The Twenty Minute VCOpenRouter CEO: Why Chinese Open Models Are Beating the US | Why Enterprises Fear OpenAI & Anthropic
EVERY SPOKEN WORD
55 min read · 11,393 words- 0:00 – 1:29
Intro
- AAAlex Atallah
It's gonna be, like, the biggest, biggest market in tech ever. A lot of companies are making routers because it's fashionable. The model apps have several incentives to go after you eventually.
- HSHarry Stebbings
Today, we have Alex Atallah, Co-Founder and CEO of OpenRouter, the unified interface, the gateway to the world of LLMs. They reportedly have had offers from Stripe for $10 billion. They've raised at a valuation of over a billion and a half. They are the market leader, and this interview could not come at a more prescient time.
- AAAlex Atallah
In July, we launched 70 models, about one model every 10 hours. America is very, very behind still. GLM 5.2 was a really big, big step for open-weight models.
- HSHarry Stebbings
There are reports that you are selling to Stripe for $10 billion. Is that gonna happen? Ready to go? [upbeat music] Alex, I am so excited for this, dude. I have wanted to make this one happen for a while. I've heard so many things from Matt at Menlo. I've stalked the shit out of you speaking to Anjani, even your roommate before this show. Um, so thank you for joining me, dude.
- AAAlex Atallah
Thank you. It's great to be here. [chuckles]
- HSHarry Stebbings
Now, I wanna start with a little bit pre-OpenRouter and start on OpenSea. It was a pretty incredible journey. What did you take with you to OpenRouter, having seen all that you saw with OpenSea?
- 1:29 – 4:05
What Alex Took From OpenSea Into Open Router
- AAAlex Atallah
Yeah. So [sighs] OpenSea, uh, we- we started as the first NFT marketplace, and, uh, similar to OpenRouter, it was very small for a long time. Um, like, we kept the team very small until the Series A, roughly, or, you know, a little bit afterwards. Um, and this was before AI. So, uh, right after NFTs started blowing up in, in 2020, October of 2020, we were like, "Oh my goodness," like, "We are understaffed. Um, the servers are melting." All kinds of... Like, our search index was exploding. Um, we had a couple big outages. It was tough to, like, keep the site up, and it was like, "Oh my God. We're gonna become like the Twitter fail whale," but, like, applied to crypto. My biggest goal was to have us not be the Twitter fail whale-
- HSHarry Stebbings
[laughs]
- AAAlex Atallah
... um, for crypto. And, uh, and it took a little bit to, like, create the team, get platform and infrastructure under control. Uh, like, make sure we, we could sc- we could predictably scale. In other words, like, do load testing to, like, help the site sustain 10X load, um, even when we weren't seeing that load, because with crypto, you just don't know. There were, like, these moments where we would get these incredible traffic spikes, and it would be very dependent on the content and the community. And, uh, so I built, like, a lot of, um, infrastructure and scaling, I think, responsibilities then that I took to OpenRouter and spent a lot of time, like, thinking about, "Okay, how do we, you know, make something that is gonna basically be always up and that, that people can really count on from an infrastructure point of view, um, even when there are huge surges in, in really, like, tumultuous markets?" Um, which has been very helpful for AI, of course, because, like, you know, all companies, like, especially Anthropic, have seen, like, g- unpredictable growth and, uh, and we have as well. And, you know, we've had, like, a couple bumps, but overall it's been, like, significantly better, and I ju- like, OpenSea just kind of, like, drilled that into me in a way where I could, like, take it productively to OpenRouter.
- HSHarry Stebbings
Can I ask you, when you go back to the founding thesis of the company-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... what has happened in the ecosystem, in the model landscape that you did not expect to happen?
- 4:05 – 5:47
The Rise of Inference Provider Ecosystem
- AAAlex Atallah
Um, okay, well, one thing that we did not expect was that a, an ecosystem of companies would emerge to host and serve the open-weight models. Um, like, early on, it wasn't clear that, that that market wasn't going to be a monopoly, where, like, just, you know, the three hyperscalers serve all the open-weight models and, uh, and st- and startups don't... You know, they're, they're really far behind. In reality, like, you know, how often do you hear people running, you know, GLM on a hyperscaler? Never. Like, they're using the, the inference providers like Fireworks and Together and, um, there's, like, you know, big lists that we, that we see doing the best job of hosting all the, the open-weight models. And, um, in the early days, we, um, we had, I think we called it Provider 1 and, and Provider Fallback. We didn't, like, show which providers were actually doing the hosting, 'cause, I mean, we weren't really a marketplace. We were kind of a ex- like, we were an exploration tool for, like, finding and discovering new LLMs, and we wanted to get... We wanted to build, like, a marketplace of model labs, but, like, the inference provider layer, we weren't sure would actually be a marketplace. And it turned out that those companies were doing a way better job than the hyperscalers, were way faster to host the models and figure out these edge cases to hosting them, and, um, and uptime was just gonna be a, a constant problem. It wasn't going to, like, magically get solved by the supply side of the market.
- 5:47 – 9:20
Are Inference Providers Commoditized?
- HSHarry Stebbings
A lot of people suggest that that inference provider layer is a commoditizable element or layer that will be removed or see margin reduction competed out over time. What would you say to that theory?
- AAAlex Atallah
Right now, we're in a massively supply-constrained market where Um, but a- and it's likely gonna be supply constrained for a while, where all the inference providers are, are short, pretty much constantly short. Uh, and, and you're like, "Okay, so GPUs are, are really, really beneficial, and like why doesn't Google or Amazon or Azure run around and like buy up all the GPUs and take all these inference providers out of business?" Well, the people making the GPUs don't want that. Like, one of NVIDIA's top priorities is not having customer concentration. They want lots of customers to all have like separate like allocations of GPUs. Um, they want the, the heterogeneity of the market. They want like competition on the compute layer. Um, and I... And this is good for the ecosystem. Like, like users also want this. This, like, like this is-- it's good for NVIDIA and it's good for end users as well. It like allows these inference providers to kind of like come up with new innovations on like how to serve the models better. Even a single model like Kimi K3, um, uh, like Moonshot just posted a benchmark showing all the inference providers and how well they're serving Kimi K3. Um, and, uh, the nu- the, they're, they're pretty different numbers for like for benchmarks that are really static, that are well known. We post this continuously all the time. We always are like benchmarking all of the models on all of the inference providers, all the open weight providers, and finding really different results con- constantly, and the results change over time. Um, these models are like very... They're very emotional. [laughs] They're, they're very like, they're very like, uh, uh, non-deterministic. So, uh, um-
- HSHarry Stebbings
I had Lynn-
- AAAlex Atallah
Yeah
- HSHarry Stebbings
... on the show from Fireworks, and she said that-
- AAAlex Atallah
Uh-huh
- HSHarry Stebbings
... you know, I said about Gavin Baker, "And a token is a token," is what he said.
- AAAlex Atallah
Uh-huh.
- HSHarry Stebbings
And she kind of corrected me that a token is not a token actually, because one provider can make a token go so much further than another token. It's like how do you get to the store where you can drive around the whole block-
- AAAlex Atallah
Yes
- HSHarry Stebbings
... or you can drive straight to the store? Tokens can be made more efficient and go further, and that's the job of the provider.
- AAAlex Atallah
Yeah. I, I, um, I agree with that. I think that, uh, you know, i- in some ways we are providing a service to help people discover providers, and, um, and, and like ultimately when, when one provider is making a token go further, we o- we spend an enormous amount of time on our router, central router tech, so that that provider immediately gets more traffic. As soon as q- as soon as we detect that like there's a quality improvement or a speed up or a price reduction happening, um, immediately starts getting more traffic. Uh, and this stuff happens like 24/7 every single, like every t- every five minutes there are big changes for the big models. Um, and so it like actually does make the experience better.
- HSHarry Stebbings
You can only invest in one inference provider, which one do you invest in?
- 9:20 – 10:55
If You Could Only Invest in One Inference Provider
- AAAlex Atallah
I probably have to stay, you know, stay neutral on this. I, I do... I really like the, the, the, uh, like, you know, inference providers that are doing, um, that are doing like custom hardware and, uh, and very, very like low level optimizations. Um, I like providers that are also trying to figure out how to make, um, customization easier. So like today you fine-tune models and, um, and you create this like new, like fully independent model from the, from the base model. Um, many infer- inference providers are kind of like, you know, creating these LoRAs or some, some call them like cartridges that are much more portable potentially between models, and, and we might see a future where like when you do a fine-tune and you wanna like change the base model layer, it only costs like maybe a few hundred dollars, maybe a few dozen dollars to change it.
- HSHarry Stebbings
It's okay. I understood that Fireworks is your favorite. It's okay. I get it.
- AAAlex Atallah
[laughs]
- HSHarry Stebbings
Uh, mine too. Uh, my question is wh- when Lynn was on the show, she was like, "Oh, you don't wanna rent your own, rent intelligence-
- AAAlex Atallah
Yeah
- HSHarry Stebbings
... you wanna own it, and we're gonna see companies have specialized models which is trained on their own data and proprietary to them." In a world of every company-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... having specialized models that's really f- tuned to them and their preferences-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... is that good for an OpenRouter business or not?
- 10:55 – 14:33
In a World of Specialized Company Models, Is OpenRouter Still Relevant?
- AAAlex Atallah
Oh, definitely. I mean, our goal is-
- HSHarry Stebbings
Why? Because you'd stick on one model-
- AAAlex Atallah
[laughs] Uh-huh
- HSHarry Stebbings
... which is yours, proprietary, trained on yours, and not be open to the diaspora of models that is available.
- AAAlex Atallah
I... No, I disagree. I, I, I think our mission from the very beginning has been to increase neurodiversity in AI for the whole ecosystem, and we really believe that like a multi-model future is inevitable. And when you start... Let's say like, let's say there's one model that like, you know, hypothetically, let's say you're right. Let's say there's one model that fulfills all of your desires, um, either within your company or like as a consumer. Um, and every, you know, more and more people start using that model. And then, um, someone decides, "You know what? I'm going to like create a neurodivergent model. I'm gonna create a model that's like a little bit different, that like talks a little differently, that has ideas that the, the first model like could never have come up with 'cause it's like completely different data, um, that's being used to train it." Then it kind of creates inevitable demand to use both models. But creativity is not a, um- It's not verifiable. [laughs] There's, there's... Like, you can't really put an easy number on, on creative ideas, and when you use two models together, um, you're more likely to get creative ideas than if you just use one. It's, it's just a fact. If, if that other model was trained in a different way on a different data set, like, or has, like, made a big update. So, um, consolidation on one model just seems like i- i- i- it just doesn't make any sense to me. [laughs]
- HSHarry Stebbings
Totally get you. So you'll have companies which have, like, a core workflow or their core, which is their own specialized model, and then they'll use a plethora of other models, and they'll use OpenRouter for those other model selection.
- AAAlex Atallah
Yes. And, and I think that when companies make, like, to get back to your question, when they make their own, you know, their own model trained on their own data, um, like, you are... The, the ecosystem around you is all doing the same thing. You have to, like, play out the game theory for these things a little bit. Like, if everybody is doing this as well, and cr- and all the model apps are creating new models constantly using new data that they've acquired, that they've bought from other companies, that's all g- like, potentially data that's valuable to you, what is in your best interest? It's to go and try out those other models and, like, see if you can be more productive with them, if you can, like, you know, merge them together to get better state-of-the-art performance, if you can reduce your cost using these other models. They're all b- if you-- Whether your goal is to, like, reduce your, or, or, um, improve your margins or grow your company, like, y- you are incentivized to go use what the ecosystem creates. So the, the, the model that you made, you're gonna have to continuously improve it to keep up, and it's never gonna win the whole market. So this is gonna be a massive market. This is gonna be, like, the biggest, biggest market in tech ever, and, uh, biggest market probably in human history. No one's gonna win all of it. Um, you're not gonna build a model that wins all of it. So you might as well build a model that, like, is known to specialize in something very useful and that's very important to your company and your business and be known for that specialty. And I think a lot of enterprises are gonna move that direction, make their own models, make their own, um, branded intelligence. Your brand is a big part of your moat, and that model will, like, be a way your brand carries around.
- 14:33 – 18:48
Are We Seeing Commoditization of the Routing Layer?
- HSHarry Stebbings
You mentioned the immense time that you spend on the routing technology that you have.
- AAAlex Atallah
Um.
- HSHarry Stebbings
A lot of people are thinking that we're seeing the commoditization of the routing technology. You're seeing Ramp release products like this. I mentioned earlier of, uh, Merge, a company we invested in, has released their product. Um, several are releasing kind of routing technology similar or claiming to be similar. Are we seeing the commoditization of this layer?
- AAAlex Atallah
I think a lot, yeah, a lot of companies are making routers because it's fashionable. Um, I think they're, you know, they're seeing growth happen here and, uh, or they, they're making gateways at least. Um, uh, first, uh, I think there's two, there's two issues with that. First, it immediately puts you in the mindset of copying instead of, like, you know, winning something. Um, you're sort of, you're playing to play, or you're playing to exist rather than playing to win. Um, and, uh, and maybe you're just trying to, like, play to serve your, your existing customer base, uh, and you, you wanna see some AI growth happen. Um, I think, you know, immediately kind of, like, puts that gateway, like, many, many months behind, um, the companies that are fully focused on it. Like, I am 100% focused on building the best router and gateway and, and LLM marketplace, um, and it shows in our product and, um, and, you know, the, the benchmarks that we create internally and how we see ourselves compared to the competition. Um, this is not a side quest for us like it, it may be for some other companies. Um, the other problem is that it, uh, it reduces the leverage of all of your users. So, um, like I really deeply believe in giving users and developers more leverage. Like, fundamentally, giving them access to more models is about giving them more leverage over all the innovations that happen in AI. You want to be able to, like, access them all. You wanna reduce your dependency on any individual one. If, um, you know, you build on top of a, a router or a gateway that, you know, doesn't give you access to the full market or full flexibility or full customizability, um, it doesn't give you, like, the f- the full leverage of the whole ecosystem, then you're, you're kinda, like, being cut out. You're cutting out all your employees at your company of things that they, they need. And so, like, OpenRouter is fundamentally about giving people more choice 'cause that gives them more leverage.
- HSHarry Stebbings
You do that at a price, at 5.5% take.
- AAAlex Atallah
That, that was sort of our, our, uh, pay-go plan. Um, we then added an enterprise plan with, like, a totally different pricing model, and, um, it's been very successful so far. You k- It's kind of based on, like, committed spend and then, uh, you know, no fees on that committed spend. We're-
- HSHarry Stebbings
'Cause that was gonna be my question. Uh, ultimately, companies-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... will, like, love it small, and then as you scale, you're like, "Shit, this is really freaking expensive. I- I'll just build my own routing tech now because it's become such a significant part of my cost base, actually."
- AAAlex Atallah
I mean, I kinda figure it, it, like some of those companies don't, you know, just haven't realized we have, like, an enterprise plan [laughs] and, uh, some of them, uh, a- and some, uh, some of it is, like, our fault for not having, like, a better, I think, more detailed pricing model. We're soon gonna introduce, like, a kind of a business, uh, self-serve plan that- Um, that also just, you know, makes, makes it make a lot more sense. And if you, if you have your own inference, like if you bring your own inference to OpenRouter, if you bring your own keys, um, that fee goes away. So it's a fairly like, you know, for inference that we are providing you, like when you go into OpenRouter's capacity and you're not on our enterprise plan, that's when that fee comes in. Um, otherwise, like, w- you know, it-- w- we need to be able to like predict demand a little bit, so that's why we do these committed spend things.
- HSHarry Stebbings
What would be the main revenue line of OpenRouter in three years' time?
- 18:48 – 21:13
What Will Be OpenRouter's Main Revenue Line in 3 Years?
- AAAlex Atallah
I, I think, I think it's gonna depend on, on the economy in so many ways. Like, if the overall AI market keeps growing the way it's been growing over the next four years, you know, you know, with like 10 to 15X every year, um, or potentially more, it's a lot of growth. Um, you know, I think under that, under that world, um, I would expect people to continue to underestimate how much inference they're going to need, and thus our, our revenue is gonna be dominated by, you know, the same things that dominated today, that dominate it today, which is like in, you know, it like us helping people with, with an unplanned inference capacity, um, both enterprises and startups. And, uh, like that, that's what OpenRouter is best at. Like when you, when you need to try models that you weren't expecting you need to try, when you're like, um, using more inference than you thought you were going to use on particular models. Like we make, we make sure that that is not going to be an issue for your company, um, by providing the best failover and best uptime. And, and this is really, really a good thing to do when the market is like continuously underestimating its inference needs and growing at this rate. Um, if this, uh, growth rate continues over the next four years, um, I mean, it's going to be a, a wild amount of growth, like the economy, and the economy has some limits [chuckles] to it. I can see like major SMB, you know, SaaS like growing for us, um, and needing to grow for us whenever, uh, you know, you know, if, you know, if growth like does not keep going 10X to, what, 15X per year.
- HSHarry Stebbings
We, we've seen token prices fall 90%-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... give or take, in, in like 18 months. Is the reduction of token prices helpful or hurtful to your business? 'Cause obviously you have a take-
- AAAlex Atallah
Yeah
- HSHarry Stebbings
... on spend. If they come down and spend is-
- AAAlex Atallah
Yeah
- HSHarry Stebbings
... more efficient, seemingly it's bad for your business. You have a shrinking pie to, to take from.
- 21:13 – 23:15
Token Prices Down 90% — Good or Bad for Open Router?
- AAAlex Atallah
Well, [sighs] a lot of people talk about the Jevons paradox, that, you know, when, when, uh, prices go down by 10X, the usage increases by more than 10X. Um, but like no one has really done a great job modeling it. There are, uh, we do have a lot of spot stories that confirm it. For example, uh, GPT 5.6 Luna on OpenRouter. Um, OpenAI cut prices by 5X, and then in coordination with us, by another 2X. So in total, price has-- the price of Luna has dropped 10X on OpenRouter over the last two weeks. And, uh, guess how much usage has grown? 13X. So it's a close to perfect Jevons paradox story, where you drop prices 10X and usage grows by more than 10X, um, just a bit more. And, uh, and the, also the, the usage is pretty stable. Like it like grew, you know, it like flattened out, but at, at 13X and then, you know, it's been kinda like growing at the same rate that it was growing before it hit the, the 13X multiple. Um, so that's pretty interesting and it's a pretty like, uh, low variable. Like there are, there are, there are few other confounding variables in the story. And, and it was also done in the middle of DeepSeek launching and having a really, really good price, and GLM having a really good price. Like now Luna is being used more than GLM on OpenRouter. GLM used to be like one of the top like f- three, four models by token volume, and now Luna is past it. This is the first time OpenAI has had a model on our platform in the top, you know, three to five-
- HSHarry Stebbings
Wow
- AAAlex Atallah
... models by token volume in an extremely long time. So it, it was a really big and interesting
- 23:15 – 25:28
How Representative Is Open Router Data of the Real Market?
- AAAlex Atallah
move.
- HSHarry Stebbings
How reflective of the market are your token volumes? 'Cause it's about, I, I'm may get this wrong, about maybe 1.5, 2% of say token volumes. And so how reflective are they? Because a lot of people when I say, "Oh, the top five models when I look at OpenRouter are all Chinese. What does that mean?" They'll go, "Oh, well, Harry, no offense to OpenRouter, but like it's not-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... reflective of the market, and most people who use Frontier, it doesn't go through that. Like they use Frontier APIs-
- AAAlex Atallah
[clears throat]
- HSHarry Stebbings
... and so it's not counted." To what extent-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... are your rankings reflective of true token usage?
- AAAlex Atallah
We try to estimate how they're off, um, by, you know, just surveying people sometimes or looking at like the surveys other people have done. Um, I think we, we have a, uh, we definitely have a bias to people who believe our thesis, which is that the future is multi-model, and companies who want multiple models. And there are still companies out there, I basically- Rarely run, very rarely run into them now. But there's still companies out there that are just like, "Oh yeah, we're an OpenAI shop." Like, "We have one... You know, we only do OpenAI models." And so we're not gonna see any of those companies. Um, and I think those companies are primarily focused on, like, the, you know, the hyperscalers, OpenAI, Anthropic, and Gemini. Um, so we do probably, like, undercount the, the frontier models. Um, but I think, like, over time, our thesis is becoming more and more common to see in other companies, and the moment that, like, they're like, "Oh, yeah, like, we need to use other models," um, then our data becomes more representative. And as we scale up, the data becomes more representative in general. Um, so my hope is that, like, that we- that the- that it just becomes, like, better and better data over time.
- HSHarry Stebbings
Can I ask you, Alex Karp said on CNBC in his rather wonderfully energetic way-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... that companies are terrified of working with frontier model providers.
- AAAlex Atallah
Mm-hmm.
- HSHarry Stebbings
Do you think they are?
- 25:28 – 27:55
Are Enterprises Terrified of Frontier Model Providers?
- AAAlex Atallah
So I haven't seen what he is... What he talked about there when I talk to our customers. Um, but there was definitely, like, a little... Uh, uh, there was some skittishness that the, uh, particularly when, when Claude Design came out at, uh, around Figma. And that part, um, I did see, and I do think that there are, like, real concerns for a company that, um, it is kind of building like a, you know, thin, uh, uh... You know, for... Like, Figma's very different, but i- if a, like, a, a startup is only building, um, a, like, go-to-market wrapper around intelligence, like, "Hey, we are... You know, we're a company that kind of like brings AI to this market," and does so by, like, doing the right integrations and, like, customizing the system prompt, um, you're gonna be fine if the model labs don't care about that market, which there will be many markets like that. But the model labs have several incentives to go after you eventually. One is, uh, getting multiple teams within companies they do care about to be dependent on them. So th- this is my theory behind why, like, Claude Design was strategic. While it's not, like, a massive amount of revenue for Anthropic, like a... Not probably, like, a significant amount of revenue, um, it does get the design team to really care about Anthropic models. And so the companies that, like, they want, like, they now have another team that really wants to stick to Anthropic. So those team... That team strategy, uh, makes this, you know, like, can make you compete with the model labs. And so I think, like, companies like that, that find themselves like, "Oh, we're, like, building a product for a team that has now become strategic for the model labs for, like, companies they actually care about," um, that's where I see probably the most n- near to- near-term threat.
- HSHarry Stebbings
Do you think Claude Design will have a meaningful impact on the Figma business? I speak to many founders today who are bluntly switching from Figma to Claude Design, and it's cannibalizing their Figma usage. Do you see that, and do you think
- 27:55 – 29:13
Will Claude Design Have a Meaningful Impact on Figma?
- HSHarry Stebbings
that will happen?
- AAAlex Atallah
So I, I, I saw a lot of designers try out Claude Design, including our own. Um, but so far I haven't heard of the repeat story. I don't know. Honestly, like, I have not talked to very many designers about this. Um, I certainly haven't heard a lot of chatter about Claude Design. Um, and, like, if you just look at the numbers for Figma, they're quite good. Like-
- HSHarry Stebbings
They're astonishing
- AAAlex Atallah
... they had a very-
- HSHarry Stebbings
Yeah
- AAAlex Atallah
... very incredible earnings. So, um, I-
- HSHarry Stebbings
This is why you don't want to be public, dude. You see, like, you know-
- AAAlex Atallah
[laughs]
- HSHarry Stebbings
... great numbers, "Figma down." I'm like, "Poor Dylan."
- AAAlex Atallah
[laughs]
- HSHarry Stebbings
Like, give... What? [laughs]
- AAAlex Atallah
Yeah. I... That was crazy.
- HSHarry Stebbings
Do you know what I mean? It's like-
- AAAlex Atallah
Yeah. [laughs]
- HSHarry Stebbings
... really? Come on. Um, we were talking about, like, the, the different models that we have on offer-
- AAAlex Atallah
Uh-huh
- HSHarry Stebbings
... and whether companies are willing to work with frontier models. The rate of model development feels immense. Do you think we will see the same rate of model development continue over the next year, two years, three years?
- AAAlex Atallah
Frontier model development or just general model?
- HSHarry Stebbings
General model-
- AAAlex Atallah
General models
- HSHarry Stebbings
... both frontier and open.
- AAAlex Atallah
Yeah. Yeah. Yeah.
- HSHarry Stebbings
Just 'cause, I mean, every single day there's, there's two, three, four new models.
- 29:13 – 31:06
70 Models Launched in July
- AAAlex Atallah
In July, we launched 70 models. It's about one model every 10 hours. There's some agent labs starting too that are all, that are all gonna kinda like probably make models eventually. Like Je- Jeff Dean is starting an agent lab right now, um, from Google. Uh, the, the companies that are, like, that are known for making agents have an incentive to create their own model, a very clear incentive to create their own models and distribute it through the agent, and, and we haven't even seen the start of that. Like, like... Sorry, we've seen the start of it, but we haven't seen it really pick up. Like, Cognition has a model. Cursor has a model. Does Lovable have a model yet? I don't think so.
- HSHarry Stebbings
Not, not publicly.
- AAAlex Atallah
Yeah. So the agent labs are gonna, I think, develop models. This pressure from both the GPU, uh, makers, like NVIDIA, to, like, create more competition in the space and create more diversity in the, in the space, plus us, plus investors who just want to try new things that, like, all could, like, improve intelligence in some neurodivergent way, um, I think those are, those are strong incentives. I think that there's still... There are- They're enough to like incentivize more founders to make Neo labs. Um, and the, and if like o- if American open weight models pick up in steam, then it gives these Neo labs a base to train on that's not Chinese, um, which will then probably create more American Neo labs
- HSHarry Stebbings
Do you think we should be concerned by the rate and quality of Chinese open models?
- 31:06 – 31:47
Should We Be Worried About the Rate of Chinese Open Source Models?
- AAAlex Atallah
We should. We're behind. I... America is very, very behind still. Um, I think things are picking up. I, and I think, I think, um, you know, we, we have poolside, we have thinking machines, we have RC.
- HSHarry Stebbings
Do you feel a sense of responsibility for that? And what I mean by that is like, you know, you are a routing business and you could route a company to a Chinese model that, who knows, people are worried about back doors, Chi- ICCP involvement. You could be the, the deliverer of that to those models. Do, do you feel a sense of responsibility for that?
- 31:47 – 34:18
Does Open Router Feel Responsible for Routing to Chinese Models?
- AAAlex Atallah
So we, we do feel a responsibility to have safe access for all of these models. Like, um, customer trust is like our, you know, paramount goal. Uh, if, if one of these models is unsafe to use, you know, generally considered unsafe, we pull it from the platform. If there's like a way to use it in an unsafe way, I mean, there's a way to use like all the models in an unsafe way, then we believe in using technology to make it safe and to like work with the model labs themselves to figure out how they're doing it on their side so that we can be state-of-the-art or better. We spend an enormous amount of time, um, making sure that like, that our practices like match what the best things that we're seeing coming out of the, the labs or are better. Um, and because we're like a very good... Because we're, we're a way of like exploring all the models and finding them for the first time, um, we're a good focal point for like deploying safety measures across your whole company. For example, we have prompt injection protection. You can just turn it on and immediately flag prompts that look like prompt injection, um, that's trying to happen. Um, we have PII redaction. We have, uh, we have like a couple different things that you can automatically just turn on with a click and, and get an added safety layer on top of all of your inference. Um, and we build that so that enterprises feel like they can safely de- like deploy new models and that their, their, um, employees can try them out. I think of the models a little bit like the internet. You know, you can't y- you can't just like ban the internet at your company because there are, there's some like bad things on the internet. Um, you can create guardrails, and you should. You need to use AI to build the best possible guardrails that you can.
- HSHarry Stebbings
I'm with you-
- AAAlex Atallah
So that's what we're most focused
- HSHarry Stebbings
... but do you think you actually know what's going on within Moonshot or Alibaba with Qwen? Like these are, these are incredibly secretive organizations in the depths of China.
- AAAlex Atallah
Can't pretend I know w- like what's going on inside of them. You know, as a US company, like we're gonna follow like, like the best practices of what happens in the US to make sure that we're not doing something irresponsible.
- HSHarry Stebbings
What do you think US companies are more nervous of, frontier models or Chinese models?
- 34:18 – 38:09
What Are US Companies More Nervous Of — Frontier or Chinese Models?
- AAAlex Atallah
I think they're, they're more nervous about frontier models usually, part because there's just like a m- a much... There's much more confusion around the data policy, about what's like actually happening to the prompts that I'm sending and, um, where they're being stored and how they're being looked at. Um, and you can't run them on your own machine or in a provider of your choice. Uh, and so that just immediately creates all of this uncertainty in a lot of enterprises, and it's uncertainty that they can also pattern match. It's very similar to like, you know, running on their own infra versus running in their VPC, um, and, and knowing like who can see the data. Um, yeah
- HSHarry Stebbings
How extraordinary is that though? Like they're more nervous of like US companies headquartered in Silicon Valley, where you can see and touch and feel the headquarters and the leaders. It's just like what a strange world to be in.
- AAAlex Atallah
Yeah. It, it is very strange, especially with these, with the frontier models like doing the, like having the biggest cyber posture right now and like the, the, um-
- HSHarry Stebbings
What, what do you-
- AAAlex Atallah
... being the best at-
- HSHarry Stebbings
What do you make of-
- AAAlex Atallah
... at cyber attacks
- HSHarry Stebbings
... every company kind of posturing, "Ha ha, we hacked someone"? First you had OpenAI, then you had Anthropic, and then you had, uh, Zuck coming out, "I didn't wanna miss the party. We did too."
- AAAlex Atallah
Yeah. Well, I think they have, they, they have to talk about it. Like the right thing to do is to reveal when there's been a cyber incident involving your model. Um, covering it up doesn't work. Well, it's not gonna work in the long term. And it certainly looks like they're all bragging about it. Um, but really if you were in their position and, you know, something happened with one of the models, um, and you had to make the choice about r- whether to publish it or not, like I think the right thing to do is to publish it regardless of what, like how people are gonna spin it. So I don't know. I d- like I s- I doubt that, that they're, [laughs] you know, they're actually y- you know, thinking of the felony bench or-
- HSHarry Stebbings
Yeah [laughs]
- AAAlex Atallah
... whatever it's called. [laughs]
- HSHarry Stebbings
How significant was the latest Kimi model which got so much attention? Was it- As significant as everyone thought?
- AAAlex Atallah
It's quite good. It's, it's, uh, it's not cyber capable in the way, the same way the frontier models are. And long ra- long horizon tasks, I think it's still a bit behind, um, the frontier models. But GLM 5.2 was a really big, big step for open weight models. Kimi was kinda like moonshot getting up to that step. That's a little bit how I see it. Um, and Kimi's also a very good writer. Like, the voice and tone are both pretty good. Um, whereas like some of the frontier models like have like voice degradation [chuckles] that happens when they get better at coding especially, and it was like, "Oh my God, like the... Ugh, I can't, I can't read this output anymore." The output sounds like, "Three of the four arguments you made are right, and one is a turning point. You know, and da, da, da, da, da, here's the rub." Like, I... [laughs] It's just... It, it... Sometimes just impossible to read what, what they're saying. Um, a- and, and this stuff is fixable, but, uh, but Kimi I think has always had pretty interesting writing.
- HSHarry Stebbings
In 12 months-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... will the chasm between US open source and Chinese open source be bigger or smaller than it is today? And my
- 38:09 – 41:34
In 12 Months — Will the Gap Between US and Chinese Open Source Be Bigger or Smaller?
- HSHarry Stebbings
fear is that-
- AAAlex Atallah
It'll be worse
- HSHarry Stebbings
... bigger because when you have DeepSeek-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... it becomes the national champion in China, and I mean, Xi Jinping is going, "This is our AI horse. I will concentrate all of my money and efforts behind this, and I will supplement this ecosystem to the end. This is the winner." And then when you see another moonshot come out, suddenly all regulation gets moved aside, all policy gets pushed aside, all funding becomes available.
- AAAlex Atallah
Mm-hmm.
- HSHarry Stebbings
Everything is allowed. You are free to run, and these guys are unabridged in their ability to do whatever they want to get to their end goal. Whereas OpenAI and Anthropic-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... and all, and all the other providers in the US, especially open source, fuck, you go and try raising billions of dollars for a US open source model. Bit tough, actually. Um, not impossible at all, but tougher. Business model questionable. Uh, AI research is super expensive, and you're competing against OpenAI and Anthropic. I think the comparative landscapes they sit in mean that the Chinese open source providers are just inherently advantaged, sadly.
- AAAlex Atallah
And they have like very, very good researchers, and I think Americans underestimate that a lot. I do think they're gonna be concerned about the cyber posture of their models, and they do seem very concerned about, like, censoring the models and, and censoring the, um, the information that the models can provide to people. So, you know, while today people complain about American models censoring more due to cyber, I'm not sure that's always gonna hold. And as, like, the Chinese models, like, grow in importance for China, I mean, what, what are they gonna do? Are they gonna like drop the great firewall? Are they gonna like give up on, on putting the firewall around the models? Like, like I, I, I don't know that much about China, but it does seem like kind of strange that they don't seem to care more that the models or... I've never seen anyone do a profile of like what you can do with DeepSeek that you can't do with the internet in China that's available to you within the border, like what information you can access. I've never seen anyone kinda like do a real deep dive. Like, how f- past... How far past the firewall does DeepSeek go? If the firewall matters to China, if it's gonna matter in 10 years, like that's gonna... Something's gonna change. [laughs]
- HSHarry Stebbings
Well, what's interesting is like obviously the abilities of the Chinese models outside of China is immense.
- AAAlex Atallah
Yeah.
- HSHarry Stebbings
The abilities of the Chinese models inside China is actually relatively limited.
- AAAlex Atallah
Oh, the, the guardrails that go up.
- HSHarry Stebbings
The guardrails are-
- AAAlex Atallah
Yeah. Yeah
- HSHarry Stebbings
... incredibly stringent and prohibitive.
- AAAlex Atallah
Huh.
- HSHarry Stebbings
So i- it's ironic-
- AAAlex Atallah
I hadn't seen that. Yeah
- HSHarry Stebbings
... that they are incredibly superior to us. Shit domestically, terrible.
- AAAlex Atallah
Interesting.
- HSHarry Stebbings
I literally just had-
- AAAlex Atallah
Yeah
- HSHarry Stebbings
... my dear friend Jason who run SaaS to come back and be like-
- AAAlex Atallah
Yeah, makes sense
- HSHarry Stebbings
... "Couldn't figure out what time Starbucks opened on DeepSeek." Like w- wasn't on offer. Would say like, "Not allowed." [laughs] Yeah, yeah. Wild.
- AAAlex Atallah
Wow.
- HSHarry Stebbings
Very basic rudimentary requests. Um, w- we're speaking about all of these different models.
- AAAlex Atallah
Yeah. Yeah.
- 41:34 – 1:01:43
Do Developers Show Any Loyalty to Models?
- AAAlex Atallah
Honestly, we do see some. We, you know... We, we try to make switching costs close to zero so that when new models come out, um, people can try them out really easily. But we also measure retention and churn from all the models. We share this with, this data with model labs too when they ask for it, um, so they can know like, "Oh, well, for my model that just came out, like which models drove traffic to it? And like for those users, like when they leave, which models are they leaving to?" Um, and we'll like make this more and more, uh, available to the, to the world, um, soon. And, and we do notice in the churn data there are developers who kinda like continuously stick to models even when there are better models out there, better models for their use cases. Um, I think it's a combination of like a couple probably root factors. One is like my app works and I don't wanna break it. You know, if the support bot starts saying something weird that I didn't expect, like why, why add more headache? I've already done all this optimization and like I've already put all these guardrails around it. Um- Another is, uh, new, new models are not necessarily going to make your pricing better [laughs] . In fact, um, in general, what happens is that the current models, like price goes down over time, and especially when new advancements in, in, uh, in the labs happen. You know, you'll see like intelligence jump, but like the price curve like also jumps, and then will start going down over time. So it's not necessarily the most like price-effective thing to do to like shift over to the, the, the newest model, even for open weights. The third reason is it, it's just fundamental like trust in the outputs. Like if I'm using a model to do my work, um, and I like the way it talks, um, I probably have like some eval, like a personal eval. A lot of people have these personal evals that are just these random tests that they give the models, and if the random test doesn't look really good on the new model, they'll just be like, "Good. I liked, you know, Kimi K 2.6 anyway." [laughs] And keep going.
- HSHarry Stebbings
People thought before-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... that memory would be the retentive mechanism. While OpenAI has all of my previous query, like prompts, it knows that I live in London, I do podcasting and da, da, da-
- AAAlex Atallah
Yeah, yeah
- HSHarry Stebbings
... and that will make it a better model for me moving forward. Is memory no longer a retentive mechanism?
- AAAlex Atallah
Memory is really interesting. I, um, I've always thought it like is a retentive mechanism, and the question is where it lives. Is it gonna live with the model? Is it gonna live with the inference provider? Is it going to live with the app? Is it gonna live with the infrastructure provider, the, the router? You know, my, my guess is that all of those layers are going to try to own memory in different ways. Um, there, and there are gonna be advantages to sticking your memory in each layer. You know, if you stick it with the app, then the memory has like the most app-related context and is model-agnostic. If you stick it with the model, the memory might perform the best on personalized benchmarks and perhaps have the best like ultimate intelligence, and I think the model labs are gonna work on memory. And, and then the ultimate thing might be like is there a good combination? Like, can I use memory in the model and memory at the infrastructure layer or the app layer at the same time? Like is, is that gonna confuse the model? We don't know yet. I, I do think that like it's impossible for one layer to capture all valuable memory because the apps own so much important context that the model labs don't have. Um, and they, the model labs, in order to get this to work, they'll have to incentivize the apps to like give them that context and state it.
- HSHarry Stebbings
Speaking of the apps and the models there, Claude Code, Cursor, Bundle, Model, and Harness, is the router absorbed into the agent framework before it ever has the chance to be independent when you have the agent and the harness together?
- AAAlex Atallah
The harnesses are pretty interesting because, like in our early days, o- one of our early bets was that most apps were underestimating the desire for users to choose the model. Like most apps in the very early days, in like 2023 and 2024, it wasn't even clear which model was being used under the hood. They were like, "Ah, people are not gonna care about that. They ju- they just want AI." Um, and our-- And one of our like strong convictions then was that no, like people are going to want to like use particular models. They're going to care about who they're talking to. It's like, you know, I, I wanna know which employees I'm talking to when I'm trying to solve a problem, and models will be kind of like that. Um, and that has played out. You know, like in Notion you can like choose the model that you, you talk to, um, even though you would think an app like that might wanna like obscure it completely. Um, sim- similar, a similar thing happened with harnesses where d- uh, particularly with developers, um, they started to build an affinity to different harnesses and, uh, and that's 'cause it's like a, it's a user experience. So I think that is my favorite argument for why harnesses are gonna stick around. Um, not that like they're being bundled with the models, 'cause in fact like a- as models get better, they get more resourceful, and the, the junk that gets thrown in the system prompt just becomes a handicap. Um, Anthropic I think published like a good, a good, uh, article about this where they showed that like, oh, we got like, we got rid of stuff from the system prompt and suddenly fewer contradictions showed up later on with user prompts and the model performed better. Um, and we, and we're seeing like a lot of the harnesses right now are like deleting code in order to perform better with the latest frontier models. I don't think means that harnesses are bad. In fa- in fact, I think we'll see more harnesses come up in the future because it's a way of building a user experience on top of models. It's a way for developers who are not model labs to own a user relationship, and that is just going to be im- you know, incredibly valuable for the economy to have that layer.
- HSHarry Stebbings
I'm gonna get killed for this. What's the difference between a harness and an app? Feels like it's word wank of like everyone talking about harnesses and the harness, and I'm like, "Is that not an app?" Like, hello. Yeah.
- AAAlex Atallah
The nice thing about the harnesses compared to the apps is that they're more composable. Like I can have a harness call another harness. I can have a harness spin up another harness in a sandbox in the cloud. Um-
- HSHarry Stebbings
Is that not what APIs did for apps?
- AAAlex Atallah
Yes, but, uh, it's much more reliable and, and, uh, deterministic and sort of easy for users to grok with a harness because- Um, the harnesses are Unix-based. They all have... And, and the models are so well-trained on, on Unix, on Bash commands. Whereas, like, you know, if I'm, like, telling a harness to go orchestrate an app in the cloud, it's gonna be like, "Oh, boy, does this app... Like, how do you log into this app? Is it, like, do I need your password? Do I need a, a... Do I need to fire up a virtual browser? It's gonna be pretty slow. I'll figure it out. Okay, I th- I, like, fired up a browser, and, like, now I need your password, and I'm gonna, like, try to find the input where to put it in, and apparently there's probably an API in this app somewhere. I need to, like, look up the docs to figure it out. And okay, now I've got the API." But there's so many, like, unknown unknowns when you're composing around an app. Very, very, very, very few unknown unknowns when you're composing around a harness. So I ju- I think it just gives developers more flexibility, um, and flexibility that they can inspect. Like, API calls, you're just seeing a whole bunch of code flying around the screen. A harness, oh, I can, like, jump into the harness and, like, look at what's going on and talk in English about it. So it's much more user-friendly.
- HSHarry Stebbings
But we've seen Meta and Muse-
- AAAlex Atallah
Mm-hmm
- HSHarry Stebbings
... really be a focus for Zuck. We've seen Alex Wang front and center much more. Were you impressed by what Meta delivered with Muse?
- AAAlex Atallah
They've been doing a good job, yeah. Um, I mean, like, it takes a while to set up a whole new model lab from scratch, and, you know, I'm sure a lot of, like, uh, organizational debt to deal with. Um, you know, like-
- HSHarry Stebbings
Do you think they will be a serious challenger?
- AAAlex Atallah
I do. I think they're... I think they, they're, they have the resources. Um, there's, uh, the... I think there's some competitive things they can do, uh, around the model that, like, helps people in ways that the, the model labs are not as interested in doing. Like, just having, like, a social network, um, and, like, a focus on people, uh, you know, it, it's, like, something for the brand that n- maybe Groq and, like, Spa- like, X-AI, SpaceX AI have it too. They do need to find their niche. Like, I'm not quite su- I think people don't quite know what to do with Muse Spark yet, like when to use it or when to go for it or what its, like, c- like, core advantage is. Like, they just released a coding harness. Uh, they are... They're trying to be, like, a generally capable model right now. Um, I expect that in the future they're going to be like, "Look, we are way better at this thing." And that's, that's gonna be a really important moment for them.
- HSHarry Stebbings
I see. I have to say, I was impressed by it actually. Do you know what I use now?
- AAAlex Atallah
No.
- HSHarry Stebbings
Uh, pl- maybe plug in one of our, our mutual friends, but Anastasios and Arena, and it's so weird. So I'll put my prompt in Arena, and then obviously it comes back with a load of different model options.
- AAAlex Atallah
Yeah.
- HSHarry Stebbings
And, you know, I come back with... I used one the other day, Pergamom?
- AAAlex Atallah
Pergamom.
- HSHarry Stebbings
Yeah. And it w- it was, like, Kimi and Pergamom.
- AAAlex Atallah
Yeah.
- HSHarry Stebbings
And it offers you four different options.
- AAAlex Atallah
Yeah.
- HSHarry Stebbings
And it, it takes me to models that I would never have used before, and actually Muse has come up a couple of times as being pretty impressive. But I love that in terms of this, like, discovery mechanism to models that I would never have used. I would never go to Kimi-
- 1:01:43 – 1:08:14
Quick-Fire Round
- AAAlex Atallah
Sure.
- HSHarry Stebbings
Okay. So what is the most underrated model on OpenRouter today?
- AAAlex Atallah
Ooh, good one. I mean, first I- I-- Pool- like, Poolside's models are great. I, um, I'm... That- that's probably, like, my fire round answer. Good Am- like New American Lab, um, building interesting coding models that are very... they're, they're small, um, but highly effective and, uh, um, and they're, like, like, building a lot of to- useful tools for accessing them. Um, good team.
- HSHarry Stebbings
70% of neo labs will die in the next three years. Agree or disagree?
- AAAlex Atallah
Disagree. 70 seems very high.
- HSHarry Stebbings
Hmm.
- AAAlex Atallah
Of neo labs. There aren't that many neo labs. If, like, getting acquired by one of the model labs counts as die, uh, I do think there, there'll probably be some, like-
- HSHarry Stebbings
Consolidation
- AAAlex Atallah
... potential consolidation. Um, if you, if you include the consolidation, I would, I would put f- I would say 50.
- HSHarry Stebbings
Do you think Dario should be less negative and more positive as a voice in AI?
- AAAlex Atallah
I think it's important to have somebody who is very paranoid about the future and h- and how things are gonna shake up, and I appreciate that. Like, I personally appreciate Anthropic's paranoia. Obviously, there are, there are areas where, like, I, like... I want, like, other model labs to, um, not feel like they're just being, like, pushed off the table. But I'm a big believer in, in neurodiversity, and, like, Anthropic, you know, is a part of the neurodiversity map that really matters, and if, um, if no one is being extremely paranoid, then, you know, like, w- no one is, like, offering that voice. Um, and so I appreciate that they're doing it.
- HSHarry Stebbings
What's the craziest thing that you see in your seat on top of everyone's usage that you don't think people talk about enough?
- AAAlex Atallah
I mean, a lot of companies are obviously worried about cost management and, uh, and freaking out about the amount of inference they're spending, and they don't know how to think about it. It's like a whole new way of, of, like, doing business and thinking about your, your OpEx. Like, the old way of thinking about how your em- how much you give your employees, you, like, give them a salary, and you kinda forget about it. Like, someone knows what, what everyone's making, but, like, it's a static number that, like, gets readjusted on, on a quarterly basis maybe after performance reviews. Really, your, your employees all cost totally dynamic, different amounts now. And, uh, I think a lot of, like, companies are putting it on them to do routing, and I think in the future there's a good chance that it will, like, get pushed downwards to the employee level. Your employees should, like, figure out which tools and models to use that are best for their tasks, and then we should figure out how much you're costing b- like, due to the choices that you make as an employee. And, you know, your, your cost as an employee is a d- gonna be a dynamic number, and it's gonna be, you know, dependent on how much that employee is, like, effectively using, you know, expensive and cheap models to do their job. And, uh, and then I would... I, I advise companies to kinda, like, still do their normal management work. Like, have their managers kind of assess how, how effective and productive employees are, but also line it up with how much their employees cost and then kind of come up with, you know, a quadrant of, of celebration. Like, these employees are, like, doing a good job, and they're pretty price effective or cost effective. And then a quadrant of concern, and these employees are kinda maybe doing a so-so job, and whoa, they are not cost effective at all. Their AI use, their AI psychosis is off the charts. Like... And then you adr- address the quadrant of concern. So I don't think people talk about, like, basically how you think of, like, employee cost in the age of AI, and that it's, it's... really it should be a dynamic number and not a, a static thing that, like, only a few people know about and it's gone.
- HSHarry Stebbings
Uh, wonderful, but can you imagine going to someone, "Oh, I'm sorry, you, you were worth 100 grand last month. Now you're worth 50"? Uh, I think it would make planning and personal-
- AAAlex Atallah
Well, they are, they are in control of how much they cost. That's the great thing. Like, all employees are in control of how much they cost and, and can, like, influence that. Um, it's now j- now you get to think, like, okay, how good am I as an employee, and how, uh, efficient am I being as well?
- HSHarry Stebbings
Final one. When you look at the landscape today, there are so many things to be excited about. What are you singly most excited about?
- AAAlex Atallah
One is rare disease research, which I think is one of those things that has been intelligence bottlenecks or, or really just the inference bottlenecks. Like, it involves, like, trying out lots of ideas and seeing if they work. Um, the other is crowdsourcing productive urban life improvements. [chuckles] So for example, like, imagine if somebody had, like, uh... someone was curious about finding every lead pipe in America or every lead pipe in the UK, and, like, had a, an approach to it, but they really need, like, to make it mature, um, and, and stress test it. Like, now you can use AI to do that, and we just might solve some weird problems that everyone's just kinda given up on because, like, you, you need, like, a crazy idea to come from somewhere. Like, brilliant ideas are sort of evenly distributed all over the world. Like, they, they, they can come from anywhere. Um, and now you just give them leverage to actually work. So I'm excited about sort of very, like, broad kinda urban, um, like, or, or urban or, or rural, like, quality of life improvements that we'll be able to make.
- HSHarry Stebbings
Alex, dude, I've wanted to do this one for a while. I'm so glad we could do it in person as well. I was worried that we were gonna have to do it remote. It is so much nicer to do it in person. You've been fantastic. So thank you so much for doing it with me.
- AAAlex Atallah
Likewise. This was great.
Episode duration: 1:08:25
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Transcript of episode K72oZoloA4M
