there was like a three month period where people were like, oh, perplasticity. What's happening with perplasticity? Do you pay attention? Do you give a shit? Of course I pay attention to all of that. 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?

1:07:26Aravind Srinivas: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. You don't give a shit? I feel like we're all doing well. Cursor, I think, is getting sold to SpaceX. OpenAI is 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 agree. Okay, we're going to 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?

1:08:12Aravind Srinivas: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 to move fast. In my mind, moving fast is a way of expressing humility because you're constantly making contact with the world and trying to question your assumptions all the time. Where are you still moving too slow internally today? 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 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.

1:08:50Aravind Srinivas:And so, yeah, that's 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 going to feel scary at all. We'll normalize that feeling very fast. It's just going to feel like the 10x engineer is running certain aspects of the company. If I gave you unlimited money, what would you do today that you're not doing? I'll build data centers. You would?

1:09:26Yeah. In space?

1:09:27Aravind Srinivas: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.

1:10:02Aravind Srinivas:And so we need to do that a lot for AI. AI. Yeah, that's what I would do. Of course, you cannot just be building infra. You need to be able to utilize all that infra to producing valuable output tokens to the user. But we're already good at doing that. So infra is the thing I would focus on. You can buy and hold for 10 years. SpaceX, Anthropik, or OpenAI, the three IPOs coming in the next few months, which you buy and hold for 10 years. And why? SpaceX. Why? It's an end 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.

1:10:40Aravind Srinivas: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 it. One small aspect of the business. Yeah. Yeah, like there's a lot of, I'm excited about possibilities to travel from Australia to like San Francisco in like 30 minutes. You know, all this feels like sci-fi, but I'm excited about all these possibilities. What job does not exist today that will be incredibly common in five years time?

1:11:12Aravind Srinivas:I think it already exists. So 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. 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? They're curious. Don't give into like FOMO and trying to max out on something here in the short term.

1:11:47Aravind Srinivas:Don't go to Twitter and feel like a loser that people on of frontier labs are getting so rich and like you're everything feels hopeless to you or something there's 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 we are seeing more spin outs from open ai anthropic you name it every single day do we have hundreds of these neo labs and vertical models no not 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 like i i if would you call deep seek a neolab no why i think very stupidly for me i don't call it a neolab because i i attribute neolabs like spin-outs from larger labs i see and kind of verticalized which is probably wrong on both axes but it's horizontal and it's not a spin out 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, or somebody goes out and builds for robotics models.

1:12:52Aravind Srinivas: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. Can you paint for me, what's the most plausible story where Populacity becomes a trillion-dollar company? What do you do then? The orchestration layer? I mean, accuracy and orchestration And it's 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?

1:13:22Aravind Srinivas:So what would I do once that happens? I don't know. We'll chart our path to 10 trillion. 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. For example, these numbers like getting to$2 trillion or$20 trillion are exciting, but that doesn't motivate me. It's hard to get motivated by wealth. You want to get motivated by impact. Who's the smartest person you've met, final one?

1:13:58You've met Jensen Huang. You've met the best of the best. I've been fortunate enough to. Who's the smartest?

1:14:04Aravind Srinivas:People are smart in their own ways. It's hard to compare. I've met Jensen, Elon, all these guys, and Bezos. What was it like meeting Elon? Amazing. I mean, 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 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.

1:14:45Aravind Srinivas:It's very hard to do. Even within perplexity, I cannot just focus on one part of the business alone. It's very difficult. I'm always looking at other things simultaneously. 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. Was Jensen Huang what you thought he'd be? 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.

1:15:23Aravind Srinivas:And like he's so intense that he tells everybody around him that they're 30 days away from going out of business. Think about it. $5 trillion guaranteed to make$500 billion 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 30 days away from going out of business. That is what it takes to be Jensen Huang. And there's so much to learn from these guys. There's so much to learn. There's one aspect of being comfortable where you are, thinking you've made it, that that feels good to get here so far. But these guys are not stopping.

1:15:57Aravind Srinivas:I don't think Elon wants to stop it. 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 motivating to be worth a 10 trillion in net worth or something. If he does these things, I'm sure he's going to get there. But it's more motivated around making the impossible things happen and having that long-term outlook. I think that has been the biggest thing to learn from maybe these two individuals in particular. A lot of people view this entrepreneurship as like, oh, if it wins, if I win and I have a great outcome and I sell my company, I would have generational money.

1:16:38Aravind Srinivas:I don't have to work ever again. And then what? You end up just staying at home and your kids will obviously have trust funds and they're not going to get inspired watching their dad - Play a paddle? Yeah. You're not going to set the right example for them. They're not going to be able to take your wealth and multiply it because they didn't watch somebody who actually did that you did it before they were like adults and so i think you always need to be doing something jensen said some 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 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.

1:17:19I was like, oh.

1:17:21Aravind Srinivas:Yeah, I think it's pretty hard, but you do it despite that. I think that's how it works. You do it despite that. Aaron, listen, this has been so fantastic to do. I so appreciate you taking the time while you're in London. So thank you so much for joining me. Appreciate it. But before we leave you today, if you're a finance professional, you know the month-end nightmare chasing down missing receipts and fighting with outdated tools that your employees hate using. It's time to enter the era of Navan. Navan is an AI powered travel and expense platform that gives you control and real-time visibility into every dollar spent.

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