This post hits the nail at a bit of an angle.

The AI agents are great, and any expert can prompt them correctly to get good code. LLMs occasionally pick wrong patterns and start digging a hole, but this is why an expert is required. The code itself is just not worth writing when a detailed prompt can get you the same code typing 20x less text.

Where I agree with the post is:

The adoption of AI agents into software engineering is a problem. Solo projects are great, but our teams have not adjusted to the speed-of-change to a mental model of a project. So I see orgs making a choice to either: slow down or forgo the shared mental model.

Anybody choosing to forgo the mental model is building crooked legacy slop at scale. You can and should save the mental model to an AGENTS.md, but devs need it in their brain to prevent the digging a hole behavior.

To be fair the digging a hole behavior is something humans do just as well. But in teams you'd communicate enough to catch it - hopefully^1. It's the combination of higher speeds and teams that's creating a bit of a disaster.

I'm not sure what a good solution is either. There is a case for solo devs running for 2-month sprints with much more freedom. Perhaps we'll have an "AI Agile manifesto" within a year.

[1] Though you should not underestimate the amount of poor code being created before LLMs. There are enough teams for whom LLMs are practically all upsides. Stay very far away from those.