YC is wrong about LLMs for chip design

Anything that requires deep “understanding” or novel invention is not a job for a statistical word regurgitator. I’ve yet to see a single example, in any field, of an LLM actually inventing something truly novel (as judged by the experts in that space). Where LLMs shine is in producing boilerplate -- though that is super useful. So far I have yet to see anything resembling an original “thought” from an LLM (and I use AI at work every day).

Experiment: you think LLMs can innovate on chip design? Ask it to do something much simpler: invent a new better sorting algorithm. We use names such as Timsort or Djikstra for a specific reason: because it requires rare human ingenuity to invent such things. If an LLM can’t invent a new sorting algorithm that is meaningfully better in some way than existing known algorithms, then good luck on something much harder like chip design.

You can set the bar lower. Have it invent another n log n sorting algorithm. Or omit all merge sort implementations from training data and see if it can re-invent it.

But I certainly agree in general. It’s been years and there are still no independent novel discoveries afaik.

As long as the chip isn’t expected to count the number of Rs in strawberry, I don’t see why an LLM couldn’t design a better chip.

Define novel

There are many LLMs that are producing original "thought".

ESM3: https://www.evolutionaryscale.ai/blog/esm3-release

AlphaProof/AlphaGeometry2: https://deepmind.google/discover/blog/ai-solves-imo-problems...

MatPilot discovering new materials: https://arxiv.org/abs/2411.08063

Then of course NVidia Omniverse with their digital-twin learning.

https://blog.google/technology/ai/google-ai-big-scientific-b...

Taking a quick glance at all of these, they seem to be aspirational or a “brute force” type of search, which computers have always been good at, before AI. Does not seem like any novel research to me. The parameters and methods are set by humans and these systems search within a well defined space.