AI-generated bug reports are overwhelming PostgreSQL triage

What Christophe Pettus reported

Christophe Pettus, a PostgreSQL consultant at PGX Inc., wrote on The Build blog (republished on Planet PostgreSQL) that the project's bug report list started receiving "flurries" of 10 to 15 bugs at once, all coming from the same email address and clearly AI-generated. He himself picked up one of these batches to triage and spent about 90 minutes on the work, which, according to him, is "about 10 times longer than it took the person to run the AI and paste the result into the email."

The triage result is a portrait of the problem. Among the items reviewed by Pettus were: a "bug" that was actually expected behavior (manual data corruption triggering an error, which is exactly what should happen); at least two cases already under discussion on the -hackers list; a duplicate of a previous report from the same person; at least three issues already fixed in version 19; some misdiagnoses even when there was a real bug; and one case where the diagnosis was correct, but the suggested patch would have made the problem worse.

None of these reports, according to him, went through any human verification before being sent. The person ran the AI, received a confident response saying there was a bug and already had a patch ready, and simply forwarded everything.

Why this matters for people who develop with Postgres

Pettus's central point is not against AI as a tool. It's about triage capacity: the number of people qualified to analyze PostgreSQL bug reports is small, and their time is scarce. Shifting onto this small group the work of figuring out whether the AI got it right or wrong is, in his words, "rude at the very least."

There's an even worse side effect. Those who repeatedly send low-quality reports earn a reputation for noise, and their future reports tend to be ignored, including the ones that might eventually be serious. This is especially sensitive in a project like PostgreSQL, where a poorly evaluated security fix has real costs for those running databases in production.

AI bug reports injected into the pipeline without human intervention are worse than useless.

Christophe Pettus, a PostgreSQL consultant at PGX Inc.

For those who develop with and depend on PostgreSQL, the practical consequence is twofold: maintainers' time that could go toward reviewing real patches is consumed discarding noise, and trust in the bug report channel (a resource any dev uses when they find strange behavior in production) becomes more fragile for everyone.

The counter-argument that deserves a response

The most generous reading is to ask whether this criticism isn't just gatekeeping against people who are starting to contribute and use AI as a legitimate crutch. Pettus anticipates this objection: he's not against AI, he uses it as a coding and writing assistant, and he explicitly says using AI to find bugs is welcome. The problem he describes isn't the tool, it's the complete absence of human verification before submission.

This pattern of low-effort contribution in search of recognition (appearing in the release notes of a high-profile project) already existed before generative AI. What changed is the speed: a person without deep knowledge of Postgres can now produce, in minutes, a technically formatted report, complete with stack trace and suggested patch, at a scale no manual triage process was designed to absorb.

I disagree, however, with one implicit point: treating this as a passing etiquette problem. In practice, open source projects are going to need a structural filter, whether a mandatory "AI-assisted report" label or a separate triage queue, because relying on contributors' good will doesn't scale when the cost of generating a report has dropped to practically zero.

How I would use AI to report a PostgreSQL bug

Editor's note: the code in this section was not run in a real environment. Validate before using in production.

The difference between using AI as a discovery tool and becoming a source of noise lies in who signs the report and the work that comes before submission. A reasonable approach, for those who work with databases day to day:

1. Reproduce the problem in isolation: a minimal script with psql, without depending on the whole application, that any maintainer can run and confirm.
2. Check the execution plan with EXPLAIN ANALYZE before assuming the behavior is a bug and not a characteristic of the optimizer.
3. Check whether the problem has already been fixed by looking at the CHANGES file for the version under development and recent commits in the repository.
4. Search the -hackers and -bugs lists to see if anyone has already discussed the same symptom before opening a new report.
5. Use AI to draft the technical explanation if that helps, but read and validate every statement as if it were yours, because it is yours: you're the one who will need to justify and defend the report if someone asks.

In summary: AI can speed up the investigation part, but it doesn't replace the judgment of someone who understands the database's execution engine and concurrency model. Submitting without this filter is outsourcing to the maintainer work that was yours.

What remains open

Pettus doesn't propose a formal filtering mechanism, and that's the real gap in the post.

What can't be ignored is the perverse incentive: contributing to a high-profile project has become social currency, and AI has made the cost of trying one's luck far too cheap. Solving this will require open source projects to define something PostgreSQL still doesn't have publicly stated: an explicit policy on what counts as an acceptable report when AI is involved in producing it.