Some codebases grown with AI assistance must be getting pretty large now, I think an interesting metric to track would be percent of code that is AI generated over time. Still isn't a perfect proxy for how much work the AI is replacing though, because of course it isn't the case that all lines of code would take the same amount of time to write by hand.
Yeah, that would be very helpful to track. Anecdotally, I have found in my own projects that the larger they get the less I can lean on agent/chat models to generate new code that works (without needing enough tweaks that I may as well have just written it myself). Having been written with models does seem to help, but it doesn't get over the problem that eventually you run out of useful context window.
What I have seen is that autocomplete scales fine (and Cursor's autocomplete is amazing), but autocomplete supplements a software engineer, it doesn't replace them. So right now I can see a world where one engineer can do a lot more than before, but it's not clear that that will actually reduce engineering jobs in the long term as opposed to just creating a teller effect.
It might not just be helpful but required one day. Depending on how the legality around AI-generated code plays out, it's not out of the question that companies using it will have to keep track of and check the provenance and history of their code, like many companies already do for any open source code that may leak into their project. My company has an "open source review" process to help ensure that developers aren't copy-pasting GPL'ed code or including copyleft libraries into our non-GPL licensed products. Perhaps one day it will be common to do an "AI audit" to ensure all code written complied with whatever the future regulatory landscape shapes up to be.
