So the two biggest examples I am currently aware of are googles AI for unfolding proteins and a startup using one to optimize rocket engine geometry but AI models in general can be highly efficient when focussed on niche tasks. As far as I understand it they’re still very similar in underlying function to LLMs but the approach is far less scattershot which makes them exponentially more efficient.

A good way to think of it is even the earliest versions of chat GPT or the simplest local models are all equally good at actually talking but language has a ton of secondary requirements like understanding context and remembering things and the fact that not every gramatically valid bannana is always a useful one. So an LLM has to actually be a TON of things at once while an AI designed for a specific technical task only has to be good at that one thing.