> Fact is, vibe-coded projects devolve over time into an unmaintainable mess. The reason is simple, yet hard to fix: code maintainability and good architecture don’t have good measurements that we can apply, because it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
    > 
    > For one, AI is not trained on what it means for code to be maintainable. For instance, any reinforcement learning done needs a reward signal that can be measured immediately, not in months or years.

Sad to say, but this is no different from human written code. Human written code just takes even longer to realize the mistakes because the pace is slower.

I think at the end of the day, it is not impossible to have AI write "good" or "high quality" code. If anything, once the patterns are established, AI will be more likely to adhere to the patterns and rules than any human team. It requires the most experienced engineers on the team to split their time writing the core patterns and documenting them in references/skills.

But it takes a lot of "taste" and a willingness to slow down a bit with AI (to create necessary artifacts), something teams find hard to do when you can ship so fast now.

My experience has been that there is a camp of very senior engineers that are unwilling to adapt to reality and focus on documentation and writing (effectively producing skills and agent guidance which multiplies their effectiveness); they will cling to their knowledge thinking coding a sacred art.