> The real question is what existing language is perfect for LLMs?

I think verbosity in the language is even more important for LLMs than it is for humans. Because we can see some line like 'if x > y1.1 then ...' and relate it to the 10% of overbooking that our company uses as a business metric. But for the LLM would be way easier if it was 'if x > base overbook_limit then ...'.

For me, it doesn't make too much sense to focus on the token limit as a hard constraint. I know that for current SOTA LLMs we still have pretty small context windows, and for that reason it seems reasonable try to find a solution that optimizes the amount of information we can put into our contexts.

Besides that we have the problem of 'context priming'. We rarely create abstract software, what we generally create is a piece of software what interacts with the real world. Sometimes directly through a set of APIs and sometimes through a human that reads data from one system and uses it as input in another one. So, by using real world terminology we improve the odds for the LLM to do the right thing when we ask for a new feature.

And lastly there is the advantage of having a source code that can be audited when we need.