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

27:09You have to give it the spark. Like you have to define – we say we're shifting from an era of instructions to objectives, right? So you have to define what are 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, 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 and different use cases. And then the third piece is good taste, right?

27:53Only 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 fact-checking, error correction, those are going to be essential skills. But it goes both ways. Like, as I said, with taxes, there's plenty of errors that humans are making right now. And let's use AI to catch those.

28:24Big Technology Podcast Host: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, ultimately, that's going to create a headache for you if you use a bad AI or not using it properly. That's also on you. so accountability doesn't go away with AI and yeah we need to develop a good sense of how do we I have a good way of spot testing when I get an output from AI like what are the things I'm going to double click on to make sure there was no silly mistakes and I love the final pass idea 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?

29:22Big Technology Podcast Host: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. management consultancies, they publish all kinds of reports. And we had a lot of fun showing them some errors in some of the public reports they found. And the people that worked on it were in the room.

30:00And so they were giving each other some trouble for it. But yeah, I think there's still a lot of value to unlock and using AI to fact check humans. Okay.

30:13Big Technology Podcast Host: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. 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.

30:48Big Technology Podcast Host: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. Like 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're ultimately, you know, you're choosing to initiate the task. Like nothing is happening kind of autonomously, right?

31:28Like, again, the agency is still, you know, human triggered. Like 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 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?

32:10And people make mistakes too, right? They tend to work slower than the AI does. Yeah, and again, another crawl, walk, run that I would suggest is we have the capability for businesses to allow for read access but not giving write access, meaning it can create the daily digest but it won't send the emails on your behalf, right? Which is like that's the part where people are like, well, what if it goes and spams 1 ,000 folks with the wrong –

32:42Big Technology Podcast Host:Or leaks, confidential information. Yeah. So again, so that's like the read-write. I think that's like a way – and again, 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. 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 making those interactions, first of all, transparent to the user. 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.

33:30Big Technology Podcast Host: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. Where 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 – because the inference is not yet happening locally, right?

34:13It'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.

35:04Yeah, 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 % look, 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.

35:49But there's nothing I see that indicates that that is a bubble or anything like that.

35:53Big Technology Podcast Host:Yeah. Okay. 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, 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 this super app with codex at the heart of it that obviously will take your computer over.

36:32Big Technology Podcast Host:They call it a new way of using a computer. And then, of course, Anthropic has done this with CloudCode 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.

37:22That 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'll like to use Opus for planning the task.

38:07It 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, like they're not going to have GPT models. Gemini is not going to have models.

38:58So 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.

39:49You 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 in 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.

40:39When 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 do they culturally, how do they through training, 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 you're seeing the necessity.

41:18Big Technology Podcast Host: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 with that. I mean, it would be much more... It would be lower margin. 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 – how do you create that pressure for that middle line worker so they feel that?

41:56And 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. Like a blank page. 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.

42:36I 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, right? Right, right, right. Objectives. And so, yeah, so 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 like a core principle, and that's where we've made our big technology investments along with orchestration.

43:21and 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.

43:32Big Technology Podcast Host: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. What should take 20 minutes of homework ends up taking two hours and usually ends in tears. And every good tutor, well, they're fully booked for months. This episode is brought to you by Brainly.

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46:29Big Technology Podcast Host: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. Dmitry, 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'll build the, they have the demo products like the ChatGPT.

47:12Big Technology Podcast Host: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, 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 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?

47:57Yeah. 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.

48:48All 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?

49:35And 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.

50:08Big Technology Podcast Host: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.

50:43So I can't speak to it out of first-hand exposure.

50:48Big Technology Podcast Host: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. 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.

51:31Big Technology Podcast Host:I guess like that was a long wind up 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 actually see even the local inference becomes more relevant because there will be more investment there.

52:19I 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.

53:01And 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?

53:14Big Technology Podcast Host: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. Download the weights. We have 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 things that are not accurate from them. Well, like different countries might have certain political agendas that they try to integrate into models.

54:04And you find those in the models? I mean, we've published some research on that with DeepSeq. If you go back to it.

54:11Big Technology Podcast Host:One of the 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. 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, adhering to certain task workflows.

55:04Big Technology Podcast Host: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 dialing 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, you know, software drives the hardware cycle, right?

55:59And 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 then 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?

56:22Big Technology Podcast Host:Oh, that makes a lot of sense. Yeah. So then China can export to control 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 that Dwarkesh interview? It's like a very straightforward answer anyway. The, well, no, Jensen is very good at comms. 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. 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.

56:59They're not at the frontier, but they're pushing it.

57:01Big Technology Podcast Host:Yeah. All right. I want to end here. There is this interesting argument, and I think you have a perspective on it at Perplexity. 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, 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.

57:52Big Technology Podcast Host: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 uh you know we're thank you you're you're contributing to our success. You're welcome. And we see great retention. So clearly, folks are finding value there.

58:35And 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 that much. And then you have certain tasks that cost, you know, five cents. 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.

59:19And then you have everything you're buying in the Costco. You have confidence that there's like a max margin, right? And those are kind of like computer credits, right? And it's – some people go to Costco and they just buy the hot dog. And then there's people who go and spend thousands of dollars every trip and that depends on their needs. But I don't – 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?

1:00:06Like 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 compute and data centers.

1:00:24Big Technology Podcast Host:Okay, really the final question. I mean, 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 strategic direction and product plans with all these capabilities just kind of blasting all the time? Yeah. I think part of it is keeping a very lean team. As we've increased our ARR by 5x from 100 million to 500 million, We only grew a headcount 34%. You only have 300 people.

1:01:08Yeah. That's crazy. So that is – and I mean this is what I try to share with companies outside our walls is you're going to be – the world will keep changing faster. And so your only way to adapt to that is to be quick at making decisions and not like tying yourself to one path. That's also a lot of the, not to bring it back to why perplexity computer is great, but you don't want to be tied into one model if another model is going to be better three weeks from now. 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.

1:01:54Right. 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.

1:02:06Big Technology Podcast Host: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. Would love to see you there. And until then, we'll see you next time on Big Technology Podcast.

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