In my experience, even Claude 4.6's output can't be trusted blindly it'll write flawed code and would write tests that would be testing that flawed code giving false sense of confidence and accomplishment only to be revealed upon closer inspection later.
Additionally - it's age old known fact that code is always easier to write (even prior to AI) but is always tenfold difficult to read and understand (even if you were the original author yourself) so I'm not so sure this much generative output from probabilistic models would have been so flawless that nobody needs to read and understand that code.
Too good to be true.
I remember how website security was before frameworks like Django and ROR added default security features. I think we will see something similar with coding agents, that just will run skills/checks/mcps/... that focus have performance, security, resource management, ... built in.
I have done this myself. For all apps I build I have linters, static code analyzers, etc running at the end of each session. It's cheapest default in a very strict mode. Cleans up most of the obvious stuff almost for free.
Let's say you dont review. Those two extra months probably turns into four extra months of finding bugs and stuff. Still 8 man months vs 54.
Of course this is all assuming that the original estimates were correct. IME building stuff using AI in greenfield projects is gold. But using AI in brownfield projects is only useful if you primarily use AI to chat to your codebase and to make specific scoped changes, and not actually make large changes.