Y Combinator Startup Podcast - How To Build The Future: Aravind Srinivas

Episode Date: February 21, 2025

YC General Partner David Lieb sits down with Aravind Srinivas, the co-founder and CEO of Perplexity, to discuss his origins in Silicon Valley, what it's like to compete with Google, and what the future of search could look like.Apply to Y Combinator: https://ycombinator.com/apply

We released the ability to ask follow-up questions. That doubled engagement time on the site and also increase the number of questions every day. So I was like, okay, there's something here. It's not worth killing and pivoting to enterprise. It was not like I want to go and kill Google, like that sort of motivation. It was more like what is an idea of that scale and ambition is something like this. Today, my view of perplexity is a more intelligent Google search that's really useful in certain scenarios.

What do you want me to think of it in three or four years? Welcome back to another episode of How to Build the Future. Today we're joined by Arvin Srinivas, co-founder and CEO of Perplexity, which in less than three years has grown to more than a $9 billion valuation. Thanks for joining us. Thank you for having me, David. How did you get into this world? I was pretty interested in AI, deep learning research.

That's actually what got me into the U.S. I was an undergrad in India. came here to the US doing my PhD here at Berkeley. Life really changed when I got to do an internship at OpenAI, and Ilya Sutskiber was there. I still remember the day I first met him when I was very prepared and had all these fancy ideas

that I thought were very interesting, and he listened for five minutes and said, all this research is useless. It feels really bad to hear that. So I got used to, you know, hearing the right things, even if they're uncomfortable. And then he told me the only thing that matters is he drew two circles.

One big circle called it unsupervised learning. And then inside he said reinforcement learning, another circle. And he said, this is AGI. Every other research doesn't matter. This was around the time when they were building GPT1. They didn't even call it GPT1. When I saw that research, I went back to Berkeley and said, I was working a lot on R.R.L.

That was the rage at the time because of AlphaGo and DeepMind. But that was kind of like chasing the trend. So I went back to my professor and said, hey, we have to actually go and study unsupervised and generative models and generative AI. So then I got into that and did more internships at Google. And during my Google internship, I stumbled upon this book called Indiplex. So I would launch jobs during the day, training runs, and then go and read these books in the library because interns don't have any other thing to do, right? And we feel amazing that, oh, like these guys actually were once upon a time grad students like me. and now I'm working as an intern in their offices.

Reading the book about them. It feels nice. It would be amazing start a company like that in future where there's a lot of research, there's a lot of like AI. At the same time, it's very grounded in product building. It's very difficult to do that. And I spent a lot of time thinking about it.

I even spoke to Ilyosutski about it and where we said there are probably only two problems where you can work on AI and also build product at the same time. One is like search, and the other is sub-driving car. Because all your product rollouts are becoming data points for improving the underlying AI in the product, and that will make the product even better,

and that will lead to more users, and more usage will lead to more data points, and it'll become a flywheel. And it should also be on the AI completeness path. It's sort of a buzzword to say this, but basically what it means is better AI should keep making, your products better. So that that way you can keep working on your company until AI is solved. And once it's all, okay, sure, we'll worry about all those, you know, implications.

But your company gets better as AI gets better, as opposed to your company gets run over by somebody else. Exactly. So search is like one of those problems. Yeah. So you're at this moment where you kind of have this realization that you want to start a company. How did you get the kind of activation energy to quit your great job at Open AI and go do that? How did you find your co-founders? I came across this blog, that one of the former YC partners, Daniel Gross wrote, but it was like how to build the next Google. And I think basically the core idea is like,

you could do so much more with better query reformulation. So you take a query and you just add some suffixes. So if someone's looking for reviews of a movie, just suffix, side colon, rotten tomatoes.com. If someone's looking for reviews of some new gadget, do site colon, colon, that corresponding subreddit. You can get away with a lot of these suffixes and like our special strings to like filter results and already make Google so much better, even with the existing Google ranking. I'm not even talking about the ads problem, just simple ranking.

And then you can do more sophisticated things of like classifying queries. And he was talking about how LLMs could automatically figure out these suffixes. And I was pretty interested in that. Okay, that seemed like, okay, maybe generative AI might be, as in like, I used to just call it LLM, so general models could be a better way to build search engines too. I also was pretty interested in like trying to do something, agent like when DeepMind had this Android environment that they built,

where like they kind of wanted to prototype mobile app using agent that knows when to use what apps and control the apps. That's when I spoke to my co-founder and CTO, his name is Dennis. We had written the same paper a day apart. So we knew each other and he was a visiting student in my lab. And we used to talk about, we'd bring some ideas of how like we can build agents to control the Android environment. So we were definitely chatting about doing a lot of things, but never concretely about any company or product. The first thing that anyone would tell you is like, why? Why even work on this? Of course Google's going to do this. Right?

It's not even like you go build a better Google Docs and Google will eventually do it because it's a secondary thing for them so companies like Notion can still be funded. This is their core crown dwell so why would you even try? I think the reason it actually made sense this is again after launching the product we realized this not before so there's like some benefit to being ignorant ignorance is bliss is that if people stop clicking on links the ad economy kind of dies now you can there's a lot of you know no ones to this but that core insight was only realized by us after launching.

So once we realized that, I thought, okay, we were onto something, and that kind of took us last two years. Walk us through, like, the first iterations of your experimentation. Like, I know you did a bunch of demos that were very dissimilar from perplexity. Yeah, so I was like, a dashes enough to go and pitch to the first seed investor of ours, Elad Gill, that, like, hey, like, you know, I want to disrupt Google, but I kind of want to do it from pixels, from a glass. And I think that's the way, you know,

you're not competing with people typing on the search bar. They're just seeing. But even at that point, you, like, knew in your mind, I want to go after Google. Yeah, yeah. It was not like, I want to go and kill Google, like that sort of motivation. It was more like, what is an idea of that scale and ambition?

It was something like this. It was also around the time in multimodal models who were slowly beginning to work. So I thought, like, if you were on the trajectory of improving technology, you could build something pretty amazing. My investor rightfully said not to work on it in the beginning. So we focused more on searching over like specific verticals or

datasets or databases, tables actually. And we were an enterprise kind of focus company, except like nobody wanted to give us their data. I remember I used to hustle for calls with like pitch book or crunch base. Because I kind of wanted to build a demo that would first make sense to an investor. That way we can keep, you know, raising some capital and then actually hiring good people and then go and, like, do the real thing. And so crunch base add all this data, pitchbook as well, they just don't want to give it to us.

And so... Next best step, Twitter. Yeah, Twitter. Pre-Elon CEO moments, academic access was allowed, legal. So we built a database of Twitter. We organized it in the form of tables. We try to do it with the OpenAI Codex models. This was even pre-GPT3.5.

We wrote a lot of templates. Oh, for these kind of queries, these are example sequels. And then the model would do kind of rag, the full relevant queries in the templates, and then write the actual SQL based on the template sequels. And that was the only way to get it to work reliably. And then we had a lot of callbacks in case errors happen. It will automatically correct it.

And then it will go and query the database and then retrieve the records. Got it. It was very nice. And it was a chat UI. You could chat. You could converse. You could plot. And this was like the first real like product or demo that you guys.

Yeah, yeah. We did it very fast. Like it took only a month to do this because of like three people only. But that energy in the beginning was insane. Yeah. And I showed it to a bunch of people and they all allowed it. Mainly there are two reasons. One, something like that never existed before.

Like you could never actually. Twitter searches. Exactly. To this day. Even today, right? And then also people. loud finding all these, I think the social search of like knowing who other people are following, whose tweets are they liking, who's tweets are they not liking, who did they

unfollow this week? You know, those kind of diffs. It's all like funny. So you launched this Twitter search thing. How did you transition from that to now what we all know as perplexity? Yeah. So we had that, right? And then we were trying to do something similar to that for many different databases,

Like if coders could go and search about repos or LinkedIn, if you could like almost be a recruiter, just say, but even now it's pretty hard. I want to say like I want all the people who worked who have been YC founders, you know, and who also worked at a COD startup because they would know what it means to be scrappy. It's very difficult to like do these queries. Using the LinkedIn UI to do that. You cannot do that, right? For whatever reasons, people don't want to give their data, their paywall, like, you know, such technology. if it exists, we'd be creating way more value, but it doesn't exist for many other reasons. We were beginning to see how, like, even with the capability of the models at that time in 2022,

pre-3.5 turbo, things were actually pretty reliable to the extent where, like, people would, like, use this and find value. I actually read this Powell Graham tweet, I think, like, where if you try to, often when you, when you, when you, you, you, you, you, figure out the better solution when you try to solve a harder version of it, but you end up with a simpler solution that's more general and scalable. That's what we realized. Like, okay, like, there's one way of doing these things where we go to each of these domains and, like, try to build an index of it and put it into specific formats like tables and then have the LLM, like, read that in a structured language of SQL. Or you could do the other way where you just keep it unstructured and expect the LLM to do most of the work at the inference time,

at the time of the query, and don't do all this work in the indexing time. And clearly we knew that if second is where the world is headed, where the models will get smarter and smarter, it gives you an advantage to build it that way because it's more general. And you also stand a chance against the legacy system that Google is built, which is a lot more in the first style.

So we thought, okay, we would try to build a more general solution, and then we prototyped this thing one weekend, actually. Actually, John Shillman's team had already published this thing called WebGPT at the time, so I was pretty aware of it. OpenEaI. even had a bot when I worked there called the TruthBot, which John built with this team, where you could ask it a question and it'll go and search the web, and then it'll give you an answer with some sources. And it was very slow. And it was built with the 175BGP3 model, so incredibly slow and inefficient. It was more agentic, like it would actually be like an RL agent that decides if it wants to click on a link and browse it, scroll. Okay.

This is very slow. So what we tried is a very simple heuristic version, but much faster, which is, okay, you always take the top K links that a search API provides you. You always only take the summary snippets that the index is already cached. So there's no scrolling. there's no clicking. And you always feed all those links into the prompt. So there's no selection.

Ask it to write a summary with sources in like the academic format. And that's it. When these models were getting to a point like 3.5 turbo sort of models were beginning to come. This actually started working much better. Instruction following capability increased enough that you didn't have to do it very, very rigorous. So you kind of did like the dumb. approach, betting on the fact that the AI would get good enough, that would make all of

that irrelevant? Right timing, I would say. One year ago when John and his team tried, like, the models were just so much worse that like if you tried the dumb approach, it just wouldn't work. And so therefore you would conclude that you need a smarter approach. But then when the models began to be much better instruction following, the dumb approach actually works. And that fixes a core product UX problem.

of latency. You are used to links appearing instantly on a traditional search, right? Even then, by the way, the first version we launched, which is the answer version, took seven seconds or something to, because we didn't even have this concept of streaming answers. We would wait till the entire answer was generated. We couldn't control the verbosity, so sometimes the answer would be very, very big. We even had to hard code a prompt saying only write five sentences or something like that, or 80 words. Keep it fast. Yeah, exactly.

Okay, so you launched this. When was the first moment that you thought, like, oh, I'm onto something here? So we tweeted it. Okay. I was, while writing the tweet, I was like, you know, people are going to ridicule it. It's going to make mistakes, blah, blah, blah. First moment of virality came when one annoyed, like, intellectual academic came, searched for herself.

It said she, it gave a biography in the past tense. And she's like, I'm still alive. What the hell? But actually what happened was there was a person with the exact same name and spelling who died. And the L.L.M. thought she died and she gave a past tense. I actually thought that was pretty clever reasoning on the motto, except it's not even higher order to know that they're different people. So then that got us a lot of attention. People were beginning to start thinking, okay, look, the sources thing is good.

But can we really trust the answers? these things are saying. And then that got into the strength of people like searching for themselves. This is something that keeps happening time and again with all consumer products. When I got a chance to speak to Mike Krieger that vacation, he said the same

that even though you can click on your own profile icon and go back to your photos, people always love to go to their profile on Instagram by typing their username on the search bar. It's such a human habit. So a lot of people start putting their Twitter handles or social, like, usernames, and then it would mash all their activity across the internet, including stuff they did in the childhood like many years ago, and then give like this interesting summaries, and

they would screenshot it and share it. So I thought there was something there. So there's something driving it that you... But I still wasn't sure. And then we released the ability to ask follow-up questions. That doubled the engagement time on the site and also increase the number of questions every day and a number of people, number of questions every day was increasing exponentially. So I was like, okay, this is, that's something here.

It's not worth killing and pivoting to enterprise. You have this like initial momentum. And you said earlier, it wasn't until hindsight that you had the idea that like, oh, we actually have a chance of competing with somebody like a Google. When did that realization happen in this journey? How'd that go down? Yeah. So I never really thought about the Google competition in a serious way, to be very honest, because

I knew that, like, they cannot make this exact product on the Google homepage. It's so hard to know when a query is purely informational or not. And then the Google search page is already, like, so cluttered. That's the answer box, the knowledge panel. There's some ads. There's some links. There's, you know, like perspectives from socials, all these social cards. It's already too much information.

So there, it's clearly like, feels like, you know, fast food and, like, healthy meal. difference for using Google and perplexity on even informational queries. I was more worried about like Microsoft in the beginning because they were launching Bing Chat. In fact, on the day we agreed to have a term sheet like hands shook on a term sheet with one of the venture capital investors NEA here in Sandinville Road after like one week of torturous pitches and we're just having like like coffee and then the words leak screenshots of Bing chat. And I was like, okay, like there's this 30-day due diligence period, right? And one of the other investors would give me a term sheet. He just increased it to 45 days.

You know, you could see the diff. Yeah, right. It was in sneakily. And I knew why, clearly. And he also texts, so what do you think about this thing? Okay, okay, I get it. I get it. You're getting a little sheepish. And then the other person I handbook with, like, he texts me the night saying, hey, do you have time for a call tomorrow? Okay, like, clearly like, this is a it, right? So I told my co-founder, okay, maybe they're going to back out or ask us to pivot. So maybe we should just try to sell the company and get it done, you know, this is not going to go anywhere. The person actually who handshift said, look, I'm not going to ask you to pivot. I'm not going to ask you to like do anything different. You guys keep going and we already

word is word. I was like, damn, that's pretty impressive. And then the next week, actually, Google also releases a blog from Sundar saying they're announcing something called the Bard, which is screenshots. So we knew that this is going to get pretty big and competitive, but we were like, look, at the end, Microsoft was never really good at consumer products for a long, long time. We can't suddenly change that. So they actually messed up the opportunity, in my opinion. Totally. Google, obviously, I knew they're going to have their own problems and challenges, so I felt like there was space for someone else here. Yeah, having spent, almost a decade at Google myself, I see a lot of the culture of the early days of Google,

like the things I've learned about Larry or about Sundar. And I see a lot of that in the way that you have built your product. Like there's a lot of attention to detail. It feels like you are the primary user of the product yourself. Like is that a thing that you deliberately tried to do? Yeah, I did. I did deliberately try to do it. One thing that Larry said is like, you know, we, I keep reminding everyone in our company about it. The user is never wrong. So even today, while testing a new feature, it didn't work. But there was some ambiguity in the query. So the person, I was talking to the engineer and saying, hey, you know, this is not good. What else could the AI have done here? And you know what the AI should have done? It should have come and

clarified to me. And asked me, hey, I'm not sure either this or this. Which one did you actually want and then I should have clarified and then it should have gone and done it instead of saying I don't know that is the user is never a wrong principle the other way of designing products is like make the user be a better prompt engineer blame the user and tell them to be a better prompt teach them educate them yeah get them to do it the way that the product wants to do it yeah exactly enterprise software is more like second kind yeah but magical consumer products are more the first kind right like in Google what why should Google have handled typos they needn't have, right? We should have all been great at English. It's like Larry says he was never

good at spellings and that's why. I think the true story is YC partner Paul Buchay, he was just annoyed by it and he's like someone should build that. Yeah, exactly. And spell check corrector and it's all there. Similarly, auto suggest, why is it there? Like easier, right? Similarly, cashed results. I was even reading somewhere where Larry wanted the home page to have the simulation of the weather outside your home so that you don't even need to type the weather query. It's just already there. So I was very influenced by that style of design, like, including like subtle things, the Chrome search bar. If you've already gone to a site, it's already there. You just have to click enter after typing the first two letters. So that influenced

me to make sure we have the cursor ready to type on the search part. You don't need to take your mouse and place it there. It sounds like your main metric that you care about is number of queries per day. Which is exactly what Google did. Exactly. In the early days, right? It's hard to grow that without like retention. In the long run, I can just pay for a user and get that number up. User could install your app, and maybe you can even game it where when they install that's one query automatically submitted,

but a repeat query doesn't need to be submitted. Yeah, I think the only counter example, which I don't think is happening in your case, is the product is not serving their needs, and so they need to issue a bunch of queries to get what they want, which is kind of the opposite of Larry's approach on Google was, you should be on Google as you should be on Google,

short as possible because we're trying to get you somewhere else to solve the problem. So that's not happening. I mean, sure, I'm sure there are some errors and stuff, but most of the follow-up queries actually we see are completely irrelevant to the first query because they just want to keep continuing the session or questions that they never even knew they wanted to ask, but they want to keep asking. So I presume your team has grown, a bunch you raised a bunch of money. How do you manage the team?

How do you operate your team on a week-to-week or cycle-to-cycle basis? With that number of queries per day is our primary metric. So every all hands, we start with that number. I don't believe in this putting a TV and having a metric being seen every day because I think that's also distracting. But I do think it makes sense to take a look every week, see the weekly growth rates, see the monthly growth rates, and if something declined and discuss about it, figure out a ways to actually freak out if something declines.

we do and something grows like look into why where so we are very data driven and we shared across the company actually I've been trying to share to the users too so that they feel like you know it's something that's actually happening right in front of their eyes and they want to be part of it there's no hierarchy like if there's some bug to be fixed

if I know some particular person's working on it I can go and talk to the person directly nobody else feels threatened because I'm going and talking to that person, there is no feeling that because I'm raising a bug, it's like, oh, they're going to be fired or something. Because I raised like 50 bugs a day. So, you know, like it's more like they understand, okay, it's important for the product to feel great.

And if it doesn't feel good for ourselves, the user is also not going to feel that. In fact, we have way more incentive to go use our own product, but the user doesn't. So the standards for the user should be even higher. So always feel like a user. I think that culture is there in the company. I love that.

And did you intentionally select for that when you were hiring? Like people who are very product-centric and in the details? I wouldn't say I explicitly have that as a criterion, but I look for people who cared about doing good work. If you don't care and you're just treating it as a job, then it's very hard for you to get excited about things. And I think so much of it feeds off of the founders and like your culture, your DNA.

Yeah. Sounds like you're that type of person that obsesses over the time. details and you're just going to naturally want to hire people who share that trade. Yeah, I do get pissed off if answers are wrong and I do get pissed off with people on Twitter saying like reflexity is degrading or like, you know, but a lot of the things, some things are actually not true, but I do try to see, you know, leave aside the cynicism, even if it was someone who's like a hater. But if there was something true there and I want to still know.

Yeah, I love seeing you engage on Twitter. with customers. Is that the primary way that you talk to users? Or are there lots of other ways that you are talking to your users? So I mainly use X, Twitter. People are just like super, like brutally honest there. And I think in email people a lot more polite. But it's okay too. I like both sides. But I think the brutal honesty brings out the worst bugs and things that people are afraid to say. Oh, and in person is the worst where you go show someone something and they're just going to tell you good things, even if they hate it. I kind of like don't like any, hey, tell me what do you think?

Yeah. You're always going to say nice things. You're going to grow your company, presumably. You're going to need to hire more people. How do you avoid the fate of becoming a big, slow company? Well, it's beginning to happen already a little bit, right? We're not as fast as we used to be. I think some of it is not because of people.

It's also because things breaking in production, people start losing trust in the product, like today we deployed some change and then someone got frustrated that there was some frontend bugs somewhere. It was actually something back in, but people are just assuming things. I think like fast loading, all that stuff matters and not every new engineer has the full context of the code base in their head, the earlier ones too. There's some tension in like moving fast and breaking things if you do want to like grow to mass market usage. So that's mainly slowing is not, I would say. And we haven't quite figured out, like, the best way to do this fast. I mean, we do have staging, deployment testing, A-B-Test, and all that stuff's happening.

And that's naturally slowing us down and, like, getting things out to production widely. Other than that, I would say the obsessive, detail-oriented people, there are only that many people in the world. So obviously, you cannot expect engineer number 250 to be like that. But I try my best to still, like, you know, go flagbugs to whoever's working on whatever new feature. And I kind of like know who's working on what, even at the size. I still try. I think our co-founders are amazing. They care and they push that principle when they're, like, building their teams.

So we are trying our best. I'm not saying it's a figure it out or cracked it. But at least, like, we are trying to fight the entropy here. I think that's the only thing you can try, right? Yeah. And it's an uphill battle, but if you keep on it, yeah. Okay, let's talk a little bit about the future. You know, I've seen you, your most recent launches are kind of like in different directions,

more verticalized or more specific around shopping or other things. Where do you want to take it? Like, today, my view of perplexity is a more intelligent Google search that's really useful in certain scenarios. What do you want me to think of it in three or four years? If you go and research what's the best sweater to buy or which is the best hotels stay in this location? Perplexi will give you a great answer, but where do you actually go and fulfill the demand? You go to Google. And who gets credit for that monetarily?

Google. We got nothing. Maybe we get your pro subscription, but then someone else will undercut us and give it away for free with like cheaper models or whatever they have. models or whatever they have bigger cash or so the challenge is like you want to be a one place where people can have the end-to-end experience they start with a problem in their mind and they seek your help you give them the answers and you also help them fulfill the action it's difficult because people think like at the app if you have an answer of like oh like what watch does Bezos wear. I think he wears some Omega something. I personally thought it would be amazing

if it not only gave the answer, but it also had a product card for the specific Omega Watch with a buy button and I just click Buy and it's done. But there are other people in the world who think that's an ad. It's not even an ad, right? They think like that company is paying us to do this. So this is where some of the tension with the early adopters who love the ad-free informational experience with like what is actually needed to get mass market and really be useful on a daily basis comes from. There are so many other things like checking the score of a game or quickly getting to a website. If you just wanted to get a docs link of an API or if you just wanted to go and book a flight on United, the answer could just be a link. The answer could be the weather for tomorrow. That's the temperature or someone's age should be like, you're going to type like Elon Musk network.

You'll just get the answer and like less than a second on Google. Right. In perplexity, it'll pull their rights. Maybe it might be more accurate than Google, but people don't care about like some of those minor details. So what you need to build is this amazing orchestration of small models, typical knowledge graphs, widgets, it's LLM streaming answers and more complicated multi-step reasoning answers. But user doesn't care. Like users not going to tell you like when to use what.

You decide that AI, nobody talks about like when to use Wad, that's our router, that orchestrator. I think that's the hardest thing to build. And whoever builds that and can operate that at a scale of billion users and also knows how to monetize like some of those queries really well. is going to be the next Google. Because they'll have the search bar. Everything will, they know exactly what to do. They'll go and ask clarifying questions.

It truly understands the user and also does tasks for you. And also that you surf the web in a typical way, all in one experience. You could even argue maybe nobody will ever be able to build this because it feels like a daunting task. But I could say whatever Google has already built is the closest system to something like this. So the next generation of this clearly can be built.

You just have to like persevere and work for a decade or two on this problem. If I talk to people at Google, they would say, yep, that's what we're building. In fact, I know they've been saying that for more than a decade. Probably same at OpenAI, probably the same at Anthropic. When you look at the people that you likely will be competing against in the next 10 years, what do you think is the piece that's maybe going to give you the edge to win? Obsession about the user and good product taste. There's a lot of these things that require a lot of domain knowledge.

Out of the list you mentioned, Google is the only company that actually has the product taste to do this. And arguably, like, you know, all the distribution in the world, everything, right? Except the dilemma is also there. It's funnily, like, you know, it's a search company, but it's also an ads company. And search is kind of almost exists in service of the ads company. Not in the other way. And you could argue, okay, that's outside. the search revenue every quarter, which is like close to 200 billion a year, there's still like

100 billion, other other places, YouTube and cloud. But the margins are all coming in search, right? Cloud is only like recently profitable. YouTube is not, never going to be a high margin business because number one, they don't serve ads on subscription like users. And number two, like They have to pay the creators. They have to pay the media partners. So it's never going to be as high margins as search. So you're arguing basically the stock price is going to be their encumbrance. Correct.

Because like Wall Street is like crazy. It just automatically, you know, panics if search revenue goes down. But search revenue has to go down in a world where people are just directly talking to AIs and agents are doing stuff for them. That doesn't mean they're not going to do anything about it. They're still building Gemini and like on the new. the new app. The hypothesis is that they're not going to be able to easily put it on the core Google where they already have all the billion users. And that's true, right?

Right. Yeah, you're arguing that whoever wins this in the long run will kind of by definition need to come up with a new monetization model, a new business model. Yeah, there's like a ton of other problems to solve, like, for shopping or travel, like, all these things, like, which merchants do you use? Or like, which hotels do you plug into, or who's the middleman there and who handles the booking and if a customer wants to cancel stuff, Google actually solved a lot of these problems too, right?

They're not just like, oh, a page rank or like map reviews or, you know, all these advances that they made in like visual, like deep learning and like bird, transformers. It's not just that. That is great, but they also did a lot of other boring work of bringing Google finance, Google Shopping, Google Flights.

I feel like for Poplexy is a better position to do these things. than Open Air Anthropic because we have it in our DNA to care about the user and the product. We're not just talking about reasoning and models, right? But we are pretty much familiar with all those things. And we are very much capable of taking the latest open source models and serving them ourselves and fine-tuning them, post-training them, and doing evals. We're not like AI illiterate.

We're not going to like spend all our bandwidth building data centers and chips and like trying to just talk about like breaking the most reason, coding a math benchmark. I think there's value in that, but it's quite orthogonal to like building the next generation information experience. All right, Arvin, thanks so much for joining us. It's great chatting. Thank you for having me again.