Couldn't agree more.
This paper (https://arxiv.org/abs/2206.07682) also touches on a pretty fascinating phenomenon - that when scaling up large language models they seem to "naturally" obtain new emergent abilities that do not exist on smaller models.
I'm a big believer in the "capability overhang" idea, which is that the existing language models still have a huge array of capabilities that we haven't discovered yet.
That theory seems to be proved correct on a constant basis. Even the classic "let's think about this step by step" paper came out less than a year ago: https://arxiv.org/abs/2205.11916 - May 2022.