The reason for this is: the only way to show productivity gains enabled by AI is to lay off people and pretend you are doing the same amount of work (while in reality you are severely dropping quality and accumulating technical debt).

Dunno how it is these days, but that reads like Android roughly 2012-2020.

I once found a looooong bug report thread on their issue tracker 7ish years old that had all the usual waves of promises that a fix might make the next release, then silence, then repeat, and the usual challenges to the bug’s status every time a release happened, plus it saw community members correctly diagnose the problem in the first couple years, then by like year 5 there’s was a (small!) patch posted by a community member with multiple posters confirming it was good and fixed the issue, that the author and others had been begging Google to apply and get in a release for a couple years. There’d been no responses from Google folks for a while.

That might be the worst one I saw, but encountering something like that was a few-times-per-year thing in my android app dev years.

I'm certain that if they would start doing that, without a proper strategy / workflow when it comes to QA, it will be GitHub reloaded. You'll be able to watch the decline in real-time.

> I have never interacted with a piece of software that, for every single problem I found, there was an open issue always at least 4-7 years old

You have never interacted with Jira?

Even slop-maker-makers themselves struggle: https://github.com/anthropics/claude-code/issues

What hope slop-maker-users have then?