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This reminds me how when GPT3 was just released, people pointed out that it sucked at logical problems and even basic arithmetic because it was fundamentally incapable of having a train of thoughts and forming long inference chains, it always answers immediately, based on pure intuition so to speak. But to me it didn't look like a very fundamental obstacle, after all most humans can't multiply two four digit numbers in their head, so give the GPT virtual pen and paper, some hidden scratchpad where it can write down its internal monologue, and see what happens. A week later someone published a paper where they improved GPT3 performance on some logical test from like 60% to 85% by simply asking it to explain its reasoning step by step in the prompt, no software modification required even.
I think that that, and what you're talking about here, are examples of a particular genre of mistaken objections: yes, GPT3+ sucks at some task compared to humans because it lacks some human capability, such as internal monologue or long term episodic memory or can't see a chessboard with its mind's eye. But such things don't strike me as fundamental limitations, because, well, just implement those things as separate modules and teach GPT how to use them! They feel like some sort of separate modules in us, humans, too, and GPT seems to have solved the actually fundamental problem, of having something that can use them, a universal CPU that can access all sorts of peripherals and do things.
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