My understanding/experience is that LLM performance in a language scales with how well the language is represented in the training data.
From that assumption, we might expect LLMs to actually do better with an existing language for which more training code is available, even if that language is more complex and seems like it should be “harder” to understand.
In the long term I expect it won't matter - already GPT3.5 was able to reason about the basic semantics of programs in languages "synthesised" zero-shot in context by just describing it as a combination of existing languages (e.g. "Ruby with INTERCAL's COME FROM") or by providing a grammar (e.g. simple EBNF plus some notes on new/different constructs) reasonably well and could explain what a program written in a franken-language it had not seen before was likely to do.
I think long before there is enough training data for a new language to be on equal grounds in that respect, we should expect the models to be good enough at this that you could just provide a terse language spec.
But at the same time, I'd expect the same improvement to future models to be good enough at working with existing languages that it's pointless to tailor languages to LLMs.