The problem is that this approach is not sustainable. Errors compound. The cost to fix one issue might seem small at first, but over a stretch of time all these "oopsies" become architectural spaghetti that can only be fixed with a complete rewrite, which will certainly become more expensive than getting the code "organically" developed.
The only way I see AI coding working in the long run is if we go back to a Waterfall/BDUF process and having actual engineering. Let engineers really own the architecture. Enforce that any new feature - no matter how small - to be specced out with complete sequence diagrams. Ensure that every new software package needs to be put on an UML component diagram for the team to review and see each addition interacts with the whole system, etc.
If we do that, then we can just give all the documents to a coding agent and say "go ahead and implement this" with a minimal amount of confidence. But in doing this, I bet we will realize the following:
- the "effort" has never been about writing code itself. The code is just the material manifest of all the thought that went to think over a solution into the problems that the product is attempting to solve.
- we will likely be better off by using code generation tools (i.e, UML-to-code) and a "weak" LLM (than can run locally) than by playing the token lottery at the Anthropic Casino.
I mirror your thoughts. I think we'll end up with "perfect map" paradox = you cannot be vague or indecisive on what you want (and if you are then these decisions don't matter) and you're creating a 1:1 representation of what the code needs to be.
I'd substitute "owner" for the team and in that sense the owner will not need to be human.
We're at this state where Claude is great at doing the "middle" part of work, but it's crap at gathering requirements and verification of what it has done. I also don't see people caring about these aspects of software development as shown in the article