I have yet to see LLMs solve any new problems. I think it's pretty clear a lot of the bouncing ball programming demos are specifically trained on to be demoed at a marketing/advertising thing. Asking AI the most basic logical question about a random video game like, what element synergies with ice spike shield in Dragon Cave Masters and it will make up some nonsense despite it being something you can look up on gamefaqs.org. Now I know it knows the game I'm talking about but in the latent space it's just another set of dimensions that flavor likely next token patterns.
Sure, if you train an LLM enough on gamefaqs.org, it will be able to answer my question as accurately as an SQL query, and there's a lot of jobs that are just looking up answers that already exist, but these systems are never going to replace engineering teams. Now, I definitely have seen some novel ideas come out of LLMs, especially in earlier models like GPT-3, where hallucinations were more common and prompts weren't normalized into templates, but now we have "mixtures" of "experts" that really keep LLMs from being general intelligences.