Hacker News users say AI disasters stay silent — raising questions about whether real-world AI failures are being hidden or simply rare

A Hacker News thread asked the community to share AI project failures and disasters, but received minimal response — suggesting either most AI deployments are succeeding quietly, or people are reluctant to publicly admit expensive mistakes with AI systems they've built or deployed.

The thread highlights a visibility gap: tech news constantly covers AI announcements and successes, but almost no one publicly documents when AI projects fail, cause financial loss, produce wrong outputs at scale, or need to be shut down — making it hard for others to learn from real mistakes.

For business decision-makers and teams considering AI adoption, this silence is a practical problem: without public case studies of AI failures (what went wrong, why, and how much it cost), it's harder to anticipate risks, plan contingencies, or avoid repeating the same costly mistakes other companies have already made privately.