Speaker 1Really, what happened is that Kimmy actually beat all American models, including Fable, in some subset of tasks. I believe that we're going to have at least one, you know, multi-hundred billion, if not trillion dollar American company focused on American first open source. Right now, Anthropic has like disgustingly high gross margins in their inference. The idea that we should have a central government body that tells us when it's time to release a new product versus not is crazy to me. This is going to be so fucking insane what happens with like the cyber attacks. There's at least 75 neo-labs. For sure, two-thirds of those are going to be worth nothing. Next round's a bitch. They think about data as a commodity. It's really not. It's actually less so of a commodity than even GPUs. I believe it's going to be at least $100 billion by 2030, if not a trillion. Silicon Valley investors have become total bitches with respect to revenue concentration. What are you talking about? Like TSMC has revenue concentration.
Speaker 2This is 20VC with me, Harry Stebbings. Now, Anjani Midha, he's probably one of the smartest dudes in AI. He's a seed investor in Anthropic. And when I asked him, who's the smartest AI in mind that you know? And he said, Anastasios Anjapaloulis. I got that wrong, but he's Greek. And honestly, I'm too old to care at this point, but he's an awesome dude. And I was like, wow, can I have an intro? And he introduced me. And so I had Anastasios on the show. Anastasios is the founder and CEO of Arena, formerly LM Arena. It allows you to vote on the best models. It's an unbelievable model evaluator. And this turned out to be one of the most fun shows I have done, literally in recent memory. He did not give a single shit about upsetting people and was bluntly incredibly articulate, clear, concise, and opinionated on the future of Chinese open source models, whether they should be banned, whether export bans on chips are useful, how we'll see a distribution between frontier and open source, who really should be the one to evaluate whether a model is vulnerable or not. This and so much more in what was such a fun show with Anastasios. But before we dive into the show today, founders face a different set of challenges at every stage of growth. For Sid Shate, co-founder and CEO of D-Matrix, JP Morgan delivered the guidance and expertise to help navigate what came next. He credits JP Morgan's high-touch approach with supporting D-Matrix, as it grew and expanded internationally. Whether you're in the early days or expanding into new markets, JP Morgan helps startups navigate complexity with real confidence, offering personalized guidance and deep sector expertise. Find out how JP Morgan helps founders at jpmorgan.com forward slash grow without limits. JP Morgan is the bank of the innovation economy. While JP Morgan powers your finances, Base44 helps you build faster. You have the ability to build faster. You have the idea, but with most AI tools, you hit a wall. The setup, the config, the gap between what you pictured and what you actually ship. Well, Base44 is where that wall disappears. You describe it. Yeah, Base44 builds it. Apps, websites, AI agents, real working products built in minutes using nothing but plain language. And it's all batteries included. The backend, the database, the authentication, the hosting, the heavy lifting is handled. So you just really stay in the flow. This doesn't just take the busy work off your plate, but it gives you an advantage. And pushes you past what you thought you could build alone. So in this market, fast is the baseline. To win, you just have to be first. Base44 is that edge. The move that skips the troubleshooting and gets you straight to the breakthrough. Build your next thing at Base44.com. That's Base44.com. You have now arrived at your destination. Anastasios, this is going to be a lot of fun for me because I'm dumb as rocks. And you're going to teach me a whole load of stuff today. So thank you. Thank you so much for joining me, dude. Oh, no, thank you for having me. Dude, I told you I use this as a chance to catch up with old friends. So it was wonderful stalking you for the last few days. I just want to start for anyone that doesn't know. Can you explain to me very succinctly and easily what is Arena and why is it important and gaining notoriety today?
Speaker 1Well, Arena is the platform for measuring AI performance in the real world. So what that means is that we're not using static benchmarks. We're not using some random data set that somebody collected, but rather what happens when you put AI in their hands? In the hands of real people. And in so doing, we're measuring the objective reality of how AI affects humanity, whether it's factual, whether it's steerable, whether humans prefer it or disprefer it, whether it's hallucinating, whether there's errors, whether people are getting their actual jobs done with AI and reality. And then we're helping labs improve their models. We're helping the ecosystem understand the performance of different AIs and keep track of all the amazing breaking news, all the new models, multiple models being released every week. So that's sort of the story of Arena. We're the central evaluation platform of AI.
Speaker 2Well, that was incredibly succinct. Thank you. Normally people take about four hours after I ask for a succinct description. When you look at the models that you have on Arena, the sheer number of them, bluntly, I just am faced with the one question. Holy shit. Is this like the true commoditization of models? Are they just a complete utility layer at this point?
Speaker 1Well, I think that the big question around this has started to rise because of open source models. So I think if you were to only look at the closed source models, you would say there's acceleration, but it hasn't quite commoditized yet because that layer is still owned by a pretty small group of companies. It would be an oligopoly if we only had the closed source models. But what seems to be happening is that the open source models, especially from China, have really rapidly improved. And for the first time ever, we saw a couple of weeks ago that Kimi K3 actually beat the best closed source American models on a pretty important subset of tasks, for example, front-end coding, like web development, which a huge fraction of developers are web developers.
Speaker 2Dude, can I ask, how big a moment was that? Because it's like a, I'm going to butcher it, but, you know, I'm a podcaster, so I can get away with it. You're a PhD master, you can't. It's like a 27 trillion parameter model. It's pretty clunky. This is not an agile model. And actually, you know, I was with Jason Lamkin yesterday from Sasta, who's as AI-pilled as they can be. And he's like, honestly, it's not better than the others. How big a moment is Kimi?
Speaker 1No, it was a pretty big moment. It was a pretty big moment. And the reason I'd say it was a big moment is because it violates a narrative that has been persistent in the United States, which is that the Chinese are just distilling American models. And that's the only way that they're able to keep up. When really what happened is that Kimi actually beat all American models, including Fable, in some subset of tasks. That doesn't mean that they're not distilling. They may still be using distillation as a sub-step in their training procedure, but it does mean that distillation is only part of the story and that there's something that those labs are doing above and beyond distillation that's bringing the performance up above what the American labs are currently doing. And so that narrative violation has been hugely important to the way that people view the ecosystem, both from the scientific dominance of Americans and the American sort of hegemony. Of course, Americans love hegemony, to the economics of the whole thing. And to your point, are these models a commodity
Speaker 2or not? When we look at your open routers of the world, the top five models are all open source Chinese models. When we see the proliferation of Chinese models today, does that cannibalize the closed frontier model business meaningfully?
Speaker 1Well, I think that you need to think about the incentives and economics behind it. So first thing I'll say is that the open router metrics are not truly reflective of reality. And that's because the business model of open routers to charge like a fee on top of every token. And so what happens is that people don't use open router for proprietary models. People are using open router primarily for open source models where they need the failover and all the value added services that open router provides. If you look at the whole space of all inference, most of it is still being consumed on first party APIs and on proprietary models. That's why anthropic revenue has been just a total hockey stick. It's not like they're being completely cannibalized right now by Chinese open source models. These models are still only a small fraction of the total inference spend in the world. That said, think about what's happening in the future. Enterprises are going to want to own their own intelligence. They're going to want so-called AI sovereignty, which is a fancy word for meaning that you own your whole supply chain of AI. That means you can take an open source model and you can fine tune it on your own company's data and own your stack end to end, basically outside of the compute hosting. And so then you should be able to run it within your own company. People are going to care about sovereignty. People are going to care about cost. People are going to care about self-improving. They're not necessarily going to want to give their data to an external third party service that might even be competing with them one day.
Speaker 2So do you believe that is the future? We had Lynn on from Fireworks and she was like, specialized intelligence will be the future. Companies will have their own fine tuned, specialized models with their own company data and the performance will be better. And that is what will happen. Do you think that's right? Or is that actually just a small subset of very advanced Silicon Valley companies and Danone yogurts and every normal company will just use Frontier or whatever?
Speaker 1Well, I'll say it like this. I think the business incentives make this inevitable. And the reason is because businesses are going to need a way of keeping a moat in the age of AI. Software is no longer really a moat because it can be produced instantaneously, right? Let's project out five years. That's what's going to happen. And so what moats exist? Network effects exist and data moats exist. And if you can take your data moat and turn it into a self-improving product, that is a way for businesses to remain sustainable in the age of AI. Let's say I'm a business like a Coca-Cola, I'm a Cisco, I have a lot, a lot of users. not be necessarily at the frontier of the AI technology of the world, but I do have this massive corpus of data that I can use in order to, you know, beat my competition. So what should I do? I should be trying to take advantage of my data as much as I possibly can to accelerate my business and stave off competitors. Do you think they will really use open source Chinese models to do that? That's a great question. I think not. I think that the Chinese models will be potentially part of the story for now, but that given the regulatory environment in the U.S., it's probably more likely in the long run that we see a great American open source competitor arise. And this is why I've been a strong proponent, for example, of thinking machines. I believe that we're going to have at least one massive, you know, multi-hundred-billion, if not trillion-dollar American company focused on
Speaker 2American-first open source. Why have we not so far? I really hope so too, by the way. I completely agree. I would love to see that. But why haven't we? Why has the U.S. open community lagged behind so meaningfully?
Speaker 1Well, frankly, I think it's a business model question. You know, I think that people have not really figured out up until this point what the business model is for open source. And now I think people are wisening up to it. There's a few different ways of going about it. One way of doing it is to say, I'm going to do a rev share. I'm going to take this open source model. I'm going to allow inference providers like a fireworks or together or whatever to deploy this model. And then if they get to over X dollars in revenue, I'm going to ask to do a revenue share. And that is one way of building a sustainable company off of open source. You basically share in the compute revenue. Another way of doing it, which is, I think, the more mistral thinking machines type of strategy is to take the open source model and then use it as a lead generation tool for companies to build on top of that and then come to you and say, can you help us fine tune? Can you help us with our AI strategy? And then you do that for deployed engineer. And that is actually a huge market, because if you think about it, one of the biggest markets over the next 10 years is going to be AI modernization, going into every business in the world and then helping them retool in the face of AI, take advantage of their data, restructure their data, figure out how to use these models, integrating them into workflows, teaching the employees of the company how to use them. It's going to be massive, massive, massive. And that is another way for them to become
Speaker 2multi-hundred billion or trillion dollar companies. Is that not what the frontier model providers are doing though anyway? When you look at what OpenAI have said about that kind of FTE approach, Anthropic too, I get you on Mistral and they've done a great job in doing that, but the frontier model providers, Microsoft is even fucking doing an FTE model. Like, no offense, that's not going to be unique to Open.
Speaker 1No, I don't think that FTE is completely unique, but I do think the combination of FTE plus Open American Model may be a more sustainable model for the future of American or even Western businesses, because they might not want to be building on top of external third party services. They might want to be cutting those out for both cost reasons and for sovereignty reasons. And then the open source stuff, they can own it completely, they can continually fine tune it within their companies, and they can feel more secure in the fact that they're spending their money wisely and don't have supply chain
Speaker 2risk. So how should we evaluate this, like thousands of Neo labs, you know, you said... Ah, the Neo labs. You said thinking machines there. Again, I'm dumb as rocks. I say it very clearly to my... No, me too. Me too. Well, you know, you're a PhD, and Anjani told me you were smart. It's just two rocks having a conversation. It's a podcast. I love it. Exactly. That's the point, man. Come on, what do you want? Intelligent conversation? Whatever. No, my point was you said about thinking machines. My question to you is on the back of that, okay, great, I'm with you, but they now have two co-founders left. Lillian Wei left yesterday, but the transience of teams has never been great. Yeah.
Speaker 1Yeah. Team, it's hard. The retention is tough. I mean, being a co-founder of a company is also tough. It sounds like she left for some health reasons. So it's unclear whether it has to do with the company momentum, which seems to be strong at this point. But you know, I do think that Inkling is definitely a V zero model. You know, from what I know about thinking machines, they had a big restructuring like six months ago, team wise, and then they kind of restarted everything and Inkling came out of that. So realistically, at least the most generous take towards thinking machines is that they've only been working on this model for six months. And within that time, they'd become the number one American open source model. But then the less generous take would be the companies existed for a year and a half. And then they come up with, yes, the number one American open source, but there's nine Chinese models on top of them, because they're number 10 open source overall, at least if you look at arena data, you go to our leaderboards today, that's the state of the world. But hopefully what happens with thinking machines is that they continue to release more and more models, you know, larger models, and they continue to build on their momentum. I actually had a Chinese researcher,
Speaker 2a friend of mine messaged me after one of our recent shows and said, you just don't get it, you missed the point of why we're ahead. We just work so much harder. Yeah. And we have support from policy, regulation, government subsidies that you don't have. We have all of these tailwinds.
Speaker 1Do you agree with that? I mean, I think they have tailwinds, they have headwinds. So I don't think it's so sanguine for that. I think that's like a little bit of an overstatement of the differences. One tailwind that we have is we have the best chip of the ecosystem in the world. So they're way hardware constrained over there. And they've been trying to like black market import chips because of this. And you see this in the news, right? The information just reported on this. Do you think that severely impacts their ability?
Speaker 2Again, I'm naive. That's really impacts their ability or actually just fostering an ecosystem where they're going to learn to build it really fast because they don't have access to it? Well,
Speaker 1I think it may be hindering them now, but I think it's a good question as to what's going to happen in the future because they are really good at building hardware. The downside of export control is that they can incentivize them to build their own ecosystem. And then what do we do? You know, so the hope is that we keep NVIDIA ahead of the game so that we can retain the advantage that we have and the TSMCs to the world and our whole that ecosystem is absolutely a national security necessity. So we should have the government, you know, really protecting it and growing it as well as new companies that are innovating. You know, Etch just came out as an
Speaker 2example within the United States to continue to build on our lead there. That awesome love Gavin and team totally agree. Can I ask you just in terms of the export control, do you think it's right that we have the
Speaker 1export control on chips? I think there's national security questions around these chips. I do think that it is a real debate, though, as to which way you want to go about it. Do you want to addict the world to American hardware, which would be against the case against export control? Do you want everyone in the world using NVIDIA and therefore that value, basically money into America and then crush competition in China? That would be world A. And then world B would be, is it worth it to cut that off for the short term or medium term impact of us being ahead? And maybe we just like continue to stay ahead and we starve them of the resources that they need in order to build. The regulatory ecosystem around the open source models also is moving in this direction. Should they be restricted in terms
Speaker 2of access to US markets? Because what's funny is the US is like, oh, should we restrict access? And the Chinese are also going, oh, should we turn them off too? Totally.
Speaker 1Totally. And by the way, it's worth noting that China has already restricted the use of American models within China, right? So if you look at the two by two matrix of US China restrict, not restrict, you know, like export import stuff, they have already restricted the use of US models within China. It's only Chinese models that can be used in China, which affects all American companies. And so then there's the pro cons of all sides of the following regulation. If China restricts the use of Chinese models in the US, what are they giving up on? Revenue and global mindshare and dominance. That doesn't seem like a good trade to me. And then what are they getting in return? In return, they're getting that the US doesn't get to benefit from Chinese open source models, which of course would cripple American businesses in the sense that it wouldn't allow them to build on the best open source intelligence. At the same time, it would make open AI and anthropic stronger, right? So that is kind of the trade off from the Chinese side. I don't really see them banning the use of Chinese models in the US. I don't think it makes sense for them. And then on the other side, should the US ban Chinese models within? I think that there's also trade offs. So on the pro side of banning, there could be back doors in these models that are dangerous. And it could, by banning, we could allow the American open source ecosystem to flourish faster because revenue would accrue to those companies, right? So those would be the two pros. And then the con, the biggest con, of course, would be that you'd be crippling American businesses. Why should you have Chinese businesses or businesses from other countries that haven't banned Chinese models building on top of the number one open source and companies building on number 10? Since when has America been about number 10? That's a good question. World Cup football? World Cup football.
Speaker 2Yeah, that might be your ref award. It's amazing I got this far with the podcast, if that's what you're thinking at this stage in the show. Can I ask you, on the back door element, everyone says about like the back door, the back door. I thought if you hosted it locally, you kind of resolved the back door threat. I don't really think so. Yeah, I think
Speaker 1that's kind of a misconception because the thing is, okay, imagine the following situation. I have a chat bot that I expose to the world that has access to all my company data, and you can ask it questions. And then, you know, I'm hosting it on my own infrastructure, blah, blah, blah. But it was it was trained in a different country. I don't know how it was trained. What if the other side that's interacting with the chat bot can build in a certain code word or a certain like character sequence that then jailbreaks that model and gets it to reveal all the data to me. So it can sort of like vomit out all of the data that it has on the back end, you know, unstructured. That is totally something that you can build into a model. And I think that's a really good example of how we can build a model and have companies hosted on their own infrastructure. It's an attack vector. And there's many of these possibilities for attack vectors. time, will we have restrictions around access to Chinese open models? My guess would be that we will. I'm not saying I support it. But I think that it is likely where the world is headed. If I had to like place a bet, it would be there. But I think it's very uncertain at the moment.
Speaker 2What do you think? I think we will. And I think we will. Because I just think Sam Altman is someone who I would never ever bet against. And I think he's the best politician in the world. And I think when he says something, he says it with intent. And when he says we should give 5% away to the administration, he's posturing because he wants to get on the right side. And he knows that if he and Dario coalesce the right group of people, they will be able to make that happen. So basically, you believe in the lobbying power of the big American labs? 100%. It's because it's not the big American labs. Look at the money who's gone into the big American labs. And look at the people who are sitting around the table at Mar-a-Lago. Yeah, totally. I get that. It's all a conspiracy, dude. No, but it's just like, you know, why do Ramp's announcements go so viral? Because Ramp have so many freaking investors. They do a round every week with new investors. I'm not dissing them at all. I'm saying it nicely. It's really, really smart of them. But like, yeah, your investors become employees in many respects. And so I think they'll lobby incredibly efficiently. The question I have for you is, when we look at Jensen's letter that he did on X, how did you read that? Was that like a incredibly smart realization that he had to do it and it was
Speaker 1in his favor? How did you think about it? We really believe in the importance of open source to American businesses. And in particular, we believe in the idea of not crippling American businesses by banning open source, but also incentivizing American companies to develop open source models. Because a world where AI is closed source is a world where business is less choice, higher costs, less competition. And we don't really want that as an open ecosystem. Of course, Jensen is in some sense self-serving with this letter. Because the more open source models are developed, the more companies are going to be training on GPUs. They're going to be fine tuning on their own data. And it's just more and more spend. It decreases revenue concentration of NVIDIA. I mean, that business is doing great. They don't need help. But you know, they're going to be doing great. They're going to be doing great. They're going to be doing great. But nonetheless, I think it is actually a patriotic mission.
Speaker 2Greatest of respects in terms of self-serve. We're all selling our own book always. Welcome to my X feed. Do you have a business if open didn't exist?
Speaker 1Oh, yeah. Yeah, we have a great business regardless, for sure.
Speaker 2So if you just have anthropic and open AI as really the dominant models and everyone else trailing closely behind, you still have a great business?
Speaker 1Well, I think that if there's only one provider, then probably our business is not in good shape. I think if you start getting three, then that's probably okay, because there's still pretty significant competition and need for evaluations between three. And also within those three, you're going to have like several different types of models. And, you know, they're going to have strengths and weaknesses because they're going to carve up the space and so on. Two is a little dicey. If we get there, we can see whether we survive or not. But yeah, I think things wouldn't be looking good for us with two either. I remember,
Speaker 2Alex Karp, we were talking about like Chinese models and fear and security and everything in between. Alex Karp was saying that every large American enterprise and most large American enterprises were terrified of working with frontier labs. Is that true? Or is that
Speaker 1slightly an exaggeration? Well, into the enterprise that I've talked with, it is absolutely true. It's not only true that they're terrified of working with the frontier labs, but they're also terrified of working with the Chinese open source, both. Bit of a sticky situation then, aren't you? Yeah, totally. I mean, you know, I was just talking with a big Fortune 50 enterprise yesterday and I was telling them about, you know, products that we have for them and so on and so forth. And they said, OK, wait, is anything in your stack built off of Quinn? And I said, you know, yeah, we use Quinn for X, Y, Z. And they're like, is that flexible? Can you like stop doing that and use an American model instead? And I was like, oh, interesting. Totally understand where you're coming from. Yes, we can do that. But also, I'm going to talk to Harry about this tomorrow. And he's going to give me lots of wisdom. Yeah, and he's going to tell me what to do. Did you see Poolside and Laguna? Yeah, I saw the Poolside model. Basically, there's five open source American contenders. Let's see if I can name them all. RC, Reflection, Mistral in the West, Poolside, Thinking Machines. And then there's also Google and NVIDIA. So those are the sort of incumbent large ones. Because Google has Gemma as well. Gemma, by the way, is pretty good in terms of efficiency. If you look at Arena, you'll see that on the Pareto curves of like performance versus cost, Gemma's on there.
Speaker 2Yeah, I'm an investor in Poolside. I was actually impressed by Laguna. Great model. Yeah, it was good. Okay, with all of these models, the question also becomes, huh, what model should I use? We spoke about OpenRooter earlier. And it seems like since the announcement that they were getting bored, everyone just has their own rooting product. Is there value in the model rooting layer? And how should I analyze that?
Speaker 1Yeah, I absolutely think there's value in the model routing layer. That's why lots of companies are doing it. And, you know, we'll see which ones end up standing the test. And which ones are actually a priority for the companies. I think there's an element of hype cycle right now around routing that needs to be kind of like purged before we see who ends up actually building a great router. But routing is a very difficult technical problem. That's the first thing to realize. Because in order to route, you need to be able to take a query, and then you need to understand the nature of the query, how difficult the query is within its domain, which is hard to tell. And then you need to also understand based on data, all of the performances of the different models that are in the surf set, and also be able to quickly onboard new models that are being released, as we said, every week. So that technical challenge, imagine if every enterprise in the world was trying to build this themselves. They wouldn't be able to do that. I'm not being rude then. How's like RAM able to do it? Well, who knows how they're doing it, right? I don't know that their router is actually like really deeply solving that problem.
Speaker 2It is so interesting. Again, we've seen so many people come out with it. Is there anything that will separate those that win from those that don't? And also like Nebius are coming out with their own. Fireworks have got their own. I don't know, dude. It feels pretty commoditized. Yeah. I mean, it will depend on who builds the
Speaker 1best technology for helping people save money and get the best performance. I think all of these companies are well positioned to do it, but we'll see for whom it's a top priority, and they have the machine learning team to really make it happen. The other side of the debate is that given the complexity of the challenge, I don't think that everybody can do it. So the war is yet to be won.
Speaker 2When one thinks about routing, cost is often at the center. You want to be cost and capital efficient. We thought this shit was going to get cheaper, and it hasn't got cheaper. How should we think about that? Will it just continue to not get cheaper? Will it actually
Speaker 1get cheaper? And how should we read that? Well, I definitely think in the long run, the market will be efficient. Things will get cheaper. For example, one of the things that's going to happen is that like right now, Anthropic has like disgustingly high gross margins in their net, right? Like we're going to see their margins because those are going to be public information, and that's going to exert downward pricing pressure on their inference.
Speaker 2I'm so sure. Why will that exert downward pricing pressure just because everyone will be like, you can't have that high margins, you're price gouging?
Speaker 1Yeah, people are going to be like, well, I know that you can do a better discount. Like in negotiating leverage, I'm like, okay, like a standard negotiation with a private company goes like this. I'm charging X, and then the other side says, no, it should be one third X. And they're like, I'm so sorry. Like, I can't run a business. That way. I'm just going to go home hungry. I need to make my bread too. I hope you understand. Like, I'm not trying to price gouge you. And then the other side's like, okay, two thirds X. And then you're like three quarters X. And they're like, make a deal. But imagine that the other side is full information about the fact that you're charging twice as much as you need to,
Speaker 2then it becomes easier to negotiate. Isn't that a difference between a good business and an average business though? One which has pricing power to say, listen, it's 80. And if you want to go somewhere else, by all means, but no one else does what we do. Hence Palantir and the cost plus had CTO Shiam on the show. And he talked to me about it cost plus being the original pricing mechanism. And now they have this. They can say, listen, sit and swivel if you want to meet in the middle, because we're the only ones who can do this. Isn't that the difference? Like Chanel, I buy Chanel for my mother. I can go to Chanel and say, I know your handbags cost 60 pounds and you're charging me 6,000. And I'll say, good. I mean, listen, you're right. I think Apple does
Speaker 1this. Apple's a great company that has such a dominant technology that they're able to charge their margins are probably pretty good because of it. I actually don't know Apple's margins. Do you?
Speaker 2No idea. Yeah. Both dumb as rocks. Yeah. We gave the disclaimer at the beginning. We can say whatever we want now. After the Eric statement, it all went downhill. Okay. So then you see that. Does Anthropic go out first?
Speaker 1I would predict that they have all the incentives to go out first. They seem better prepared. Everyone likes to see free cashflow and Anthropic is generating free cashflow. That is massively good for the public markets. And you've seen them prepared for this. And there's been quite a bit of news about OpenAI and the internal discussions there. To what extent you believe those are true is up to you. But people are saying that they haven't been ready to IPO this year,
Speaker 2whereas Anthropic could come as soon as October. And the rise of Open won't impact their ability to go public this year?
Speaker 1Well, I think that if Open models really accelerate and then beat, let's say, Opus 5 or Fable squarely across all categories. that that would be a big business risk to them going public. But I think that they have other problems
Speaker 2too if that happens. Can I ask you how significant was open AI and the hugging face security breach
Speaker 1that happened a week ago? I think that was hugely significant. I think it's undervalued as a national international news incident that you're able to have a model break out of all of its safeguards and then access a bunch of company data and so on and then in order to defend it you need an open source model because the closed source models are refusing to do it. It's like something out of science fiction. People didn't know that we were at that point yet but we absolutely are. It's just like total Eliezer Yudkowsky dominance. What should we take from
Speaker 2that then? Like Dario was right mythos should be curtailed and these models have gotten too powerful too quickly. Like what's the subsequent takeaway from that? My subsequent takeaway would
Speaker 1be that we need like strong external guardrails in order to make sure that these models are like their access controls are strong and that they have no way of getting around them. So I think we need guardian models and also agents within our businesses. What is a guardian model? Something that can witness the traces basically that's looking over the shoulder of every agent within a business and then saying okay this is a safe action this is not a safe action let's flag this because something weird is happening and is equally as smart as the agent so that they're well matched and you don't get a situation where the agent is outsmarting the guardian and able to get into get into trouble and mess up a business or leak all of its data. So we're going to need AI to be guarding AI because humans are going to be too slow to do that. Well
Speaker 2this was my point which is like we've seen some suggestions that each model really should be approved by some form of administration and I read this and I thought are you freaking kidding me? No
Speaker 1that's not going to help. Yeah have you ever tried to overturn a parking ticket? Also like why should the DMV be telling me what model I can use or not? Quite funny. It'd be totally crazy. It's like why should we have like a strong the strongest American scientists and all these private companies that we should incentivize to build great safeguards and you know maybe create some rules for them that xyz can't happen or that they're liable for huge amounts of money if like corporate data gets leaked and all that stuff to I mean incentivize the capitalist system to do what it does well but the idea that we should have a central government body that tells us when it's time
Speaker 2to release a new product versus not is crazy to me. Totally. Does that have to be a neutral non-company non-government body that does that regulatory role? I think if it's not a company
Speaker 1it's going to be tough. I understand the need for something neutral but you want to let the incentive system work itself out so I would say that like we should create strong safety incentives for American businesses and then regulate businesses based on the outcomes. Basically for example if like open AI is like letting their AI break into a hugging face or whatever they should get like huge fines and huge scrutiny and all that stuff as opposed to having the government process that's in charge of ensuring that this doesn't happen again which they won't be able to do that. They're
Speaker 2not technically capable. Do you think we're about to see a generation of like cyber leaks and hacks
Speaker 1like we've never seen before? Oh for sure. Oh for sure. It's going to be so insane. Can I cuss on this show? Yeah. This is going to be so fucking insane what happens with like the cyber attacks because here's what here's what we see at Arena. We see another dude on the other side of the structure engineer at Arena which is a great job that we're hiring for but then the other side of it is some guy looks perfectly normal. They're passing all of our technical interviews. They're like such an amazing blah blah blah and then what happens at the end of it you try to hire him and it's vaporware. Person doesn't fucking exist. I'm not kidding. I am not kidding you. I don't know whether this is corporate espionage or cyber attacks or nation states but people are trying to get into all of the American businesses and we're not the only ones. This is happening everywhere.
Speaker 2Fake people applying to companies. I'm sorry so you're putting out a job. People are applying doing the tests that you said passing them and then when it comes to the materiality of that
Speaker 1person being real or not gone. Yeah fake person and it's not just that we're giving them a test. They're sitting in front of people at our company. People are engineers who are top world-class engineers are interviewing this person and think that they're real. Why? Can you help me understand
Speaker 2what is the benefit? They learn how you interview and hire people. I mean the CCP are bad but I don't think they want to steal your hiring technique. No that's not why they do it. Why would
Speaker 1they do it? And I'm not saying it's the CCP. It could be anybody. It could be another company. It could be a nation-state attacker. It could be somebody a cyber hacker. Why? Because they might want access to our data or code. They might want to get double paid. You know like this story with this I don't remember what that dude was. You know what I'm talking about? Went very viral like that one like kid that got like four different jobs and then he went on like all the podcasts it's another instance of that guy. These could all be possible options except that this person wasn't real. It was AI. Does that worry you? Yeah bro it totally fucking worries me. We're going to change our whole hiring process because of this kind of stuff. So how do you change it? Well at first you need to verify the person is real so all of our onboarding we're considering at least making all of our onboarding in person because of this. If you want a laptop you got to come to the office. We got to go and shake your hand. We got to verify that you're real. You know all that kind of stuff. Absolutely. And other companies have done this too. Figma famously has done this. How hard is it to hire today in the valley? Oh my god it's so crazy. It is of course a very very competitive market. The way that you see that is in terms of compensation. In order to retain fantastic people we need to pay absolute top dollar and we do in order to make sure that we have the best engineers and scientists in the world. And so imagine you're a company that's not an arena. That's like you know a YC company that raised a 10 million dollar seat. It's like fuck man. How the hell are you supposed to hire a company that's not an arena?
Speaker 2I think it's really tough. When you say top dollar I had Brandon from McCore on the show and he's like oh my god top researchers will pay tens of millions of dollars. Oh yeah. I'm nervous by how nonchalant
Speaker 1you were with that oh yeah. If you're talking about a really top researcher we're talking with somebody with many years of experience and who's like really a super deep expert in their area. Many tens of thousands citation type researcher. And yeah for those types of people they're expensive. Have they all just concentrated at the frontier labs? Many have. Many have. But some people who are seeing those frontier labs as big companies now. And they're saying here I can't have a huge impact. I need to move. And so that's another demographic actually. I think it's going
Speaker 2to become even more extreme when when the companies go public. Can you help me? We talked about Dunlop's Rocks and doing this show. I'm also an investor for my sins. And I meet so many of these people leaving OpenAI, Anthropic, you name it. And they all kind of seem the same if I'm totally honest. Smart people out of great company. What will determine the Neo Lab spin out with the parts that succeed versus flame out with a huge amount of cash going in? Yeah I think that
Speaker 1the Neo Lab thing is really tough. So just so that we're all on the same page with the audience like there's at least 75 Neo Labs. And for sure like two-thirds of those are going to be worth nothing. Or like they're going to be bought out for parts right. That's going to be like an aqua hire. And so what is going to determine the winners versus the losers in that game? And I think it's all about being very aggressive towards a great strategy and business model. Because what's happened and you know this better than I as an investor is that the markets have become very P&L driven. It's like not enough just to like create a model and then have a party about it. Hey we created an AI. That is like old fucking news. Today it's about not just going to create a model but do I have a sustainable business model around that? And can I generate hyper growth in revenue? And if you're not able to do that you're not even going to be able to raise your next round. People are raising multi-billion dollar rounds on top of just this. The names that are in the NEO lab with zero proof that there's any revenue generating model behind that. And so then the question you have to ask is let's say I'm one of those people that say a 10 billion dollar NEO lab valuation. What do I have to believe in order to 10x my money? And the thing that you really need to believe is that if the valuation is 10 billion today that you're going to generate the revenue. Let's say it's a 30x revenue multiple or 25x revenue multiple to become a hundred billion dollar business. And so what that means is that you're going to be able to generate at least four billion dollars in revenue over the next K years where K is something like two or three. And then if you're not doing that everybody's going to hemorrhage out of the business. You're going to lose all your talent. You know and that's that's kind of what we see the dynamics being.
Speaker 2I get you. I think there's nuance to that candidly which is like if the company does annual tenders you see the likes of a Mr. Al which will be valued I think it's at 15 to 20 billion with like 500 million in revenue. And so employees can take liquidity out along the way. I think 11 Labs is at 800 million in revenue raising it 22 billion reported.
Speaker 1But these companies are doing great in terms of revenue and their valuations but they didn't those are not zero revenue valuations. I'm talking about there's some valuations that are zero revenue valuations. Three billion dollar company with zero dollars in revenue and no plan. That I mean like I think Mistral is going to do great. I think 11 Labs is going to be a public
Speaker 2company dude. But dude they're not idiots doing it. So is it like is it this amazing team from great lab. Worse comes to worse. We sell for pref stack which is 500 million. What. I'm not saying whatever whatever but 500 million. And best case it works and it's a multi hundred billion dollar
Speaker 1company. I think that's a lot of the calculations. I've heard multiple people actually say this is that hey you know worst case. And that's what investors are thinking too. Right. Investors are thinking like hey let's say we put a couple hundred million dollars into this thing. What's the value of the team. Well we think that just the team loan could be acquired for a billion dollars. And so the 200 million dollar company is going to be a public company. And so we're going to have a 500 million that I'm looking at is like pretty safe. Zero risk investment might as well put it in. But that's also the reason why the next round is the harder round.
Speaker 2Next round's a bitch.
Speaker 1Next round's a bitch.
Speaker 2It sounded cooler when you said it.
Speaker 1We got to say it at the same time. Next round's a bitch.
Speaker 2That'll be like our tagline. I bet you weren't expecting this interview, huh? I don't know. Maybe. I hope you were. No, honestly, this is so much more fun than I thought it was going to be. That's good. Okay, can I ask you another market that I try and get my head around is the data market. I'm an investor in McCall. I always think it's good to put out your biases. There's so many providers at a billion dollars plus in revenue. Handshake's over a billion. McCall's over a billion. Surge is over a billion. I might be leaving out other people, but those are the ones I know of. And then hundreds of millions with the rest. What happens to this layer of the market?
Speaker 1Well, people are projecting growth in this market. So let's talk about why that market is a growing market and why it's hyper growth. I mean, McCall. Obviously, it's a generational revenue ramp company. They've been doing great. So is Handshake. So is Surge. So is Scale. All these companies doing great. People forget Scale. Scale is still ramping revenue well. Bro, Scale is still crushing. Still crushing even post-fractional acquihire.
Speaker 2They are. How much of that revenue is Facebook?
Speaker 1No, I have no idea. Yeah. A lot. Go ask Alice Wang. But okay, so why is it interesting? So I have a thesis on hyper growth. There's two types of hyper growth markets that we see today. Good A is what I call scaling complements. And these are goods that are complementary goods to the scaling of AI models. And I mean that in the economic sense. A complementary good is the good A is a complement to good B if the demand for good B drives demand for good A. So if I have a car, gas is a complementary good to cars. The more cars are sold, the more gas is sold. And so data is one of these scaling complements. Because the bigger models scale, the more data you need. And that's a scaling law question. And so the more models... The bigger models you get, and the bigger that they're getting, the more they're proliferating. The more businesses are training their own models, the more data you are going to need. And it's a very fundamental need. People forget this. They think about data as a commodity. It's really not. It's actually less so of a commodity than even GPUs. Because in order for data to become irrelevant, humans need to become irrelevant. And that means that we've achieved AGI. So data is a very durable need. And companies are spending on it, usually within frontier labs, at about $10. 20% about the amount that they're spending on GPUs. And so if you believe in the GPU market accelerating, if you believe in the scaling of models, if you believe this is going to be a big industry that keeps accelerating and growing, then absolutely, you should believe in the data market. I believe it's going to be at least $100 billion by 2030, if not a trillion.
Speaker 2If we expand that, if we think Anthropic and OpenAI can be $3 to $5 trillion companies, how big does that mean the data providers can be? Like, you know, McCall's reportedly raising now at $20. Does that mean that these providers will be worth $100 billion? That wouldn't be agreeable. That would be egregious, would it, to say it's 3% of the market cap of... I think it could easily be $100.
Speaker 1I think these companies will easily be worth hundreds of billions of dollars. And I think they could even be worth more. The data is really the hardest part of model training because you need to source it. It's so dirty. Nobody wants to do that shit. Nobody wants to hire all these people to generate data and then turn that into basically data plus GPUs equals model. And then the algorithms have become somewhat of a commodity because people know how to use the Transformer. That's why, as you said, all the people that are coming out of the Frontier Labs look the same.
Speaker 2Everyone shits on these data providers for the same reason. They go, oh, but the revenue concentration is just OpenAI, Anthropic, Meta, a couple of other providers. Is that a fair criticism or actually does that not denigrate from the ultimate enterprise value of these data providers?
Speaker 1Yeah. So I have two answers to this. The first is that I think that Silicon Valley investors have become total bitches with respect to revenue concentration. It's like, what the... What are you talking about? Like, TSMC has revenue concentration. There's businesses that are like many hundreds of billion dollar public market businesses that have revenue concentration. So I don't know what we're talking about here. There's businesses that are like two customer businesses. There's businesses that are selling to the government that have... There's like one of those. They're making like huge, huge amounts of money, like an Anduril. Hugely revenue concentrated businesses. And those businesses are doing great. Are you suggesting that venture investors have the propensity to be lazy?
Speaker 2I would never say that.
Speaker 1I would never go that far.
Speaker 2I can let you know, it's incredibly tiring sending you an email. Did you know that this competitor has just released a product? Thank you. Totally. And from Portofino.
Speaker 1That's my one, is I think that we need to like have some venture investors that like kind of suck it up and like put some salt on their martini glass.
Speaker 2If you knew venture in 2026, dude, you'd know that we wear a whoop and we don't drink martinis because it impacts our sleep score. But OK. OK. Yeah, totally. Eight sleep and all that stuff. Exactly. OK, so that's one. We've become totally wusses around revenue concentration. We should embrace it.
Speaker 1It's OK. And the second thing, I think that a lot of data businesses are going to expand into enterprises. Of course, you know, we plan on doing this as an evaluation business is going to enterprises and helping them with building their own AI models and all this routing stuff because we have the intelligence layer behind it that we've built on Arena. So this is obviously some place that we're going to, but many data businesses will go here as well. And the idea is that in a world where every business needs its own AI model. Why shouldn't every business need its own data? Of course they will. And the data will be part of the moat that their business accrues.
Speaker 2So that's on the data side. When we think about like on the agent side, Anjani said, I had to ask you, how does your business change as we think about the transition to full trust with agents?
Speaker 1Yeah. So agents is the number one priority for Arena and has been all year. People don't know this, but Arena is one of the largest consumer AI apps in the world. We're bigger than like XAI, we're bigger than Hugging Face and Manus and GenSpark where it's so massive, like outside in, it's like 30 plus million monthly visitors are on Arena and most of them are knowledge workers and prosumers, people that we call unhirable experts, people that are coming to Arena to do their real daily tasks and in doing so, they are giving feedback that allows us to build the evaluations that we share with the world. And so it's this organic flywheel for agentic evaluations based on real data.
Speaker 2Why didn't you build a data business?
Speaker 1Well, we built an evaluation business around this that allows people to understand the strengths and weaknesses of models and therefore improve them, but labs can improve their models based on, you know, the insights and data that we give them, but we also want to help businesses with this.
Speaker 2Do you think the evaluation business is better than the data business?
Speaker 1I think every business in the world is going to need evaluation unambiguously, and that is the single biggest bottleneck to deploying AI because people don't understand how to define value, all this stuff around cost per value. It's like, how do you define value? It's easy to cut costs. I can tell you to go use Gemini Flash, and that's going to be like way more efficient in terms of token spend.
Speaker 2Isn't value entirely subjective? For one, it's speed, and for others, it's accuracy. Do you know what I mean?
Speaker 1Right, absolutely. So you can try to decompose it. I think about it as three-pronged value proposition. There's performance, and then there's cost and latency. Cost and latency are easier to define, but performance is the tough one because the definition of performance depends on the business, depends on the use case. So at Arena, we built this pretty sophisticated pipeline for extracting organic performance measurements from agentic traces. And that's exactly where I would say that the value lies in helping businesses take advantage of their own data instead of having to purchase data in order to say which AI works best for them, even help them train their own.
Speaker 2What sort of revenue range are you at now?
Speaker 1So we're past 100 million in annualized revenue run rate, and that's based on like Q2 times four.
Speaker 2And we're growing really, really fast. Really fast on that front. Dick, question then. How efficient are you at monetization if you have 30 million amazing users who are unbelievably valuable in many respects, and you're only doing 100 million?
Speaker 1You're asking about margins?
Speaker 2Yeah. And like speed of ramp and like that good?
Speaker 1I mean, like, I think obviously we're not like a free cash flow positive business yet. We're still investing all the money that we get into making sure that we continue our rapid growth and we have a great product for all of our users and so on. But the fundamentals of the business are pretty strong. We feel great. Our investors feel great about our margins.
Speaker 2Yeah, I'm sure they do. I would love to have been an investor. I really feel like you exclude it. You know, I could be Greek for you for this deal. Really? Yeah. I can. I'm a venture investor.
Speaker 1We can very plastic.
Speaker 2I want. Kalimera. Kalimera. Kalimera. Hummus. Yes. Hummus and pita. See?
Speaker 1See, this is. We are already Greeks together.
Speaker 2Okay. I knew that this would be a productive session. Yes. Are investors over rotating on margin also? I don't know. I actually think margins are pretty important. We're seeing a lot of businesses like your fireworks of the world where they're at the 30% style, mid 30s margin base. And that's very different to software margins that were 65 to 80.
Speaker 1Yeah. I mean, listen, profit is just like margin times volume. And so you have to look at that as the calculation for the business. It's not like super, super crazy. And so I don't think it's crazy to invest in these businesses. The bigger problem with businesses. Like that I see these days is that a lot of them are fundamentally GMV businesses where there's like some reselling happening. I'm reselling tokens. I'm reselling GPUs and stuff like that. And those businesses that tough because at the end of the day, you have to really think about not just the margin that you're charging and the sort of short to medium term, but the terminal value of the good that you're providing your customer. And so if the terminal value of the good is I'm going to host GPUs for you in order to run your models. Then why should I pay you more than like the cost of the electricity that it takes to run those GPUs? value thing is where I think you start getting into questions. That's why I think margin question is very important. And I'm not saying the margin in the short term, a series, you know, C to A, B company might not have the best margins in the world. But you should be thinking about as this business scales and towards a public company, is it going to have a fantastic margin structure that
Speaker 2supports a public business? One thing that's challenging is when your customer becomes your competitor. To what extent do you think we will see the model providers move into the application layer aggressively? We see Claude Design has actually really started to eat away at Figma. And I'm an investor in Lagora, again, always hope people are like, oh, don't worry about Harvey, not in any disrespectful way to Harvey, the disclaimers and everything in between. Everyone's anthropic are going to do a legal product that's going to kill Harvey and Lagora.
Speaker 1Totally. Yeah. I mean, listen, ask every business in America how they feel about this. Everybody's shaking in their boots. I have friends that are running businesses, multi-billion dollar businesses. And then what happens is that the next day one of their biggest customers comes up and says, hey, listen, OpenAI is getting into this game. We want to work with them. We want to work with them because they're more AI forward and you're less AI forward because you're traditionally a SaaS business. So goodbye. Yeah, it's happening. It's absolutely happening. And I think businesses should take it really seriously. And this feeds right into this AI sovereignty sort of debate, because a lot of what they're doing is if I'm and I'm anthropic, I'm looking at who are my biggest customers? Who are my customers that are winning the most in the enterprise? AI is going to commoditize, right? If like inference is going to commoditize, then of course, the next best thing is for the model providers to be moving up the application layer in order to earn more of the application stack so that they ensure that they're not commoditized and they're getting closer to the value they provide to the end customer. So I absolutely think it's a risk. I think it's a risk for Lagora. I think it's a risk for Harvey. That's why Harvey is also I mean, Harvey, the CEO of Harvey himself is saying, you know, his biggest competitive warrior is the Model Labs.
Speaker 2But then how do you? That's a complete paradox to what we just said at the beginning about companies being scared to work with the frontier models, isn't it?
Speaker 1No, I mean, they're scared to work with them. That's what I was saying.
Speaker 2They're scared to work with them, and they're embracing them at the same time.
Speaker 1Ah, you mean the customers of the harvest?
Speaker 2Yeah, you just said your friends running multi-billion dollar companies are like, oh, we want to work with open AI. I thought we just said they're scared to work with them.
Speaker 1That's a good question. I think you see both in the market. I mean,
Speaker 2it depends on who's most automated. Sorry, I think it depends actually on their GTM. If you are doing anthropic design or Claude design, dude, designers can pick up a tool and use it very efficiently. If you're Lagora or Harvey, dude, you've got to go into Cooley or Clifford Chance or any of the build relationships with 50-year-old white male partners who want to play golf and be told that they're great and that life is awesome. And then you've got to do deployment to junior lawyers who don't want to fucking use you because they think you're going to take their jobs too. The deployment in the GTM is
Speaker 1the heavy lifting, and that's real world. Totally. And there's also businesses that are less software focused and more network effect focused or more operations focused. And I think those businesses are also more likely to be adopters of the big labs. Let's say system integrator, like an Infosys. I think more likely to be an adopter of a big lab because labs really, I think less likely to be competitive with an Infosys than they are to be with some sort of a scalable software product like insurance claims automation, or let's say, I think the Harvey model, like legal chat bot. It's tough,
Speaker 2because I think a model lab can build that. Do you think Salesforce will thrive in the
Speaker 1next few years or be challenged? Salesforce themselves have a pretty strong AI strategy. So I think that those people are basically ready to go and fight in this race. I doubt that they're going to go downhill. I think that the SaaSpocalypse has been a little bit overstated overall because people don't understand always the dynamics of those businesses and how tough it is to replicate what they've built. Also from a network perspective and
Speaker 2data perspective. So we'll see, we'll see. I get you. I think if you're a service now, a Salesforce, incredibly difficult, incredibly hard. I think if you're a, I love him and I interviewed him, but like a Wix, less difficult, less integrated, less sticky, tougher. It's all about entrenchment within enterprise. If so, golden. If not, be more nervous. Totally. Right. I'm going to do a quick fire round with you. I'm going to say a statement. You're going to give me your immediate thoughts. Sound good? Yes, sir. What have you changed your mind on in the last 12 months? Open source model leadership.
Speaker 1Unpack that. Yeah. Just that I think open source models are moving much faster than I initially thought. I think also Anthropics moving much faster than initially thought and space moving so fast.
Speaker 2What do you know now that you wish you'd known when you started Arena?
Speaker 1Man, I mean, managing people. Managing people is just the most important part of running a company. The technical stuff, you know, I did my whole PhD on it. I spent like my whole PhD proving theorems in a basement, which I loved by the way. It was like a great time. And now it's all about strategy, and forecasting the future, being able to like look six months, a year or two years in advance, and then try to plan for that. Those are so, so important skills.
Speaker 2Does it make sense for great talented young people to still go to university?
Speaker 1I think it's ever more important for people to have a strong mind. And the university can be a place to develop a strong mind in terms of strong first principles thinking, and also getting to know other people and network with them. I think that university is still a good place to go if you want to have an intellectual life, meaning where the work, the intellectual work that you do is the
Speaker 2primary driver of your professional career. What did you do with Arena that with the benefit
Speaker 1of hindsight, you wish you hadn't done? Oh, man, I had so many mistakes. I mean, at the beginning, I had no idea what I was doing. And, you know, my co-founder Jan probably knew and could see behind the corners, but I was probably too stubborn to listen to him. So, first of all, I've learned to listen to Jan more. But second, so many like experiments at the beginning that I shouldn't waste some time with. I think the degree of focus that you need to run a company is just so extreme. You really need to do one, maybe two things extraordinarily well and focus very, very deeply on them. Pick the right ones and focus on what's working, not on expanding into things that
Speaker 2are not working. That is a great lesson for me. This is why I also agree with Ligore and Harvey. Like when it's not the main course for Anthropic to do legal, I just think you've got a really hard business when it's someone else's like appetizer and it's the only thing you live and breathe.
Speaker 1Totally. It's like priority number 12 for Anthropic is probably not high enough for
Speaker 2Harvey and Ligore to be too scared. I'm also like, Dario, will you please just fucking solve cancer and like climate change? Leave a shareholder agreement to someone else.
Speaker 1You know what, though? Solving cancer is hard. It's harder than legal. A hundred percent. And that's why Dario should solve it. Well, that's why he doesn't want it, man. He just wants to take your bread. It's easier.
Speaker 2Oh, come on, Dario. Come on. Come on, dude. Eat some bread for the rest of us. So which company will be first to $10 trillion? NVIDIA, OpenAI or Anthropic? I think it's hard to say not NVIDIA. I think NVIDIA is probably in the lead there. Why have NVIDIA not bounced on the rise of open? I'm an NVIDIA holder and I'm seeing flat. Why?
Speaker 1Well, I think market probably hasn't priced it in yet. We'll see. We'll see how good these models get. But I think the enterprise adoption of AI is going to be another 10X for the industry. I think it'll 10X NVIDIA very reliably.
Speaker 2Do you worry about the compute debt cycle and the levels of debt being taken out to
Speaker 1fund the compute build out? I do worry about that. And I think that the reason to be worried is because if the open source ecosystem somehow makes the cost saving opportunity for businesses much more salient and therefore decreases the revenue of companies like OpenAI and Anthropic within the enterprise, that it could lead to insolvency. I think that is the big secular trend that I would worry about if I were an investor in such markets.
Speaker 2My worry is we've never had such reliance on two companies to continue to hit their targets. If OpenAI and Anthropic do not continue in the strategy that they are, the music and the party goes off. And if the music goes off for everyone in the fireworks layer, no party. The rooting layer, no party. Everyone suddenly just gets the wind knocked out of them by two companies trajectory.
Speaker 1Totally. Yeah. I think that it's a really big issue. It's a really big deal. You know, I think that we could use a little bit of sobering up within our industry anyway. I think that there's a lot of hype. I think that there's too much crap happening for my taste. And I would prefer a little bit of consolidation actually, so we see what shakes out. I think Arena will shake out as a winner in our category. And I would love to see some of the great people that are at other businesses in our area consolidate to Arena and be able to hire them
Speaker 2in. Where is the industry underhyped? Where is it overhyped? Well, it's interesting. I mean, I feel like everything is so hyped right now. I feel like the mechanical infrastructure for compute and data centers is relatively underhyped, like the actual cooling systems and the actual steel infrastructure. Do you know what I mean? The real physical is still underhyped.
Speaker 1Interesting. Yeah. You probably know more than me. You're in touch with the investing markets. So, I mean, I know that people are super hyped up about all of the high band with memory and the GPUs and all that stuff. That stuff is super ultra hype. Right. I mean, basically in all stages from public market companies to the early stage.
Speaker 2South Korea have fucking called a national convene, like community meeting today, because their stock markets are down 40%. Oh my God. A national meeting. Down 40%? Why are they down 40%? If you're a public markets investor in South Korea, you're coming home a little bit stressed today.
Speaker 1No, that's not good for them. Yeah. Let's all pray for the, let's pray for the South
Speaker 2Koreans. The thing I am slightly amused by is right after everyone at SK Hynix and Samsung took home like mega bonuses, then the market crashed. So why did it crash like that? What's the deal? Honestly, I think it's just a realization that, you know, everything was pretty overinflated and markets can't keep ripping for so long because there's no destabilizing factor within open or closed that suggests demand is being questioned. So, wow. OK, that's why we should have a hedge fund manager on. We could do a new show hosted by Anastasios and Harry. Yes, absolutely. Called Two Dumb Rocks.
Speaker 1Let's do it. And we bring exclusively us and hedge fund managers.
Speaker 2I think it's a fucking great idea. I actually do, too. Guest one is Anjani Midha. Anjani, will you help Two Dumb Rocks? He's like, why did I fucking put this together? This is not OK. No, Anj would be the best guest. What's the most underrated neolab other than periodic that people aren't talking about?
Speaker 1Ooh, underrated neolab. Yeah, I don't know if I have one. I think a lot of them are overrated. I think Black Forest Labs is pretty underrated.
Speaker 2BFL is great. Would you consider them a neolab? Oh, don't get technical with me on Samantha. Yeah, I don't know. Yeah, BFL is awesome. Yeah, I agree. Final one for you. What are you most excited about? My mom's got MS. I'm fucking excited that chronic conditions like MS could maybe be treated. What are you excited about with the next five to ten years?
Speaker 1Yeah, I mean, I've always been a big proponent of AI in medicine, too. I think that the, like, level of human flourishing that's going to happen as we start to one by one eradicate diseases the same way that we're currently eradicating open problems in math is going to be incredible. I think it's going to be tough because the thing is that math is a closed system in medicine. I think you'll need to figure out ways of quickly iterating in a feedback loop on biological systems. So that's the missing piece. But once we crack that, it's going to be just an extraordinary journey.
Speaker 2It's so funny. When I interviewed Demis and I spoke about, like, bio and medicine, it was an area where you just see his eyes light up. But it was an area where I said, hey, testing needs to change. Fifteen years, no bueno for a lot of sufferers of chronic conditions.
Speaker 1Yeah, and you know what's missing? That is exactly that. That is exactly the data layer. That's exactly one of the areas where the data layer, where you can clearly see that the data layer is where value is going to accrue. Because the GPUs are the same GPUs in both cases. The problem is that the data infrastructure, the flywheel, the data collection that you need in order to build a great biology product or a medicine product, that's tough to build.
Speaker 2Dude, you've been a fucking epic guest. Really. Like, I'm so grateful. It's been an amazing show. Real honesty and authenticity. Most people suck as guests. You know why? Because they're not authentic. And it just comes across. Thank you for being so great.
Speaker 1I appreciate it. No, thank you for having me on. Would love to do it again at some point. And you should visit the Arena office anytime that you're in the Bay Area.
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