I've noticed a huge gap between AI use on greenfield projects and brownfield projects. The first day of working on a greenfield project I can accomplish a week of work. But the second day I can accomplish a few days of work. By the end of the first week I'm getting a 20% productivity gain.
I think AI is just allowing everyone to speed-run the innovator's dilemma. Anyone can create a small version of anything, while big orgs will struggle to move quickly as before.
The interesting bit is going to be whether we see AI being used in maturing those small systems into big complex ones that account for the edge cases, meet all the requirements, scale as needed, etc. That's hard for humans to do, and particularly while still moving. I've not see any of this from AI yet outside of either a) very directed small changes to large complex systems, or b) plugins/extensions/etc along a well define set of rails.
This is where engineering practices help. Based on 1.5 years data from my team I can say that I see about 30% performance increase on mature system (about 9 years old code base), maybe more. The interesting stuff - LLMs is leverage, the better engineer you are the more you benefit from LLM.
This has not been my experience at all. The only time I even got close to this is multiple long sessions that had multiple compacts.
The key is if you hit compact, start a new session.