Comment by vidarh

I mostly agree, and I think a combination of good representation and tooling that lets it self-correct quickly will do better than new language in the short term.

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.