I think that LLM's ability to understand any language, programming or otherwise, is almost entirely a factor of how much training data it can get its claws on. In the cases of new programming languages, it probably won't be able to do much of anything.

That's a shit take, it's assuming that the LLM has the same thought process for learning a programming language instead of being autocorrect no steroids.

I'm pretty sure LLMs will be shit at Lisp for the foreseeable future just because the language is sort of created by the programmer.

There's more to languages than ease of use. D is often cited having a "poor library infrastructure", by those who leave it. They often deal with Rust, Go, or even C/C++ instead of writing their own libraries and/or library bindings.

I don't think this is linearly correlated. Despite being called a "Language Model", it does not mean it processes language as humans do. If an LLM is good at supporting you with a programming language mainly depends on the amount of available training data.

Let's take esoteric languages as an example - there are languages that only work via weird Unicode symbols or other cryptic commands. A human will have a hard time to understand that language, the LLM may not have any problem at all to give working comprehensive examples (as in will be useful).