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 Zaveri, 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, on 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 Dmitri Shevilenko 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 CloudCode 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. Dmitri, 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. It's a nice growth area for them. Yeah. So that is – we 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. 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 has always been an incredible hardware company. And I think this is an era where hardware will matter even more because software is going to face waves of commoditization pressure. So I actually think it's 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. Aravind, the perplexity CEO, was very vocal in saying, we're going to take on Google. We have this new way of doing search and lookout. 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 use is 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 perplexities 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 Aravind recently shared that 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 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, 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 couldn't have built something like computer before November, December of last year. because model capabilities advance 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 that's where we focus. But I think revenue is a much more honest metric than kind of top line MAUs, which I think 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? So... Well, we're not talking about average revenue. We're talking about total revenue, right? So pay... I would say that's the next step. Yeah. 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. And 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 doing crazy amounts of revenue. So if you figure out this enterprise use case, you could be a massive company. We're looking at – they're both – Anthropik 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 – if you – 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, you know, they were like ChatGPT was the fastest growing consumer product ever. I guess it still is. But that growth has tailed off. What do you think is behind this flattening of consumer AI product growth overall? Let's just take it with the whole industry, because it's certainly happening. Is it just that they kind of hit saturation? Or is it we know there are fears about AI? Is it people are just too afraid of AI? What's your best diagnosis there? I think there's 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. Um, but, but I also think there, there's a fusion of consumer and prosumer that we find very interesting. Um, a lot of people are now empowered to, uh, explore launching a side business, uh, 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, uh, you're more than happy to spend 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. People refer to this as the capabilities overhang, where the models got a lot more powerful, especially in the last six months, and people are still using them in a very Web 1.0 way. And that's just going to take time for that discovery to catch up. But 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, just like the most basic. Yeah, like, okay, like sports scores, you know, like weather, you know, basic news. Like 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. 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 built the foundation. 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. Then images, the Studio Ghibli moment still was just one of the like, I know somebody that created like seven OpenAI accounts just 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 individual users. So that to me is like, as companies have shifted away from those things, we know that Sora is going away 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 opening eye 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 people in And then people have to you know discover their own kind of habitual use cases But you can't, yeah, 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, there's been a reduction there too. ultimately like we we see value in the most economically productive aspects of ai right and that's why you know for us a 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, yeah, 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, and 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 is crazy being so heavily invested in learning the tools, what you can do. 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 running your own media business that hopefully one day becomes a media empire. And you're able to make very quick, rapid progress on it because you have a team. I think of it like we all just got 100 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, OK, what are the useful tasks that I can deploy the 100 agents that are on standby to grow this thing? And so that's very different than casual chat and generating images. I think those things feed into each other because sometimes the spark of curiosity requires the quick question and answer. And so 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 the future of AI is what you're doing. Yeah, and it is interesting because I do use these. 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 I'll have a little extra margin because I don't have that marketing agency, well, maybe I can use that to host an event. which, by the way, folks, we're going to be doing on June 18th. Arvind Srinivas, CEO of Perplexity, is going to come speak with us. I'll link it in the show notes. If there are still tickets, you should definitely join. But that's something that exists because there's a little bit higher margin, and we can invest in doing an event because of that. So I think we'll see a very interesting transformation of the economy if this stuff works the way that many anticipate that it will. And I've never really been bought into the gloom and doom hypothesis around it. But I guess that's a different discussion. Let me just sort of ask the natural follow-up to what you just said, though, which is if chat, images, voice were part novelty to cause this explosion of interest in generative AI, 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 these apps and saying, oh, that might be useful, but then there could be a pullback from it. I'll just give one example, then I'll turn it over to you. I'm sinking my teeth into a perplexity computer, which is perplexity's agent or super agent, I guess, is the best way to describe it. And I added suggestion, created a daily digest email for myself. So it's connected to my Gmail. It's connected to my calendar. It tells me which emails I need to respond to, what's going on today, what I should be thinking of, the headlines. It's pretty cool. But is there also a chance that that could just potentially be like, Like, oh, that was kind of a cool new use case, but not like a revolutionary use case. Because you could have said the same thing about chat, images, voice, that they were cool use cases, potentially revolutionary. Maybe they're not. Maybe they have potential to be that way. So why is this not, you know, another one of those novelty use cases? Yeah. So what we're seeing with computer is people are generally using it the way you were describing the way you're running your business, where it's like you now don't need to hire dedicated staff or a dedicated agency to do your marketing, to do event production. You're gaining leverage from these tools, right? And what we're seeing is the longer people have had access to computer, I mean, this stuff is still brand new, but they're using it, consuming more computer credits every week than the previous week, right? So we're actually just in the extreme upward part of the ramp. That's a big part of why revenue is ramping as well. So we're certainly not seeing that. And I think the fact that 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 sure, the workers have to show up and do a good job to earn their paycheck, just like people do. But their capabilities are increasing, and we're getting better every day of connecting the models to different tools, improving 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 or 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 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. Yeah. What are some of the use cases that you're seeing? I mean, my email, I think, is 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 your internal data systems, your Snowflake, your Databricks. Just last night, I ran an analysis of what are the models that are being used inside of Perplexity right now. What's the distribution of between Opus 4.7 and GPT and Gemini? And it got a very elaborate result back. And I know zero SQL. 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 pull in that analysis within a few minutes that in a previous world, 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 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? 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 would 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. Oh, okay. So actually, one of the workflows that we're most excited about is 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 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 or basically 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 human activities when it comes to using AI. One is we talked about curiosity, right? You have to give it the spark. You have to define, we say, we're shifting from an era of instructions to objectives, right? So you have to define where the objectives for, 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 error correct and double check the work of a human, we need to get really good at understanding where AI might go sideways and do validation testing. And that's going to mean different things in different use cases. And then the third piece is good taste. Only humans are going to 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 fact checking, error correction, those are going to be essential skills, but it goes both ways. As I said, 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 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 do that. Yeah. I mean just like you're responsible if you hire a cheap accountant and they mess up, like ultimately that's going to create a headache for you. If you use a bad AI or you're not using it properly, that's also on you. So accountability. Accountability doesn't go away with AI. And yeah, we need to develop a good sense of how do we – like I have a good way of spot testing. 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 will 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 presented to the senior leadership of Bain, a management consultant. You know, management consultancies, they publish all kinds of, you know, reports. And, like, we had a lot of fun, you know, showing them some errors in some of the public reports they found. And, like, the people that worked on it were in the room. And so they were giving each other, you know, some trouble for it. But, yeah, 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 just my daily email. See and download. 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 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 ultimately you know you 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 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 many businesses is, you know, start with zero connectors and just, you know, see the value there because there's a lot you can do with, you know, just interfacing with all the outside world's data and making more sense of it. But you're ultimately, you know, to unlock the full value, if you think about this as a digital worker, you know, if you hire people, you also give them access to even greater permissions, right? And people make mistakes too, right? They tend to work slower than the AI does. Yeah. And, you know, again, another like, you know, crawl, walk, run that I would suggest is we we have the capability for businesses to allow for read access, but not giving right access, meaning it can, you know, you know, it can create the daily digest, but it won't send the emails on your behalf. Right. Which is like that. That's the part where people are like, well, what if it like goes and, you know, spams a thousand folks with, you know, with the wrong confidential information. Yeah. So again, so that's like the read, right. I think that's like a way, you know, and again, And we, you know, with our business versions, we offer very granular controls. And I think 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. first of all, transparent to the user, right? You were able to know exactly what you're giving us permissions for and then make sure that 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 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 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. Do you think it will? Well, I certainly think that as models get more powerful, you will certainly be, and as 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 a timeline on 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 the cloud and certain will run locally, I think that's a pretty safe bet to assume that that will be the right way to anticipate how these systems will work in the near future. Yeah, that's the bare 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% low. 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 – there's kind of – I think we're under-anticipating all of the broad types of computation that more powerful models will bring to bear. And so I – 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 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. And we've just heard at length from OpenAI on this show about their ambitions to build this super app with codex at the heart of it that obviously will take your computer over. They call it a new way of using a computer. And then, of course, Anthropic has done this with Cloud Code and Cloud Cowork, which I can't believe. I'm still stunned at how much permission I've given these things. But the payoff is pretty intense in a good way 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 kind of very contrarian at the time because the easiest way to raise capital in 2022 was to say you're training a model, especially with our founder's background, that could have been a very easy story for them. They believed back then, and it's proven to be the case, that models would end up specializing. And 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 whenever I'm trying to get them to learn about things, I'll create like mini podcasts for them. They're very personalized. And when I do that, computer will use – this is kind of – and this changes week to week. But it will 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 single 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. It will always be in the GPT family. Same thing for Claude. They're not going to have GPT models. Gemini is not going to have Grok models. So our value as a multi-model 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 I spoke to briefly earlier is accuracy. When we were focused on the V1 of perplexity, which was 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. 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 I, you know, 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 do they culturally, how do 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 wouldn't, you know, like you're seeing the necessity. Yeah, I'm a psycho who likes to pressure test these things. No, but you're seeing – But it's very useful to me. Yeah, like you wouldn't be, I mean, I don't think your type of business model would work necessarily. I mean, it would be much harder. It would be smaller. Yeah, you wouldn't be able to grow this fast, right? And so if you're 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 pressure for that middle line worker? so they feel that. And we need to do our part in that in making Proxy Computer super easy to use. That's why we're launching workflows because 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 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. It's like a blank page for a writer. Yeah, it's a blank page. Exactly. It's the new writer's block. 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, takes all these complicated, you know, scenarios and use case of AI and just breaks it down into a simple UI where you don't need to provide open-ended instructions or objectives. Summing it up, the reason we're going to continue thriving in a very competitive space is we're the best orchestrator and aggregator of all the intelligence. We're the only AI company fundamentally committed to accuracy as a core principle, and that's where we've made our big technology investments along with orchestration. And usability, which is really a design problem as much an engineering problem, it matters. And it's something that we've always had an edge in and we're going to keep innovating on. Yeah. Well, the question is if these AI providers allow you to 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 Kimi K2 in there. So let's do that right after this. Look, if you have a kid in school right now, you know the drill. 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Visit Progressive.com and give the Name Your Price tool a try. Take the stress out of shopping and find coverage that fits your life on your terms. Progressive Casualty Insurance Company and Affiliates. Price and coverage match limited by state law. And we're back here on Big Technology Podcast with Dmitry Shevilenko. He's the chief business officer of Perplexity. Dimitri, this is a really great, rich conversation. I appreciate it. 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 have the demo products like the ChatGPT this is the previous way of operating and they 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, now some will not be competing, but there's interest to have their own products like Cloud Cowork, like Codex, be the 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 aggressively petition us to use their models. They give us early access. They want us to run evals. And so we have the exact opposite dynamic right now where they're more than happy to take revenue from us. And they're the beneficiary of more consumption of computer credits as well. And I think they – because they are all competing with each other on their platform businesses as well, and there's open source, which is 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 since this industry has kicked off, there's never been a moment where the delta between 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? For each use case. Yeah, it's the subspecialization, right? And so the specialization is also a hedge against those sort of competitive dynamics. So I don't – I lose more sleep about us preserving our execution velocity and continuing to build our culture and our company through the intensity of the space rather than 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 think – I ask everybody this, so I'm curious. I think everyone will have their own – I don't think – we don't have access to a mytho. You don't. So I can't speak to it out of firsthand 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, yeah, 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 before Mythos. So like I think this has been building up for a while. I guess like that 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 – It would be good for you. Yeah, that would 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 the local inference becomes more relevant because there will 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, when they're baking the cake of a new model, 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'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, in some ways, that's a core skill we've developed at Perplexity is like zeroing in on when a new model becomes available, where is the actionable value for a user? Yeah. Yeah. I mentioned this before the break, but you used the Chinese models. Kimi K2 is in Perplexity. I don't see DeepSea getting 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 are developed by Chinese labs. We run those in U.S. data centers. We post-train them for accuracy and removing things that are not accurate from them. Well, like different countries might have certain political agendas that they try to integrate into models. And you find those in the models? I mean we've published some research on that with DeepSeek if you go back to it. answer questions on Tiananmen Square. Yeah, there's those sorts of – now, again, like that's – we also solve for that with grounding, with accurate search, right? And that ends up – if you're using the model fundamentally for reasoning, that becomes less of an issue. But 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. is 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, adhering to certain task workflows. When Jensen says it's important for the entire world to have their AI built on a Western or US AI infrastructure stack, if you could do what you just did, what you just told me with Kimmy K2, which is down the weights, post-train it the way that you want, why does it matter where the models are developed? What 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, rightfully so, is where software drives the hardware cycle, right? And where, you know, imagine the flip of the scenario where right now Chinese companies are trying to get access to NVIDIA chips because that's where the model architecture is, right? And they need the NVIDIA chips to be able to run them in an efficient way. What if it was flipped the other way around where, you know, it's the Huawei chips are the ones that U.S. companies would need to get, right? Oh, that makes a lot of sense. Yeah. So then China can export control of the U.S. and control AI. Yeah. So I think that's the – I think that is when you have this like – Why didn't he just say that in that Dwarkesh interview? It's like a very straightforward answer anyway. Well, listen, Jensen is very good at comms, so I wouldn't – I think there's a – I mean there are certain things he can't say probably too that can't say certain names. Yeah, we can say it here on the show. That's fast. But 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 I want to end here there is this interesting argument and I think you have a perspective on it at Perplexity that this is a great article from CNBC that Deirdre Bosa wrote AI demand is inflated and only anthropic is being realistic I think that the crux of the argument is that people have been running massive amounts of workflows on these $20 or $200 a month plans. And there's 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 everything is 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 pro or max plan, thank you. You're contributing to our success. You're welcome. And 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 cost you $50 for, say, it's like video generation. and it's like long horizon running, one task can cost up to that much. And then you have certain tasks that cost five cents. And so there's no way to encapsulate all of that in a subscription product, right? So I think the mental model I would have is AI is gonna 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, I think it was Cursor kind of advanced this data point that like Claude Code was subsidizing a subscription tier. I think that will normalize over time. But the behavior we're seeing with computer credits where like people are paying for usage, right? Like there's no subsidization. There's no kind of breakage that's driving it and finding value and paying more every month. as they use it more, I think it's a safe investment in all the computing data centers. Okay, really the final question. I mean, how do you keep up? Perplexity has been, I would say, early on three trends, right? AI search, AI browsers, and now this computer use. It 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 100 million to 500 million, we only grew a headcount 34%. You only have 300 people. Yeah. That's crazy. So, you know, that is – and I mean this is what I try to share 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 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 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 is going to look like two years from now is a big part of being successful in that world. Yeah. I mean, 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 take charge. And you guys have been good at doing that. So, 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 618 event. We'd love to see you there. And until then, we'll see you next time on Big Technology Podcast. Thank you.