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

Aravind Srinivas
Today's models are just giving you the output. Tomorrow's models will start with an output reason, elicit 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 or the application layer companies. This is 20 VC with me, Harry Stubbings, and what a show we have for you today with Aravind Srinivas, co founder and CEO at Perplexity.

Harry Stubbings
As Gary Tan described it in a. Tweet, perplexity is actually just better than. Google for clear, well cited answers. The company has 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, Aravin cut his teeth at OpenAI and DeepMind.

Gary Tan
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Harry Stubbings
For joining me today. Thank you for having me, O'Harry. I've watched all of your episodes, so looking forward to it. That is very, very kind of you, my friend. Listen, I want to start with a little bit on you.

Gary Tan
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? More like an accident. I was just yet another electrical engineering or computer science undergraduate doing my courses and doing some interesting projects along the side. 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.

Aravind Srinivas
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 against what is correct and what you predict. And whoever wins the most number correct predictions wins the contest, and you get the price. And I go and 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 forests.

Like, none of these things made any sense to me. Literally just did what an AI 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 Sam Altman once telling me, I asked him this question, like, two, three years ago, hey, like, how do you identify something where 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 AI. So I got into it and I did a lot of courses. Pattern recognition, machine learning, the book written by Christopher Bishop, I bought it second hand in India, something like two, $3 or something, 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 Bard, a student, and he was teaching at my undergrad institute. I told him to advise me, give me a project. And he was a reinforcement learning guy. So reinforcement learning is when I actually got into AI, because we have all written AI for, like, checkers or like tic tac toe or something like that. Like, you know, or even when you play chess as a kid, when you play against a computer, you always ask this question, how does computer play, like, what is it like?

And then they go, oh, yeah, it's like an AI. You don't worry about it. So they use AI loosely there. But the real definition of AI, what it is like, oh, it's an agent. It's an environment.

Receives a reward signal. It optimizes for an objective. All that framework mathematically made sense to me once I studied RL. And then he told me at the end of the class, hey, I have a friend of mine from UK, David Silver. We used to know each other from PhD days, and his startup just got bought by Google for half a billion dollars because they wrote this paper that learned to play Atari games just from the screen pixels.

They've open sourced the code. Why don't you take it and now figure out how to play all the games simultaneously? Not just one single game. You learn to play pong. 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 loved the idea of all the papers that DeepMind did, and I would just like, literally be in the lab all the time and keep reading their papers, trying to implement them, and borrowing gaming GPU's from other people in the lab and using it to train neural nets on it. I was thinking before this, what are the single most pressing questions right now? And what do I most want to ask?

Harry Stubbings
And I think the first one that came to mind for me, and when I had so many people messing 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? Yeah, I think it's a nuanced answer. I can just say no.

Aravind Srinivas
And you'd be like, okay, if I brute force still works. But that's not the reality, either. It's not like if I suddenly came and say, hey, Harry, take my $500 million and go build a big cluster, take, like, trillion tokens, 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 curating the data. Otherwise, it's just not worth it. 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. 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 Shinshill 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, be more computational efficient? All these things matter. And, like, that's where I think, like, those who do it right, those who get these 128 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. When Xai released the model, the open source first crocodile, and Arthur tweeted saying, that's a lot of superfluous parameters because the model was 300 b or something and was not even as good as the mistrals, seven times 856 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.

Harry Stubbings
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 verticalization of models, that you will use different models, models for different things. Is that where it leads to then? Is that what you're pointing towards? No, I actually think that viewpoint is flawed.

Aravind Srinivas
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.

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

Aravind Srinivas
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 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 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. Now, that doesn't come from any domain specificity. It comes from the emergence of 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 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 going to go and make it domain specific, how many tokens you do even have in the domain? 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, 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 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. Do you think models are good at reasoning one? And then 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? I mean, it really depends on what you call as being good at reasoning.

Are they better than an 8th grader? I think so. Are they better than, like, 75% of the 12th graders? Most likely. Are they, like, gonna win the imo or ioi?

No, definitely not. So there's, like, a spectrum, right, of people good at reasoning, even among humans. And I'm sure, like, AI 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 super intelligence. And, like, I think when we achieve that, it'll break all this $20 a month business models.

Have you watched this movie prestige, where there's, like, magicians, you know, competing with each other in that. Like, there's Edison. 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 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 an investor, right? If I literally told you which companies. Hey, Harry, listen, I got all the insider information and all the revenues, blah, blah, blah.

If I came and told you, let's say, even if I didn't have any insider information, if I was such a good reasoner and I gave you Harry, this is going to be the set of companies that actually mattered two years from now. And I gave you such amazing reasoning that you probably would have had to spend like two months talking to 100 people then. Would you have paid ten k for just that answer? You're probably paying 10 million. Exactly.

So even if you pay 1% of the ROI, it'd be worth it. People at the level of, say, demis as Sabbath, look, they're like incredibly smart. Like, who's going to advise them is you can count the number of people in your hand, right? And if demos feels like there's an AI that can advise him, what's the value of that AI? It breaks all your mental models of like $20 a month.

I think that's what is lacking. If you say, do we have true reasoning? The benchmark for true reasoning is an AI that can advise Sabbath. We don't have that today, but there are AI's that can advise a person, maybe making one hundred twenty k a year in UK that I think we can get there. This is where you got to clearly be precise on what good reasoning is.

Harry Stubbings
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? I don't think we know the secret sauce yet.

Aravind Srinivas
At least according to writers, media writers, they claim, like OpenAI has some new thing called Q star they're working on to make these models use their own data to bootstrap and make themselves more intelligent. Xai recently hired this guy, Eric Zelikman from Stanford, who's written these papers on something called the Star, self taught 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. Whatever 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 basically are training on not just the output, but also the explanation that was used to achieve the output. 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, elicit 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 will 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. 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 feel like this is where companies with a lot of capital are going to be way more advantaged to pursuing this research. 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 compute at it and get a big lead over the other models. We are absolutely going to talk about the funding required to finance these models.

Harry Stubbings
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? There are two things here to consider.

Aravind Srinivas
What does memory mean? Is it, like, 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, and I think we don't even have the algorithms for it yet today. And then there's another AI that sort of like, is like Gmail sort of a thing, where it starts off with a sufficiently large storage, it's practical enough, and it keeps expanding over time, and then now it's like throttle, beyond which you have to pay $10 a month or something. That seems more like where we are headed right now.

People are expanding the token window from 128k SAR with 32k, then it goes to a million. Then DeepMind announced 2 million. I feel like that is already good enough where at least we can prioritize and throw out what's not relevant and keep using memory. And 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 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 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 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.

Harry Stubbings
When we look at the different foundation 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? Are they getting commoditized, as people say?

Aravind Srinivas
I think today the word commoditization, it's sort of driven, a sense, a model that's like 75, like GPD 3.75 level model is commoditized. There are like too many models like that today on 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 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 4.5 or five?

That's way better. TBD, the training run is happening. 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 more reliable, better. It's faster, cheaper, but it's not like how four blue, 3.5 out of water, that sort of thing. Whether fi can do that to four would answer your question of whether these models are getting commoditized. This is not like a bad business unlike any other before, whereby you every six months have your core product basically made redundant. Is that true though?

Like, I mean, I saw your interview with Altman and Brad Lightcap, but is it actually true that like a product gets redundant because the model gets an upgrade? I think so. Is GPT-3 not like redundant now that you got GPT four? 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, sort of a thing. Google and anthropic meta Mistral. And after Xa Eisenhower funding round, 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. Are you not in that same arena? We keep train models, we post train them. We're not foundation model trainers, for example. We can take any model that's there in 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 that's like a llama 370 B, there's a base model that's just trained on predicting the next token. And then there's the supervised fine tuning and RHF steps that train them to be very good at chat and like instruction following summarization and like translation and all these skills. Now that 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. We're not doing the first part. It's a losing game almost to play the first part, because every time you end up finishing a large training run, you earned a lot of money, you have a great model, and then you watch it destroy in the leaderboard by the next update. And then you have to again catch up. You go spend more money.

So 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. 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. 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 model becomes redundant.

I think the second tier models, that the models are not the most cutting edge, but cheap enough to operate a business on top of it will get commoditized. But there will be some frontier models that are so smart. And I think those are still, that's still a game being played by like three or four people today. Does that end as three or four people or does it end as one person? I think the answer to that really lies on 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. Who do you think that person is most likely to be? It's likely to be OpenAI or anthropic.

I can make a good case for both of them. OpenAI, because they are far ahead in terms of the lead they had 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 advantage on capital and speed.

So it really is like a question of who you know, which matters more. Is it, is it clever brains 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 open AI. If it's the first, it's anthropic.

Harry Stubbings
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 complementaries, features that they already provide. 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? I don't think so. Why?

Am I wrong? 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. 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.

Aravind Srinivas
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 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. When you say models are getting commoditized, so open air anthropic are not that valuable, I disagree, because these are the same guys who will produce the next model. Are those guys getting commoditized, like 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 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 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. It's the commodity is not in the model, the commodities and 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 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 cloud to sell to make, continue to retain the lead in the cloud market, so they have no need or like desperation to get acquired. Open and anthropic, I don't think are going to get acquired. Now the flip side is that models, they don't produce any breakthrough 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 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.

We have to play it out. I think both of these things could be true. I think these guys will still produce breakthroughs. So that's why I have a different prediction. But time will tell us honestly who's right.

Harry Stubbings
We mentioned access to capital that obviously OpenAI slightly more than anthropic. The thing that struck me was when I heard that Mistral's new funding round in terms of size was about 30 hours of Microsoft's free cash flow in Microsoft dollar 330 million in free cash flow per day. In a world where that is the case, 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? That's why you got to build a business. First of all, 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 Mistra scientists to come work for Microsoft? Like they could take that money 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.

Aravind Srinivas
No longer need to work at OpenAI, just directly build AI's here. Whatever GPU's 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 other best people. So 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 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 difficulty here. 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 a lot. They can just you know, like, get it from open source and sell the same models and, like, make a lot same amount of money with less spending, then that is bad news for the foundation models.

As for, like, what is the way out of here? I think you got to build a business yourself. It's fundamentally every company that raises capital has to eventually build a business or hope that, like, their algorithmic prowess keeps staying forever. I would bet on, like, those who are serious about building, like, OpenAI is building a business for what it's worth. I think they had, like, what, about 2 billion in revenue annually, which is like higher than snowflake or at least 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 trading these models, but also like, getting into the market through products and making revenue out of it, there is a potential for you to, like, be independent and self sustaining. Are you focused on building a business today? Yeah, we're going to move away from the 20 pounds per user. It's exactly what you are, 20 pounds per month.

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.

50 to 100 million people paying for you. Definitely. That's a great business. I don't think we are at a point where these AI's are so fundamental to people's lives that like 100 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 100%.

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. Whatever we criticize Google for, the greatest business model in the last 50 years is that click based advertising.

It's just insanely good business model. 80% margins. 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 and how did it net out?

You know, this is whole Larry and Sergey page rank paper that said, like, advertising is fundamentally incompatible with serving good results to the user in a search engine. I mean, they truly believed 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 invest the 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 80% 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. 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 idea to explore.

That's why we have other surface areas for ads too. Like even the discovery feature in perplexity, which has like, you know, a bunch of threads, interesting threads every single day to, like, read, that's just going to 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. 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. Do you think you've cracked the relevance code?

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, but 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. And he said the gap between these two is so high this 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.

Jeff Bezos has this code read that asymptotically the shareholder and the user should be aligned. If not, then you don't have a customer focused business. 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.

Harry Stubbings
You mentioned OpenAI is 2 billion in revenue. A lot of that is enterprise. And they built our 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?

Aravind Srinivas
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 cannot 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 leak 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 that problem for you. We'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. Now, that's just the 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 in a pretty differentiated way, rethink what even internal search means.

Like, not just build pipes to every single enterprise tool, like slack or notion, but really think about like 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 good readable pages, organize it by book, like as a knowledge base, index it yourself. That can be a good enterprise offering. I think we'll work on that.

I'm not saying we'll succeed at it, but we'll try to do something 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 big beast. To get your head around.

Harry Stubbings
It's a challenge. You said that 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? My sense is that AI is still so early today that nobody is locked in and loyal to any particular enterprise tool in AI.

Aravind Srinivas
And none of them even have a lock in effect to make your data live on one single tool. I'm not even talking about things like why is it hard to migrate from Snowflake to databricks? Because 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.

Your custom prompts that you wrote for chat shift can be taken over easily to perplexity. It's very easy. 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? No, it's just game begins today. That's how I see it. I think this is exactly the whole wrapper thing.

And if the value you add is very little on top of the model, or 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 feeds that are not just LLM spaced or like have 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 AI overviews was bad? They have the world's greatest index.

They have the world's best models too, but it wasn't good enough? Or why is it that people still think chat GPT browsing is not as good as perplexity, despite them making so many updates over the last one year? Why is perplexity's browsing better than chat GPT? I think it's just a lot of small details. I'm a big believer in those who can orchestrate models and data sources and build great ux and keep innovating here all the time will survive this whole rapper argument.

I think it's just like going to be difficult until you build a business where everyone's always afraid you're going to die. But as you are accumulating the users, as you are figuring out the business, it feels to me more like the biggest beneficiaries of the commoditization of foundation models or the application layer companies. Why is that? 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, packaging it into like great product experience and utility value, directly own the relationship with the customers and 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 want to 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 SVP 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 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 build product because they're always going to 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 altogether, 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. Have you been surprised by the fundraising process?

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 race it's pretty difficult actually. Everyone's asking all the questions that people on Twitter roast the rappers with.

What happens if opening analysts says, what happens if Google does this? Why would they not stop giving you models? How will you build your own models? How are you going to build a search index? That's really good.

How do you compete on the enterprise sales? All these are questions everybody asks. When you don't have a good model of the future yet you have to give them good arguments. At the end of the day, it's all like arguments, nothing is there. And 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 of the cash raised. How much goes to compute? Most of it? Like 50%?

Harry Stubbings
Like 75%? Just first of all, let me give you like two things. We have not spent a lot of money. Most of cash we raised has already gone away to compute. No, whatever money we spent, majority of it has gone to compute.

Aravind Srinivas
And the compute is either us buying GPU's 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 GPU's at once if you want to compete in that game. 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 get users. And getting users 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 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. Every business has a core monetization engine. They have ancillaries, but there tends to be one which is dominant. When you look at perplexity in five years time, what is your dominant engine? Is it consumer subscription?

Harry Stubbings
Is it advertising? Is it enterprise. 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 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.

Aravind Srinivas
Either way, we can be profitable. I think with advertising we can be really, really profitable. And then you can ask him, hey, Armin, why do you care about profits? 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 he already told publicly in an interview that even if we spend $50 billion in 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 OpenAI competitor at all. We're not an AGI lab. You can say perplexity and chat GPT are products in a similar space and there's like some competition for mind sharing 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. What do you think is the best question you are never asked?

I think someone asked me, like, why are you doing this? This is not a 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, 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 started with something else completely.

That's a question that I actually don't have a clear answer to. But I really like the question because it's a question worth asking yourself constantly. Like, why are you even working on this? Steve Jobs has this thing, right? Like, if you internalize death, if you normalize death.

And everyday 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 yes, go and give your best that day. If the answer to that question is consistently no on 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. You still look incredibly young, so don't worry, it hasn't aged you, Arvind. So, all good there? Thank you.

I'm hiding my gray hair very cleverly. Listen, I do want to do a quick firearm. 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? Long term view on people seen some people like, not immediately hit the ground running, but give them sufficient time, they are able to truly transform themselves.

It's something that I didn't have the right attitude towards in the beginning, where I always thought those who hit the ground running immediately are the best. But different people have different styles of showing their true talents. What's the biggest misconception in AI today, do you think? Short term thinking. Anytime somebody comes up with an update, everyone's like, the other company is done, this is over.

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 going to get really surprised that it's not a bubble, it's not overhyped, it's actually under hyped. These things, when taken in the right workflows and form factors that you're already familiar with, will have a lot of impact. Chat UI is a new UI.

We are not used to using it. We are 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. 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. Have you seen WhatsApp's integration. It's not the right way to do it. Why?

I'm not going to WhatsApp to search for anything. I'm going to WhatsApp to text people or reply to. But my WhatsApp most of the times is just having 2030 notifications and by the time I'm done with them, I just want to get away from the app. I'm not going there. I'm not pressing on the WhatsApp icon to 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 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. And that only works because you're starting with the existing user flow.

You're already going there to check out other people. So you have to really think about not just why this feature is added, but what is the existing user intent in your app and how can you make sure the new feature you're adding ties into the existing intent? That's very important. What's your vision for the future of browsers? 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 browser is going to be disrupted because you get answers instead of links. People still want to 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 going to be more productive. But you need the traditional browser functionality a lot. What will change though is you go to a browser, you just say start the podcast Aravind and already knows exactly riverside.

It has to go like fill up your logins. After that it's just over. That would be amazing. That would change everything. Or like buy me this thing on Amazon.

It's sort of like completely. Then you can go a step further and say, what is the future of the OS? What's the future of? Mac was the future of Windows. So browser is just an OS too, right?

Harry Stubbings
What do you think is the future of Os then? I mean, something like the her movie can work. I'm not talking about the voice, but the OS itself. Being an AI, completely AI, native OS, it's not organized in a traditional way and you just talk to it and it just worked for you. That's amazing vision to have.

Aravind Srinivas
And that's the sort of thing that doesn't work today. GPT four cannot do it yet. What is the hardest element of your role today that people don't think about? That people don't consider dealing with contradictions all the time. 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's all about contradictions. Should you take a risk or should you like double down on what you have? Should you 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 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 constantly deal with these contradictions in so many different dimensions. That's tiring. Penultimate one, if we were to write, you know, we write premortums 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.

Harry Stubbings
Google innovating and killing you. What is that reason? Didn't execute well. Competitors don't kill startups. Startups kill themselves.

Aravind Srinivas
It's not that Google Drive killed robots. People say that as an example, but there was like a great enterprise business to build on Dropbox that they didn't move really fast compared to like other companies, like box compared to Narco, starups kill themselves. So if there was a pre model to be written about us, it's like CEO not making, being decisive, execution of the company not being good. Lack of focus, inefficient use of capital largely comes to whatever 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 would be the premortal 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 it's a good question.

I think I would just wanted to be the assistant for facts and knowledge you just cannot live without. You can ask me would ten 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 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 AI 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. Arvind, listen, I've loved doing this. Thank you so much for putting up with my straying questions, but you've been a fantastic guest and I so appreciate the time. Thank you Harry, that was great.

Harry Stubbings
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 20 VC. That's 20 VC.

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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

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1: Introduction

2: Foundation Models and Performance

3: Future of AI Reasoning

4: Competitive Dynamics

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