SITUATION EXPLAINED: What happens to AI labs' margins when the gap between open source and frontier models shrinks to a month? We asked @braeden_norris, investor at @PropelVC "The labs have had this incredible ramp in terms of revenue. But the social tide is changing. People are thinking more about costs." "They're in this constant battle between each other and also between open models, keeping that gap between near frontier and frontier alive. The existence of these margins is probably reliant on there being a gap that's somewhat substantial and isn't like a month long or even shorter." "There are plenty of use cases, drug discovery, material science, where the marginal cost you're willing to pay for a token is basically infinite. And so this defends the eighty percent plus gross margin potentially." "As an investor, that's a precarious spot to be in as a lab. It's a running race. You have to raise a lot of capital. You're investing more capital over time in R&D even if you are spinning off good margin on the inference side."

SITUATION EXPLAINED: Why haven't the most intelligence-intensive fields dominated compute demand yet? We asked @braeden_norris, investor at @PropelVC: "We are still early to the higher returns on intelligence fields really capturing more of the compute supply out there. It's probably because it's just more nuanced in how it's applied. It's research-y. It's frontier. It's discovery. The capabilities are harder to manifest in the models as well." "People at the labs have talked about how Eli Lilly should be willing to spend basically infinite inference and time for a model to discover drugs for them." "Right now it's much more focused on: Eli Lilly wants to speed up their clinical trial administration process. Which is very much the same shape of work and probably not as token consumptive. And maybe can even be put on an open model because it's the same shape of work."