Joining us is Aravind Srinivas. He is a co-founder and CEO of Perplexity.
Perplexity has been on quite the tear, rolling out its AI browser Comet to all users this week.
The data center is coming to your laptop. When we started, people made fun of us. We did it in a way that essentially became the blueprint for every other chatbot out there.
A lot of brands have really bad aura. You guys have great aura.
Our brand is fundamentally to celebrate curiosity. We think that'll be the only quality we human beings can be proud of. Search is actually compounding. Every form of agentic action requires access to web, access to a cloud browser, access to context across connectors, files, and tools, and putting it all together and orchestrating across models. I can't think of another company that's better positioned than us to do that. We don't have competitors. We have contributors. We're very glad to partner with amazing chip companies like Nvidia and Apple, Intel. Like, we're going to work with all of them.
As Nvidia reportedly weighs investing in the company at a more than $30 billion valuation.
Aravind, thank you so much for having us here at Perplexity. I know you guys just moved into this office.
Thanks for coming by, and maybe once we're fully settled in here, we can do a proper tour around the office, but I'd be happy to talk to you.
The walk-and-talks have been really popular. It's funny. We were just in Italy with Bending Spoons and they were calling it a walkie-talkie. So, we'll do a walkie-talkie with you.
So before we started rolling, we were talking about all the AI brands and, you know, there's a war between all of them, but the one thing that I've particularly noticed with you and Perplexity is you've withheld an above-board brand the entire time.
Our brand is fundamentally to celebrate curiosity. We picked that because we think that'll be the only quality we human beings can be proud of and lean into even more as AIs get better and better. Like, you could argue that AI is the best accelerant of our own curiosity. Like, almost the purpose of AI is to accelerate human curiosity. And David Deutsch, the famous Oxford physicist, he has this quote, almost a thesis, that human beings are the only species that are capable of being curious about what is already familiar to us. Like, we might know something and then we could take a look at it again and try to think about it from a different perspective. Animals don't tend to do that. And that's very interesting because you could argue that, oh, AIs can solve all the problems, AIs can answer whatever question there is. So what's our role? Our role is to actually go and ask the next set of questions. We actually were designed to ask questions. But we grow up in settings where we're rewarded for answers until AI came to exist. And now it's like it's not necessarily cool to be a know-it-all anymore because an AI can answer everything. Like nobody needs to go and find an expert to learn about a topic. You can actually just go to the AI, whatever AI you trust for accurate answers, whatever it is. But then what is your purpose? Your purpose is to actually lean into your curiosity even more and not be afraid of asking whatever questions they may be, however dumb they are, however of an outsider you feel to the topic. You don't have to feel that bottleneck or fear of being judged. And so our brand is built around that. And we think that the best companies always have a fundamental brand that has nothing to do with the technology, more to do with humanity and people. Like Steve Jobs built a brand for Apple about putting beautiful things out there because that's the best way to celebrate and give back to mankind. Nvidia's mission was just make things possible that only supercomputers can. It was never about AI. It's still even today, if you go to their website and read about their mission, it's about doing things that are beyond the realms of regular computing, and that applies to AI today, but it could apply to quantum computing tomorrow. It could apply to something else later. It could apply to cryptocurrency or Bitcoin. Whatever it is, I think it's very important that you build a brand around things that have nothing to do with technology, but fundamentally accelerating something related to human progress and human qualities. And that's what we're very proud of getting that right from the beginning.
Even walking around this floor, every room is named after an academic, right? So, can you explain that?
I don't want to take credit for it because I like giving credit to other people. Our brand is all about citations and sources. So that was inspired by my time at DeepMind. Demis actually was the OG of that idea. And I'll tell you why it had a deep impact on me because I've interned at multiple companies when I was a PhD student. I interned at Google, Google Brain, and all the meeting rooms were named after like, like Procure and Pac Heights and like Mission and all that. Like in the SF office or in Mountain View, it was named after like movies. And OpenAI named its offices after like sci-fi movies. And these were kind of cool, but nothing different, you know. Even this particular office, we're subleasing it from another company and they're named after like a bunch of movies. And Demis actually named it after a scientist. And not only that, like there was a lot of thought put behind like rooms named after philosophers having no TVs to join meetings. It was actually meant to discuss in person with whiteboards. And so I really wanted to do something similar. I was like, my dream was if I ever had a company, I want to definitely copy this one thing. And so I'm really glad I got a chance to execute on it. And it also inspires people, like if the scientists are celebrated because they all ask questions. Every great invention is about asking one question. The transistor was all about, like back then there were as many telephone engineers as there are software engineers today. And only three people were asking the question of like, can we have something fundamentally better than hot vacuum tubes? And that led to the invention of the junction transistor. And that fundamentally changed modern day computing. Like we wouldn't have everything around us if that didn't happen. And so every great invention is about like some people asking some interesting questions, right? And so I wanted to create that spirit. So it may not matter, like you could have named it something else, but every time someone enters into a meeting room, every time someone books a meeting room, it has an impact. You're like constantly reminded like you're working here because you fundamentally, we celebrate the spirit of asking questions.
I actually like Ramanujan a lot. I think he's, of course, he's Indian and all that. I respect him a lot. He's from the same place as I am in India. But I think Ramanujan is like the spirit of human intuition. And we as human beings, as we get more AIs and more tools and more access to context and more compute power, we tend to rationalize a lot of things. We tend to think deeply through things, but we don't celebrate fundamental human intuition and human curiosity. And it's still very powerful. Like it exists for a reason. And it guides us. It guides us on things we're unable to explain, but it kind of knows exactly what we need to do or what we want to do. And he's the perfect example of intuition. It's almost like it was revealed to me in a dream, right?
Mhm. We were talking about that before we started rolling, too. And we were talking about our travel schedules and managing jet lag because I just came from Italy. But to your point, you were saying how you can build a trillion-dollar company just based on America. So could you talk us through that?
There are examples. Berkshire Hathaway is worth a trillion. JP Morgan is worth almost a trillion, like 940 billion, and largely the customer bases of these companies are in the US. US is such a recursive country, like people not only spend once, once they love a product, they spend a lot. The lifetime value of a customer is very high and spending potential is high. GDP growth is really good and a very industrious country, like people always want to try new things. People have all sorts of creative ideas to start new businesses that never existed before. And so it's the only country that has that constant entrepreneurial creative spirit. And so you're going to capture a lot of value by serving that need. And so that's exactly why we built Perplexity Computer. We want to maximize the amount of useful work people in these new companies that'll be created here delegate to agents, and we want to do that by being the most trustworthy model-neutral agent platform. We're orchestrating frontier models and open-weight models. That's basically the strategy. It's very simple. And then the more we do that, the more they trust the AI and the agent to do more of their workloads, and they don't have time to go hire a lot of people. It's not about wanting to do everything with AIs. It's more that it's very hard to hire the right people when you're a new company. It's very hard to know who to hire. I didn't know who to hire at all in the beginning. I actually forced the first 10 to 20 people to do trials. It was not a strategy, it was a survival mechanism. Like it's not like I was such a genius that I came up with a different strategy to recruit people by asking them to do trials. I really did not know how to recruit. It's because there's so many characteristics that need to be right for a founding engineer, and that characteristic would be very specific and idiosyncratic to your company, and there's no way to have a rubric for that. And so I would just say, hey, let's work together for a week. And the ones who ended up joining, like usually within two days we knew it was the right fit. And the ones who didn't work out even after seven days, we weren't sure. So that was also a good signal. But these kind of flexibilities do not exist for the average person starting a new company in the US. And so if you're able to do that effectively, you can get to build an extremely valuable company. And also, like, you don't think about how to be worth a trillion, you think about how to be useful. And if you're useful at scale to a lot of American consumers, prosumers, small and mid-market companies, eventually, like after a half a decade or a decade, you're like, wow, like I'm worth a trillion now, but I didn't optimize for it. It's also very hard to be motivated by it. And Berkshire and JP Morgan, though I gave them as examples, I think they kind of did that in a financial way, like they maximize returns by being holding companies or lending money. But also the product companies that became worth, you know, Apple, like say 5 trillion, you could argue Apple is the largest SMB and enterprise company too, but they didn't get there by wanting to be that. They just built world-class products that we all love and use. And they didn't want to become a billion-user company. It just happened over time. They just wanted to be very specific about who they serve and what kind of products they build and slowly ramped up usage. And so that's the way we want to go about our business. A lot of future companies will be way smaller and will be able to make a lot of money. It's not so people make a big deal about selling products to like Fortune 100s and 500s and measure their success based on that because that's what a growth-stage investor wants to see because it gives them confidence that that revenue is durable and does not disappear overnight. But it's possible that the future of American GDP growth comes more from smaller and mid-market companies that are going to be far more productive, capital efficient, headcount efficient, and create value way faster using these tools by being more AI-native and less bureaucratic. And if you're not benefiting from all their progress by providing them the tools to succeed, then obviously you get to succeed as well, you can't be that successful. And so I think the fact that we built it for people like us gives me confidence that there'll be a lot more people like us in future, except they don't have to build these tools themselves. We can provide these tools to them.
I think a really good point about that really lends itself to the American dream and how it's still alive. I know you have a really good story behind this and what you're doing and how you're allowing more access to knowledge for these SMBs. So I'm curious for your take on the American dream because we are at a weird place where graduates hate AI but everyone's using AI in their tools. A lot of brands have really bad aura. You guys have great aura. I'm really curious how this all blends together and, you know, your perspective of the American dream and building a business here and what that opportunity means.
I think there is a massive opportunity for an outsider here that does not exist in any other country. I cannot think of another country where you're an outsider and you can come here, you want to improve the state of things here, and people actually not only say you're allowed to do it, they actually invest in you to do it, and they share the profits from your success, of course. But that mentality of taking risks, the spirit of encouraging others who want to take risks by providing them venture capital or friends-and-family kind of capital and helping them succeed and being happy for someone's success by, you know, truly watching them solve really hard problems and feeling part of the journey, that's a very unique American, Silicon Valley thing. But that spirit is, Silicon Valley is essentially a manifestation of the American dream in terms of tech entrepreneurship. But fundamentally, the American dream is you can dream of solving really hard problems here and be rewarded for it no matter who you are. And that's what excites me still today. Like I still think it's the only country, and we don't actually appreciate it that much, but it's still the only country that allows us to do this. And not only that, like people are very ambitious and they compound on their successes here. And, you know, everybody comes and initially for me it was like, oh, let me just get a job, it'll be great, it'll be a success from where I came from, being a middle-class kid in India. But as you see other people, their ambition is infectious. It makes you dream bigger. So it's not just about a static dream. You come here, you see other people dreaming bigger and bigger, and you kind of want to aspire to be that. That peer pressure here is incredible, and it's still incredible. I still sometimes think how lucky I am to be here.
What does greatness as a founder mean to you? And is there anybody who's inspired you?
I think, you know, I've listened to your podcast where people answer this question and they all say Elon and Steve Jobs. And like, it's kind of true. Like the reason they say that is, like, you know, they both have been incredible. Obviously, my entrepreneurial hero was Larry Page, and he's not that talked about. And the reason for that is he's just not very active in public image, whatever. But I think there are some fundamental philosophies that he's had about product design and companies that has impacted me. And one of that is the user is never wrong philosophy. I still follow that today. When a product doesn't work for me, the instinct of the engineer is to always say, you should go do this, you should go do it this way, then it works, because their mind is always thinking, how do I fix the problem for the user rather than how do I fix the product in a way where the user doesn't even have to think about the problem. And so that has impacted us a lot. Like we really try to design our product in a way where even if you did the wrong thing, we would guide you to do the correct thing, or we would anticipate what you really wanted to do and do it. And this is very important because as AI's capabilities advance, you cannot expect people's understanding of those capabilities to advance too. So you cannot blame them for not being good prompters or like writers of skills files or sub-agents. Like you don't want everyone to learn these concepts. You want the product to do its magic. You don't want everyone to understand what is Astra good at? What is Fable good at? What is Opus good at? What is like Saul good at? Like no way am I going to be able to keep track of these things. And so that's essentially our purpose. And I think he himself probably was inspired by Steve Jobs because in the early days Steve Jobs used to say our goal is to make computers usable by the mere mortals because back then computers were exactly like how AI models are today. It was meant for the geeks and the nerds, and mainframe computers were meant for like office workers, knowledge workers. And somebody had to have that inherent drive to make it so intuitive like a toaster, you know, that anybody can use. And the iPhone is a computer and your grandma, my grandmom can use it. And that's the power. And so making things very intuitive for the average user. Because imagine if the iPhone needed an FTE to onboard everybody, right? That's how AI is today, right?
If the product doesn't work, what do people do? Put FDEs and like force them to onboard everybody. But if you crack the design, the onboarding, and the experience and make it so intuitive that you don't have to figure out which model to use for what task, the harness takes care of it itself. You don't need anyone to onboard you. Like it's self-intuitive. You can pick it up, use it for whatever task you want, automate whatever workflows you want. Like it should be that obvious. And we're not there yet as a product. Like, but that's what we strive to be. And we think if we do that job, we can be successful at serving any enterprise. It doesn't matter if it's an SMB or large enterprise because nobody's like saying, you know, I only want a product with an FTE. That's the philosophy people have because that's what bad products forced people to have. Like, you know, the FDE is needed because the product is bad. That isn't to say you don't need FDEs at all. You probably need it to help people fix bugs and all that. But I think product should get to a point where they're so intuitive and high quality and easy to onboard. And AI should be made so intuitive that it shouldn't be seen as a skill issue if you're not able to get the AI to do something for you. It's a product issue. And I think that's deeply impacted me, and I would give Larry Page a lot of credit for this.
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Well, let's get into your product development. So, Perplexity has been on quite the tear since you released Perplexity Computer. I want to talk about the evolution there, how you got to that point and the different tools and various things that you're offering now. I know a lot of people might know you as an answer engine, right? But you have agents, you have all this stuff. So I would love to walk through the evolution there.
So yes, we started as an answer engine in 2022. We evolved to a multi-step search agent pretty fast actually in 2023, late 2023, and then deep research in '24, browser agents last year. So we've been doing agents when before it was called an agent. The thing that I'd like to think, like the easiest thing to understand of why Perplexity has been able to benefit a lot from the rise of agents even though we started as an answer engine is very simple. If agents are meant to do a lot of work we do on our own computers, they need access to the internet. If I shut off Wi-Fi, can you get any work done? It's impossible. Like you need access to the web. You need access. You do a ton of searches online every day. When you want to like understand what is this, what is that? Like full context from here, put it there. Like sometimes you're just researching, let's say, your own work. Like you have to research the person you're interviewing, the company.
Or in this case, you guys research me and you have a set of questions you think that I'm going to ask.
Exactly. Like it's very part of our life to ask questions every day.
You can't get any work done without that.
And so when agents start doing all this work for you, they also need access to internet. They need access to a browser and they need to be able to take all this information they mine from the web and your own personal context, synthesize it, put it together in a usable output that you can take. There's no surprise as to why we benefited so much from that. Like you need the infrastructure for search and browser and a multi-model orchestration because different models are good at different things. Just like how if you have a team, you want people with different skills to be able to do the job. We were perfectly positioned for that. And so that's why our revenue is 5x year-over-year because if you wanted to do agents properly, if you wanted to actually have agents do real work that people do on their computer, you need search and a browser and a multi-model harness. And you need to be able to run this all on cloud on sandboxes with low serving costs and an interface that's super intuitive and easy to use, which is still the same interface, which is that of a prompt box because there's no need to change how we use these products and it should just work from your phone. You should be able to dictate a task and it should just work. We had all these pieces. So we perfectly captured that moment really well. Credit to frontier labs for pushing that paradigm. Like they made their models extremely good at writing code that it made sense to have a sandbox where the agent is using search as code or browser as code, having access to files to be able to put intermediate outputs and expand the context window effectively through effective compaction and then run on the cloud so that you don't have to figure out how to onboard like you would have to if you would do OpenClaw, right? And so that is essentially what led to our success and it was so intuitive that people started onboarding themselves through self-serve enterprise and we saw a lot more growth in the enterprise this year compared to even prosumers because the product started unlocking business operations and workflows for real people. There's one solopreneur, like literally one-person company, who's spending upwards of like a million dollars a year on our product.
Because he's making 3 million a year. He's making 3 million a year by automating newsletters for others.
And the people who are using that are getting clients for their business. So it's very recursive. And so that's why I like to think of this product, our product, our company, and in general products as the recursive effect. It's not the network effect. And this is actually the thing that a lot of people get wrong is they think of AI should follow the same network effect theory of previous era of consumer.
products. So they're like, oh, you don't have a billion users, you're a failure. No, you don't need to have a billion users. You actually need to have a lot of depth in the users you have. You still need a lot of users. You need real distribution. You need scale at millions of users. But you need high depth, highly recursive usage among those users where using your product actually lets them make a lot of money and that lets you make money because they rely on you for that. That's basically what's happening with a product like Perplexity Computer. Search is actually compounding. Every form of agentic action requires access to the web, access to a cloud browser, access to context across connectors, files and tools and putting it all together and orchestrating across models. I can't think of another company that's better positioned than us to do that. And that's why we like to think we don't actually have competitors. We are the only neutral model orchestrator for real knowledge work and research. We have contributors. Their success is recursive to us too. Every time there's a new frontier model, we have some new capability to offer to our users and the competition between them lowers prices that helps lower serving costs and that helps to serve more users and their usage even the existing users when the serving cost lowers they're going to use the product even more and so it's recursive. So I think we only have contributors. We orchestrate across models, across chips, across tools and that only positions us better and better as time progresses. And so that's why our business is growing tremendously.
Wow. I want to talk a little bit deeper on the technical layer behind computer because now you've extended to hybrid and local. So what does that mean? And also talk through maybe the partnerships associated with them.
Sure. So as a company, we believe that we should be orchestrating across everything in AI and AI is a five-layer cake as Jensen described it and the chip layer is very important too and most of AI inference runs on the cloud today, the data center, there's a reason for that. Batch inference gets you high throughput and a lot of repeatable workloads with small context windows benefits from batched inference. That's just the nature of how caching works. But as context expands and as individual user has a lot of personal context, the context window increase dominates the batch size and your context is unique to you. When everybody wants personal agents or every business wants its own personal agents that's always on and working 24/7 and running a lot of automations, the context window matters even more than the model weights in the memory. And so, for example, chat is best suited for data center inference. It would be 30x more expensive to do that on local hardware. But as things get more and more into the long context trajectories, especially like 100k context and so on, the data center only buys you like 3x or even like close to 1x over running it on local hardware as far as like cost efficiency is concerned. And so that's when local hardware starts to matter. And couple this fact with the fact that the biggest bottleneck to diffusing AI right now to more people who want like more agents is the data center buildouts, the long interconnection queues, the permits, people protesting against the buildouts, the gigawatt requirements they have, the need for them to have their own substations and gas turbines to do that. It's just very slow and like very problematic. Also consider that there are like millions, hundreds of millions of devices that we already have that can potentially run models up to like 100 billion parameters even in future and can be plugged into our own power which is already running on the grid. If you just multiply 50 watts per Apple silicon and 100 million Apple silicons, it's already gigawatts of power. Of course, it's envelope math. It's not that always all of them are plugged in for AI and so on. I get that. But there's a lot of unique workloads that can run uniquely to you that we're not even tapping into yet. Think of the data center like a bus. Shared ride is obviously cheaper, but that didn't stop people from wanting to have their own car. And having your own car is the equivalent of having your own local hardware. And you're going to have both. Just like how sometimes you might want like the frontier to do repetitive tasks that are very very reliable, but a lot of personal context and long-running agents where the context in the harness matters even more than the model, it'll be better for you to actually run it on your own local hardware for various reasons. And there's obviously the security reasons and the privacy reasons of wanting to have AI that runs on hardware you own and control. Like I'm not sure if many users are very comfortable giving access to like all their home sensors and Ring and like you know like speakers inside home and music systems and what's happening their personal life their photos everything to a frontier lab you never know like you know especially there's a lot of like controversy today about the Navy SEALs situation right and again like you can't blame them they're going to train on all our data as long as their policy permit that and that's only going to force more people to want to have at least some version of AI that they can own. It feels like their car and they can have their own experience, customize it and don't have to pay a frontier lab for it as long as they pay for the grid and they just have to pay for the orchestrator and we want to be that orchestrator orchestrating between local hardware that they own and control and the frontier on the cloud and benefit from progress everywhere. We are betting on progress in chips competition in the chip layer to build really good local hardware. We are betting on progress in open weight models that are going to be effective and you know very compact very power efficient can be running on local hardware and we are simultaneously betting on frontier models that can run in the cloud and we think there's an orchestration to be done between the two and we want to stay there and be the neutral orchestrator for people.
