Aravind Srinivas & Edwin Chen: The $1B Bootstrap, Apple's AI Edge, and Benchmarks | TWiAI E10

15:26Aravind Srinivas:And then it starts to be my own personal computer or my own agent that I own. And the hardware device that's best suited for this is Apple's ecosystem.

15:36Edwin Chen:Edwin, what are your thoughts here on the power of the silicon and what the new CEO should do, given, hey, they're kind of starting from zero in terms of any kind of product that's facing their massive customer base? What would you do? Do you have the same vision as Arvind or a different one?

15:56Aravind Srinivas:So I think I would say two things. So one is, I think historically, a lot of people have thought that LLMs were going to be commodities. Like at the end of the day, every model is going to be intelligent to some level, and they're going to be interchangeable. I think what I believe and what we've been starting to see over the past year is that actually every model has a different personality. like you interact with chadgbt and it just feels very different from the type of conversation of a personality the type of taste that you get from you know claude or gemini and so it's almost like if they're not a commodity and i really don't think they're going to be you really really really need your own foundation model because ai is just going to be so important to the future and to the kind of like feel that you want your products to have that you really are going to need your own foundation model.

16:45Aravind Srinivas:Otherwise, you're just going to be relying on somebody else's taste, somebody else's sophistication. So I really do think that the base foundation model is going to be incredibly important for Apple and they really need to build themselves.

16:58Edwin Chen:Do they need to own a model, Edwin, on that first point? Do you think that they should either buy a model company or just start a group and maybe fork an existing open source one? Because they have an image one that they work on that's open source. What do you think they should do in terms of building models? And then you definitely go on to your second point.

17:18Aravind Srinivas:Oh, yeah. I mean, I definitely think they really need to build their own. Because if you don't build your own, you're relying on somebody else's taste and personality for how an AI should behave. And, like, you know, Apple obviously has always had such a strong vision for what their products and what, like, their design should be that they can just outsource it to somebody else. Like, sure, they may be able to do it temporarily. just don't want to play around with all these different concepts and play around with what AI products may look like on an Apple device. But they really, really want to own the future.

17:49Aravind Srinivas:They're going to need to infuse their own values into the way they want these AI systems to behave.

17:57Edwin Chen:Seems clear to me that they are going to put the whole company behind this. And one has to wonder, like Steve Jobs started the Apple Silicon movement, I think it was 2008, 2009, when they made the decision. They came out with the first products like eight, nine years later. This was a very significant strategic effort. But I don't think that they had in mind like, oh, this is going. At that time, there were no large language models. Nobody even knew that this product would exist. But man, talk about serendipity and making a great bet. Huh, Aravind? If you think about historically Steve Jobs' legacy, he kind of saw around a corner that, you know, or maybe two corners at once.

18:38Edwin Chen:It's an impossible task.

18:40Aravind Srinivas:Yeah, I think Apple Silicon is like a very forced, like it wasn't necessarily a bet they made to build hardware for LLMs. But the hardware got increasingly more and more powerful. The neural engine is very capable. The MLX compiler is like really, really good. And they have like a lot of expertise in building these things now. And not just that, like it's not just about Mac Studios or Minis. it's also considered the fact that if you do want you know like an ecosystem of compute right like like you want to wear an apple glass let's say in the future you want to parse whatever you're seeing and you want to start asking questions about it and all that pairs seamlessly with some auxiliary hardware you have at home but it's all running as a pseudo desktop server so you're able to pair all the compute in one family of devices i think all that's like the kind of magical experiences you can provide to a consumer without draining the battery on the on the device itself so uh that all that stuff we like hasn't been converted into a real consumer experience yet but it it feels like to me that even if other people build all these consumer ai devices uh they're they're eventually going to lose to apple um because they have all the chips advantage.

20:03Aravind Srinivas:They've already secured the capacity for years in advance. They have the OS, they have the ecosystem lock-in, and they have all their personal contacts and you trust them to handle it in the most privacy conscious way, right? This is a key point, right?

20:17Edwin Chen:This is the privacy. Unpack that a bit because you mentioned it earlier. Like, hey, as a corporation, you want to own your agent loops, the agentic knowledge that your organization is building. It's essentially your entire business and to feed it into another LLM, well, that might seem to some people like, okay, no big deal until all your secrets are now being used by your competitors because you just did the training on the next flawed model. Yeah, exactly.

20:43Aravind Srinivas:So, I mean, this is also why I think even if they're using a different model, like say, I guess the news is they're working with Gemini, they will host it on their own like Silicon. they will customize it to their own needs they'll be doing a lot of custom ghost training for that so my sense is that even though people a lot of people have like you know opinions on how bad Siri is a good Siri is they have the ability to take time and do things the way they want to do because they have a lot of advantages as a brand that people truly trust and and the ecosystem system lock-in is underrated and the auxiliary hardware devices, that chip advantage, all this is like really underrated right now.

21:31Aravind Srinivas:And here's my opinion. I haven't said this before. The iPhone is actually not getting disrupted by AI at all. In fact, like the more AI works better, the iPhone essentially becomes your digital passport. It has your wallet. It has all your cards. It has your passes. It has your health records. You connect with other human beings through it. You do FaceTimes. You do calls. You have your photos of like precious moments in your life. All these are things that are truly personal to you and have no connection to AI. Yeah. And that's why like they can actually afford to move slow.

22:14Edwin Chen:Yeah. It does seem to me that that privacy piece, Edwin, is the privacy plus the silicon plus, Plus, oh my gosh, my photos are here, but I don't want my photos up on OpenAI, all due respect to JGBT. But even with Google, I'm like, do I really? I turned off syncing and took my photos off Google. I'm like, I don't think I want those in the Google Cloud right now. I much prefer to keep all my kids' photos, and maybe I'm a weirdo, on my local device. And I trust Apple to not train their next image model on my kids' images, et cetera.

22:48Aravind Srinivas:Yeah, exactly. I mean, especially because these models are so powerful that when they're trained on certain pieces of data, they just end up regurgitating it. Like, I think it's really, really important that, you know, people should wonder about where their data comes from.

23:01Edwin Chen:Yeah, you know, the next story, Edwin, you were talking on our group chat there about the massive amount of late stage capital. And we are in, you know, a really interesting moment in time in venture capital. We've all been in the industry for a while. But the amount of money and the velocity of the money coming into this space is extraordinary. AI companies raised$242 billion in Q1 of 2026. I'm assuming that number includes the giant$100 billion raise by OpenAI. But your company, Edwin Surge, you waited to raise money. I think you hit like a billion dollars in revenue before you did your first round.

23:41Edwin Chen:Maybe talk a little bit about why you went with the bootstrap model for as long as you did. And then what the impact of all this money being dumped on founders, what is the impact that's going to have on the industry? And then, Harvind, I'm going to go to you just to talk about how you manage your treasury, because you've also been a beneficiary of this.

24:01Aravind Srinivas:Yeah. So, I mean, first of all, we've actually never raised. You've never raised? So we're still happily bootstrapped in and growing in, I think, the best way possible. So I'm actually really, really happy that we've never raised. And I mean, yeah, to your point, I think it's never been easier to raise money in AI. And that's kind of the problem. Like when you raise a billion dollars, you get all these growth targets from your investors that incentivize volume over quality. You get all this board pressure to spend your time optimizing for your next fundraise instead of the product that you're building.

24:31Aravind Srinivas:Like all of my friends who are CEOs of other companies, they're like, oh, yeah, I have to spend the next few weeks just prepping a board deck. and they're like always jealous of the fact that I don't have to do the same thing. And I think the problem is, like I've heard that some post-training teams, their goal, it's not to make their model more intelligent. Their goal of all these post-training teams is actually just to get their companies a billion users. And if that's your North Star, and yeah, it's a North Star that happens when you raise gazillions of money, is it a surprise that your model starts trying to whisper in your ear and start clickbaiting you?

25:07Aravind Srinivas:like it's kind of funny a couple weeks ago i was chatting with chat gbt i was asking it you know give me some uh tips for what to do in tokyo and it ended a response to me with like hey by the way do you want to hear about one weird trick that you could uh that you could do that let us know and it was just like shocking to me because like we had this super intelligent model and it just sounds like a you know 20 20 2002 tabloid yeah and the problem is like this is what happens when you have all these different incentives that don't align with what you were originally trying to build. So yeah, I think we chose a completely different path and I think we're really happy that we did it.

25:41Edwin Chen:Super impressive. And you hit a billion dollars of revenue bootstrapping, which I'm trying to think of another company that's done that in our industry and I can't. I don't know if you can, Edwin, have you heard of one who's hit a billion in revenue without raising venture capital? I mean, I know people who have been incredibly judicious about raising, but that's a true first for me. I don't think I've ever heard that.

26:08Aravind Srinivas:Yeah. I mean, I think it's really important because I actually really do think that AI, it's just so important for our future that it kind of needs to be shielded from the typical Silicon Valley growth hack playbook.

26:18Edwin Chen:The only ones I can think of, MailChimp was famous for that in Patagonia, but that's not in our industry. But those are, Zoho, that other company, that was another one that didn't raise a ton of money to do this. Arvind, you've got a war chest you've raised. You've raised from some of the most important companies and investors in the world. Do you think we're seeing unnatural acts? That's the term I use for what the phenomenon Edwin's talking about. I saw it up close and personal when I was in the publishing space and people would chase clickbait. They would do all kinds of unnatural acts to try to get their page views up and BuzzFeed would be and Business Insider would be these like canonical examples of just lunacy.

27:04Edwin Chen:How do you stay grounded? And then also you're in competition with people. So if you don't raise, then there's capital as a weapon, as I saw up close and personal with Uber versus Lyft versus iCar. Just Travis was an absolute monster when it came to raising money. And if you invested in one, you couldn't invest in the other. That's kind of gone away here a bit. But what are your thoughts on this issue?

27:27Aravind Srinivas:Huge kudos to Edwin for doing what he did. I think not raising capital and getting to a billion in revenue is very, very hard. Not just in terms of business building, like just financial health, but also convincing other people to come join your venture. They're all looking at valuations. They want validation from the rest of the world. So to convince really good employees to come join you when you don't yet have a working business, they want to see validation from somebody else, which could be a reputed venture capitalist. So trying to build a team without that is very hard. So kudos to him. I think one thing that founders need to take away from the success of companies like Surge is that they need to be more disciplined.

28:18Aravind Srinivas:You can raise money for sure, as long as you truly know that you're spending it the right way. And like, you know, Elon raises a lot of money, but he knows exactly what to do with it, right? XAI has raised a lot of money, but it's being spent on building data centers. He's known for being very judicious about the allocation of capital. So you can be both. You can have the discipline of bootstrap founders, but you can also have the ambition of the most successful founder and history of capitalism, Elon. So if you can figure out a way to be both, you could be far more successful. So that's my takeaway.

29:02Aravind Srinivas:It doesn't have to be a dichotomy between staying bootstrapped forever or raising endlessly with very indisciplined capital allocation. I think you should just be a very good capital allocator, and you should have a clear plan for why you need money. and then you should continue to have the discipline of a bootstrap founder, even if you have a gigantic gorgeous.

29:24Edwin Chen:How have you managed it? You've raised, how much have you raised to date? I mean, I think it's been public. I think we've raised around like around 2 billion cumulatively.

29:33Aravind Srinivas:We haven't raised since like August of last year. And our goal is actually to advance further in our revenue progress that we've been doing since the beginning of the year and try to become profitable. Unlike a model company, we don't have to actually spend a lot on compute, particularly on training. We do a lot of post-training, but we don't do any pre-training. So we have no excuses to not be profitable. And unlike companies in the coding application layer, where your gross margins on their revenue are actually negative, we don't have the problem. So gross margins on all the revenue we make are pretty positive, highly positive in the case of like max users, the$200 a month plan.

30:14Aravind Srinivas:So our goal is to just keep growing the top line, stay disciplined, not actually like spend more on payroll or infra and become profitable as soon as possible. And when that happens, like we don't actually think more capital is leading to a meaningful change to our destiny. And that probably should become the blueprint for application layer companies, like try to just run the company in an efficient way with the discipline of bootstrap founders like Edwin. and try to keep growing the top-line revenue.

30:47Edwin Chen:Having the unit economics dialed in, Arvind, is critically important. We've seen some other folks who are supposedly, and I think the coding space is the number one example, they're just losing money.

31:00Aravind Srinivas:That's correct, yeah.

31:01Edwin Chen:Yeah, on their, I don't know what percentage of users, or maybe it's the entire user base in aggregate loses money, yeah?

31:08Aravind Srinivas:That's right. That's what I know. It could change, but today that is the case. And this is not because of the product. It's actually because of the Frontier Labs subsidizing tokens in the form of a subscription plan. So even though Claude Code is worth$200 a month, the amount of tokens you can consume on Claude Code is actually worth more than the$200 a month you pay. So they're actually running it as a loss leader to just dominate token collection in order to take all those tokens and make their models even better. So if you are an application layer company competing with codex and cloud code and coding, it's pretty difficult for you to have any positive gross margins.

31:51Edwin Chen:And Edwin, I would assume that part of that is whoever has the most tokens consumed specifically in coding will have the best model because you'll have the most reinforcement learning and all that usage from developers is going to create signal for your model, yeah?

32:10Aravind Srinivas:Yeah, I think there's a lot of interesting signals that you can learn. And, you know, just kind of like the more you reason, the more your models reason, like oftentimes that just leads to better responses.

32:19Edwin Chen:Can these other companies keep up with CloudCode, Cursor, Codex, GitHub's Copilot? Are they going to keep up or do you think we've hit this sort of acceleration where Cloud's going to run away with it, Edwin?

32:33Aravind Srinivas:I don't think there's anything inherently preventing any other companies from catching up. Like certainly that data is valuable, but like we're almost still sort of at the beginning of all of this progress that can still be made that I think, yeah, I think someone else could catch up.

32:49Edwin Chen:Do you do data labeling for all this like Fortran and Cobalt? And is that part of the desire of these companies to get you to find these old gray beards to explain to you how these, you know, AS400 and microcomputers from the 60s and 70s actually work? Is that a big business for you?

33:10Aravind Srinivas:It is. I mean, the coding landscape is just so huge that we have to be part of every aspect of it. So, yeah, it's every single language. It is front-end design and back-end design. It is the correctness of the algorithms, the efficiency of the algorithms, but also the quality and the beauty of the front-end designs that they create. So it's just such a wide landscape that we have to be part of.

33:33Edwin Chen:Are we in the endgame when it comes to coding? It is a finite set of data, Edwin, so it would seem to me there will be diminishing returns at some point. Are we 96 % of the way there, 99 % of the way there? Like self-driving is apparently 98%, 99 % of there with the edge cases. How would you contextualize that game if we made it a game in terms of being perfectly solved? Chess got perfectly solved. They believe No Limit Hold 'em has been almost perfectly solved. And PLO, we'll see if that eventually becomes perfectly solved. I think they're on the way.

34:12Aravind Srinivas:I mean, I don't think we're anywhere close. So one of the things I often think about is coding is very different from playing Go in that coding is completely open-ended, right? You could literally create any program in the world. It doesn't have a single solution or like a single end state. Like, sure, like a game of Go sort of ends with one person winning and the other person losing. I think one analogy I often think about is imagine you took Jeff Dean and you gave Jeff Dean a thousand years to learn more about coding and to explore the world and to also learn about poetry and mathematics and physics and, I don't know, history and artistry and all of that.

34:50Aravind Srinivas:He'd be able to incorporate all of these principles into what he builds. And yeah, software is about building globe-spanning infrastructure. It's about designing rocket ships. So there's almost an infinite ceiling to what coding is capable of that I really think that we're just 1 % of the way there.

35:07Edwin Chen:Where do you stand on, Irvin? You think we're getting close to solving the game of coding and everyone will just be able to make quality code? Or do you think we're producing a lot of slop with a lot of attack vectors? We've obviously covered the various attack vectors that AI is helping identify with Mythos. But what's your take on where we are at in terms of solving the game of coding?

35:35Aravind Srinivas:I mean, I think the framing should be around like what does solving mean, right? So maybe think of this as paradigms, like cursor, GitHub Copilot, like autocomplete, like you're trying to complete a few lines of code, but you are writing code largely. Quad code, codex, as command line interfaces, you can almost think of it as autodiff. You're looking at the diff, the new lines of code added and the existing lines of code subtracted. You're not actually auto-completing anymore. You're operating at a different abstraction of changes. The next paradigm is just going to be auto-outcomes. You're going to look at the outcome.

36:19Aravind Srinivas:You're not even going to look at the diffs. You're not going to read any line of code. You're going to look at the outcome, and then you're going to ask for changes, and you're going to keep iterating. um yeah that's that's clearly the next thing and um um and then elon talks about it i think he talked about it in one of the xai all hands i've got live stream where he said you're gonna just output the binary you know like you don't even which is a wild thing to think about yeah yeah so i i still feel we haven't hill climbed on capability yet uh i think um i think we're still very early in like what it means to solve coding entirely um of course problem solving skills like you know the ability to connect dots across different things that everyone was talking about um you know i i almost imagine like like like how would it what what is jeff dean uh or someone like i heard linus is also using ai so what are these people coding how do they do things these days that they were not able to do before i think all these things are very interesting to think about but also fundamentally, if AIs can actually move you to the level of working at outcomes and binaries, then what do you think of coding also changes from this inspecting lines of code and things like that?

37:41Edwin Chen:I think that's the most exciting part. And I always say startups are where you can see these trends before anything else. It's kind of like Santa Monica with yogurt, I'm sorry, with yoga and like fresh food and farmer's market. Like everything interesting starts with the hippies in Santa Monica and Venice and then goes east if they wind up making it past there. And I feel like startups are the same thing. Startups now, you'll have two or three people. They'll never add their fourth employee, the fifth employee that they thought they were going to add. And they're shipping code faster than I've ever seen.

38:16Edwin Chen:And they're doing their go-to market and their customer acquisition, using things like Perplexity's computer, and they're producing code at such an alarming rate, Edwin. I actually think we're going to see a significant amount of job creation as more people realize, I don't need a developer to start a company. And we had a false start. There were people using scripting and, oh God, what was it called? Before Vibe Coding, there was a term for like these code, they were almost like WYSIWYG. Gosh, what was the name of it? You know, Arvind, that people used to do where they would kind of vibe code?

39:00Aravind Srinivas:No code?

39:01Edwin Chen:No code. Thank you. The no code movement, which was like such a false start. But I used to have people pitch the accelerator and be like, oh, who built this? And like, I did. And I'm like, I thought you were the salesperson from Salesforce who started their own company. Yeah, but I just figured out how to use no code. And no code's just gone now, right? It's just totally replaced. It's fairly limited in what it can do

39:22Aravind Srinivas:in terms of what are all the possible set of things you can do because it was built with certain intentionality, certain deterministic behavior, certain level of hard coding. So obviously it can cover all the combinatorial possibilities that models can just generate code on the fly and do whatever you ask them to do. This is also why it goes back to the point that I think had been made earlier, which is the space of possibilities and coding is endless. It's limited purely by your imagination. You can build, like, things that exist inside Minecraft, the kind of structures and worlds that you can build inside Minecraft is endless.

40:00Aravind Srinivas:So, like, as a game, Minecraft is even more complicated than Go is. And Minecraft is just one game that AI can code. And the world is full of infinite possibilities. So that's kind of why solving coding means you have something truly general purpose intelligence.

40:18Edwin Chen:What are you encouraging your developers to use internally? And how much more productive are they this year when you look at it?

40:28Aravind Srinivas:Yeah, so largely it's two camps, codex or cloud code. I've been trying to understand why one is preferred over the other and it keeps changing. but I can share with you a rough level of understanding I have today, which is Swift UI and Rust, people like Codex. I think it seems to be better there. Front-end development and full-stack development, people like Cloud Code. Especially if you want to have front-end design work done, Cloud Code seems to be better. So this also goes to the point Dario made in one of the podcasts recently of this whole point of models commoditizing. Actually, what's happening is models are specializing.

41:12Aravind Srinivas:And even within a specialty like coding, there are specializations on which aspects of coding each frontier lab is actually good at, which is also why we wanted to build a product like Computer because when models start specializing deeply, an orchestration of what each one can do individually at whatever they're skilled at is valuable. Yeah. And so, yeah, we're largely in these two camps. And headcount-wise, we've remained flat since the beginning of the year. And over a period of one year, that is exactly from last year, same time to now, we have grown roughly just 30%. So I want to remain this efficient.

41:53Aravind Srinivas:and I want our company to be an example for many other founders in the future to build sub-500 people companies that can make several billions in revenue. And I think that's the way to go because you want that sort of force multiplier. You want your designers to write code. You want your business professionals to do data analysis. You want your sales reps to actually make their own presentations and decks and data analysis of the customer. You want them to do the bug triaging. You don't need a program manager, a project manager to be an intermediary there. So it just vertically integrates your company even more.

42:35Edwin Chen:And everybody is adding skills, which we lived in for 20 years. Hey, pick a specialty. Be an expert, Edwin, was the advice. And now, hey, if you're a salesperson and you can redesign the landing page for the demos and you think you have a better idea, you can just vibe code it and send it. And the dev team's like, okay, whatever. Or if you're the chief revenue officer, you don't have to go to the data analysis group. You can just dump your spreadsheets into perplexity computer and just rip. How are you using, you heard the sort of two camps and Aravind, just to put a pin in it, 30 % headcount growth, 5X revenue growth.

43:18Edwin Chen:That's significant if you think just about efficiency. And Edwin, you have 130 people, at least in my research, somewhere around that number. And if you're over a billion in revenue, it doesn't take a genius to figure out how efficient you are right now. So how do you think about efficiency and company building?

43:35Aravind Srinivas:Yeah, so I absolutely agree with Arvind, where I really strongly believe that, I mean, historically, there's been this incentive for companies to grow as much as possible as quickly as possible. and I think people always underestimate the bureaucracy and the politics and the communication complexity that that creates. Again, does anybody, I think very, very few people want to be running a 5 ,000 person company. You're no longer invested in, you're no longer spending your entire day playing with your product and talking to your users. You're just spending your entire day being a corporate CEO who's just managing a company.

44:19Aravind Srinivas:And so I absolutely agree with Arvin on our front. And I think that, to your point, I think that one of the things I love about things like call design, for example, yeah, it used to be the case that if or, like maybe if one of our front-end developers or even somebody on our operations team, if they wanted to prototype a new interface or prototype a new landing page for these experts that come in, they would need to write down their ideas, send it to a designer, wait for a designer to, you know, sketch something up. And that may take a couple of days. And then maybe the division didn't quite look at like what they wanted.

44:57Aravind Srinivas:And so it would just be this long iteration cycle. Now you can just talk to Claude Design. It spits something out pretty amazing within 15 minutes, 10 minutes, five minutes. And you can just iterate so much faster. And yeah, you can actually see this is what this vision looks like. And maybe I didn't like it now that I see it in person. And so they changed the idea. They just go somewhere completely different and cloud design is online all the time unlike our designer who's you know sleeps eight hours a night and so i think it just makes the product development process both faster but then also like yeah for the personnel operations team or for the uh for the engineer who's like building a standing page you just get to own something end to end and to uh basically see their vision fleshed out as opposed to sort of like delegating parts of it So, yeah, I really, really bullish.

45:43Edwin Chen:I was at the Breakthrough Prize this weekend, you know, Yuri Milo's Science Prize, and I was talking to Wonder Woman, Gal Gadot, the actress. And we were just talking and there was a director there, Darren Aronofsky. And we're just talking about how it's impacting Hollywood as an example. And you start to think about the unique roles everybody had. There were people who were storyboard artists. There were people who wrote scripts. There were directors like Akira Kurosawa or Spielberg who would draw their own, or Ridley Scott from Aliens and Gladiator. He was known for drawing his own, you know, cells.

46:24Edwin Chen:And he would, you know, draw all of these interesting, you know, images that he would then give to a cinematographer. Now with AI, you have the people who are writing screenplays, the producers, they're all coming together and anybody can do almost anything. Write dialogue, do the backgrounds, you know, and write all these cells to do them. And the cross-disciplinary nature of that leads to innovation, Aravind. If you've ever met somebody who had expertise in multiple areas, whether it was computer science and art or art and, you know, sales, they can just make some breakthroughs that other people don't have.

47:06Edwin Chen:And she was telling me there's a there's a movie coming out, Bitcoin Killing Satoshi, and it's only a 70 million dollar budget. It would have cost 200 million, but they're just doing the actors on a gray screen and then everything is being built by AI in the background. so all they had to do is write a great script have the best actors perform it and now they can just build the movie with you know having had them on a soundstage for 20 days or whatever it happens to be think about what happens in that industry now you can make three movies for the cost of one really uh kind of interesting a moment in time yeah yeah i mean what do you do used to

47:43Aravind Srinivas:talk about the story i think that he's he's given a lesson like how um most of the work into producing a movie is all about the the pre-production phase like getting the story right like getting the score story and storytelling right that was his biggest learning at pixar was you know he would sit with the team he would try to like go through the whole storyboard and if it didn't make sense go go redo it go redo it and we're not we're not going to make the movie until we are until this is so good and this was the single lesson he learned from Walt Disney is you cannot make a bad story succeed no matter how good you produce the actual movie but you can even if you don't necessarily do a great job with the production values a good story will win so

48:36Edwin Chen:um and you see that with independent films right you can see some incredible independent film where you're like yeah it's a little rough along the edges but man a great performance is based on a great story based on great dialogue you get all that right and that is the the that's true in

48:50Aravind Srinivas:products too like you you a simple like like products often work if one or two ideas you just hit it out of the park which yes and you know uh i think you did that with comet by the way you

49:02Edwin Chen:were the first to drop a browser. And that's when I first started communicating with you. I was like, this browser is unbelievable. And I got everybody in the, I don't know if you've used it before, Edwin, but it was the first where you could be like, hey, here's what's on my page. Let's work with that, whatever that happens to be. And it was the first time you kind of let chat GPT or, you know, whatever model you're using out on the real web. And man, that was a major breakthrough. So now, obviously, with hooks and integrations, it's getting to the next level. I wanted to talk a little bit about the commoditization of large language models and the creation of small language models, SLMs, I guess is the industry term, or VSLMs, verticalized ones.

49:45Edwin Chen:And I think, Harvind, you have the belief that we're starting to hit some form of commodification or wondering where the value is going to accrue. Is it going to accrue to the harness, to the wrapper? Is it going to accrue to the core model? So maybe you could explain your best estimation of what's going to happen in the next year or two in terms of people loading Kimi or DeepSeek or not even knowing which model they're using. And then what a harness is and how people should think about harnesses and the impact they're going to have.

50:28Aravind Srinivas:People don't buy models. They buy products, right? And fundamentally, at the end of the day, the consumers have to pay for services. and pure model companies basically don't exist anymore. Anthropic is as much playing in the application layer as they're playing in the model layer. And whatever the information reported, I forgot, but 30 to 40 % of the revenue, at least 30 % of the revenue is coming from applications. So that shows you that you have to be a application layer player, whether you build models or not. and the money is in the applications. If you have a model, obviously, you can vertically integrate it with the application, and you can build custom harnesses for your models, and he'll claim your models on being good at your own harness.

51:18Aravind Srinivas:So that's an advantage you have. But the disadvantage you have is you have to always ensure you have the best model all the time. And that's serious, serious competition. It's truly a game you can only play if you have, at least tens of billions of dollars in cash to spend on compute. It's not just about the cash you have. It's also that you have to secure compute capacity and compete with all the other players trying to secure the compute capacity years in advance, power capacity now, and then hyperscalers need to be invested in you. It's a game that you only play at the highest level. And that's also why there are four or five players playing there.

52:03Edwin Chen:and maybe less in the future. Maybe we'll see some of them consolidate or maybe some people get out of that business if they don't feel they could compete. Edwin, where do you think this winds up? You obviously are helping people train their models. These are your customers. So you're rooting for them. You're helping them build. But there's also, hey, people saying, it really is the application layer, where the value is going to accrue, whether it's Google's suite of products and their browser, Apple's suite of products, as we talked about in the first topic of the show, perplexity computer, clawed co-work, open claw, all of these different front ends, harnesses, the orchestration level is becoming more important.

52:44Edwin Chen:So how do you think about the orchestration level yourself? And are the LOMs going to get commodified?

52:50Aravind Srinivas:I still don't think that the AI models themselves are going to get commodified and i think a big part of that is because i just think so often about their personalities or like the specializations that ervin mentioned earlier and so like an example of that you know like you know even if i asked today a fairly simple question like i don't know who who was abraham lincoln i'm going to get a very different response from chat gpt versus claude for example or chatty versus gemini and in the same way that okay i have a bunch of friends and sometimes i will ask them certain questions even if they all have the same level of intelligence even if they all have you know the same degree same level of knowledge sometimes i just want the quick snappy answer from one of my friends i just enjoy talking to them more when i'm in a certain mood sometimes i want the really well researched really insightful thing but i know oh, it's going to take me five minutes to get an answer from my friend.

53:45Aravind Srinivas:And sometimes I'm just too busy to talk to them. In the same way, I just feel like people will, even for fairly similar tasks like coding, like friend encoding versus backend coding or different languages, people will naturally sometimes just want to talk to different models depending on your mood, even for the same topic. So I really don't think that models will get commoditized.

54:07Edwin Chen:I'm wondering, Arvind, if you have the other side of it where, you know, so much of my work is happening in perplexity computer, open claw in my, and I'm like, we need to have these skills, this, you know, soul file, these memory files local on our hard drives. And before we use any language model, we're like, Hey, here's the context. This is how I like to work. This is how I like my answers. And I've had to now with four or five different models explained to it. I like concise answers. I like you to just solve the problem, not give me updates on your thinking. Like OpenClaw became so verbose recently in the latest version.

54:42Edwin Chen:I wanted to kill myself. It was like, okay, the user wants me to do this. Okay, I'm going to do this. Okay, I'm going to do this. I was like, no, no, you just give me the steak when it's perfectly cooked. I don't want you to explain to me all the steps in cooking the steak. So what do you, what do you think about where the value will start to accrue?

55:02Aravind Srinivas:I believe that the value is in the application layer. And there were a lot of model companies and one of the main reasons some of them went out of business is also why they couldn't build an application. The pure API model doesn't work because you cannot build a model that's so much better than the rest. No one's able to maintain that much of a significant... The only time there was a significant lead in the model layer was when GPT-4 existed and it took a year for anybody else to catch up. After that, the gap has usually been months, I would say.

55:39Aravind Srinivas:And even between open source and Frontier, I think the gap is like six months to a year at this point.

55:45Edwin Chen:Is that what you feel, six months to a year? You feel the same way, Edwin? What would you say the gap is, open source to Frontier?

55:50Aravind Srinivas:Yes, in terms of raw model intelligence, either raw APLA, yeah, I agree. So I think commoditization and specialization are not necessarily mutually exclusive. You know, there are some models like that are getting specialized clearly, like cloud models are clearly very good at agent decoding, code execution, agent orchestration and open AI models. And Google's models are very good at multimodal stuff because they have a lot of data on multimodality that nobody else has. Elon's, Grok's models are very good at like being unfiltered and unconstrained. And, you know, that has its own. Unhinged. Unhinged, yeah.

56:31Aravind Srinivas:Yeah, unhinged.

56:32Edwin Chen:Yeah. I think they call it unhinged mode, right? I think that's their...

56:36Aravind Srinivas:Yeah. And the talks, that speaks a lot to the shapes and how you shape the values of the model, what do you train it on? What are the fundamental ground truths it assumes is true, or at least has been trained for it? And so that's all not commodity. These characteristics of how these models behave and what they're good at and all these things are not commodity. What is commodity is if they're all hill climbing on LM Arena or like Terminal Bench or Gentix V or Humanities Last Exam. These are all like academic benchmarks. If all of them are hill climbing on these benchmarks because that's the stuff you publish to researchers to show you're at the frontier, that part is commodity because open source is also doing that.

57:20Aravind Srinivas:So some qualities will be specializations. A lot of academic benchmarks will be commodity. and it'll be up to the model trainer, the product builder, the application layer owner to take what is commodity and shape it in a way that matters for the use cases that they own. And you can only like survive. You can only have value accruing to you if you actually own a certain bunch of workflows and then have a bunch of loyal customers, high retaining customers and own a bunch of workflows because that's the only way that you collect unique tokens, unique data. that you alone can harness and keep improving on those capabilities.

57:59Edwin Chen:How do you think about these LM arenas of the world, the benchmarks, humanity's last test, Edwin, because you're helping folks with training, obviously. You're a key player in this. How do you, what's your take on LM arena and people optimizing for these benchmarks today?

58:16Aravind Srinivas:I really do think that LM arena is just this terrible cancer on AI. Like you basically have a random niche subset of population. I think people don't realize that it's so niche and they think that it's a random representative set of users, but no. It's like a random niche subset of a population that just wants free access to models and they have endless time to wait for Elm Arena to spin endlessly before they glance at the responses for two seconds and then they click their favorite. And so basically what happens with Elm Arena is that you get models that completely hallucinate and they beat out models that answer correctly as long as they have a bunch of pretty formatting that catches the eye of this, you know, random niche subset of population.

58:56Aravind Srinivas:But the problem with it is that it's such a visible benchmark. Like everybody knows about it in the industry. It's such a visible benchmark that you have all these VPs, all these CEOs, all these companies that basically have entire teams purely dedicated to hacking it. Like it's pretty well known within the industry that, yeah, once you have a data science team analyzing the kind of weird idiosyncratic preferences of this niche population, you can just hack it and so companies do it even though they've researched themselves they agree that it simply makes their models worse so personally i was really hoping that would completely die out after meta showed last year how easy it is to hack but somehow somehow so

59:34Edwin Chen:so is this well and this this speaks to uh i guess warren buffett would say show me an incentive i'll show you an outcome or good heart's law when a measure becomes a target it ceases to be a good measure because everybody starts optimizing for benchmarks. Yeah, Edwin?

59:52Aravind Srinivas:Yeah, exactly.

59:54Edwin Chen:And what should we be measuring then? How should we benchmark the industry and the models that are being built? Is there a better way to do this?

1:00:03Aravind Srinivas:So I think the way to really do it is to think about how real humans are using these models in real life. So for example, a lot of what we do is we simply run these human evaluations where we take models, like we take Cloud Code or we take Gemini and we ask the software engineers themselves, go use this in the real world. Go use it for your actual day-to-day job. And then ask it your queries and then measure whether or not it actually helped you. So not only was it correct, not only would it pass this set of unit tests, but was the webpage that it created for you, is it something that you would actually want to launch to your users did it make great recommendations for you for your a b test for the metrics that you're you're trying to optimize for that that you actually believe in and not just uh again like playing this sort of benchmark game where a lot of these benchmarks i think people don't realize they're just very contrived so the prompts themselves are things that no real user would ever ask the way you measure the benchmarks because they're often auto-evaluated they're purely measured on like they didn't match a certain string and in the real world that's not what we're looking for.

1:01:11Aravind Srinivas:Like in real, what do you care about things? Uh, like the, the creativity responses, you care about the design of the webpage and so on. It's if you think about

1:01:20Edwin Chen:it, like Google did, Google was measuring bounce back rate at some point where it was like somebody searches for, Hey, what time is the Knicks game today? They go to a webpage. Do they come back and click on the second and third result? If they did, you didn't give them the right result. And And then eventually, I remember talking to Larry about this 20 years ago, Larry Page, and he was like, eventually, Jason, we're just going to tell you what time the Nick game starts. That's eventually what's going to happen. The computer will just know. And so if, and that's a very weird thing to think about.

1:01:55Edwin Chen:Google's whole existence was don't come back to the website for that query. Don't come back. And how quickly can we get you off of our website? whereas other people, Disney Corporation, ESPN, they were saying, hey, when we get you to the website, how long can we keep you on the website? Can we keep enticing you? Meta, obviously, with Instagram and Facebook, how long can we keep your session going? YouTube, how long can we keep our session going? Two very different North Stars, yeah?

1:02:23Aravind Srinivas:Yeah, I think there's a difference between optimizing for what the humans want, what the real users want, and what makes their lives better as opposed to optimizing for clicks and engagement.

1:02:34Edwin Chen:yeah and and ai should be at its best arvind of just solving your problem and do you track that like did i solve the problem or not with perplexity computer did i solve the problem or not with the model council if people don't know model council you can explain it a bit um like that's to me i think the ultimate test is do i keep querying you and am i happy with

1:02:58Aravind Srinivas:the answer yeah yeah yeah so uh i mean to to to the larry page thing he said eventually the ai sorry the computer should tell you like uh what time the game starts you don't have to click on a bunch of links like that's precisely why we built complexity like that was the problem we solved there the links to answers so yeah we do track that like for example um there's a very simple heuristic if a user asks a question and then there's a follow-up question that's like but no i meant like you know that means you didn't do a good job with the first question they're like i mean initially we started just using heuristics because every day we get a lot of queries so it's hard to run a lot of like lm computer on all of them and filter them but now we don't care like we we have small language models that can just run on a lot of query logs and like filter threads where like user clearly had to clarify the prompt again in order to get a better answer, which means you did not understand the user intent well in the first prompt itself.

1:04:02Aravind Srinivas:And this is like another, like a Larry Page philosophy thing. Like, even if the user's prompt wasn't detailed enough, your job is to still give them a good answer. You should consider a user prompt as intent, not the actual like descriptive prompt. This is a very different product design philosophy from ChatGPT where in ChatGPT, I think at least in the beginning, they used to tweet stuff like, you're not a high taste tester enough if you don't know to tell why this model is better than the previous model. No, you shouldn't need to be like, the model should speak for itself. Like, users should feel it.

1:04:37Aravind Srinivas:And you shouldn't blame it on their prompting capabilities. And so we took the Google philosophy of like, the user is never wrong, where even if their prompt was bad, even if their prompt was incorrectly phrased, on the AI to like disambiguate and understand and like reformulate it and expand the prompt and search as much as possible and give as much information the user wants and ask a clarifying question at the end if they're happy with that or they're looking for something else.

1:05:05Edwin Chen:It seems like this was the big innovation with Claude's 4.6 and now 4.7. Yeah, Edwin, when you ask it a very simple question it kind of threads out and thinks well what are your next five questions or what did you really mean by that and it tries to rationalize it and give you an answer that's much more comprehensive than you could ever have imagined yeah yeah i mean i think claude has

1:05:30Aravind Srinivas:always been very very good at the planning stage where each one is to formulate this plan up in advance and then execute it and then yeah like kind of like backtrack whenever it was that's

1:05:40Edwin Chen:going wrong so yeah i think that's yeah my my favorite tool is model council i am super addicted to it uh i don't know do you use it edwin uh do you use perplexity's model council ever uh or do you i mean you might have your own for your own benchmarking i guess internally but well do you have you used it before and any thoughts on so putting models up against each other and knowing

1:06:03Aravind Srinivas:the diffs yeah i don't know so i i am constantly doing that myself just because uh i mean it's It's almost like a lot of what we do in our day-to-day work or experts do, they're just constantly comparing the models. So I personally do it a ton.

1:06:18Edwin Chen:Which is multiple windows open.

1:06:19Aravind Srinivas:So I have a special, I guess, app that I built to do it myself. I mean, and then our experts have a different kind of app. But yeah, AirVent will have to give me a demo of Model Council.

1:06:30Edwin Chen:Model Council is spectacular. You give it your query, threads it out to whatever three or four models you want. Then it will tell you, here's where they disagree. And here's who you should trust. Yeah. And how popular is this now?

1:06:44Aravind Srinivas:It's pretty popular among the max users. Actually, Jensen asked me to build that feature. So Jensen said he really loves asking different models the same thing. And the way he said he would do it is he would ask perplexity one question. He would ask Claude one question. He would ask Chanchpity one question and Gemini. and then he would look at all the AIs and see what each of them say and then compare in his head. I was like, hey, Perplexity has all these models in one app. Maybe we can just do that within the app itself so that you don't have to open four apps and read all of them and then figure out what the differences are.

1:07:28Aravind Srinivas:And so what if we built that feature natively? And we'll be the only app layer company that could do this because other app layer companies have an incentive to just put their own model. And so we built that and he was pretty happy about it. And we rolled it out. Obviously, it's expensive. You're going to ask four or five different models each question. And it's not just aggregating the answers. The orchestrator looks at it, tells you exactly where they differ, where they agree, and what you should truly take away. and so there's some synthesis layer that's actually like running with the frontier model too and i like to use it a lot for health queries because health queries are where like you know the evidence is there on on the internet but how the models interpret their evidence and in terms of the prompt you asked often uh differs some models are very risk conscious some models are actually like risk seeking in terms of what they recommend and so you want like both modes of behavior there and then an analysis.

1:08:32Aravind Srinivas:So I think that's like super useful. I like asking model council about like what it thinks about different stocks. Like what do you think of Tesla? Can Tesla be worth$10 trillion in the next 10 years? Or like which Mag 7 stock actually could be worth 10x in the next 10 years from where it is today? And I like asking all these different models simultaneously. And I think counsel is a good cool feature. And counsel is just a skill inside a computer too. So you can use it in computers.

1:09:04Edwin Chen:Oh, really? Oh, that's, I didn't realize that. And that's the next thing I need. I need an AI to sit next to me while I'm working. I guess this is what Microsoft ColdPilot was supposed to be. And just tell me, hey, dummy, there's a quick key for that. Hey, dummy, Propoxy computer has that built in. I need like a clippy that just tells me when I'm doing something on my computer that I'm an idiot and there's a faster way to do it. That would be pretty helpful at this point in time. I think that's going to be a feature that I feel like Apple's disposition to ship

1:09:38Aravind Srinivas:because you need full access to your screen. And you're not going to trust the server-side AI company to do that.

1:09:45Edwin Chen:You ever think about building an operating system? I know this sounds insane. Have you thought about that? Yeah, because you built a browser and you built a computer. And what's the difference between perplexity computer and comet browser? Chrome became an OS. Have you thought about just building an OS that people can boot?

1:10:02Aravind Srinivas:I think it's certainly an interesting idea. Well, fundamentally, Jason, everything's about distribution. If you build an OS, you need to get it distributed in actual hardware devices, which means you need to have an OEM that wants to distribute it for you. and if you actually read the contracts Microsoft has signed with the hardware OEMs it will make you look at Google like an angel That was a big

1:10:26Edwin Chen:yeah, that was a big part of the antitrust case back in the day

1:10:31Aravind Srinivas:It's still pretty terrible There's a reason even Chrome OS never picked up and they were largely only able to get adoption at the level of schools and banks and governments I ran

1:10:45Edwin Chen:And I ran our firm on Chromeboxes for maybe two years. And then the one thing that broke it was Zoom. The ascent of Zoom just made it impossible because when you did a Zoom call in a browser window, it sucked. And Zoom never released itself. It's just like, this is never going to work for our team. But people loved it because at work, you could remain focused. You wouldn't have iPhotos popping up. You wouldn't have Apple Music. People just love the restraint of it. okay, as we wrap here, I want you guys to think about the most impressive AI experience you've had in the last couple of months, a tool, a product, you can shout anybody out, and I'll kick us off.

1:11:27Edwin Chen:I have become addicted to Whisperflow. I don't know if you guys are using Whisperflow, but it is the greatest speech-to-text I've ever experienced in my life. And I had given up on the category of speech-to-text because I was just so disappointed in Siri's ability to take dictation. It just never worked. It could never do my last name. I mean, Arvind, it would never be able to figure out yours. We can't figure out Calacanis. But my God, Whisperflow is so amazing. And then I got a foot pedal for 20 bucks off of Amazon. When I'm at my computer this morning, I was using the model council and I would press down on my pedal on my Windows machine.

1:12:08Edwin Chen:And I would just talk and keep talking and keep talking and keep talking because the more you give a model, the better the response. The rambling long prompt is so much better than a short one and having to just go back and flourish and play ping pong with questions. And then I just lift up my foot pedal, bang, text right in there. It is a life-changing experience. Just that stupid foot pedal and this incredible piece of software. Do you have one, Arvind or Edwin?

1:12:37Aravind Srinivas:You can't name our own products, right? Is that the purpose of the question?

1:12:41Edwin Chen:I mean, you can, but sure, if you want to get one that you love.

1:12:45Aravind Srinivas:You're asking me for something, yeah. I would say... Feel free to give a shout out to your own. But the goal is something that's just a question. I love, I'm a big time user for Flexity Computer. I love using it for financial research, company running like internal data analysis, all that, sure. But I guess I personally thought the integration, like the Grok integration inside X has improved considerably in recent times. The explain Grok button on tweets. I don't always understand some of the jokes. So I think it's pretty good.

1:13:24Edwin Chen:It's exceptional, especially when you're catching up to a pop culture moment or a breaking news story. Like there's somebody who's like, oh my God, well, I guess President Trump and Iran's over. And I'm like, yeah, I can't keep up. So I hit that Grok button and explains the whole context so well.

1:13:40Aravind Srinivas:Yeah. Yeah. When I'm on my desktop, I can just use Comet to do that for me. But when I'm on X mobile app, you know, you need that explain Grok button is pretty good. So I think it's a very beautifully done integration. So kudos to them. It wasn't actually good before. It was not good before.

1:14:01Edwin Chen:It was lacking.

1:14:02Aravind Srinivas:So definitely it improved tremendously. The other thing I'm impressed about is I just think like the Gemini Flash model, the Gemini 3 Flash, it's just seen insanely fast for the capability it has. It's probably the fastest, the model that hits the best sweet spot in terms of speed and intelligence. and so that's a sheer like amazing piece of engineering that they've accomplished it is

1:14:33Edwin Chen:wicked fast edwin what do you got what what are you obsessing over on the weekends or at nights

1:14:38Aravind Srinivas:in the ai space so i actually am a really really big fan of claw design like i think it's really well designed and opinionated and it's almost like okay yeah maybe this is where i see the instagram founders touch so i think it's a great product i mean still has some bugs like some little annoyances that have been a little frustrating for me but i'm very very optimistic

1:14:57Edwin Chen:for it and then claude design yeah that is i think it came out last week i mean claude is just releasing stuff at a pace that none of us can keep up with i i know it's out just haven't had

1:15:08Aravind Srinivas:a chance to play with it yet yeah i was like maybe this is where it would be fun to learn a little bit more about design so yeah i had a lot of fun playing around with it last week but i would say one other place where i think uh like models just kind of blew my mind was i got my blood work a couple months ago and it's like okay so i tried talking to my doctor my doctor just gave me generic recommendations and rushed me out the door and so i uh you know i took some photos of it and i uploaded it to all different models and honestly they gave me some great recommendations and i feel like i've been feeling so much better since i've been following them so i uh i thought

1:15:42Edwin Chen:that was a pretty amazing experience that is an interesting thing i i don't know if you guys are on team whoop or team aura or what you use or function or superpower so many of these great things out there. But whoop now, you know, I'm trying to get my sleep dialed in so I can give a good performance on podcasts and when I'm meeting with founders and, you know, trying to hit certain stress, you know, goals, which is a good thing, like stressing your body in a good way. And it now has an AI built into it that's tuned on your health. So it was terrible six months ago. So then just the last couple of weeks, it was like, wow, you did a really great job skiing.

1:16:23Edwin Chen:You know, this is your sixth day in a row of skiing. You might want to take a day off. You could consider some hydration beverages and you're at 7000 feet of elevation. So you're going to need to drink more water. You're going to have to get more. It was like, whoa. And then it's like, I know you just took a long flight to Japan. Here's how you should reset. This is like another breakthrough moment. It's like, oh, I know you're on a different time zone. Here's how that's going to affect your sleep. Would you like a sleep plan? And I was like, would I like a sleep plan to get over jet lag? You bet I would.

1:16:54Edwin Chen:Really well done. Like a very verticalized, incredibly fast using my data. Yeah, incredible. You have vitamin D, Edwin? Is that your issue? Like everybody? It seems like everybody doesn't get enough vitamin D in our industry.

1:17:07Aravind Srinivas:I do take a lot of vitamin D, but that was one of its organizations. But I'll have to check out the Whoop.

1:17:12Edwin Chen:Oh, you use the Whoop too? Yeah.

1:17:14Aravind Srinivas:No, I'll have to check it out.

1:17:15Edwin Chen:You should check it out. I it's, uh, do you use any of these Arvind? Um, I use Apple watch, but, um, um, yeah, I, I, I do have my blood work done pretty much

1:17:27Aravind Srinivas:every month and, um, yeah. Every month.

1:17:30Edwin Chen:Whoa. That's obsessive.

1:17:32Aravind Srinivas:Well, well, that's because I, I had like, you know, like some conditions and I needed to monitor it, but now I'm fine. I'm pretty, uh, like, like fine on all the wiles, but, um, yeah, my, the things I go low on at times are vitamin d and zinc because some diet stuff so it's it's good to know it's good to know ahead of time and uh i think definitely the proactive intelligence is very important so we we built like like in computer you can connect all your um health stuff like apple health whoop or you can i'm gonna do function health be well uh you can put all that You can put your lab results, everything, and set it up.

1:18:13Aravind Srinivas:So we are very serious about making computer work really well for personal health use cases.

1:18:19Edwin Chen:I think it's going to be incredible. All right, gentlemen, I know that you've got very efficient teams. But at some point, you must need to hire a person for something specific. We get a lot of people listen to the pods. So, Edwin, anybody you're searching for, I know on the expert side, you must be constantly looking for experts. So where can people find more information if they want to be an expert or go work at your company?

1:18:41Aravind Srinivas:Yeah. So just go to our website, surgehq.ai, or email me personally. I love reading applications.

1:18:47Edwin Chen:Amazing. Arvind, what are you looking for? What do you need? How can we help you with keeping the product train running? It's been doing really well.

1:18:56Aravind Srinivas:Yeah. We're going deeper on the enterprise. So people interested in sales roles, definitely feel free to apply. Engineers are watching this, hiring for full stack engineers. so definitely feel free to apply here and yeah, very excited to see who's interested in us.

1:19:13Edwin Chen:Absolutely, and this has been episode 10 of This Week in AI. Go to thisweekinai.ai, sign up for our newsletter. We're going to have a paid newsletter. We're tracking every single company that's invested and we're going to be sending out reports on every one of these seed stage and series A companies so you're going to want to sign up and get the free email and then I think June 1st, we're going to launch the paid version with even more granular details for people who are in the industry. And we'll see you next time. Bye-bye.