Moravec's Paradox is certainly interesting and correct if you limit its scope (as you say). But it feels intuitively wrong to me to make any claims about the relative computational demands of sensi-motor control and abstract thinking before we’ve really solved either problem.
Looking e.g. at the recent progress in solving ARC-AGI my impression is that abstract thought could have incredible computational demands. IIRC they had to throw approximately $10k of compute at o3 before it reached human performance. Now compare how cognitively challenging ARC-AGI is to e.g. designing or reorganizing a Tesla gigafactory.
With that said I do agree that our culture tends to value simple office work over skillful practical work. Hopefully the progress in AI/ML will soon correct that wrong.
Keeping the paradox would more logically bring you to the conclusion that LLMs’ massive computational needs and limited capacities imply a commensurately greater, mind-bogglingly large computational requirement for physical aptitude.
While the linguistic representation of thought space may be discrete and appear simpler (even the latter is arguable), the underlying phenomena are not.
Current LLMs are terrific in many ways but pale in comparison to great authors in capturing deep, nuanced human experience.
As a related point, for AI to truly understand humans, it will likely need to process videos, social interactions, and other forms of data beyond language alone.
If you put an AI like AlphaZero in a Go environment it explores so much of the game space that it invents its own Go culture from scratch and beats us at our own game. Creativity is search in disguise, having good feedback is essential.
AI will become more and more grounded as it interacts with the real world, as opposed to simply modeling organic text as GPT-3. More recent models generate lots of synthetic data to simulate this process, and it helps up to a point, but we can't substitute artificial feedback for real one except in a few cases: like AlphaZero, AlphaProof, AlphaCode... in those cases we have the game winner, LEAN as inference engine, and code tests to provide reliable feedback.
If there is one concept that underlies both training and inference it is search. And it also underlies action and learning in humans. Learning is compression which is search for optimal parameters. Creativity is search too. And search is not purely mental, or strictly 1st person, it is based on search spaces and has a social side.
So, it doesn't make sense to say that what LLMs do is Moravec-easy, and therefore can't be extrapolated to predict near-term progress on Moravec-hard problems like robotics. What LLMs do is, in fact, Moravec-hard. And we should expect that if we've got enough compute to make major progress on one Moravec-hard problem, there's a good chance we're closing in on having enough to make major progress on others.