Training cost is actually not that high — it’s fixed and amortizable across the lifetime of the model. Inference is expensive, and open weights don’t solve that problem — in fact, they might even encourage it, since a high cost of entry means consumers will pay for inference directly from the labs anyway.
Unfortunately it seems likely the winner will be the cloud providers. If anyone can run inference on open models, then profit will flow to the vendors who can afford the capital to run them. That’s the CSPs.
(It’s basically the same business model as pharmaceutical R&D, but the major difference is that nobody has even talked about patenting the models like a pharmaceutical company patents each new drug. I’m surprised about that, tbh — why give all the leverage to the cloud platforms? They aren’t training frontier models…)