No, so technically you really are gatekeeping slop rather than "AI" specifically.

So there can be the code itself, so this example from a recently claimed 'user level anti-cheat', we have a purported "kernel integrity checker": https://github.com/TuncorReUnion/TLAC-MODERN-LOCAL-ANTI-CHEAT-REUNIONED/blob/main/kernel/tlac_kernel.c

So a relatively intact structure for a kernel module, but just the dumbest concept imaginable (a kernel is 'good' so long as the words 'rootkit' nor 'suspicious' appear in /proc/modules, since rootkits obviously must name themselves 'rootkit' and other suspicious software must name itself 'suspicious'. Basically look at almost any file in that repository and you'll see similar stuff, in-tact, even suspiciously complete boilerplate with stupid logic. Alternatively, crazy verbose.

The there's the git activity, the project above has a git commit history: https://github.com/TuncorReUnion/TLAC-MODERN-LOCAL-ANTI-CHEAT-REUNIONED/commits/main/

Activity comes in crazy number of commits, and you can kind of see for each one what kind of prompt would have driven that change, with the disconnect between syntax correction but the underlying concept is just stupid.

Then sometimes, it's not the code or code activity itself, but the commentary. So this change looks plausible to most folks without much tell: https://github.com/rhinstaller/anaconda/pull/7074/changes

Then there's the discussion, which mostly proceeded in a "no one has any idea what any of this is about, but everything sounds plausible and it's niche enough that heck if I know if it really makes sense". Eventually this comment: https://github.com/rhinstaller/anaconda/pull/7074#issuecomment-4509336034

It has that LLM smell "That's a great question", and the expanded description is enough for a broader audience to start seeing jargon that doesn't make sense. But it's still niche enough for most people to have no idea what to make of it.

No. But there are code smells, that might reveal it.

It is the same with any kind of output generative deep learning models produce.