How To Build The Future: Aravind Srinivas

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

• 0:00Speaker 00

  We released the ability to ask follow up questions. That doubled our engagement time on the site and also increased 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.

• 0:20Speaker 01

  Today, my view of perplexity is a more intelligent Google search.

• 0:25Speaker 00

  Yeah.

• 0:26Speaker 01

  That's really useful in certain scenarios. What do you want me to think of it in three or four years?

• 0:37Speaker 01

  Welcome back to another episode of How to Build the Future. Today, we're joined by Aravind 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.

• 0:50Speaker 00

  Thank you for having me, David.

• 0:51Speaker 01

  How did you get into this world?

• 0:52Speaker 00

  I was pretty interested in AI, deep learning research. That's actually what got me into the US. I was an undergrad in India, came here to the US to do my PhD here at Berkeley. Life really changed when I got to do an internship at OpenAI. And Ilya Sutskever was there. I still remember the day I first met him and 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.

• 1:37Speaker 00

  And he said, this is AGI. Every other research doesn't matter. This was around the time when they were building GPT-1.

• 1:45Speaker 01

  Okay.

• 1:45Speaker 00

  They didn't even call it GPT-1. When I saw that research, I went back to Berkeley and said, hey, I was working a lot on RL. That was the rage at the time because of AlphaGo and DeepMind.

• 1:57Speaker 01

  Right.

• 1:57Speaker 00

  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 In the Plex. 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.

• 2:33Speaker 01

  Reading the book about them.

• 2:34Speaker 00

  It feels nice. It would be amazing to 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 Ilya Sutskever about it, and he, like, 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 self-driving car. Because all your product rollouts are becoming data points who are 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.

• 3:18Speaker 00

  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.

• 3:33Speaker 01

  Right.

• 3:34Speaker 00

  And once it's solved, okay, sure, we'll worry about all those, you know.

• 3:36Speaker 01

  But your company gets better as AI gets better, as opposed to your company gets run over by somebody else.

• 3:42Speaker 00

  Exactly. So search is like one of those problems.

• 3:44Speaker 01

  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 OpenAI and go do that? How did you find your co-founders?

• 3:56Speaker 00

  I came across this blog that one of the. Former Y C partners, Daniel Gross wrote, but it was like how to build the next Google. Mm hmm. Uh, 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. Uh, so if someone's looking for reviews of a movie, uh, just suffix site colon rotten tomatoes dot com. If someone's looking for, uh, reviews of some, uh, new gadget, uh, do site colon that corresponding subreddit. You can get away with a lot of these suffixes and like, uh, spe- special strings to like filter results. And, and already make Google, uh, so much better, even with the existing Google, Google ranking.

• 4:41Speaker 00

  Uh, I'm not even talking about the ads problem, just simple ranking. Right. And then you can do more sophisticated things of like classifying queries. And he was talking about how L L M's could automatically figure out these suffixes. And I was pretty interested in that. Okay, that, that seemed like, okay, maybe, uh, generative A I might be, um, as in like, I, I used to just call it L L M's or general models could be a better way to build search engines too. I also was pretty interested in like trying to do something, uh, agent like when DeepMind had this, um, Android environment that they built where like they, they kind of wanted to prototype, uh, mobile app using agent, uh, that knows when to use what apps and control the apps. Um, that's when I, I spoke to my, uh, co-founder and C T O, uh, his name is Dennis.

• 5:26Speaker 00

  We had written the same paper a day apart, so we, we knew each other and he was a visiting student in my lab. And we used to talk about, I, we'd bring some ideas of how like we can build agents to control the Android environment. So we, we were definitely chatting about doing a lot of things, but never concretely about any company or product. The first thing that anyone will tell you is like, why, why even work on this? Of, of course, Google's gonna do this, right? It's not even like you go build a better Google Docs, uh, 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 crunch well, so why would even try? I think the reason it actually made sense, this is again, after launching the product, we realized.

• 6:10Speaker 00

  This not before. So there's like some benefit to being ignorant. Ignorance is bliss. Uh, is that if people stop clicking on links, the ad economy kind of dies. Now you can, there's a lot of, you know, nuance to this, but that core insight was only uh 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.

• 6:34Speaker 01

  Yeah. 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.

• 6:42Speaker 00

  Yeah. So I was like adocious enough to go and pitch to the first and seed investor of ours, Elon Gill. That like, hey, like, um, you know, I want to disrupt Google, um, but I kind of want to do it from pixels from a glass. And I think that's where people, you know, you aren't competing with people typing on the search bar. They're just seeing.

• 7:03Speaker 01

  But even at that point, you like knew in your mind, I want to go after Google or like.

• 7:07Speaker 00

  Yeah. It was not like I want to go and kill Google, like that that's our motivation. It was more like, what is an idea of that scale and ambition is something like this. It was also around the time in multimodal models who are 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 like not to work on it in the beginning. So we focused more on searching over like specific verticals or data sets or databases, tables, actually. And we were an enterprise kind of focused company, except like nobody wanted to give us their data. I remember I used to, you know, hustle for calls with like PitchBook or Crunchbase. As because I kind of wanted to build a demo that would first make sense to an investor.

• 7:51Speaker 00

  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 Crunchbase had all this data, PitchBook as well, but they just don't want to give it to us. And so.

• 8:03Speaker 01

  Next best step, Twitter.

• 8:04Speaker 00

  Yeah, Twitter. The pre Elon CEO moments, uh, academic access was allowed legal. So we um, um, built a database of Twitter. We organized it in the form of tables. We tried to do with the OpenAI Codex models. This was even pre. G P T three point five. We 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 sequel based on the template sequels. And that was the only way to get it to be work reliably. And then we had a lot of callbacks in case errors happen, it'll automatically correct it. Uh, and then it will go and query the database and then retrieve the records.

• 8:47Speaker 01

  Got it.

• 8:48Speaker 00

  It was very nice. And it was a chat UI. You could chat, you could converse, you could plot.

• 8:52Speaker 01

  And this was like the first real like product or demo that you guys.

• 8:56Speaker 00

  Yeah, yeah, we did it very fast, like it took only a month to do this. Um, because I like three people only, uh, but that energy in the beginning was insane.

• 9:03Speaker 01

  Yeah.

• 9:04Speaker 00

  And they 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, uh, you could never actually go on.

• 9:12Speaker 01

  Twitter searches.

• 9:13Speaker 00

  Exactly.

• 9:14Speaker 01

  To this day.

• 9:14Speaker 00

  Even today, right? And then, uh, also people allowed I finding all these, I think, I think the social search of like knowing who other people are following, whose tweets are they liking, whose tweets are they not liking, who did they unfollow this week? You know, those kind of diffs. It's all like funny.

• 9:32Speaker 01

  So you you launched this Twitter search thing.

• 9:35Speaker 00

  Yeah.

• 9:35Speaker 01

  How did you transition from that to now what we all know as perplexity?

• 9:39Speaker 00

  Yeah, so we had that, right? And then we were trying to do something similar to that for many different databases. GitHub, 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, uh, who who've been V C founders, you know, and and who also worked at a C C O D startup. Cause they would know what it means to be scrappy. It's very difficult to like do these queries.

• 10:08Speaker 01

  Using the LinkedIn UI to do that.

• 10:09Speaker 00

  You cannot do that, right?

• 10:10Speaker 01

  Yeah.

• 10:10Speaker 00

  For whatever reasons, people don't want to give their data, their paywall, like. You know, such technology, if it exists, we'll be creating like way more value. But some, 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 twenty twenty two. Um, pre three point five turbo. Uh, things were actually pretty reliable to the extent where like people like use this and find value. I actually read this um, Paul Graham tweet, I think uh, like where if you try to. Often when you, when you figure out the better solution, when you try to solve, solve a harder version of it. But you end up with a simpler solution that's more general and scalable.

• 10:53Speaker 00

  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 L L M. Like, like, read that uh, in a structured language like SQL. Or you could do the other way where you just keep it unstructured and expect the L L M to do most of the work at the inference time, at, at, at the time of the query. Yep. And, and don't do all this work in the indexing time. And clearly, we knew that if, if second is where the world is headed. Uh, where the models will get smarter and smarter. It gives you an advantage to build it that way because it's, it's more general.

• 11:35Speaker 00

  And uh, you also stand a chance against the legacy system that Google has built. Right. 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 Shulman's team had already published this thing called Web G P T at the time, so I was pretty aware of it. OpenAI even had a bot when I worked there called the Truth Bot, which John, John built with this team. Okay. Where you could ask it a question and it'll go and search the web and then it'll give you an answer uh, with some, with some sources. And um, it was very slow. And it was built with a hundred and seventy five B G P three model, so incredibly slow and inefficient.

• 12:15Speaker 00

  It was more agentic, like it would actually be like an R L agent that decides if it wants to click on a link and browse it, scroll. Okay. This is very slow. Tried as 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. So our models were beginning to come. This actually started working.

• 12:58Speaker 01

  Got really good.

• 12:59Speaker 00

  Yeah, the instruction following capability increased enough. That you didn't have to do it very, very hard rigorously.

• 13:06Speaker 01

  Got you. So you kind of did like the dumb approach.

• 13:09Speaker 00

  Yeah.

• 13:10Speaker 01

  Betting on the fact that the AI would get good enough. Correct. It would make all of that irrelevant.

• 13:16Speaker 00

  Uh, right timing, I would say. One year ago, when John and his team tried like the models were just so, so much. Uh, 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.

• 13:29Speaker 01

  Okay.

• 13:29Speaker 00

  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 like 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, um, 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 verboresity, 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. Uh, or 80 words.

• 14:08Speaker 01

  To keep it fast.

• 14:09Speaker 00

  Yeah, exactly.

• 14:10Speaker 01

  Okay, so you launched this. When was the first moment that you thought like, oh, I'm on to something here?

• 14:16Speaker 00

  So we tweeted it.

• 14:17Speaker 01

  Okay.

• 14:17Speaker 00

  I, I, I was, while writing the tweet, I was like, um, you know, people are gonna ridicule it. It's gonna make mistakes, blah, blah, blah. First moment the virality came and one annoyed, uh, like intellectual academic. Came search for herself. It said she, it, it, it gave a biography in the past tense. And she's like, I'm still a liar, what the hell? But actually what happened was there was a person with the exact same name and spelling. Uh, who died and LM thought she died and she gave a, it gave a past tense.

• 14:52Speaker 01

  Okay.

• 14:53Speaker 00

  I actually thought that was pretty clever reasoning on the model. 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 uh saying? And then um that got into this trend of people uh 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 uh that vacation. Uh, he said the same that even though you can click on your own profile icon and go, 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.

• 15:37Speaker 01

  Yeah.

• 15:38Speaker 00

  So we, 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.

• 15:54Speaker 01

  Yeah.

• 15:54Speaker 00

  So I thought there was something there.

• 15:55Speaker 01

  Something driving it.

• 15:56Speaker 00

  Yeah.

• 15:57Speaker 01

  That you.

• 15:57Speaker 00

  But I still wasn't sure.

• 15:58Speaker 01

  Yeah.

• 15:59Speaker 00

  And then we released the ability to ask follow up questions. That doubled our engagement time on the site and also increased the number of questions every day. And the number of people, number of questions every day was increasing exponentially. So I was like, okay, this is, there's something here. It's not worth killing and pivoting to enterprise.

• 16:16Speaker 01

  You have this like initial momentum and you, 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. Um, when did that realization happen in this journey?

• 16:28Speaker 00

  Yeah, so I never really thought about the Google competition in a serious way, to be very honest. Um, because I knew that like. The 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. There's the answer box, the knowledge panel, uh, 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.

• 17:09Speaker 00

  I was more worried about like Microsoft in the beginning, uh, because they were launching Bing Chat. In fact, on the day we agreed to, uh, have a term sheet, like hand shook on a term sheet uh, with, uh, with, with, with one of the venture capital investors NEA here, uh, in, in San Antonio Road. After like one week of torturous pitches and we're just having like, uh, uh, like coffee. And then the words leak screenshots of Bing Chat. And, um, I was like, okay, like, uh, there's this thirty day due diligence period, right? And one of the other investors would give me a term sheet. He just increased it to forty five days. You know, you could see the diff. Yeah, right. It was in sneakily. And I knew why clearly. And he also texts, what do you think about this thing?

• 17:54Speaker 00

  Okay, okay, I get it, I get it. You're getting a little sheepish. And then the other person I handshake with, like, he texts me the night saying, hey, do you have time for a call tomorrow? Okay, like clearly like this is it, right? So I told my co-founder, okay, maybe they're gonna, um, uh, back out or ask us to pivot. So, um, maybe we should just try to sell the company and like get it done, you know? This is not gonna go anywhere. The person actually who handshake said, look, I'm not gonna ask you to pivot. I'm not gonna ask you to like do anything different. Uh, you guys keep going and, uh, we already word is word. I, I was like, damn, that's, 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. Sure. With the screenshots.

• 18:39Speaker 00

  So we knew that like this is gonna get pretty big and competitive, but we were like, look, it's at the end. Um, Microsoft was never really good at consumer products for a long, long time. You can't suddenly change that. Uh, 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.

• 19:02Speaker 01

  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?

• 19:22Speaker 00

  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 AI have done here? And you know what 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 wrong principle.

• 20:06Speaker 00

  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 engineer.

• 20:14Speaker 01

  Teach them, educate them, get them to do it the way that the product wants them to do it.

• 20:17Speaker 00

  Yeah, exactly. Enterprise software is more like second kind. But magical consumer products are more the first kind. Right? Like in Google, why should Google have handled typos? They need to, right? We should have all been great at English. It's like Larry says, he was never good at spellings and that's why.

• 20:34Speaker 01

  I think the true story is YC partner Paul Buchheit, he was just annoyed by it and he's like, someone should build that.

• 20:40Speaker 00

  Yeah, exactly. And spell check corrector, it's all there. Similarly, auto suggest, why is it there? Like, easier, right? Similarly, cached results. I was even reading somewhere where Larry wanted the homepage to have the a simulation of the weather outside of your home. So that you don't even need to type the weather query. This is already there. So I was very influenced by that style of design, like in- including like subtle things like 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 like, make sure we have the cursor ready to type on the search bar. You don't need to take your mouse and place it there.

• 21:19Speaker 01

  It sounds like your, your main metric that you care about is number of queries per day.

• 21:22Speaker 00

  Yeah.

• 21:23Speaker 01

  Which is exactly what Google did, I think, in the early days, right?

• 21:26Speaker 00

  It's hard to grow that uh uh without like retention.

• 21:31Speaker 01

  In the long run.

• 21:31Speaker 00

  You cannot just uh 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.

• 21:44Speaker 01

  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 short as possible because we're trying to get you somewhere else to solve your problem.

• 22:00Speaker 00

  So that's what's happening. I mean, sure, I'm sure there are some errors and stuff, but most of the follow-up queries actually we see are like 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.

• 22:16Speaker 01

  So, so I presume your team has grown a bunch, you've raised a bunch of money. How do you manage the team? How do you operate your team on a week to week or, or cycle to cycle basis? With that, you know, number of queries per day is our primary metric.

• 22:32Speaker 00

  So every all hands, we start with that number.

• 22:34Speaker 01

  Okay.

• 22:34Speaker 00

  I don't believe in this um putting a TV and having a metric, you know, being seen every day because I think that's also distracting. But I, I do think like it, it makes sense to take a look every week, see the weekly growth rates. See the monthly growth rates and like if something declined and discuss about it, figure out ways to, actually freak out if something declines. Like we do. And if something grows, like look into why, where. So we are already data driven and we share it across the company.

• 23:06Speaker 00

  I've been trying to share to the users too, so that they feel like, uh, you know, it's it's an act. Something that's actually happening right in front of their eyes and and they wanna be part of it. There's a hierarchy, like if if there is 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, uh, uh, it's like, oh, they're gonna be fired or something.

• 23:33Speaker 01

  Right.

• 23:33Speaker 00

  Uh, because I have raised like fifty bugs a day. So if you know, like, uh, it it's more like they understand, okay, this. It it's important for the product to feel, uh, great. And if it doesn't feel good for ourselves, then the user is also not gonna 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.

• 23:55Speaker 01

  Mm hmm.

• 23:55Speaker 00

  So always feel like a user. I think that culture is there in the company.

• 23:58Speaker 01

  I love that. And and did you intentionally select for that when you were hiring? Like people who are very product centric and in the details?

• 24:06Speaker 00

  I wouldn't say I I explicitly had that as a criterion, but I look for people who cared about doing good work.

• 24:12Speaker 01

  Mm hmm.

• 24:12Speaker 00

  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.

• 24:18Speaker 01

  And I think so much of it feeds off of the founders and like your culture, your DNA and.

• 24:22Speaker 00

  Yeah.

• 24:23Speaker 01

  Sounds like you're that type of person that obsesses over the details and.

• 24:26Speaker 00

  Yeah.

• 24:26Speaker 01

  You're just going to naturally want to hire people who share that trait.

• 24:30Speaker 00

  Yeah, I I do get pissed off if answers are wrong and I do get pissed off if people on Twitter are saying like perplexity is degrading or like. You know, but but there's a lot of the things, there's some things are actually not true. But I do try to see, you you know, leave aside the cynicism, um, even if it was someone who's like a hater.

• 24:49Speaker 01

  Yep.

• 24:49Speaker 00

  But if there was something true there and I wanna still know.

• 24:52Speaker 01

  Yeah, I 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?

• 25:01Speaker 00

  So I I mainly use X, uh, Twitter. People are just like super like brutally honest there. And, uh, I think in email, people are a lot more polite.

• 25:11Speaker 01

  Yeah.

• 25:12Speaker 00

  This is okay too, like I I like both sides, but I think the brutal honesty brings out the worst bugs. And, uh, things that people are afraid to say.

• 25:20Speaker 01

  Oh, and in person is the worst, where you go show someone something and they're just gonna tell you good things.

• 25:25Speaker 00

  Yeah, yeah.

• 25:25Speaker 01

  Even if they hate it.

• 25:26Speaker 00

  I kinda like, don't like any, hey, what, hey, tell me, what do you think?

• 25:29Speaker 01

  Yeah.

• 25:30Speaker 00

  You're always gonna say nice things.

• 25:31Speaker 01

  Right. You're gonna grow your company presumably. You're gonna need to hire more people. How do you avoid the fate of becoming a big, slow company?

• 25:40Speaker 00

  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 front end bug somewhere. It was actually something in the backend. 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 do. There's some tension in like moving fast and breaking things if you do wanna like grow to mass market usage. So that's mainly slowing us down, I would say.

• 26:24Speaker 00

  And I haven't, 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, like getting things out to production widely. Other than that, I would say

• 26:40Speaker 00

  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 flag bugs 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, 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, we've figured it out or cracked it. But at least like we are trying to fight the entropy here.

• 27:18Speaker 01

  I think that's the only thing you can try, right?

• 27:20Speaker 00

  Yeah.

• 27:21Speaker 01

  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.

• 27:42Speaker 00

  Yeah.

• 27:42Speaker 01

  That's really useful in certain scenarios. Where do you, what do you want me to think of it in three or four years?

• 27:49Speaker 00

  If you go and research, what's the best sweater to buy? Or uh which the best hotels stay in this location. Perplexity will give you a great answer. But where do you actually go and fulfill the demand? You go to Google. Mm hmm. And who gets credit for that monetarily? Google us. Google, we we got nothing. Maybe we get you a pro subscription. But then someone else will undercut us and give it away for free with like cheaper models or whatever they have be bigger cash or sells. So the challenge is like you wanna be uh one place where people can have the end to end experience. They start with a problem in their mind. Mm hmm. And they seek your help. You give them the answers. And you also help them fulfill the action. It's difficult.

• 28:32Speaker 00

  Because uh people think like at the app, if you have an answer of like, oh, like what watch does uh Bezos wear? Mm hmm. I think he wears some Omega something. Uh I personally thought it would be amazing if it not only give 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. Hmm. Right? They think like the company is paying us to do this. So this is where like some of the tension with uh 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.

• 29:17Speaker 00

  There are so many other things like checking the score of a game. Or quickly getting to a website. Uh 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 a the weather for tomorrow, that's the temperature. Or someone's age should be like, you go and type like Elon Musk network, you just get the answer in like less than a second on Google, right? Perplexity it'll pull the right source, 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.

• 29:55Speaker 00

  Uh typical knowledge graphs, uh widgets, uh L L M streaming answers and more complicated multi-step reasoning answers. But user doesn't care, like user is not gonna tell you like when to use what. You decide. That AI, nobody talks about like when to use what that that sort of router, that that orchestrator. I think that's the hardest thing to build. And whoever builds that and and and can operate that at a scale of billion users and also knows how to monetize like some of those queries really well.

• 30:27Speaker 01

  Right.

• 30:27Speaker 00

  It's gonna be the next Google. Because they'll have the search bar, everything will, they they know exactly what to do. They'll go and ask clarifying questions. They it it truly understands the user and also does tasks for you. And and also that you like surf the web in a typical way, all in one experience.

• 30:42Speaker 01

  Mm hmm.

• 30:43Speaker 00

  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.

• 30:53Speaker 01

  Agreed.

• 30:54Speaker 00

  So the next generation of this clearly can be built.

• 30:57Speaker 01

  Mm hmm.

• 30:58Speaker 00

  You just have to like persevere and work for a decade or two on this problem.

• 31:01Speaker 01

  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. Um, 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 ten years, um, what do you think is the piece that's maybe gonna give you the edge to to win?

• 31:21Speaker 00

  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 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.

• 31:43Speaker 01

  Yeah.

• 31:44Speaker 00

  But it's it's also an ads company. And search is kind of almost exists in service of the ads company.

• 31:50Speaker 01

  Yes.

• 31:50Speaker 00

  Not in the other way. And you could argue, okay, that's out out outside the search revenue every quarter, which is like close to two hundred billion a year.

• 32:00Speaker 01

  Mm hmm.

• 32:01Speaker 00

  There's still like hundred billion in some other other places, YouTube and cloud, but the margins.

• 32:09Speaker 00

  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 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.

• 32:29Speaker 01

  So you're arguing basically the stock price is going to be their encumbrance.

• 32:33Speaker 00

  Correct. Because like Wall Street is like 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 the new app. The hypothesis is that like 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?

• 33:01Speaker 01

  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.

• 33:10Speaker 00

  Yeah, there's like a ton of other problems to solve like for for shopping or travel or like all these things like which which merchants do you use? Or like which hotels do you plug into or, you know, who's the middleman there and who handles the booking? And if a customer wants to cancel stuff, Google actually solved these a lot of these problems too, right? They're not just like, oh, a page rank or like a map reduce or, you know, all these advances that they made in like visual, like deep learning and like BERT 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 perplex is better positioned to do these things than Open Air Anthropic because we have it in our DNA to care about the user and the product.

• 33:55Speaker 00

  We're not just talking about reasoning and models, right? We 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 recent coding and math benchmarks. I think there's value in that, but it's quite orthogonal to like building the next generation. Information experience.

• 34:26Speaker 01

  All right, Arvind, thanks so much for joining us. It's great chatting.

• 34:29Speaker 00

  Thank you for having me again.