My current intuition on this topic is that they are right about scaling but they are training on the wrong data.
LLMs were not intended to be the core foundation of artificial intelligence but an experiment around deep learning and language. Its success was an almost accidental byproduct of the availability of large amount of structured data to train from and the natural human bias to be tricked by language (Eliza effect).
But human language itself is quite weak from a cognitive perspective and we end up with an extremely broad but shallow and brittle model. The recent and extremely costly attempts to build reasoning around don't seem much more promising than using a lot of hardcoded heuristics, basically ignoring the bitter lesson.
I've seen many argue that a real human level AI should be trained from real-world experience, I am not sure this is true, but training should likely start from lower-level data than language, still using tokens and huge scale, and probably deeper networks.