#1296 Aravind Srinivas | Perplexity CEO Wants To Destroy Google

Aravind Srinivas is the Founder & CEO of Perplexity AI. Perplexity is on a mission to build the world's most known centric company. In this conversation, we talk about how Perplexity is building Larry Page’s dream of a true search engine, Jeff Bezos backing Perplexity, artificial intelligence, hardware, advertising, trends, his learning experiences, and more. ======================= Introducing Espresso - the world’s most interactive portable display. They have a portable screen that is incredibly light, comes with a nice stand, and the user interface is very easy. Anyone who listens to this podcast can go to us.espres.so/pomp. They have a brand new offer waiting for you. ======================= BetOnline.ag is a proud sponsor of the the Pomp Podcast. Use crypto to bet on sports, play poker and enjoy casino games at BetOnline. Visit https://promotions.betonline.ag/pomp and use promo code POMP100 to receive a 100% matching bonus on any crypto deposit. BetOnline boasts no crypto transaction fees, and processing is anonymous, instantaneous and secure. ======================= Pomp writes a daily letter to over 250,000+ investors about business, technology, and finance. He breaks down complex topics into easy-to-understand language while sharing opinions on various aspects of each industry. You can subscribe at https://pomp.substack.com/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:02Anthony Pompliano?

  What's up everyone? This is Anthony Pompliano. Many of you know me as Pomp. You're listening to the Pomp Podcast, which is my effort to find the most interesting people in the world and sit with them for hours while I ask questions in an effort to learn. So it would mean the world to me if you would subscribe to the show on your favorite audio platform, watch episodes on YouTube, and tell your friends and family about the podcast. My goal is to help millions learn from the world's most interesting people. So let's get into today's episode. Today's conversation is with Aravind Srinivas, the founder and CEO of Perplexity AI. In this conversation, we talk about how Perplexity is building Larry Page's dream of a true search engine, how Jeff Bezos, being an early angel investor in Google and now backing Perplexity,

• 0:47Anthony Pompliano?

  gives great credence to what they're doing. And then we dive deep, deep, deep down the artificial intelligence rabbit hole, including what is going on with the hardware, how advertising is going to work with an artificial intelligence search engine, where do you determine authority from? How do you choose what to surface? And then what he learned from working at OpenAI, Google, and DeepMind. This conversation was a ton of fun. I learned a bunch, and I think that you will find it very, very interesting. Perplexity has become my go-to search engine for about 50% of the search queries that I do on a daily basis. And I think if you check out the product, you'll like it as well. So here's my conversation with Arvid Srinivas.

• 1:25Speaker 01

  Anthony Pompliano runs Pomp Investments. All views of him and the guests on his podcast are solely their opinions and do not reflect the opinions of Pomp Investments. You should not treat any opinion expressed by Pomp or his guests as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of his personal opinion. This podcast is for informational purposes only.

• 1:50Anthony Pompliano?

  Today's episode is brought to you by Espresso, the maker of the world's thinnest portable display. Now, listen up. If you're like me, you feel like you are at a command center when you sit down at your desk. I got a gazillion tabs open and different windows for different activities. There's my web browser, my text messages. I have Slack open, and I got a Notes app. I normally work on a desktop. can be very, very productive. But everything falls apart the second I leave my desk. If I'm traveling, if I go to a coffee shop to do some work, or just want to work from the kitchen table, my laptop doesn't have enough screen space. I lose my command center and my productivity falls off a cliff. It's a major problem. But this is where Espresso comes in. They have a portable screen that is so beautiful that you think Steve Jobs came back from the dead to create it. The thing is incredibly light.

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• 3:19Anthony Pompliano?

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• 4:03Anthony Pompliano?

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• 4:11Anthony Pompliano?

  All right, guys. Bang, bang. I thought a great place to start this conversation would be on Twitter. You shared this clip of Larry Page talking about the dream search engine and kind of what he hoped Google would become. When you listen to his description, it sounds like he's describing perplexity much more than he's describing what Google has become. Talk a little bit about maybe the evolution of these search engines and why is perplexity able to build something that's much more closer to even the founders of Google's vision for the perfect search engine?

• 4:39Speaker 02

  Yes. So I mean, I'm a big fan of Larry Page and most people you would talk to about Larry would always just say, the guy was living in the future, like he's always imagining the future many years before it happens. And why this moment has arrived much later than he envisioned is because, first of all, his vision was the ultimate version of Google is artificial intelligence. And if you have an AI, it would exactly understand what you're asking and just directly give you the answer to it instead of you having to open

• 5:17Speaker 02

  links. That's basically the video that I shared. And so why is it that like he couldn't do it and we are doing it? I think it's not due to lack of technology that he couldn't do it. Obviously, you know, he's no longer running the company. And Google has a big business model to protect today, which is based on how many people view link and how many people click on a link. They basically optimized their front end so much that the entire company's destiny relies on that 10 link user interface. It's one of those rare things where you know, most trillion dollar companies have divested their bets a lot. Like, for example, Microsoft, if there is a new LinkedIn

• 6:01Speaker 02

  competitor, it's not a big deal. There are a lot of money from Azure, there are a lot of money from gaming, there are a lot of money from operating systems and Windows. Similarly to Apple, like you can maybe argue iPhone is the most important thing, but the whole end to end packaging of the ecosystem is so strong that it's very hard for anyone to imagine taking them over. But for Google, the scariest moment is that interface is disruptive, where people don't have to like actually interact with links in the way they do today. Because And that's something that Larry cannot just go and change immediately because he is

• 6:41Speaker 02

  running a public company that has billions of dollars in market cap and like constantly answered with the Wall Street expectations and Wall Street just wants them to increase that advertising revenue, right? But this is disruptive to that. So hence why an opportunity exists for a new startup like ours, which can go back to the whiteboard, empty whiteboard, think from scratch versus having a lot of whiteboards with lots of equations and trying to see which we're able to change to adjust. That's, that's Google. And, and that's like a classic innovator dilemma there and like we are benefiting from that.

• 7:18Anthony Pompliano?

  Now, one of the things inside of your interface that I think a lot of people immediately notice is I ask a question, I get the answer. I don't get links, I get the answer. But there's also citations and I can kind of go to the original source. I can go deeper into that answer if I would like to. Talk about maybe the, the philosophy as to why to present, even though you can give the answer, to still give the source material and the links for allowing that exploration into these answers.

• 7:44Speaker 02

  Yeah. So our philosophy has always been give the power to the user as much as possible. Now have an opinion about the product. Like, don't try to do something like you put a summary at the top and have the ten blue links at the bottom and I give you both. That's sort of like the Google UI today. You know, have a clear opinion that, hey, like links shouldn't be a prominent part of the real estate of the product anymore. It should be answers and it should be conversations, it should be ability to dig deeper and ask follow-ups. But make sure the user can still go and find out the truth if they want to in case you got it wrong. Because these AIs are still not accurate enough for you to

• 8:25Speaker 02

  just blindly believe what they say. They still make mistakes. Like maybe eighty, eighty to ninety percent of the answer is accurate, but like maybe there's one sentence there that's inaccurate. So even if the user believes something is like not likely to be true, then they can still go and check the source and read for themselves. Until AIs get to a point where they're like super accurate, right? I always believe like there's some idealistic way of thinking about things, which is like the ultimate version of AI would not even require you to type and stuff. You'll just exactly know what you want in your head and like you don't even have to ask. That's all in the future. But and part of product engineering is to Give some elements of the future today with hacks, right?

• 9:09Speaker 02

  Ten buildings was a hack to get you to the answer. Now, answers with sources and citations is another hack. Now, five years from now, maybe, you're like, I know what, I hardly see myself clicking the source. Like, it's dramatically dropped. I'm just reading your answer. Your AI is like one in a thousand times inaccurate. I don't care, right? And then at that point, you're just gonna talk to it like a human. You don't even have a type. You can just have an AirPods and you can just walk around and ask questions, right? So, I believe like this is the right interface today to make a paradigm shift. And that's why we went for it. And also, another reason we did it is like, I come from an academic background. Another of my co-founder also comes from an academic background.

• 9:53Speaker 02

  And the first thing you're taught in academia when you're writing a research paper is like, always never write anything in a paper that you cannot actually attribute a source from a peer-reviewed article. And we took that philosophy. What if an AI chatbot was designed with this rule? That everything it says has to come from the web. Fundamental difference from ChatGPT. ChatGPT can say whatever it wants. Perplexity can only say what exists on the web. And it also has to attribute what it says in every single sentence to a particular source it's taking from. And so that's part of like our background that's, you know, going into the product too.

• 10:33Anthony Pompliano?

  Talk to me about the future. You just described walking around with the AirPods on and just asking questions and getting answers. Is that what you think is kind of the end state of this innovation? Or, you know, maybe it's 10, 15, 20 years away. But like, where is this all headed? And how far into the future do you see right now?

• 10:51Speaker 02

  Um, I would say we can create a really good voice-to-voice experience in a couple of years. Maybe I'm even overestimating that timeline.

• 11:07Speaker 02

  But for you to like not even look at a computer or a phone will take a while. Like, for example, if an answer is actually detailed, you don't want it to be read out to you, right? Like, it's kind of laborious to hear the whole thing. Um, so you want it to be visually rendered. And then the AI has to know exactly when to, when to render things, when to just speak it out. And that depends on like, you know, it should already know to what extent you're interested in without you having to prompt engineer it. So those are the kind of like, truly understanding your intent is gonna take a while.

• 11:47Speaker 02

  Models have to be really smart and also be able to run on device as much as possible to minimize computer overload. So these are all like, you know, engineering details that'll take a while to figure out. Um, I believe that we, uh, if we have glasses, not just AirPods, the visual rendering part will also like can be taken care of. You can just project things out and read it for us if we want to. And that means like our interaction with, uh, computing is obviously going to change to new form factors. Glasses, AirPods, and more human-like interactions with how we, you know, interact with fellow humans.

• 12:26Speaker 02

  Um, and we, and whenever we're still doing work, like, let's say we are actually doing work, writing, writing something, uh, creating something. We'll still go back to our computers and like, you'll still work on it. Uh, but it'll feel more like you wanna work rather than you have to work. That, that, that's sort of the future, I think, we are going towards.

• 12:46Anthony Pompliano?

  Explain that a little bit further in terms of, uh, there's a lot of fear, I think, of AI taking jobs and kind of what's everyone gonna do? I usually point back to, at one point, it was like ninety-eight percent of people in North America were farmers. Now it's less than two percent, right? And so we found other things to do, other work to do. Um, but you're talking about a world where maybe we work because we want to, not because we have to. How do we get there and what is that world like?

• 13:13Speaker 02

  So I think today, there's a lot of jobs that just require you to manually do a lot of things. For example, there are still jobs, you won't believe this, but there are still jobs that just take physical records of people's application forms and trying to digitize that. There are still jobs that try to like edit any mistakes in someone else's article. There are still jobs that, you know, like do brute force, stitching together pipes of different APIs because someone else doesn't want to do those integrations. Translating code from one language to another because the company's moving frameworks. These are all like super boring, but the only way to solve them today is like dump it on an agency.

• 13:58Speaker 02

  They'll hire a bunch of people and then they'll do this for you and give it back to you. Now, we're not going to enter a world where that agency is fully deprecated. But if that agency had hundred people, now instead it can actually have five to 10 people and have 80% of the work done by an AI. So does that mean 90 people are losing their jobs? Yes. You know, the 10 people will be retained and these are the 10 who are actually really good at it. Like they can quickly look at the mistakes and identify mistakes and point out. So that naturally leads to the conclusion that those are going to retain their jobs or those who embrace AIs have a knack of where they go wrong and right.

• 14:42Speaker 02

  Keep up to the latest, up to date speed of, you know, whatever the latest cutting edge AIs are. And have really good judgment. Human judgment is going to be more valuable than human labor and creation. Like it's, it's a little bit hard to

• 15:02Speaker 02

  internalize this because we always have the opinion that we are the most creative people on the planet. But we, it's sort of what Rick Rubin says, you need to have incredibly good judgment. And the actual process of creation, like knowing which tools to use, you know, like which instruments to use, like which software to use, all that is gonna be like not super valuable anymore. It's more like, can you look at the end output and say like what's actually going wrong there? And then give that as feedback to an AI or a computer that you're interacting with? And make good use of it to be able to get the same end output much faster than was possible before. And people are gonna employ you, probably they're gonna pay you even more than what you got paid earlier.

• 15:46Speaker 02

  But people are gonna employ fewer people. And, and, and that's gonna take a while for the population to internalize.

• 15:54Speaker 02

  But that's guaranteed to happen.

• 15:56Anthony Pompliano?

  Talk about the constraints to getting there. Obviously, hardware is a, is a constraint. Some of the sophistication and accuracy of these models is a constraint. But maybe we can start with the hardware first. There's really two sides to the hardware. There's the consumer interface, which you're talking about, whether it's glasses or, or other types of devices. But also, these models run on hardware, right? And we've seen Nvidia and many of these other companies that are even tangentially related to hardware do very well in terms of investor returns. So how do you see the constraints on that hardware side in terms of where this, these LLMs and software is running? And then on the consumer side, it sounds like there's a couple of years away still.

• 16:35Speaker 02

  Yeah, so let's talk about the work part. You know, AI is replacing humans at work. For that, you actually don't need consumer hardware to run these models.

• 16:49Speaker 02

  Except maybe in factory inspection or something like that, where you have to wear a glass and you have to identify defective items or something of that nature. Where something on device can help you do the computer vision much faster and better than a human eye can. Like those are exceptional outcomes, but a majority of digital work, you don't actually need the large language models or like generative models to be running on your consumer device. It can run on the cloud and still fine. So what are those constraints there left? I believe that GPT-4 is already like, you know, sixty percent median human worker today.

• 17:26Speaker 02

  You know, just the rough numbers, why the sixty percent? It's like eighty percent of all tasks times eighty percent human level. Uh, it's like eighty, eighty by hundred times eighty by hundred is like approximately sixty four percent. Uh, now that sixty four percent gets up, let's say to eighty percent with four point five or five. And all these models are running on the cloud. Then we already are at a moment where like we, we would need a way fewer humans to do the task. As long as they can work together with AIs as co-pilots. Now that's going to create new jobs, but it's going to deprecate a lot of existing jobs. So that's for the work part. Um, now for consumer uh, uh, device hardware. I think the person with the biggest edge here is Apple.

• 18:12Speaker 02

  Uh, because of their custom silicon chips. They no longer rely on Intel or Qualcomm to provide chips for them. They've been working on this even before large language models was a thing. Uh, for, you know, like for augmented reality and things like that. And they're naturally extremely well positioned to run on device. Large language models on Apple phones or MacBooks. And we're going to have these assistants that control the operating systems natively. Um, and we're, we're going to first time have a new kind of computing experience on personal computers where that could be something like instead of opening a computer and seeing a desktop screen, you, you probably see like a search bar. Where like, tell me what to do and, you know, those kind of experiences are going to be possible for the first time.

• 18:55Speaker 02

  It's gonna, it's gonna take its own gradual progress where it's going to start something like Apple Watch one to seven or Ultra that we have today is like, you know, totally different beast, right? Uh, so that, but that's going to take its own uh, progression. And you're also going to see a lot of computing on the go. Like, for example, you're wearing a glass, like, you know, I, this, this is for uh, my eyesight, but in general, you can see a lot more people wearing glasses for, as, as a thought partner. Uh, while, while you're walking, you want to know if you want to buy something or not, or like something is healthy for you to eat in a restaurant. Or it could be even something that you don't have to actually ask, like when you're like in a restaurant and you're trying to eat something and you

• 19:36Speaker 02

  clearly know the AI knows it's unhealthy for you and you've been eating unhealthy for the last few days. It's gonna come and warn you, hey, like you're not supposed to eat this thing. You've been eating an healthy for the last like one week. This is not good. This is the effect it's gonna have. And then all these sort of like surreal experiences are gonna be possible.

• 19:57Anthony Pompliano?

  When you think about this hardware kind of revolution, if you will, that that starts to occur, is it gonna be custom-built hardware? Or will we still have a lot of general purpose built? And what I start to think about is the more custom, the harder it is to get into a lot of these devices. Like, obviously, we see Humane, we see Tab. We, you know, there's all these kind of consumer hardware that people are trying. And so what I'm wondering is like, do we have to go through a whole life cycle of product iteration, right? Do we have to go make custom hardware that's large, that can run the models, et cetera? And then we have to start shrinking it and basically go right back through what the phone, for example, just went through for, you know, 20, 30 years and try to compress it in the shortest period possible?

• 20:42Speaker 02

  I think we'll see a lot of custom hardware, yes. Uh, because these models don't need, they're all like standardizing to this one kind of computing. Matrix multiplies, done really fast, efficiently, uh, dealing with the key value, uh, caching of the memory, uh, and so on. So I think we'll see a lot of people trying to squeeze as much juice out of it for inference on the device without consuming a lot of power. And I can definitely see a lot of people like trying to take on Nvidia on the inference side, not on the training side. Uh, because the inference side, so there'll be some people who are gonna work at the chip layer, there are gonna be people who work

• 21:24Speaker 02

  on top of that, like how to combine it together to put together an end-to-end consumer hardware device. And I can see some more players in this ecosystem outside of Nvidia, too. Uh, and of course, Apple's gonna keep everything locked end-to-end, that nobody even finds out what they're doing. That's their style. So it'll be very interesting to see different players, but I'm, I'm, I can see Nvidia and some competitors helping others to build their own devices here, too.

• 21:51Anthony Pompliano?

  When you look across the landscape, you're very unique in that you worked, I think, at OpenAI, at Google, and at DeepMind, and now you're building Perplexity. Uh, there are very few people who have the diversity of experiences and the cutting edge of this technology. What is something that you learned at each one of those organizations that helped you now go and launch Perplexity?

• 22:11Speaker 02

  I would say at DeepMind, I learned the importance of having one or two major strike efforts or moonshocks that, you know, the whole company is like rallying behind to do things. And also the importance of good top-down leadership. It's one of their big strengths. In fact, why they perform better than Google Brain is very good top-down leadership and hiring really good people. Of course, they made a few mistakes, which I also learned what not to do there. But the good part is like how well they led and executed. And at OpenAI, I just learned the importance of being relentless.

• 22:54Speaker 02

  Like, they're just relentless. They just never stop. And the mentality is just like, keep going, keep pushing. And taking a more long-term view on things, like, you know, not overreacting to short-term failures. If, you know, this whole Peter Thiel thing, if there is a secret that you know that's working and the rest of the world doesn't believe in it, it's actually a good thing, so you keep going. They were very good at cultivating that because GPT-1, GPT-2 was already killed at, and like, you know, people didn't really think it was a real thing. People thought it was a toy, OpenAI was hyping it up. But OpenAI was internally looking at the metrics and evals, and like, wait, this really looks like a new kind of computing.

• 23:35Speaker 02

  Like, how, when have you ever seen before that AIs can learn just from a few instructions and learn it to task? Even if it's not reliable today, it's going to get reliable more with scale. And that raw belief in data and computing power was kind of like obvious now in hindsight, but at that time it was a secret, only they believed in it and the rest of the world did not believe in it. And Peter has obviously said this before, but when it happens in real life in a field that you relate to, you kind of internalize it even more.

• 24:08Anthony Pompliano?

  When you think of those secrets, what are the secrets that you guys believe you know?

• 24:14Speaker 02

  Well, what we know is the importance of working on this. A lot of people might think, okay, this is like one narrow kind of experience of AI chatbots. When I think of AIs, I think of what it can do for me, not like coming and searching for things. Or like when I think of AIs, I think of agents. I don't care about chatbots. Like, everybody has these opinions. But one thing we really know is whatever AI you build, it needs to be grounded in facts about the world, or else it's not going to be very trustworthy. And in order to do that, you actually need to build a great index of the web that's particularly suited for day-to-day question answering. And that's a problem that nobody is really focused on. And we also know that the only way of making

• 24:58Speaker 02

  progress on that is to actually go build a product with users and then work backwards in terms of how we can establish the flywheel and continue to make the index better and better. So a lot of people will write us off today, saying we can never do this. Google will obviously be able to do it better than us. But the thing they don't realize is every single day we are making progress. Like, the product, three months from now is going to be way better than what is today. And today it's way better than what was six months ago. And they are underestimating that steep curve of like improvement that we're going through.

• 25:30Anthony Pompliano?

  One person who, I guess, does believe that maybe you got a shot is Jeff Bezos. A lot of people probably don't know that Bezos was an angel investor in Google, you know, 25, 27 years ago, whatever it was. He also invested in the latest round of perplexity. How did that come about? And then what is the significance of somebody who obviously saw Google, made the bet, was right, now saying, look, there's time and opportunity for the next, you know, search engine really to, to become popular?

• 26:02Speaker 02

  Yeah, so we, you know, we got connected to Bezos Expeditions Fund. That's the vehicle through which he's investing companies. And I believe he's also like looking at other AI startups. So perplexity, you know, we presented a, you know, in the Bezos style, we were like a small document for their...

• 26:25Speaker 02

  For them to write a memo for on us and also we gave them a presentation and they were pretty impressed and he approved it, approved the investment. We did not, I did not directly pitch to Jeff, but he did see through all the material and approved it at the end.

• 26:42Anthony Pompliano?

  That's very cool. And how do you think about, you know, in terms of them looking at Google and obviously having, you know, belief there and now looking at perplexity?

• 26:52Speaker 02

  Well, I'm pretty sure that they're all aware that search is going to change to answers using AI. And this is like a unique moment in time where AI is definitely going to make life easier for people to consume information on the web. And there's constantly going to be ever increasing amount of information that such a tool exists is actually essential. And our mission of making the planet smarter is also like something that appeals to them likely. So that's, that's my hunch on like, you know, why they believe in us. And whether Google succeeds at this or not, it's good for the world if perplexity succeeds. That's

• 27:32Speaker 02

  how I see it. I'm not, I'm not saying perplexity successes can be dethroned and kill Google. I don't want any company to be dethroned and killed. Like it's not, it's not, it's not a good thing for anyone. But I think it's good for the world that in, in the AI native world of search, in the AI chatbot market for search, generic chatbots, entertainment, Google shouldn't have monopoly there, just like how it had monopoly in the traditional search market. That's what we want to ensure. And we want to be one of the players to making sure it's a competitive landscape there.

• 28:06Anthony Pompliano?

  So I'm going to read to you a part of an answer that you gave in a Barron's interview. It's a little technical for some of the audience, but I thought it'd be really interesting for you to describe it. You said, "The real solution is to combine the traditional search index with large language models through a paradigm called retrieval augmented generation. That's what we built. We call it an answer engine." Can you explain what a retrieval augmented generation is and then why you believe it's this answer engine?

• 28:35Speaker 02

  Yeah, absolutely. So retrieval augmented generation means you retrieve facts from the internet relevant to what query the user asked first. You feed that as context to these large language models,

• 28:51Speaker 02

  which you can consider as amazing reasoning engines. And these models look at that context and your query together, and then synthesize an answer with citations. That's what we do. Now, that whole process, the chatbot part of the reasoning engine and the pulling the facts part of a retrieval engine, combined together, creates this new engine called the Answer Engine. That's what you're interacting with on the front end of Perplexity. It's called Retrieval Augmented because you're always making sure you retrieve and then you generate. So every time you ask a query, we always look at the web for that. Maybe it's not the optimal thing to do when you're asking, "Give me some names for my baby boy," or something like that, right?

• 29:35Speaker 02

  But maybe that's also like the trade-off we're making with the product. In order to get as much accuracy as possible for almost any question, we are giving up on the creativity and entertainment part of the product. And that's this new paradigm.

• 29:51Anthony Pompliano?

  Now, when you look at the way people are using the product, what trends are you seeing there? Is it just, "Hey, I used to go and ask the question to Google, and now I'm asking it to Perplexity." I see a lot of people tweeting, saying, "I use it five, 10, 15, 20 times a day." People are saying, "I can't live my life without it," right? I mean, these are very bold, powerful kind of user testimonials. So what are they actually doing with the product?

• 30:16Speaker 02

  They can use it for anything. For example, a lot of people use it for work. Like, they look up the people they're trying to meet before then, first time in a meeting. I use it a lot during our fundraising, honestly, because I really wanted to know what were the typical valuations, you know, what were the typical consumer metrics that other past legendary consumer companies achieved at various stages of their funding cycle? How many monthly actives do they have? What does retention even mean? I mean, the the story behind perplexity itself, I've said this before, is like, we did not seek to go build a search engine. We built this as a tool that we can ask questions ourselves for.

• 30:58Speaker 02

  And we realized it needed to have web search grounding, or else it would hallucinate. And then we had this amazing tool and like, we we gave it, we gave it access like, you know, to other people. And they all said, okay, this is like, not just a useful tool, this actually seems like an alternative to Google. That's when we realized we had built something much more useful than what we thought. And we put it out to the rest of the world. Now, people can still use Google for like, Google Maps, weather, flights, shopping, like, you know, we have not yet expanded our product surface area to cover all of that. So you cannot just completely deprecate Google today. But there are just certain questions that you, instead of doing three or four searches and opening tabs, you can just come to us and ask questions.

• 31:39Speaker 02

  And you and you and you're, you get the answer in an instant versus you get answers after like fifteen, twenty minutes on Google.

• 31:45Anthony Pompliano?

  Every founder, when they look at their product, they see like an ugly baby, right? They're like, I love this thing, but there's things I want to change. There's things that, you know, I I just can't get over the frustration of. What are those things for you?

• 31:58Speaker 02

  The, I I can say, you know, first of all, it's not always having all the information. Um, up to date, sometimes you just see the search results are not having enough information about something because the index is not refreshed or up to date yet. So making an even better index is obvious, low hanging fruit for us. Um, mathem- mathematical expressions, like, I'm a big, I do a lot of math on the Chrome search bar. It's my, different people have different habits, but this is my thing. Some people use command spacebar on Mac and they just do it on the Mac spotlight. So I I don't do that. And then. We are like slower than Google's calculator.

• 32:41Speaker 02

  Um, time and weather in certain locations. Um, I'm, I follow like a sports and I like to look at the live scores. We don't cover that today. Um, shopping, uh, we don't have all the catalogs in the world on our index. So these are just like so easy things that, you know, might seem to the user, it's like easy. But for us, it's actually like prioritization. Uh, and how to roll this out into one single product. Cause our product is still like retrieve and then summarize with an L L M. But many of these use cases don't need L L Ms. It needs like accurate access to data sources. But then packaging it all together in one product takes time. And, um, I hope that over the next few months, we can like slowly address all these surface areas.

• 33:24Speaker 02

  And make perplexity a product where you don't actually have to leave perplexity to go to Google. You can just stay here. So that's when I think the baby will mature into like a nice looking three or four year old, uh, kid.

• 33:37Anthony Pompliano?

  Now, um, hallucinations seem to be more and more of a, uh, of an issue. You're talking here about a little bit of, uh, data accuracy. What can you do to help people who maybe are new to using some of these products? And don't know that hallucinations can be a problem. Uh, we've seen lawyers, you know, use this in like legal briefs and all these stories that have come out now. How do you think about that user experience where it, it provides the answer so authoritatively? But also the user needs to make sure that it's accurate.

• 34:06Speaker 02

  Yeah. So that's actually why we put all the sources at the top. And we also give the sources for every sentence. And we want to get the accuracy of what sources we put at the end of each sentence. Also like as accurate and precise as possible. So that we don't link the wrong webpage for the wrong attribution. Um, and then we can go a step further and say like, which part of the answer? We are not very confident about and highlight that to the end user. Um, but we also present other options. Like in case you want to rewrite the answer with our co-pilot, which is like even more thorough researcher. Um, think about it as research mode, deeper work, larger model.

• 34:48Speaker 02

  Um, it's going to get many of the inaccuracies sorted out there. So a larger or better model and a.

• 34:59Speaker 02

  Way of like doing the whole answering with parsing webpages on the fly instead of using your index can address like 90% of hallucinations already are sorted out there. Now, the remaining 10% is still going to happen where it's still going to say something that's not very accurate. And I think there are two ways to address that. One is like short term, try to give some measure of confidence in your answer to the user. And long term, actually just try to like address a long tail through AI and machine learning. And I believe that with sufficient scale of usage and data flywheels, we can do the long term thing pretty well. That's the core bet in the company.

• 35:41Anthony Pompliano?

  Last thing I want to talk about is Twitter or X. You have a sneaky good Twitter account. I don't think that it is very planned. It seems like you kind of just share the things that you want to say as they come up. If I'm wrong, correct me. But talk about Twitter and maybe what you guys have used it for, both positive and maybe there's negatives as well.

• 36:06Speaker 02

  I like Twitter. I mean, I'm very grateful also to Twitter because I remember when, you know, I was drafting the first tweets from the company account on the day we created the account to announce our product. And I was like, man, I'm going to look like a joker. Everyone's going to make fun of me here. And they'd be like, oh, why the hell did this guy leave his job and do this thing? He could have been writing papers and now look, he's making a fool of himself. 10 people are going to like the tweet and it's going to look ugly. That was the honest fear I had when I was drafting those tweets and clicking the send button. But we benefited a lot from Twitter, like organic growth. A lot of users discover us.

• 36:51Speaker 02

  A lot of potential employees have seen us on Twitter. So it's really helped us a lot. Like in a world where, I mean, the core word is called distribution, right? You want to distribute your brand, your product as much as possible. You need to have channels for that. And for us, Twitter has been an incredibly good channel. Obviously, my personal account is like more raw and unfiltered than the company account. And some of the things I'm not very proud of what I said, some of the things I kind of like, you know, played along with the crowd. Definitely like, you know, how I mean, you obviously, the master of this whole game is Elon Musk. He created a whole fan fan club for Tesla and SpaceX through Twitter. And now he's like enjoying the benefits

• 37:36Speaker 02

  of it so much. He loves it so much. And he basically ended up buying the product, right? So I like Twitter. I think it's a cool platform. There's like negativity, a lot of toxicity there. I get a lot of comments saying like, how dare you think you're better than Google? How dare you compete with them? You're gonna die. You're just a rapper. Like, like perplexity is gonna be dead in a year. Like, I get that pretty much every day. And like, I think all that is keeps me checked, too. So it's not, it's fine.

• 38:05Anthony Pompliano?

  Yeah. Well, if you use it for positive motivation, then maybe it's a good thing. It's a necessity.

• 38:11Speaker 02

  Yeah, you have to. I mean, they're not wrong. Like, in a rational world, like, we could definitely very much still be defeated. And I should be aware of that all the time. But, I mean, again, like entrepreneurial journeys are meant for like those kind of pursuits where rest of the world thinks you're gonna die, right? Because if your success is low odds, that's when the success is gonna be very high magnitude.

• 38:41Anthony Pompliano?

  I think that's a great place for us to end. Where can we send people to find you on Twitter or find out more about perplexity?

• 38:48Speaker 02

  Yeah, it's perplexity underscore AI is our company account. And Arav Shrinivas is my personal account.

• 38:54Anthony Pompliano?

  Awesome. And then the website?

• 38:57Speaker 02

  perplexity.ai

• 39:00Anthony Pompliano?

  Awesome. All right. Well, thank you so much for doing this. I learned a lot today. I think everyone else did. We'll definitely do it again in the future.

• 39:05Speaker 02

  Thank you, Anthony.