Electricity becoming a commodity doesn't necessarily make nuclear reactors become commodities. And as we can see, the publication of open-weight models doesn't really diffuse the know-how of training SoAT models as we expected. Even the major cloud providers are still unable to build competitive ones from scratch on their own. That's very different than the traditional FOSS ecosystem, where everyone can copy, learn, and evolve on one's own (like, Bitcoin -> altcoins).

On token pricing, I think it's very much bottlenecked by hardware (the aggregate of compute) rather than the number of competing models. Assuming that the ceiling of the token price is determined by the economic value a unit of compute can provide, then the less efficient ones would be priced out of the compute allocation. It's not consumers bidding up a limited number of different models, but more like tokens of different models bidding up the limited computing resource. Less-intelligent tokens (which are generated by weaker models) are crowded out by smarter tokens from the limited compute. My prediction is that we'll see a meaningful downward pressure on token prices only when the new batches of next-generation hardware get mass-deployed.

castform founder here. despite our bet on fine-tuned smaller open-source models, i'm still quite bullish on the big labs. i think scaled closed models will continue to dominate for more general purpose use-case like codegen, search, etc. but intelligence has lots of long-tail applications and i think for these longer-tail applications, finetuned custom models will rule

Small models aren't going to take people's jobs. Agents using large models might. That's how their numbers make sense.