“Open models are Trojan horses for the infrastructure that they bring with them. So you have an open model, but that alone is not very useful. So you buy into the entire software ecosystem and infrastructure ecosystem of a given country.”
No Priors · AI Research & Frontier Labs · October 2026
Laskin runs Reflection AI, which is building an American open model, so he has a stake in this argument. He was explaining what China gains from releasing open-weight models: a US ally may run a Chinese model on American chips today, he says, but Chinese companies will soon offer the full stack, and whoever supplies that has leverage of the kind railroads once gave.
Transcript
earth minerals
or something. It's you have a component that other people will use for all sorts of purposes, and therefore you have geopolitical leverage for that.
Yes. So, you know, one way I kind of think about it is that open models are Trojan horses for the infrastructure that they bring with them. So you have an open model, but that alone is not very useful. So you buy into the entire software ecosystem and infrastructure ecosystem of a given country. So that would be, okay, maybe today allied country, US allied country X, you know, might run a Chinese model on American chips. But pretty soon, if not, I mean, and we're seeing this already, is that Chinese companies like Huawei are going to be coming in and offering a full stack solution and then locking into that country's supply. So I think that if we kind of think about AI or like chips and data centers almost as like railroads or something like that, like it's kind of fundamental infrastructure. And you like everyone, but no other country can actually build their own railroads. Like they all have to go to you or another country. You have a lot of geopolitical leverage.
I see. And so, for example, inferencing a specific model will occur in a more performant way on a specific chipset. And that chipset happens to be provided by Huawei or something.
Yeah. So these stacks are in real time getting optimized end-to-end because in some sense the paradigm has been set, right? Like this is that there are going to be other ways to scale intelligence possibly, but this is clearly the first one that really scaled. So now everything's being optimized…
they'd only accelerate with the models, right, in terms of model usage to design next generation chips and things like that.
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