I have not worked on a project with 20 AI-assisted contributors. I have worked on a team with 4. While smaller, I can already see that the code churn, and conflict resolution is more a kin to a larger team. However, we also have been introspective about how we can interoperate with these new disruptive tools -- ritualizing the LLM's ability to create and maintain documentation and perform code reviews has been indispensable. Perhaps 20 on a project is unsustainable, and even unnecessary.

I've running local models; before having a coding harness, i'd basically do the same misteps the AI does; add the same logging lines, and trace with the errors are, etc. This was exhausting so no docs and tests were rarely if ever created.

Now, to actually get the AI to do anything complex, it's basically required to both write docs and write tests because solidifying behavior only works when there's AI tests it can run to verify some behavior I've verified once.

Then there's things I'm never going to remember the AI had to do; recenly it was "blitting" to /dev/fb0 and it was trying to remember which encoding was which and what order, etc. Things I simply do not want to have to get a detail account of nor is it something I need to remember above the statement "some video drivers have their own RGB, BGR encoding standards" and "an image has a bit depth and size" etc.

These things I would have learned doing it myself, but I would also had to learn where it all breaks and how to interoperate between pillow and video drivers, etc. If I ever do this again, i'll still point the LLM at it with or without this library, and it'll do the same abstract reasoning.

So my knowledge is compact because I don't need to know the implementation for this video display code; I just need the tests and docs and I'll point the LLM at it if I need to extend it.