# OpenRouter Acquisition: Securing the AI Token Economy

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-09-26

## Transcript

Okay, we are here in Andres' house.
It was just where all great startups in San Francisco start.
Howdy.
And congrats on Cursor, Mistral.
God knows what else.
You've got so much stuff going on.
There's a lot going on.
Well, Open Router is probably the most has been...
The most, I would say, one I'm excited about recently.
Yeah, yeah.
And we have Alex, first time on the pod, but you've been there 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 sort of product person is PubSub as a product principle.
And I wanted you to maybe explain how you think about what should exist in the world.
Yeah, the PubSub 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, easy, easy example of this.
You have suppliers that are publishing some kind of product to a SKU.
And the skew is kind of like a pub-sub topic that a consumer is subscribing to and just going to consume whenever they want.
And 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.
So Open Router is sort of 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 product SKUs that you can subscribe to.
And then you can continuously derive value and make decisions based on those consumers.
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 sort of realization moment that this would be it?
You've given a talk at AIE about Alpaca as one of your inspiring moments.
Alpaca, I can like rehash the Alpaca moment for a sec.
the very beginning at the end of 2022, OpenAI was the only game in town.
There was like OpenAI, Cohere, and then a smattering of like early attempts at open weight models.
Yeah.
When WAMA came out in January of 2023, it was like, whoa, really exciting.
This is really big.
It outperforms GPT-3 on one or two benchmarks.
But you can't chat with it.
It wasn't like an...
It wasn't actually 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, fine-tuned Llama, and made Alpaca, 7 billion parameter model.
Maybe it was 13 billion parameters.
And it was so good.
Like, I was just...
like on an airplane using it.
I, you know, in many cases, like you could not discern a chat GPT 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.
When you say monetizing your data as what eventually will become an MCP endpoint or as a 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.
It makes sense for me in my product, but 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 you know, 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 kind of framework for imitating it in your own flavor, you have an immediate ecosystem of like...
An immediate ecosystem should arise because there's just a huge gap between the decisions that the single company is making and all of the variations in those decisions that a wider ecosystem can create themselves.
And so then you need a marketplace to discover all of those.
services and all of those products, there wasn't any place on the internet that 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.
The closest would be Hugging Face.
Hugging Face was the closest.
They just started Hug like a few years ago before that.
Yeah, and Hugging Face also didn't have the closed source models.
And they didn't, you couldn't use the models at the time.
And there wasn't data about who was using that.
There are like a bunch of differences between open router 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 these different little ones that are emerging over time.
Got it.
And then Anj, no stranger to wanting more model diversity, at the time, you're a couple of years into your anthropic journey, which we've covered in the previous podcast as well.
What was your introduction to Alex?
Well, the introduction was, I think, 13 years before that.
But the open router handshake actually happened right over there, if you remember.
Alex and I met, I believe it was sophomores now, if I remember, at a Stanford review.
That's what I think for the first time.
I think so, yeah.
Yeah.
So Stanford Review was the libertarian newspaper on campus at Stanford that Peter Thiel started back in the day.
And 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 wanting to, it was kind of surprising to me that at the time there was no dedicated technology section in the newspaper.
Because it's political, right?
It primarily started as like a political, yes.
Yeah, states and all those things.
But to take us back in time, you may remember this, but there was this technology kind of legislation that was being debated called the Net Neutrality Act.
And net neutrality is like inherently this political concept, right?
It's about the regulation of internet broadband access.
And so there was a community of us who were kind of 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 you should start a technology kind of section.
And 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 12.
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.
But along the way, Alex and I have had a chance 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 kind of platform for crypto 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.
And the phishing.
The phishing, the social engineering attacks, that Katana DDoS that we were getting hit by.
It's around the time I started.
start teaching security at Stanford, CS153.
And Alex was at OpenSea at the time, and I was trying to figure out how we could defend against all these attacks that we were...
And at peak, I forget if you remember how much NFT volume was running through Discord, but it was a meaningful amount of 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 buy, sell, trade servers.
I mean, the D in DAO is Discord.
So that's when I think we had hung out professionally.
But a year after that, OpenAI gave Discord early access to GPT.
Sorry, GPT-3.
No, it was GPT-3.5, actually.
GPT-3.5, which is the RL version of GPT-3.
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.
What was the use case?
There were two.
And there's actually a post now called Discord is your place for AI with friends that somebody sent me recently that I wrote and published in 2023.
But there were two use cases.
One was Clyde, which was like a first 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.
One of the realizations we had with content moderation was it would refuse to moderate.
Like, we just refuse our prompts because the RL, the post-training was, we were very early in the post-training era and it would just, our prompts would trigger it.
It's like guardrails.
And we told OpenAI, hey guys, we need access to the weights because if we're going to be doing content moderation at scale, we have 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 kind of control plane or management system to orchestrate these open models.
But there were no good open alternatives until maybe six months later when LAMA came out.
And six months after that, I led the Series A into Mestral, which was started by Guillaume and the LAMA team.
And around that time is when I remember hearing about Alex launching OpenRouter and going, these worlds are going to 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 an easy layer to manage for it, especially for, I was approaching it from the enterprise perspective because I'd been that, like 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 or their safety policies forbid the model from responding to your prompts, that was quite catastrophic.
Yeah, but moderation is a thing that they want to support.
And obviously beyond that, they would work, OpenAI would work with you.
you know, presumably to give you a moderation endpoint, which they offer for free.
It was an interesting use case that they, 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, they often like every subreddit, Discord servers, public ones have their own rules that the user.
Oh yeah, we run the Linux-based Discord.
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 are like millions of users.
So we had a 5,000 plus person team globally on the Discord content moderation team.
These are outsourced contractors.
They 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 in-context moderation for that server.
And many of those servers norms just violated OpenAI's rules.
And so that, it was like we had our own custom evals.
So each server had its own custom eval, but at the time OpenAI's evals, we were all so primitive in our thinking about how to deploy these LLMs that often the post-training prompts were super heavy-handed.
It said, oh, anything about, Harry Potter, anything that has trademarked content, you know, don't refuse.
And if it was a Harry Potter fan community, this is a real use case, that had a content moderation, the LLM would just refuse.
And that was just not precise enough.
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 kills someone, the LLMs would just refuse to like help with that part.
of the story.
Yeah.
And then they, like, these would 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.
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 some of the struggles?
Basically, I like getting stories out of him about how other VCs don't get it.
So anything you want to talk about now that the early journey of OpenRouter is done, you can obviously talk about some of the early day stuff.
Well, I was going to say that the Biggest objection we got is big model win, which is all the value.
Scaling laws.
Yeah, scaling laws.
And natural network effects are just going to kind of accrue to one company, which will be like, it'll be a Google-style monopoly, just like how Google won the search market by a large, large margin.
And you'll just be fighting for scraps at the end, basically.
That was probably the biggest objection we got.
You know, it is interesting that Google won the search engine race with such a huge margin.
You know, I think, like, had there been more interesting benchmarks or had, like, search engines been, you know, had people, like, seen them a little bit more like LLMs where there are 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, you know, a Google-level monopoly would be like the Dutch East India Company times, you know, quadrillion in magnitude because the whole economy ends up, like, depending on the one monopoly as well.
So it didn't seem like, you know, 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.
Everything Alex said is true.
And I came at it from a completely different perspective, which is...
Yes, this is why we're here.
The scaling laws were never like, 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 group, and 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'd already invested in Mistral and Black Forest Labs and Luma.
So there was multiple model companies and teams that I was...
working with.
But you did other modalities, whereas this is literally text.
Across different modalities, yes.
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 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, you know, 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, how like similar the early pre-training teams at OpenAI, sorry, Anthropic, BFL, Mistral.
in their default approach to taking their research out of the lab and kind of 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 Cloud, the first Cloud 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 Cloud version externally.
And they had no plan, like no plan for how to get developers to actually try it out.
And so if you go to the Claude1 blog post, you'll notice they're like three kind of developer examples for users of the API.
And one is a Discord bot and the second is Vivian, my wife's startup called Juni Learning.
And then there was like Notion because these were all friends of like the Anthropic team 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, you know, endpoint management, versioning control, like all these things that the scientists and researchers go, I mean, that's plumbing, I don't really think about it.
Implementation detail.
Right, and instead Alex came at it from that perspective, and so, you know, it was so obvious to me that like, Every single lab I was funding would spend literally sometimes billions of dollars into training and then a checkpoint would be done and there'd be crickets during early access because they were like, oh, that's right.
Like, 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.
And so by the time, I think we did, it was so obvious to me that a distribution platform like Open Router was critical to have in the ecosystem if we wanted there to be competition to Google.
Like unless, you know, with Google, DeepLine 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, you know.
Everywhere, even if I don't know.
Everywhere.
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.
You know, at Anthropic, I think it took more than 12 months to get to our first 10 million in revenue.
And in contrast with Blackforce Labs, I remember the early days, you guys had a conversation with the BFL team, and it was so simple for OpenRodder to say, oh, no problem, the day you launch, we can send a million developers to you.
That was crazy.
That was like a step function change in power.
Is that a real number, a million?
I think today it's like four million.
How many developers are on OpenRodder today?
Over 10 million, but it's hard to...
We do a lot of account deduping work, but no one...
If you could get 1,000 developers, just to put it in context, if you get 1,000 developers who actually try the model on day one after you release it and just 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, BFL had a reputation, but 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 like torrents.
It was like torrent weights.
Yeah, they just put up a magnet link.
There was no API.
Because they weren't in for people.
It's like, okay, download these weights and you guys go through it.
He has a story on his side, yeah.
Yeah, I mean, in addition to the building a really good developer experience around it, the marketing that we do, like four different models is totally different and perceived totally differently from the marketing that a model lab does for itself.
Yes, 1,000%.
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 and trying to figure out what objects to grab off the tables and like build into their companies.
It's an insane way of working.
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 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 specialized in.
So the...
In addition to developer experience, there's also a very important marketing and product packaging component and a way of routing and discovering models becomes critical to your go-to-market as a provider or a model lab or a server tool and more in the future.
And this value, to your earlier point about how many VCs just don't...
One of my biggest frustrations is that...
venture capitalists, many of them 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 an associate working on financial modeling or maybe hasn't 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 a year after running the platform.
And so I knew what the challenges were of building a real great developer experience and actually 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 Open Router and felt at the time, when I would compare notes with people, that it was just, I quote unquote, just a marketplace.
Yeah, just a thin layer, just a proxy.
A wrapper or whatever on other people's APIs.
And I was like, you have no idea how strategic the value that Open Router 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 actually 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.
He just understood from a systems perspective, how do you 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 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, post-training.
It's a linear stage.
It's this linear pipeline.
It's no loop, 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, mostly we did a lot of ML when I was in grad school on a laptop.
So you just download a data set.
ransom 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 came much, much, much later.
And it was very counterintuitive to the science, like the traditional AI mindset.
I do remember doing the investment phase for Open Router.
I just didn't try and re-educate 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'm going to like take the opportunity to partner with Alex.
And if not, no other VCs get it.
That's totally fine.
Because 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.
I was like, you know what?
I don't have time to debate you.
I'm just, we're going to invest.
And then I think like a month later, Matt Murphy marked it up by 10x.
Like I think, I forget what the exact post money was and so on.
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.
And if you're like, oh, an app is just a wrapper on a model, then Open Router is this wrapper on top of other APIs.
This is the most stupid.
reductive framework.
So it's clearly somebody who has no experience deploying products in the field.
It's the thing you dismiss other things with.
Everyone's a rapper on everything, right?
There's some point, some rappers have value.
I mean, investors are rappers on LPs, right?
I mean, yeah, it's all rappers down to bare metal, I guess.
When I started the whole AI engineer, I guess, the coining.
in 3.3, that was the number one pushback is that this is no value.
You should actually just train models.
And yeah, I mean, obviously this is like, you guys are one of the testaments to the fact that you can actually build very valuable wrappers, but also very valuable model companies.
It's so hard to be like the day a model launches, the fact that you have an open router endpoint for that.
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.
You know, that's not...
Yeah, we've covered some of the inference engineering that goes behind some of the base 10 and all those.
Well, today you have all those cool codename things where people guess what OxyAlpha 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, right?
Because today you have your scale and your reputation and all these things.
So obviously you drive immense distribution.
But when you're early on, when it's mostly...
The bootstrap, yeah.
What is the bootstrap like?
I mean, to bring it back to...
early Discord days.
I think we initially connected with, this is an OpenSea story, technically, but we initially connected when you were at Discord and we talked about the Axie Infinity server.
Oh, yes, yes.
This server was the biggest server at the time at Discord.
That's right.
And you were constantly bumping up the limits of how many people could be in the server.
For those who don't know, 10% of the Philippines was...
was on that server.
It was like a meaningful contributor to the GDP of the country.
It was an NFT crypto game.
It was like a Pokemon breeding thing.
Similar.
There was battling, there was breeding, and then there was a marketplace for trading.
Play to earn as well.
Yeah, play to earn.
And like the graphics were really cute and fun.
And you kind of like, you know, you get kind of 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 basically every early project for us to create a marketplace for it, we need to make sure that like the community actually wants it.
And it's kind of like building something that people want and going and telling them about it.
Like you can do that on a one-on-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 building things that the community really wanted.
We did the same thing for Open Router.
And the Axie community was one of a zillion communities we did that with.
And Ange saw us doing it because 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, you know, 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?
You know, in early LLM days, it was, you know, OpenAI refusing to finish the prompt or like to like complete the task.
It was also, you know, 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 sort of learn about and dive into and explore.
Something that really struck me at that time, as I was just hearing your talk, I remember noting how, you may not remember this, but we had these like working Zoom calls that we were doing as Sprint.
around for like this OpenSea integration with Discord.
And, you know, it was myself, my engineering team, I think you were there.
And I remember, you know, Alex in the middle of one of those calls, just like, there was like silence.
You know, we were all like, oh yeah, this totally makes sense.
Let's do this.
And then there's some, everybody aligned.
And Alex was like, no, this makes no sense to me.
And everyone's like, I remember going.
what?
It works.
You click on a link and then it bounces you out to 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 person out of all of us to actually raise his hand and go, yes, it made sense from a technical implementation perspective.
We were bouncing the user out into OpenSea.
And so it kind of checked the box of the product manager's requirements on both sides.
Alex went one step further and was like, you know 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.
And not one person on the call, there were like seven of us who had met like, you know, week after week.
And it's the guy who doesn't work for Discord.
And it's the guy who doesn't work for Discord.
Like technically you benefit if they bounce.
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.
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 was one of those moments where I go, I realized I got to 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.
I think that went into one of our case studies for the PM training program at this point.
I don't know if it's there.
You need an Alex, this is the conclusion.
Yeah, you need an Alex.
And this is why 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.
And putting all that together has resulted in this extraordinary scale that very few other...
marketplaces have been able to achieve over the last five years.
Yeah, well, we should talk about the other reasons for acquisitions, which you've written about.
I want to sort of proceed somewhat chronologically as well.
So there is a point that, you know, one of the questions that Dave from HF0 sent in was, when did you know it really started to work?
And you brought up Mixed Draw.
I don't know if you want to bring up that story.
Oh, yeah.
Which obviously you overlap with, so.
Yeah, the MOE was, I don't know when, I mean, There's no like one moment where I was like, oh, this is, you know, officially starting to work.
It was like moments of kind of increasing connection.
Like really super early on.
Oh, yeah.
But well, yeah.
So before Open Router, I wanted to like explore a bring your own model experiment.
Which anyone familiar with crypto is like, you know, Phantom and all these things.
Yeah, yeah.
So it felt like doing a MetaMask analogy for AI would be kind of a fun way of exploring that.
At the time, there were no AI apps.
There were probably as many AI apps that were like hitting an LLM via an API call as there were like games just doing it in JavaScript.
Basically, like there was a moment in time where it could have been the case that web apps call LLMs through the browser, like through some kind of desktop managed app.
that is controlled by the user.
And of course, there are like, I think many reasons that that did not happen.
But back when the days were that primordial, I built a Chrome extension called Window AI.
And...
With Plasma.
With Plasma.
I had come across early on and I was like, who's going to actually use this?
You did.
Plasma had a couple, like, I think Phantom was using it.
There were some other like...
real companies using it.
There's like a shim, basically.
It compiles to all these.
Yeah, kind of like Next.js for Google.
Next.js.
And yeah, built Window.ai on top of it.
The creator of Plasma started contributing code to Window.ai in GitHub.
And that turned out to be Louis Vichy, who is the co-founder of Open Router.
You have told me this is how you met Louis.
Yeah.
So that...
That allowed users to kind of 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.
You know, not the right form factor for LLMs.
But, you know, it's like fun experiment.
You learn a lot.
And like I, you know, open sourced it.
And, you know, 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 going to help me discover.
It's not enough real estate.
I need more space.
I need visuals.
I need graphs.
I need, you know, examples.
I need images.
I need to like, I need to be able to like explore both as a human and as an agent.
So that's kind of how OpenRider came to be.
You know, a meta point that, 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 kind of like a dress rehearsal for generative models, right?
If you think about the Axie experience, you know, Alex is totally right.
There were not that many AI apps at the time.
And while I was dealing, you know, 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 20% of my time I was spending with a friend who would get hotpot with me and ask me for, we'd 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.
Was that David?
That was David Holtz.
He was a good friend.
And David and I have both been sort of failed AR, VR founders 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 abstractions and the infrastructure decisions we made to scale Axie.
happened just in time because they actually did this and then fell off a cliff.
And then as MidJourney was taking, we like explicitly decided to help David make the server, 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.
And so the single-player MidJourney web app on its own, like midjourney.com, had terrible retention because people would show up, they'd see this empty field.
It was kind of like Dali 2, and they would type in cat or dog.
And it was 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 prompts, and the engagement was off the charts.
And so scaling MidJourney from zero to 10 million monthly actives was a much smoother approach.
post-AXIE Infinity.
And so...
Don't forget the best of four pictures, and then you choose one.
Which is the feedback loop.
The RLHF feedback loop.
Which, by the way, separately, Tom Brown, David and I used to play Magic the Gathering on weekends.
And so it was one group of friends who would hang out.
These concepts were all being discussed all the time.
But there was...
I think there were few of us who bridged both the crypto worlds and the AI worlds.
And compared to crypto, where the question was always, what's the use case?
you know, for this technology, there was never any need to ask that for, yeah, 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.
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 mid-journey, you know, Claude was a Discord bot pre-launch, you know, that we were using internally as an LLM.
11 Labs 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 Open Router gave the world like a public home store or like a, you know, storefront.
Discord was this like almost kind of petri dish storefront that was kind of 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 Open Router 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, how come you were, my perception is Open Router is not that Discord centric, right?
You have a Discord.
Yeah.
And you use it to engage your community, but it's not like mid-journey where like, no, that is like the primary way people experience Open Router.
Yeah, mid-journey like, it really helps us see visually really quickly how people are using the model and how to prompt it.
And I think that is partly why the server was so critical.
It's like, it is the user experience.
It actually adds a ton.
Yes.
And you can go the whole mile with just like prompting via mid-journey, like via the mid-journey 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 LLM to make them really usable.
Yeah, but 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.
You need code-based integration, not possible to do in a Discord server.
You need, or technically, it's possible.
I shouldn't say that.
It's just not a great developer experience.
You need governance for...
At the point where you got code-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 like, it's not the right.
Well, in addition, you're not wrong, but also there's the very important distinction that, you know, Midjourney was an end-user application.
And, you know, that's why Discord has 250 million monthly end consumers.
it made sense for Discord to be 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, 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.
The Stability Discord?
It was the Lion.
Yeah, the Lion.
The image community that spawned Stable Diffusion.
And so 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.
So they were a full stack company, right?
They were training their own models and they were deploying them as an application.
But if you want to build your own MidJourney, there was no API.
of that quality.
And I think DALI too was still quite primitive.
Like MidJourney actually had great quality.
And then when Stable Diffusion came out, suddenly there was this new person who could, 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 had the kind of creativity of David Holes 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.
When you have open model alternatives to closed sort of applications, OpenRouter's value in the world becomes extraordinary because now any developer can just show up and...
You just want model diversity.
Did you just say the shape of open router?
Oh, no.
That's the real Ange.
I'm misaligned now.
I've been overtrained.
I've been using cloud way too much, haven't I?
Clawdish is what people are saying.
Clawdish.
Oh, God, I got to untrain myself.
Okay, and I just want to cap off the Mistral side.
My TLDR is there was a mixed trial price war is what they called it, right?
Like roundabout in Europe of 2023 or 2024.
They launched the Mistral 8x7B and the price went down like 80%.
To me, that's very positive because it's like the first real competition to host Mistral.
Is there more?
Yeah, that was...
I'm trying to remember it.
all the things that happened.
Like, we saw that model come out and immediately saw people say that it was the best model in the world.
Yes.
Like, this was, to my knowledge, the first time an open weights model was called that in real seriousness.
It's hype, right?
Is it, you know?
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 GPT-4.
So people really wanted to try it out and see, is this going to be true for me too?
And if so, at what price?
And the like inference landscape was really messy.
Yes.
We cleaned it up.
It allowed like providers to compete on price so we could give users 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 developers.
Sean, 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 at BFL as well.
And Guillaume was there.
I was at NeurIPS at that time.
You were there too.
And we had just announced the Mixtral investment and Guillaume was over there and I remember turning to Guillaume and asking him, like, how are you feeling after the launch of Mixtrol and 7B?
And, you know, him in his typical French fashion was like, I mean, it's an okay model.
It's not that good.
It was so, you know, 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 GPT-4 was because of the speed.
You know, it was an MOE model that they had, like, absolutely kind of figured out how to make super efficient.
It was on the periodo 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, you know, these common evals that are seven, you do seven tries.
And I don't actually remember.
I think we should go back and figure out what the data says.
But I wouldn't be surprised if it turns out on an N of seven attempts, GPT-4 was smarter on evals.
But the perception of on like, or correctness would be smarter or more accurate.
People from a human preference perspective felt that it was faster because it was smarter because it was so fast.
And actually, most queries do not take that level of intelligence.
So this is the start of humans as router, which then eventually becomes open router as router of the auto mode.
Because humans are the routing mechanism.
I will ask the fast model first, and then if not good enough, I'm going to upgrade manually.
But then he's going to auto it.
I hadn't thought of it that way, but that makes sense.
Which then 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 sort of early years.
One thing that I observe, which you are also an investor in Arena.
Right.
And we talked about mid-journey having that feedback loop of ABCD and choosing that being very important.
And 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.
Yeah, Elemsys.
And then it became a company.
Elemsys, yeah.
And I know you had some Arena experiences, like the heads-up comparison type things.
Yeah.
But you never really went as hard as Arena did.
In doing heads-up experiences.
And Elemsys actually did have a router project based on Elemsys, 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.
So, you know, I think there is like a branding reason that there are two companies here.
Like when you set up OpenRouter, there's no training or no prompts, like aside from what your provider policies set.
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 Elmarino is like, their business model is like oriented around the labs.
Because they give it for free, right?
You don't give it for free, they give it for free.
Yeah.
I mean, we do give some, we like have free endpoints too, but like those free endpoints, I think we're not collecting any prompts.
We're not like monetizing the data unless you, you know.
opt into it for some reason.
This compares, I mean, you're not the first person to ask me this, and Alex knows this, but I, you know, I was the first CEO of Arena for the first five months when we were helping Anastasius and Waylon kind of spin out of Berkeley.
And I did invest in that before OpenRouter, but it was very strange to me the comparisons that outside folks would make between the two projects 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 finger in the wind.
That's kind of what Anastasios and Wayland's PhD work was as scientists at Berkeley, was on statistical methodologies for sort of correcting, you know, eval estimates based on like intrinsic biases and how you collected the data.
Style control.
Style control and stuff like that.
Which is very much like, hey, if you're a scientist and you're trying to kind of, the highest expectation customer for Arena was always like a post-training.
like a research at a lab.
Whereas the highest expectation customer from my perspective that Alex really understood and the mission was to serve was like a developer, right?
Who then takes the result of the research and then produces an application that's deployed to the world.
It was actually a completely different problem and person that these two teams were focused on.
And so from the outside in, actually, 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 Open Router, I'd given you a call because we were trying to get a pooled data set together from Open Router and from Arena to create like an open source repository of prompts.
I mean, these projects were so kind of different in their goals that it was totally normal to me to be like, oh yeah, let's call Alex and see if he'd want to team up on pooling data because they're so different.
We actually don'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.
Does that make sense?
And so to this day, I think you see this difference, even though at a 30,000 foot level, I guess you could kind of conclude that Arena and OpenRouter are adjacent, but the roadmaps, the missions, and so on at the time at least were like, in very different sort of directions.
That ideal customer, I totally get that.
As a founder, I want to own everything.
That's possible.
This is clearly the adjacency that I'm going to explore that.
Own everything meaning you don't know what to do yet, so you want to make sure you catch PM.
No, I think what he says is you want to own the entire infrastructure space, and so you kind of expand to whatever demand you can capture.
Yeah, I think that's hard in reality, because Serving multiple customers is critical.
Clearly, this is when it's a focus, right?
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 like, 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.
So, like, if I want real attention on this issue, like, 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 to, like, people think that the early days of Anthropic were, like, super easy because they were on their GPT-3 guys who left.
But it was actually very competitive.
The company was starting $10 billion behind OpenAI.
And so to get to the frontier, the big question was, what do we want to be known for?
What's the mission?
And the mission was AGI repair programming.
And so 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.
And today you can see the results.
It's a trillion dollar company within five years.
And that focus, I think, the focus on coding 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 so even more difficult.
It's part of the reason why OpenRouter succeeded and Anthropica as well.
Was the focus on coding that early, though, or did it come later?
Literally from day one, it was AI pair programming is responsibly commercializing AI pair programmer was the seed memo.
That was when I invested, right?
We actually got like...
refined that memo a lot.
Well, you've got to ask Dario and Tom for permission on that.
But it's an extraordinary piece of writing that they had put together.
And AI, you know, commercializing, responsibly commercializing an AI pair program was the mission, you know, from day one.
And I would say there was maybe like a couple moments in the company's history where like they did experiments to kind of see if like little detours made sense, like a general chatbot, like Cloud AI when ChatGPT was really taking off.
But, you know, at the end of the day, especially once They got their really significant pre-training computer online, I think.
All the main evals at the company, for example, have always been coding evals, long horizon, agentic programming.
From day one, that was always the plan.
Because when Claude Instant came out and Claude 2 came out, I remember the marketing mostly being focused on pros.
Like this model.
Yeah.
Good writer.
Write better.
Yeah.
Long context.
Long context.
This directly affected me because I know something like that.
What did you make?
A small developer, which was my Devin before Devin.
Oh, yeah.
Yes.
That's small.
Yes.
And, you know, so I think there's all that really, like, good, like, focus is another thing.
That is a question that people do want to ask.
You know, you could have built any other things, like, and obviously OpenRod was working, working, working.
Were there other ideas that you wanted to pursue that you, turned down, you know, just the paths, 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.
Lots of that, open pipe and all those things.
But it kind of was in a very consumery form factor where you would give us a YouTube video or two or three.
We would then extract.
all the transcripts from it and try to fine-tune a model to talk like the person in the YouTube video or the people in the videos that you sent.
So like a really, really easy way of creating a fine-tuned model based on like some kind of videos that you like.
That would be so useful.
We made it too.
It was like kind of a...
You don't think so?
It was...
Nobody used it.
It was...
We didn't actually like test it with that many people because the model marketplace was our main...
our main focus and it was like growing and we were building more conviction in it over time.
Just as a creator, I've been pitched many, like, I have 500 hours of recorded voice of myself, make a thing of you, charge access to it.
Works for OnlyFans, doesn't work for us as regular people.
I think this is mostly, it's just a...
glorified rag bot.
Whether it's in the weights or it's outside the weights, it doesn't really matter.
You're just doing rag on the videos.
And people ultimately always just 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 what, like the way I practice for a job interview or like I'm hiring a candidate or public speaking or whatever.
I wish there was like a good mini-me that I could like critique because it's kind of hard to pull yourself out.
I would never offer it to other people in service.
pick your top five mentors that didn't talk to them instead of talk to yourself.
That'd be cool too, yeah.
That's the character AI, that's 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.
And AI Steve Jobs was the initial use case.
Even though it's not allowed.
That's a common prototype, yeah.
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?
Like, why don't you do that?
Because if people are running already, they're inferenced through you, store everything, log everything, fine-tune to a smaller model that is cheaper, faster, all these things that's within your control, right?
You didn't do that, but other people would have pitched that in the general state of an infra startup.
I think you were just maybe a little bit early because today that's an extraordinarily fast-growing segment.
Like, you know, from Astral, where they do a lot of enterprise deployments.
It's often fine-tuning custom models for ASML or whatever.
But not as a router.
They're just like, I come to you because I like your Astral models.
I want custom Astral model, right?
It is not I want to run all my OpenAI prompts, store all my results, and then just move off of OpenAI.
They're not doing that.
As like a way to export off of dependency on a...
Frontier Lab, I have not seen that yet.
Which was your kind of your...
I mean, we decided, really, we like leaned into our focus and figured that like, we just saw the ecosystem develop over time.
All these inference providers that do want to help companies do that.
Like, it makes sense for us to partner with them and to like give users lots of choice and to like, you know, figure out what makes them...
what gives them competitive advantages.
It's a whole new business, basically.
And there's value in being a neutral marketplace that just kind of like works with those companies.
Could you share a little bit, to Sean's point, like how you prioritized, what are some ways you prioritize features?
Because you've always done it so elegantly that it never, you know, just happens.
And you make all the right decisions that always have product market fit from the outside looking in.
But consistently, you seem to have prioritized.
you know, a lot of hit features that worked and maybe I have a sample set bias or whatever, but Sean's question.
Can you list what you think hit features worked?
Oh, the leaderboards.
Leaderboard, okay.
Yeah, you know, like...
From day one, it's charting.
That's the feedback.
Charting, BYOK.
But he had plugins.
I think there was a whole thing I want to get into about completions versus check completions versus completions.
And then also, let's call it the rise of the reasoning models and how you deal with those, multi-modality, all those things.
BYOK.
That was a huge one.
There was one, I think it was in early 2024.
Very early 2024, we thought it might be interesting to fuse the results of multiple models together.
And we launched a prototype called MOM, mixture of models, that let you pick a couple models, we'd pick them for you.
And then it would fuse the results together at the end.
And it would show you all the intermediate results in this like big Kanban board looking product.
What does the fusion at the end?
Another model?
Another model.
The smartest of the set.
Of the three, of the set.
So this is like a council idea?
It was like a very early LLM council.
This is a multi-agent swarm as like they would call it at one of the frontier labs in the early days, you know?
Yeah, like some of those ideas are like going the right direction, but the devil's in the details.
There's a lot of like product refinement needed to make them really work.
They take your focus away from, you know, whatever else you have going on.
And there's a lot of like community building and learning that you need to do.
And the technology might be too early.
So there are like all kinds of reasons they might go wrong.
And in our case, the technology was a little too early.
In other words, the fused result was...
A little bit worse, sometimes the same as the best model that was being used to fuse because the best model was so far ahead of options two and three at the time.
over time, the top three or four LLMs have gotten closer together, still neurodivergent, but like all capable of inserting like pretty interesting ideas.
Like RL has basically like expanded the surface area of creativity for machine learning researchers within each lab.
And so they can, you know, diversify the reasoning power of different models more effectively.
At least that's my theory for why.
Fusion works better than it used to early 2024.
So the technology was a little bit too primitive.
The form factor was not right.
And so we would have had to go through a couple more iterations.
And so we decided to just delete all the code.
And then years later, middle of 2026, or early 2026, we're like, Let's bring it back.
Like the research is looking kind of promising for Fusion.
The models now have like two, three, four top frontier models that are all really good.
And like I'm frequently trying to like consult multiple models to get the best results.
And then I, you know, I ran a little personal experiment where I was like, I'm going to like do an architecture.
plan for a code change.
I'm going to give it to all the models.
I'm going to fuse the result.
And I'm going to ask all the models if the fused result is better than the individual result each model came up with.
And they all said yes, that the fused result was better.
And this happened a couple times.
And I was like, okay, spot check, pretty good.
We should benchmark this.
And that's how we built Fusion.
Yeah, and it came on your Fable.
So you were like, this is Fable level.
Yeah, yeah.
Let's start leading up to this year.
which we haven't gotten to this year.
Can you mark out the main milestones in the journey?
I think it seems like your promise was routing.
You decided the business model very early.
You take a cut.
And what are the major milestones that inflect the growth?
You're growing like 9% week on week now?
Is this the official number?
In terms of token volume?
I think that sounds about right.
Yeah.
So just like, can you mark out like the sort of brief history of OpenRouter up to, you know, the acquisition?
Let's call it, we're just talking about, you know, people are, you have sort of your birth moment with the Mishal stuff where people are really competing.
You have your state of AI thing where it's very cute.
You have a hundred trillion tokens, ha ha ha.
Because now you're doing 10 a week, you know.
Yeah.
We're doing 10 a week.
Day.
10 a day now?
Yeah, more.
So yeah, you do this in 10 days.
Like, what are the major points there?
You know, I just want to, like, there's a smooth curve, but like, you feel the infections.
A lot of this is kind of oriented around model launches.
We had, you know, a huge focus on pros all the way up through May of 2024.
because coding was just not there and no apps were able to build much on top of it.
So, you know, a diversity in models, but not a wide diversity and not a wide diversity in use cases.
Dream Tavern was one of our top apps at the time.
The creator of Dream Tavern now runs product at Cognition, Devon.
Then we, in the middle of 2024, we saw Claude Sonnet 3.5.
That came out, incredible leap forward encoding.
And we saw the dynamics of like apps spilling on top of us change.
We saw a huge surge in volume in like users using Open Router.
And...
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.
And shortly after that, I think it was after Sonic 3.5, Mixtrol 8X7B came out.
And everyone was like, whoa, this is the model.
Like, the open weights community delivered.
And so it was really good timing from the strong.
Basically all of Anja's portcullis are just helping you out.
It takes an ecosystem to grow an open router, you know?
Yeah, that was the, yeah, it was.
Like, it was this early ecosystem.
It was like a swing action where, like, model labs would come up with some sort of frontier innovation.
Like, usage would surge.
Then users, you know, look at their invoices 30 days later and are like, whoa, what's going on here?
And then...
Open weight models would deliver cost-effective options two, three months later.
We saw that happen several times.
One thing you also did with the coding agents was that you broke out which are the top coding agents, and they love that.
They love that leaderboard.
The Klein versus the Rue code versus the what have you.
Yeah, yeah.
Klein was the top of our leaderboard at the time.
We then, at the end of...
And I'll skip forward a little bit.
At the end of 2025, there were quite a few coding apps on the leaderboard, but they were all IDs or terminal-based agents.
And at the end of 2025, we saw OpenClaw appear.
And OpenClaw was particularly interesting because, one, it was like a new form factor that...
like brought in a new type of user, not just a developer, but like a productivity or sort of like an internet creator came to AI for the first time.
And it also had an interesting architecture where it was like calling your chosen model for these heartbeats to see if it was still alive in addition to actually using the model for real tasks.
And the heartbeats are like, they're kind of, you don't want to pay a lot of money to do a heartbeat.
the auto-router that we provided was really, really useful to this wide range of users all of a sudden.
And so we just saw it rocket exponentially.
And then we saw OpenCloud just blow up and a couple other apps lean into that new paradigm and do something similar.
Hermes came out and really leaned into things like the auto-router.
and built like a really good community and leaned into like, like basically skill management and making it really easy and effective for people like, like set their memory in the agent and, and build really good skills.
Which another thing you never did, memory skills, sandboxes, all these like adjacent things you could have done.
Could have, but like, it's, I think like, it's hard to bet.
They're also very.
There are things that really matter for the developer use cases that were coming out at the time.
Developers wanted to architect those things.
Those are kind of critical to building a good user experience.
It's been hard for companies to find abstractions that work for all developers on the memory layer.
There are some, like Mastra has done a pretty good job, for example.
But developers have lots of varied preferences for them.
And then we saw, you know, 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 just sort of like go to the Wayback Machine and look at the rankings leaderboard and the apps leaderboard over time, it sort of shows you like what's happened in AI over the last couple of years.
To me, the coming of age moment was Andre Karpathy was like, I no longer read Local Llama because I just go to Open Router's leaderboard.
Which I remember.
I think he probably said, sorry guys, I'm going to send a bunch of traffic to you.
So I was going to bring it into the Stripe thing.
How does that kind of conversation start?
We had this long-standing relationship with Stripe though 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 10x as much dollar volume last month as the month before.
And the types of token fraud are diversifying quite a bit.
There are fraudsters.
going after typical stolen credit cards, but there are also, you know, people trying to resell traffic against the terms of service.
There's like hacked accounts.
There's people who just lose access, you know, like their whole company is compromised and they don't even realize it.
And we help them like regain control and detect it.
There are accounts that are like reselling inference on the side.
There are accounts that are dealing with, you know, like an accidental runaway agent and they don't realize it.
Not a hack, but it's something that blows up and the company doesn't want it.
And so our trust and safety team like works a lot on all of these like categories of problems and helps block it and detect it.
And so we've built these, you know, we have models around them.
We have, we worked closely with Stripe for a while on this.
And I think it's going to become a huge problem in the ecosystem.
Like we're already seeing a lot of companies start to see these fraudsters like spread and look for other ways other than open router to other fraud vectors.
And if you're making a gateway or selling like generalized inference, you are a target for fraud.
If you're selling very discreet.
like intelligence products, intelligence products that are like doing something pretty specific, but not like, you know, just reselling inference with some added capability, then you're way less likely to get these fraudsters.
So I think we'll see companies also move away from just reselling inference with some sort of like added capability and move towards sort of like discrete tasks and charging for those tasks.
and charging for those enhancements and letting people bring their own inference like 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, you know, I think like the data dog pricing page is a good look at like the future to come.
It's like companies, like infrastructure companies will like charge for different.
types of events that they're providing.
And there'll be lots of like continuous pricing models that look like that.
And of course, there will be like, if you go down towards consumer apps, you know, simpler pricing, more subscriptions, you know, fewer events to worry about.
And ones that like are not focused on just adding a markup on top of inference.
Not just because fraud is hard, but.
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.
Any comments?
Two.
One, I think Alex has done a very eloquent job of describing something.
Counterintuitively, I knew would be a thing at scale four years ago because of Discord.
And the particular experience that taught me this was As we started scaling MidJourney, one of the primary ways that we used to give away or get people to try MidJourney early on to get their first 10 generations, because 10 images generated was roughly the magic moment activation point we found.
Once you've done 10, you were like, this is extraordinary.
But for that, we had a free trial with MidJourney.
And one day I woke up because they had a platform and had to monitor all these dashboards.
You know, I had like three missed calls from David and it turns out like there had been this flood of new users overnight.
And we were like, this is great.
And he was like, no, actually we shut down the free trial.
And I was like, why is that?
And he said, I took the geolocation IP addresses and basically somebody in China had started to resell mid-journey free subscriptions with the free trial as a way to like basically...
You know, it was fraud abuse, right?
Even for a specialized model like MidJourney.
Yeah, and that was actually an application.
So this idea that, I think the big picture realization I had back then was, hey, there's a new type of unit of value that's 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 like the more valuable tokens got, the more bad actors were going to try to get their hands on those tokens.
And anytime you scale something and the payload gets more and more valuable, more bad things, people try to get access to that value.
And so it was very obvious to me back then.
And so, look, to this day, I don't think there's a free turn.
Like, I don't think Majority 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.
That and the anthropic learnings, to me it was clear that the need for security at scale was going to be enormous a few years from then.
Because if you just do the math, think about if where online payments started roughly in the 80s and 90s and grew to over a trillion dollars over the next 10 years and 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 even five years, we're expecting the token economy to get to like roughly $5 trillion.
And over the next 10 years, I'd be shocked if we went to $10 trillion of token flow.
And so if we were starting to see such aggressive abuse and fraud at subscale, mid-journey, mid-journey at this point was like less than 300 million revenue run rate a year.
I just realized we were going to need like entirely new like systems to deal with the fraud that was going to happen for trying to get into the token flow.
And so my, I, you know, when I forget the board meeting it was when you brought up that, you know, Stripe wanted to partner up and it made so much sense to me because Stripe Radar, when I was a Kleiner 10 years ago, we invested in Stripe and the whole pitch that, you know, Patrick and John communicate so eloquently was like, hey, unlike traditional payment tools like Braintree that do a seven day verification, like KYC and email to get, 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, like, just use five lines of code and we start accepting your payments in five minutes.
And what will happen is over time, we'll collect all this data on the developers.
Cloudflare model.
Is the Cloudflare model, right?
And they did.
Five years later, they launched Stripe Radar.
And Stripe really today is a security company.
That's the real.
People think it's a payments company.
No, the reason, there's lots of other payments providers today that give you like cheaper payments transmission.
But the reason Stripe keeps...
you know, being the dominant one here in Adyen and in Europe is because they have extraordinary fraud detection that they've built, you know, over the years.
This is the same story with Elon and Max Levchin and...
And Affirm, yeah.
So, you know, I think the story shows up over and over again where every time you have value streamed across the world in large amounts, you need new protection and security infrastructure to fight to keep the bad guys out and allow the good people to like have their transactions happen really fast.
And so I think, you know, this is why...
From my perspective, the Stripe and Open Router story is a security story for the internet ecosystem, for the Frontier AI ecosystem.
Without a partnership like that, it becomes very hard to defend the quality of experience and 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 about the fact that you need to...
All the bad things that Alex described as being perpetuated by humans right now, is going to be perpetuated by AI agents over the next 10 years.
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.
And 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 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.
And so what you need is defenders, new sheriffs in town, which a cowboy has, that can...
can see all the bad behavior from AI agents across the ecosystem from different model labs and 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, you know, the amount of fraud we're going to see of this $10 trillion in GMV and global GDP growth is like a huge percentage of that, I think is going to be fraud, abuse.
And we might never get there if people just don't trust tokens, right?
And 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, like bad behavior perpetuated by AI agents, is about to hit us like a tsunami.
Yeah, I mean, 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?
I mean, I think this is a really good way for us to accelerate go-to-market and to go upmarket more quickly.
It's also, you know, as Ange eloquently described, there's a really clear better together story here when it comes to improving trust and safety and making it really easy to like accept tokens and let people bring their own inference to your app and to help developers just like build on top of inference going forward.
We have a really strong brand with Open Router and we're keeping the brand.
So like Open Router, like as a product and the roadmap and the name and the brand, like, you know, is staying the same.
And so what, like, you should expect, you know, in the next six months is that most things will be like what we would have done had we been independent, except everything will be moving faster.
And that's kind of like our, you know, 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.
I mean, this is one of the most beautiful bromances I've seen in AI.
Just starting out.
Starting from Stanford to here.
Lots more to do.
Lots of sheriff policing to do of the token economy.
We need new sheriffs for sure.
Yeah.
Awesome.
Thank you.
Thank you.