Yes, and most likely more of a paradigm shift. The way deep learning models work is largely around static statistical models. The main issue here isn't the statistical side, but the static nature. For AGI this is a significant hurdle because as the world evolves, or simply these models run into new circumstances, the models will fail.

Its largely the reason why autonomous vehicles have sorta hit a standstill. It's the last 1% (what if an intersection is out, what if the road is poorly maintained, etc.) that are so hard for these models as they require "thought" and not just input/output.

LLMs have shown that large quantities of data seem to approach some sort of generalized knowledge, but researchers don't necessarily agree on that https://arxiv.org/abs/2206.07682. So if we can't get to more emergent abilities, it's unlikely AGI is on the way. But as you said, combining and interweaving these systems may get something close.

a sufficiently capable fakery which still has limitations can still upend the world quite a bit

Maybe but we are essentially throwing petabyte sized models and lots of compute power at it and the results are somewhere on the level where a three year old would do better in not giving away that they don't understand what they are talking about.

Don't get me wrong, LLMs and the other recent developments in generative AI models are very impressive but it is becoming increasingly clear that the approach is maybe barely useful if we throw about as many computing resources at it as we can afford, severely limiting its potential applications. And even at that level the results are still so bad that you essentially can't trust anything that falls out.

This is very far from being sufficient to fake AGI and has absolutely nothing to do with real AGI.