This discussion is an excellent instance of a pattern I see often, which I should write a post on at some point.
Another mildly-hot-take example: the bitter lesson. The way I view the bitter lesson is:
(I think most people today interpret the bitter lesson as something like "brute force scaling beats clever design", whereas I think the original essay reads closer to my interpretation above, and I think the history of ML also better supports my interpretation above.)
For another class of examples, I'd point to the whole "high modernism" thing.
I revisited the Bitter Lesson essay to see if it indeed matches your interpretation. I think it basically does, up to some uncertainty about whether "ontologies" and "approximations" are quite the same kind of thing.
The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.