For what it's worth, I still parse code changes and feel uncomfortable if I struggle to understand code output without assistance.

On the "languages for LLMs front", isn't that a bit "cart-before-horse"? Doubt we are anywhere near the limit of what a model could do with a static verifiable language, even more so since stuff like TS has massive training data, any new language wouldn't benefit in such a way.

Add to that, I'd rather be able to understand the output, partly for aforementioned reasons, partly because we have seen with post Spud GPT reasoning/summarisation issues what happens when being able to read model output becomes even slightly harder. Having model-focused languages would only exacerbate such issues.

Also, absolutely not a Maths expert so take this for an utter layman regurgitating of what I have read, but apparently the Lean proofs by OpenAI are very hard to parse even for the experts in the field, lovingly called "Clanker Proofs", even when they are valid.

Having not just correctness but also human understanding as a goal has merit, so there is that to consider too. Rather models get more efficient at using what we have (and can understand+maintain as humans if need be) then make it more opaque.