Yeah I think LLMs really help with the chicken-egg situation in language adoption. Contrary to many opinions that predict programming homogenizing around the big 3 languages that exist today (because that's what the LLMs currently write) I think in the future more nice languages will gain adoption as they are written by LLMs, who as you note don't care about a lack of community surrounding those langs -- if they need a missing library the AI can just write it. Maybe they even add it to the language ecosystem for other AI or humans.
I think Python is actually kind of the worst language of the top langs to be the lingua franca of AI, where more niche statically typed languages like Nim are better suited.
As a Pythonista I tend to agree. I had high hopes for Mojo but it's taking its due time to become usable outside the narrow focus of GPU programming, whereas Nim fits multiple niches surprisingly well.
Python is a way, is Lisp's revenge after its AI winter dismissal.
Finally, due to AI market share pressure, JITs in Python are a real thing, after all those years of PyPy being largely ignored.
Python at least has type annotations these days, even if they aren't enforced.
One of my concern is LLMs are going to generate a lot of low quality code for languages that do not have sufficient discussions on forums like Stackoverflow.
That's why these niche languages need state-of-the-art compilers that enforce invariants more strongly. This way, they can catch most of the subtle bugs the LLM produces, sort of like antibodies.