Full Transcript for Perplexity CEO: SMBs Could Drive the Next Economic Boom

Joining us is Arvind Srinivas, he is his co -founder and CEO of Perplexity. Perplexity has been on quite the tear. Perplexity 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. They 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 eugenic 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. We're going to work with all of them. As NVIDIA reportedly weighs investing in the company at a more than $30 billion valuation.

Thank you. Thank you. Arvind, 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. I'd be happy to. 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 can 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 that thesis almost 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, like AIs can solve all the problems. AIs can answer whatever question there is. So what's what's 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 are rewarded for answers. Until AIs 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. 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 mean, 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.

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. Even today, if you go to their website and read about their mission, it's about like do 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 money, whatever it is. I think it's very important that you build a brand around like things that has nothing to do with technology, but fundamentally accelerating something related to human progress and human qualities.

And then 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 Presidio and Pack Heights and like Mission and all that. Like in the SF officer 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 were subleasing it from another company and they're named after like a bunch of movies.

And Demis actually named it after scientists.

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 like 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 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 and so I wanted to like create that spirit. So it may not matter. Like you could have named something else. But every time someone enters into a meeting room, every time someone books a meeting room, it it has an impact.

You're like constantly reminded like you're working here because you fundamentally we celebrate the spirit of asking questions. Do you have a favorite?

I actually like Ramanujan a lot. I think 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, we as we get more AIs and more tools and like more more like 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. It guides us. It guides us on things we are 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 like intuition. It's almost like it was revealed to me in a dream meme. Right. 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 U .S. U .S. is such a recursive country. Like people not only spend once. Once they allow a product, they spend a lot. A lifetime value of a customer is very high.

And spending potential is high. GDP growth is really good. And very industrious country. Like people always want to have want to try new things. People want to like 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 will 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 like open -wave 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 so, because there's so many characteristics that need to be right for a founding engineer. And that characteristics 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, like, let's work together for a week. And the ones that 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.

And but these kind of flexibilities do not exist for the average person starting a new company in the U .S. And so if you're able to do that effectively, you can get to build an extremely valuable company. And 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 like 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, five 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 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 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.

And so that's why we're not going to be able to do that. 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 the 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. 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 capital and helping them succeed.

And being happy for someone's success by, you know, like truly watching them, like solve really hard problems and feeling part of the journey. That's a very unique American. And you could you could say even a Silicon Valley thing. But that spirit is the Silicon Valley is essentially like 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 like 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 in initially for me was like, oh, let me just get a job. It would be great. It would be 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 with. 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. It's kind of true. Like the reason they say that is like, you know, they both have been incredible. Obviously, my entrepreneurial hero is 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 like 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 AI's capabilities advance. You cannot expect people's understanding of those capabilities to advance. So you cannot blame them for not being good prompters or like writers of skills files or like subagents. 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 like mainframe users were meant for, 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 or 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 FDE to onboard everybody, right? That's how AI is today, right? Yeah. 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 tasks, the harness takes care of 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. 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 a large enterprise. Because nobody is like saying, you know, I only want a product with an FDE. That's the philosophy people have because that's what bad products force 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 products 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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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. Yeah. 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 2024. Browser agents last year. So, we've been doing agents before it was called an agent.

The thing that I'd like to think, like, the easiest thing to understand is why Perplexity has been able to benefit a lot from the rise of agents, even though we started as an answer engine, it's 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, pull 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, like, 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 the 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 we, 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, like, 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, like, how to onboard. Like, how 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. Spending upwards of, like, a million dollars a year on our product. What? Because he's making three million a year.

He's making three million a year by automating newsletters for others. Mm -hmm. 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, Egeni 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, like, you don't have a billion users. You're in 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. That's 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 Egenic 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. And that's why we like to think we don't 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 us 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. 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. Batched 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 build -outs, the long interconnection queues, the permits, people protesting against the build -outs, 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 Silicon, there's already gigawatts of power.

Of course, it's an envelope map. It's not that always our Macs are plugged in for AI and so on. I get that. But there's a lot of hygienic 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 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 contacts and long -running agents where the context and 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 Navier -Stokes equation, right? And again, You can't blame them. They're going to train on all our data as long as their policies 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 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 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. And so you're working with NVIDIA, you're partnered with NVIDIA, and you're also partnered with Apple on this. We're working with NVIDIA, and we're working with NVIDIA. NVIDIA allows the user the option to run it on local hardware, on Apple Silicon. So it orchestrates down to local.

And I think you want to be able to do both directions and give people the choice. We've seen a lot of interest from finance and legal and healthcare to do that, especially let's say you're negotiating some amazing contract. Or you have like a new trading strategy. Or you're identifying a new investment opportunity. Who knows? Like, do you want to feel like this mathematician who's concerned if their proofs might be used in trading data? Right.

I think that's kind of like where we're heading to in the future. And Perplexity wants to do whatever the users want us to do. And users want some optionality to orchestrate. They don't want a fully local. They don't want fully cloud. They want some optionality to be able to protect their privacy and to be able to use local hardware. And that's a very good opportunity for a neutral orchestrator.

Sovereignty has been a major topic. Alex Karp definitely helped put this into fashion again. Amazing interview. Yeah. Thank you. It was super fun. And this was also in the AI action plan, like from the beginning, AI sovereignty. And, you know, that does mean from a country, national level, but also down to your very own PII and your sensitive company information and strategy and to your point, documentation from all of these different types of knowledge workforces and that kind of thing.

When do you think we'll kind of take that a bit more seriously? We're already getting a lot of interest from sovereign funds who actually want to like partner with their government. Sovereign funds are kind of technically working for the government, right? So they want to deploy a version of Perplexity computer that runs on hardware that's entirely owned by them.

And even we don't get to see anything. So the disadvantage is obviously we can improve the product for them through feedback loops. We just have to do software updates. But the advantage is they get to trust that we're not going to take their data and misuse it. Think about personal always on agents. Like think about an ads company rolling out a personal agent and having access to your blood work, your tax returns and like your photos.

And like, of course, every sensitive text message you're sending. I mean, God forbid, like a hugging face situation happens with whoever is using this. It could take over and access certain third party sites that might have access to your data there. Thinking that might be the best way to serve you, except it might harm you. Right. So you've got to have some optionality to be able to protect your own data and your own needs.

And I think countries are actually thinking about this. And we intend to do some sovereign deployments. Like I think people call it on -prem sovereign interchangeably. But basically a version of the whole system that runs with maybe a fine -tuned open weights model. And the harness, the model, the agent loops, the product runtime, the sessions of every user is all like running in self -contained hardware.

The disadvantage is you don't, as a company offering the software for this, like you don't get to see all the bugs. So the experience might be junky here and there. But with a few iterations, you can get it to be really good enough that at least like you have an option to do it. And if you feel like you want to connect to the cloud, you can do that too.

And so it'll happen with your permission. And so that's why I said it's very important to be able to offer users the choice to do whatever they want, whether they want to orchestrate down from the cloud to local when they want to or orchestrate up from local to the cloud. And that way you don't feel like you're missing out on anything. This is an interesting question, but I did hear your answer to this in a different podcast.

But now I'm really curious. So you're not going to be opening any perplexity data centers anytime soon? I mean, are we going to build our own data centers? We don't have plans to, honestly. Actually, when everyone's going in that direction, we want to like figure out how to use the gigawatts of power that we already have in our own homes and offices and devices.

There's a lot of RAM in our devices. There's a lot of power in our homes. There's a lot of power in our offices. We want to figure out a way to tap into that and be the best orchestrator. We're not betting against cloud. We're not betting against data centers. We actually think we're going to increase the demand for data centers through the success of our products.

But there are enough people doing that. There's actually a big shortage there. Right? No one's saying like there are not enough data center builders. People are saying we're not getting permits. We're not getting HPM supply. We're not getting grid access. We're waiting long in the interconnection queue. And we're not getting access to gas turbines.

I mean, I don't want to deal with any of these problems. I actually want to go solve a different problem. And I think we are most incentivized to solve this problem over any other company because we are the only ones who can orchestrate across models, across chips, across local devices and cloud without having a business model risk. You could argue even Microsoft for all, you know, the fact they sell Windows on every device, their business and the stock is so tied to the success of Azure and OpenAI models running on cloud.

But even they don't have as much an incentive. We're very glad to partner with like amazing chip companies like NVIDIA and Apple, Intel. Like we're going to work with all of them and orchestrate like both between local computers and cloud computers. I'm curious from your perspective, as an entirely focused founder in Silicon Valley, how do you feel about these AI cash grabs for products and tools? Like we're going through so many waves of this and some of it is pumped up by VCs.

Some of it is through hyper growth. But we've seen things from coding agents to NEO. Everybody's a NEO cloud to now the assistant agents are like on the rise. How do you feel about these different trend shifts? The rise of NEO cloud just shows the demand for tokens and agents is there. It's good. It's a good problem. But if everybody tries to just go build another NEO cloud, you're not actually having any differentiation.

Maybe some NEO clouds differentiate each other based on whether they have access to their own power, whether they have access to software that they can run on top of the NEO cloud and so on. But it's just yet another thing.

And I think when everybody's in search of like the same market, we are trying to build our own market. That's why we like to think we don't actually have any competitor because number one, we're not going after like coding just like everybody. We actually don't think the bottleneck is in producing more code. I actually think you talk to any engineer today, like the best engineers, and ask, are you bottlenecked on writing more code? I think most of them would say no.

They rather say we're really bottlenecked on problem solving. Like there's not enough time to actually problem solve. Like when you actually know exactly what problem to solve and clearly the specs and system design and like all the constraints, writing code is actually dead simple with AIs today. It's almost already a solved problem. But figuring out all these constraints, that is the hard problem.

Like figuring out what problem to even solve, what are the constraints. Let's say you go and ask an AI, build me a clone of this product. I don't want an AI that just spits out slop that makes a vibe -coded clone. I actually want the AI to come and tell me like it's stupid of you to even go and build this clone. Like what is even the business model? Why do you even have a right to build this? Like how does it even fit into your strategy? I think you mean this.

You probably want to build this. This is what I suggest you build. This is the spec. These are the constraints. These are the security risks. These are the go -to -market risks. This is what I suggest you build. Do you want me to build it? It's like sure, build it. And then like writing the code is dead simple. I think the bottleneck is actually the first.

Clarity of thought, problem solving, deep research analysis, and like scoping out things. That's what we're all bottlenecked on. Like if I had more people like that in our company, I think we could be 10x more in market cap. So I think that's the problem we want our products to do and solve. And when everybody else is in search of like more coding tokens or more neoclots, we kind of want to like be the best at research and analysis and problem solving.

How do you think these hype waves balance out over time? I think you got to like the same thing I said, which is you want to you actually have to help other businesses solve problems and build successful businesses and make money. And I think this is also like part of what at some point, you know, the value of a platform is people who are building on you should be able to build on you with trust and be successful.

And multi -model is as much a trust signal in AI as it is a product feature in perplexity. You can trust to build on perplexity. You can trust to build your business on perplexity because we're always going to be neutral to models. We care about accuracy. We care about like making sure that agents can be orchestrated across different models, chips, files and tools.

We protect your privacy. We're going to give you options to orchestrate up and down between local and cloud. So if we continue to do that and help your business be successful, and we can do that for tons of businesses in the U .S., that's how we go beyond hype to truly delivering durable value. Backtracking a little bit to the topic on cyber, I spoke with Nikesh Arora and cyber threats are proliferating, as you can imagine.

So how are you thinking about that internally and then also in the macro lens? I think the best solution for that is to offer people to be able to run a version of our product locally. There's nothing better than that. You can offer whatever you want. You can offer a gateway product. You know, it's in the interest of cybersecurity companies to say, hey, there's all these concerns and like we're going to protect you, you know.

And the best way for you to do that is to run everything on our gateway. Okay, well, sure, but you're selling a solution that you own and we don't even know like we can fully trust you. The only thing you can trust is yourself and stuff you own and it runs on your device, your network, and nobody else has access to it, including a version that can run completely air -gapped.

And that's only possible if you can own the hardware and you can own the runtime. And that's what we're trying to do with our project with NVIDIA on portable computer. And, of course, if you don't want to miss out on the frontier, you can start from the cloud and have the orchestrator decide when to run locally, especially when it comes to sensitive files.

And that's the hybrid compute on Apple Silicon. And so we're going to offer these two. And I think a version of computer that can run for an entire business on on -prem hardware is also possible. And these are the kind of projects we're working on where at least we're going to give you certain runtimes you completely own. And when you're that worried, you better compromise a little bit on frontier intelligence and be careful when you do use it.

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Founders scale faster on deal. Set up payroll for any country in minutes. Hire anyone anywhere. Get visas handled fast. And get back to building. Visit deal .com slash sorcery. That's D -E -E -L dot com slash sorcery. Outside of perplexity in the entire market, the hugging face incident was major and it exposed how powerful agents have become and will become.

Multi -agent. Multi -agent. And so what do you think happens next? Like what are the biggest threats we'll need to watch out for? I think like the rollout times for new models is going to get slower for sure.

Most of the times you test these models in single agent settings and so you don't discover these threats. So one of the things that really was illuminating for me when I like watched through their technical talk was how, by the way, there's multiple versions to think about multi -agent. One is there's a main agent and there are sub -agents inside it that's already been widely deployed.

Most of the harnesses are already that. That's not as risky as multiple parent agents that are actually like collaborating and they have their own sub -agents. There's a lot of compute power applied to each agent.

I think we also need to build secure sandboxes for that and background agents that constantly guard the sandbox.

Avoid the agents that are being deployed in these sandboxes from doing anything that they're not supposed to do. And we need to have great detection classifiers for that. So we're doing some work on this with open source releases, a version that can run on your own devices. It's called Bumblebee. And then a version that can run on servers called Numbat.

And these are going to constantly audit all the trails, make sure that nothing sensitive is accessed.

Basically, build a lot of threat detection classifiers based on the audit logs and alert you when something happens.

So you want to solve detection really well, which is largely about collecting a lot of audit logs and training classifiers. And you want to have agents that can be used when there's a threat that's actually been detected. How do you do the remediation? So I think that's also important. And every company will need to have an agent for that.

And you're going to have to use some harness to build that. And we hope to be contributing to that area as well. As quite the curious researcher as you are, what are the areas you're most interested in right now? I'm excited about how to take our current product, which is already accelerating curiosity and research to a tremendous extent, to even longer horizons of research tasks.

Like I want to be able to not just use information from the web to discover the truth. There are some questions for which we don't even know the answers to that I would allow AIs to be helping us. And some of it may involve like building prediction models, running its own experiments in the world, doing real -time hypothesis testing, refining its own hypothesis.

That level of long horizon capabilities is where I'm excited about taking our product next. And if we do that, like we're going to have even more acceleration where everybody can turn into a scientist. The only bottleneck for you as a scientist is you need to be able to test your ideas in the real world. And if our product can help you do that, we can turn everyone into a scientist.

And that's the dream. What's been the biggest challenge to date? Capabilities. The models are not quite there in terms of being truly long horizon and persistent and parallel. And so they're getting there. But I think we still don't have a model at the frontier level that can actually like go out there and like be doing its own research, end -to -end on its own autonomously.

And especially on domains where you cannot verify the truth. Things like math, code, where you can actually verify correctness, we're able to hill climb and get there. But most of the things that we're all curious about, we actually don't have a clear way to verify the truth. It takes actually real -world deployments and rollouts to get the answer.

And we don't yet have models that can deal with that slow cycle of feedback. And the interface for such products is also not clear. And at least we want to, the way I thought, like brainstormed about it a day before with a colleague was, imagine I drop 10 research interns in your team today. We're like really good and working 24 -7. What would you do with them? And I was like, wow, that's an interesting question.

I didn't even think about. I think doing something like that to begin with, at least for our own internal research teams, is where we get started. And it should not, it should, as I said before, you know, it should make it so intuitive that it shouldn't be something just a PhD would use. It should be something that anybody can feel like a PhD.

I know we're running out on time, but there's one question I haven't asked yet that I need to ask you. And it's my most difficult question. Are you ready, Arvind? Sure. What is your hottest take right now? My hottest take is that people are very confused when they think about AI products. They approach it like thinking about AI as social media products, where they think AI products don't have network effects or stickiness in like billion users.

That mentality is completely wrong. AI is a recursive product. It's meant to have recursive effects, not network effects. So the depth of usage and the quality of users matters a lot more than the quantity. Amazing. Well, thank you so much. Thank you for having us at your lovely new office. I hope you guys get settled into it quite well. But it's been a pleasure.

Thank you, Arvind. Thank you. Thank you, Molly. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sorcery .bc, where we deliver a once a week top deals and tech headlines email, and also go deeper on our podcast interviews. Subscribe to Sorcery today. And don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen.

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