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From the earliest days of open-weight models to becoming the neutral routing layer for more than 10 million developers, OpenRouter is one of the clearest bets that the future of AI will be multi-model. In this episode, OpenRouter co-founder & CEO Alex Atallah, with AMP’s Anjney Midha returning with swyx to unpack how OpenRouter emerged from the first wave of Llama, Alpaca, Mistral, and Midjourney, why model diversity mattered before it was consensus, and how a company dismissed as “just a wrapper” became critical infrastructure for the AI ecosystem. We go deep on the product and distribution lessons behind OpenRouter: why model labs can spend billions training a checkpoint and still struggle to get it into developers’ hands, how Mistral helped prove the value of a competitive inference marketplace, why OpenRouter chose focus over expanding into fine-tuning, memory, and other adjacent products, and how its rankings became a real-time map of how AI usage was changing. Alex also explains OpenRouter’s early experiments with model fusion, why they deleted the first version and brought it back years later, and how the platform grew to more than 10 trillion tokens per day. Finally, Anjney explains why Stripe and OpenRouter fit together, why token fraud may become one of the defining security problems of the AI economy, and why the next wave of fraud won’t just come from humans but from autonomous agents attacking increasingly valuable token flows. We discuss: Why OpenRouter bet early that no single AI model would win everything Alpaca, Llama, and open models becoming impossible to ignore Why Discord’s early AI deployments exposed the limitations of closed models Why model labs can spend billions on training and still fail at distribution How OpenRouter became a neutral distribution layer for model developers Why VCs dismissed OpenRouter as “just a marketplace” or “just a wrapper” The Mistral price war and the first real proof of an inference marketplace How Midjourney scaled through Discord and what it taught the AI ecosystem Why crypto infrastructure became a dress rehearsal for generative AI OpenRouter vs. LM Arena and why their missions are fundamentally different Why focus became one of OpenRouter’s biggest strategic advantages Anthropic’s early focus on AI pair programming and coding The OpenRouter products that were prototyped but never launched MOM, OpenRouter’s early Mixture of Models experiment Why model fusion failed in 2024 — and why it works much better now How OpenRouter’s leaderboard became a live map of the AI industry OpenClaw, auto-routing, and agents reshaping AI usage How OpenRouter reached 10+ trillion tokens per day Why inference gateways are increasingly becoming targets for fraud Why Stripe’s fraud infrastructure is strategically important to OpenRouter The coming rise of agentic fraud and attacks on the token economy What changes and what stays the same as OpenRouter joins Stripe Alex Atallah LinkedIn: https://www.linkedin.com/in/alexatallah/ X: https://x.com/alexatallah Website: https://alexatallah.com Anjney Midha LinkedIn: https://www.linkedin.com/in/anjney/ X: https://x.com/AnjneyMidha AMP: https://www.amppublic.com/ Timestamps 00:00:00 Introduction 00:02:12 Alpaca, Llama, and the Multi-Model Bet 00:06:04 Discord, Open Models, and OpenRouter’s Origins 00:14:28 Why “One Model Wins” Was the Wrong Bet 00:17:27 Why Model Labs Struggle With Distribution 00:23:04 “Just a Wrapper”: Why VCs Misunderstood OpenRouter 00:27:58 Bootstrapping OpenRouter Through Community 00:36:16 Crypto, Midjourney, and the Early Generative AI Ecosystem 00:43:38 Mistral and the Birth of the Inference Marketplace 00:47:10 OpenRouter vs. LM Arena 00:52:08 Focus, Anthropic, and Roads Not Taken 00:59:34 Mixture of Models and Model Fusion 01:02:44 Sonnet, OpenClaw, and OpenRouter’s Explosive Growth 01:09:03 Why Stripe Acquired OpenRouter 01:12:45 Fraud and the Emerging Token Economy 01:17:47 The Coming Wave of Agentic Fraud 01:19:07 What’s Next for OpenRouter at Stripe Transcript Introduction: OpenRouter, Marketplaces, and Pub-Sub as a Product Principle Swyx [00:00:00]: Okay, we are here in Anja’s house, which is where all big startups in San Francisco start. Anjney Midha [00:00:08]: Howdy. Swyx [00:00:08]: And, congrats on Cursor, Mistral. I don’- God knows what else. You got so much stuff going on. Anjney Midha [00:00:17]: There’s, there’s a lot going on. Well, OpenRouter is probably the - has been the most, I would say, like, one I’m excited about recently. Swyx [00:00:24]: Yeah. And we have Alex, first time on the pod, but, Anjney Midha [00:00:27]: Thanks for having me. Swyx [00:00:27]: You’ve been in the IE a few times. I appreciate every time you’ve shown up, for the community. Congrats. I just, like, what a journey. When I was looking back at your past posts, one of the earliest principles that I saw you write as a product person is sub as a product principle. And I wanted - you to maybe explain how you think about what should exist in the world. Anjney Midha [00:00:49]: Yeah. The sub piece, which was early 2023, I didn’t think about it until we talked like 10 minutes ago, is about how there is like a way of thinking about products as an intersection between subscribing to data and publishing data. And marketplaces are an easy example of this. You have suppliers that are publishing some product to a SKU. And the SKU is like a sub topic that a consumer is subscribing to and just going to, like, consume whenever they want. And humans consume in a very, like, discreet, ad hoc way. It’s not very scalable. all their attention is on the topic when they’re buying the thing, and their attention is nowhere else when that happens. agents and consumers of inference don’t act like that. They’re consuming continuously, and they’re changing the SKUs that they consume from all the time. So OpenRouter is like a blend between a normal API experience and a marketplace where we create model slug. We have the auto router. We have all kinds of, like, product SKUs that you can subscribe to. And then you can, like, continuously add, like, derive value and make decisions based on those consumers. Alpaca, Llama, and the Multi-Model Bet Swyx [00:02:11]: Yeah. This is something that was more consensus now, but not consensus when you guys started, which was that there is such a demand for swapping models and changing things out and, that people would not use the native SDKs. I guess, for each of you, what was your realization moment that this would be it? I, - You’ve, you’ve given a talk at EIE about Alpaca as, Anjney Midha [00:02:33]: Yeah. Swyx [00:02:33]: One of your inspiring moments. Anjney Midha [00:02:35]: Alpaca, I can, like, rehash the Alpaca moment for a sec. Like, the very beginning, at the end of 2022, OpenAI was the only game in town. There was, like, OpenAI, Cohere, Swyx [00:02:47]: Yes. Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models. Swyx [00:02:54]: Yeah. Anjney Midha [00:02:54]: When Llama came out in January of 2023, it was like, “Wow, really exciting. This is really big.” It outperforms 3 on, one or two benchmarks. but you can’t chat with it. It wasn’t like - It wasn’t an engaging model, but it seemed like someone just needed to fix a couple things and do some RLHF on it to get it all the way there. And Alpaca was the first model that I saw that did that. It only took $600 to do. A team at Stanford generated a bunch of synthetic data, tuned Llama, and made Alpaca, billion parameter model. Or was - Maybe it was thirteen billion parameters. And it was so good. Like, I was just, like, on an airplane using it. I, - in many cases, I, like, you could not discern a ChatGPT versus an Alpaca result. And I figured if it was this easy to make a model, one, we have a whole new way of monetizing data for the first time. you can just, like, take really valuable data and turn it into a service in $600. and that cost will probably go down over time. Swyx [00:04:03]: When you - So sorry. when you say monetizing your data as, what eventually will become an MCP endpoint or as a training data for a model? Anjney Midha [00:04:12]: Yeah, training data for a model. Swyx [00:04:13]: Awesome. Anjney Midha [00:04:13]: Like, an abstract way of saying like, “Hey, I have this data.” Swyx [00:04:15]: Compress it into a model. Anjney Midha [00:04:16]: Like, it makes sense for me in my product, but, like, I could repackage it in the form of a model and sell it. And so it’s just a whole new business model for the economy. It also, of course, provides, like, a way of following what Frontier Labs are doing, but in a way that, like, a single developer or a small team of developers can roll on their own. And so - Whenever you have an example of that, like a breakout app that’s doing really well, and then some framework for imitating it with - in your own flavor, you have an immediate ecosystem of, like an immediate ecosystem, like, should arise because there’s just a huge gap between the, like, decisions that the single company is making and all of the variations in those decisions that, like, a wider ecosystem can create themselves. And so then, you need a marketplace to, like, discover all of those, services and all of those products. There wasn’t any place on the internet that, like, was like a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why. Swyx [00:05:29]: The closest would be Hugging Face. Anjney Midha [00:05:30]: Hugging Face was the closest at the time, yeah. Swyx [00:05:31]: They just started Hugging, like, a few years ago before that. Anjney Midha [00:05:34]: Yeah, and Hugging Face also didn’t have the closed-source models. Swyx [00:05:37]: Yeah. Anjney Midha [00:05:38]: And they didn’- you couldn’t use the models at the time. and there wasn’t data about who was using them. There were, like, a bunch of differences between OpenRouter and Hugging Face, and those differences felt really critical to me, especially when I was just trying to learn about LLMs and, like, why people are choosing, like, Different little ones that are emerging over time. Discord, Open Models, and the Origins of OpenRouter Swyx [00:06:03]: Got it. And then, Ansh, no stranger to wanting more model diversity, at the time, you’re a couple of years into your Anthropic journey, which we covered in the previous podcast as well. What was your introduction to Alex? Alex Atallah [00:06:16]: Well, the introduction was, I think, thirteen years before that. Swyx [00:06:20]: Oh. Alex Atallah [00:06:20]: But the OpenRouter handshake happened right over there, if you remember. Anjney Midha [00:06:23]: Yeah. Alex Atallah [00:06:24]: Which - So Alex and I, met, I believe as sophomores now, if I remember at the Stanford Review, Anjney Midha [00:06:32]: That’s right Alex Atallah [00:06:32]: Meeting for the first time. Anjney Midha [00:06:33]: I think so, yeah. Alex Atallah [00:06:35]: Yeah. Anjney Midha [00:06:35]: Yeah. Alex Atallah [00:06:35]: So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day. And, whatever-- for whatever reason, I, Alex and I both showed up to one of the meetings, and I remember, the editor-chief was a mutual friend of ours. Lisa was really a really great editor-chief, where, part of an editor-chief’s job is to assign responsibilities to people and make sure the work gets done. and I, I may be misremembering the details, but I remember wanting to. It was surprising to me that at the time there was no dedicated technology section in the newspaper. Alex Atallah [00:07:11]: You Swyx [00:07:13]: Because it’s political, right? Alex Atallah [00:07:14]: It is primarily Swyx [00:07:14]: Like, it’s talking Alex Atallah [00:07:15]: It originally started [truncated for AI cost control]