The problem with AI SE is that AI writes un-architecture-ed code. The problem #2 is that a lot of people suck even more....
I personally like using openspec for this, because it makes the model iteratively ask you architectual decisions, and then manages creating and accessing the documents as needed.
I work at a ~15y old codebase; guess that the famous "architecture" is - a glue of half-baked solutions, unfinished ideas and inconsistencies. Recently I discovered there are 6 different ways used to create audit trails in the app. The "human memory" lives until person decided to switch job or is fired. Then the next person re-invents the wheel and cycle continues.
I suggest taking an honest look at "the architecture" people are so proud of - you might realize there is not much moat there after all. But then again - I mostly write cruds - so that's probably core of the problem, lol
Not necessarily. I use SOTA models with established stacks like .NET which allow my agent to easily follow decades of best practiced patterns (DI, EF Core, ASP.NET Core...) so you still and up with proper architecture.
In fact I can easily still manually inspect everything bc I already know how it's supposed to be written.
Completely, 100% agree on these exact points. I have access to every frontier model at work. None of them write what I would call clean code. At work the teams that have gone fully vibe-coded have repos full of broken abstractions that just accumulate over time. Eventually most of the effort of the team and their AI tokens seemingly goes to fixing issues that then break other things. Any attempt at 'refactor this mess, make no mistakes' turns into a _huge_ churn of changes ("shuffling the deck chairs") without ever really fixing things.
As I said: prompting correctly and reviewing are still indispensable skills.
Perhaps a lot of devs still have a flawed mental model of how these things even work.