Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Perplexity's Aravind Srinivas on Will Foundation Models Commoditise, Diminishing Returns in Model Performance, OpenAI vs Anthropic: Who Wins & Why the Next Breakthrough in Model Performance will be in Reasoning About this episode Aravind Srinivas is the Co-Founder & CEO of Perplexity, the conversational "answer engine" that provides precise, user-focused answers to queries. Aravind co-founded the company in 2022 after working as a research scientist at OpenAI, Google, and DeepMind. To date, Perplexity has raised over $100 million from investors including Jeff Bezos, Nat Friedman, Elad Gil, and Susan Wojciki. In Today’s Episode with Aravind Srinivas We Discuss: Biggest Lessons from DeepMind & OpenAI What was the best career advice Sam Altman @ OpenAI gave Aravind? What were Aravind’s biggest takeaways at DeepMind? How did DeepMind shape how Aravind built Perplexity? What did Aravind mean by “competition is for losers?” What did he learn about talent assembly at DeepMind? The Next AI Breakthrough: Reasoning Does Aravind think we are experiencing diminishing returns on compute & model performance? Does Aravind agree reasoning will be the next big breakthrough for models? What are the reasons Aravind thinks models suck at reasoning today? What is the timeline for reasoning improvement according to Aravind? What does Aravind think are the biggest misconceptions about AI today? Will Foundation Models Commoditise? Does Aravind think foundation models will commoditise? What will the end state of foundation models look like? Why does Aravind think the second tier models will get commoditised? Why does Aravind think the subscription model will not work for AI models with true reasoning? Why does Aravind think the application layer companies will benefit from foundation models commoditising? Why does Aravind think foundation models will not verticalize? When does Aravind think is the right time to go enterprise? What is his strategy to differentiate Perplexity from its competitors? AI Arms Race: Who Will Win? Who does Aravind think will be the winners of foundation models? What do AI companies need to do to win the model arms race? How does Aravind think startups can compete against incumbents' infinite cash flow? What are the reasons Aravind thinks Perplexity’s browsing is better than ChatGPT? What is Aravind’s biggest challenge at Perplexity today?
Transcript 11,386 words · 3 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 Today's models are just giving you the output. Tomorrow's models will start with an output, reason, illicit feedback from the world, go back, improve the reasoning. That is the beginning of, I would say, the real reasoning era. The biggest beneficiaries of the commoditization of foundation models are the application layer companies.
0:18 Harry Stebbings? This is 20 VC with me, Harry Stebbings, and what a show we have for you today with Aravind Srinivas, co-founder and CEO at Perplexity. As Gary Tan described it in a tweet, "Perplexity is actually just better than Google for clear, well-sighted answers." The company's raised over $100 million to date from the likes of Jeff Bezos, Nat Friedman, Eli Gill, and many more incredible investors. And prior to Perplexity, Aravind cut his teeth at OpenAI and DeepMind. But before we dive in, I want to talk about Cooley, the global law firm built around startups and venture capital. Since forming the first venture fund in Silicon Valley, Cooley has formed more venture capital funds than any other law firm in the world, with 60-plus years working with VCs. They help VCs form and manage funds, make investments,
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3:02 Speaker 02 You have now arrived at your destination.
3:05 Harry Stebbings? Aravind, I'm so excited for this. I've been looking forward to this one. So first off, thank you so much for joining me today.
3:11 Speaker 00 Thank you for having me, Harry. I've watched all of your episodes, so looking forward to it.
3:16 Harry Stebbings? 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?
3:26 Speaker 00 More like an accident. I was just yet another electrical engineering or computer science undergrad, doing my courses and doing some interesting projects alongside. There was a point when one of my friends in undergrad told me, hey, there's this contest where you could win some prize if you came first. And I think I was like, kind of like in need of money because I wasn't sure I was going to get an internship. So I tried the contest out. It was a machine learning contest, but I didn't even know what machine learning was. All I knew from that guy was that, hey, you're going to be given some data. You can use some of the patterns in the data and use it to make predictions on held out data that you don't have access to. The server will have it. You submit your algorithm and it'll score
4:11 Speaker 00 against what is correct and what you predict. And whoever wins the most of our correct predictions wins the contest and you get the prize. 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. Literally just did what an A I would do, brute force, random search, but as a human. I did all that and we won the contest, I won the contest and then that gave me a lot of confidence, okay. I beat people who actually knew machine learning in it. And that gave me a lot of confidence that like this is something I could be pretty good at. I remember uh Sam Altman once telling me, I asked him this question like two, three years ago.
4:54 Speaker 00 Hey, like, how do you identify something where you you're naturally good at? And he said, whatever comes easy to you, but seems hard to other people. Like, that's a good heuristic to identify things that you could be like mu plus two sigma at compared to the rest. So I felt like, okay, this machine learning is a good thing. It was not called A I. So I I got into it and I did a I did all the courses, pattern recognition, machine learning, the book written by Christopher Bishop. I bought it second hand in India, something like two, three dollars or some and and and started reading it. And I really enjoyed it, like it was pretty mathematical, but also intuitive at the same time. That got me access to a professor, Rich Sutton and Andy Barde's student, uh and and he was teaching at my undergrad institute. I told him to advise me, give me a project.
5:38 Speaker 00 And he was um a reinforcement learning guy. So reinforcement learning is when I actually got into A I. Because we have all written A I for like checkers or like tic tac toe or something like that, like, you know. Or or even when you play chess as a kid, when you play against a computer, you always ask this question, how does this computer play like? What is it like? And then they go, oh, yeah, it's like an A I, you don't worry about it. So they use A I loosely there, but the real definition of A I, what it is like, oh, it's an agent, it's it's it's an environment. Receives a reward signal, it optimizes for an objective. All that framework mathematically uh made sense to me once I studied R L. And then he told me at the end of the class, yeah, how a friend of mine uh from U K, David Silver, you know, we used to know each other from P H D days.
6:23 Speaker 00 And his startup just got bought by Google for like half a billion dollars because they wrote this paper that learned to play Atari games just from the screen pixels. Um. They've open sourced the code. Why don't you take it and now figure out how to play all the games simultaneously, like not just one single game. Uh. You learn to play Pong, uh, you should be able to play breakout much faster than learning to play breakout from scratch. Transfer learning. So that was the first project I actually worked on. I I loved the idea of all the papers that DeepMind did and. I I would just like literally be in the lab all the time and keep reading their papers, trying to like implement them. And like borrowing gaming GPUs from other people in the lab and using it to train neural nets on it.
7:03 Harry Stebbings? I was thinking before this, like, what are the single most pressing questions right now? And what do I most want to ask? And I think the first one that came to mind for me and when I 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?
7:29 Speaker 00 Yeah, I think it's a nuanced answer. I can just say no. And you'd be like, okay, if I, brute force still works, but that's not the reality either. Like, it's not like if I, if I suddenly came and say, hey, Harry, take my five hundred million dollars. And go build a big cluster, take like, uh, you know, like trillion tokens and and get a model better than OpenAI. It's not going to 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, it's just not worth it. Like, I know so many research labs, I can't obviously mention who they are, but. Who trained really big models on a lot of data and ended up with nothing.
8:12 Speaker 00 It's a lot about what data you train on, how you mix English and like other languages and code and like math. And like all the chain of thought reasoning. And then how does it play out in the scaling law, like in terms of Shinshila optimality? And like, we later discovered even Shinshila was not optimal. It was just some guideline. And then. How do the mixture of expert models like like be more computationally efficient? All these things matter and like that's where I think like those that do it right, those that get these hundred and twenty eight details right, are the ones who end up benefiting more from more scale. And that happens to be like three or four labs at this point. And I'll just give you an example, don't judge me here, judge Arthur of Mistral.
8:56 Speaker 00 When XAI released a model, the open source first GROK, and Arthur tweeted saying that that's a lot of superfluous parameters. Because the model was three hundred B or something and was not even as good as the Mistral's seven times eight, fifty six B. You could train a model that's like six X larger and end up still with the worst model. You could have spent a lot more money and ended up with the worst model.
9:20 Harry Stebbings? So if you talk about the kind of curation of data there as kind of the central factor in terms of determining quality of performance. I had Reid Hoffman on the show actually earlier this week and he said actually that we will see the kind of verticalization of models. That you use different models for different things. Is that where it leads to then? Is that what you're pointing towards?
9:39 Speaker 00 No, I actually think that viewpoint is flawed. I used to think that'll happen too, but I can give you another example that defeats that purpose. Bloomberg spent a lot of money training Bloomberg GPT. They even wrote a paper on it, saying they trained their own foundation model. And that model is beaten convincingly by like a GPT four on all the finance benchmarks.
10:01 Harry Stebbings? 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?
10:11 Speaker 00 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. You're not training them for what you're using them at test time. The way you prompt and use these models as if they were a human in the chat window. It's 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 the 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.
10:57 Speaker 00 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. They 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 you are saying, oh, I'm just gonna go and make it domain specific. How many tokens you do even have in the domain? Like, think about it. Code is probably the only domain that actually has a lot of tokens. You can throw like a lot of enterprise data at a model and say, I have a lot of internal data that nobody else has.
11:36 Speaker 00 But that doesn't mean that these models will absorb a new kind of reasoning that they couldn't get from the internet. It's very, it's it's like one of those things that very few people understand, why are these models even good at reasoning? It's not well understood. Is it because they're training on math? Is it because they're training on code? And even that is not well understood today. Like, had you trained a model on just textbooks, would you have not gotten reasoning? Like, these are questions that we don't yet have good answers to.
12:05 Harry Stebbings? Do you think models are good at reasoning, one? And then, I I think like a breakthrough in reasoning will be one of the biggest breakthrough moments in the next wave. How do you feel about where we are today in terms of quality of reasoning? And what is required to break through in the next wave of reasoning quality?
12:21 Speaker 00 I mean, i- it really depends on what you call as being good at reasoning. Are are they better than an eighth grader? I I think so. Are are are they better than like seventy five percent of the twelve graders? Most likely. Are they like gonna win the I M O or I O I? No, definitely not. So there's like a spectrum, right? A lot of people good at reasoning even among humans and I'm sure like A I is like somewhere like in the median right now. Of like high schoolers. Can it get to like a median college undergrad? Definitely. It seems like we're on the pathway to getting there. Would it be like talking to Faraday or Einstein? Not anytime soon. Some people call it as artificial superintelligence. And like I think when we achieve that, uh it'll break all this twenty dollar a month business models.
13:06 Speaker 00 Have you watched this movie, uh Prestige, where the there's like magicians, you know, competing with each other? In that, like, there's Edison and and this magician wants to steal a trick from Edison on how to make things disappear. He's willing to pay like a lot of money just for that one trick. And I think that's the sort of thing ma- thing you would get to if models got really good at reasoning. Where just for the output alone, you're not even paying for a monthly subscription, you're paying for one single session. One single chat, one single output. You would pay a lot of money. You're you're an investor, right? If I literally told you which companies, hey, Harry, listen, I I got all the insider information and all the revenues, blah, blah, blah, if I came and told you. Or or you, let's say, even if I didn't have any insider information, if I was such a good reasoner.
13:49 Speaker 00 And I gave you, Harry, this is gonna be the set of companies that actually matter two years from now. And I gave you such amazing reasoning that you probably would have had to spend like two months talking to a hundred people. Then would you have paid ten K for just that out answer?
14:05 Harry Stebbings? You'd probably pay ten million.
14:06 Speaker 00 Exactly. So even if you pay one percent of the R O I, it'd be worth it. People at the level of, say, Demis Sabas, look, they're they're like incredibly smart, like who's gonna advise Demis? Uh you can count the number of people like in your hand, right? And if Demis feels like there's an A I that can advise him, what's the value of that A I? It it breaks all your mental models of like twenty dollar a month. I think that's what is lacking. If you say, do we have true reasoning? The benchmark for true reasoning is an A I that can advise Sabas. We don't have that today. But there are A I's that can advise a a person maybe making hundred twenty K a year in U K. The I think we can get there. The this is where like you you gotta like clearly be precise on what good reasoning is.
14:47 Harry Stebbings? I understand that in terms of the precision around good reasoning. When you think about the trajectory of reasoning quality, how do you think about the timeline there? Do you think it goes up, flat up? Is it a continuous gradual increase? How do you think about the trajectory and slope of reasoning improvement?
15:03 Speaker 00 I don't think we know the secret sauce yet. At least according to writers, media writers, they claim like OpenAI has some new thing called Q star. They're working on to like make these models like use their own data to bootstrap and make themselves more intelligent. XAI recently hired this guy, Eric Zealickman from Stanford, who's written these papers on something called the STAR, self thought automated reasoner. Basically taking the model itself and trying to make the model explain its own outputs. And then whatever is the right output, you train on that. Or is the wrong output? You take the right output and then you ask the model to explain why that was right and train on that. You're basically training on not just the output, but also the explanation that was used to achieve the output.
15:48 Speaker 00 And if you can do that, you're basically training a model that can think and reason and get to an output, see if it's correct, go back, reason again and iterate. That is what is lacking in today's models. Today's models are just giving you the output. Tomorrow's models will start with an output, reason, illicit feedback from the world, go back, improve the reasoning and until they converge, they'll keep on trying to improve the output. And I think when that is achieved, I don't know when that's going to be achieved. Maybe it'll be achieved in a year or two, maybe it'll take three, four years. But I think when that is achieved, that is the beginning of, I would say, the real reasoning era, where we'll figure out how to make these things more efficient, we'll be throwing a lot.
16:32 Speaker 00 The only, only problem here, this is a game that won't be played by academics like before. Because just to do the inference compute, to do all these reasoning, getting an output, going back and reasoning, building a rationale, then going back and getting another output, just to even do this process, takes you a lot of inference compute, you have to pay money for this. And so even a single experiment costs you a lot of money until you arrive at the truth. Of the algorithm. And then that algorithm to run it is going to cost you a lot of money to get all the data, synthetic data to train on. So I I feel like this is where uh co- companies with a lot of capital are going to be way more advantaged to pursuing this research.
17:11 Speaker 00 So if at all it happens that there are only like four or five contenders to do this and whoever ends up with the algorithm the first has a massive advantage because it seems too good to be true sort of thing where once you crack it, you can just keep throwing more computer and like get a big lead over the other models.
17:29 Harry Stebbings? 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?
17:43 Speaker 00 There are two things here to consider. 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 remembers all your life, every single aspect of it, every single detail. That is like infinite memory. I 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 thing where, you know, it starts off with like a sufficiently large storage. Like it's practical enough and it it keeps expanding over time. And then now it's like throttle beyond which you have to pay ten dollars a month or something, right?
18:23 Speaker 00 That seems more like where we are headed right now. Like, like people are expanding the token window from one twenty eight K, like start with thirty two K. Then it goes to like a million, then DeepMind announced two million. 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 using memory. And I 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 a prompt, you you have the memory, but Models can hallucinate or get confused because of so much information to focus on. So you need to ensure that the instruct following capability has no degradation
19:08 Speaker 00 despite adding all this long context capability. I think that's not the case today, which is why like these models are not so good that like, you know, they can just write an entire code base yet. But all that will happen. I think, I think, I think it's, it's just a matter of time before, you know, they run another training run and like figure out all these bugs. But the second thing I'm not sure how to do, like infinite context, I'm not sure.
19:30 Harry Stebbings? When we look at the different foundation model providers, I do just wanna 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? Are they getting commoditized as people say?
19:48 Speaker 00 I think today the word commoditization, it's sort of true in the sense, a model that's like seventy five, like G P T three point seven five level model is commoditized. There are like too many models like that today in the market, some open source and some closed source. I think G P T four quality models are not yet commoditized. There's only probably one or two alternatives for the people today, like Claro Opus or Gemini, let's say. So 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 four point five or five that's way better? T B D, the training run is happening. My, my prediction would be there would be another great model after four.
20:34 Speaker 00 That's like very good. Like I wouldn't say G P T four O is like a lot smarter than G P T four turbo. It's more reliable, better, it's faster, cheaper, but it's not like how four blew three point five 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.
20:54 Harry Stebbings? This is not like a bad business unlike any other before whereby you every six months have your core product basically made redundant.
21:03 Speaker 00 Is that true though? Like, I mean, I, I saw your interview with Altman and Brad Lightcap, but is it actually true that like a product gets redundant because a model gets an upgrade?
21:13 Harry Stebbings? I think so. Is G P T three not like redundant now that you've got G P T four?
21:17 Speaker 00 Yeah, but your product is never the model. Let's maybe decouple this. If there are companies that are working on foundation model competitor to OpenAI. It is definitely like one of the worst arenas to be part of. Almost like I think there are five men standing today, it's all a thing. Google and Anthropic, Meta, Mistral, and after XAI's new funding run, maybe you can include them too. But that's a game that like is so hard to play and I'm very impressed that Mistral was even in the arena with like ten X lower capital than the rest.
21:47 Harry Stebbings? Are you not in that same arena?
21:49 Speaker 00 We post-train models, we post-train them. We're not foundation model trainers. For example, we can take any model that's there on the market today and shape them to be really good at what our product does. Including like open source models and like making them really good. Have we trained a base model? When you say there's like a Lama three seventy B, there is a base model that's just trained on predicting the next token. And then there's the supervised fine-tuning and R H F steps that train them to be very good at chat and like instruction following, summarization and like translation and all these skills. Now, the second part is what adds magic to the product. Without that, you're not going to have these good chatbots. But the first part has the base IQ that builds the base IQ for these models. Are we not doing the first part?
22:35 Speaker 00 It's a losing game almost to play the first part because every time you end up finishing a large training run, you burn a lot of money, you have a great model, and then you watch it destroying the leaderboard by the next update. And then you have to again catch up, you go spend more money. So where, how are you recovering all that money back? You may recover that through the APIs. Nobody wants to use the APIs if somebody else is just offering a better model at a cheaper price and faster. That's why I think it's a hard game. Now, it's not a hard game because it's hard to train these models. Sure, the science behind it and the people. Difficult to assemble, but ROI wise, like business wise, it's very difficult to compete here.
23:16 Harry Stebbings? Is that not what we're saying though about the commoditization of models being you get to a stage, oh shit, everyone's at that stage, we have to do the same again, we have to do the same again, same again, your, your model becomes redundant.
23:26 Speaker 00 I think the second tier models, the models that are not the most cutting edge, but cheap enough to like operate a business on top of it will get commoditized. But there will be some frontier models that are like so smart. And I think those are still, that's still a game being played by like three or four people today.
23:43 Harry Stebbings? Does that end as three or four people or does it end as one person?
23:46 Speaker 00 I think the answer to that really lies on uh who cracks bootstrap reasoning. You know, the thing we talked about a little bit earlier about models using their own outputs to reason and improve. Whoever cracks that first, if they allocate all their capital on just scaling that up, I think it'll end up as one person. But if they're hedging, hedging, hedging, it won't end up as one person.
24:08 Harry Stebbings? Who do you think that person is most likely to be?
24:10 Speaker 00 It's likely to be uh OpenAI or Anthropic. I can make a good case for both of them. OpenAI because they're far ahead in terms of the lead they had in, in doing these things first. Anthropic because they're algorithmically a superior company. They got whatever OpenAI got to with lower capital. They have better post-training and things like that. So OpenAI is on the other hand, like advantaged on capital and speed. So i- 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 first, it's Anthropic.
24:50 Harry Stebbings? I think that you're going to see the kind of large cloud providers realize that they need to acquire these models in different forms. And they will continue their core cash cow businesses as cloud providers, but they will acquire these models and add them in as complementary features that they already provide. And you'll see your anthropics, you'll see your coheres, you'll see your adapts acquired or aquahired 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 aquahires?
25:20 Speaker 00 I don't think so.
25:21 Harry Stebbings? Why am I wrong?
25:22 Speaker 00 I think with OpenAI and Anthropic, the value of those companies is not in the models they have. That is a very first order approximation. I think the second order approximation is it's in the machine that's building the machine. The 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 are valued at this price, where the revenue and the valuation make no sense. But they are because always I think about valuation is like how easy or difficult it is to reassemble this whole thing. And the thing is not just the output, the thing is also the machine that gave you that output.
26:06 Speaker 00 When you say models are getting commoditized, so OpenAI and Anthropic 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 I recently got to hang out with one of a very great researcher. I even made a joke that the best research is the one that's not being published today. And like, so there's nothing to read on archive anymore. So even the guys at Stanford who wrote all these reasoning papers, they are now like Musk paid them a lot of money to work for him. So he's not going to publish anymore. That's what's happening.
26:48 Speaker 00 It's the commodity is not in the model, the commodity is in the people who produce the models, and that's not a commodity yet. So that's why I feel like these companies are valued a lot and and they have so much leverage that they won't get acquired. Like if these people don't want to go work at a big company and the big company needs the output to keep doing their business, like Microsoft needs GPTs to sell and make Azure the number one cloud. AWS needs Claude to ma- to sell, to make, continue to retain the lead in the cloud market. So they have no need or like desperation to get acquired. OpenAI and Anthropic, I don't think are going to get acquired. Now, the flip side is that models, they don't, they don't produce any breakthrough.
27:29 Speaker 00 Scientifically, it's not possible to keep cramming more and more tokens at this and keep seeing the juice. That's when what you said is likely to happen. If like, say, after even one year, O- OpenAI doesn't have a better model. Yeah, then the leverage goes away because it's over this, you've got to actually produce a new thing and the people are unable to produce it. So their value goes down. And we have to play it out. I, I think both of these things could be true. I think these guys will still produce breakthroughs. So I, I, that's why I have a different prediction, but time will tell us honestly who's right.
28:00 Harry Stebbings? We mentioned kind of a- access to capital that obviously OpenAI slightly more than Anthropic. The thing that struck me was when I heard that, you know, Mr. Al's new funding round in terms of size was about thirty hours of Microsoft's free cash flow. In Microsoft, do three hundred and thirty million dollars of free cash flow per day. In a world where that is the case, I, I, not cynically, but just genuinely, how does anyone compete? Like, you know, you're rumored to be raising and it's 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?
28:36 Speaker 00 That's why you got to build a business. First of all, let, let's, let's separate the two things. Why, if Microsoft is generating that much cash flow, why are they not able to poach all the OpenAI scientists or Mr. Al scientists to come work for Microsoft? Like, they could take that money and, and, and ask one of those people to like, ask like ten of those people to, you know, come work here and I'll pay you a lot of money. 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, people want to work with the other best people. So you say it's not enough to get one person, you want, you have to get the whole thing. That's why they were all jokes, you know, when, when the whole board drama was happening, that Satya acquired OpenAI like at a small price. Because he got the whole team out.
29:20 Speaker 00 I, I think that's the difficulty here. It's, it's, it's cash flow doesn't change the dependence issue. And if they can get these models from people other than these two companies, yes, that changes the equation. Lot. They can just, uh, 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. As for like, what is the way out of here? I think like you gotta build a business yourself. Uh, it's fundamentally every company that raises capital has to eventually build a business or uh, hope that like their algorithmic prowess keeps staying forever. I would bet on like those who are serious about building a business, like OpenAI is building a business for what it's worth.
30:05 Speaker 00 I think they they have like whatever two billion in revenue annually, which is like higher than Snowflake or at least like as good as Snowflake. They're not as capital efficient as Snowflake, but in the same league in terms of recurring revenue and growing faster. So that shows you that like, you know, if you are serious about not just training these models, but also like getting it to the market through products and making revenue out of it, there is a potential for you to like be independent and self-sustaining.
30:29 Harry Stebbings? Are you focused on building a business today?
30:31 Speaker 00 Yeah.
30:31 Harry Stebbings? You said we're gonna move away from the twenty pounds per user. It's exactly what you are, twenty pounds per month.
30:37 Speaker 00 And I don't think that business is actually that good. It's not high margins enough. It's okay. If you can get to like a YouTube level thing, sure. Netflix, YouTube, user base. Fifty to hundred million people paying for you. Definitely, that's a great business. I don't think we are at a point where these AIs are so fundamental to people's lives that like hundred million people are subscribing to it. If they can get there, if you can build a product that's not just AI but has a lot more things to it and people pay a lot of the monthly fee for it and the retention is like close to hundred percent. Yes, that's a phenomenal business and I think we will try to do that too. But all these great subscription businesses are also doing ads for a reason. Margins, right?
31:21 Speaker 00 Whatever we criticize Google for, the greatest business model like in the last fifty years is that. Click-based advertising. It is this insanely good business model. Eighty percent margins.
31:31 Harry Stebbings? What was the internal discussion with you and the team when you were talking about adding advertising as a monetization engine? Just take me inside that conversation. How did it go? Um. How did it net out?
31:42 Speaker 00 You know, there's this whole Larry and Sergey PageRank paper that say, uh, said like advertising is fundamentally uh incompatible with like serving good results to the user in a search engine. I mean, they truly believe that and like I've read books that said like they push back on introducing ads as much as possible until they gave up to investor pressure. 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. You don't have to aim for that eighty percent margins. Like as long as you can get a good reasonably good high margin business without failing on your duties to user, be happy, like, don't be greedy.
32:25 Speaker 00 What is the way to do ads without corrupting the answer? As in you make sure that the answer is not like 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 in perplexity, which has like, you know, a bunch of threads, interesting threads every single day to like read. That's just gonna be like an endless scroll at some point. Instagram does ads in that format, TikTok does ads in that format. So ads is a great business model and when it's relevant, it's amazing. Like I've literally not met one single person who came and told me Instagram ads suck.
33:05 Speaker 00 It's actually pretty good. It's all about cracking the relevance code. Like if you crack the personalization and relevance code, ads is like pretty amazing.
33:13 Harry Stebbings? Do you think you've cracked the relevance code?
33:15 Speaker 00 No, not yet. If you cracked it, I think we should be worth way more. 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 in recent months and he told me how like in advertising, it's like three tiers where like the top tier is like Google and then like one and a half, 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. Then there's like companies like Twitter and like Reddit and Snap.
34:00 Speaker 00 And and he said the gap between these two is so high. This is like almost climbing the peak of the mountain and this is just like somewhere in the bottom. That is the extent to which ads have been dominated by like Google and Meta at this point today. My point is that if we can get the fundamental mistake that Google made right in our journey very early on, where we are not overly greedy on one source of revenue and are diversified enough through subscriptions, advertisements, APIs, enterprise. I think we have a chance to build something that achieves the alignment between shareholders and users a lot more. Like Jeff Bezos has a score read that asymptotically, the shareholder and the user should be aligned. If not, then you don't have a customer focused business.
34:45 Speaker 00 This is where Google got it wrong because asymptotically, they couldn't achieve that alignment between the user that is you using Google and the shareholder. Wall Street loves it when Google puts more ads. You hate it.
34:56 Harry Stebbings? You mentioned OpenAI is two billion in revenue. A lot of that is enterprise and they built out enterprise actually incredibly well. You kindly mentioned my show with Brad where we actually kind of touched on it. How do you think about when's the right time to build out perplexity's enterprise division?
35:10 Speaker 00 The number one insight that motivated us to build this was what is the most used enterprise tool today? Google. You search every single day at work. All the data is something internal to your company, as in the specific queries, but nobody cares because you need it. You can't live without it. You pay for it through your time and you pay for it through your data. This thing changes in the AI native search world where they're always worried about data leaking to AI. They don't care if data leaked to a traditional search engine, but if the search engine now has a lot of AI in it, they're worried. So we said, okay, if you want to use perplexity at work and your employer doesn't let you use it, we'll solve the problem for you. You'll offer an enterprise pro. With compliance and security and data governance and literally offer you the same product with all these security features and that became our enterprise pro.
35:55 Speaker 00 Now, that's just a start. You need features too that are more catered to the enterprise than just the consumer. And that's what we will build and we want to build it in a pretty differentiated way. Rethink what even internal search means like not just can build pipes to every single enterprise tool like Slack or Notion. But really think about like, like what, what, what is like the ranking problem? Why is it hard for the enterprise compared to consumer? And like, if we can build like one UI where all the proprietary data, external data, internal data, all the different models, open source, closed source. Live in like one single platform, you know, you can take your output, convert to good readable pages, organize it by like as a knowledge base. Index it yourself, that can be a good enterprise offering. I think we'll work on that.
36:39 Speaker 00 I'm not saying we'll succeed at it, but we will, we'll, we'll try to do something.
36:42 Harry Stebbings? With total respect, are you nervous about building out an enterprise product? When you look at the GTMs, it is a very different motion. Enterprise is a, is a big beast to get your head around. It's a challenge. You, you said there about the scale of OpenAI sales team. How do you think about getting your head around the GTM building exercise of an enterprise division? Do people buy perplexity enterprise and OpenAI enterprise or either or?
37:07 Speaker 00 My sense is that like AI is still so early today that nobody is locked in and loyal to any, any particular enterprise too in AI. And none of them even have a lock in effect to like make your data live on like one single tool. I'm not even talking about things like, why is it hard to migrate from Snowflake to Databricks? Cause the SQL format itself is so different. And once you wrote all the SQL queries in one format, it's so hard to change. It's not even things like that in AI, like your custom prompts that you wrote for chat G P can be taken over easily to perplexity. It's very easy. I, I think enterprises are still willing to tinker and experiment and try different tools. That said, if there is no differentiation, they will win. In the beginning, the one with the bigger brand and bigger team has an advantage. But is it game over?
37:52 Speaker 00 No, it's just game begins today. That's how I see it. I think like this is exactly the The whole wrapper thing and I if the value you add is like a very little on top of the model. Or if the model is the one that's adding most of the value and all the stuff you built around it don't matter, yes. But if you build enough value around the model, that is very difficult to do without coordinating a bunch of other hard to achieve engineering feats. That are not just L L M spaced or like how a lot of human element involved in it. It is difficult to see a world where like like that is not valuable and people don't want that. You know, the specific search thing, why is it that like Google A I overviews was bad? They have the world's greatest index, they have the world's best models too, but it wasn't good enough.
38:35 Speaker 00 Or why is it that people still think chat G P T browsing is not as good as perplexity despite them making so many updates over the last one year?
38:44 Harry Stebbings? Why is perplexities browsing better than chat G P T?
38:46 Speaker 00 I think it's just a lot of small details. I'm I'm a big believer in those who can orchestrate models and data sources and build great U X. And so and keep in innovating here all the time will survive this whole wrapper argument. I I think it's just like gonna be difficult until you build a business where everyone's always afraid you're gonna die. But as you are accumulating the users and as you are figuring out the business. It feels me to be more like the biggest beneficiaries of the commoditization of foundation models or the application layer companies.
39:21 Harry Stebbings? Why is that?
39:22 Speaker 00 Yeah, if models get commoditized, then the price of the models goes down. And then those who directly reach the user using those models, harnessing the power of those models, but packaging it into like great product experience. And utility value directly on the relationship with the customers, the users have a lot more advantage because they are able to like. Take something that's a commodity and sell it at a premium, which is a great business. If models get commoditized, I'm happy. If models don't get commoditized, I still wanna figure out a way to benefit from that. That's why this is a great difficult company to build. It's not something where you just hire an S V P of product from Twitter or Meta, ask them to figure out product for you. It's not easy, they don't have the mental models of like what happens.
40:06 Speaker 00 When the next AI model is so much better, how to rethink the whole product strategy? Similarly, it's not something where you hire a great AI person and ask them to like build product. Because they're always gonna think the model is the most important thing and keep trying to do everything through the model. You need the right sweet spot of design and product and AI and search all together. And that assembly is not easy. And that's why we are able to do things as a rapper that other people are not able to do.
40:34 Harry Stebbings? Have you been surprised by the fundraising process?
40:37 Speaker 00 Fundraising processes are brutal. I think most people think like you just go to like, there's always these memes about like, if it's an 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 raise? It's pretty difficult, actually. Everyone's asking all the questions that people on Twitter roast the rappers with. What happens if OpenAI does this? What happens if Google does this? What, what, what, you know, why would they not stop giving you models? Like, how will you build your own models? Like, how are you gonna ever build a search index that's like really good? You know, how do you compete on the enterprise sales? All these are questions everybody asks and like, when 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.
41:22 Speaker 00 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.
41:34 Harry Stebbings? Of the cash raised, how much goes to compute?
41:36 Speaker 00 Most of it.
41:37 Harry Stebbings? Like fifty percent, like seventy-five percent, just?
41:40 Speaker 00 First of all, let me, let me give you like two things. We have not spent a lot of money. Most of the cash we raised has already gone away to compute, no. Whatever money we spent, majority of it has gone to compute. And the compute is either us buying GPUs and serving models or post-training models, or money we pay for APIs like Anthropic or OpenAI. That's fine, as long, so that's why it's very advantageous to us to not train our own foundation models. Because if we were doing that too, most of the funding would have run out. Because the way it works is you have to pay three years in advance to get a big cluster. Like, you have to commit to that. It's not like all the money goes away immediately, but you have to commit to three year to get like thousands of GPUs at once if you wanna compete in that game.
42:24 Speaker 00 On the other hand, we're not doing that and we benefit from any commoditization in the models. We have all the money to go and get users. And and getting users not not simply through like marketing, but actually more in the Amazon Prime sort of way. Giving a lot of great features at like amazing prices, getting to retain you through superior product execution. And then like, you know, building like sufficiently large user base and brand loyalty. That is the model that we are going for in such a world like advertising can be pretty powerful at that scale.
42:54 Harry Stebbings? 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?
43:09 Speaker 00 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, 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, hey, 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 even if we spend fifty billion dollars on AGI, it doesn't matter. So that's a different company. That's why, like I'm saying, we're not, we shouldn't be seen as an open AI competitor at all. We're not an AGI lab.
43:52 Speaker 00 You can say perplexity and chat G P T are products in a similar space. And there's like some competition for mindshare users. But even that will like be pretty clear in like two years from now. You're not going to keep asking how is perplexity different from chat G P T? 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.
44:10 Harry Stebbings? What do you think is the best question you are never asked?
44:14 Speaker 00 I think someone asked me like, why are you doing this? This is our question where you don't actually know yourself. I think a lot of people give these made up answers. Like, oh, I had an existential crisis. I needed to save humanity. Reality is like, like you just look up to some people you want to be like them. And you try to carve your career path according to what they have done. But then you end up like figuring out there are things that you really like and you shape it to the style. You want. And at least that's how it's been for me. I have been a big fan of Larry Page and I always wanted to do some things of that scale of ambition. That was not the reason we did search engine though, like we we started with something else completely.
44:54 Speaker 00 That's a question that I actually don't have a clear answer to, but I really like the question because it it's a question worth asking yourself constantly. Like, why are you even working on this? Uh, Steve Jobs has this thing, right? Like, if you if you uh internalize depth, if you normalize depth. And every day morning you stood in front of the mirror and asked, if today was my last day, would I still be doing this? And if the answer to that question is a yes, go and give your best a day. If the answer to that question is consistently no on a, you know, a regular basis, you really have to rethink your life priorities. And for me, like perplexity is yes, like hell, yeah, like every day, even though it's painful, takes a toll on mind and body, I think it's worth it.
45:33 Harry Stebbings? You still look incredibly young, so don't worry, it hasn't aged you, Aravind, so all good there.
45:37 Speaker 00 Thank you. I'm hiding my gray hair uh very cleverly.
45:40 Harry Stebbings? Listen, I do want to do a quick fire round. So I say a short statement, you give me your immediate thoughts, and I'd love to start on. What have you changed your mind on most in the last twelve months?
45:50 Speaker 00 Long term view on people, seen some people like not immediately hit the ground running, but give them sufficient time. They are able to like truly transform themselves. It's something that I didn't have the right attitude towards in the beginning where I always thought like those who hit the ground running immediately are the best. But, you know, different people have different styles of showing their true downs.
46:12 Harry Stebbings? What's the biggest misconception in A I today, do you think?
46:16 Speaker 00 Short term thinking, like anytime somebody comes up with an update, everyone's like the other company is done, this is over. The the usual Twitter mob, but I would say the biggest misconception among even the more well informed people. Is that because majority of the people in the world are not using chatbots, they just think this is a bubble. They are 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. Chat U I is a new U I. We are not used to using it. 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.
46:58 Speaker 00 On the other hand, Word, Docs, Gmail, Google Search. I'm not even talking about the specific products, but more like the form factors or 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.
47:17 Harry Stebbings? Have you seen WhatsApp's integration?
47:19 Speaker 00 It's not the right way to do it.
47:21 Harry Stebbings? Why?
47:22 Speaker 00 I'm not going to WhatsApp to search for anything. I'm I'm going to WhatsApp to text people or reply to. But my WhatsApp, most of the times is just having like hard twenty thirty notifications and I, by the time I'm done with them, I just wanna get away from the app. I'm not going there. I'm not pressing on the WhatsApp icon to like search for something. Same thing with Instagram. I'm just going there for pretty pictures. I'm not going there for searching about like who's won the NBA. It's just the user intent behind opening the app matters a lot. This is the same reason why they failed multiple times at doing uh stories and reels. Stories started off as a way to copy Snapchat as a separate app first that didn't work. Then they tried so many different variants. What really ended up working is the top bubbles.
48:05 Speaker 00 And that only works because you you're starting with the existing user flow. You're you're already going there to check out other people. So you you have to really think about like not just like why this feature is added, but what is the existing user intent in your app? And how can you make sure the new feature adding ties into the existing intent? That's very important.
48:24 Harry Stebbings? What's your vision for the future of browsers?
48:26 Speaker 00 I think you can reimagine the browser when agents start working. There's a reason why we never did a browser. I don't think browsers gonna be disrupted because you get answers instead of links. People still wanna browse and get to a new website, get to a specific website, enter details, fill up forms, all those kind of things. That's not really getting disrupted with the traditional chat UI. Just because you can type in like on perplexity on the search bar, let's say that integration is done. I don't think you allow the browser more or something. It's it's gonna be more productive, but you need the traditional browser functionality a lot. What will have?
49:01 Speaker 00 Change though is you go to a browser, you just say, start the podcast at our event and it already knows exactly Riverside, it has to go like, fill up your login. After that, it's just over. That would be amazing. That that would that would change everything like, or like, buy me this thing on Amazon like, like it's it's sort of like um completely. Then you can go a step further and say like, what is the future of the O S? Like, like, what's the future of Mac? What's the future of Windows? So browser is just an O S two, right?
49:31 Harry Stebbings? What do you think is the future of O S then?
49:33 Speaker 00 I mean, something like the her movie can work, like, like, you know, not talking about the voice, but the O S itself being an A I. Completely A I native O S like, you know, it's not organized in a traditional way and you just talk to it and it just work for you. That's amazing vision to have and that's the sort of thing that doesn't work today. G P T four cannot cannot do it yet.
49:54 Harry Stebbings? What is the hardest element of your role today that people don't think about, that people don't consider?
49:59 Speaker 00 Dealing with contradictions all the time. I believe the brain is not very good at dealing with contradictions. It it actually tires us out when we can't arrive at a convergence point on something. And startup C E O 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 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 your competitors are doing the same thing. You have to like constantly deal with these contradictions in so many different dimensions. That's tiring.
50:37 Harry Stebbings? Penultimate one, if we were to write, you know, we were at pre-mortems as investors, a reason why a company doesn't work when we write an investment. If you were to write a pre-mortem on perplexity today, what is the reason why you don't achieve your goals? Access to compute, Google innovating and killing you. What is that reason?
50:56 Speaker 00 Didn't execute well. Competitors don't kill startups, startups kill themselves. It's not that Google Drive killed Dropbox. People say that as an example, but there was like a great enterprise business to build in Dropbox that they didn't move really fast and compared to like other companies like Box. Competitors do not kill startups, startups kill themselves. So if there was a pre-mortem to be written about us, it's like C E O not making, being decisive. Execution of the company not being good, lack of focus, inefficient use of capital, largely comes to whatever.
51:27 Speaker 00 Decisions are made, correctness of them, the speed of them and execution of them and whether we are focused or not. If these things are not true on a consistent basis, yeah, I think, I think we would die and that that would be the pre mortem.
51:39 Harry Stebbings? Final one for you. It's twenty thirty four. Where would you most like perplexity to be then? If we do a show then, where is the business then?
51:47 Speaker 00 It's a good question. I think I would just want it to be the assistant for facts and knowledge you just cannot live without. You can ask me, would ten years later, do 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 ten 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 A I agency and less of human agency, people would still want to know what's true and what's not true. 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 ten years from now.
52:21 Harry Stebbings? Aravind, listen, I've loved doing this. Thank you so much for putting up with my my stray in questions, but you've been a fantastic guest and I so appreciate the time.
52:28 Speaker 00 Thank you, Harry. That was great.
52:31 Harry Stebbings? I have to say, I do just feel so lucky to do what I do. That was such a fantastic conversation. If you want to watch the full episode, you can watch it on YouTube, of course, by searching for twenty V C. That's two zero V C. I always love to hear your thoughts and feedback there. But before we leave you today, I want to talk about Cooley, the global law firm built around startups and venture capital. Since forming the first venture fund in Silicon Valley, Cooley has formed more venture capital funds than any other law firm in the world. With sixty plus years working with V C's, they help V C's form and manage funds, make investments and handle the myriad issues that arise through a fund's lifetime. We use them at twenty V C and have loved working with their teams in the U S, London and Asia over the last few years. So to learn more about the number
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