基本信息

要点解读

这是什么

这是一段对话式播客，讲述 OpenRouter 从开源模型路由起家，成长为 AI 开发者常用的中立分发层，并涉及产品聚焦、模型市场与安全等议题。

用在哪里

适合想了解 AI 基础设施演进、模型分发策略以及平台安全趋势的工程师、产品负责人或投资者。

可以推断的

  • 推测：在多模型共存的生态里，提供统一路由和调度的服务具有持续价值。
  • 推测：随着 token 调用量提升，针对 token 流动的欺诈风险将成为平台运营的关注点。

来源摘要/节选

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 as like a

Anjney Midha [00:07:16]: Yes.

Swyx [00:07:17]: Yeah, states and all those things.

Alex Atallah [00:07:17]: Correct.

Swyx [00:07:18]: Yeah.

Alex Atallah [00:07:18]: But it, - To take us back in time, you may remember this, but, there was this technology, legislation that was being debated called, the Net Neutrality Act. And net neutrality is, like, inherently this political concept, right? It’s, it’s about the regulation of - internet broadband access. And so there was a community of us who were technologists, but also debating the politics of the technology. And I thought the Review would be a great place - to, like, write about that. And I was working on, I think, a net neutrality article, and I remember proposing, “Well, maybe we should start a technology section.” And Alex was one of the only people who said, “Yes, that would be cool.” And said. I forget whether we ended up writing stuff together, but - that’s when we first met,

Alex Atallah [00:08:03]: Was 2011 or twelve. I forget which year it was. It was one of those.

Anjney Midha [00:08:09]: Yeah.

Alex Atallah [00:08:09]: It was at Old Union, if I remember correctly.

Alex Atallah [00:08:11]: That’s where we used to meet. But, along the way, Alex and I have had a chance to, To hang out often. And probably the time when we had the most professional overlap was when I was running the platform at Discord, and it had become this explosive platform for crypto

Swyx [00:08:32]: Yeah

Alex Atallah [00:08:32]: And NFTs in the middle of the pandemic.

Swyx [00:08:35]: Which also, by the way, you were in charge of safety and security as well, right?

Alex Atallah [00:08:38]: I was the head of platform, which meant all of the crypto - the DAO and NFT launch security debugging fell on

Swyx [00:08:45]: And their phishing and.

Alex Atallah [00:08:47]: The phishing, the social engineering attacks, the katana DDoS that we were getting hit by. but it’s around the time I first started teaching security at scale at Stanford, CS 153. And Alex was on the, - at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were. Like, and at peak, I forget, if you remember how much NFT volume was running through

Swyx [00:09:10]: Discord

Alex Atallah [00:09:10]: Discord, but it was, like, a meaningful amount of, like, it was, like, several billion dollars in NFT volume of GMV, so to speak, were running through the platform, and it was all coming from OpenSea. It was these, like, buy, sell,

Swyx [00:09:20]: The

Alex Atallah [00:09:21]: Servers

Swyx [00:09:21]: The D in DAO is Discord.

Alex Atallah [00:09:25]: Yes. And so that’s when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT. Sorry, three. No, it was five. Yeah, five, which is the RL version of three. And that’s around the time we made a Discord bot with, OpenAI for internal deployment, and that’s when I realized we would need. Like, since I was part of the deployment team.

Anjney Midha [00:09:50]: What was the use case?

Alex Atallah [00:09:51]: There were two that were. And there’s, there’s a post now called “Discord is Your Place for AI with Friends” that somebody sent me recently that I wrote, and published in twenty-three. But There were two use cases. One was Clyde, which was the - like, a party friend inside of Discord that could help you set up your Discord server and talk to you about onboarding and get your friends to hang out more. and then there was content moderation. And one of the realizations we had with content moderation was - it would refuse to moderate. Like, it would just refuse our prompts because the The training was. We were very early in the training era, and it would just. Our prompts would trigger it, its, like, guardrails. And we told OpenAI, “Hey, guys, we need access to the weights because if we’re gonna be doing content moderation at scale, we had 250 million monthly active users, we need more reliability that the model will do what we need it to.” And they said, “Well, sorry, guys, that’s not how this works. We’re a closed-source company.” And so that was my first realization that we needed open models, and the enterprises would need more control over capabilities, and then ultimately would need some control plane or management system to orchestrate these open models. But there weren’t no good - there were no good open alternatives until maybe

Alex Atallah [00:11:10]: Six months later when Llama came out. And six months after that, I led the series A into Mistral, which was started by Guillaume and the Llama team. And - That, - Around that time is when I remember hearing about Alex launching OpenRouter and going, “These worlds are gonna collide, and I don’t know when it’ll make sense to team up.” But Alex was so early and could see. I think he was totally right about this ecosystem starting with Llama that then needed, like, a, an easy layer to manage for, especially for. I was approaching it from the enterprise perspective because I had been that, like, the. As the VP of platform at Discord, it was my job to ensure that when we deployed models to, like, 250 million users, they did what we wanted them to. And that was very hard, because if you outsourced it to the labs and they controlled the guardrails and their guardrails are their safety policies. Forbid the model from responding to your prompts. That was quite catastrophic.

Swyx [00:12:05]: Yeah. But what, a moderation is the thing that they want to support. And obviously, beyond that, they would - OpenAI would work with you, presumably to give you a moderation endpoint, which they offer for free.

Alex Atallah [00:12:16]: It was an interesting use case, that - So they did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini deployment of itself. And so the use case was instead of having human moderators that have to interpret the norms of the community, you just give the, - Often, like every, subreddit, Discord servers, public ones have their own rules that the user, the users create.

Swyx [00:12:41]: Oh, yeah. We run the LinkedIn Discord in. Yeah.

Alex Atallah [00:12:43]: And then humans used to read those norms and then enforce it every day manually, like observing each message in these communities. And these communities have like millions of users. So we had a 5,000+ person team globally in the, on the Discord content moderation team. These are outsourced contractors who had a really tough job. And so the idea was instead, if you could give the norms of that server To the LLM, then the LLM would do custom moderation for that server. It’s almost like a, like context moderation for that server. And many of those servers’ norms just violated OpenAI’s rules. And so - It was like we had our own custom eval. So each server had its own custom eval. But Discord– at the time, OpenAI’s evals, we were all so

Alex Atallah [00:13:28]: Primitive in our thinking about how to deploy these LLMs that often the training prompts were super handed. It said, “Oh, anything about Harry Potter, anything that has trademarked content, don’- refuse.” And if it was a fan - Harry Potter fan community, this is a real use case, that had content moderation, the LLM would just refuse.

Swyx [00:13:48]: Yeah.

Alex Atallah [00:13:49]: And that was just not precise enough.

Anjney Midha [00:13:52]: Another one that we heard was like if someone was trying to write like a detective story, and there’s one chapter with a lot of violence, like maybe someone

Alex Atallah [00:14:01]: Right

Anjney Midha [00:14:01]: Like kills someone, the LLMs would just refuse to, like, help with that part of the story.

Alex Atallah [00:14:07]: Yeah.

Anjney Midha [00:14:07]: And then - like, we used to be like, okay, this is not like structurally inherent to LLMs. There must be, like, some choice out there so that I can, like, switch to another model, when I’m getting, like, a refusal or a bad result from the main one that I have. And that, like, tension also drove me for a marketplace.

Why “One Model Wins” Was the Wrong Bet

Swyx [00:14:28]: Yeah. I think that is well accepted now. What was it like back then when you were raising or, starting this? did people get it? what was the, some of the struggles? I like getting stories out of him about how other VCs don’t get it. So like anything you wanna, talk about, now - Let’s, let’s call it, that the early journey of OpenRouter is done, right? You can obviously talk about some of the early days stuff.

Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of the

Swyx [00:15:03]: Scaling laws.

Anjney Midha [00:15:04]: Huh?

Swyx [00:15:04]: Scaling laws.

Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It’ll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you’ll just be fighting for scraps at the end. That was probably the biggest objection we got. it is interesting that Google won the search engine race with such a huge margin. I think, like, had there been more interesting benchmarks or had, like, search engines been, - had people, like, seen them a little bit more like LLMs where they’re services that you can build companies on top of, that might not have been the case. but LLMs don’t merely have a user interface. They’re also, like, ways of building entirely new businesses. And, a Google-level monopoly would be like the Dutch East India Company times, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well. So it didn’t seem like would be a really crazy outcome if that happened. And it’s also less likely because the economics of, like, creating good competitors are much, like, much more decentralizable.

Alex Atallah [00:16:25]: Everything Alex said is true, And I came at it from a completely different perspective, which

Swyx [00:16:31]: Yes, this is why we’re here.

Alex Atallah [00:16:32]: The scaling laws were never - In my mind, were always a feature, not a bug for why OpenRouter would be very valuable. Because, I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends - I went to grad school for machine learning, and I just had a lot of friends in the ML community who it was very obvious to us that the bitter lesson holds. And so I was like, “Oh, fantastic. Now we have at least two proof points that compute scaling works.” It was OpenAI and Anthropic. and by the time I think we decided to team up on OpenRouter, I had already invested in Mistral and Black Forest Labs and Luma. So there was multiple model companies and teams that I was, working with.

Why Model Labs Struggle With Distribution

Swyx [00:17:14]: But you did other modalities, whereas this is literally

Alex Atallah [00:17:16]: Across different modalities, yes

Swyx [00:17:17]: Text.

Alex Atallah [00:17:18]: Exactly. And it was so obvious to me that an ecosystem of different kinds of models were being created, and that this whole narrative of, like, Only one company will dominate like Google was, well, like maybe true, but one, I don’t believe that. But two, there was so much extraordinary innovation happening across several different research teams. But the shared problem I was noticing across all of them was often, the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but never - like, are not developer mindset-oriented. Like, what happens after the training is done and the checkpoint comes out? Like, you’d be shocked how, like, similar the early training teams at OpenAI, sorry, Anthropic, BFL, Mistral, were in their, like, default approach to. Taking their research out of the, lab and scaling their impact, which is often, oh, the checkpoint is done, put it out as an API, done, and then there’d be crickets. in the case of Claude, the first Claude checkpoint was done a year before they released it internally. And then ChatGPT came out, and we decided, okay, yes, it’s a good idea to release a Claude version externally.

Alex Atallah [00:18:34]: And they had no plan, like no

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