In other words, its to generate stupid metrics for stupid employers.
I'd like to emphasize the "stupid" bit when it applies to "employers" more than "metrics". As an interviewer, I have used, among other things, an applicant's public Github as part of my process. But I'd like to think I do it right because of two reasons: I look deeper than just the history graph, and I only use this (among other metrics) for ranking resumes.
I'll look at their history, sure, but I'll also look more in depth at repos, PRs, comments, issues, etc. I'll clone their repos and try running their code. I'll review their public PRs and read their comments and discussions, if any. I try to get an idea of if I'd like working with this person. If I saw someone with a constant feed of PRs to seemingly random open source projects, that would cause me concern for this exact reason.
And all that is one of the things I do to rank resumes in order of interview preference and to give me questions to ask in the interview. I'll look for things that suggest the candidate has already been vetted successfully by others (e.g., Ivy League school, FAANG, awards, etc.). I'll look for public content that suggests the candidate knows what they are doing. But all this does is sort the resumes for me. My entire decision-making process is fed by the interview.
Granted, AI assistants are getting good enough that they can potentially coach candidates through remote interviews (and eventually in person interviews, with glasses or earpieces or something.). Eventually we'll have to put candidates in Faraday cages with metal detectors for interviews (that is unless AI takes over all development). I'm hoping to be retired by then.
