You're spot-on that excel managed to break through because it's one of the few kinds of rigidness that was both legible and familiar enough to the average person. But very little else is, because the average person doesn't think in precision.
> they lack the training to fully specify their ideas (it's tedious for a reason)
> almost no one wants to spend the day trying to get it out a probability machine
These are opposing statements. What AI does best is taking fuzziness or under-specification and making sense out of it. For the first time in history, computers don't need precise instruction to be useful.
Excel is so usable because that rigidity eliminates an entire class of bugs
No race conditions, no side effects, no confusing state to debug
Functional languages like Nix is also extremely inflexible by design but there we recognize it as a "smart" feature, not a "dumb" limitation
> For the first time in history, computers don't need precise instruction to be useful.
They still do. What we've done mostly is extracting common meaning out of textual languages so that we can map out a prompt to an interpretation. But the issue is that a single sentence can have many and some are plainly wrong. The training data is also not so clean.
The computer is still using precise instructions. Now we only have a mapping program that's using probability and weights to match natural languages to those instructions with no limit on the gap between what it chooses and what was needed (aka error).
The standard way was to have the idea going through multiple people (or persona), each refining it until it's precise enough to be done by a computer. Yes, the final result, the program, is restrictive. But like checklist, the dependability matters more than creative flexibility. Especially in cases like "Transfer N dollars from account X to account Y". You don't want LLMs to decide it's fine for N to be negative or for N to be greater than the balance of account X.