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Comment by corn-cheese | Hacker News Reader

For reasonably complex boards, it’s often not possible to know if it’ll work as intended until you have an assembled prototype in hand… even if you have the best SPICE and RF simulations ever, data sheets for many components can be missing important details, or components can have errata.

LLMs may be able to accelerate time to first prototype, but I don’t think it’ll be possible for them to revolutionise electronics design in the same way that’s happened for software - there’s not enough data, and it’s not cheap to gather more.

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That sounds a great deal like what AI has done for software. Today in software, the work has gone from coming up with approaches and initial attempts to verification and making sure that what AI decided to do is fit for purpose.

The amount of skill needed has gone down dramatically.

The skill floor has gone down dramatically, but the skill ceiling is still quite high depending on the domain.

I'm not sure how true that is. When Claude made progress on the Riemann conjecture, here are the kind of prompts used:

> Jarred's input was mostly limited to sending Claude messages of encouragement (mostly variants of “keep going” or “believe in yourself”).2 This seems to have helped Claude overcome some initial skepticism that it could make meaningful progress.

It seems like the kind of prompts a high schooler could come up with. What kind of problems were you thinking of as high skill?

https://www.anthropic.com/research/riemann-zeta

The full transcript is here: https://www-cdn.anthropic.com/8a0d1add3c637b858a9a181e98c40e...

I think we'll may see something like:

1.AI design tool

And in the warehouse of a big electronics distributor: 1.something like 3D printing that can make a single board rapidly 2.An automated assembly for a single board 3.AI based Automated testing for prototypes, with remote access.

This may reduce the loop to under a day.

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