Scott debunks the "all exponentials become sigmoids" argument against AI risk by showing how forecasters consistently predict premature flattening of exponential trends, and argues that without deep understanding of AI dynamics, we should expect current AI progress to continue for roughly as long as it's already been going.
Longer summary
Scott argues against the "all exponentials eventually become sigmoids" talking point often used to dismiss AI capability concerns. While technically true that exponential growth must eventually level off, he demonstrates through examples (UN birthrate predictions, solar power deployment forecasts, and AI capability projections) that people consistently misidentify when this flattening will occur, often predicting it prematurely. He explains that while some technological progress does follow sigmoid curves (like airspeed records), predicting when a trend will flatten requires either deep understanding of the underlying process or, in the absence of such understanding, applying Lindy's Law - which suggests a trend will continue approximately as long as it has already lasted. Scott concludes by challenging AI skeptics to either provide detailed models explaining why AI progress will slow down, or explain why they're not using Lindy's Law as their default assumption.
Shorter summary