Scott argues that even if AGI requires a new paradigm beyond LLMs, we shouldn't expect significant delays, since Lindy's Law suggests major paradigm shifts could occur within 3-5 years, and new paradigms typically emerge precisely when scaling hits limits. Longer summary

Scott addresses the objection that AGI is far off because LLMs need a 'new paradigm' to reach AGI. He traces the evolutionary tree of AI development from neural networks through transformers to modern LLMs, then applies Lindy's Law to show that even paradigm shifts as major as deep learning or transformers should be expected within 3-5 years at the 25th percentile. He argues this timeline is comparable to LLM-only predictions anyway. Scott also makes a subtler point: new paradigms historically emerge when old ones hit scaling limits, meaning they won't cause delays but rather continue progress from where scaling left off. He concludes that extrapolating from current LLM scaling remains the best forecasting method whether or not LLMs themselves reach AGI. Shorter summary

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