Track Records

This section documents the epistemic track records of influential figures in AI. Understanding where experts have been right or wrong—and their patterns of over/underconfidence—helps calibrate how much weight to give their current views.

Available Track Records

Person	Summary	Key Pattern
Yann LeCunYann Lecun PredictionsDocumenting Yann LeCun's AI predictions and claims - assessing accuracy, patterns of over/underconfidence, and epistemic track record	Strong on long-term architectural intuitions; underestimates near-term LLM capabilities	Consistent skeptic
Sam AltmanSam Altman PredictionsDocumenting Sam Altman's AI predictions and claims - assessing accuracy, patterns of over/underconfidence, and epistemic track record	Directionally correct on AI trajectory; overoptimistic on specific timelines	Safety rhetoric vs. deployment tension
Eliezer YudkowskyEliezer Yudkowsky PredictionsDocumenting Eliezer Yudkowsky's AI predictions and claims - assessing accuracy, patterns of over/underconfidence, and epistemic track record	Early timeline errors; vindicated on AI generalization; core doom predictions unfalsifiable	Updated from early overconfidence
Elon MuskElon Musk PredictionsDocumenting Elon Musk's AI predictions and claims - assessing accuracy, patterns of over/underconfidence, and epistemic track record	Prescient safety warnings; consistently missed product timelines by 6+ years	Shifting goalposts

Methodology

Each track record page documents:

1. Resolved predictions - Claims that can now be evaluated with ✅/❌/⚠️ status
2. Pending predictions - Testable claims with target dates
3. Unfalsifiable claims - Positions that cannot be empirically tested
4. Accuracy analysis - Patterns of where the person is right/wrong
5. Position evolution - How their views have changed over time

All claims include source citations where available.