We understand the aerodynamics of bird flight, and used it to make fixed-wing planes optimized for carrying lots of cargo. Once we understand the principles behind intelligence, we can make very efficient AI optimized for our usage. But we're still at the point where we don't understand intelligence as well as we understood aerodynamics when building the first planes, so we still have a lot to learn from "birds" - animal brains.
I mean, we could quibble about exactly where we are pre- or post-Wright Flyer, but given the amount of AI research that amounts to brute-force flailing about in search of incremental improvements, disagreements on the importance of "biological plausibility" and so on, it's pretty clear that, roughly speaking, AI is currently somewhere in the equivalent of the Lilienthal-Langley-Wright-Curtis continuum (ie. 1890-1910-ish) and still prior to the most important theoretical breakthroughs. IOW, AI has not in my opinion yet achieved an equivalent to aerodynamics' Prandtl lifting-line theory: https://en.m.wikipedia.org/wiki/Lifting-line_theory
I believe AI will start as a basic principle or idea that can be applied to any sufficiently big state machine that controls e.g. an RC airplane or traffic lights. That idea will be obvious in a hindsight. I'd even make a guess that it will be like a "stateful" state machine that accumulates state in a particular manner and uses that to control the underlying state machine. We still will be nowhere near understanding intelligence, but that clever trick will be enough in most cases.
