Scott explains why Ajeya Cotra's influential 'Biological Anchors' report correctly predicted the AI scaling boom but got AGI timelines wrong by twenty years, due to severely underestimating the rate of algorithmic progress. Longer summary

Scott analyzes why Ajeya Cotra's landmark 2020 'Biological Anchors' report predicted AGI around 2050, when current estimates now center on the late 2020s to 2040s. The report correctly predicted the scaling hypothesis and AI boom, but underestimated one crucial parameter: algorithmic progress was actually 200% per year instead of the predicted 30%. This single error, compounded across exponential growth, threw off the entire timeline by about twenty years. Scott examines various contemporary critiques of the report, finding that most concerns about the methodology were actually non-issues, while one throwaway concern (about algorithmic progress estimates being poorly researched) turned out to be the fatal flaw. He concludes this demonstrates both the power and limitations of probabilistic forecasting. Shorter summary

Scott Alexander reviews Tom Davidson's model predicting AI will progress from automating 20% of jobs to superintelligence in about 4 years, discussing its implications and comparisons to other AI forecasts. Longer summary

Scott Alexander reviews Tom Davidson's Compute-Centric Framework (CCF) for AI takeoff speeds, which models how quickly AI capabilities might progress. The model predicts a gradual but fast takeoff, with AI going from automating 20% of jobs to 100% in about 3 years, reaching superintelligence within a year after that. Scott discusses the key parameters of the model, its implications, and how it compares to other AI forecasting approaches. He notes that while the model predicts a 'gradual' takeoff, it still describes a rapid and potentially dangerous progression of AI capabilities. Shorter summary