Exactly. The real speed up from AI will come when we can under specify a system and the AI uses its intelligence to make good choices on the parts we left out. If you have to spec something out with zero ambiguity you’re basically just coding in English. I suspect current ideas around formal/detailed spec driven development will be abandoned in a couple years when models are significantly better.
> in a couple years when models are significantly better.
They aren't significantly better now than a couple of years ago. So it doesn't seem likely they will be significantly better in a couple of years than they are now.
For now I would be happy if it just explored the problem space and identify the choices to be made and filtered down to the non-obvious and/or more opinionated ones. Bundle these together and ask the me all at once and then it is off to the races.
This is humans have traditionally done with greenfield systems. No choices have been made yet, they're all cheap decisions.
The difficulty has always arisen when the lines of code pile up AND users start requesting other things AND it is important not to break the "unintended behavior" parts of the system that arose from those initial decisions.
It would take either a sea-change in how agents work (think absorbing the whole codebase in the context window and understanding it at the level required to anticipate any surprising edge case consequences of a change, instead of doing think-search-read-think-search-read loops) or several more orders of magnitude of speed (to exhaustively chase down the huge number of combinations of logic paths+state that systems end up playing with) to get around that problem.
So yeah, hobby projects are a million times easier, as is bootstrapping larger projects. But for business works, deterministic behaviors and consistent specs are important.