> Do you take issue with ... the idea that these were a gigantic leap?

Yes, the gigantic leap was transformers. No one thought we peaked with 340M or 1.5B parameters, in fact expectations from early work was that massively scaling was going to achieve zero-shot capabilities rather the emergent capabilities you're alluding to which are essentially variations of in-context learning, a relative disappointment.

Subsequent improvements in GPT-4, which seem to be mostly just more RLHF and MoE, are similarly not surprising and are temporizing measures while hardware and datasets are limited. Jury is still out whether the billions spent are worth it in terms of actually getting us closer to AGI.

It seems to be worth it for OpenAI/MS who are trying to be first to market and establish vendor lock-in.

Emergent capabilities are interesting but at the moment I don't think anyone has any idea how interesting they really are, or if they are truly a gigantic leap in the direction of AGI. I've skimmed a handful of papers trying to answer that question and there has hardly been a slam dunk proof that indeed they mean anything other than the fact that the transformer model can generate reasonable facsimiles of what a human might say.

The reality is that GPT isn't even great at answering factual questions at this point, despite all the hype. I have a modest amount of expertise in music theory and I've found that asking even relatively basic questions resulted in completely incorrect answers coming out of GPT. It's been impressive that in some cases if you say, "No, that's incorrect." it will actually go back and spit out a new answer which in some cases is correct, but that's hardly any fundamental advancement in "understanding", and just more evidence that it's parroting what it's been trained on, which includes substantial amounts of misinformation.