I agree with your take, and actually go a bit further. I think the idea of "diminishing returns" is a bit of a red herring, and it's instead a combination of saturated benchmarks (and testing in general) and expectations of "one llm to rule them all". This might not be the case.

We've seen with oAI and Anthropic, and rumoured with Google, that holding your "best" model and using it to generate datasets for smaller but almost as capable models is one way to go forward. I would say that this shows the "big models" are more capable than it would seem and that they also open up new avenues.

We know that Meta used L2 to filter and improve its training sets for L3. We are also seeing how "long form" content + filtering + RL leads to amazing things (what people call "reasoning" models). Semantics might be a bit ambitious, but this really opens up the path towards -> documentation + virtual environments + many rollouts + filtering by SotA models => new dataset for next gen models.

That, plus optimisations (early exit from meta, titans from google, distillation from everyone, etc) really makes me question the "we've hit a wall" rhetoric. I think there are enough tools on the table today to either jump the wall, or move around it.