alt Hacker NewsI 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.
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> 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.
Have you tried Astra?
I'm designing an analog ASIC. It will suggest e.g. transistor matching. You can also give it hints for layout.
You still need to validate with SPICE simulation (and obviously silicon).
I'm also looking into non-LLM scripts that use brute force aided by heuristics (e.g. lookup tables) to efficiently search the solution space.
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.
So its a thing that we can simulate every single aspect you mentioned.
Btw. the expert had to learn this, its probably available in written form one way or the other on the internet and LLMs have read that too.
You should bet on it.
> 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.
All this emi stuff is easily caught in a few rules that you use over and over again. Exactly the sort of thing an LLM could easily do.
Also, Claude can already write a multiphysics simulator for you. Doing EM analyses on the fly during design is something that will make LLMs much better designers than humans.
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.