这期访谈请了 OpenRouter 的联合创始人兼 CEO Alex Atallah 与 AMP 的 Anjney Midha。文中给出的规模是：OpenRouter 已成为服务 1000 万以上开发者的中立路由层，每天处理超过 10 万亿 token，而它最早是被第一波 Llama、Alpaca、Mistral 与 Midjourney 推起来的。叙事的重点是分发——模型实验室可以花数十亿训练出

正文

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 OpenRoute r: 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

00:02:12 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

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,

Anjney Midha [00:02:48]: And then a smattering of, like, early attempts at open weight models.

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.

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.

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.

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

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,

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.

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

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 plan for how to get developers to try it out. And so if you go to the Claude one blog post, you’ll notice there are, like, three developer examples for users of the API, and one is a Discord bot, and the second is Vivian, my wife’s startup called Juny Learning, ‘- And then there was, like, Notion, because these were all friends of, like, the Anthropic Because that’s how - like, last minute the planning was around, hey, once the model’s done training, how do you get it out to the world? There was no distribution platform that understood what developers needed, all the key management, provisioning, like, simple, like, endpoint management, versioning control. Like, all these things that the scientists and researchers go, “ that’s plumbing. I don’t really think about it.”

Swyx [00:19:15]: Implementation detail.

Alex Atallah [00:19:16]: Right. And instead, Alex came at it from that perspective. And so, it was so obvious to me that, like, every single lab I was funding would spend - like, literally sometimes billions of dollars into training, and then a checkpoint would be done, and there’d be crickets, like, during early access because they’re like, “Oh, that’s right.”

Alex Atallah [00:19:35]: It’s hard to use a checkpoint to make anything. You need a whole bunch of plumbing around it to make it usable by a developer. And so by the - I think - it was so obvious to me that a distribution platform like OpenRouter was critical to have in the ecosystem if we wanted there to be competition to Google. Like, unless-- ‘cause with Google, DeepMind is done training a new checkpoint, and then they push a button, and it gets blasted out across all their surfaces from Google Docs to,

Swyx [00:20:01]: Everywhere, even if I don’t want it.

Alex Atallah [00:20:02]: Everywhere. You wanna know about, like, on Android, like, overnight, they can deploy a new checkpoint to, like, a billion devices, right? And that invisible infra advantage, distribution advantage, most people don’t realize, but until OpenRouter showed up, - you had to think about all of that yourself as a model lab. And it was very daunting. at Anthropic, I think it took, well, more than twelve months to get to our first 10 million in revenue. And in contrast with Black Forest Labs, I remember the early days, you guys had a conversation with the BFL team, and, it was so simple for OpenRouter to say, “Oh, no problem. Like, the day you launch, we can send 1 million developers to you.” that was crazy. That was like a step function change in, like, an hour.

Swyx [00:20:46]: Is that a real number, a million?

Alex Atallah [00:20:47]: I,

Swyx [00:20:48]: Okay. All right.

Alex Atallah [00:20:48]: I think today it’s, like, 4 million. How many developers are on OpenRouter today?

Anjney Midha [00:20:52]: Over ten,

Alex Atallah [00:20:54]: Yeah.

Anjney Midha [00:20:54]: Over 10 million, but, like, it’s, it’s hard to, you

Alex Atallah [00:20:59]: I, yeah, I don’t know how to. Yeah.

Anjney Midha [00:21:00]: We do a lot of, like, account duping work, but, no

Alex Atallah [00:21:04]: If you could get 1,000 developers, just to put in context If you get 1,000 developers who try the model on day one after you release it and just, like, do inference and give you feedback, that’s a thousand

Anjney Midha [00:21:15]: That’s huge

Alex Atallah [00:21:16]: More developers than they knew how to get to on their own.

Swyx [00:21:19]: Well, BFL had a reputation, but yes.

Alex Atallah [00:21:21]: They had one in Stable Diffusion.

Alex Atallah [00:21:23]: And with Mistral, I don’t know if you guys remember, but the first checkpoint they released was, like, torrents. It was, like, torrent weights.

Swyx [00:21:31]: Yeah, they just put up a magnet link.

Alex Atallah [00:21:33]: Yeah, there was no API.

Anjney Midha [00:21:34]: Yeah.

Alex Atallah [00:21:34]: Because they didn’- they weren’t infra people.

Alex Atallah [00:21:37]: ? Like, it’s like, okay, download these weights, and you guys go figure out how to host it.

Swyx [00:21:39]: Well, he has a story on his side, yeah.

Anjney Midha [00:21:41]: Yeah, in addition to the, like, building a really good developer experience around it, the marketing that we do on, like, for different models is totally different and perceived totally differently

Alex Atallah [00:21:54]: Right

Anjney Midha [00:21:54]: From the marketing that a model lab does for itself.

Alex Atallah [00:21:56]: Yes, 1,000%.

Anjney Midha [00:21:57]: Right? We are like a, neutral layer looking at this market like it’s a big dark room with all the corners completely obscure to users, and users are walking into the room and, like, feeling around

Alex Atallah [00:22:09]: Yeah

Anjney Midha [00:22:09]: And trying to figure out what objects to grab off the tables and, like, build into, their companies. And it’s just an insane way of working. Like, models are not products where you can just enumerate all their features onto a web page. They’re all black boxes, including the open weight ones. So you need to, like, shine lights on all corners of this room, so that people can see what makes this model good, and you need the company shining that light to be a neutral third party, which is what we specialize in. So the, like. It’- In addition to developer experience, there’s also, like, a very important, like, marketing and product packaging component

Alex Atallah [00:22:50]: Yeah

Anjney Midha [00:22:50]: And a way of, like, routing and discovering models becomes, like, critical to your market as a provider or a model lab or a server tool and more in the future.

“Just a Wrapper”: Why VCs Misunderstood OpenRouter

Alex Atallah [00:23:03]: And this value, to your earlier point about how many VCs, like, just don’t. One of my biggest frustrations is that venture capitalists, many of them, like, just don’t have any operating experience in the field. so unlike a traditional investor who’s just maybe come up through the ranks as, like, a associate working on financial modeling or maybe hasn’t been a real operator in the field for, like, more than ten years, which is a big part of the industry now, I had just arrived at a16z, like, a year after running the platform. And so I knew what the challenges were of, like, building a real - great developer experience and like, being able to create a working piece of software with a model. And there were a few, I won’t name names, but there were investors who were looking at OpenRouter, and, felt at the time, like, when I would compare notes with people, that it was just, I quote unquote, “just a marketplace.”

Swyx [00:23:59]: Yeah, just a thin layer, just a

Alex Atallah [00:24:00]: Correct

Alex Atallah [00:24:01]: A wrapper or whatever on other people’s APIs. And I was like, “You have no idea how strategic the value that OpenRouter has created by being able to orchestrate even three.” APIs in production. The amount of both engineering work and community design that goes into getting that live and running in production at the scale the OpenRouter team had started just doesn’t happen by default. And that was one of the things that stood out to me about Alex from the earliest days. Like, he just understood, like, - from a systems perspective, like, how do you get these flywheels going? Like, that stood out to me with OpenSea when we were working together on the NFT integration at Discord. Like, Alex had a level of community-- like, systems thinking on how you get these flywheels going that most scientists and machine learning people just don’t

Alex Atallah [00:24:48]: Think of. Like, we often think in terms of training.

Swyx [00:24:52]: It’s a linear stage.

Alex Atallah [00:24:53]: It’s this linear pipeline.

Swyx [00:24:53]: There’s no loop yet.

Alex Atallah [00:24:54]: Yeah. It wasn’t until much later that the modern context feedback loop cycle really got standardized in the industry. But at the time, if you remember, machine learning was like. Like, mostly we did a lot of ML, like, when I was in grad school on a laptop. So you just, like, download a dataset, ran some ablations, and you looked at the loss curves, and you’re like, “Great, I made AI.” And the idea that you have to, like, deploy those capabilities, collect feedback trajectories, then, like, put those into a continuous loop, like, came much later. And it was very counterintuitive to the - like, the traditional AI mindset. I do remember doing the investment phase for, OpenRouter, I just didn’t try and educate a bunch of other VCs on why it was not just a marketplace. I was like, “ what? I’m just gonna invest.”

Anjney Midha [00:25:41]: Yeah.

Alex Atallah [00:25:41]: And I’m going to, like, take the opportunity to partner with Alex, and if - no other VCs get it, that’s totally fine. ‘Cause at the time, - it was not obvious, I think, to several of the investors that, like, OpenRouter was not more than just a wrapper around APIs. And - that infuriated me. And I was like, “ what? I don’t have time to debate you. I’m - we’re gonna, we’re gonna invest.” And then I think, like, a month later, Matt Murphy marked it up by 10x. Like, - I think. I forget what the exact money was and so on, but, to his credit, Menlo Ventures realized, “Okay, there’s much more strategic value here as well.” Maybe you didn’t hear all these conversations behind the scenes But that frustrated me a lot. there’s a lot of this, like, opining about wrappers. and if you’re like, “Oh, an app is just a wrapper on a model,” then, like. And, OpenRouter is, like, this wrapper on top of other APIs, and this is the most stupid, reductive framework.

Alex Atallah [00:26:31]: And so it’s clearly somebody who has no experience deploying product at scale.

Swyx [00:26:34]: It’s the thing you dismiss other things with. Like, you’re a - everyone’s a wrapper on everything, right? Like, and there’s, there’s some Some wrappers have value.

Alex Atallah [00:26:40]: Investors are wrappers and LPs, right?

Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it’s all wrappers down, all down to bare metal, I guess, and like energy.

Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models.

Anjney Midha [00:26:56]: Right.

Swyx [00:26:57]: And, yeah, obviously this is, like. you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies.

Alex Atallah [00:27:06]: It’s so, hard to be. Like, the day a model launches, the fact that you have an OpenRouter, endpoint for that model frequently at the top of Hacker News on day one, people don’t realize the amount of work that goes into accomplishing that. And OpenRouter used. Like, that would happen over and over again, and I remember going, “People have no idea how hard that is.”

Alex Atallah [00:27:30]: That’s not.

Swyx [00:27:31]: Yeah, we’ve covered some of the inference engineering that goes behind,

Alex Atallah [00:27:34]: Yes

Swyx [00:27:34]: Some of - with Base Ten and all those. Well, today you have, all those, like, cool code name things that people guess what Oxy Alpha is and all those things. But, like, I guess one of the things that you’re teasing is, how do you get that initial flywheel going, right? Because today you have your scale and your reputation, all these things, so obviously you - you’re driving immense distribution. But when you were early on, when it’s mostly

Bootstrapping OpenRouter Through Community

Alex Atallah [00:27:55]: The bootstrap, yeah.

Alex Atallah [00:27:56]: What was the bootstrap like?

Anjney Midha [00:27:58]: To bring it back to early Discord days, I think we, like, initially connected with. This is an OpenSea story, technically. But, and we initially connected when you were at Discord, and we talked about, like, - the Axie Infinity server.

Alex Atallah [00:28:13]: Oh, yes. Yes.

Anjney Midha [00:28:14]: This server was, like, the biggest server at the

Alex Atallah [00:28:17]: Yeah

Anjney Midha [00:28:17]: At Discord.

Alex Atallah [00:28:18]: That’s right.

Anjney Midha [00:28:19]: And you were like, constantly bumping up the

Alex Atallah [00:28:22]: The limits on the server. Oh, my God

Anjney Midha [00:28:24]: Of how many people could be in the server.

Swyx [00:28:24]: For those who don’t know, like, 10% of Philippines was Axie.

Alex Atallah [00:28:29]: Was on that server. That’s a big hit.

Swyx [00:28:31]: It was, like, a meaningful contributor to the GDP of the country.

Alex Atallah [00:28:33]: It was an NFT, like, crypto game, but it

Swyx [00:28:35]: It was like a Pokémon breeding thing.

Anjney Midha [00:28:36]: Yeah.

Alex Atallah [00:28:36]: Yeah. Similar. Yeah. There was battling, there was breeding, and then there was, like, a marketplace for trading.

Swyx [00:28:43]: Earn as well.

Alex Atallah [00:28:45]: Yeah, earn. And, like, the graphics were really cute and fun, and you like, you get emotional about your Axie that you make. So to, like, start a community like that, which we had to do many times at OpenSea with every early project, for us to create a marketplace for it, we need to make sure that the, like, the community wants it.

Anjney Midha [00:29:09]: Right.

Alex Atallah [00:29:09]: And it’s like building something that people want and going and telling them about it. Like, you can do that on a one basis, but there’s way higher leverage to do that in a community where everyone can talk to you at the same time. So we spent a lot of time, like, building things that the community really wanted. We did the same thing for OpenRouter. And, like, the Axie community was one of, like, a zillion communities we did that with. And Anj, like, saw us doing it and. ‘Cause you could just see people sharing OpenSea links constantly in that Discord. Like, users sharing links is a really clear indicator that, like, something important is going on. So we spent, a lot of time, like, first figuring out what the gap is in the technology that people care about. Like, what was the actual problem that needs to be solved? in early LLM days, it was, OpenAI refusing to finish the prompt or,

Anjney Midha [00:30:09]: Yeah

Alex Atallah [00:30:10]: To, like, complete the task. It was also.

Anjney Midha [00:30:13]: Inability to customize models. and so there are communities that, like are just completely blocked on that issue, and those are the communities that are most useful to learn about and dive into and explore.

Alex Atallah [00:30:28]: Something that really struck me at that time, - as I was just hearing your talk, I remember noting - you may not remember this, but we - we had these, like working, Zoom calls that we were doing a sprint around for, like this OpenSea integration with Discord. and, we’d, we’d - it was myself, my engineering team. I think you were there. And I remember, Alex, in the middle of one of those calls, just like there was like silence. we were all like, “Oh, yeah, this totally makes sense. Let’s do this.” And then there’s - every, like everybody aligned. And Alex was like, “No, this makes no sense to me.” And everyone’s - I remember going, “What? Like, it works. Like, you click on a link and this, then it bounces you out to, like, OpenSea.” And he was like, “It’s not a good user experience. Yeah, we should not do this.” And I remember going, he was the only one person out of all of us to raise his hand and go, yes, it made sense from a technical implementation perspective. Like, we were bouncing the user out into the, into OpenSea. And so it kinda checked the box of the product manager’s requirements on both sides. But Alex went one step further and was like, “ what would be better, guys? If we just embedded the experience right here inside of Discord so the link opened up as an embedded iframe, and you can just check out right there.”

Alex Atallah [00:31:47]: And not one person on the call, and there’s like seven of us who had met, like, week after week.

Swyx [00:31:52]: And it’s the guy who doesn’t work for Discord.

Alex Atallah [00:31:53]: And it’s the guy who doesn’t work for Discord.

Swyx [00:31:55]: Like, technically, you benefit if they bounce.

Alex Atallah [00:31:57]: Exactly. And that was, like, adversarial. To keep the user inside of Discord would be adversarial to OpenSea. And yet Alex put that user experience first. And I was like, “That’s special.”

Alex Atallah [00:32:08]: Because it’s very hard to have somebody who’s technical like Alex and understands the developer flow, but also understands the best user experience and wants to prioritize that. And that’s two sides of the flywheel that if you can get spinning, like is often hard to stop. And you just reminded me, like that one was one of those moments where I go, I - I realized I gotta be better at user experience because I should have been the one who came up with that, and I didn’t. And I learned from you. And, I think that went into one of our case studies for the PM training program at Discord.

Alex Atallah [00:32:36]: I don’t know if it there is Because of

Swyx [00:32:38]: You need an Alex is the conclusion.

Alex Atallah [00:32:40]: Yeah. You need an Alex. And this is why I’m not, nobody should be surprised why Stripe decided like they had to buy OpenRouter because it’s a really rare combination of people who understand the machine learning community, the developer experience, and the user experience. And putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieve

Window AI, BYOM, and Finding the Right Form Factor

Alex Atallah [00:33:02]: Over the last, five years.

Swyx [00:33:04]: Yeah. Well, we should talk about the other reasons for acquisitions, which

Alex Atallah [00:33:07]: Yes, we should.

Swyx [00:33:07]: You’ve written about. I wanna proceed somewhat chronologically as well. So - there is a point that, one of the questions that, Dave from H of Zero sent in was, when did it - really started to work? And you brought up Mixtral. I don’t know if you wanna bring up that story.

Alex Atallah [00:33:22]: Oh, yeah.

Swyx [00:33:23]: Which obviously you overlap with, so.

Anjney Midha [00:33:26]: Yeah, the MoE was. I don’t know when. there’s no like one moment where I was like, “Oh, this is, officially starting to work.” It was

Swyx [00:33:36]: The moment where you had a Chrome extension, like, really super early on.

Anjney Midha [00:33:39]: Oh, yeah. But, well, - yeah. So before OpenRouter, I wanted to, like, explore a bring-your-own-model experiment. And,

Swyx [00:33:47]: Which anyone familiar with crypto is like, yeah, Phantom and all these things.

Anjney Midha [00:33:50]: Yeah. So it felt like doing a MetaMask analogy for AI would be a fun way of exploring that. And at the time, there were no AI apps. There were probably as many AI apps that were, like, hitting AI - like, hitting an LLM via an API call as there were, like, games just doing it in JavaScript. like there was a, there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some desktop

Alex Atallah [00:34:27]: Yes.

Anjney Midha [00:34:27]: Managed app that is controlled by the user. and of course, there are like, I think, many reasons that did not happen. But back when the days were that primordial, I built a Chrome extension called Window AI

Swyx [00:34:43]: With Plasmo.

Anjney Midha [00:34:44]: With Plasmo.

Swyx [00:34:45]: I had come across early on, and I was like, “Who’s gonna use this?” You did.

Anjney Midha [00:34:49]: Plasmo had a couple, like, I think Phantom was using it. there were some other, like real companies using it.

Alex Atallah [00:34:56]: It was like a shim.

Swyx [00:34:57]: React for Chrome extension. It compiles to all

Anjney Midha [00:35:00]: Yeah.

Alex Atallah [00:35:00]: I see.

Anjney Midha [00:35:00]: Like Next.js for Chrome extensions.

Swyx [00:35:01]: Next.js, Next.js.

Alex Atallah [00:35:02]: Okay.

Anjney Midha [00:35:03]: And yeah, built Window AI on top of it. The creator of Plasmo, like started contributing code to Window AI, in GitHub, and that turned out to be Louis Vicchi

Alex Atallah [00:35:15]: Oh, you’

Anjney Midha [00:35:15]: Who is the founder of OpenRouter.

Alex Atallah [00:35:17]: That’s right. You have told me this is how you met Louis. Yes.

Anjney Midha [00:35:19]: Yeah.

Alex Atallah [00:35:19]: Okay.

Anjney Midha [00:35:20]: So, that allowed users to like configure which model they wanted to use for a web page in their browser, and then, like the app would just call out to that model when it needed to do things. not the right form factor for LLMs, but, it’s like fun experiment. You learn a lot, and like I open sourced it. And the main learning is like, okay, this has to be an API, and it has to look a little bit - like, there has to be more of a developer experience here and more of a discovery experience as well. Like, I don’t know where to use these models, and a little Chrome extension is not gonna help me discover. It’s not enough real estate. I need more space. I need visuals. I need graphs. I need, examples. I need images. I need to, like, I need to be able to, like explore both as a human and as an agent.

Crypto, Midjourney, and the Early Generative AI Ecosystem

Alex Atallah [00:36:10]: Yeah.

Anjney Midha [00:36:10]: So that’s how OpenRouter came to be.

Alex Atallah [00:36:13]: A meta point that.

Alex Atallah [00:36:16]: I think is underappreciated, but Alex is reminding me, is that we were quite lucky that we were so. we were, like, adjacent to the crypto community in those days. Because in hindsight, crypto ended up being like a dress rehearsal for generative models, right? If you think about the Axie experience, Alex is totally right, there were not that many AI apps at the time. And while I was dealing-- my job was to be the head of platform at Discord, which meant to be a general purpose place for communities and friends to create-- for developers to create apps and bots and, other services that could be deployed across Discord. And while 80% of the attention at the time was being spent on crypto, because that’s where all the NFT volume was, there was, like, twenty percent of my time I was spending with a friend, who would get hotbot with me and ask me for. We would play Magic: The Gathering on weekends, and he was working on a little Discord bot that could take a text input and turn it into an image, and it was called Midjourney. You

Swyx [00:37:15]: Is that David?

Alex Atallah [00:37:15]: It was David Holz.

Alex Atallah [00:37:16]: He was a good friend. And David and I have both been failed ARVR founders, in the before that. And, I remember this. Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter off. And many of the, like, the abstractions and the infrastructure decisions we made to scale Axie happened just in time because they. Axie did this and then fell off a cliff. And then as Midjourney was taking off, we, like, explicitly decided to help David make the server, the Midjourney server, as the primary place for interaction with the model, because it was very hard for people to understand how to use the model if they couldn’t see other people using it and copy them. And so the single-player Midjourney web app on its own, like midjourney.com, had, like, terrible retention because people would show up, they’d see this empty field. It’s like E 2, and they would type in, like, cat or dog. And it was, like, paralyzing for them to have this blank canvas that they had to fill because they’d never used an AI model before. But instead, in a Discord server, you could see other people using it and riff off of their prompt, and the engagement was off the charts. And so scaling, Midjourney from zero to, like, 10 million monthly actives was a much smoother approach Axie Infinity. And so,

Swyx [00:38:29]: Don’t forget the best of four pictures, and you choose one.

Alex Atallah [00:38:31]: The best, yeah, and then the other, we

Swyx [00:38:32]: Which is the feedback loop.

Alex Atallah [00:38:33]: The RLHF feedback loop, which, by the way, separately, like, Tom Brown, David and I used to play Magic: The Gathering on weekends. And so, like, it was one group of friends would hang out, and we’d. Like, these concepts were all being discussed all the time. But, there was.

Alex Atallah [00:38:47]: I think there were few of us who bridged both the crypto worlds and the AI worlds. And compared to crypto, where it was - the question was always, what’s the use case, for this technology? There was never any need to ask that for AI because it’s, like, the use case was so visceral. It was like, I can create now anything at - I can imagine. I can write novels, I can code. And the infrastructure that those of us who believed in the distributed systems, like, value of crypto, like the censorship resistance part, found this use case that was explosive. And I think between Midjourney, the, Claude was a Discord bot launch, that we were using internally as an LLM. ElevenLabs had a TTS model that we had on Discord as well. Like, Discord became this petri dish for, like, early apps to innovate. And I don’t think it’s a coincidence that they found a home there before OpenRouter gave the world, like, a public home store or, like, a, storefront. Discord was this, like, almost petri dish storefront that - had, like, piggybacked on the infra we’d built for crypto communities. And then I think Alex was one of the first people to realize, wait a minute, like, these apps need their own home, on the internet. And then OpenRouter, to me, was a continuation of that community’s needs. And of course, there was the crazy distribution that you enabled for a lot of these developers.

Why OpenRouter Couldn’t Just Live Inside Discord

Swyx [00:40:07]: So then my question is, how come you were. My perception is OpenRouter is not that Discord-centric, right? You have a Discord.

Anjney Midha [00:40:14]: Yeah.

Swyx [00:40:14]: And you use it to engage your community, but it’s not like Midjourney where, like, no, that is like the primary way people experience OpenRouter.

Anjney Midha [00:40:21]: Yeah, Midjourney, like, it really helps to see visually really quickly how people are using the model and how to prompt it.

Anjney Midha [00:40:29]: And I think that is partly why the server was so critical. It’s like it is the user experience. It adds a ton.

Anjney Midha [00:40:37]: And you can go the whole mile with just, like, prompting via Midjourney, like, the, via the Midjourney Discord server, getting your images and then sharing them and having fun. For OpenRouter, for LLMs, like, you need a lot of user experience around LLMs to make them, like, really usable.

Swyx [00:40:54]: Charge point.

Anjney Midha [00:40:55]: And yeah.

Anjney Midha [00:40:57]: The, like, seeing the examples of other people is also not as useful because it’s a lot of stuff to read. It takes a long time.

Anjney Midha [00:41:04]: You need, like, based integration. Not possible to do in a Discord server. You need, Or technic- it’s possible. I shouldn’t say that. It’s just not a great developer experience. you need, like, - you need governance for. At the point where you got based integration, now you need governance for managing the LLMs that have access to it, the data policies, which teams. All that stuff needs a lot more than a Discord server can provide. So it’s just

Anjney Midha [00:41:30]: It’s not the right.

Alex Atallah [00:41:32]: Well, in addition, you’re not wrong, but also there’s the very important distinction that, Midjourney was an end user application.

Alex Atallah [00:41:40]: And, that’s why Discord, which has 250 million monthly end consumers, made, it made sense for Discord to be a host for that application experience. What I knew was gonna happen soon after Midjourney found explosive product-market fit, because we. I think when Midjourney launched, from launch to $100 million revenue run rate, it was less than eight months. And shortly thereafter, Stable Diffusion launched. And, all of us used to hang out in the Discord server. There, I think it was the,

Swyx [00:42:13]: The Stability Discord?

Alex Atallah [00:42:14]: It was the

Swyx [00:42:16]: Yeah, LAION.

Alex Atallah [00:42:16]: Yeah, the LAION Discord server.

Swyx [00:42:17]: The image community that spawned Stable Diffusion.

Alex Atallah [00:42:19]: The image community. Yeah. And so when Stable Diffusion came out, I realized- Oh, now other people can build their own Midjourney.

Alex Atallah [00:42:27]: Because until then, Midjourney did not have an API, so they were a stack company, right? They were training their own models, and they were deploying them as an application. But if you wanted to build your own Midjourney, there was no API of that quality. and I think E two was still quite primitive. Like, Midjourney had great quality. And then when Stable Diffusion came out, suddenly there was this new person who - there was - this new capability in the world, which is a developer could create their own Midjourney. And that, I think, created the need for something like OpenRouter, because then you need an API to. If you - if you had the creativity of David Holz and you had Stable Diffusion as the model and you wanted to put these things together, how could you do that without having to figure out how to host the weights? And what OpenRouter, - the shape of OpenRouter enabled is that. Right? When you have open model alternatives to closed applications, OpenRouter’s value in the world becomes extraordinary because now any developer can just show up and use the

Stable Diffusion and the Need for a Model API Layer

Swyx [00:43:20]: You just love model diversity.

Anjney Midha [00:43:21]: Did you just say the shape of OpenRouter?

Alex Atallah [00:43:23]: Oh, no.

Anjney Midha [00:43:25]: Were you in cloud? What is this the real Han?

Alex Atallah [00:43:26]: I’ve been, I’ve been - I’m, I’m misaligned now. I’ve been overtrained. I’ve been using Cloud way too much, haven’t I?

Swyx [00:43:34]: Claude-ish is what people would say.

Alex Atallah [00:43:35]: Claude-ish. Oh, God, I gotta untrain myself.

Swyx [00:43:38]: Okay. - And I just wanna cap off the Mistral side. my TLDR is there was a Mistral price war, is what they called it, right? Like, round about NeurIPS is twenty-three or twenty-four.

Mistral and the Birth of the Inference Marketplace

Anjney Midha [00:43:47]: Yes. December

Swyx [00:43:48]: They launched, the Mistral 8x7B, and like the price went down like 80%.

Anjney Midha [00:43:54]: Yeah.

Swyx [00:43:54]: To me, that’s very positive because it’s like the first, like, real competition to host Mistral. Is there more?

Anjney Midha [00:44:01]: Yeah, that was. I’m, like, trying to remember it, all the things that happened. It. Like, we saw that model come out and immediately saw people say that it was the best model in the world.

Alex Atallah [00:44:15]: Yes.

Anjney Midha [00:44:15]: Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.

Swyx [00:44:22]: It’s hype, right? Is it?

Anjney Midha [00:44:25]: It was hype. It was hype. It was also, like, hype from AI influencers at the time. And there were many examples where it was, like, outperforming four. So people really wanted to try it out and see, is this gonna be true for me too? And if so, at what price? And, the, like, inference landscape was really messy.

Alex Atallah [00:44:49]: Yes.

Anjney Midha [00:44:50]: We cleaned it up. - it allowed, like, providers to compete on price, so we could give you just the best price in one spot. And so it was, I think, the first clear example of, like, a provider marketplace working in a way that adds value to end developers.

Alex Atallah [00:45:08]: Sean, you may not remember this, but I think we met for the first time a few days after Mistral came out at NeurIPS

Anjney Midha [00:45:15]: Yeah.

Alex Atallah [00:45:15]: At a luncheon.

Swyx [00:45:16]: Yeah. That’s where I also met BFL as well. Yeah.

Alex Atallah [00:45:18]: And Guillaume was there.

Anjney Midha [00:45:19]: I was at NeurIPS at that time.

Alex Atallah [00:45:20]: You were there too. And, we had just announced the Mistral investment, and I remember Guillaume was over there, and I remember turning to Guillaume and asking him, Like, “Is it is all the. Like, how are you feeling after the launch of Mistral and seven B?” And, him in his typical French fashion was like, “ it’s a, it’s an okay model. It’s not that good.” And I was like. It was so, in contrast. But I remember him also saying that part of the reason he felt a lot of people Thought that it was better than four was because of the speed. - it was an MoE model that they had, like, absolutely figured out how to make super efficient. It was on the Pareto frontier. And this is an important thing about LLMs, right? Sometimes when they’re faster, you think they’re smarter, even though, like, if you did, N of, these common, like, evals that are - you do seven tries, and I don’t remember. I think we should go back and figure out what the data says, but I wouldn’t be surprised if it turns out, oh, on an N of seven attempts, four was smarter on evals, but the perception of on, like, or correctness would be smarter or more accurate. But, people, like, from a human preference perspective felt that it was faster because it - or smarter because it’s so fast.

Swyx [00:46:36]: Yeah. And most queries do not take that level

Alex Atallah [00:46:39]: Don’t take that. That’s true.

Swyx [00:46:40]: Right? So this is the start of humans as router

Alex Atallah [00:46:42]: Yes.

Swyx [00:46:42]: Which then eventually becomes OpenRouter as router of like the

Alex Atallah [00:46:45]: Oh, that’s interesting way to think about it. Yeah.

Swyx [00:46:47]: Like, because humans are the routing mechanism. Like, I will ask the fast model first, and then if, like, oh, not good enough, I’m gonna upgrade manually.

Alex Atallah [00:46:52]: Yes.

Swyx [00:46:53]: But then he’s gonna auto it.

Alex Atallah [00:46:54]: I didn’t, I hadn’t thought of it that way, but that makes sense.

Swyx [00:46:57]: Which then there’s, there’s a lot more techniques, like fusion. Fusion is the thing that we should talk about. Before I move on to those things, I just want to close off the early years. one thing that I observe, which you are also an investor in Arena.

OpenRouter vs. LM Arena

Alex Atallah [00:47:10]: Right.

Swyx [00:47:10]: And we talked about Midjourney having that feedback loop of, A, B, C, D, and choosing that very. being very important. And you understand the flywheel. So how come you didn’t build Arena, and how come Arena didn’t build OpenRouter?

Anjney Midha [00:47:23]: Well, Arena started before OpenRouter, right?

Swyx [00:47:27]: They had the school project

Anjney Midha [00:47:29]: Yeah, LM

Swyx [00:47:29]: And then it became a company.

Anjney Midha [00:47:31]: LM Arena, yeah.

Swyx [00:47:32]: So, but, and I know you had some Arena experiences, like the up comparison type things.

Anjney Midha [00:47:37]: Yeah.

Swyx [00:47:37]: But you never really went as hard as Arena did.

Anjney Midha [00:47:40]: In doing up experiences?

Swyx [00:47:42]: Yes. And LM Arena did have a router project based on LM Arena ELOs, which they never commercialized.

Anjney Midha [00:47:48]: It’s hard to do a company that does both because one company is taking data and selling it, and the other company really can’t by default. So, I think there is, like, a branding reason that there are two companies here. like, when you set up OpenRouter, there’s no training, there are no prompts, right, aside from what your provider policy set. Like, OpenRou- like, OpenRouter can’t see your prompts or completions. If you want to see that as an org, you have to opt into it and enable it. And so we’re, like, pretty conservative and careful about data policy and security. And privacy. And LM Arena is like, their business model is like oriented around the labs and,

Swyx [00:48:34]: Because they give it for free, right? You don’t give it for free to give it for free.

Anjney Midha [00:48:37]: Yeah.

Anjney Midha [00:48:38]: But we do give some. We like have free endpoints too, but like those free endpoints, we, I think we’re not collecting any prompts. We’re not like monetizing the data unless you, opt into it for some reason.