Anecdote◆Article
Teams using AI to fix bugs repeatedly end up depending on the AI (e.g., Claude) for answers, and then cannot tell whether anything the AI outputs is actually true.
Illustrates a recurring bug that the AI keeps failing to fix; the developer readily defers to Claude, and both watch an output wall whose truth—or falsehood—they cannot verify. ✦ AI generated
Florian Herrengt · Simon Willison's Weblog · 2026-08-12 · original ↗
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it. I mean... asking AI to fix it. Unfortunately, it seems like not even Fable can figure it out. You go talk to the person who worked on this feature. "So where does the data come from?" "Hmm... actually I don't know. Let me ask Claude." You sit next to each other watching an endless wall of text appear on the screen. Neither of you has any idea whether any of it is true but Claude seems very confident.
Read full article ↗excerpt · fair-use quotation
- ·Teams repeatedly ask AI to fix the same bug
- ·4th attempt still failing
- ·Developer cannot say where data comes from
- ·Team defers to Claude for answers
- ·Watch endless wall of output text
- ·No one can tell if any of it is true
- ·Claude appears very confident
Around this claim
This moment responds to
supports → The real bottleneck in production AI isn't performance or benchmark overfitting, it's reliability, trustworthiness, and understanding what your agents are doing.Scott Clark · The TWIML AI Podcastgives example → The one-shot game produced by Codex contained a visual bug where each raccoon's eyeball was enlarged into a giant sphere floating over its head, which Codex failed to spot despite reviewing screenshots during development.Author · Simon Willison's Weblogsupports → AI is given too much credit for what it can actually do—when you look closely at the details, it fails at a lot of things despite being amazing and powerful overall.Avishai · 20VCprovides context → Engineering teams need to be helped to avoid 'AI slop' — low-quality AI-generated code.ByteByteGo · ByteByteGo Newslettergives example → At a company that ranks employees on an AI-usage leaderboard, an engineer secretly checked out a parallel copy of the Go repository and had an AI rewrite the entire codebase in Zig on the side, purely to generate visible AI-usage metrics and protect their job.anonymous engineer · Simon Willison's Weblogsupports → Managing code quality and human attention in an era of abundant AI-generated code is the biggest unsolved problem — an open season for new companies to solve.Elad · No Priors