AI Agents: Mirage Or Real Revolution? — With Dmitry Shevelenko

Is the near-uniform move of AI companies to agent super apps going to pay off? Let's ask Perplexity's Chief Business Officer right after this. This week, I'm live at Knowledge 2026, ServiceNow's annual conference in Las Vegas, where enterprise AI moves from promise to production. I'm sitting down with ServiceNow's President and CPO, Amit Zavery, on the platform strategy powering it all, their people and technology leaders, on what AI means for the workforce, the engineering team behind ServiceNow's NVIDIA partnership, and what it really takes to ship AI at scale, and Ultra Beauty on deploying AI across 1,300 stores. These are the conversations you won't hear anywhere else, and new episodes are dropping this week on my YouTube page. We've all heard the stat, 95% of AI initiatives fail. It's not because the technology isn't ready. It's because you don't have the right process or the right partner. Meet Aboard. Aboard is your partner for AI transformation, which means they listen, use their very own powerful software tools, and deliver exactly what your company needs to thrive in the age of AI. Working with big and small clients, Aboard always delivers in weeks, not months. Your AI revolution is just beginning. Visit aboard.com to get your AI rollout right. Welcome to Big Technology Podcast, a show for cool-headed and nuanced conversation of the tech world and beyond. We have the chief business officer of Perplexity here with us. Dmitry Shevelenko is here with us in studio, and Perplexity, as you may know, is one of the many companies moving towards this agentic super app style product with Perplexity computer. Now they are joining OpenAI with Codex and Anthropic with Claude Code as one of the many companies moving towards this agent that can control your computer and get stuff done for you. And today we'll talk about where that's going and whether it's going to be a real business. Dmitry, great to see you. Welcome to the show. Thanks for having me. Looking forward to the conversation. So we're here in mid-2026 and I got to be honest, I thought at this point you would be a subsidiary of Apple. Hasn't happened yet. Well, sorry, your polymarket bet there didn't pan out. Just to be clear, there was no polymarket bet. I just thought it was a good idea, but it hasn't happened. We have a great blossoming partnership with Apple. They actually are really excited about what we're doing with personal computer and how it uses Mac minis. That's one area for them. Yeah, so that is, we've found a way to work together there, but we're having too much fun being independent and a lot of the world is realizing that the power of multimodal orchestration, mass multimodal orchestration, what was first a wrapper is now a harness. And so we're really excited about the future ahead. Yeah. And that's, to me, the main criticism I was obviously very vocal saying Apple should buy Perplexity. I think they actually gave you a call. I'm not taking credit, but maybe I contributed. The reason why I thought it would be a good tie-up is because, you know, all the criticism was, oh, Perplexity is just a wrapper company. And I was like, these guys actually know how to build AI products. Obviously, the search engine, the browser comment looked pretty cool. And then this new computer application where Perplexity will take over your computer on your request and do things for you is really where AI is heading. And as you mentioned, it accesses multiple models as opposed to just being tied to one. So I thought that would be a good acquisition for Apple, which has clearly struggled to take these models and translate them into working products, at least so far. Maybe they'll figure it out with Gemini. What do you think about their CEO, John Ternus, or their incoming CEO, John Ternus? Well, Apple's always been an incredible hardware company. And I think this is, you know, an era where hardware will matter even more because software is going to face waves of commoditization pressure. And so I actually think it's, you know, a really smart pick and we're excited to see what they build. And, you know, we want to build really powerful solutions that work well with Apple hardware. Okay, we're going to get, you have a partnership with Samsung, so we'll get to that in a bit. Let's not bury the lead here, though, which is that, you know, Perplexity gained, I would say, mass awareness, at least in the tech industry, because of the search engine that you built. Arvind, the Perplexity CEO, was very vocal in saying, we're going to take on Google. We have this new way of doing search and look out. And when we look at the usage of consumer AI, something very interesting has happened over the past, I would say, six months, which is that U.S. has pretty much flatlined. If you look at the DAUs of generative AI apps from Aptopia, for instance, there is sort of a flattening that starts late 2025. Even looking at Perplexity's market share of AI search, it was close to 20%. I think this is again, according to Aptopia, mid-2025, and it really has decreased, kind of flat over the past month or so. According to SimilarWeb, your traffic about 5.2 million average daily visits, up 2% over the past month compared to 182 million for ChatGPT, which also isn't growing too significantly. That's up 5%. The question for you is, everyone is now pivoting to this super app, this app that can control your computer, you guys, OpenAI, Anthropic. I'm wondering, is this happening from a position of strength, which is that, okay, we're just going to move here because the technology is so strong, or is it potentially a reaction to the fact that consumer AI hit a ceiling and you need something else? So, well, I'll tell you that I don't know those metrics that you shared, but the stats I look at every morning is our revenue. And we started the year at under $250 million ARR and recently shared that, you know, as of a month ago, we crossed $500 million ARR. And so clearly, we're creating value for our users. And when we actually go back and understand who was using Perplexity, even when it was, you know, more focused on, let's say, consumer AI, as you define it, people were actually using Perplexity for work and knowledge-related tasks. So they were coming to us, you know, as much as we were talking up, you know, this is the Google search killer, people were using Perplexity to get ahead at work, even when they weren't using the enterprise version, this was their secret weapon to be more productive, have greater leverage as they build businesses, create businesses. And so in some ways, you know, we're not, we haven't shifted our focus. We're really going to meeting our users where they always were. And what's possible now is, and this really started, you know, you couldn't have built something like Computer before November, December of last year because model capabilities advanced where you can have longer time horizons for running tasks, right? Where you're not just answering a question, but you're actually doing work as an agent on behalf of the user. And one thing Perplexity has always prided ourselves on is being the best at understanding what the new emergent capabilities are and finding ways to make that accessible and useful for a broader population. And, you know, that's where we focus, but I think revenue is a much more honest metric than kind of, you know, top-line MAUs, which I think, you know, can include in it a lot of hype and exploratory activity, but aren't as tightly coupled with value. Okay, but I'm going to give the alternative perspective here, which is that the MAUs matter. Like typically, MAU, of course, monthly active user, when you're typically in a growth surge, you start talking. I mean, every company, every tech company, they grow users. And then they have this big user base. And then when the growth slows, you start hearing about average revenue per user. You need more users to have a bigger user, to have a bigger revenue base, don't you? Well, we're not talking about average revenue. We're talking about total revenue, right? Right, total revenue. I mean, I would say historically, that's been true for consumer internet companies because MAU is a proxy for ad revenue, right? And as has been reported, like we're not focused on advertising-based monetization. We realize that there is, when a core value prop of Perplexity is accuracy, it's really hard to reinforce that to users when you also have ads running alongside the answer. And so I think some of why MAU matters less is, at least for us, is we're not trying to go to advertisers and say, look at all these users that you can show ads to across all these different demographics. So that might be part of the shift in focus as well. Yeah, I mean, to support your argument, Anthropic does not have the lead in users whatsoever and is doing crazy amounts of revenue. So if you figure out this enterprise use case, you could be a massive company. I mean, we're looking at, they're both going to, Anthropic and OpenAI are both going to have trillion dollar IPOs and we'll have many large companies, I think, that will follow them in the generative AI world. But let me get your take on, well, let me just get your take on the consumer side of things and then we'll move more on the enterprise side. I mean, even if people are using these products for work, they're such powerful tools. And, Some of the use cases got ahead of where people were curious to explore, like, what is this AI thing, but their behaviors didn't change. But I also think there's a fusion of consumer and prosumer that we find very interesting. A lot of people are now empowered to explore launching a side business or, you know, explore like doing that, you know, you know, that project that they never had the activation energy for. And now, because you have these super powerful tools at your disposal, you're more than happy to spend, you know, money behind that because you feel like you get leverage there. So I think consumer to us is not just people using Perplexity to look up the weather, right? You don't need AI for that. And so I think part of what the broader industry needs to do is educate users on what is possible now. Like people refer to this as the capabilities overhang, right, where the models got a lot more powerful, especially in the last six months, and people are still using them in a very, you know, Web 1.0 way. And that's just going to take time for that discovery to catch up. But we're, you know, I'd say this is less relevant for Perplexity, but I'm confident that everyone will prefer to have a more intelligent set of software they use to help run their life. Web 1.0 meaning like information retrieval. Yeah, that's just like the most basic. Yeah, yeah, like, okay, like sports scores, you know, like weather, you know, basic news, like that, that's, you know, that's where still a lot of people are. You don't necessarily need, you know, these new agentic capabilities for that. There's all kinds of, you know, other things people can be doing. And the thing that we're going to realize is the constraint on making the most of AI is our own curiosity, right? Like, you know, that's the bottleneck. And that's why, you know, Perplexity is, you know, we design our products to spark curiosity, to activate it, to, you know, that's a big part of our brand is curiosity, because like when we kind of zero out, like, you know, what gets commoditized, what doesn't, the uniquely human ingredient to taking advantage of all this will be curiosity and agency. Let me give you my belief on why we're seeing this slowdown. And we can sort of, because this does lead right into the agentic use cases. When we've seen the biggest spikes, they've been around some of these multimodal use cases, so not text. I mean, ChatGPT got to 200 million users because of text. People were interested to see what AI could do. So I think that novelty and that interest, you know, built the foundation. But where we were, I'll just use OpenAI for example, where OpenAI saw the biggest surges was after voice hit. Remember that demo where it sounded so much like Scarlett Johansson, she threatened to sue OpenAI. You see an inflection point in growth there. And then images, the Studio Ghibli moment still was just one of the like, I mean, I know somebody that created like seven OpenAI accounts just to, because they kept getting rate limited on the usage. And so, of course, you'll probably see a user spike there, even if it's not, you know, individual users. So that to me is like, as that, as companies have shifted away from those things, we know that Sora is going the way out at OpenAI. Obviously, they're still doing images. They just released a great second generation of their latest image product OpenAI did. But there is going to be this sort of moment of adjustment among people from going from what the AI companies were initially telling them, you know, chat and images and voice to this new use case, which is like, we think that the model should take your computer over, or whatever the model through a harness should take your computer over and let you do stuff. And that will naturally lead to a divot. Yeah, I mean, I think, I agree with the thesis, right? A lot of those spikes in usage were novelty driven, right? Like, I mean, your friend that created the seven OpenAI accounts, you know, I bet they haven't created any Studio Ghibli images in the last 30 days, right? Like, I don't see those around anymore. It's probably gone from the family chat. Yeah, yeah, it is. Though you still see some people's profile pictures are like Studio Ghibli. And so that is a warm reminder of that era of AI. I think the novelty spikes are great because it raises, you know, broad awareness and it brings, you know, it brings people in. And then people have to, you know, discover their own kind of habitual use cases. But you can't, yeah. I, you know, novelty is what it is. I mean, Nano Banana had a similar, you know, moment for Gemini. And I think you could see now it's kind of, you know, that there's been a reduction there too. Ultimately, like, we see value in the most economically productive aspects of AI, right? And that's why, you know, for us, a core foundational investment has been accuracy. And you almost think of search and accuracy as, you know, two sides of the same coin, right? You need to have best-in-class search so that whatever you're doing with AI is grounded in the most up-to-date, you know, highest quality sources, best snippets of that information working for you. And so I do think the, you know, I don't think it's fair to call us what we're doing a pivot, but I think we're mapping our investments towards what are the most economically productive uses of AI that have the most enduring value. And effectively, what's happened now, I mean, you're probably a great example of this. You know, you're running, you know, an independent business, right? That previously, if you were not using AI, which I'm sure you're using in many big and small ways, you'd probably need to hire, you know, a lot of people. Web developer, marketing agency, maybe a software developer. It's crazy. I'm being so heavily invested in learning the tools, what you can do with these tools. Yeah, so like, I mean, you're like the, you know, we should do a case study on you because you're exactly like what we see as the future of the economy, right? Like someone with high agency, right? You had a vision of, you know, running your own media business that, you know, hopefully one day becomes a media empire. And you're able to make very quick, rapid progress on it because you have a team, you know, I think of it like we all just got a hundred employees, right? And the shift we're seeing in both prosumers and in the workforce is everyone now gets to operate as an executive because your job is to wake up in the morning and think about, okay, what are the useful tasks that I can, you know, deploy the hundred agents that are on standby to grow this thing? And so that, that's a, that's very good and very different than like, you know, you know, casual chat and generating images. Like I think those things feed into each other because sometimes, you know, the spark of curiosity requires kind of the quick question and answer. And so you want to make that minimally, you know, you want to make that delightful, easy, low friction, so then people are inspired to go after the longer horizon tasks. And so we see them working well together. But, you know, the future of AI is what you're doing. Yeah. And it is interesting because I do use these and, you know, I just cited the groups I wouldn't need to hire because I'm using this stuff well. But by having access to the tools, I'm actually able to do a lot more, I would say economically productive activity than I would have been if I wasn't constrained by them. So for instance, because like, I'll have like a little extra margin because I don't have that marketing agency. Well, maybe I can use that to host the, host an event, which by the way, folks, we're going to be doing on June 18th. Arvin Sarnevas, CEO of Perplexity, is going to come speak with us. I'll, I'll link it in the show notes. If there are still tickets, you should definitely join. But that's something that exists because, you know, there's a little bit higher margin and we can invest in doing an event because of that. So I think there's like a, we'll see a very interesting transformation of the economy if this stuff works the way that many anticipate that it will. And I'm, I'm not, I've never really been bought into the gloom and doom hypothesis around it, but I guess that's, that's a different discussion. Let me just sort of ask the natural follow-up to what you just said, though, which is if, if chat, images, voice were part novelty to cause this explosion of interest in generative AI, why, why are you sure that this computer style use or super agent use case is not going to be similar? For instance, just to make the bear case, maybe it is also a lot of people trying out this, these, these apps and saying, oh, that might be useful, but then there could be a pullback from, I'll just give one example and then I'll turn it over to you. I stick my teeth into Perplexity computer, which is Perplexity's agent or super agent, I guess is the best way to describe it. And I, I, at its suggestion created a daily digest email for myself. So it, it connected to my Gmail, it's connected to my calendar. It tells me which emails I need to respond to, what's going on We're actually just in the extreme upward part of the ramp. It's a big part of why revenue is ramping as well. So we're certainly not seeing that, and I think the fact that people are now, the mental model is not, this is like, I'm spending on software. People are thinking about this as, you know, this is actually part of my payroll budget, right? I have a team of digital agents, digital workers. And, you know, sure, like the workers have to like show up and do a good job to earn their paycheck, just like, you know, people do. But their capabilities are, you know, increasing, and we're getting better every day of connecting the models to different tools, you know, improving, you know, the virtual machine that it runs on. And so I think the, nothing, none of the usage of computer right now that we're seeing has a novelty effect. It's all kind of, you know, being tied in where people are willing to pay for it. It's tied into those economically productive scenarios. So we're incredibly bullish on it. And as people in AI like to say, like, the models are only going to get better from here, right? So the capabilities will increase. I think consumer is really hard to get right if you don't have network effects. And so again, I think some of, you know, the Studio Ghibli, like the voice, those early video gen examples, I think that's very different than what we're seeing with computer now. So what should, I mean, you mentioned that people as they use it, they use more credits. What are some of the use cases that you're seeing? I mean, my email, I thought, I think it's pretty fun. I let that go. But I also see taxes. Yeah, I mean, it's any, so we actually are launching this week 36 different workflows that go on top of computer. So this is everything from building a financial model of a company to filing your taxes if you're a wealth manager, prepping for a meeting with a client. And again, this takes advantage of connecting to, you know, your internal data systems, your, you know, your snowflake, your Databricks. Just last night, I ran a analysis of, you know, what are the models that are being used inside of Perplexity right now? Like, what's the distribution of between, you know, Opus 4.7 and GPT and Gemini? And got a very elaborate result back. And I know zero SQL. I don't, I can't code if my life depended on it. And I didn't bug a single data scientist at Perplexity. And I was able to do this because we connected Perplexity computer to our snowflake. And I was able to, you know, pull in that analysis, you know, within a few minutes that in a previous world, you know, that would have been 10 emails and I certainly would not have been able to get it at midnight as I wanted to kind of dive into that, right? So, what we're seeing people do is be able to operate with much greater velocity, whether they're accomplishing marketing objectives, analytical objectives, like building products. You know, we're now able to prototype new features instantly. We have people on our content team that submit pull requests, basically ship, you know, code that goes live into production without engineers being in the loop. And that's all being run through Perplexity computer. How much can you trust this stuff? You know, again, going back to this taxes example, I don't trust it to do my taxes. Am I just a Luddite or is there legitimacy to the worry that if it gets something wrong, I could get a letter from the IRS? Well, actually, I'll flip it the other way. The way people are using computer is to double check the work done by their accountant and finding significant errors done there, right? So, it's actually one of the workflows that we're most excited about. It's called Final Pass. And you submit a PDF, a presentation, a spreadsheet, and it basically does a detailed fact check on every assertion and claim in that document. And both in terms of fact-checking against the outside world and then for internal consistency. And we actually did, you know, ran through a Gartner press release about their earnings and found like four glaring, you know, like mistakes in it where they like misstated the earnings. And, you know, we're going to have a fun marketing exercise where basically we go through public companies' press releases and run Final Pass through them and show just how much, you know, error lives in the world right now. And so, I think, you know, there's, but to get to the heart of your question, I think there's always going to be three fundamentally like human activities when it comes to using AI. One is we talked about curiosity, right? You have to give it the spark. Like you have to define, you know, we say, you know, we're shifting from an era of instructions to objectives, right? So, you have to define what are the objectives for, you know, what is the marketing success that you want to see and then the AI will accomplish it for you. So, you need the agency. The second part is just like you need to, you know, error correct and double check the work of a human. We need to get really good at understanding where AI might go sideways and, you know, do validation testing. And that's going to mean different things in different use cases. And then the third piece is good taste, right? Only humans are going to deeply know what other humans will find interesting and cool. And I don't think AI is going to, AI can be a great brainstorming partner, but ultimately that's going to require discretion. And so, yeah, I think, you know, fact-checking, error correction, those are going to be essential skills, but it goes both ways. Like, you know, as I said, you know, with taxes, there's plenty of errors that humans are making right now. And let's use AI to catch those. The question is if people will stop. Stop at people will use these tools the way that you intend or whether they will just say, all right, screw it. I'm going to replace my accountant entirely. But I guess you're responsible for that if you, if you do that. Yeah. I mean, just like you're responsible if you hire, if you hire a cheap accountant, you know, and they mess up, like ultimately, you know, that's, that's going to create a headache for you if you use a bad AI or not using it properly. You know, that's also on you. So, you know, accountability, accountability doesn't, doesn't go, you know, go away with AI. And yeah, we, we need to develop a good sense of how do we, you know, like I, I have a good way of spot testing, you know, when I get an output from AI, like what are the things I'm going to like double click on to make sure there was no silly mistakes. Yeah. And I love the final pass idea. I mean, I've been doing that for all my stories. I like, we'll upload the interviews and then upload my draft and be like, where did I miss? What outside context is there that I should be considering? And so it's just natural that that type of approach would be applied to other things like taxes, financial projections, even, I don't know, marketing presentations could be thrown in and be like, just triple check the numbers, which I've been doing and it's quite good at that. Yeah. I mean, the really fun one was I, I presented to the senior leadership of, of Bain, a management consultant, you know, management consultancies. They publish all kinds of, you know, you know, reports and, and like, we had a lot of fun, you know, showing them some, some errors in, in some of the public reports they, they found and like the people that worked on it were in the room. And so they were, they were giving each other, you know, some, some trouble for it. But yeah, there's, I think there's still a lot of value to unlock in using AI to fact check humans. Okay. But to get this to work right, you have to trust a company like yours tremendously, actually. Let me just read you some of the permissions I had to enable for my, just my daily email. See and download. I don't, I can't believe I actually went through with this, by the way. See and download contact info automatically saved in your other contacts. See and download your contacts. See the list of Google calendars you're subscribed to. See, add and remove Google calendars you're subscribed to. View and edit events on all your calendars. View availability in your calendars. See and download any calendar you can access on your Google calendar. Read, compose and send emails from your Gmail account. See and download your organization's Google workspace directory. I guess I see now why people are working on the Mac mini because, you know, and this is enabled for me right now as we speak that perplexity has all this access to like, you know, all of my mission critical, you know, technological infrastructure. I mean, maybe computer right now is like writing up client emails and sending them. I don't know. Well, you do know, right? Because you're ultimately, you know, you're, you're choosing to initiate the task. Like nothing is happening kind of autonomously, right? Like again, the agency is still, you know, human triggered. Like you're, you're ultimately still directing and you know, you don't need to give all those permissions to get a lot of value out of perplexity computer. I mean, this is a conversation I have with, with many businesses is, you know, start with zero connectors and just, you know, see the value Yeah, with the wrong access. Yeah, so again, so that's like the read-write. I think that's like a way, you know, and again, we, you know, with our business versions, we offer very granular controls. And I think that that's the path forward there. But we spend a lot of time getting the engineering on this right. You know, one of our advantages in the space is the only thing we do is build the product. We don't train pre-trained foundation models, which means all our locus of effort is exactly on, you know, making those interactions, you know, first of all, transparent to the user, right? You know, you were able to know exactly what you're giving us permissions for and then make sure that, you know, it is error-proof in terms of adhering to those permissions. So do you think that the technology today is trustable enough that what I did is not crazy? And if so, why do you think so many people are running this on a Mac Mini? I mean, there was a Mac Mini in your ad for Perplexity Computer. Oh, so the Mac Mini is, it's actually the other way where it lets you get even more, right? Because with the Mac Mini, you can then get access to your iMessages, which you can't with the permissions you got there. With the Mac Mini also, the agent can run 24-7, right? Even when your laptop is closed, it can, you know, run those long horizon tasks. So I wouldn't necessarily interpret the Mac Mini as like a, I want, because the inference is not yet happening locally, right? It's still happening. Not yet. Do you think it will? Well, I certainly think that as models get more powerful, you will certainly be, and as, you know, local CPUs get more powerful as well, you're going to be able to distill powerful reasoning models to a size where they can run on a Mac Mini. Now, I'm not going to offer you like a timeline on, you know, when that's going to, you know, when you're going to get the 80-20 where some of these workflows can shift towards local inference. But I think hybrid compute where certain tasks will run in the cloud and certain will run locally, I think that's a pretty safe bet to assume that that will be like the, you know, the right way to anticipate how these systems will work in the near future. Yeah, that's the bear case to the data center build out is that eventually, like, you do all the training in these massive data centers, and then you sort of distill it and run locally on a Mac Mini. Well, again, I didn't say 100%—I said hybrid. Well, but like if the work that you're doing in the cloud is so computationally intensive, you might still need all that data center build out, right? So I don't, you know, there's kind of—I think we're under-anticipating all of the broad types of computation that more powerful models will, you know, bring to bear. And so I, you know, from the perplexity point of view, like, we don't have strong opinions on the data center build out, but there's nothing I see that indicates that that is, you know, a bubble or anything like that. Yeah. Okay, so just to sort of wrap this part of our discussion, the Mac Mini is not a way to ensconce the agent away. It's to give it access to more and let it work harder. Yeah, and again, with kind of even more granular control, right, and more access to your local files, obviously you're giving those granular permissions. But yeah, we're currently those systems don't support local inference. Obviously, you're doing this. We've just heard at length from OpenAI on this show about their ambitions to build a super app with codex at the heart of it. That obviously will take your computer over. They call it a new way of using the computer. And then, of course, Anthropic has done this with Cloud Code and Cloud Co-Work, which I can't believe how I'm still, like, stunned at how much permission I've given these things. But the payoff is pretty intense when, in a good way, when you when you do. I guess you got to take risks in life. Why is Perplexity going to be able to compete with these two giant companies in the same product arena? Yeah. So when we first set about building Perplexity, we made a very intentional decision to be model agnostic. And that was that was kind of very contrarian at the time because the easiest way easiest way to raise capital in 2022 was to say you're training a model, you know, especially with with our founders background. That could have been a very easy story for them. And they believed back then, and it's proven to be the case, that models would end up specializing. And that is that is actually one of the most powerful things about computer is on a single given task. It will use different models for different parts of that task. Right. So I have little kids and I love like when whenever I'm trying to get them to learn about things, I'll create like mini podcasts for them. They're they're very personalized. And when I do that, computer will use this is kind of and this changes week to week, but it'll it'll like to use Opus for planning the task. It will use GPT models for writing the script because GPT is a good writer. It will then use Gemini models for generating the audio. It will then sometimes actually use Grok for fast research because Grok is a very fast model. It will use Sonnet for writing the Python code to stitch together all the audio clips. And that's just in one, you know, single, you know, deliverable task. It used four different models. So the one thing that codex is never going to be able to support is running Gemini models. You know, it will always be in the GPT family. Same thing for, you know, Claude, like they're not going to, you know, have GPT models. Gemini is not going to have Grok models. So our value as a multimodal orchestrator and being an aggregator is we can tell a user whatever is the best intelligence that exists in the world today that can help you accomplish your task. We're going to be using it and we're not going to be discriminating because of the models we happen to train or the ones we have a special relationship with. And that is a very powerful value prop. And that's something that endures over time. I think the second piece that is foundational that, you know, I spoke to briefly earlier is accuracy. You know, when we were focused on, you know, the V1 of perplexity, which was, you know, ushering in this transition from links to answers, the core technology investment we made in our own tool was search. You need the most accurate grounding so that whatever the intelligence is processing, the source input is as high quality as it can be. And so that's something where we have a very powerful data flywheel that's been running for over three years of compounding, you know, as people use the product, we see which snippets the models use, which ones they don't. That reinforces the intelligence of the index and what we do on search. And so accuracy is another thing that is very differentiated in perplexity computer compared to some of those other products. And so, you know, and I'd say the third structural differentiator, this one you're going to say might be like soft and fuzzy, but I think it matters, is usability. You know, when I talk to businesses, something comes up often is the alpha for a company that is not an AI company is not in them building their own internal tools with AI necessarily. It is in the depth of their adoption, right? Like how did they culturally, how did they through training, you know, through the right type of management, actually get everyone to use these superpowers the way you're using them, right? And you're doing it because you have to, right? Because like you, you wouldn't, you know, like you're seeing the necessity. And because I'm, yeah, I'm a psycho who likes to pressure test these things. No, but you're seeing, but you're seeing. Yeah, like you wouldn't be, I mean, I don't think your type of business model would work necessarily with that with, I mean, it would be much harder. Yeah. It'd be smaller. Yeah, it wouldn't be, you wouldn't be able to grow this fast, right? And so if you're, you know, part of a 5,000 person organization, you don't necessarily feel that same pressure that you feel, right? And so I think the organizations need to figure out how do you actually, you know, how do you create that, that pressure for, for that middle line, you know, worker? So, so, so they feel that. And we need to do our part in that in making Perplexity Computer super easy to use. That's why we're launching workflows because the, you know, the example you had of, you know how to prompt AI to do the fact checking on your articles, right? And you probably have a certain, you know, process that you use there that you repeat. For a lot of folks, they look at the open prompt and it's terrifying. Yeah. They don't know, like. It's like a blank page for a writer. Yeah, it's a blank page. Exactly. It's the new writer's block. It's the scariest thing you could ever look at. Yeah. And it's like, and you hear about, you know, I mean, all your reporting is like, oh my God, AI is changing everything. I need to, you know, you need to be ahead. You're going to get disrupted. And, you know, that's again, why we need something like workflows, which, you know Continue to use the models because they have shut down competing companies. So I want to take a break and I want to go over that with you and then talk a little bit about the variety of models you do orchestrate, including the Chinese models. You have GimiK2 in there. So let's do that right after this. Most leaders know how work is supposed to happen, but when it comes to how it actually gets done day-to-day across tools, teams, and handoffs, they're mostly guessing. That's exactly the problem Scribe Optimize was built to solve. 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He's the Chief Business Officer of Perplexity. Dmitry, this is a really great, rich conversation. I appreciate it. Though, I've written about this. One of the big problems with all these AI use cases converging is that it used to be for these big AI model providers, they'll build the... They have the demo products like the ChatGPT. This is the previous way of operating. And they'll offer their model that you can, you know, pay for intelligence and build whatever you want on top of it. But as we get to this style of agentic use case where everybody wants to build this stuff, you know, some will not be competing, but there's interest to have their own products like CloudCoWork, like Codex, be these sort of system or agent of record, so to speak, that handles all this stuff. And I think they might even prefer a world where, you know, that would just be the single app to rule them all. You're orchestrating their models. So long-term, aren't you sort of at least dependent on their benevolence to allow you to use these models, even as you compete with their core products now? Yeah, I think ultimately all these companies are platform businesses in addition to product businesses. And they, you know, they aggressively petition us to use their models. They give us early access. They want us to run evals. And so we have, you know, the exact opposite dynamic right now where they're more than happy to take revenue from us. And, you know, they're the beneficiary of, you know, more consumption of computer credits as well. And I think they, you know, because they are all competing with each other on their platform businesses as well, and, you know, there's open source, which is, you know, continuing to push at the frontier, not necessarily at the frontier, but pushing at it. All those competitive dynamics are very healthy for us. Now, I agree with you if we lived in a world where there was just one frontier model that was twice as good as the next best model, that would be a bad scenario for Perplexity. I wouldn't deny that. But, you know, since this industry has kicked off, there's never been a moment where the delta between the, you know, the best model and the second best model was like more than maybe like a 10-15% gap. And again, like best model is probably I shouldn't even be using that phrase because it's best model at what, right? Yeah, there's, you know, it's the sub-specialization, right? And so the specialization is also a hedge against, you know, those sort of competitive dynamics. So I don't, I lose more sleep about us preserving our execution velocity and, you know, continuing to build our, you know, our culture and our company through the intensity of the space rather than, you know, a us getting cut off scenario because I'm not seeing indicators of that. If the models, your example of the models sort of competitiveness is very interesting. I mean, we're at this point where the models are very smart, right? We have Anthropic, for instance, won't release mythos because it believes it's too intense for cybersecurity. Great marketing, by the way. You think it's marketing? No, I'm saying regardless of whether it is or isn't, it is great marketing. Do you think it's mostly marketing or truth about the product? I ask everybody this, so I'm curious. I don't, I think everyone will have their own. I don't think, we don't have access to mythos, so I can't speak to it out of, you know, first-hand exposure. But the people you speak with in the industry, believers or mostly skeptical? I think there is a, I think what is a real concern is that models will be better at exploiting cyber vulnerabilities than they are at fixing them, right? Just like you can find these problems in the consultant presentations. Yeah, so I think that arbitrage, I think that's a real concern. I think that has already, you know, but I don't know if there's been some new capability that like didn't already exist. I mean, you've been noticing, like there's been more hacks and things over the last few years, you know, before mythos. So like, I think this has been building up for a while. I guess like there was a long windup on my question to say, isn't there going to come a point where these models are just all kind of smart enough and compute becomes a commodity that like, right now we're in this buildup and eventually we just see parity among models, even though they're unbelievably smart and just like a lot of compute infrastructure and then sort of a price war that brings the price of all this stuff way down. Well, if a... Be good for you. Yeah, that'd be good. I mean, that's like in that scenario because again, open source would catch up too, right? But again, like you start, if we reach some kind of plateau, then you'll actually see even, you know, the local inference becomes more relevant because there'll be more investment there. I think it's really hard to make long-term predictions in this space. I'm fond of saying that the thing I'm most confident in is that six months from now, I'm going to personally have a perplexity, a top three priority that today I don't know what it is. And the model companies themselves, you know, when they're baking the cake of a new model, like they don't know what it's going to taste like until it comes out, right? Meaning the capabilities, like when you train a model, you're not necessarily training it, you know, you're making improvements, but you don't know exactly what the new capabilities are until it's out there and people start using it. And that is, you know, in some ways, it's, you know, that's a core skill we've developed at perplexity is like zeroing in on when a new model becomes available, where's the, you know, actionable value for a user. Yeah. I mentioned this before the break, but you use the Chinese models. Qimi K2 is in perplexity. I don't see DeepSeek in there anymore. So to clarify, we never integrate into perplexity any product or API that is hosted in China. We have ourselves post-trained open-source models that come, that are developed by Chinese labs. We run those in US data centers. We post-train them for accuracy and removing, you know, things that are not accurate from them. It's really impressive what the Chinese labs are doing and the progress they're able to make. I think open source is good overall for users. It's ensuring that pricing remains competitive. And obviously, there's more we can do in the post-training space on an open model than a closed model. And so that lets us kind of accelerate our work around accuracy, conciseness, you know, adhering to certain task workflows. When Jensen says it's important for the entire world to have their AI built on a Western or U.S. AI infrastructure stack, if you could do what you just did, what you just told me with Kimi K2, which is download the weights, post-train it the way that you want, why does it matter where the models are developed? Why does it matter if, let's say, China has the lead in open source? What would be a bad scenario is, say, that the best open source models, their architecture is done in such a way where they don't run on NVIDIA chips. They only run on Huawei chips, right? So that the kind of, I think the scenario Jensen is concerned about, and rightfully so, is where, you know, software drives the hardware cycle, right? And where, you know, imagine the flip of this scenario, where right now Chinese companies are trying to get access to NVIDIA chips because that's where the model architecture is, right? And that they need the NVIDIA chips to be able to run them in an efficient way. What if those flip the other way around, where, you know, it's the Huawei chips are the ones that U.S. companies would need to get, right? And that makes a lot of sense. So then China can export control to the U.S. and control AI. Yeah. So I think that's the, I think that is, you know, when you have this, like... Why didn't he just say that in the Dworkish interview? It's like a very straightforward answer anyway. Well, Jensen is very good at calm, so I wouldn't, you know, I think there's a new, I mean, there's certain things he can't say probably too, that, you know, can't say certain names, but yeah. Yeah, we can say it here on the show. That's fast. But the model of the Chinese models are good. They are, you know, they're pushing the frontier. They're not at the frontier, but they're pushing it. Yeah. All right. I want to end here. There is this interesting argument, and I think you're, you have a perspective on it at Perplexity, that, and this is a great article on, from CNBC that Deirdre Bozer wrote, AI demand is inflated and only Anthropic is being realistic. I think that the crux of the argument is that, like, people have been running massive amounts of work, of workflows on these like $20 or $200 a month plans. And, you know, they are, there's like a lack of ability to serve them, and so therefore, these AI companies are showing immense demand and going and raising money based off of it, where, like, everything's going to change once you have to actually charge per token as opposed to unlimited. Like, you wouldn't do an unlimited electricity plan or an unlimited fuel plan, but for some reason, a lot of these companies have been doing this. Do you think that this is, like, a legitimate issue that she's pointing out, that basically, like, we don't really know what AI demand is because it's been subsidized so heavily for so long? And if so, what's the answer here? So we, at Perplexity, we've never subsidized paying users. So if you're on a, you know, pro or max plan, you know, thank you. You're contributing to our success. You're welcome. And, you know, we see great retention, so clearly folks are finding value there. And that's actually why computer credits are so important, right? So that as you have, because you can have a certain computer task costs you $50 for, you know, say it's like video generation and it's like long horizon running, you know, one task can cost up to that much. And then you have certain tasks that cost, you know, $0.05. And so there's no way to encapsulate all of that in a, you know, subscription product, right? So I think the mental model I would have is AI is going to become a lot like Costco, where you pay for the membership, right? And that gets you in the store. And that's actually the part of Costco's business that is, you know, the highest margin. And then you have, you know, everything you're buying in the Costco, you know, you have confidence that there's like a max margin, right? And those are kind of like computer credits, right? And it's, you know, some people go to Costco and they just buy the hot dog and then, you know, there's people who go and spend, you know, thousands of dollars every trip and that depends on their needs. You know, but I don't, I think, I think she's reacting to some, to, I think it was Cursor kind of advanced this, this data point that like Claude code was subsidizing, you know, a subscription tier. I think that will normalize over time, but the, the behavior we're seeing with computer credits where like people are paying for usage, right? Like there's no, there's no subsidization. There's no, there's no kind of breakage that that's driving it and finding value and paying more every month as they, as they use it more. I think we're, you know, I think it's a safe investment in all the compute and data centers. Okay. Really the final question. I mean, how do you, how do you keep up? Like Perplexity has been, I would say early on three trends, right? AI search, AI browsers, and now this computer use must be tough to set strategy as a company with things changing as quickly as they do. So what is the process that Perplexity uses to make decisions about, you know, strategic direction and product plans, you know, with all these capabilities just like kind of blasting all the time? Yeah. I think part of it is keeping a very lean team, you know, as we've increased our ARR by 5X, you know, from a hundred million to 500 million, we only grew headcount 34%. You only have 300 people. Yeah. That's crazy. So, you know, that is, and, and I mean, this is what I try to share with, with companies outside our walls is, you know, you're going to be, you know, the world is, will keep changing faster. And so your, your only way to adapt to that is to be quick at making decisions and not like, you know, tying yourself to one path. That's also a lot of the, you know, not to bring it back to why Perplexity computer is great, but you don't want to be, you know, tied into one model if another model is going to be better three weeks from now. Right. The world is very unpredictable. And so you want to have agility and, and you want to make quick decisions and be willing to revisit your decisions. Right. And, you know, I think, you know, I think having the humility of not knowing what the world's going to look like two years from now is a big part of being successful in that world. Yeah. I mean, it's, I mean, I wrote a book with this title, but it is always day one. Really, really sort of felt that way beforehand, but in this world, you can't be tied to any legacy. You have to just basically see what the new is today and how it works and, and take charge. And you guys have been good at doing that. So thank you. Dimitri, it's great to see you again. And thank you again for coming on the show. Hopefully we can do this again soon. My pleasure. Thank you. All right, folks. Definitely check out the link in the show notes for the 6-18 event. Would love to see you there. And until then, we'll see you next time on Big Technology Podcast. Looking for the best place to shop this Mother's Day? Go with the brand that makes it easy to send something thoughtful to everyone on your list. 1-800-Flowers.com. Right now at 1-800-Flowers, order one dozen roses and get another dozen free. 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