20VC: Will OpenRouter Sell for $10BN to Stripe? | Why Chinese Open Models Are Beating America—and What Happens Next | Why Enterprises Are More Fearful of Anthropic and OpenAI Than China | Is the Routing Layer Becoming a Commodity with Alex Atallah

This is going to be like the biggest, biggest market in tech ever. A lot of companies are making routers because it's fashionable. The Model Apps have several incentives to go after you, eventually. In July, we launched 70 models, about one model every 10 hours. America is very, very behind still. But GLM 5.2 was a really big, big step for open-weight models. There are reports that you are selling to Stripe for $10 billion. Is that going to happen? This is 20 VC with me, Harry Stabbing. So now, the only thing that I really care about anymore is providing the best, most relevant interviews at the right time to you. So today, we have Alex Attala, co-found and CEO of OpenRouter, the gateway to the world of LLMs. They reportedly have had offers from Stripe for $10 billion. They've raised at a valuation of over a billion and a half. They are the market leader. And this interview could not come at a more pressient time. It'll be very interesting to see whether the company chooses to stay private or sell to Stripe. We shall see, but this interview was recorded before. So we will check back in a couple of weeks. This was an incredible show and it was awesome to have Alex in the studio. But before we dive into the show today, founders face a different set of challenges at every stage of growth. For Sid's shade, 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/grove. And if you're running a business right now, you already know this pain all too well. 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Tap capital that grows with your revenue and pay vendors in 170 countries across 32 currencies. Plus the whole back office. Bills, expenses, accounting, all in one place. So you spend less time reconciling and more time growing. That's why thousands of owners use flex, named one of fast companies' most innovative companies of 2026. Visit flex.1. That's F-L-E-X. O-N-E and use the code 20VC. You have now arrived at your destination. Alex, I am so excited for this dude. I have wanted to make this one happen for a while. I've heard so many things from Matt at Manlo. I've stored the shit out if you're speaking to Anjani. Even your roommate before this show. So thank you for joining me, dude. Thank you. It's great to be here. Now, I want to start with a little bit pre-openruder and start on OpenC. It was a pretty incredible journey. What did you take with you to OpenRuda, having seen all that you saw with OpenC? Yeah, so OpenC started as the first NFT marketplace. Similar to OpenRouter, it was very small for a long time. We kept the team very small until the series A, roughly, a little bit afterwards. And this was before AI. Right after NFTs started blowing up in 2020, October of 2020, we were like, "Oh my goodness, we are understaffed. The servers are melting. Our search index was exploding. We had a couple big outages. It was tough to keep the site up." And it was like, "Oh my god, we're going to become the Twitter fail-wheel, but applied to crypto." My biggest goal was to not be the Twitter fail-wheel for crypto. And it took a little bit to create the team, get platform and infrastructure under control, make sure we could predictably scale. In other words, do load testing to help the sites sustain Tenex load, even when we weren't seeing that load? Because with crypto, you just don't know. There were like these moments where we would get these incredible traffic spikes. And it would be very dependent on the content and the community. So I built like a lot of infrastructure and scaling responsibilities then that I took to OpenRouter and spent a lot of time like thinking about, "Okay, how do we make something that is going to basically be always up?" And that people can really count on from an infrastructure point of view, even when there are huge surges in tumultuous markets. Which has been very helpful for AI, of course, because like all companies, especially in Thropic, have seen like unpredictable growth. And we have as well. And we've had like a couple bumps, but overall it's been like significantly better. And like OpenC just kind of like drilled that into me in a way where I could like take it productively to OpenRouter. Can I ask you, when you go back to the founding thesis of the company, what has happened in the ecosystem in the model landscape that you did not expect to happen? Well, one thing that we did not expect was that an ecosystem of companies would emerge to host and serve the open-weight models. Like early on, it wasn't clear that that market wasn't going to be a monopoly where like just the three hyper scalers serve all the open-weight models and startups don't, you know, they're really far behind. In reality, you know, how often do you hear people running GLM on a hyper scaler? Never. Like they're using, but the inference providers like fireworks and together and there's like big lists that we see doing the best job of hosting all the open-weight models. In the early days, we had, I think we called it Provider 1 and Provider Fallback. We didn't like show which providers were actually doing the hosting. Because we weren't really a marketplace. We were kind of an exploration tool for like finding and discovering new LLMs and we wanted to build like a marketplace of model labs, but like the inference provider layer, we weren't sure would actually be a marketplace. And it turned out that those companies were doing a way better job in the hyper scalers. We're way faster to host the models and figure out these edge cases to hosting them. And uptime was just going to be a constant problem. It wasn't going to like magically get solved by the supply side of the market. A lot of people suggest that that inference provider layer is a commoditizable elemental layer that will be removed or see margin reduction competed out over time. What would you say to that theory? Right now we're in a massively supply constrained market. And it's likely going to be supply constrained for a while where all the inference providers are short, pretty much constantly short. And you're like, okay, so GPUs are really, really beneficial. And like why doesn't Google or Amazon or Azure run around and like buy up all the GPUs and take all these inference providers out of business? Well, the people making the GPUs don't want that. Like one of the Nvidia's top priorities is not having customer concentration. They want lots of customers to all have like separate allocations of GPUs. They want the heterogeneity of the market. They want like competition on the compute layer. And this is good for the ecosystem. Like like users also want this. It's good for Nvidia and it's good for end users as well. It like allows these inference providers to kind of like come up with new innovations on like how to serve the models better. Even a single model like Kimi K3, like moonshot just posted a benchmark showing all the inference providers and how well they're serving Kimi K3. And they're pretty different numbers for benchmarks that are really static that are well known. We post this continuously all the time. We always are like benchmarking all of the models on all of the inference providers, all the open-weight providers, and finding really different results constantly. The results change over time. These models are like very, they're very emotional. They're very non-deterministic. I had a link on the show from Fireworks and she said that, you know, I said about Gavin Baker and a token is a token is what he said. And she kind of corrected me that a token is not a token. Actually, because one provider can make a token go so much further than another token, it's like how do you get to the store where you can drive around the whole block or you can drive straight to the store. Tokens can be made more efficient and go further. And that's the job of the provider. Yeah, I agree with that. I think that in some ways we are providing a service to help people discover providers. Ultimately, when one provider is making a token go further, we spend an enormous amount of time on our router, central router tech, so that that provider immediately gets more traffic. As soon as we detect that, like, there's a quality improvement or a speed-up, or a price reduction happening, immediately starts getting more traffic. The stuff happens like 24/7, every five minutes. There are big changes for the big models, and so it actually does make the experience better. - You can only invest in one inference provider, which one do you invest in? - I probably have to state neutral on this. I really like the inference providers that are doing custom hardware and very, very low level optimizations. I like providers that are also trying to figure out how to make customization easier. Today you find two models, and you create this new, fully independent model from the base model. Many inference providers are creating these loras or some call them cartridges that are much more portable, potentially between models. And we might see a future where when you do a fine tune, and you want to change the base model layer, it only costs maybe a few hundred dollars, maybe a few dozen dollars to change it. - It's okay, I understood the firewires, is your favorite, it's okay, I go. Mine too. My question is, when it was on the show, she was like, "Oh, you don't want to rent your intelligence, you want to own it, and we're going to see companies have specialized models, which is trained on their own data and proprietary to them." In a world of every company having specialized models that's really tuned to them and their preferences, is that good for an open router business or not? - Oh, definitely. I mean, why? Because you'd stick on one model, which is yours, proprietary, trained on yours, and not be open to the diaspora of models that is available. - No, I disagree. I think our mission from the very beginning has been to increase neurodiversity in AI for the whole ecosystem. And we really believe that a multi-model future is inevitable. When you start, let's say there's one model that like, hypothetically, let's say you're right, there's one model that fulfills all of your desires, either within your company or as a consumer. More and more people start using that model. And then someone decides, "You know what, I'm going to create a neurodivergent model." I'm gonna create a model that's like a little bit different that talks a little differently, that has ideas that the first model I could never have come up with, 'cause it's like completely different data that's being used to train it. Then it creates inevitable demand to use both models. But creativity is not verifiable. Like you can't really put an easy number on creative ideas. And when you use two models together, you're more likely to get creative ideas than if you just use one. It's just if that other model was trained in a different way on a different data set or has it made a big update, consolidation on one model just seems like it just doesn't make any sense to me. - Totally get you. So you'll have companies which have a core workflow or their core, which is their own specialized model. And then they'll use a plethora of other models and they'll use OpenRuda4, those other models selection. - Yes. And I think that when companies make, like to get back to your question, when they make their own model trained on their own data, the ecosystem around you is all doing the same thing. You have to like play out the game theory for these things a little bit. Like if everybody is doing this as well and all the model labs are creating new models constantly, using new data that they've acquired, that they've bought from other companies, that's all potentially data that's valuable to you. What is in your best interests? It's to go and try out those other models and see if you can be more productive with them, if you can merge them together to get better state of the art performance, if you can reduce your cost using these other models, whether your goal is to improve your margins or grow your company, you are incentivized to go use what the ecosystem creates. So the model that you made, you're gonna have to continuously improve it to keep up and it's never gonna win the whole market. So it's gonna be a massive market. It's gonna be like the biggest, biggest market in tech ever and biggest market probably in human history. No one's gonna win all of it. You're not gonna build a model that wins all of it. So you might as well build a model that like is known to specialize in something very useful and that's very important to your company and your business and be known for that specialty. And I think a lot of enterprises are gonna move that direction, make their own models, make their own branded intelligence. Your brand is a big part of your moat and that model will like be aware your brand carries around. You mentioned the immense time that you spend on the routing technology that you have. A lot of people are thinking that we're seeing the commoditization of the routing technology. You're seeing a ramp release product like this. I mentioned the idea of merge company. We invest in its release that product, several releasing kind of routing technology similar, what kind of means to be similar. Are we seeing the commoditization of this layer? I think a lot, yeah, a lot of companies are making routers because it's fashionable. They're seeing growth happen here or they're making gateways at least. There's two issues with that. First, it immediately puts you in the mindset of copying instead of like winning something. You're playing to play or you're playing to exist rather than playing to win. And maybe you're just trying to like play to serve your existing customer base. And you want to see some AI growth happen. I think immediately kind of like puts that gateway, many, many months behind the companies that are fully focused on it. Like I am 100% focused on building the best router and gateway and LLM marketplace. And it shows in our product and the benchmarks that we create internally and how we see ourselves compared to the competition. This is not a side quest for us. Like it may be for some other companies. The other problem is that it reduces the leverage of all of your users. Like I really deeply believe in giving users and developers more leverage. Fundamentally, giving them access to more models is about giving them more leverage over all the innovations that happen in AI. You want to be able to like access the mall. You want to reduce your dependency on any individual one. If you build on top of a router or a gateway that doesn't give you access to the full market or full flexibility or full customizability, it doesn't give you like the full leverage of the whole ecosystem. Then you're kind of like being cut out. You're cutting out all your employees at your company of things that they need. And so like open routers fundamentally about giving people more choice because that gives them more leverage. You do that at a price at 5.5% take. That was sort of our pay go plan. We then added an enterprise plan with like a totally different pricing model. And it's been very successful so far. It's kind of based on like committed spend and then you know no fees on that committed spend. - 'Cause that was gonna be my question. Ultimately companies will like love it small and then as you scale, you're like, "Shit, this is really freaking expensive. I'll just build my own routing tag now because it's become such a significant part of my cost base actually." - I mean, I kind of figured like some of those companies just haven't realized we have like an enterprise plan. And some of it is like our fault for not having like a better, I think more detailed pricing model. Or soon gonna introduce like a kind of a business self-serve plan that also just makes it make a lot more sense. And if you have your own inference, like if you bring your own inference to open router, if you bring your own keys, that fee goes away. For inference that we are providing you, like when you go into open routers capacity and you're not on our enterprise plan, that's when that fee comes in. Otherwise like, you know, we need to be able to like predict demand a little bit. So that's why we do these committed spend. - What will be the main revenue line of open router in three years time? - I think it's gonna depend on the economy in so many ways. Like if the overall AI market keeps growing the way it's been growing over the next four years with like 10 to 15 X every year or potentially more, it's a lot of growth. I think under that world, I would expect people to continue to underestimate how much inference they're going to need. And thus our revenue is gonna be dominated by you know, the same things that dominated it today, which is like us helping people with an unplanned inference capacity, both enterprises and startups. That's what open router is best at. Like when you need to try models that you weren't expecting, you need to try when you're like using more inference than you thought you were going to use on particular models. Like we make sure that that is not going to be an issue for your company by providing the best failover and best uptime. And this is really really a good thing to do when the market is like continuously under estimating its inference needs and growing at this rate. If this growth rate continues over the next four years, it's going to be a wild amount of growth and the economy has some limits to it. I can see like major SMB SaaS like growing for us. You know, if growth like does not keep going 10X, what 15X per year. - We've seen token prices for 90%, you've all taken in like 18 months. Is the reduction of token prices helpful or hurtful to your business? Cause obviously you have a take on spend. If they come down and spend is more efficient, seemingly it's bad for your business. You have a shrinking pie to take from. - Well, a lot of people talk about the Jevons paradox that when prices go down by 10X, the usage increases by more than 10X. But like no one has really done a great job modeling it. We do have a lot of spot stories that confirm it. For example, GPT 5.6 Luna on OpenRouter. Open AI cut prices by 5X and then in coordination with us by another 2x. So in total, the price of Luna has dropped 10X on OpenRouter over the last two weeks. Guess how much usage has grown? 13X. So it's a close to perfect Jeven's paradox story where you drop prices 10X and usage grows by more than 10X, just a bit more. And also the usage is pretty stable. It grew, it flattened out at 13X, and then it's been kind of growing at the same rate that it was growing before it hit the 13X multiple. So that's pretty interesting. And it's a pretty low variable-- there are a few other confounding variables in the story. And it was also done in the middle of deep-seek launching and having a really, really good price and GLM, having a really good price. Now, Luna is being used more than GLM on OpenRouter. GLM used to be one of the top three, four models by token volume. And now Luna is past it. This is the first time opening I has had a model on our platform in the top three to five models by token volume in an extremely long time. So it was a really big and interesting move. How reflective of the market are your token volumes? Because it's about-- I may get this one about maybe 1 1/2, 2% of say token volumes. And so how reflective are they? Because a lot of people when I say, oh, the top five models when I look at OpenRouter are all Chinese. What does that mean? They'll go, oh, well, how are you? No offense to OpenRouter. But it's not reflective of the market. And most people who use Frontier, it doesn't go through that. They use Frontier APIs. And so it's not counted. What extent are your rankings reflective of true token usage? Yeah, it's a really good question. We try to estimate how they're off by just serving people sometimes or looking at the surveys other people have done. We definitely have a bias to people who believe our thesis, which is that the future is multi-model. And companies who want multiple models. And there are still companies out there. I basically rarely, very rarely run into them now. But there are still companies out there that are just like, oh, yeah, we're in OpenAI shop. We only do OpenAI models. And so we're not going to see any of those companies. And I think those companies are primarily focused on the other hyperscalers, OpenAI, Anthropic, and Gemini. So we do probably undercount the Frontier models. But I think over time, our thesis is becoming more and more common to see in other companies in the moment that they're like, oh, yeah, we need to use other models. Then our data becomes more representative. And as we scale up, the data becomes more representative in general. So my hope is that it just becomes better and better data over time. I like the columns that on CNBC and his role are wonderfully energetic way that companies are terrified of working with Frontier model providers. Do you think they are? I haven't seen what he talked about there when I talked to our customers. But there was definitely a little-- there was some skittishness that particularly when Claude design came out around Figma. And that part I did see. And I do think that there are real concerns. Like Figma is very different. But if a startup is only building a go-to-market wrapper around intelligence, like, hey, we are a company that brings AI to this market and does so by doing the right integrations and customizing the system prompt. You're going to be fine if the model labs don't care about that market, which there will be many markets like that. But the model labs have several incentives to go after you, eventually. One is getting multiple teams within companies they do care about to be dependent on them. This is my theory behind why Claude design was strategic. Well, it's not like a massive amount of revenue for an anthropic, not probably a significant amount of revenue. It does get the design team to really care about anthropic models. And so the companies that they want, they now have another team that really wants to stick to anthropic. So that team strategy can make you compete with the model labs. And so I think companies like that that find themselves like, oh, we're building a product for a team that has now become strategic for the model labs for companies they actually care about. That's where I see probably the most near-term threat. Do you think Claude design will have a meaningful impact on the Figma business? I speak to many founders today who are bluntly switching from Figma to Claude design. And it's kind of realizing that Figma usage. Do you think that will happen? So I saw a lot of designers try out Claude design, including our own. But so far, I haven't heard of the repeat story. I don't know. Honestly, I have not talked to very many designers about this. I certainly haven't heard a lot of chatter about Claude design. And if you just look at the numbers for Figma, they're quite good. They had a very, very incredible earnings. This is why you don't want to be a public dude. You see great numbers. Figma down in my portfolio. Like, what? What? Yeah. That was crazy. Do you know what I'm going to say? We're going to come on. We were talking about the different models that we have on offer. And where the companies are willing to work with frontier models, the rate of model development feels immense. Do you think we will see the same rate of model development continue over the next year, two years, three years? Frontier model development or a general model? A general model. That was a year until you opened it. Yeah. Just because I mean, every single day, there's two, three, four new models. In July, we launched 70 models. About one model every 10 hours. There's some agent lab starting too that are all going to probably make models eventually. Like Jeff Dean is starting an agent lab right now from Google. The companies that are known for making agents have an incentive to create their own model a very clear incentive to create their own models and distribute it through their agent. And we haven't even seen the start of that. Sorry, we've seen the start of it, but we haven't seen it really pick up. Like cognition has a model, cursor has a model. Does it loveable have a model yet? I don't think so. Not publicly. Yeah. So the agent labs are going to, I think, develop models this pressure from both the GPU makers like Nvidia to create more competition in the space and create more diversity in the space plus us, plus investors who just want to try new things that all could like improve intelligence in some neurodivergent way. I think those are those are strong incentives. I think that there are enough to like incentivize more founders to make meal labs. And if like, if American open-weight models pick up in steam, then it gives these meal labs a base to train on. That's not Chinese, which will then probably create more American meal labs. Do you think we should be concerned by the rate and quality of Chinese open models? We should work behind. America is very, very behind. Still, I think things are picking up. I think we have poolside, we have thinking machines, we have RC. Do you feel the sense of responsibility for that? And what I mean by that is, you are a routing business and you could route a company to a Chinese model that who knows people are worried about back doors, CCP involvement, you could be the deliverer of that to those models. Do you feel a sense of responsibility for that? So we do feel a responsibility to have safe access for all of these models. Like customer trust is like our paramount goal. If one of these models is unsafe to use, generally consider unsafe, we pull it from the platform. If there's a way to use it in an unsafe way, I mean, there's a way to use all the models in an unsafe way. And then we believe in using technology to make it safe and to work with the model labs themselves to figure out how they're doing it on their side so that we can be state of the art or better. We spend an enormous amount of time making sure that our practices match with the best things that we're seeing coming out of the labs or better. Because we're a way of exploring all the models and finding them for the first time, we're a good focal point for deploying safety measures across your whole company. For example, we have prompt injection protection. You can just turn it on and immediately flag prompts that look like prompt injection. That's trying to happen. We have PII reduction. We have a couple different things that you can automatically just turn on with a click and get an added safety layer on top of all of your inference. And we build that so that enterprises feel like they can safely deploy new models and that their employees can try them out. I think of the models a little bit like the internet. You can't just ban the internet at your company because there's some bad things on the internet. You can create guard rails and you should. You need to use AI to build the best possible guard rails that you can. I'm with you, but do you think you actually know what's going on within MoonShort or Ali Barbar with Quen? These are incredibly secretive organizations in the depths of China. Can pretend I know what's going on inside of them. There's a US company. We're going to follow the best practices of what happens in the US to make sure that when I'm doing something irresponsible. What do you think US companies are more nervous or frontier models or Chinese models? I think they're more nervous about frontier models usually. Part because there's just like a much more confusion around the data policy, but what's like actually happening to the props that I'm sending and where they're being stored and how they're being looked at. And you can't run them on your own machine or in a provider of your choice. And so that just immediately creates all of this uncertainty in a lot of enterprises. And it's uncertainty that they can also pattern match. It's very similar to like, you know, running on their own in for a versus running in their VPC and knowing like who can see the data. How extraordinary is that? Like they're more nervous of like US companies had courted into the company where you can see and touch and feel the headquarters and the leaders. It's just like what a strange world to be in. Yeah, it is very strange, especially with the frontier models having the biggest cyber posture right now. I'm like, what do you make of every company kind of posturing? We have someone. First, you had an open air, then you had an anthropic and then you had a zuck coming out. I don't want to miss the party. We did too. Yeah. Well, I think they have to talk about it. The right thing to do is to reveal when there's been a cyber incident involving your model, covering it up, doesn't work. It's not going to work in the long term. And it certainly looks like they're all bragging about it. But really if you were in their position and something happened with one of the models and you had to make the choice about whether to publish it or not, I think that's the right thing to do is to publish it regardless of how people are going to spin it. So I doubt that they're actually thinking of the felony bench or whatever it's called. How significant was the latest Kimi model which got so much attention? Was it as significant as everyone thought? It's quite good. It's not cyber-capable in the way the same way the frontier models are and long horizon tasks. I think it's still a bit behind the frontier models. But GLM 5.2 was a really big, big step for open weight models. Kimi was kind of like moonshot getting up to that step. That's a little bit how I see it. And Kimi is also a very good writer. Like the voice and tone are both pretty good. Whereas like some of the frontier models have like voice degradation then happens when they get better at coding especially. And it was like, oh my god, I can't read this output anymore. The output sounds like three of the four arguments you made are right. And one is a turning point. You know, and here's the rub. It's just sometimes just impossible to read what they're saying and this stuff is fixable. But Kimi I think has always had pretty interesting writing. In 12 months, well the chasm between US open source and Chinese open source, the bigger or smaller than it is today. Then my fear is that it will be bigger because when you have deep seek it becomes a national champion in China. And I mean Xi Jinping is going, this is our AI horse. I will concentrate all of my money and efforts behind this and I will supplement this ecosystem to the end. This is the winner. And then when you see another moonshot come out suddenly all regulation gets moved to the side, all policy gets pushed aside. All funding becomes available. Everything is allowed. You are free to run. And these guys are unabridged in their ability to do whatever they want to get to the angle. Whereas open AI and anthropic and all the other providers in the US especially open source. Fuck you got try raising billions of dollars for a US open source model bit tough. Actually, not impossible at all, but tougher business model questionable AI researchers super expensive and you're competing at so now anthropic. I think the comparative landscapes they sit in mean that the Chinese open source providers are just inherently advantaged. Sadly. They have like very, very good researchers and I think Americans underestimate that a lot. I do think they're going to be concerned about the cyber posture of their models and they do seem very concerned about like censoring the models and censoring the information that the models can provide to people. So while today people complain about American models censoring more due to cyber, I'm not sure that's always going to hold. And as like the Chinese models like grow in importance for China, what are they going to do? Are they going to like drop the great firewall or are they going to like give up on putting the firewall around the models? Like I don't know that much about China, but it does seem like kind of strange that they don't seem to care more that the models or I've never seen anyone do a profile of like what you can do with deep seek that you can't do with the internet in China that's available to you within the border. Like what information you can access? I've never seen anyone kind of like do a real deep dive. Like how far past the firewall does deep seek go? If the firewall matters to China, if it's going to matter in 10 years, like something's going to change. Well, it's in chains like obviously the abilities of the Chinese models outside of China are immense. The abilities of the Chinese models inside China is actually relatively limited. Either the guard rails, the guard rails are in a friendly stringent and prohibitive. It's ironic that they are incredibly superior to us. Shit domestically, terrible. Interesting. Unless you just had my dear friend Jason Lankin, who runs SASTA come back and be like couldn't figure out what time Starbucks opened on deep seek. Like what was an on offer would say like not allowed. Wild. Very basic rudimentary requests. We're speaking about all of these different models. And the thing I think is like about loyalty and you have this incredible seat in the ecosystem where you can see everything. Do we see any developer loyalty today with models? Honestly, we do see some. We try to make switching costs close to zero so that when new models come out, people can try that out really easily. But we also measure retention and churn from all the models. We share this data with model labs too when they ask for it so they can know like no, for my model that just came out, which models drove traffic to it. And like for those users, like when they leave, which models are they leaving to and we'll like make this more and more available to the world soon. And we do notice in the churn day, there are developers who kind of like continuously stick to models. And when there are better models out there, better models for their use cases, I think it's a combination of like a couple probably root factors. One is my app works and I don't want to break it. You know, if the support bot starts saying something weird that I didn't expect, why add more headache? I've already done all this optimization and like I've already put all these car wheels around it. Another is new models are not necessarily going to make your pricing better. In fact, in general, what happens is that the current models like price goes down over time. And especially when new advancements in the labs happen, you'll see like intelligence jump, but like the price curve like also jumps and then we'll start going down over time. So it's not necessarily the most like price effective thing to do to like shift over to the the newest model, even for open weights. The third reason it's just fundamental like trust in the outputs. Like if I'm using a model to do my work and I like the way it talks, I probably have like some eval, like a personal evil. Well, a lot of people have these personal evals that are just these random tests that they give the models. And if the random tests doesn't look really good on the new model, it'll just be like, "Good. I liked Kimei K2.6 anyway." People thought before that memory would be the retentive mechanism. Well, OpenAI has all of my previous query, like prompts. It knows that I live in London, I do podcasting and that will make it a better model for me moving forward. Is memory no longer a retentive mechanism? Memory is really interesting. I've always thought it like is a retentive mechanism and the question is where it lives. Is it going to live with the model? Is it going to live with the inference provider? Is it going to live with the app? Is it going to live with the infrastructure provider, the router? My guess is that all of those layers are going to try to own memory in different ways. They're going to be advantages to sticky your memory in each layer. If you stick it with the app, then the memory has the most app-related context and is model-agnostic. If you stick it with the model, the memory might perform the best on personalized benchmarks and perhaps have the best ultimate intelligence and I think the model labs are going to work on memory. And then the ultimate thing might be like, is there a good combination? Can I use memory in the model and memory at the infrastructure layer or the app layer at the same time? Like, is that going to confuse the model? We don't know yet. I do think that it's impossible for one layer to capture all valuable memory because the app's own so much important context that the model labs don't have. And the model labs are going to get this to work. They'll have to incentivize the apps to give them that context. Began to do it. Apps in the model site, claw code, cursor, bundle, model, and harness, is the rusa absorbed into the agent framework before it ever has the chance to be independent when you have the agent and the harness together. The harnesses are pretty interesting because in our early days, one of our early bets was that most apps were underestimating the desire for users to choose the model. Most apps in the very early days in like 2023 and 2024, it wasn't even clear which model was being used under the hood. They're like, ah, people are not going to care about that. They just want AI. And one of our like strong convictions then was that, no, like people are going to want to use particular models, they're going to care about who they're talking to. It's like, I want to know which employees I'm talking to when I'm trying to solve a problem, and models will be kind of like that. And that has played out, you know? Like, in notion, you can like, choose the model that you talk to. Even though you would think an app like that might want to like, obscure it completely. A similar thing happened with harnesses where, particularly with developers, they started to build an affinity to different harnesses. And that's because it's like, it's a user experience. So I think that is my favorite argument for why harnesses are going to stick around. Not that like, they're being bundled with the models. Because in fact, like, as models get better, they get more resourceful. And the junk that gets thrown in the system prompt just becomes a handicap. And Thropic, I think, published like a good article about this where they showed that like, oh, we got like, we got rid of stuff from the system prompt. And suddenly fewer contradictions showed up later on with user prompts and the model performed better. And we, and we're seeing a lot of the harnesses right now are like deleting code in order to perform better with the latest frontier models. That I don't think means that harnesses are bad. And I fact, in fact, I think we'll see more harnesses come up in the future because it's a way of building a user experience on top of models. It's a way for developers who are not model labs to own a user relationship. And that is just going to be incredibly valuable for the economy to have that layer. I'm going to get killed for this. What's the difference between a harness and an app? It feels like it's wordwank of like, ever talking about harnesses and harnesses. I'm like, it's not not on an app. Hello. The nice thing about the harnesses compared to the apps is that they're more composable. I can have a harness call another harness. I can have a harness spin up another harness in a sandbox in the cloud. I know what API's did for apps. Yes, but it's much more reliable and deterministic and sort of easy for users to grok with a harness because the harnesses are unique space. They all have and the models are so well trained on Unix on Bash commands. Whereas like, you know, if I'm like telling a harness to go orchestrate an app in the cloud, it's going to be like, oh boy, does this app like, how do you log into this app? Is it like, do I need your password? Do I need to fire up a virtual browser? It's going to be pretty slow. I'll figure it out. Okay. I like fire it up a browser and like, now I need your password and I'm going to like, try to find the input where to put it in and I there's probably an API in this app somewhere. I need to like, look up the docs to figure it out. Okay. Now I've got the API, but there's so many like unknown unknowns when you're composing around an app. Very, very, very, very few unknown unknowns when you're composing around a harness. So I think it just gives developers more flexibility and flexibility that they can inspect like API calls. You're just seeing a whole bunch of code flying around the screen. A harness, oh, I can like jump into the harness and like, look at what's going on and talk in English about it. So it's much more user friendly. We've seen meta and muse really be a focus for Zark. We've seen Alex, Y and front and center much more. Were you impressed by what matter delivered with muse? They've been doing a good job. Yeah. I mean, it takes a while to set up a whole new model lab from scratch and, you know, I'm sure a lot of like organizational debt to deal with. Like, do you think they will be a serious challenge? I do. I think they they're they have the resources. I think there's some competitive things they can do around the model that helps people in ways that the model labs are not as interested in doing. Like just having like a social network and like a focus on people, you know, it's like something for the brand that maybe rock and like a SpaceX AI have it too. But they do need to find their niche. Like, I think people don't quite know what to do with muse Sparky at like when to user when to go for it or what it's like core advantages. They just released a coding harness. They're trying to be like a generally capable model right now. I expect that in the future, they're going to be like, look, we are way better at this thing. And that's that's going to be a really important moment for them. I think I was impressed by that. You know what I use now? Maybe plugging one of our mutual friends, but Anastasia and Arena. And it's so weird. So I'll put my prompt in Arena. And then obviously it comes back with a load of different model options. Yeah. And you know, I come back with I use one the other Purgamomb. Purgam. Yeah. It was like Kimi and Purgamom and they offer you four different options. And it takes me to models that I would never have used before. And actually, muse has come up a couple of times to be pretty impressive. But I love that in terms of this like discovery mechanism to models that I would never have used. I would never get a Kimi. Yeah, obviously dude. I just get a fucking chat GPT. It's really interesting. It basically goes to the point of the model. It's just becoming a utility there. What do you mean by that? Well, actually, I have no loyalty to them. I have no affiliation with brand. I go to Arena and I want to see what you got for me. Show me the results. I can't have it's Kimi or muse or Claude or Sonnet or do you know what I mean? And actually, I just want to see the options you got. And I'll pick the best from there. I'd rather run four in parallel. Do you buy this whole we're going to have one frontier model run four open models. And the frontier model might be 160 IQ points and their open models might be 120 IQ points. But that will be a model infrastructure structure that we'll work with. Totally think that that is a great architecture that everybody needs to explore. We've been helping lots of developers do this. You have sub agents. We have a sub agent server tool that we like to be really, really good at using models generally. And then you have a an orchestrator model that calls out to the sub agents when it wants particular tasks to get done. And these sub agents are just very, very low cost and they're focused on deterministic tasks. This is what open weight models are generally really good at compared to frontier models. When you have a deterministic task where you know the shape of the output, you know the type of problem that you're working on. And it's a type of problem that has been solved like classifying some text, for example, then you should definitely use like a low cost model from open router and then have the orchestrator model read the results and then go and continue working on the like unknown non-deterministic task that it was set out to do. I want to create a open American ecosystem. Yeah. I want more amazing open American models. And I make you head of this program. What would you do to encourage incentivize the open US ecosystem to compete more vociferously with the Chinese? I think I would spend time talking to the current American labs a little bit more to figure out what distilling the Chinese models looks like for them and how effective it is. You can probably get pretty far distilling the Chinese models. The nice thing about the open weight models and the Chinese models that they allow distillation and they're like most of them. And that means that you can like take the outputs of these models to do reinforcement learning on top of the model that you're building. This is just like a very important and common practice in AI that all labs do. And also when you distill you see the outputs. You can like inspect them to make sure that they are aligned. So there's anything about the like you know the open weight models that you're worried about not being aligned with like the voice or constitution of the model you're creating. You have a much better shot at catching it when you're doing these RL roll outs. The other thing I would try to figure out is the compute question. Like compute is just a huge advantage that I think we still have relative to China. And these neolabs need a shot. And they're like there needs to be an easier way to like get compute to the right talent in all countries. But especially if we're trying to create like a competitive American neolab system. Like Nvidia's been doing a good job of this. But there's Google or TPUs. There's Trainiam from Amazon. I would like work with all of the hardware companies and also like the neo chips to help with compute. I don't think we will have that compute advantage for long. I think you see deep sea combined dance both aggressively pursuing their own chips now. Yet sport controls mean that they have to. And this is like the number one problem for Xi Jinping and his race to win the AI war. If they build a bridge in four weeks, I think they'll manage a chip in six months. Yeah. Like staying ahead on the on the chip war is critical for America. It's distillation wrong. I mean distillation is it's a technique to build models like the people like view it with cynicism and shade. Well, and then just distill models. It's a technique to build models. The close weight model labs distill models to like like sonnet is a partially distilled version of Opus. And like this is how you like make smaller models out of bigger models. Yeah, it's an important way to just teach your model new things when when you find like something useful in the ecosystem. We do think that labs have a right to say it's not allowed in their terms of service. A company can like cut off access to someone who is trying to build a competitive model. If you're just trying to build like a smaller model that's like really focused on doing one specific thing that's not competitive. Most of the frontier labs don't prohibit that to my knowledge, but they're going to be markets for companies that allow it and companies that don't. And we we make sure that we help like both companies uphold their terms of service. I have to ask you one question before we do a quick fry around. I'm going to get killed if I don't ask. There are reports that you are selling to strike for $10 billion. I can't comment, but whatever happens, we're going to execute on the vision. What we're doing is critical for the ecosystem. And we believe for like safe access to AI where one monopoly doesn't take over, where we have like a vibrant ecosystem of models that everyone can explore and when new providers and new server tools, a new inference adjacent tech comes online, there's a really easy way to discover it and connect it with all of your existing AI. I was seeing these changes. Like my response to me when I owned 22% of the company, $10 billion, $2.2 billion. Now I'm a venture capitalist. But is it a hard not to think like that? I don't really think about it. Do you know? I don't spend a lot personally what I think about when I do with like personal capital. I really want to help people work on problems that are not, that just don't lend themselves very well to venture capital. They're sort of falling in this gray area of problems that like people need to solve but are really tough to fund because they don't come with a business model attached. And I think they're like very cool things to do. Now in the nonprofit space, because you can use AI to review way more data than you ever could before. I'm not quite ready to talk about it publicly yet, but I do want to like do something that like helps researchers work on those problems and like get grants to do it. One really cool example of this is David Fialkau, who's one of the founders of General Catalyst, who basically finds incredible stories that won't get funded for movies and funds to shine a light on them because he thinks they're very important. So like the dissident which obviously told the story of Koshogi and Koshogi being, you know. And then Icarus, which is the story of the Russian Doping and like these were films that would not get funded, had it not been for his funding because they were politically sensitive, charged and he's like, I'm going to enable the stories of these forbidden tales. Yeah, it's kind of like that. Well, I love that stuff. He's great. He's fucking awesome. Anyway, are you ready for a quick fire round? Sure. Okay. What is the most underrated model on OpenRooted today? Ooh, good one. I mean, first I've put like pool sides models are great. I'd probably like my fire round answer. Like new American lab, building interesting coding models that are small, highly effective and building a lot of useful tools for accessing them. Good team. 70% of neo labs will die in the next three years. Agriode disagree. Disagree 70 seems very high of neo labs are not that many neo labs. If like getting acquired by one of the model labs counts as die. I do think there'll probably be some like potential consolidation. But if you include the consolidation, I'd say 50. Do you think Dario should be less negative and more positive as a voice in AI? I think it's important to have somebody who is very paranoid about the future and how things are going to shake up. And I personally appreciate in Throbics, Paranoia. Obviously there are areas where I want other model labs to not feel like they're just being pushed off the table. But I'm a big believer in neurodiversity. And like, the Throbics is a part of the neurodiversity map that really matters. And if no one is being extremely paranoid, then no one is like offering that voice. And so I appreciate that they're doing it. It's the craziest thing that you see in your seat on top of everyone's usage that you don't think people talk about enough. I mean, a lot of companies are obviously worried about cost management and freaking out about the amount of inference they're spending. And they don't know how to think about it. It's like a whole new way of like doing business and thinking about your your up-ex. Like the old way of thinking about how you're how much you give your employees you like give them a salary and you kind of forget about it. Like someone knows what what everyone's making, but like it's a static number that like it's readjusted on on a quarterly basis maybe after performance reviews. Really your your employees all costs totally dynamic different amounts now. I think a lot of like companies are putting it on them to do routing. And I think in the future there's a good chance that it will like it pushed downwards to the employee level. The employees should like figure out which tools and models to use their best for their tasks. And then we should figure out how much you're costing like due to the choices that you make as an employee. Your your costs as an employee is a going to be a dynamic number and it's going to be you know, dependent on how much that employee is like effectively using expensive and cheap models to do their job. I advise companies to kind of like still do their normal management work. Like have their managers kind of assess how effective and productive employees are. But also line it up with how much their employees cost and then kind of come up with you know, a quadrant of celebration like these employees are like doing a job and they're pretty price effective or cost effective. And then a quadrant of concern these employees are kind of maybe doing a so-so job and whoa, they are not cost effective at all. They're the eiceyecosis is off the charts. And then you address the quadrant of concern. So I don't think people talk about like basically how you think of like employee cost in the age of AI and that it's it's really it should be a dynamic number and not a static thing that like only a few people know about it. It's gone. It's wonderful. But can you imagine going to someone, oh, I'm sorry. You were with a hundred grand last month now you're worth 50. I think it would make plan. They are in control of how much they cost. That's the great thing like all employees are in control of how much they cost and and can like influence that. Now you get to think like, okay, how good am I as an employee and how efficient am I being as well? Final one, when you look at the landscape today, there are so many things to be excited about. What are you singly most excited about? Two things come to mind. One is rare disease research, which I think is one of those things that has been intelligence bottleneck or really just the inference bottleneck. It involves like trying out lots of ideas and seeing if they work. The other is route sourcing productive urban life improvements. For example, like imagine if someone was curious about finding every lead pipe in America or every lead pipe in the UK. And like had an approach to it, but they really need like to make it mature and stress test it. Like now you can use AI to do that and we just might solve some weird problems that everyone's just kind of given up on because you need like crazy idea to come from somewhere. Brilliant ideas are sort of evenly distributed all over the world. They can come from anywhere. And now you just give them leverage to actually work. So I'm excited about sort of very like broad kind of urban urban or or rural quality of life improvements that will be able to make. And I said I wanted to do this one for a while. I'm so glad we could do it in person as well. I was worried that we were going to have to remote. It is so much nicer to do it in person. You've been fantastic. So thank you so much for doing it with me. Likewise, this was great. But before we leave you today, founders face a different set of challenges at every stage of growth. For Sid's shade, co founder and CEO of D matrix, JP Morgan delivered the guidance and expertise to help navigate what came next. Whether you're in the early days or expanding into new markets, JP Morgan helps start-ups, navigate complexity with real confidence, offering personalized guidance and deep sector expertise. Find out how JP Morgan helps founders at JP Morgan.com/growwithoutlimits. 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