As we've seen in the recent past, it's difficult to predict what the possibilities are for LLMS and what limitations will hold. Currently it seems pure scaling won't be enough, but I don't think we've reached the limits with synthetic data and reasoning.
I think that LLMs are plateauing, but I'm less confident that this necessarily means the capabilities we're using LLMs for right now will also plateau. That is to say it's distinctly possible that all the talent and money sloshing around right now will line up a new breakthrough architecture in time to keep capabilities marching forward at a good pace.
But if I had $100 million, and could bet $200 thousand that someone can make me billions on machine learning chip design or whatever, I'd probably entertain that bet. It's a numbers game.
Problem with this reasoning is twofold: start-ups will overfit to getting your money instead of creating real advances; competition amongst them will drive up the investment costs. Pretty much what has been happening.