For example, why are you working on a four-year-old issue, and a trivial one at that, when you're already behind schedule on the tasks assigned to you? Now someone else who has their own things to get done has to review it? And even trivial changes can be annoying to truly review beyond a blind LGTM.
Just one of the many ways that pressure builds against the utopia of burning through old tickets.
Aside, watch out for the double standard we have for AI on forums like this. AI is expected to be so good that it can magically overcome the forces that keep engineers from working on old tickets (which were never related to engineer productivity) and, when AI can't, well of course it couldn't because AI sucks.
Productivity gains can also be achieved by reducing scope. The coming issues will be that because of increased productivity (idea -> working code) that software is too bloated, does too much, that product managers will and can say "yes" to everything. Until it becomes unmanageable.
And that's not a new problem, it's what basically every programming adage / wisdom going back 70 years is about.
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.
So nothing really changes in terms of product development velocity, it’s just headcount reduction.
But that’s not what their own marketing strategy communicates.
Has any of the companies who went all in on AI gotten better at their job because they went all in on AI?
You have never interacted with Jira?
What hope slop-maker-users have then?
On the other hand, LLMs seem perfect for triage and finding duplicates, so it's still surprising that they've let it get this bad.
(Source: I build tooling around Claude Code and have spent hours swimming in the GitHub issues based on downstream user feedback)