We tracked specific, dated predictions from CEOs, researchers, skeptics, and Reddit crowds — from 1965 to 2025 — and scored every one against reality.
We collected 69 specific, dated AI predictions from CEOs, researchers, independent bloggers, professional skeptics, and anonymous Reddit commenters. We scored 57 of them against what actually happened (the remaining 12 are still pending).
The average accuracy score across all scored predictions is 0.51 — barely better than a coin flip. That number should make you uncomfortable, because these aren't random people. This group includes the CEO of OpenAI, the co-founder of Google DeepMind, Nobel laureates, and tenured professors at Princeton and Stanford.
Three findings stand out from the data:
1. The extremes score worst. Maximum doom (Yudkowsky, 0.12) and maximum hype (Patterson, 0.10) are equally wrong. The most confident people in either direction cluster at the bottom of the leaderboard.
2. People closest to production consistently outpredict everyone else. NVIDIA engineers who ship actual products (Briski 0.92, Deierling 0.94, Das 0.88) outscored CEOs, academics, and futurists by a wide margin. Proximity to real-world deployment is the single best predictor of prediction accuracy.
3. Specificity correlates with accuracy. Vague predictions ("AI will transform everything") score poorly. Specific predictions ("agentic scaffolding will drive the next leap, not bigger models") score well. The more precisely you can be wrong, the more likely you are to be right.
Each dot is a scored prediction. Higher contrarian scores mean bolder claims.
These eight people got it significantly more right than wrong. The pattern is striking: they were close to production, specific about mechanisms, and honest about limitations.
These predictions scored worst. The striking thing: they come from both extremes. Maximum optimism and maximum pessimism are equally wrong.
Professional AI skeptics have built careers on "AI can't do X" claims. How did they actually perform?
Skeptics as a group scored 0.30 — about half the accuracy of the optimists. But one skeptic stands apart from the rest.
Princeton's Narayanan was the only skeptic who scored well, and the reason is instructive. He was specific about what he criticized. He didn't make sweeping "AI is fake" claims. Instead, he targeted predictive AI in hiring and criminal justice — and he was right. Those products genuinely are unreliable.
His second prediction — that AI wouldn't cause mass unemployment — scored 0.80. By April 2026, no mass job apocalypse had materialized.
The most durable pattern in AI prediction history: every generation of researchers believes AGI is just around the corner. It never is.
Each prediction was scored on three dimensions, weighted and combined into a 0–1 overall accuracy score:
| Dimension | Weight | What It Measures |
|---|---|---|
| Direction | 40% | Did the predicted thing move in the predicted direction? Binary 0/1. |
| Timing | 30% | How far off was the timeline? Measured in months early or late. |
| Magnitude | 30% | How close was the scale of the prediction to reality? 0 = wildly off, 1 = nailed it. |
Scored: Prediction window has passed, fully evaluable. Partial: Some evidence available, preliminary score assigned. Pending: Can't score yet — prediction window still open. Pending predictions are excluded from averages.
Predictions sourced from published interviews, blog posts, research papers, official company announcements, Forbes roundups, McKinsey surveys, LessWrong prediction markets, and Reddit threads. All quotes are verbatim with original source citations.
Outcomes assessed against publicly available data as of April 2026: company earnings reports, industry surveys, product launches, market data, and independent research.
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