In all seriousness using AI for codegen is at best shortsighted negligence. You know that problem huge long running software projects have where it becomes a nightmare to change anything? That’s some proportion of poor architectural design, lack of cleanup or refactor time, and poor understanding of the code by developers. Poor architectutal design can be repaired by cleanup and refactoring, so both of those issues end up being management/planning failures more than anything. Not understanding the codebase is much more complex. It can be caused by attrition causing loss of institutional knowledge, the code base growing faster than anyone can keep track of, the team being so large no one can stay on top of things, too much time passing since anyone has looked at or changed parts, lots of reasons. The only solution is doing a long audit and associated cleanup and refactoring. If you don’t it just takes forever to change anything because of all the knock on effects that no one can predict, meaning delays and bugs. When you use AI tools the code base grows very quickly, too quick to really comprehend, and you get shitty architecture to go along with it. You’re just speedrunning enterprise software or spending all your time reviewing slop code. It’s like a drug, the first time it does something fast and well you feel it’s so great, but it will never live up to that because it secretly sucks and can only ever suck. Best case it slows you down and you get good software at the end. Worst case you spend all your time wrestling with it and never get a finished product.