Bitter Lesson 2.0: AI progress is stacked sigmoids, not one exponential
Dan_Jeffries1 · x · 2026-11-08
@kuchaev proposes "Bitter Lesson 2.0": AI progress isn't a single exponential but a stack of overlapping sigmoids. Within one sigmoid, scaling compute is necessary; jumping to the next requires innovation. Dan Jeffries adds that every exponential is a hidden S-curve before it breaks out.
The framing is often used to explain scaling plateaus: compute buys you the current paradigm's gains, but paradigm shifts need algorithmic novelty.
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