So I tend to err on the side of biology being more complex than not.
But it's absolutely possible that the high-level behaviour is simple while the underlying implementation is complex and chaotic, as in the gas laws.
In principle one could imagine a processor design that works on these intermediate states that somehow vastly exceeds the computational power of a modern system despite using the same base transistors; in practice we have no idea how to build such a thing, and if we did, we wouldn't know how to build a second one of the same thing reliably either.
Biology lacks this restriction. That doesn't mean it's pure and utter chaos, either, there's bounds on that because it still needs systems to at least be metastable. But where humans engineer almost exclusively with stable systems, biology freely uses metastable systems all over the place. And then, even more remarkably, it deals with the question of how to replicate such a strange system in a way that no modern human engineer ever would by making every instance unique, and still somehow functional.
It's a tough act to follow.
Often the most efficient solution to a problem ends up being a simple one. It would be very surprising if the human brain's Rube Goldberg machine was anywhere near close to a mathematically optimal implementation of intelligence.
It's possible that much of the complexity in a biological neuron is simply working around other complexity introduced by biology, solving problems that we programmers do not even have to think about because we can simply directly use matrix multiplication.
Humans can attempt to multiply numbers and frequently get the wrong result. That doesn't scream "pushing the limits of intelligence".
Maybe there will be transistor-based human-level AGI soon, but I guess it would require several kilowatts of power compared to the 20 or so watts a human brain requires.
Given that entire datacenters don't come close to an AGI, let alone a human level one, I fear "several kilowatts of power" is lowballing it by a significant number of orders of magnitude.
More to the point, we don't even know how or what gives rise to a general intelligence, and even defining it is basically a philosophical question. To me, the optimism of some AI enthusiasts (and I don't mean specifically the parent) feels like cavemen contemplating an expedition to the stars shortly after they invented the sling.
I think the chances of us arriving to anything close to an AGI iteratively based on our current capabilities is a pipe dream.
There are already several computers more powerful than this that don’t appear close to a working full brain emulation, and we definitely don’t understand intelligence well enough yet to engineer something like ourselves, so it’s reasonable to be skeptical of estimates saying we’ll be at the kilowatts level “soon” even if it turns out we’re just missing a step which will be obvious in hindsight.
(Unless by “soon” you mean 15 years; I don’t want to bet on anything on that timescale).
Well, I'm old so by "soon" I mean within my lifetime, e.g. the next 3 to 4 decades.
edit: to clarify a little, when the term AI was first coined by McCarthy in 1956, researchers were confident in cracking AGI within a decade. Then AI-Winter came and people became more cautious. So when I say "soon", I mean it's probably not going to be another 65 years, but also not 5 months or 5 years (unexpected breakthroughs aside).
"You think thermodynamics means using humans as batteries is a dumb idea? Where do you think you learned thermodynamics?"
faint nyan cat music in the background