The $10 Trillion Token Economy — Alex Atallah, OpenRouter & Anjney Midha, AMP

There’s a new type of unit of value being streamed across the internet called a token. Over the next 10 years, the entire internet value chain was going to have to deal with the fact that the more valuable tokens got, the more bad actors were going to try to get their hands on those tokens. Anytime you scale something and the payload gets more and more valuable, more bad actors try to get access to that value.

Online payments started roughly in the 1980s and 1990s and grew to over $1 trillion over the next 10 years. We needed to build entirely new payment solutions to deal with online fraud. Where we are today is roughly there on tokens, but over the next 5 years, we’re expecting the token economy to get to roughly $5 trillion. Over the next 10 years, I’d be shocked if we got to $10 trillion in token flow. We blocked 10 times as much dollar volume last month as the month before, and the types of token fraud are diversifying quite a bit.

1. Introduction

Okay, we are here in Anj’s house, which is where all great startups in San Francisco start. And congrats on Cursor and Mistral. I don’t know—God knows what else. You’ve got so much stuff going on.

There’s a lot going on. OpenRouter has probably been the one I’ve been most excited about recently.

Yeah. And we have Alex—first time on the pod—but, Anj, you’ve been a few times. I appreciate every time you’ve shown up for the community. Congrats. 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 was “pub-sub as a product principle.” I wanted you to explain how you think about what should exist in the world.

The pub-sub piece, which was early 2023—I didn’t think about it until we talked 10 minutes ago—is about how there is a way of thinking about products as an intersection between subscribing to data and publishing data. Marketplaces are an easy example of this. You have suppliers that are publishing some kind of product to a SKU, and the SKU is kind of like a pub-sub topic that a consumer is subscribing to and just going to consume whenever they want.

Humans consume in a very discrete, 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. OpenRouter is sort of like a blend between a normal API experience and a marketplace, where we create model slugs. We have the Auto Router. We have all kinds of product SKUs that you can subscribe to, and then you can continuously derive value and make decisions based on those consumers.

2. Alpaca, Llama, and the Multi-Model Bet

This is something that’s more of a consensus now, but it wasn’t consensus when you guys started: that there is such a demand for swapping models and changing things out, and that people would not use the native SDKs for each model. I guess, for each of you, what was your realization moment that this would be it? You’ve given a talk at AI Engineer about Alpaca as one of your inspiring moments.

I can rehash the Alpaca moment for a second. At the very beginning, at the end of 2022, OpenAI was the only game in town. There was OpenAI, Cohere, and then a smattering of early attempts at open-weight models. When Llama came out in January 2023, it was like, “Wow, really exciting. This is really big. It outperforms GPT-3 on 1 or 2 benchmarks.” But you couldn’t chat with it. It wasn’t actually an engaging model. It seemed like someone just needed to fix a couple of things and do some RLHF on it to get it all the way there.

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, fine-tuned Llama, and made Alpaca—a 7-billion-parameter model. Or was it maybe 13 billion parameters? It was so good. I was just on an airplane using it, and in many cases I could not discern a ChatGPT result from an Alpaca result.

I figured that if it was this easy to make a model, we had a whole new way of monetizing data for the first time. You could take really valuable data and turn it into a service for $600, and that cost would probably go down over time. It also, of course, provides a way of following what frontier labs are doing, but in a way that a single developer or a small team of developers can roll on their own.

Whenever you have an example of a breakout app that’s doing really well, and then some kind of framework for imitating it in your own flavor, you have an immediate ecosystem that should arise. There’s a huge gap between the decisions that a single company is making and all of the variations in those decisions that a wider ecosystem can create themselves. Then you need a marketplace to discover all of those services and products. There wasn’t any place on the internet that was a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.

When you say monetizing your data, do you mean as what eventually will become an FCP endpoint, or as training data for a model?

Yeah, training data for a model—an abstract way of saying, “Hey, I have this data—”

Compress it into a model.

—so it makes sense for me in my product, but I could repackage it in the form of a model and sell it. It’s just a whole new business model for the economy. There also wasn’t any place that was a home base for LLMs in terms of seeing how much they were being used and seeing who was using them and why.

The closest would be Hugging Face.

Hugging Face was the closest. They had just started the Hub a few years ago; before that, they didn’t have it. Hugging Face also didn’t have the closed-source models, and you couldn’t use the models at the time. There wasn’t data about who was using them.

There are 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 why people were choosing these different little ones that were emerging over time.

3. Discord, Open Models, and OpenRouter’s Origins

Yeah.

Got it. And then, Anj, you’re no stranger to wanting more model diversity. At the time, you were a couple of years into your Anthropic journey, which we covered in a previous podcast as well. What was your introduction to Alex?

Well, the introduction was, I think, 13 years before that.

Oh.

But the OpenRouter handshake actually happened right over there, if you remember.

Yeah.

Alex and I met—I believe we were sophomores, if I remember correctly—at a Review meeting for the first time.

I think so. Yeah.

Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel had started back in the day. For whatever reason, Alex and I both showed up to one of the meetings, and I remember the editor-in-chief was a mutual friend of ours. Lisa was a really great editor-in-chief. Part of an editor-in-chief’s job is to assign responsibilities to people and make sure the work gets done.

I may be misremembering the details, but I remember thinking it was kind of surprising that, at the time, there was no dedicated technology section in the newspaper.

Because it’s political, right?

Yes. States and things.

Yeah.

But to take us back in time, you may remember this: there was this technology legislation being debated called the Net Neutrality Act. Net neutrality is inherently a political concept, right? It’s about the regulation of internet broadband access.

There was a community of us who were technologists but also debating the politics of technology, and I thought the Review would be a great place to write about that. I was working on, I think, a net neutrality article, and I remember proposing, “Maybe you should start a technology section.” Alex was one of the only people who said, “Yes, that would be cool.” I forget whether we ended up writing stuff together, but that’s when we first met. It was 2011 or 2012—I forget which year it was. It was one of those.

It was at Old Union, if I remember correctly. That’s where we used to meet.

Along the way, Alex and I have had a chance to hang out often. Probably the time when we had the most professional overlap was when I was running the platform at Discord.

It had become this explosive kind of platform for crypto.

Yeah.

And NFTs in the middle of the pandemic, which also, by the way, you were in charge of safety and security as well, right?

I was the head of platform, which meant all of the crypto DAO and NFT launch security debugging fell on me, along with the phishing and social engineering attacks, and a ton of DDoS attacks that we were getting hit by. This was around the time I started teaching security at Stanford, CS53, and Alex was at OpenSea at the time. I was trying to figure out how we could defend against all these attacks, and at peak—I forget if you remember how much NFT volume was running through Discord—but it was a meaningful amount. It was several billion dollars in NFT volume, or GMV, so to speak, running through the platform, and it was all coming from OpenSea. It was these buy-sell-trade transactions.

I mean, the server—the DMs in Discord?

Yes. That was when I think we had hung out professionally. But a year after that, OpenAI gave Discord early access to GPT-3—sorry, GPT-3.5, actually. GPT-3.5, which is the RLHF version of GPT-3.

That was around the time we made a Discord bot with OpenAI for internal deployment. Since I was part of the deployment team, there were 2 use cases. There’s actually a post now called “Discord Is Your Place for AI With Friends” that somebody sent me recently, which I wrote and published in 2023.

One use case was Clyde, a first-party friend inside Discord that could help you set up your Discord server, talk to you about onboarding, and get your friends to hang out more. The other was content moderation.

One of the realizations we had with content moderation was that it would refuse to moderate. It would just refuse our prompts because the RL and post-training were very early, and our prompts would trigger it. It was like guardrails.

We told OpenAI, “Hey guys, we need access to the weights, because if we’re going to 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.”

They said, “Sorry, guys. That’s not how this works. We’re a closed-source company.” That was my first realization that we needed open models, and that enterprises would need more control over capabilities. Ultimately, they would need some kind of control plane or management system to orchestrate these open models.

But there were no good open alternatives until maybe 6 months later, when Llama came out. Six months after that, I led the Series A into Mistral, which was started by Guillaume and the Llama team.

4. The Coming Wave of Agentic Fraud

That was around the time I remember hearing about Alex launching OpenRouter and thinking, “These worlds are going to collide. I don’t know when it’ll make sense to team up.” Alex was so early and could see it. I think he was totally right about this ecosystem starting with Llama, which then needed an easy layer to manage.

I was approaching it from the enterprise perspective because I had been the VP of platform at Discord. It was my job to ensure that, when we deployed models to 250 million users, they did what we wanted them to, and that was very hard. If you outsourced it to the labs and they controlled the guardrails, and their guardrails or safety policies forbade the model from responding to your prompts, that was quite catastrophic.

Yeah. But moderation is something they want to support, and obviously they would work with you to give you a moderation endpoint, which they offer for free.

It was an interesting use case. They did give us a moderation endpoint. However, as you guys know, every Discord server is like a mini-deployment of itself.

The use case was that, instead of having human moderators who had to interpret the norms of the community, you could give those norms to the model. Every subreddit and public Discord server has its own rules that users specify in Discord.

Humans used to read those norms and enforce them every day manually, observing each message in these communities. These communities had millions of users. We had a 5,000-plus-person team globally on the Discord content moderation team. These were outsourced contractors who had a really tough job.

The idea was that, instead, if you could give the norms of that server to the LLM, the LLM would do custom moderation for that server. It was almost like in-context moderation for that server. Many of those servers’ norms just violated OpenAI’s rules.

We had our own custom evals, so each server had its own custom eval. But at the time, OpenAI’s evals were so primitive in their thinking about how to deploy these LLMs. The post-training prompts were often super heavy-handed: “Anything about Harry Potter, anything that has trademarked content—refuse.”

If it was a Harry Potter fan community—and this was a real use case involving content moderation—the LLM would just refuse.

Yeah. And that was just not precise enough.

Another example we heard was when someone was trying to write a detective story and there was 1 chapter with a lot of violence, where maybe someone killed someone. The LLMs would just refuse to help with that part of the story.

Yeah.

Then the user would be like, “Okay, this is not structurally inherent to LLMs. There must be some choice out there so that I can switch to another model when I’m getting a refusal or a bad result from the main one that I have.” That tension also drove me toward a marketplace.

5. Why “One Model Wins” Was the Wrong Bet

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 were some of the struggles?

I like getting stories out of you about how other VCs don’t get it. Anything you want to talk about now, now that the early journey of OpenRouter is done? You can obviously talk about some of the early-days stuff.

The biggest objection we got was big-model wins, which is where all the value—

Scaling laws.

Yeah, scaling laws and natural network effects are just going to accrue to 1 company, which will be a Google-style monopoly, just like how Google won the search market by a large margin. You’ll just be fighting for scraps at the end, basically.

That was probably the biggest objection we got. It is interesting that Google won the search-engine race by such a huge margin. Had there been more interesting benchmarks, or had search engines been a bit more like LLMs—where they’re services that you can build companies on top of—that might not have been the case.

LLMs don’t merely have a user interface. They’re also ways of building entirely new businesses. A Google-level monopoly would be like the Dutch East India Company times a quadrillion in magnitude, because the whole economy ends up depending on the 1 monopoly as well. It didn’t seem like that would be a really crazy outcome if it happened.

It’s also less likely because the economics of creating good competitors are much more decentralizable.

Everything Alex said is true, and I came at it from a completely different perspective.

Yes, this is why we’re here.

The scaling laws were never, in my mind, a bug. They were always a feature for why OpenRouter would be very valuable. I was one of the first investors in Anthropic, and it was obvious to me that other researchers in our friends group—and I went to graduate school for machine learning and had a lot of friends in the ML community—understood that the bitter lesson holds.

I thought, “Fantastic. Now we have at least 2 proof points that compute scaling works: OpenAI and Anthropic.” By the time I think we decided to team up on OpenRouter, I had already invested in Mistral, Black Forest Labs, and Luma. So there were multiple model companies and teams that I was working with.

But you did other modalities, whereas this is different modalities.

6. Why Model Labs Struggle With Distribution

Exactly. It was so obvious to me that an ecosystem of different kinds of models was being created. This whole doomsday narrative that only 1 company will dominate like Google may be true, but, 1, I don’t believe that, and, 2, there was so much extraordinary innovation happening across several different research teams.

The shared problem I was noticing across all of them was that the research teams were fantastic at figuring out how to reason about new capabilities. They think in terms of capabilities, but they’re not developer-mindset-oriented. What happens after the training is done and the checkpoint comes out?

You’d be shocked how similar the early pre-training teams at Anthropic, Black Forest Labs, and Mistral were in their default approach to taking their research out of the lab and scaling their impact. It was often, “The checkpoint is done. Put it out as an API. Done.” Then there would be crickets.

In the case of Claude, the first Claude checkpoint was actually 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. They had no plan—no plan—for how to get developers to actually try it out.

If you go to the Claude 1 blog post, you’ll notice they had 3 developer examples for users of the API. One was a Discord bot, and the second was my wife’s startup, Juni Learning. Then there was Notion, because these were all friends of the Anthropic team.

That’s how 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: key management, provisioning, simple endpoint management, version control—all these things that the scientists and researchers would say, “That’s plumbing. I don’t deal with that,” right?

Instead, Alex came at it from that perspective. It was so obvious to me that every single lab I was funding would spend literally, sometimes, billions of dollars on training, and then a checkpoint would be done and there’d be crickets. They’d start doing early access because they’d realize, “Oh, that’s right. It’s hard to use a checkpoint to make anything.”

You actually need a whole bunch of plumbing around it to make it usable by a developer. By that time, 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.

With Google DeepMind, once it’s done training a new checkpoint, it pushes a button and blasts it out across all its surfaces, from Google Docs to everywhere you want to know about—even on Android. Overnight, they can deploy a new checkpoint to a billion devices. That invisible infrastructure and distribution advantage is something most people don’t realize.

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 more than 12 months to get to our first 10 million in revenue.

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. The day you launch, we can send a million developers to you.” That was crazy. That was a step-function change in power.

Is that a real number? A million?

I think today it’s like a million. How many developers are on OpenRouter today? We have over 10 million, but it’s hard to know exactly. We do a lot of account deduping work.

If you could get 1,000 developers to actually try the model on day 1 after you release it, do inference, and give you feedback, that’s 1,000 more developers than they knew how to get to on their own. Well, you know, Black Forest Labs had a reputation.

Yes, they had one with Stable Diffusion.

Yeah.

And with Mistral, I don’t know if you guys remember, but the first checkpoint they released was through torrents. It was torrent weights.

Yeah. They just put up a magnet link.

Yeah. There was no API.

Because they weren’t in it for people. It was like, “Okay, download these weights and you guys go.”

He has a story on his side, yeah.

Yeah. I mean, in addition to building a really good developer experience around it, the marketing that we do for different models is totally different and perceived totally differently from the marketing that a model lab does for itself.

Yes. We are a neutral layer looking at this market like it’s a big, dark room, with all the corners completely obscured to users. Users were walking into the room, feeling around, trying to figure out what objects to grab off the tables, and building them into their companies.

It’s an insane way of working. Models are not products where you can just enumerate all their features onto a webpage. They’re all black boxes, including the open-weight ones. You need to shine lights on all the corners of this room so that people can see what makes a model good.

You need the company shining that light to be a neutral third party, which is what we specialized in. In addition to developer experience, there’s also a very important marketing and product-packaging component. The way of routing and discovering models becomes critical to your go-to-market as a provider, a model lab, a server tool, and more in the future.

7. “Just a Wrapper”: Why VCs Misunderstood OpenRouter

This gets to your earlier point about how many VCs just don’t—one of my biggest frustrations is that many venture capitalists just don’t have any operating experience in the field. Unlike a traditional investor who may have come up through the ranks as an associate working on financial modeling, or may not have been a real operator in the field for more than 10 years—which is a big part of the industry now—I had just arrived at a16z about a year after running the platform.

I knew what the challenges were of building a real developer experience and actually being able to create a working piece of software with a model. There were a few investors—I won’t name names—who were looking at OpenRouter and felt at the time, when I would compare notes with people, that it was “just a marketplace.”

Yeah, just a thin layer, just a proxy, just—

A wrapper, or whatever, on other people’s APIs. I was like, you have no idea how strategic the value OpenRouter has created is by being able to orchestrate even 3 APIs in production.

The amount of engineering work and community design that goes into getting that actually live and running in production, at the scale the OpenRouter team had reached, just doesn’t happen by default. That was one of the things that stood out to me about Alex from the earliest days. He understood, from a systems perspective, how to get these flywheels going.

That stood out to me with OpenSea, when we were working together on the NFT integration at Discord. Alex had a level of community-systems thinking around how you get these flywheels going that most scientists and machine-learning people just don’t think of.

We often think in terms of pre-training, mid-training, and post-training—

It’s a linear stage. It’s this linear pipeline. There’s no loop.

Yeah. It wasn’t until much later that the modern feedback-loop cycle really got standardized in the industry. At the time, machine learning was mostly something we did on a laptop when I was in grad school. You’d download a dataset, run some ablations, look at the loss curves, and say, “Great, I made AI.”

The idea that you have to deploy those capabilities, collect feedback trajectories, and then put those into a continuous loop came much, much later. It was very counterintuitive to the traditional AI mindset.

During the investment phase for OpenRouter, I just didn’t try to reeducate a bunch of other VCs on why it was not just a marketplace. I was like, “You know what? I’m just going to invest,” and I was going to take the opportunity to partner with Alex. If no other VCs got it, that was totally fine.

At the time, it wasn’t obvious to several other investors that OpenRouter was more than just a wrapper around APIs. That infuriated me. I was like, “I don’t have time to debate you. We’re just going to invest.”

Then, I think a month later, Matt Murphy marked it up by 10x from our investment. I forget what the exact post-money was, but to his credit, Menlo Ventures realized, okay, there’s actually 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 opining about wrappers. If you say, “An app is just a wrapper on a model,” and OpenRouter is just a wrapper on top of other APIs, that is the most stupid, reductive framework. It’s clearly somebody who has no experience deploying.

It’s the thing you dismiss other things with. Everyone’s a wrapper on everything, right? Some wrappers have value.

I mean, investors are wrappers and LPs, right? Venture capitalists. So, yeah, it’s all wrappers, all the way down to bare metal, I guess.

When I started the whole “AI engineer” thing—I guess, the coining—in 2023, that was the number-one pushback: “This has no value. You should actually just train models,” right?

Obviously, you guys are one of the testaments to the fact that you can build very valuable wrappers, but also very valuable model companies. It’s so hard to overstate that 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 1—people don’t realize the amount of work that goes into accomplishing that.

OpenRouter used like that would happen over and over again, and I remember thinking, “People have no idea how hard that is.” That’s not—

Yeah, we’ve covered some of the inference engineering that goes behind some of this with Baseten and all those—

Well, today you have all those cool codename things, and people guess what Oxy Alpha is and all those things. But I guess one of the things that you’re teasing is: how do you get that initial flywheel going? Today, you have your scale and your reputation, so obviously you drive immense distribution. But when you’re early on, when it’s most—

The bootstrap, yeah. How—what is the bootstrap like?

8. Bootstrapping OpenRouter Through Community

I mean, to bring it back to the early Discord days.

I think we initially connected with this. This is an OpenSea story, technically, but we initially connected when you were on Discord and we talked about the Axie Infinity server.

Yes. Yes.

This server was the biggest server on Discord at the time.

That’s right.

And you were constantly bumping up the limits on the server.

For those who don’t know, about 10% of the Philippines was actually on that server.

I was on that server. It was a meaningful crypto game, but—

There was a Pokémon breeding thing.

Similar. Yeah. There was battling, there was breeding, and then there was a marketplace for trading.

Play-to-earn as well.

Yeah, play-to-earn. The graphics were really cute and fun, and you could get emotional about the Axie that you made.

To start a community like that—which we had to do many times at OpenSea, with basically every early project for us to create a marketplace for it—we needed to make sure that the community actually wanted it. It’s kind of like building something that people want and going and telling them about it. You can do that on a one-on-one basis, but it’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 building things that the community really wanted. We did the same thing for OpenRouter. The Axie community was one of a zillion communities we did that with, and you could just see people sharing OpenSea links constantly in that Discord. Users sharing links is a really clear indicator that something important is going on.

We spent a lot of time first figuring out what the gap was in the technology that people cared about—what the actual problem was that needed to be solved. In the early LLM days, it was OpenAI refusing to finish the prompt or complete the task. It was also the inability to customize models. There are communities that are completely blocked on those issues, and those are the communities that are most useful to learn about, dive into, and explore.

Something that really struck me at that time, as I was just hearing your talk, was that we had these working Zoom calls that we were doing as a sprint around this OpenSea integration with Discord. It was myself, my engineering team, and I think you were there.

I remember Alex, in the middle of one of those calls, after there had been silence and we were all saying, “Oh, yeah, this totally makes sense. Let’s do this,” and everybody was aligned, saying, “No, this makes no sense to me.” Everyone was like, “What? It works. You click on a link, and then it bounces you out to OpenSea.”

He was like, “It’s not a good user experience. We should not do this.” He was the only person out of all of us who actually raised his hand and said that. It made sense from a technical implementation perspective—we were bouncing the user out into OpenSea—and it checked the box of the product-manager requirements on both sides. But Alex went one step further and said, “You know what would be better? If we just embedded the experience right here inside Discord. The link should open up as an embedded iframe, and you can just check out right there.”

Not one person on the call—there were 7 of us who had met week after week—had thought of that.

And it’s the guy who doesn’t work for Discord.

And it’s the guy who doesn’t work for Discord.

Technically, you benefit if they bounce.

Exactly. Keeping the user inside of Discord would be adversarial to OpenSea, and yet Alex put the user experience first. I thought, “That’s special.”

Wow.

Because it’s very hard to have somebody who’s technical like Alex, who understands the developer flow but also understands the best user experience and wants to prioritize that. Those are 2 sides of the flywheel that you can get spinning, and it’s often hard to stop.

You just reminded me that one of those moments was when I realized I had to be better at user experience. I should have been the one who came up with that, and I didn’t. I learned from you, and I think that went into one of our case studies for the PM training program. I don’t know if it’s there because—

You need an Alex conclusion.

Yeah, you need an Alex. Nobody should be surprised why Stripe decided 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 end-user experience. Putting all that together has resulted in this extraordinary scale that very few other marketplaces have been able to achieve over the last 5 years.

Yeah. We should talk about the other reasons for the acquisition, which you’ve written about. I want to proceed somewhat chronologically as well. One of the questions that Dave from HF0 sent in was: When did you know it really started to work? You brought up Mixtral. I don’t know if you want to bring that up.

Oh, yeah.

You obviously overlapped with them, so—

Yeah, there was—I don’t know when. There’s no one moment where I was like, “Oh, this is officially starting to work.” It was incremental, really super early on.

Before OpenRouter, I wanted to explore a bring-your-own-model experiment.

Anyone familiar with crypto knows MetaMask and all these things.

Yeah. It felt like doing a MetaMask analogy for AI would be a fun way of exploring that. At the time, there were no AI apps. There were probably as many AI apps hitting an LLM through an API call as there were games just doing it in JavaScript.

There was a moment in time when it could have been the case that web apps called LLMs through the browser, through some kind of desktop-managed app controlled by the user. Of course, there are many reasons that didn’t happen, but back when things were that primordial, I built a Chrome extension called Window AI.

With Plasmo, which I had come across early on. I was like, “Who’s going to actually use this?” You did.

Plasmo had a couple of users. I think Phantom was using it. There were some other real companies, basically.

It was React for Chrome extensions. It compiled to all these kinds of things, like Next.js for Chrome extensions.

And, yeah, I built Window AI on top of it. The creator of Plasmo started contributing code to Window AI on GitHub, and that turned out to be Louis Vichy, who is the co-founder of OpenRouter.

That’s how you told me you met Louis. Okay.

That allowed users to configure which model they wanted to use for a webpage in their browser, and then the app would just call out to that model when it needed to do things. It wasn’t the right form factor for LLMs, but it was a fun experiment. You learn a lot.

The main learning was, okay, this has to be an API, and there has to be more of a developer experience here and more of a discovery experience as well. I didn’t know where to use these models, and a little Chrome extension wasn’t going to help me discover them. It wasn’t enough real estate. I needed more space, visuals, graphs, examples, and images. I needed to be able to explore both as a human and as an agent.

That’s how OpenRouter came to be.

9. Crypto, Midjourney, and the Early Generative AI Ecosystem

A meta point that I think is underappreciated, but Alex is reminding me of, is that we were quite lucky to be adjacent to the crypto community in those days. In hindsight, crypto ended up being a dress rehearsal for generative models.

If you think about the Axie experience, Alex is totally right: There were not that many AI apps at the time. My job was to be the head of platform at Discord, which meant creating a general-purpose place for communities and friends, and for developers to create apps, bots, and other services that could be deployed across Discord.

While 80% of the attention at the time was being spent on crypto, because that’s where all the NFT volume was, I was spending 20% of my time with a friend who would get hot pot with me, and we would play Magic: The Gathering on weekends. 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 know, was that David?

That was David Holz. He was a good friend. David and I had both been failed AR/VR founders before that.

I remember this: Midjourney was one of the fastest-growing communities we had after Axie Infinity started to peter out. Many of the abstractions and infrastructure decisions we made to scale Axie happened just in time, because Axie did this and then fell off a cliff. As Midjourney was taking off, we explicitly decided to help David make the Midjourney server 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.

The single-player Midjourney web app on its own, like Midjourney.com, had terrible retention because people would show up and see this empty field. It was kind of like DALL-E 2, and they would type in “cat” or “dog.” It was paralyzing for them to have this blank canvas that they had to fill because they had never used an AI model before.

But instead, in a Discord server, you could see other people using it and riff off their prompts. The engagement was off the charts, and scaling Midjourney from 0 to 10 million monthly active users was a much smoother approach post-Axie Infinity.

And don’t forget the best-of-4 pictures, which is the feedback loop—the RLHF feedback loop. By the way, separately, Tom Brown, David, and I used to play Magic: The Gathering on weekends. One group of friends would hang out, and these concepts were being discussed all the time.

There were few of us who bridged both the crypto world and the AI world. Compared to crypto, where the question was always, “What’s the use case for this technology?” there was never any need to ask that for AI, because the use case was so visceral. It was like, “I can create now anything I can imagine. I can write novels. I can code.”

The infrastructure that those of us who believed in the distributed-systems value of crypto—the censorship-resistance part—found this use case that was explosive. Between Midjourney, Claude, which was a Discord bot pre-launch that we were using internally as an LLM, and ElevenLabs, which had a TTS model that we also had on Discord, Discord became this petri dish for early apps to innovate.

I don’t think it’s a coincidence that they found a home there before OpenRouter gave the world a public home or a storefront. Discord was this almost petri-dish storefront that had piggybacked on the infrastructure we’d built for crypto communities. I think Alex was one of the first people to realize, “Wait a minute. These apps need their own home on the internet.” 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.

So then my question is: My perception is that OpenRouter is not that Discord-centric, right? You have a Discord, and you use it to engage your community, but it’s not like Midjourney, where that is the primary way people experience OpenRouter.

Midjourney really helps because you can see visually, very quickly, how people are using the model and how to prompt it.

I think that is partly why the server was so critical. It’s the user experience. It actually adds a ton.

Yes. You can go the whole mile with just prompting via the Midjourney Discord server, getting your images, sharing them, and having fun.

For OpenRouter and LLMs, you need a lot of user experience around LLMs to make them really usable. Seeing examples from other people is also not as useful because it’s a lot of stuff to read, and it takes a long time. You need codebase integration—not impossible to do in a Discord server, or technically, it’s possible; I shouldn’t say that. It’s just not a great developer experience.

You need governance. At the point where you have codebase integration, you need governance for managing the LLMs that have access to it, the data policies, which teams have access, and all that stuff. It needs a lot more than a Discord server can provide.

Well, in addition, you’re not wrong, but there’s also the very important distinction that Midjourney was an end-user application. That’s why Discord, which has 250 million monthly end consumers, made sense as a host for that application experience.

What I knew was going to happen soon after Midjourney found explosive product-market fit—because I think when Midjourney launched, its path from launch to a $100 million revenue run rate was less than 8 months—was that, shortly thereafter, Stable Diffusion launched. All of us used to hang out in the Stability Discord. It was the LAION community that Stable Diffusion came from.

When Stable Diffusion came out, I realized, “Oh, now other people can build their own Midjourney.”

Because until then, Midjourney did not have an API. They were a full-stack company: They were training their own models and deploying them as an application.

But if you wanted to build your own Midjourney, there was no API of that quality. DALL-E 2 was still quite primitive. Midjourney actually had great quality, and then when Stable Diffusion came out, suddenly there was this new capability in the world: A developer could create their own Midjourney.

I think that created the need for something like OpenRouter, because then you needed an API. If you had the kind of creativity of David Holz, 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?

What OpenRouter enabled is that, when you have open-model alternatives to closed applications, OpenRouter’s value in the world becomes extraordinary. Any developer can just show up and—

Did you just say “the shape of OpenRouter”?

Oh no. The real—I’m misaligned now. I’ve been overtrained. I’ve been using Claude way too much, haven’t I? “Claudish” is what people say.

Claudish. Oh God, I got untrained myself.

10. Mistral and the Birth of the Inference Marketplace

And I just want to cap off the Mistral side. My TL;DR is that there was a Mixtral price war, is what they called it, right? Around about December—it was 2023 or ’24. They launched Mixtral 8x7B, and the price went down by about 80%.

To me, that’s very positive because it was the first real competition to host Mixtral. Is there more?

Yeah, I’m trying to remember all the things that happened. We saw that model come out and immediately saw people say that it was the best model in the world. To my knowledge, this was the first time an open-weights model was called that in serious terms.

It’s hype, right? Is it?

It was hype. It was also hype from AI influencers at the time, and there were many examples where it was outperforming GPT-4. People really wanted to try it out and see, “Is this going to be true for me too, and if so, at what price?”

The inference landscape was really messy.

We cleaned it up. It allowed providers to compete on price, so we could give users the best price in one spot. I think it was the first clear example of a provider marketplace working in a way that adds value to developers.

Alex, you may not remember this, but I think we met for the first time a few days after Mixtral came out, at NeurIPS, at a luncheon.

Yeah, that’s where I also met BFL.

And Guillaume was there. I was at NeurIPS at that time.

You were there too.

We had just announced the Mistral investment, and I remember Guillaume was over there. I remember turning to Guillaume and asking him, “How are you feeling after the launch of Mixtral 8x7B?”

In his typical French fashion, he was like, “I mean, it’s an okay model. It’s not that good.” I was like—it was so, you know, in contrast. But I remember him also saying that part of the reason a lot of people thought it was better than GPT-4 before was because of the speed.

It was an MoE model that they had absolutely figured out how to make super efficient. It was on the Pareto frontier, and this is an important thing with LLMs: Sometimes, when they’re faster, you think they’re smarter.

Even though, if you did those common evals—and I don’t actually remember; I think we should go back and figure out what the data says—I wouldn’t be surprised if it turned out that, on an N-of-7-attempts basis, GPT-4 was smarter on evals. The perception of correctness, or accuracy, would be better, but from a human-preference perspective, people felt that it was faster because it was smarter.

Actually, most queries do not take that long.

Don’t take that long, right? This is the start of humans as router, which eventually becomes OpenRouter as router of the auto—you know what I mean?

Humans are the routing mechanism. I’ll ask the fast model first, and then, if it’s not good enough, I’m going to upgrade manually.

But then it’s going to auto it.

I hadn’t thought of it that way, but that makes sense.

There are a lot more techniques, like fusion. Fusion is a thing that we should talk about. Before I move on to those things, I just want to close off the early, early years.

11. OpenRouter vs. LM Arena

One thing that I observe—which you are also an investor in Arena, right?—is that we talked about Midjourney having that feedback loop of A, B, C, D, and choosing the best one being very important. You understand the flywheel. So how come you didn’t build Arena, and how come Arena didn’t build OpenRouter?

Well, Arena started before OpenRouter, right? They had the school project, and then they became a—

Yeah. I know you had some Arena experiences, like the head-to-head comparison type of things, but you never really went as hard as Arena did in doing head-to-head experiences. LMSYS actually did have a router project based on Arena ELOs, which they never commercialized.

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. I think there’s a branding reason that there are 2 companies here. When you set up OpenRouter, there’s no training on prompts, aside from what your provider policies set. OpenRouter can’t see your prompts or completions.

If you want to see that as an organization, you have to opt into it and enable it. We’re pretty conservative and careful about data policy, security, and privacy. Arena’s business model is oriented around the labs—

Because they give it for free, right? You don’t give it for free; they give it for free.

Yeah. But we do give some—we have free endpoints, too. But for those free endpoints, I think we’re not collecting any of the prompts. We’re not monetizing the data unless you opt into it for some reason.

You’re not the first person to ask me this, and Alex knows this, but I was the interim founder—the first CEO of Arena for the first 5 months, when we were helping Anastasios and Wei-Lin spin out of Berkeley. I invested in that before OpenRouter, but the comparisons that outside folks would make between the 2 projects were very strange to me because the missions were completely different.

The founding entity for Arena, we called it the AI Reliability Institute, because it was actually there as an eval service. The data, so to speak, that they were originally offering the labs was: How do you make the evaluation of models more reliable than the state of the art at the time, which was really just a finger in the wind?

That was what Anastasios and Wei-Lin’s PhD work as scientists at Berkeley was focused on: statistical methodologies for correcting eval estimates based on intrinsic biases, how you collected the data, style control, and stuff like that. That is very much the kind of problem a scientist is trying to solve. The highest-expectation customer for Arena was always a post-training researcher at a lab.

Whereas the highest-expectation customer from my perspective—the one Alex really understood, and the mission—was a developer, right? Someone who then takes the result of the research and produces an application that’s deployed to the world. It’s actually a completely different problem and person that these 2 teams were focused on.

From the outside in, I don’t know if you remember this, but I have a distinct memory of a few weeks before we did the term sheet together for OpenRouter. I had given you a call because we were trying to get a pooled dataset together from OpenRouter and Arena to create an open-source repository of prompts.

These projects were so different in their goals that it was totally normal to me to be like, “Oh yeah, let’s call Alex and see if you’d want to team up on pooling data,” because they were so different. We actually didn’t have that kind of data at all. We didn’t have API prompts. We didn’t have what developers want to do with the models, which is very different from what researchers inside a model lab want to do before releasing the model.

Yeah.

Does that make sense? To this day, I think you see this difference. Even though at a 30,000-foot level you could conclude that Arena and OpenRouter are adjacent, the road maps, the missions, and so on, at the time at least, were going in very different directions.

The ideal customer—I get it. I totally get that. As a founder, I want to own everything, right? This is clearly the adjacency, so I’m like, “I’m going to explore that.” By “own everything,” I mean you don’t know what to do yet, so you want to make sure you capture TAM. I think what he said was that you want to own the entire infrastructure space, and so you’d kind of expand to whatever the demand is.

Yeah. I think that’s hard in reality, because serving multiple customers is hard.

Clearly, you know this is the only one to focus on, right?

12. Focus, Anthropic, and Roads Not Taken

Yeah. I still think even in the age of AI, focus is underrated and critical—not just because you end up with a better product by focusing your humans on it, but also because the world knows what your focus is.

The world can map, “Oh, I have this issue. Which brand out there is going to help me with that issue?” This is the brand that’s known for that focus. If I want real attention on this issue—this really matters to me—I should go with the brand that cares the most about it.

To underscore Alex’s point about how important focus is, in the early days of Anthropic, it was not easy. People think that the early days of Anthropic were super easy because they were the GPT-3 guys who left, but it was actually very competitive. The company was starting $10 billion behind OpenAI, right?

To get to the frontier, the big question was: What do we want to be known for? What’s the mission? The mission was AGI pair programming. To the exclusion of all kinds of other things that were really shiny at the time, like image models and video models that were getting lots of momentum, the Anthropic team was like, “We just got to focus on coding.” That is the core capability that we’re focused on.

Today, you can see the results. It’s a trillion-dollar company within 5 years. That focus—the focus on who your highest-expectation customer is and how you exceed their expectations, because exceeding anyone’s expectations is hard and doing it for multiple customers is even more difficult—is part of the reason why OpenRouter succeeded, and Anthropic as well.

Was the focus on coding that early, though, or did it come later?

Literally from day 1, it was AI pair programming. “Responsibly commercialize an AI pair programmer” was the seed memo. That was when I invested, right? We actually refined that memo a lot. You have to ask Dario and Tom for permission on that, but it’s an extraordinary piece of writing that they had put together. Responsibly commercializing AI pair programming was the mission from day 1.

I would say there were maybe a couple of moments in the company’s history where they did experiments to see if little detours made sense, like a general chatbot, Claude, when ChatGPT was really taking off. But at the end of the day, especially once they got their significant pretraining compute online, all the main evals at the company, for example, have always been coding evals—long-horizon agentic programming. From day 1, that was always the focus.

When Claude Instant came out and Claude 2 came out—

Yes.

I remember the marketing mostly being focused on prose, like, “This model writes better,” and long context.

Long context.

Long context. This directly affected me because I built something on that.

What did you make?

Small Developer, which was my Devin before.

Oh yeah, Small Developer.

Yes. I think all of that is really good. Focus is another question that people do want to ask. You could have built any other things, and obviously OpenRouter was working, working, working. Were there other ideas that you wanted to pursue that you turned down—the paths, the roads not taken?

We made a couple of prototypes for things that we didn’t launch. One was a fine-tuning model as a service.

A lot of that—OpenPipe and all those things.

It was in a very consumer-y form factor where you would give us a YouTube video or 2 or 3. We would extract all the transcripts from it and try to fine-tune a model to talk like the person in the video, or the people in the videos that you sent. So, a really easy way of creating a fine-tuned model based on some videos that you like.

That would be so useful.

We made it, too, and nobody used it. We didn’t actually test it with that many people because the model marketplace was our main focus. It was growing, and we were building more conviction in it over time.

Just as a creator, I’ve been pitched many things like this: “You have 500 hours of recorded voice of yourself. Make a thing of you and charge access to it.” It works for OnlyFans; it doesn’t work for regular people.

I think this is mostly just a glorified RAG bot. Whether it’s in the weights or outside the weights doesn’t really matter. You’re just doing RAG on the videos, and people ultimately always want to find the source video that directly answers it.

My use case was mostly to practice with myself, because I often like to see the way I practice for a job interview, or if I’m hiring a candidate, or public speaking, or whatever. I wish there were a good mini-me that I could critique, because it’s kind of hard to pull yourself out. I would never offer it as a service to other people: “Pick your top 5 mentors that then talk to them instead of talk—”

That would be cool, too.

Yeah, that was a replica, and that was the use case we were aiming at.

I see. It’s like you want to create an experience like AI Steve Jobs.

AI Steve Jobs was the initial use case. Even though it’s not allowed, that’s a common prototype.

Talking about adjacencies, fine-tuning as a service as part of the router service is something that I would typically think about as well, right? Why don’t you do that? If people are already running their inference through you, you store everything, log everything, fine-tune to a smaller model that’s cheaper and faster—all these things are within your control, right? You didn’t do that, but other people would have pitched that in the general state of an infrastructure startup.

I think you were just maybe a little bit early, because today that’s an extraordinarily fast-growing segment. From Mistral, where they do a lot of enterprise deployments, I mean, it’s often fine-tuning—custom models for ASML or whatever.

But not as a router. They’re just saying, “I come to you because I like your ML models. I want a custom model,” right? It is not, “I want to run all my OpenAI prompts, store all my results, and then just move off OpenAI.” They’re not doing that as a way to export off dependency on a frontier lab. I have not seen that yet, which was your kind of decision to make?

I mean, we really decided—we leaned into our focus and figured that there are inference providers that do want to help companies do that. It makes sense for us to partner with them, give users lots of choice, and figure out what gives them competitive advantages. It’s a whole new business, basically, and there’s value in being a neutral marketplace that just works with those companies.

Could you share a little bit—to swyx’s point—how you prioritized features? You’ve always done it so elegantly. I never—you know, it just happens, and you make all the right decisions that always have product-market fit, from the outside looking in. Consistently, you seem to have prioritized a lot of hit features that worked. Maybe I have a sample-set bias or whatever, but swyx, can you list what you think hit features worked well?

The leaderboards.

Leaderboards, okay.

Yeah, you know, from day 1, the feedback—

Charting, okay.

But he had plugins. He had—I think there was a whole thing I want to get into about chat completions versus—

Yes.

Chat completions versus completions, and also, let’s call it, the rise of the reasoning models and how you deal with multimodality. All those things—the huge ones.

13. Mixture of Models and Model Fusion

There’s one. I think it was in early, very early 2024, when we thought it might be interesting to fuse the results of multiple models together. We launched a prototype called MoM, Mixture of Models, that let you pick a couple of models—we’d pick them for you—and then it would fuse the results together at the end. It would show you all the intermediate results in this big Kanban-board-looking product.

What does the fusion at the end—another model?

Another model. The smartest of the 3 in the set.

So this is like a council idea?

It was a model. It was like a very early LLM council.

This is a multi-agent swarm, as they would call it at one of the frontier labs in the early days, you know.

Yeah. Some of those ideas are going in the right direction, but the devil’s in the details. There’s a lot of product refinement needed to make them really work. They take your focus away from whatever else you have going on, and there’s a lot of community building and learning that you need to do. The technology might be too early, so there are all kinds of reasons they might go wrong.

In our case, the technology was a little too early. In other words, the fused result was a little bit worse sometimes, or the same as the best model being used to fuse, because the best model was so far ahead of options 2 and 3 at the time. Over time, the top 3 or 4 LLMs have gotten closer together—still notably divergent, but all capable of inserting pretty interesting ideas. RL has basically expanded the surface area of creativity for machine-learning researchers within each lab, so they can diversify the reasoning power of different models more effectively. At least, that’s my theory for why fusion works better than it used to in early 2024.

The technology was a little bit too primitive, and the form factor wasn’t right, so we would have had to go through a couple more iterations. We decided to just delete all the code. Then, years later, in early 2026, we were like, “Let’s bring it back.” The research was looking kind of promising for fusion, and there were now 2, 3, 4 top frontier models that were all really good.

I was frequently trying to consult multiple models to get the best results. I ran a little personal experiment where I was like, “I’m going to do an architecture plan for a code change, give it to all the models, fuse the result, and ask all the models if the fused result is better than the individual result each model came up with.” They all said yes, that the fused result was better. This happened a couple of times, and I was like, “Okay, spot-check: pretty good. We should benchmark this.” That’s how we built Fusion.

Yeah, and it came on your Fable. So you were like, “This is Fable-level.”

14. Sonnet, OpenClaw, and OpenRouter’s Explosive Growth

Yeah. Let’s start leading up to this year, which we haven’t gotten to yet. Can you mark out the main milestones in the journey? It seems like your promise was routing. You decided the business model very early: you take a cut. What are the major milestones that inflected the growth? You’re growing 9% week over week now—is that the official number?

In terms of token volume, I think that sounds about right.

Can you mark out the brief history of OpenRouter up to the acquisition? Let’s call it—we’re just talking about people having your birth moment with the Mistral stuff, where people were really competing. You had your State of AI thing, where it was very cute: “100 trillion tokens,” because now you’re doing 10 a week—

We’re doing 10 a day.

10 a day now?

Yeah, more.

So you do this in 10 days. What are the major points? There’s a smooth curve, but you feel the inflections.

A lot of this is oriented around model launches. We had a huge focus on prose all the way through May 2024, because coding was just not there and no apps were able to build much on top of it. There was diversity in models, but not a wide diversity, and not a wide diversity in use cases. DreamTavern was one of our top apps at the time. The creator of DreamTavern now runs product at Cognition, the company behind Devin.

Then, in the middle of 2024, we saw Claude 3.5 Sonnet come out—an incredible leap forward in coding. We saw the dynamics of apps building on top of us change. We saw a huge surge in volume in users using OpenRouter, and this is when I think people started to look at the money that they were spending and get a little bit like, “Whoa, what’s going on? I might need to think about more cost-efficient but equivalent models.”

Shortly after that—I think it was after Sonnet 3.5—Mixtral 8x7B came out, and everyone was like, “What? This is the model the open-weights community delivered.” It was really good timing from the start.

Basically, all the AI labs are just helping you out.

It takes an ecosystem to grow an OpenRouter.

Yeah, that was it—the early ecosystem.

It was a seesaw action. Model labs would come up with some sort of frontier innovation, usage would surge, then users would look at their invoices 30 days later and say, “Whoa, what’s going on here?” Then open-weight models would deliver cost-effective options 3 months later. We saw that happen several times.

One thing you also did with the coding agents was break out which were the top coding agents, and they loved that leaderboard—the Cline versus Roo Code versus whatever.

Yeah. Cline was at the top of our leaderboard at the time. We then—at the end of 2025, and I’ll skip forward a little bit—had quite a few coding apps on the leaderboard, but they were all IDEs or terminal-based agents.

At the end of 2025, we saw OpenClaw appear. OpenClaw was particularly interesting because, first, it was a new form factor that brought in a new type of user—not just a developer, but a productivity or internet creator who came to AI for the first time. It also had an interesting architecture where it was calling your chosen model for these heartbeats to see if it was still alive, in addition to actually using the model for real tasks.

The heartbeats are something you don't want to pay a lot for. The auto-router that we provided was really useful to this wide range of users all of a sudden, and we saw it rocket exponentially. Then we saw OpenClaw blow up, and a couple of other apps lean into that new paradigm and do something similar.

Hermes came out and really leaned into things like the auto-router, built a really good community, and leaned into skill management—making it really easy and effective for people to set their memory in the agent and build really good skills.

Which is another thing you never did: memory, skills, sandboxes—all these adjacent things you could have done.

Could have, but I think—

It's hard to bet.

There are also things that really matter for developer use cases that were emerging at the time. Developers wanted to architect those things.

Those were kind of critical to building a good user experience. It's really been hard for companies to find abstractions that work for all developers on the memory layer. There are some—Mastra has done a pretty good job, for example—but developers have lots of varied preferences for them.

The way our leaderboard has changed over time is kind of like a movie of how the AI space has changed over time. If you go to the Wayback Machine and look at the rankings, the leaderboard, and the apps leaderboard over time, it sort of shows you what's happened in AI over the last couple of years.

To me, the coming-of-age moment was when Andrej Karpathy was like, “I no longer read r/LocalLLaMA because I just go to OpenRouter's leaderboard.”

15. Why Stripe Acquired OpenRouter

Which I remember. I think he probably said, “Sorry, guys, I'm going to send a bunch of traffic to you.” So I also want to bring it into the Stripe thing: how does that conversation start?

We had this longstanding relationship with Stripe from many different projects that we had worked on with them. We invest a lot of effort in countering abuse.

Token fraud.

And token fraud.

Can you give some numbers, just so people understand?

I think I posted about this. We blocked 10× as much dollar volume last month as the month before. The types of token fraud are diversifying quite a bit.

There are fraudsters going after typical stolen credit cards, but there are also people trying to resell traffic against the terms of service. There are hacked accounts. There are people who just lose access— their whole company is compromised and they don't even realize it—and we help them regain control and detect it.

There are accounts that are reselling inference on the side. There are accounts dealing with an accidental runaway agent, and they don't realize it. It's not a hack, but it's something that blows up and the company doesn't want it.

Our trust and safety team works a lot on all of these categories of problems and helps block and detect them. We've built models around them, and we've worked closely with Stripe for a while on this.

I think it's going to become a huge problem in the ecosystem. We're already seeing a lot of companies start to see these fraudsters spread and look for other ways—other than OpenRouter—to pursue other fraud vectors.

If you're making a gateway or selling generalized inference, you are a target for fraud. If you're selling very discrete intelligence products—products that are doing something pretty specific, but not just reselling inference with some added capability—then you're way less likely to get these fraudsters.

I think we'll see companies move away from just reselling inference with some sort of added capability and move toward discrete tasks, charging for those tasks and those enhancements, and letting people bring their own inference in a third-party way.

Whoa. Okay. And, yeah, obviously you would power that. But do people pay for outcomes—

—or per task?

I think people will pay for outcomes. The Datadog pricing page is a good look at the future to come. Infrastructure companies will charge for different types of events that they're providing, and there will be lots of continuous pricing models that look like that.

Of course, if you go down toward consumer apps, there will be simpler pricing, more subscriptions, fewer events to worry about, and products that are not focused on just adding a markup on top of inference—not just because fraud is hard, but also because the pressure from the labs and from good inference providers to do a commit and then bring your inference elsewhere is going to be very high.

16. Fraud and the Emerging Token Economy

Any comments? To anyone?

I think Alex has done a very eloquent job of describing something that, counterintuitively, I knew would be a thing at scale 4 years ago because of Discord. The particular experience that taught me this was that, as we started scaling Midjourney, one of the primary ways we used to get people to try Midjourney early on was to get them to their first 10 generations.

Ten generations of 10 images generated was roughly the magic-moment activation point we found. Once you'd done 10, you were like, “This is extraordinary.”

For that, we had a free trial with Midjourney. One day I woke up—because they had a platform and I had to monitor it, and I had all these dashboards—and I had 3 missed calls from David. It turned out there had been this flood of new users overnight.

We were like, “This is great,” and he was like, “No, actually, we shut down the free trial.” I asked, “Why is that?” He said, “I want to look at the geolocation IP addresses.” Basically, somebody in China had started to resell Midjourney free subscriptions with the free trial as a way to—basically, it was fraud abuse, right?

Even for a specialized model like Midjourney.

Yeah, and that was an actual application. The big-picture realization I had back then was, hey, there's a new type of unit of value being streamed across the internet called a token, and over the next 10 years, the entire internet value chain was going to have to deal with the fact that the more valuable tokens got, the more bad actors were going to try to get their hands on those tokens.

Anytime you scale something and the payload gets more and more valuable, more bad actors try to get access to that value.

To this day, I don't think there's a free tier. I don't think Midjourney has ever actually turned on the free trial since then, because it was really not an easy problem to solve in terms of trust and safety.

That's why I started teaching the class Security at Scale at Stanford. Between that and the Anthropic learnings, it was clear to me that the need for security at scale was going to be enormous a few years from then.

If you do the math, think about how online payments started roughly in the '80s and '90s and grew to over $1 trillion over the next 10 years. We needed to build entirely new payment solutions to deal with online fraud.

Where we are today is roughly there with tokens, but over the next 5 years we're expecting the token economy to get to roughly $5 trillion. Over the next 10 years, I'd be shocked if we didn't get to $10 trillion of token flow.

If we were starting to see such aggressive abuse and fraud at subscale Midjourney—remember, Midjourney at this point was less than $300 million in revenue run rate a year—I just realized we were going to need entirely new systems to deal with the fraud that was going to happen from trying to get into the token flow.

I forget the board meeting when you brought up that Stripe wanted to partner up, but it made so much sense to me, because when I was at Kleiner 10 years ago, we invested in Stripe.

The whole pitch that Patrick and John communicated so eloquently was, hey, unlike traditional payment tools like Braintree that do a 7-day verification—KYC and email—to get the fraud out of the way, we actually just bite the fraud cost up front as a customer acquisition cost and tell a developer, “Just use 5 lines of code, and we'll start accepting your payments in 5 minutes.”

What'll happen is, over time, we'll collect all this data on the developers.

Cloudflare model.

It's the Cloudflare model, right? And they did. 5 years later, they launched Stripe Radar, and Stripe really is a security company today.

People think it's a payments company. No, the reason there are lots of other payment providers today that give you cheaper payment transmission, but Stripe remains the dominant one here in the U.S. and Europe, is because they've built extraordinary fraud detection over the years.

The same story with Elon and Max Levchin—

—and Affirm. Yeah. I think the story shows up over and over again: every time you have value streamed across the world in large amounts, you need new protection and security infrastructure to keep the bad guys out and allow the good people to have their transactions happen really fast.

And so I think this is why, from my perspective, the Stripe and OpenRouter story is a security story for the internet ecosystem and the frontier AI ecosystem. Without a partnership like that, it becomes very hard to defend the quality of experience, the speed, and all the good stuff without letting the bad guys get in the way.

The second is that there’s this underappreciated thing: all the bad things that Alex described as being perpetuated by humans right now are going to be perpetuated by AI agents over the next 10 years.

Right? So think about the recursive scale we’re about to see of bad actors. It’s not just bad human beings; it’s all the bad agents that are going to be attacking the token flow. It’s very hard if you’re a researcher or an AI lab to reason about that problem, because the only data you have is how the agents you’re training are going rogue. But that’s just a fraction of all the bad behavior on the internet that we’re going to see.

What you need are defenders—new sheriffs in town with cowboy hats—that can see all the bad behavior from AI agents across the ecosystem, from different model labs, different post-trained deployments, and different developers, and take all of that data and say, “We’re going to build a shield for the entire token economy.” Because without that, the amount of fraud we’re going to see from this $10 trillion in GMV and global GDP growth is—like, a huge percentage of that, I think, is going to be fraud and abuse, and we might never get there if people just don’t trust tokens, right? I don’t think this infrastructure exists.

So you have your work cut out for you at Stripe, but I don’t think people have realized the scale at which agentic fraud—bad behavior perpetuated by AI agents—is about to hit us like a tsunami.

Yeah. There’s a lot to dig into there. I want to give you the last word. We do have to wrap. What can people expect from OpenRouter and Stripe?

17. What’s Next for OpenRouter at Stripe

I think this is a really good way for us to accelerate go-to-market and go upmarket more quickly. Also, as you eloquently described, there’s a really clear better-together story here when it comes to improving trust and safety, making it really easy to accept tokens, letting people bring their own inference to your app, and helping developers build on top of inference.

Going forward, we have a really strong brand with OpenRouter, and we’re keeping the brand. OpenRouter as a product, the roadmap, the name, and the brand are all staying the same. What you should expect in the next 6 months is that most things will be what we would have done had we been independent, except everything will be moving faster. That’s kind of our near-term goal. Longer term, hopefully I can comment on it soon, but I can’t now.

Okay. Well, we’ll hopefully do a follow-up at some point. But thank you for being so generous with your time, and congrats on the partnership. This is one of the most beautiful bromances I’ve seen.

Just starting.

Starting from Stanford to here.

Lots more to do. Lots of sheriff policing to do of the

Of the town—the token economy. We need new sheriffs for sure.

Yeah. Awesome. Thank you.

Thank you.