Toby Ord digs out key exponent: ~100x compute buys only 10x longer AI task horizons
tobyordoxford · x · 2026-11-09
Toby Ord points out that the power-law exponent in METR's task time-horizon scaling curve is buried in a footnote: 0.454. That means roughly 100x more compute is needed to extend the task horizon 10x. He also notes that adjusting models to their Chinchilla-optimal-equivalent yields an even better power-law fit, suggesting the relationship is robust — a crucial parameter for forecasting when AI can handle month-long tasks.
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