I remain highly skeptical. I doubt that transformers are the best architecture possible, but they set a high bar. And it sure seems like people who keep making the suggestion that "transformers aren't the future" aren't good enough to actually clear that bar.
If any midwit can say "X is deeply flawed" but no one can put together an Y that would beat X, then clearly, pointing out the flaws was never the bottleneck at all.
It's not a linear process so I'm not sure the "bottleneck" analogy holds here.
We're not limited to only talking about "the bottleneck". I think the argument is more that we're very close to optimal results for the current approach/architecture, so getting superior outcomes from AI will actually require meaningfully different approaches.
Ironically, the same could be said about Attention Is All You Need in 2017. It didn’t drive any improvements immediately- actual decent Transformer models took a few years to arrive after that.