There is a real trend of smaller models becoming more "capability-dense" - i.e. the best 8Bs of today beat the best 32Bs of 2 years ago. This is in part a product of distillation being used to train the smaller models.
But people consistently underestimate how "capability hungry" the world is. There are diminishing returns on model capabilities in narrow "summarize the search results" sorts of applications - but as capabilities improve, LLMs enter, get their footing in and begin to dominate new niches. At times, expensive, highly desirable niches.
I do not expect anyone at the frontier to pop up and say "no reason to train a new model" within the following decade. There will always be a demand for an LLM that's 5-10% more capable and more reliable at some highly advanced task, and generational upgrades will keep delivering those 5-10%. From increased scale and improved training both.
But for some classes of problems I think a model that is 10-100x smarter than the smartest expert is a huge boon. These would be problems that are very hard to solve but easy to verify that the solution is correct. Protein folding, sudoku, etc. Because of this I see the really smart models going to biomedical and pharma first and maybe a few high profit verticals rather than being widely deployed. I am sure Pfizer would be happy to pay for a 100x smarter than the smartest researcher model. But I am not certain that this kind of market fit would justify trillion dollar valuations in the long run. And in the meantime normal “human companion” models will go from Sonnet to some open weight model running on a Dell tower in your closet to maybe even on your phone in the next few years.
the relationship should be the opposite, the smartest people can write the most readable solutions