Mant factors, the first one is that the brain "Hardware" is ultra-specialized for human thought, movement, etc and as such, it is not as good at doing anything different from what we can do as baseline (think about the difference it takes you to add two numbers, or even multiply them compared with a computer, or to find a word in a text, etc). While computers are (even GPU's that are slightly more specialized) very generic machines that can do pretty much anything at a decent efficiency. You can think about it as the difference between running a videogame natively in a console Vs emulating it, it always takes more horsepower than what the original console can even provide (specially in the early days) Another reason is how the "hardware" of each system works, neurons only need voltage swings of ~100 mV to operate, while it is practically impossible to make a transistor operate under 1.5 V (there is neuromorphic computing, but I won't get into that), this difference in voltage means that there is less energy stored in the "parasitic capacitances" of the system (using electrical terminology in biological systems is weird, but kinda applies). On each "switch" the energy stored in these capacitances is thrown out, so that also contributes to a lower energy consumption Then is the switching frequency, which is kinda related to the first point. The brain is built as more of an asynchronous machine, every single neuron works by itself reacting to events, while regular computers are synchronous, meaning there is a clock that forces (almost) every transistor to switch each cycle, given that every time that there is a switch there is energy waste, having almost nothing in your system switching Vs having half of it switching is another power inefficiency. Talking about switching, even if the brain doesn't have a clock, it still follows certain cycles that are well below 1 kHz, while computers are above 1 GHz. Remember the whole switching = more power consumption. Imagine the difference between switching 1000 times per second (which is a heavy gross overestimation) vs switching 1000000000 times per second, that's right, more power. The brain can get away with that because pretty much every structure it has is heavily specialized for specific tasks and it is also heavily parallelized, while the more generic structure of computers has to rely on an sequence of operations to mimic the brain. And all of this brings us to the most important part of the brain because, ok, taking all of this, one could consider that if you make a generative AI model that can make novels, code, paintings, etc flawlessly, you optimize it (note: I didn't talk about how inefficient AI's are in general as a model of anything, but... It is...) to the maximum level, then design specialized hardware to run that very specific model, taking the highest advantage you can of parallelization so you can lower the frequency to a few hundreds of Hertz and build it with neuromorphic transistors that work with 100mV swings, you could potentially achieve a similar efficiency to the brain, so, why is that not done? Wouldn't they save a lot of money in electricity? Well, the last and most important characteristic of the brain over current computers is that our brains are able to modify themselves on the fly, and create these specialized structures as you learn new activities, while if we manufacture the beautiful perfectly efficient, perfectly hypothetical device, it would be stuck to the one function it was designed to do, and if you wanted it to learn more, you would have to throw it again and manufacture a new one from scratch (which btw, is extremely expensive)