I have to disagree here. While it's been proven that a lot of things can be done by just searching a very big solution space very fast (either by brute compute or by a sophisticated algo or both), hardware solution spaces are not that easily searched.

For a lot of applications where signal integrity is not critical, high frequency switching is not required and power density is moderate, yeah. Anyone can do that, even an AI. But you don't even need to make a pcb for those cases, just throw the components in a protoboard and fire away.

When you start adding variables to the search space (emi interference, matching routing impedances, heat management, mechanical integrity, etc) the search space grows so big that any non-expert will become lost in it.

Will future cheap compute power be able to overcome this? I'm not betting on it.

I'd say that balancing those aspects are exactly what an AI excels at. And an AI can iterate in a way a human wont. Completely redesign something because an edge-case could be slightly better.

AI isn't brute-forcing, or it would have been completely unusable for code.

> Will future cheap compute power be able to overcome this? I'm not betting on it.

Famous last words. https://en.wikipedia.org/wiki/Bitter_lesson

Also did you not see the quoted post at the end, they are already training ai on running material science labs so we are quickly moving away from the non-expert.

We can probably find people saying the search space for Go is too large and that we'll never be able to do protein folding and if we can we'll never manage protein interactions and then well never manage to simulate a whole cell (tbc). I just think at some point these machines become smarter than us, if it's 2 years or 20 years the self improvement part will happen on both the hardware and software side. Judgement day is inevitable.