I think it's because people walk every model up to its limits and become very aware of a task they can't make work. They do a lot of work simplifying and understanding limitations at that boundary. Then an improved model comes out and they immediately toe that barrier and make swift progress. They will also notice that the new model is natively doing tricks they had done manually.

The reality is likely that everyone is hitting similar barriers and the solutions are somewhat generalizable and get added to training new models.

Eventually people will reach the new limits and the cycle repeats.