By my estimation, there is a point where these models are "good enough" for the vast vast majority of all appropriate tasks, after which point further investment by the major labs will have diminishing returns. While they might stay ahead by some measure, the open models will be good enough too, and I assume significantly cheaper like they are now.

Or AGI hits and this theory collapses, but that's feeling less likely every day.

It's not a marathon, or any race. There is no a finish line. It doesn't matter that much that someone is a minute ahead.

It makes perfect sense if you think things cannot improve indefinitely

They do approximate any function... within the range they're trained on. And that range is human limited, at least today.

Also, there is a good enough point where improvements for a given use case are on heavy diminishing returns

That's fine. I can afford to wait a minute if it means I pay $10/m instead of $5k/m.