IME, not long. ChatGPT (v3) did poorly with Zig when it first came out, despite some Zig in its training code, and despite equivalent Python tasks being handled trivially. I worked around that for a bit by including a moderately lengthy prompt prefix with example Zig code and descriptions of interesting language features, which worked well for bulk boilerplate at the time. A year later, the model was updated (presumably with additional GH Zig source as training data) and works fine without any prompt prefix strategy, at least for the simple sorts of tasks I'd use an LLM for.

A question might be, will there be new languages? If we start using AI so much that humans lose there job, where will the examples come from on which we’ll train the AI?

The goal is for the LLMs we have to be so good at long context tasks and reasoning about code that you can simply paste the entire documentation for your new language and they’ll figure it out.

Once you have that working, you can generate synthetic examples to train the next version of the LLM, or for open source coding models, a small LoRA file that anyone can download to add support to their model of choice.