Hacker News Reader

Comment by donatj | Hacker News Reader

My friend is an electrical engineer. He designs circuit boards for a living. We were having dinner the other night, and when the topic of AI came up he told me rather confidently that he didn't think AI was coming for his job anytime soon.

I kind of cautiously disagreed. He told me that the applications he used had no tooling for AI.

I basically said "give it six months". I think in my googling now, it's already here.

View on HN

People are trying, but it's not here because it's a multi-dimensional problem space where there are local optimums but often no perfect solution and 'good enough' might only be judged through practical testing, integration, or supply chain realities which are at best predictable and often emergent. You can't always foresee why a design will fail until it's 80% done and then you have to go back 20% to solve it another way. This is particularly the case with power, interface, budget, thermal, EMI, radio, optical, spatial, supply chain, firmware, HR, regulatory, deployed unit, or assembly process constrained designs. Turns out that's most non-trivial designs.

Yeah, another way to say it is that the biggest inputs to any complex design aren't actually captured in the board files themselves. Everything you listed are system integration complexities that no level of autorouter will be able to accommodate for, and they make up 80%+ of the work.

While I agree they're not in the files, I don't think that's the correct root of the problem. Rather, given the LLM input assumption is that they are communicated, I think the key problem is that they can't be readily communicated because even the specifying party is unsure what they are. That is to say, it's a wicked problem. https://en.wikipedia.org/wiki/Wicked_problem

I had some success with using Claude in conjunction with my oscilloscope and spice simulations. I think it is an under explored space so far.

In case you are interested: https://lucasgerads.com/blog/lecroy-mcp-spice-demo/

I will post more updates soon.

I largely agree with your friend. There's a big difference between it being possible, and it being adopted. A startup could come out with cutting edge AI integrated eCAD tools tomorrow, and ten years from now Apple will still be using Cadence to design the iPhone.

Basically, unless the legacy eCAD companies decide to add it themselves, there's too much pain involved in switching tools — and even with that caveat, Cadence specifically is too much of a dinosaur to integrate it effectively anyway.

That said, there's a big distinction depending on whether your friend works primarily on the schematic or layout side.

I second the friend's opinion.

A lot of the people who post online have no experience with the paid PCB tools and those tools already have quite a lot of automation, and the automation interfaces work between different CAD & EDA vendors. Shared, hierarchical, and repurposed schematics are also totally a thing.

I spend almost no time on boiler plate stuff. And with good constraints, which require serious thought and understanding, tons of routing & checks can be automated too. Right now.

So, IMHO, there is not a lot of fat in the process for AI to automate away without a lot more EE and physics models, and the ability to interpret multiple specs, built in. And the current AI tools are very far from that.

> those tools already have quite a lot of automation

Not to mention the level of customization and tooling that companies like Apple have themselves built out around the PCB tool. Playing around with Cadence at home is going to be a different experience than using it at a large tier1 company.

I was mostly sticking with more systemic factors against AI adoption, but I agree with you completely.

As you said, professional PCB design has largely automated the easy stuff, and the hard stuff is going to be largely illegible to an LLM. A competent engineer could route a 10L HDI board which powers on in under a week, getting it ready for mass production is what takes the other 8+ months and 5 design spins, and I don't see much opportunity for AI to help there.

If nothing else, wouldn't access to training data be a hard limit here? I doubt one could get multiple companies to provide the complete history of their product board designs _and_ the background on why each change was made.

The new models that have come out can now see

Reply on news.ycombinator.com