It's interesting to look at what was being attempted when the vulnerability was introduced[0]

> Workflows like jira_close.yml use deprecated atlassian JIRA actions and have a dependency on the gh-actions repo. This is not ideal and unecessarily complex. PR updates jira_close workflow to use direct API calls via curl. It preserves custom fields used too.

I won't speak to this projects' management and how they prioritize things, but from my own experience, pre-AI, this type of change would have been firmly in the "this is a minor annoyance, put it in the Tech Debt Backlog alongside the 50000 other tickets" and never actually done. The cost of a human investing the time understanding how to fix the problem, doing code changes, testing them, and deploying them is just way too high for what actual value this change brings, which is close to nothing.

Now with AI, it's as simple as firing up an agent and telling them to make a change; as much effort as writing that backlog Jira ticket in the first place.

Similar to the problem open source is having with low-value PRs, companies are going to have to start realizing that code is not free to review or maintain, even when it's generated for ~free, in their internal processes. Just because an agent can fix a minor tech debt annoyance with a few lines of instructions doesn't mean it should.

[0] https://github.com/snowflakedb/snowflake-connector-net/pull/...

I have seen plenty of "my backlog has never been shorter" comments here.

I'm interested in how that turns out 6 months later.

In my team, we have plenty of enhancement requests from users. We address those that make obvious sense and are trivial to do but withhold from others, even though the code change itself is likely small. Because we don't know if there is more than a single user that can actually benefit from it, if it has unintended consequences, or if it causes maintainence issue down the road.