The Exchanges

Q Um, but people are going, wow, Micron is fully priced at this point. Why is it not fully priced?

A Because it's still the bottleneck. Whatever is the bottleneck will command the price. Um, 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, like, 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 into certain ways and generates a plot and then hosts it on a website that you can, you can share with your people. All that compute is running on CPUs. Agents are using CPUs more than humans, right? 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, um, they're more important than companies that are just building data centers and not knowing how to turn that into a valuable output.

AI assessment note: “Because it's still the bottleneck. Whatever is the bottleneck will command the price.”

Q How likely do you think that is, though?

A It's probably a 20%, 30% chance. The reason I think there are some possibilities that because of the export controls, um, you are, so the Deep Seek is not building with the Nvidia stack. They're building the Huawei stack. And because there are export controls on not just Nvidia GPUs, but also on HPMs, These, um, 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 three NANDs. So their architecture is going to look, it's not just a model architecture. The model architecture is already pretty different. They've made innovations on the attention layer. They made innovations on like the, the training algorithm so that it doesn't consume A lot of interconnect capacity. So they, they made a lot of, they, basically their whole stack is getting vertically integrated to their hardware and their chips and their fabs and so on. And so that's a very different bet from what America's making.

AI assessment note: “It's probably a 20%, 30% chance.”

Q model providers, I do just want to kind of move to the ecosystem itself. And before we touch on the funding itself, I just look at it and everyone says that, you know, we're seeing the commoditization of foundation models, as you know, I'm just interested to hear your thoughts. How do you see the end state for the foundational model layer? Like, are they getting commoditized as people say?

A I think today the word commoditization is sort of true. Uh, I mean, like it's sort of true in the sense A model that's like 75, like GPT 3.75 level model, uh, is commoditized. There are like too many models like that today in the market, some open source and some closed source. I think GPT four quality models are not yet commoditized. There's only probably one or two alternatives for the people today, like Claude Opus or some people, Gemini, let's say. If it's just like two or three alternatives, it's not, I wouldn't call it a commodity yet. But will it be commoditized? I think so. But by the time it gets commoditized, would there be a 4.5 or five that's way better? TBD, the training run is happening. My, my prediction would be there would be another great model after four. That's like very good. Like, I wouldn't say GPT four O is like a lot smarter than GPT four turbo. It's, it's more reliable. It's, it's, it's better, it's faster, cheaper, but it's not like how four blew 3.5 out of water. That sort of thing, whether five can do that to four would answer your question of whether these models are getting commoditized.

AI assessment note: “I think GPT four quality models are not yet commoditized.”

Q Do you think the Asport controls have helped or hurt us?

A Jury's still out. Short term is helping because the only reason I, my belief, the only reason where, why there's even like a 12 month gap between open source and frontier is export controls. It's definitely helped. And, and, and definitely like companies like Anthropic lobbied very hard for it. But, um, 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, a 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.

AI assessment note: “Jury's still out. Short term is helping”

Q Can you just help me? Sorry, when you say the orchestration problem.

A Yeah. So, so, okay, so there are like four objectives. Um, accuracy, intelligence and accuracy, and then privacy and cost. You know, these are all competing with each other. So you can, you could argue that, um, you could max out on intelligence and accuracy by building giant, giant data centers and spending a lot of power to, uh, you know, run them. And, uh, you could miss out on privacy and costs because everything will be centralized and, and, and you're going to be paying a lot. Um, you could argue that everything can run locally. And so that'll be good for privacy and costs. But may not be frontier intelligence, may not be frontier accuracy. So the solution is to figure out a sweet spot. You know, use local models when necessary, use server-side models when necessary, and orchestrate across local models and server-side models. Grounded and valuable personal context. Sometimes the intelligence might already be there, but the system might not work because the harness isn't grounded in the right set of tools, right? So build a world-class harness that can even make an okayish model appear great. And be able to use the right model for the right task, and the right part of the task, sub-agents, and, and even, like, utilize the compute we all have in our own devices all with that, that, you know, doesn't need to be always on a server. That is an orchestration problem, a router, …

AI assessment note: “That is an orchestration problem, a router, an awesome router, a master orchestrator router.”

Q Which did very well on launch. Is that a hundred billion dollar companies in the model selection and routing business?

A Probably not. Um, I think you can just be a provider of a router. You have to use the router to produce something meaningful. Um, actually, most of the business value of open router is less than the router, even though the product is called open router. It's not routing across models there. It's actually just routing across different endpoints of the same model. So, uh, what? Okay. So maybe Let, let's, let's, let's ask this question. If you wanted to use Claude Opus or, um, I don't know, like GPT-FIFI's 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, it might, there might be an error on open AI servers that, you know, don't guarantee you the response time you need to run your application and, uh, open router would go and earn the, uh, you know, they would pay for capacity for like one year ahead, uh, 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 lo…

AI assessment note: “Probably not. Um, I think you can just be a provider of a router.”

Q Can you just help me? Sorry, when you say the orchestration problem.

A Yeah. So, so, okay, so there are like four objectives. Um, accuracy, intelligence and accuracy, and then privacy and cost. You know, these are all competing with each other. So you can, you could argue that, um, you could max out on intelligence and accuracy by building giant, giant data centers and spending a lot of power to, uh, you know, run them. And, uh, you could miss out on privacy and costs because everything will be centralized and, and, and you're going to be paying a lot. Um, you could argue that everything can run locally. And so that'll be good for privacy and costs. But may not be frontier intelligence, may not be frontier accuracy. So the solution is to figure out a sweet spot. You know, use local models when necessary, use server-side models when necessary, and orchestrate across local models and server-side models. Grounded and valuable personal context. Sometimes the intelligence might already be there, but the system might not work because the harness isn't grounded in the right set of tools, right? So build a world-class harness that can even make an okayish model appear great. And be able to use the right model for the right task, and the right part of the task, sub-agents, and, and even, like, utilize the compute we all have in our own devices all with that, that, you know, doesn't need to be always on a server. That is an orchestration problem, a router, …

AI assessment note: “orchestrate across local models and server-side models”

Q 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, uh, increasing wealth inequality.

A It's a lot of things. It's a lot of things. Um, it's a lot of apprehensions, um, fear about like what's going to happen, channelizing in so many different ways. Um, sometimes it's channelizing through hatred for wealth inequality and like wanting to tax people. Sometimes it's channeling through like concerns for the environment and like climate change. Um, sometimes it's, uh, channelizing in a way where, um, you're all like, oh, like the price of the grid is going up because you guys are building all these data centers. And then, or like, I'm paying more for my phones and laptops now because the RAM prices have gone up because you guys went and bought all of it. So I think there's a lot of different ways in which it's getting channelized, but the common sentiment is, um, like, like a pretty bad sentiment about AI.

AI assessment note: “fear about like what's going to happen, channelizing in so many different ways.”

Q Does design not matter? Does the advertising model of the internet die completely?

A No, it doesn't. Because my belief is that The advertising model around like travel or shopping or like, uh, fashion are not getting disrupted by agents because the judgment is not objective. Any, anything where the judgment is objective, the transaction is based on objective judgment, that's going to get disrupted by agents. Anything where the transaction is more subjective, like the decisions are more subjective. Like, like what is the best piece of furniture inside this, this, this 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. I think, 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.

AI assessment note: “No, it doesn't. Because my belief is that The advertising model”

Q That is very, very kind of you, my friend. Listen, I want to start with a little bit on you. How did you first fall in love with AI and realize that actually this was what you wanted to do and spend the majority of your career on?

A It was literally not more like an accident. Um, I was just yet another electrical engineering or computer science undergrad doing my courses and doing some interesting Some projects along the side. There was a point when one of my, um, friends in undergrad told me, hey, there's this, uh, contest, um, where you could win, win some price if, if you came first. And I think I was like, you know, kind of like in need of money because I wasn't sure I was going to get an internship. So I, I tried, try the contest out and, um, It was a machine learning contest, but I didn't even know what machine learning was. Um, all I knew from that guy was that, Hey, you're going to be given some data and you can, uh, use some of the patterns in the data and use it to make predictions on, you know, held out data that you don't have access to. The server will have it. You submit your algorithm and it'll score against what is correct and what you predict. And whoever wins the most number of correct predictions wins the contest and you get the price. That's the extent to which I, I was told. And I go and, um, check out this library called scikit-learn. It's a very popular machine learning library. And I have literally no idea what any of these words mean, like decision trees and random forest, like none of these things made any sense to me. Um, and so like, I, I just like literally just did what an AI …

AI assessment note: “we won the contest. And then that gave me a lot of confidence.”

Q What's the hardest element of your role that people don't think about or consider, do you think?

A I think it's just, um, dealing with contradictions all the time. Um, I, I believe the brain is not very good at dealing with contradictions. It actually tires us out when we can't arrive at a convergence point on something. And startup CEO is all about contradictions. Should you take a risk or should you like double down on what you have? Should you, uh, move faster or should you set up the company in a way that it can scale? Is it time to like You know, try out this feature just because it's, it's, it's, uh, not something your competitors would do or continue doing what you're doing well, but, but your competitors are doing the same thing. You have to like constantly deal with these contradictions in so many different dimensions, and that's tiring.

AI assessment note: “I think it's just, um, dealing with contradictions all the time.”

Q businesses as cloud providers, but they will acquire these models and add them in as complimentary features That they already provide. Uh, and you'll see your anthropics, you'll see your coheres, you'll see your adepts acquired or aqua hired by these large cloud providers. Do you agree with me in that prediction of the next three to five years in terms of how it shakes out with those aqua hires?

A I don't think so. I don't have a prediction on cohere, but, um, I think with OpenAI and Anthropic, the value of, uh, the value of those companies is not in the models they have. That is a very, uh, first-order approximation. I think the second-order approximation is it's in the machine that's building the machine. That specific group of people with all the tacit knowledge required to train these frontier models and innovate algorithmically on what is likely to be the real reasoning breakthrough. And the accumulation of compute they have is the reason why they're valued at this price, where the revenue and the valuation make no sense, but they are because it's, I always, I think about valuation is like how easy or difficult it is to reassemble this whole thing. And, and, and the thing is not just the output. The thing is also the machine that gave you that output. When you say the models are getting commoditized, so open air and probably are not that valuable. I disagree because these are the same guys who would produce the next model. Are those guys getting commoditized? Like the, the talent? No. In fact, it's getting the opposite of commodity. Like they're all being paid a lot of money to stay in these companies. And so the knowledge only stays with them because people don't publish anymore. There was even a joke. I recently got a hangout with one of a very great researcher. I…

AI assessment note: “I don't think so. I don't have a prediction on cohere, but”

Q So we are absolutely going to talk about kind of the funding required to, to finance these models. I do just want to stay on performance and capabilities. Why is it so difficult to have models with memory? Everyone says, ah, memory is the challenge. I don't understand why. Can you help me?

A There are two things here to consider. What, what does memory mean? Is it like a sufficiently long context that's practical for most use cases? Or is it infinite long context? Like basically there's an AI for Harry that, uh, remembers all your life. Every single aspect of it. Every single detail. That is like infinite memory. I think like we don't even have the algorithms for it yet today. And then there's another AI that's sort of like, it's like Gmail sort of a thing where, you know, it starts off with like a sufficiently large storage that like, like is practical enough and it keeps it, it keeps expanding over time. And then now it's like throttle beyond which you have to pay 10 dollars a month or something. Right. That seems more like where we are headed right now. Like, like people are expanding the token window from one, 28 K, like start with 32 K. Then it goes to like a million and deep mine announced two million. So, so I feel like that is already good enough where at least we can prioritize and throw out like what's not relevant and like keep, keep, keep using memory. And I, as you said, that's not very hard to do. There's one small challenge there though. It'll be figured out, but today's case is that we have achieved long context before achieving good instruction following. So you can dump a lot into your prompt. You, you have the memory, but models can hallucinate o…

AI assessment note: “we have achieved long context before achieving good instruction following”

Q Who do you think that person is most likely to be?

A It's likely to be, uh, open AI or anthropic. And I, I can make a good case for both of them. Um, OpenAI because they are far ahead in terms of lead they had in doing these things first. Anthropic because they're algorithmically a superior company. Like they got whatever open AI got to with lower capital. They have better post training and like, like things like that. So open AI is on the other hand, like extremely like, like advantage on capital and, uh, speed. So it really is like a question of who, you know, which matters more. Is it, is it clever brains or, and like some amount of capital or is it Good brains, a lot of aggression, and a lot of capital. If it's a second, it's OpenAI. If it's the first, it's Anthropic.

AI assessment note: “It's likely to be, uh, open AI or anthropic.”

Q What's the hardest element of your role that people don't think about or consider, do you think?

A I think it's just, um, dealing with contradictions all the time. Um, I, I believe the brain is not very good at dealing with contradictions. It actually tires us out when we can't arrive at a convergence point on something. And startup CEO is all about contradictions. Should you take a risk or should you like double down on what you have? Should you, uh, move faster or should you set up the company in a way that it can scale? Is it time to like You know, try out this feature just because it's, it's, it's, uh, not something your competitors would do or continue doing what you're doing well, but, but your competitors are doing the same thing. You have to like constantly deal with these contradictions in so many different dimensions, and that's tiring.

AI assessment note: “I think it's just, um, dealing with contradictions all the time.”

Q businesses as cloud providers, but they will acquire these models and add them in as complimentary features That they already provide. Uh, and you'll see your anthropics, you'll see your coheres, you'll see your adepts acquired or aqua hired by these large cloud providers. Do you agree with me in that prediction of the next three to five years in terms of how it shakes out with those aqua hires?

A I don't think so. I don't have a prediction on cohere, but, um, I think with OpenAI and Anthropic, the value of, uh, the value of those companies is not in the models they have. That is a very, uh, first-order approximation. I think the second-order approximation is it's in the machine that's building the machine. That specific group of people with all the tacit knowledge required to train these frontier models and innovate algorithmically on what is likely to be the real reasoning breakthrough. And the accumulation of compute they have is the reason why they're valued at this price, where the revenue and the valuation make no sense, but they are because it's, I always, I think about valuation is like how easy or difficult it is to reassemble this whole thing. And, and, and the thing is not just the output. The thing is also the machine that gave you that output. When you say the models are getting commoditized, so open air and probably are not that valuable. I disagree because these are the same guys who would produce the next model. Are those guys getting commoditized? Like the, the talent? No. In fact, it's getting the opposite of commodity. Like they're all being paid a lot of money to stay in these companies. And so the knowledge only stays with them because people don't publish anymore. There was even a joke. I recently got a hangout with one of a very great researcher. I…

AI assessment note: “I don't think so. I don't have a prediction on cohere, but”

Q How do we know that's not case specific? It could be they just bluntly approached it in the wrong way, they didn't have a good enough team, whatever that is. It doesn't necessarily disprove verticalization of models, does it?

A Uh, you know, if they could have had a better team, they could have ended up with a better model. No questions about it. The question I'm trying to pose here is that, what is the magic in these models? Where is it coming from? These models are magical. Like you're, you're not training them for what you're using them at test time. Like the way you prompt and use these models as if they were a human in the chat window is not what they were trained to do. They were just trained to predict the next token on the internet. Sure. They were fine tuned a little bit to be good at chat, to be good at instruction, following all those things. Definitely. But that is just a very small amount of compute that was applied to these models. So what makes these models magical is a general purpose emergent capabilities. The fact that they can do things without being taught how to do it, or they can catch things on the fly with some little bit of prompt instructions. Now that doesn't come from any domain specificity. It comes from, from the emergence of having training on so much. These neural nets are amazing that if you just throw very diverse set of data at them, uh, the, the, the pattern match on the abstract skill required to be good at all of them at once. And that abstract skill, that, that abstract IQ is what is making these models amazing for you on practical production use cases. So when y…

AI assessment note: “Now that doesn't come from any domain specificity. It comes from, from the emergence”

Q What was the internal discussion with you and the team when you were talking about adding advertising and as a monetization engine? Just take me inside that conversation. How did it go? Um, how did it net out?

A You know, there's this whole Larry and Sergey page rank paper that say, uh, said like advertising is fundamentally, uh, incompatible with like serving good results to the user. In a search engine. Truly believe that, and like, I've read books that said, like, they've pushed back on introducing ads as much as possible until they gave up to the investor pressure. Um, we were like, look, let's be practical. This is the most highest margin business model ever invented, but let's do it in a way where we don't have to be as high margins as Google. Like you don't have to aim for that. 80% margins. Like as long as you can get a good reasonably good high margin business without, uh, failing on your duties, the user be happy, like don't be greedy. So that's our, that was our thinking. We didn't actually have much debate on this. It was like, what is the way to do ads without corrupting the, um, Answer, uh, without, without making sure, but, but, as in, you make sure that, uh, the answer is not, like, influenced by, uh, the ads, or the links that you cite are not influenced by the ads. And if you can ensure that, I think it's a great, I think it's a great, uh, idea to explore. That's why we have other, other surface areas for ads too, like even the discover feature and perplexity, which has like, you know, a bunch of threads, interesting, uh, threads every single day to like read, uh, tha…

AI assessment note: “We didn't actually have much debate on this.”

Q What's the biggest misconception in AI today, do you think?

A Short term thinking. Like, anytime somebody comes up with an update, everyone's like, the other company is done. Like, this is over. Um, but that's like, you know, I would say the The, the usual Twitter mob, but I would say the biggest misconception among even the more, uh, well-informed people, because majority of the people in the world are not using chatbots, they just think this is a bubble. They're gonna get really surprised that it's not a bubble, it's not overhyped, it's actually underhyped. These things, when taken in the right workflows and form factors that you're already familiar with, will have a lot of impact. Like chat UI is a new UI. We're not used to using it. We're all used to using WhatsApp and Signal and all that, but That's different. It's not exactly a chat. It's more like a texting service. Then, on the other hand, Word, Docs, Gmail, Google search. Like, I'm not even talking about the specific products, but more like the form factors of usage, the UIs. You're very familiar with it, and when AI is presented to you in that sort of a format where it feels so obvious and natural as a workflow, it'll have a tremendous amount of impact. And it's not really happened yet.

AI assessment note: “they just think this is a bubble. They're gonna get really surprised”

Q and when I, Bluntly had so many people message me when I put out about our show. The first one was one of diminishing returns. And it's when we look at model performance, I think we've always had this kind of belief that you throw more compute and you get much better model performance. Do you think we've gotten to a stage now where we're starting to see diminishing returns?

A I think it's a nuanced answer. I can just say no. And you would be like, okay, if I brute force still works. But that's not the reality either. Like, it's not like if, if I suddenly came and say, hey, Harry, take my five hundred million dollars, uh, and, and, and go build a big cluster, uh, take like, uh, you know, like trillion tokens and, and get a model better than OpenAI. It's not gonna happen like that. There is still some alpha left in making these models bigger and training them on more tokens. But You would only get the bang for the buck if you put a lot of effort into, like, curating the data. Otherwise, this is not worth it. Like I know so many research labs. I can't obviously mention who they are, but who train really big models on a lot of data and ended up with nothing. It's a lot about what data you train on, how you mix, uh, English and like other languages and code and like math, um, and like all the chain of thought reasoning. And then how does it play out in the scaling law? Like in terms of Chinchilla optimality. And like we later discovered even Chinchilla was not optimal. It was just a guideline. And, and then how do the mixture of expert models like, like be more computation efficient? All these things matter. And like, um, that's where I think like, uh, you know, Those who do it right, those who get these 128 details right, are the ones who, uh, end up be…

AI assessment note: “There is still some alpha left in making these models bigger and training them”

Q Do you think you've cracked the relevance code?

A I'm saying we want to try. I'm not, I'm not saying we have cracked it. And if you cracked it, I think we should be worth way more. But, but, but like, We have, I mean, first of all, it, it can only be, it's, it's like a chicken and egg problem. It can only be cracked when you have a lot of users. So advertising is one of those funny things where there's no way it can work well when you don't have a lot of users. And then when you have a lot of users, it can work really well if you get all the details right. I was talking to Mark and recent ones, and he told me how, like in advertising, it's like three tiers, but like the top tier is like Google. And then like one and a half, like one is Google, one and a half is Meta. Because even between Google and Meta, Google benefits from every other advertising other people do, because at the end, once you discover the brand, you go to Google and click on the link they have. It's amazing. Like how they benefit from everyone else's hard work all the time. And then there's like companies like Twitter and like Reddit and snap, which is like third tier. It's like, and he said the gap between these two is so high. This is like almost climbing the peak of the mountain. This is just like somewhere in the bottom. So that, that, that is the. Extend to which ads have been dominated by like Google and Meta at this point today. My point is that if we …

AI assessment note: “I'm not, I'm not saying we have cracked it.”

Q What's the biggest misconception in AI today, do you think?

A Short term thinking. Like, anytime somebody comes up with an update, everyone's like, the other company is done. Like, this is over. Um, but that's like, you know, I would say the The, the usual Twitter mob, but I would say the biggest misconception among even the more, uh, well-informed people, because majority of the people in the world are not using chatbots, they just think this is a bubble. They're gonna get really surprised that it's not a bubble, it's not overhyped, it's actually underhyped. These things, when taken in the right workflows and form factors that you're already familiar with, will have a lot of impact. Like chat UI is a new UI. We're not used to using it. We're all used to using WhatsApp and Signal and all that, but That's different. It's not exactly a chat. It's more like a texting service. Then, on the other hand, Word, Docs, Gmail, Google search. Like, I'm not even talking about the specific products, but more like the form factors of usage, the UIs. You're very familiar with it, and when AI is presented to you in that sort of a format where it feels so obvious and natural as a workflow, it'll have a tremendous amount of impact. And it's not really happened yet.

AI assessment note: “they just think this is a bubble. They're gonna get really surprised”

Q 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?

A Definitely they'll be dramatically more efficient, right? And, and that's why I, I, I am a believer in building a lot more efficient companies and being an example for all these companies ourselves. Like, like people should look at perplexity and be like, oh, like with 400 people, um, you can build like a multi, like, like, I don't know, like 20,000,000,020 billion dollar company. Um, And so that means with like 40 people, I could probably build a billion dollar or two billion dollar company, you know, and that, that's totally doable, totally doable. And, um, and, and so for us, maybe that means is with 4000 people, we could be worth two hundred billion. We could be worth two trillion dollars with like 10,000 people. You know, I think, I think that doesn't mean it's bad for all the, um, 100,000 people we did not hire for a typical two trillion dollar company. I would rather have those 100,000 people be split into groups of like hundred thousand groups like that. And each of those thousand groups are worth a few billion dollars. That's awesome. And I think a lot more people need to be entrepreneurial. Um, there are people who would be bad employees in any company because they're just like difficult to work with. They, they don't listen to like instructions. So like they don't follow like roadmaps. Uh, or not, they're not like easy to collaborate, but, but maybe the flip side of …

AI assessment note: “Definitely they'll be dramatically more efficient, right?”

Q Do you think we've done a complete disservice by having the marketing message that Dario has had that all jobs are going and it's all doom and gloom?

A Yeah. I think so. I mean, I think, you know, they have contradictory messages in their own, like, uh, different social engagements so far, where the most reason when I heard was there is no evidence that AI is taking over jobs. But so I, I, I think there, there needs to be a consistent communication around this. And I also think that, um, very little is being spoken about how AIs can help you build companies in a very, very different way. Like the current AIs, organic AI. It's already true that so many things you would hire people for, you can do it with agents. But one way of looking at it is like, oh, like what happens to all the jobs? But the other way of looking at it is like, hey, like, I can, I never had the chance to go build out a company on this idea that I've been having all this time, 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, you know, Amazon gave a lot of compute credits to a lot of startups. Like when we started perplexity, we had like around 200,000 dollars worth of Amazon credits and GCP credits and Azure credits. Um, 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…

AI assessment note: “Yeah. I think so. I mean, I think, you know, they have contradictory messages”

Q Every business has a core monetization engine. They have ancillaries, but there tends to be one which is dominant. When you look at, you know, perplexity in five years time, what is your dominant engine? Is it consumer subscription? Is it advertising? Is it enterprise?

A I would predict it'll be advertising. If we crack it, yes, it'll be advertising. If we don't crack it, if we are not, if we, if we don't, if we haven't grown to that level in user basin, or if we grew and didn't figure out how to advertise really well, I think it'll be the other two. Either way, we can be profitable. I think with advertising, we can be really, really profitable. And then you can ask him, Hey, Arvin, why do you care about profits? Like Sam Altman doesn't care, but he doesn't care because he's not interested in actually just focusing on product as a business. Like he's trying to build AGI. And like he already told publicly in an interview that, you know, even if we spend like, you know, fifty billion dollars on AGI, it doesn't matter. So that's a different company. We shouldn't be seen as an OpenAI competitor at all. We're not an AGI lab. We can say perplexity and ChatGPT are products in a similar space. And, and there's like some competition for mindshare and users, but even that will like be pretty clear, like two years from now, you're not going to keep asking how is perplexity different from chat GPT. Today you are, but two years from now, I don't think so. If that's still the case, one of us is just copying the other.

AI assessment note: “I would predict it'll be advertising. If we crack it, yes, it'll be advertising.”

Q Don't laugh. Is there an asymptote to frontier problems to be solved? I know that sounds ridiculous, But if you are continuously on the chase for the next frontier problem, you get to cancer, you get to climate change, and my word, I hope they solve both, and like, heaven, that's a huge amount to solve. But if you're on the treadmill of continuously, is there an asymptote to that?

A Do you see what I mean? There's no mathematical argument to there being a cap on the amount of economic value One can create with, with, um, AGI or ASI-like systems. Um, and Elon, Elon has a good argument for this. Like, like he already says, money loses all meaning in a post-AGI economy, because you were producing an abundance of energy and labor, and fundamentally the economy is grounded to energy and labor. If you can produce an abundance of them, Well, what, what meaning does money have? Um, and, um, and so I, I don't think we run out of things to solve at the frontier. I think we're always going to be creative. Like, like, why, why would, why did people even want to understand the universe? Like, like, why did we want to understand subatomic particles, quantum physics, black hole theory, um, you know, the origins of the universe? Like, what, what, what is the purpose? But we still went ahead and did it because That's kind of what the purpose of humanity has always been, to understand the unknown. You know, David Deutsch is famous for saying this, right? Like, we are the only species capable of being curious about what is already familiar. Like, you can stare at a fruit, and you know that it's a mango, and like, you know exactly like how it tastes, you know how it looks, you know the shape, you know what seasons it grows and stuff, but you can still look at it and ask one m…

AI assessment note: “I don't think we run out of things to solve at the frontier.”

Q Do you think it will be meaningful to the development of those data centers? I think right now, 40 out of a hundred are not being developed because of public resistance.

A Yeah. So, um, that's where the power bottleneck is. And, um, you could see maybe certain countries seize the opportunity for this and, um, um, allow these model builders to build data centers there. Um, Elon's going to space to do that. Um, so that's going to be an interesting experiment, um, because there's a lot of energy from the sun that can be harnessed there. And, uh, There's a lot of natural resources in other countries. Regulations might be more friendly. So we're still going to see data center build out. It might not happen in the US. And, um, but, 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, um, doing better marketing against the competitor's products. Like, 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, uh, 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 her…

AI assessment note: “So we're still going to see data center build out. It might not happen in the US.”

Q cash flow per day. In a world where that is the case, not cynically, but just genuinely, How does anyone compete? Like, you know, being blunt, we can take this out. You know, you're rumored to be raising this, rumored you don't need to comment at all. Like, the amount that you raise, relatively, it's just insignificant compared to Microsoft's free cash flow. How does one compete in that world?

A That's why you gotta build a business, right? So, first of all, let's, let's separate the two things. If Microsoft is generating that much cash flow, uh, why are they not able to poach Uh, all the OpenAI scientists or Mistra scientists to come work for Microsoft. Like, they could take that money and, and, and ask one of those people to, like, ask like 10 of those people to, you know, come work here and I'll pay you a lot of money. You know, you don't no longer need to work at OpenAI, just directly build the AIs here. Whatever GPUs I'm giving for OpenAI, I'll give it to you directly. It's not happening, right? For a reason. It's like, people want to work with other best people, so you say it's not enough to get one person, you want it, you have to get the whole thing. Uh, that's why they were all jokes, you know, when the whole board drama was happening, that Satya acquired OpenAI, like, at a small price. Because he got the whole team out. I think that's the, uh, difficulty here. It's, it's, it's cashflow doesn't change the dependence, uh, issue. If they can get these models from people other than these two companies, yes, that changes the equation a lot. Like they can just, uh, um, you know, like get it from open source and sell the same models and like make a lot, same amount of money with less spend. Then that is bad news for the foundation models. Um, As for like, what is th…

AI assessment note: “It's, it's, it's cashflow doesn't change the dependence, uh, issue.”

Q Have you been surprised by the fundraising process?

A Fundraising processes are brutal. I think most people think like you just go to like, there's always these memes about like, oh, if it's AI, people are just like willing to write you the term sheet without even doing any diligence. Well, like, welcome. Like, why don't you try to race? It's pretty difficult, actually. Um, Everyone's asking all the questions that people on Twitter roast, uh, rappers. What happens if, uh, opening analysis? What happens if Google does this? What, what, what, you know, why would they not stop giving you models? Like, um, how will you build your own models? Like, how are you going to ever build a search index that's like really good? Or, um, you know, what, how do you compete on the enterprise sales? Like, like all these are questions everybody asks and like, and when you don't even know, uh, And you don't have a good model of the future yet. You have to give them good arguments, but at the end of the day, it's all like arguments. Nothing is there. One thing that we do have in our favor is like a good track record of execution. We've been around for like less than two years and the amount of things we've shipped is quite a lot. Compared to the team size and funding we have.

AI assessment note: “Fundraising processes are brutal. I think most people think... It's pretty difficult, actually.”

Q Final one for you. It's 2034. Where would you most like perplexity to be then? If we do a show then, where is the business then?

A I think I would just want it to be the assistant for facts and knowledge you just cannot live without. And you can ask me, would 10 years later, do people even want facts? You know, there's this thing where you have to always ask this question, like, what is going to be true even 10 years from now? And if you work on that, you're working on the right thing. I feel like even, even in a world with a lot of AI agency, Uh, and, and less of human agency, people would still want to know what's true and what's not true. And so we are working on that. So if we are the go-to assistant for facts and accurate information and knowledge, I think we'll be fine even 10 years from now.

AI assessment note: “I think I would just want it to be the assistant for facts”