If I’m reading your question right I think the answer is:
I’m going to make a bunch of claims about the algorithms underlying human intelligence, and then talk about safely using algorithms with those properties. If our future AGI algorithms have those properties, then this series will be useful, and I would be inclined to call such an algorithm “brain-like”.
i.e. The distinction depends on whether or not a given architecture has some properties Steve will mention later. Which, given Steve’s work, are probably the key properties of “A learned population of Compositional Generative Models + A largely hardcoded Steering Subsystem”.
Regarding “posts making a bearish case” against GPT-N, there’s Steve Byrnes’, Can you get AGI from a transformer.
I was just in the middle of writing a draft revisiting some of his arguments, but in the meantime one claim that might be of particular interest to you is that: ”...[GPT-N type models] cannot take you more than a couple steps of inferential distance away from the span of concepts frequently used by humans in the training data”