According to https://www.rand.org/content/dam/rand/pubs/research_memorand... (1951) neurons (and, we know, transistors, also) wind up mapping onto (the same) basic finite automata mathematics.

Right, lots of things can map onto models of computation described in typical theory of computation textbook, and that gives you your one to one comparison to transistors. You can also find equivalences between board games, lego, conways game of life etc. etc.

But what I'm saying is, there is a layer (or layers) of organic function that is abstracted away in machine learning/AI. While the field does borrow ideas liberally from brain architecture (as it well should), you nevertheless have neurons doing things like chemical signalling, metabolic regulation, unique site-specific structural organization, and processes for dynamic reorganization of structure as needed.

That is, not just in the informational representation, which can be sufficiently abstract to model practically anything, but in the hardware itself upon which the information rests. It's there that I suspect there are efficiencies gained by brains over transistors.