[Cthulhu_]

I have a theory (armchair take here lmao) that AIs are trained on public code, but the biggest codebases are not public.

Although I suspect models from Google, Facebook and Microsoft can be trained on their massive internal codebases. Whether they are is another question.

[mbreese]

I’m not sure you’d see that big of a difference in quality. There is quite a bit of cruft that can accumulate when you know the code will never be public.

But, you would probably see a difference of scale and architecture. Larger projects that need better organization are probably more likely to be in private codebases (Linux excluded). So you might be right about the lack of private code in LLM being an issue.

[aleph_minus_one]

I have another thought on this:

At least in the past (before LLM-based code contributions got socially acceptable in some circles), in open-source project you often got very direct comments on code that was of bad quality. Yes, this was always a little bit abrasive, but it did a lot for the code quality.

For internal applications used at companies, such an abrasive behaviour is typically not accepted ("not a team player" (as if this is something bad), offended snowflakes, "not socially adept" etc.). Thus the code quality suffers quite a lot for internal applications.

Have you ever thought why salespeople have such an easy time selling some LLM for coding to big companies? Because the code quality of many internal applications is so bad, which makes even shitty LLM slop code often better than what is there.