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

Alex Atallah is the Founder and CEO @ OpenRouter, the unified interface for LLMs. The company has raised over $153M in funding, with the latest valuation pricing the company at $1.3BN. OpenRouter is reportedly in an acquisition process with Stripe for $10BN. AGENDA: 00:00 Is OpenRouter Selling to Stripe for $10 Billion? 04:05 What Did Alex Learn From Scaling OpenSea? 06:38 What Did OpenRouter's Founding Thesis Get Wrong? 14:47 Is AI Model Routing Already Being Commoditized? 19:12 Do Falling Token Prices Help or Hurt OpenRouter? 27:16 Should America Be Alarmed by Chinese Open Models? 32:43 Will US Open-Source Models Compete With Chinese Models in the Next 12 Months? 39:26 Will the Router Be Swallowed by the Agent Framework? 48:56 Is Distillation Wrong—and How Should We Look at It? 50:01 Is the Reported $10 Billion Stripe Deal Actually Happening?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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 labs 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.

• 0:25Harry Stebbings?

  There are reports that you are selling to Stripe for $10 billion. Is that going to happen? This is 20 VC with me, Harry Stebbings. 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 Atallah, co-founder 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 prescient 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.

• 1:10Harry Stebbings?

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• 3:21Harry Stebbings?

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• 3:36Speaker 02

  You have now arrived at your destination.

• 3:39Harry Stebbings?

  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 stalked the shit out of you, speaking to Angny, even your roommate before this show. So thank you for joining me, dude.

• 3:52Speaker 00

  Thank you, it's great to be here.

• 3:54Harry Stebbings?

  Now, I want to start with a little bit pre-OpenRouter and start on OpenSea. It was a pretty incredible journey. What did you take with you to OpenRouter, having seen all that you saw with OpenSea?

• 4:07Speaker 00

  Yeah, so OpenSea started as the first NFT marketplace. Similar to OpenRouter, it was very small for a long time. Like, we kept the team very small until the Series A roughly, or 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, like, we are understaffed. The servers are melting, like, our search index was exploding. We had a couple of big outages. It was tough to, like, keep the site up. And it was like, oh my God, we're going to become like the Twitter fail whale, but like, applied to crypto.

• 4:48Speaker 00

  My biggest goal was to have us not be the Twitter fail whale for crypto. And it took a little bit to, like, create the team, get platform and infrastructure under control, like, make sure we could predictably scale. In other words, like, do load testing to, like, help the site sustain 10x 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?

• 5:33Speaker 00

  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 Anthropic, have seen like unpredictable growth. And we have as well. And, you know, we've had like a couple bumps, but overall, it's been like significantly better. And I just, like, OpenSea just kind of like drilled that into me in a way where I could like take it productively to OpenRouter.

• 6:01Harry Stebbings?

  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?

• 6:10Speaker 00

  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 hyperscalers 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 hyperscaler? Never, like, they're using 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.

• 6:50Speaker 00

  In the early days, we had, I think we called it Provider One 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 a, 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 than the hyperscalers, were 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.

• 7:31Harry Stebbings?

  A lot of people suggest that that inference provider layer is a commoditizable element or layer that will be removed or see margin reduction competed out over time. What would you say to that theory?

• 7:42Speaker 00

  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 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.

• 8:26Speaker 00

  And this is good for the ecosystem, 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.

• 9:07Speaker 00

  These models are like very, they're very emotional, they're very non-deterministic.

• 9:13Harry Stebbings?

  I had Lynn 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? Well, 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.

• 9:37Speaker 00

  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. This stuff happens like 24/7. Every five minutes, there are big changes for the big models. And so it like actually does make the experience better.

• 10:13Harry Stebbings?

  You can only invest in one inference provider. Which one do you invest in?

• 10:18Speaker 00

  I probably have to stay neutral on this. I do, I really like the inference providers that are doing like custom hardware and very, very like low level optimizations. I like providers that are also trying to figure out how to make customization easier. Today, you fine tune models and you create this like new, fully independent model from the base model. Many inference providers are kind of like creating these LoRAs or some call them cartridges that are much more portable potentially between models. And we might see a future where like, when you do a fine tune and you want to like change the base model layer, it only costs like maybe a few hundred dollars, maybe a few dozen dollars to change it.

• 11:01Harry Stebbings?

  It's okay. I understood that Firewise is your favorite. It's okay. I get it. Mine too. My question is, when Lynn 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?

• 11:28Speaker 00

  Oh, definitely. I mean.

• 11:30Harry Stebbings?

  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?

• 11:39Speaker 00

  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 like 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. Let's say there's one model that fulfills all of your desires, either within your company or like as a consumer. More and more people start using that model. And then someone decides, you know what, I'm going to like create a neurodivergent model. I'm going to create a model that's like a little bit different, that like talks a little differently, that has ideas that the first model like could never have come up with because it's like completely different data that's being used to train it. Then it kind of creates inevitable demand to use both models.

• 12:24Speaker 00

  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 like made a big update. Consolidation on one model just seems like, it just doesn't make any sense to me.

• 12:47Harry Stebbings?

  Totally get you. So you'll have companies which have like 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 OpenRouter for those other model selection.

• 12:58Speaker 00

  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 like potentially data that's valuable to you, what is in your best interests? It's to go and try out those other models and like see if you can be more productive with them. If you can like merge them together to get better state of the art performance, if you can reduce your costs using these other models. Whether your goal is to like improve your margins or grow your company,

• 13:41Speaker 00

  you are incentivized to go use what the ecosystem creates. So the model that you made, you're going to have to continuously improve it to keep up. And it's never going to win the whole market. So it's going to be a massive market. It's going to be like the biggest, biggest market in tech ever. And biggest market probably in human history. No one's going to win all of it. You're not going to 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 going to 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 a way your brand carries around.

• 14:26Harry Stebbings?

  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 ramp release products like this. I mentioned earlier of Merge, a company we invest in has released that product. Several are releasing kind of routing technology similar or claiming to be similar. Are we seeing the commoditization of this layer?

• 14:48Speaker 00

  I think a lot, yeah, a lot of companies are making routers because it's fashionable. You know, 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, you know, winning something. You're playing to play. 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, you know, 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.

• 15:30Speaker 00

  And it shows in our product and you know, 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 them all. You want to reduce your dependency on any individual one. If you know, you build on top of a router or a gateway

• 16:09Speaker 00

  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 OpenRouter is fundamentally about giving people more choice because that gives them more leverage.

• 16:30Harry Stebbings?

  You do that at a price at 5.5% take?

• 16:34Speaker 00

  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.

• 16:50Harry Stebbings?

  Because that was going to be my question. Ultimately, companies will like love it small. And then as you scale, you're like, shit, this is really fricking expensive. I'll just build my own routing tech now because it's become such a significant part of my cost base, actually.

• 17:04Speaker 00

  I mean, I kind of figured like some of those companies, you know, 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. We're soon going to introduce like a kind of a business self-serve plan that also just makes it make a lot more sense. And if you, if you have your own inference, like if you bring your own inference to OpenRouter, if you bring your own keys, that fee goes away. For inference that we are providing you, like when you go into OpenRouter's 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.

• 17:43Harry Stebbings?

  What will be the main revenue line of OpenRouter in three years' time?

• 17:47Speaker 00

  I think it's going to depend on, 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. You know, 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 going to be dominated by, you know, the same things that dominate it today, which is like us helping people with, with an unplanned inference capacity, both enterprises and startups. That's what OpenRouter is best at, like when you need to try models that you weren't expecting,

• 18:30Speaker 00

  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, 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 underestimating 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 10 X, 12, 15 X per year.

• 19:12Harry Stebbings?

  We've seen token prices fall 90% people take in like 18 months. Is the reduction of token prices helpful or hurtful to your business? Because 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.

• 19:31Speaker 00

  Well, a lot of people talk about the Jevons paradox, but when prices go down by 10 X, the usage increases by more than 10 X. 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. OpenAI cut prices by 5 X and then in coordination with us by another 2 X. So in total, the price of Luna has dropped 10 X on OpenRouter over the last two weeks. Guess how much usage has grown? 13 X. So it's a close to perfect Jevons paradox story where you drop prices 10 X

• 20:15Speaker 00

  and usage grows by more than 10 X, just a bit more. And also the, the usage is pretty stable. Like it like grew, it like flattened out at 13 X. And then, you know, it's been kind of like growing at the same rate that it was growing before it hit the 13 X multiple. So that's pretty interesting and it's a pretty low variable. Like there are a few other confounding variables in the story. And it was also done in the middle of DeepSeek launching and having a really, really good price and GLM having a really good price. Like now Luna is being used more than GLM on OpenRouter. GLM used to be like one of the top three, four models by token volume and now Luna is past it.

• 20:56Speaker 00

  This is the first time OpenAI 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.

• 21:09Harry Stebbings?

  How reflective of the market are your token volumes? Because it's about, I may get this wrong, about maybe one and a half, 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, Harry, no offense to OpenRouter, but like it's not reflective of the market. And most people who use Frontier, it doesn't go through that. Like they use Frontier APIs and so it's not counted. To what extent are your rankings reflective of true token usage?

• 21:41Speaker 00

  Yeah, it's a really good question. You know, we try to estimate how they're off by, you know, just surveying people sometimes or looking at like the surveys other people have done. We definitely have a bias to people who believe our thesis, which is that the future is multimodal and companies who want multiple models. And there are still companies out there, I basically rarely, very rarely run into them now, but there's still companies out there that are just like, oh yeah, we're an OpenAI shop, you know, 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 hyperscalers, OpenAI, Anthropic and Gemini.

• 22:24Speaker 00

  So we do probably like undercount the frontier models, but I think like over time, our thesis is becoming more and more common to see in other companies. And the moment that like they're like, oh yeah, like 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 like, that it just becomes like better and better data over time.

• 22:48Harry Stebbings?

  Alex Karp said on CNBC in his rather wonderfully energetic way that companies are terrified of working with frontier model providers.

• 22:55Speaker 00

  Mm hmm.

• 22:55Harry Stebbings?

  Do you think they are?

• 22:56Speaker 00

  I haven't seen what he talked about there. When I talk to our customers, but there was definitely like a little, there was some skittishness that the, particularly when Claude Design came out around Figma. And that part I did see, and I do think that there are like real concerns, like Figma's very different, but if like a startup is only building a like go to market wrapper around intelligence, like, hey, we are, we're a company that kind of like brings AI to this market and does so by like doing the right integrations and like customizing the

• 23:39Speaker 00

  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 like Claude Design was strategic. While it's not like a massive amount of revenue for Anthropic, like not probably like significant amount of revenue, it does get the design team to really care about Anthropic models. And so the companies that like 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.

• 24:21Speaker 00

  And so I think like companies like that, that find themselves like, oh, we're like building a product for a team that has now become strategic for the model labs, for like companies that they actually care about, that's where I see probably the most year term threat.

• 24:33Harry Stebbings?

  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 cannibalizing their Figma usage. Do you think that will happen?

• 24:46Speaker 00

  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, like I have not talked to very many designers about this. I certainly haven't heard a lot of chatter about Claude Design. And like, if you just look at the numbers for Figma, they're quite good. Like they had a very, very incredible earnings.

• 25:11Harry Stebbings?

  This is why you don't want to be public, dude. You see like, you know, great numbers, Figma down, and like, poor Dylan, like, give a fuck, what?

• 25:20Speaker 00

  Yeah, that was crazy.

• 25:22Harry Stebbings?

  Do you know what I mean? It's like, really? Come on. We were talking about like the different models that we have on offer and whether 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?

• 25:41Speaker 00

  Frontier model development or a general model?

• 25:43Harry Stebbings?

  A general model, both frontier and open.

• 25:46Speaker 00

  Yeah.

• 25:46Harry Stebbings?

  Just because I mean, every single day, there's two, three, four new models.

• 25:50Speaker 00

  In July, we launched 70 models, about one model every 10 hours. There's some agent labs starting too, that are all going to kind of like 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 the agent. And we haven't even seen the start of that. Like, 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 Lovable have a model yet? I don't think so.

• 26:26Harry Stebbings?

  Not publicly.

• 26:26Speaker 00

  Yeah. So the agent labs are going to, I think, develop models. This pressure from both the GPU makers like Nvidia to like 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 Neolabs. And if like, oh, if American open weight models pick up in steam, then it gives these Neolabs a base to train on.

• 27:08Speaker 00

  That's not Chinese, which will then probably create more American Neolabs.

• 27:13Harry Stebbings?

  Do you think we should be concerned by the rate and quality of Chinese open models?

• 27:20Speaker 00

  We should work behind. America is very, very behind still. I think things are picking up. I think, you know, we have Poolside, we have Thinking Machines, we have RC.

• 27:31Harry Stebbings?

  Do you feel a sense of responsibility for that? And what I mean by that is like, you know, you are a routing business and you could route a company to a Chinese model that who knows, people are worried about backdoors, CCP involvement, you could be the deliverer of that to those models. Do you feel a sense of responsibility for that?

• 27:52Speaker 00

  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, you know, generally considered unsafe, we pull it from the platform. If there's like 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 what the best things that we're seeing coming out of the labs or are better.

• 28:36Speaker 00

  Because we're a way of like 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 redaction, we have, we have like a couple of 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. You know, I think of the models a little bit like the internet. You know, you can't just like ban the internet at your company because there are, there's some like bad things on the internet.

• 29:21Speaker 00

  You can create guardrails and you should. You need to use AI to build the best possible guardrails that you can.

• 29:29Harry Stebbings?

  I'm with you, but do you think you actually know what's going on within Moonshot or Alibaba with Quan? These are incredibly secretive organizations in the depths of China.

• 29:39Speaker 00

  Can't pretend I know what's going on inside of them. You know, as a US company, like we're going to follow like the best practices of what happens in the US to make sure that we're not doing something irresponsible.

• 29:50Harry Stebbings?

  What do you think US companies are more nervous of frontier models or Chinese models?

• 29:55Speaker 00

  I think they're, they're more nervous about frontier models usually, part because there's just like much more confusion around the data policy, about 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 infra versus running in their VPC and knowing like who can see the data.

• 30:31Harry Stebbings?

  How extraordinary is that, though? Like, they're more nervous of like US companies headquartered in Silicon Valley, where you can see and touch and feel the headquarters and the leaders. It's just like, what a strange world to be in.

• 30:43Speaker 00

  Yeah, it is very strange, especially with the frontier models having the biggest cyber posture right now.

• 30:50Harry Stebbings?

  What do you make of every company kind of posturing, haha, we hacked someone? First you had OpenAI, then you had Anthropic, and then you had Zuck coming out, I don't want to miss the party, we did too.

• 31:00Speaker 00

  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 the right thing to do is to publish it, regardless of what, like how people are going to spin it. So I doubt that they're, you know, they're actually thinking of the felony bench or whatever it's called.

• 31:37Harry Stebbings?

  How significant was the latest Kimi model which got so much attention? Was it as significant as everyone thought?

• 31:44Speaker 00

  It's quite good. It's not cyber capable in 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 like have like voice degradation then happens when they get better at coding especially. And I was like, oh my God, like I can't read this output anymore.

• 32:28Speaker 00

  The output sounds like three of the four arguments you made are right and one is a turning point, you know, and duh, duh, duh, duh, duh. Here's the rub. Like it's just, it's sometimes just impossible to read what they're saying. And this stuff is fixable. But Kimi, I think, has always had pretty interesting writing.

• 32:47Harry Stebbings?

  In 12 months, will the chasm between US open source and Chinese open source be bigger or smaller than it is today? Then my fear is that it will be bigger because when you have DeepSeek, 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 aside, 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 their end goal.

• 33:31Harry Stebbings?

  Whereas OpenAI and Anthropic and all the other providers in the US, especially open source, fuck, you go and try raising billions of dollars for a US open source model. Bit tough, actually. Not impossible at all, but tougher. Business model questionable. AI research is super expensive and you're competing against OpenAI and Anthropic. I think the comparative landscapes they sit in mean that the Chinese open source providers are just inherently advantaged, sadly.

• 33:55Speaker 00

  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, you know, 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? Are they going to like give up on, 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

• 34:38Speaker 00

  they don't seem to care more that the models are. I've never seen anyone do a profile of like what you can do with DeepSeek 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 DeepSeek go? If the firewall matters to China, if it's going to matter in 10 years, like something's going to change.

• 35:04Harry Stebbings?

  Well, what's interesting is like, obviously the abilities of the Chinese models outside of China, it's immense. The abilities of the Chinese models inside China is actually relatively limited.

• 35:12Speaker 00

  The guardrails.

• 35:13Harry Stebbings?

  The guardrails are incredibly stringent and prohibitive. It's ironic that they are incredibly superior to us. Shit domestically, terrible.

• 35:21Speaker 00

  Interesting.

• 35:22Harry Stebbings?

  I literally just heard of my dear friend Jason Lankin, who runs SAS, to come back and be like, couldn't figure out what time Starbucks opened on DeepSeek. Like wasn't 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, what 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?

• 35:49Speaker 00

  Honestly, we do see some. You know, we try to make switching costs close to zero so that when new models come out, people can try them out really easily. But we also measure retention and churn from all the models. We share this, this data with model labs too, when they ask for it, so that they can know like, oh, you know, 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 data, there are developers who kind of like continuously stick to models even when there are better models out there, better models for their use cases.

• 36:34Speaker 00

  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 guardrails 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 know, you'll see like intelligence jump, but like the price curve like also jumps and then will start going down over time. So it's not necessarily the most like price effective thing to do to like shift over to the newest model, even for open weights.

• 37:19Speaker 00

  The third reason is 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 eval. A lot of people have these personal evals that are just these random tests that they give the models. If the random test doesn't look really good on the new model, it'll just be like, good, I liked Kimi K 2.6 anyway.

• 37:45Harry Stebbings?

  People thought before that memory would be the retentive mechanism. While OpenAI has all of my previous query like prompts, it knows that I live in London, I do podcasting and that, and that will make it a better model for me moving forward. Is memory no longer a retentive mechanism?

• 38:03Speaker 00

  Memory is really interesting. I, 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. And there are going to be advantages to sticking your memory in each layer. You know, if you stick it with the app, then the memory has like 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.

• 38:47Speaker 00

  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 like it's impossible for one layer to capture all valuable memory because the apps own so much important context that the model labs don't have. And they, the model labs, in order to get this to work, they'll have to incentivize the apps to like give them that context and state it.

• 39:17Harry Stebbings?

  Apps and the models that call code, cursor, bundle, model and harness. Is the router absorbed into the agent framework before it ever has the chance to be independent? When you have the agent and the harness together?

• 39:32Speaker 00

  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 were 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

• 40:16Speaker 00

  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, the junk that gets thrown in the system prompt just becomes a handicap. Anthropic, 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.

• 40:58Speaker 00

  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. 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.

• 41:28Harry Stebbings?

  I'm going to get killed for this. What's the difference between a harness and an app? It feels like it's word wank of like everyone talking about harnesses and harness, it's not, not an app. Hello?

• 41:40Speaker 00

  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.

• 41:51Harry Stebbings?

  I don't know what APIs did for apps.

• 41:53Speaker 00

  Yes, but it's much more reliable and deterministic and sort of easy for users to grok with a harness because the harnesses are Unix based. 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 fired up 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.

• 42:35Speaker 00

  I need to like look up the docs to figure it out and 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.

• 43:07Harry Stebbings?

  We've seen Meta and Muse really be a focus for Zuck. We've seen Alex Wang front and center much more. Were you impressed by what Meta delivered with Muse?

• 43:15Speaker 00

  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 I'm sure a lot of organizational debt to deal with.

• 43:27Harry Stebbings?

  Do you think they will be a serious challenger?

• 43:28Speaker 00

  I do. I think 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 a social network and a focus on people. It's like something for the brand that maybe Grok and 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 Spark yet, like, when to use it or when to go for it, or what its core advantage is. 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,

• 44:12Speaker 00

  we are way better at this thing. And that's going to be a really important moment for them.

• 44:17Harry Stebbings?

  I say, I was impressed by it, actually. Do you know what I use now? Maybe plugging one of our mutual friends, but Anastasios 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.

• 44:30Speaker 00

  Yeah.

• 44:31Harry Stebbings?

  And, you know, I come back with, I use one the other day, Pergamom.

• 44:34Speaker 00

  Pergamom?

• 44:35Harry Stebbings?

  Yeah. It was like Kimi and Pergamom, 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, it's been pretty impressive. But I love that in terms of this discovery mechanism to models that I would never have used. I would never get Kimi, honestly, dude, I'd just go to fucking ChatGPT. It's really interesting. It basically goes to the point of the model there, just becoming a utility layer.

• 44:58Speaker 00

  What do you mean by that?

• 44:59Harry Stebbings?

  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 don't care if 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 gonna have one frontier model run four open models? And the frontier model might be 160 IQ points, and the open models might be 120 IQ points. But that will be a model infrastructure or structure that we'll work with.

• 45:34Speaker 00

  Totally think that that is a great architecture that everybody needs to explore. We've been helping lots of developers do this. You have subagents, we have a subagent server tool that we like tune to be really, really good at using models generally. And then you have an orchestrator model that calls out to the subagents when it wants particular tasks to get done. And these subagents 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, or 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,

• 46:19Speaker 00

  then you should definitely use a low-cost model from OpenRouter and then have the orchestrator model read the results and then go and continue working on the unknown, non-deterministic task that it was set out to do.

• 46:33Harry Stebbings?

  I want to create an open American ecosystem, yeah. Or 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?

• 46:51Speaker 00

  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 is that they allow distillation, and they like, most of them. And that means that you can take the outputs of these models to do reinforcement learning on top of the model that you're building. You know, this is just a very important and common practice in AI that all labs do. And also, when you distill, you see the output.

• 47:35Speaker 00

  So you can inspect them to make sure that they're aligned. So if there's anything about the open weight models that you're worried about not being aligned with 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 rollouts. 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 Neo Labs need a shot. And there needs to be an easier way to get compute to the right talent in all countries. But especially if we're trying to create a competitive American Neo Lab system. Like, Nvidia's been doing a good job of this.

• 48:20Speaker 00

  But there's Google, there's TPUs, there's Tranium from Amazon. I would work with all of the hardware companies and also the Neo chips to help with compute.

• 48:30Harry Stebbings?

  I don't think we will have that compute advantage for long. I think you see DeepSeek and ByteDance both aggressively pursuing their own chips now. The export controls mean that they have to. And this is the number one problem for Xi Jinping in 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.

• 48:49Speaker 00

  Yeah, like, staying ahead on the chip war is critical for America.

• 48:55Harry Stebbings?

  Is distillation wrong?

• 48:57Speaker 00

  I mean, distillation is a technique to build models, like...

• 49:00Harry Stebbings?

  But people view it with cynicism and shade. Well, they're just distilled models.

• 49:05Speaker 00

  It's a technique to build models. The closed weight model labs distill models too. Like, Sonnet is a partially distilled version of Opus. And this is how you make smaller models out of bigger models. It's an important way to just teach your model new things when you find 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 cut off access to someone who is trying to build a competitive model. If you're just trying to build a smaller model that's really focused on doing one specific thing, that's not competitive. Most of the frontier labs don't prohibit that, to my knowledge. But there are going to be markets for companies that allow it and companies that don't.

• 49:49Speaker 00

  And we make sure that we help both companies uphold their Terms of Service.

• 49:54Harry Stebbings?

  I have to ask you one question before we do a quick fire round. I'm going to get killed if I don't ask it. There are reports that you are selling to Stripe for $10 billion. Is that going to happen?

• 50:06Speaker 00

  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 safe access to AI, where one monopoly doesn't take over, where we have a vibrant ecosystem of models that everyone can explore. And when new providers and new server tools and new inference-adjacent tech comes online, there's a really easy way to discover it and connect it with all of your existing AI.

• 50:36Harry Stebbings?

  I've always seen these situations, like, my response would be like, well, I own 22% of the company, $10 billion, $2.2 billion. Ooh, now I'm a venture capitalist. But is it hard not to think like that?

• 50:48Speaker 00

  I don't really think about it.

• 50:50Harry Stebbings?

  Do you know?

• 50:51Speaker 00

  I don't spend a lot personally. What I think about, what I do with 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 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 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 do something that helps researchers work on those problems and get grants to do it.

• 51:35Harry Stebbings?

  One really cool example, I think of this is David Fialcao, who's one of the founders of General Catalyst, who basically finds incredible stories that won't get funded for movies and funds them to shine a light on them because he thinks they're very important. So like The Dissident, which obviously told the story of Khashoggi, and Khashoggi being... And then Icarus, which is the story of the Russian doping. And 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.

• 52:11Speaker 00

  Yeah, it's kind of like that. I love that stuff.

• 52:14Harry Stebbings?

  He's great. He's fucking awesome. Anyway, are you ready for a quick fire round?

• 52:18Speaker 00

  Sure.

• 52:19Harry Stebbings?

  Okay. What is the most underrated model on OpenRouter today?

• 52:23Speaker 00

  Ooh, good one. I mean, first, like, Poolside's 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 they're building a lot of useful tools for accessing them, good team.

• 52:41Harry Stebbings?

  70% of Neo Labs will die in the next three years. Agree or disagree?

• 52:46Speaker 00

  Disagree. 70 seems very high. Of Neo Labs, there aren't that many Neo Labs. If getting acquired by one of the model labs counts as die, I do think there'll probably be some potential consolidation. But if you include the consolidation, I'd say 50.

• 53:04Harry Stebbings?

  Do you think Dario should be less negative and more positive as a voice in AI?

• 53:09Speaker 00

  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 Anthropic's 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 Anthropic is a part of the neurodiversity map that really matters. And if no one is being extremely paranoid, then no one is offering that voice. And so I appreciate that they're doing it.

• 53:45Harry Stebbings?

  What'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?

• 53:52Speaker 00

  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 OpEx. Like, the old way of thinking about how much you give your employees, you like, give them a salary, and you kind of forget about it. Someone knows what everyone's making, but like, it's a static number that like gets readjusted on a quarterly basis, maybe, after performance reviews. Really? Your employees all cost 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, get pushed downwards

• 54:37Speaker 00

  to the employee level. Your employees should like, figure out which tools and models to use that are 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 cost as an employee is 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 good job, and they're pretty price effective, or cost effective.

• 55:22Speaker 00

  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, their AI psychosis 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 really, it should be a dynamic number, and not a static thing that like, only a few people know about, and it's gone.

• 55:46Harry Stebbings?

  Wonderful, but can you imagine going to someone, "Oh, I'm sorry, you were worth a hundred grand last month, now you're worth 50." I think it would make planning and personal.

• 55:54Speaker 00

  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 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?

• 56:10Harry Stebbings?

  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?

• 56:17Speaker 00

  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. Like, it involves like, trying out lots of ideas and seeing if they work. The other is crowdsourcing 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. Now you can use AI to do that, and we just might solve some weird problems that everyone's

• 56:59Speaker 00

  just kind of given up on, because you need like, a 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, or rural, quality of life improvements that we'll be able to make.

• 57:20Harry Stebbings?

  Nice, dude. I've 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 do it 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.

• 57:31Speaker 00

  Likewise, this was great.

• 57:35Harry Stebbings?

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