Probably more to the point is that humans are using transistors in their designs, so we deliberately confine them to their simple modes. It is an oversimplification to talk about transistors being either "on" or "off" because they technically have all sorts of intermediate states, but with a few exceptions, human designs avoid those intermediate states like the plague, because A: they defy our ability to build logic with them and B: depending on those exact behaviors means we can't mass-manufacture chips because the variance of the exact behaviors will be too high.

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.

Complexity cuts both ways. Complexity, as every programmer knows, is no friend, and can easily create more problems than it solves.

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.

Transistors were invented by humans, but chip layouts themselves involve a lot of automation and at more than one level — Verilog and VHDL both appeared in the 1980s, and there’s tools at both higher and lower levels of abstraction than those two.

Biology hasn't had time to figure out that intelligence is a good idea, up to humans the payoffs have been relatively low. The complexity in human intelligence probably focuses more on doing basic things extremely energy efficiently rather than being effective at thinking.

Humans can attempt to multiply numbers and frequently get the wrong result. That doesn't scream "pushing the limits of intelligence".