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Vote for the 2025 AI Darwin Awards

Help Decide This Year's Most Spectacular AI Misadventure

Voting Is Not Currently Active

The 2025 AI Darwin Awards voting period has concluded (January 1–31, 2026). Democracy has spoken, votes have been recorded, and the democratic process now rests peacefully in a spreadsheet somewhere. If you participated, thank you for your contribution to internet polling and the important work of publicly documenting AI catastrophes.

Our voting system operates on an annual cycle: one month for democracy, eleven months for documenting new disasters. This gives us adequate time to collect, verify, and catalogue the year's most spectacular AI failures before asking the public to judge them.

(We considered keeping voting open year-round, but realized that would require maintaining a functional website continuously, which seemed unnecessarily ambitious.)

When Can I Vote Again?

Eager to participate in the democratic celebration of AI catastrophes? Mark your calendars: voting for the 2026 AI Darwin Awards will open on January 1st, 2027.

Why the wait? Because we need the entire year 2026 to properly document, verify, and catalogue all the spectacular AI failures yet to come. Rest assured, based on current trends, there will be plenty of nominees. The tech industry shows no signs of slowing down its commitment to deploying untested AI systems in critical applications.

You can still submit nominations throughout the year as you witness AI misadventures unfolding in real-time. Every submission helps build next year's collection of cautionary tales. We accept nominations on a rolling basis, documenting disasters as they happen rather than waiting for some artificial deadline.

Pro tip: Start a spreadsheet tracking AI failures you encounter. By December 2026, you'll have forgotten which incidents were genuine disasters versus which were just Tuesday in the tech industry.

Explore the Awards

Since you can't vote right now, we encourage you to:

Remember: The AI Darwin Awards exist because learning from mistakes is theoretically important. Whether anyone actually learns from them remains an open question, but we're optimistic that someday, someone will read these stories before making the exact same mistake.