YC is wrong about LLMs for chip design

I don’t mind LLMs in the ideation and learning phases, which aren’t reproducible anyway. But I still find it hard to believe engineers of all people are eager to put a slow, expensive, non-deterministic black box right at the core of extremely complex systems that need to be reliable, inspectable, understandable…

You find it hard to believe that non-deterministic black boxes at the core of complex systems are eager to put non-deterministic black boxes at the core of complex systems?

Yes I do! Is that some sort of gotcha? If I can choose between having a script that queries the db and generates a report and “Dave in marketing” who “has done it for years”, I’m going to pick the script. Who wouldn’t? Until machines can reliably understand, operate and self-correct independently, I’d rather not give up debuggability and understandability.

I think this comment and the parent comment are talking about two different things. One of you is talking about using nondeterministic ML to implement the actual core logic (an automated script or asking Dave to do it manually), and one of you is talking about using it to design the logic (the equivalent of which is writing that automated script).

LLM’s are not good at actually doing the processing, they are not good at math or even text processing at a character level. They often get logic wrong. But they are pretty good at looking at patterns and finding creative solutions to new inputs (or at least what can appear creative, even if philosophically it’s more pattern matching than creativity). So an LLM would potentially be good at writing a first draft of that script, which Dave could then proofread/edit, and which a standard deterministic computer could just run verbatim to actually do the processing. Eventually maybe even Dave’s proofreading would be superfluous.

Tying this back to the original article, I don’t think anyone is proposing having an LLM inside a chip that processes incoming data in a non-deterministic way. The article is about using AI to design the chips in the first place. But the chips would still be deterministic, the equivalent of the script in this analogy. There are plenty of arguments to make about LLM‘s not being good enough for that, not being able to follow the logic or optimize it, or come up with novel architectures. But the shape of chip design/Verilog feels like something that with enough effort, an AI could likely be built that would be pretty good at it. All of the knowledge that those smart knowledgeable engineers which are good at writing Verilog have built up can almost certainly be represented in some AI form, and I wouldn’t bet against AI getting to a point where it can be helpful similarly to how Copilot currently is with code completion. Maybe not perfect anytime soon, but good enough that we could eventually see a path to 100%. It doesn’t feel like there’s a fundamental reason this is impossible on a long enough time scale.

> So an LLM would potentially be good at writing a first draft of that script, which Dave could then proofread/edit

Right, and there’s nothing fundamentally wrong with this, nor is it a novel method. We’ve been joking about copying code from stack overflow for ages, but at least we didn’t pretend that it’s the peak of human achievement. Ask a teacher the difference between writing an essay and proofreading it.

Look, my entire claim from the beginning is that understanding is important (epistemologically, it may be what separates engineering from alchemy, but I digress). Practically speaking, if we see larger and larger pieces of LLM written code, it will be similar to Dave and his incomprehensible VBA script. It works, but nobody knows why. Don’t get me wrong, this isn’t new at all. It’s an ever-present wet blanket that slowly suffocates engineering ventures who don’t pay attention and actively resist. In that context, uncritically inviting a second wave of monkeys to the nuclear control panels, that’s what baffles me.