Zero-tolerance no-AI policies are tiresome. I get why people have concerns. No PR reviewer wants to review drek the submitter him/herself doesn't understand. And LLMs have been a disproportionate source for such drek. But the actual problem they're trying to solve should be low-quality submissions, rather than spending energy fulfilling some notion of AI-free purity.
The linked issue raised the complaint that a bit of text as a "wall of text" (implication, I guess, too many words, too little meaning). Maybe it was or wasn't. But being generated by an LLM doesn't make it a wall of text, and being written by a human doesn't make it not a wall (speaking as somebody who has been fairly accused at times of generating 100% human-made walls of text).
No because some people are against AI for reasons other than just slop. Some people actually find it revolting that it uses so much energy and that it encourages deskilling in many. AI in my view is antithetical to human creativity and I try very hard to avoid software projects that use AI, whenever possible. And I try to only promote software that is AI-free.
Don't forget ethical concerns about using things that only exist because of boatloads of criminal behavior.
Some people are tired of situations where 14 year olds get hounded for years for downloading music but altman/amodei/musk/zuck get billions and zero consequences for stealing more copyrighted content than any individual ever could in a lifetime.
So, yes, it's amusing to see clear Claude-isms like "load-bearing", "outright", and "genuine" in a [very nice] bit of analysis like this. And there's a (maybe negative? or not?) argument to be made about the world being filled with more Claude-isms or LLM-isms in general.
But I think the data say a second thing which is just as interesting and an absolute positive for the typical source code base. Look at the clusters that shrank significantly. Most of what you'll see in there is just incomprehensible...not even English. Cluster 4 has, after "pullrequest", a bunch of seeming usernames in the top tier. Cluster 6 seems to have names of repositories or tags in the top tier. Cluster 9 has branch names in it.
Meanwhile, keep going through cluster 1 and you'll see words I don't consider Claude-isms that really, really grow in usage. Words like "died", "nothing", "worse", "ever" all have well over 10x growth. This tells me something else. That the average commit log was BARELY ENGLISH. And then the LLMs came along and made commit logs that were ACTUALLY ENGLISH.
I count this as a good thing. I don't know the cross-section of repos chosen for this analysis, and I get it...some repos are garbage/throwaway, some commits come from automated processes that generate uninteresting commit logs, etc. But I've been benefitting from my work team's actually explanatory commit logs when doing code/bug archeology for decades, when doing PR review for the last decade, and I've even seen LLMs benefit from it in the last year (granted, not as often). A large part of professional software development is communication, and while the most important communication is via the code/comments, the commit logs are not unimportant. So, if this is making the average GitHub PR better (arguably more professional) by including actual English descriptions of code changes in commit logs...well, that's a genuinely load-bearing concept for me. :)
The linked issue raised the complaint that a bit of text as a "wall of text" (implication, I guess, too many words, too little meaning). Maybe it was or wasn't. But being generated by an LLM doesn't make it a wall of text, and being written by a human doesn't make it not a wall (speaking as somebody who has been fairly accused at times of generating 100% human-made walls of text).