Scott presents Plan A, a detailed roadmap by Daniel Kokotajlo's AI Futures Project proposing a US-China regulatory agreement to safely advance AI to genius-level systems in the 2030s, solve alignment during a controlled pause, then achieve aligned superintelligence by 2040. Longer summary

Scott introduces Plan A, a detailed roadmap created by Daniel Kokotajlo and the AI Futures Project for navigating the AI transition safely. The plan envisions a trustless regulatory agreement between the US and China built on controlling chip supply and auditing data centers, followed by a 'golden mean' approach where both countries rapidly advance to top-human-genius-level AI while pausing before superintelligence. During this pause in the 2030s, billions of genius-level AIs would solve alignment and other major problems while being kept in controlled environments, eventually leading to fully aligned superintelligence around 2040 that helps chart humanity's future. The post frames this as offering a positive vision that satisfies both safety concerns and accelerationist goals through triple-digit GDP growth and rapid problem-solving. Shorter summary

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 argues AI safety regulation adds only 1-2% to training costs while America has a 10x compute advantage over China, making safety concerns irrelevant to the race; meanwhile, chip exports to China pose a far greater threat that the same critics ignore. Longer summary

Scott argues that AI safety regulation will not significantly harm America's position in the AI race with China. He breaks down the race into three levels (compute, models, and applications), showing America has a massive 10x compute advantage while China's strategy focuses on applications. He demonstrates that proposed AI safety regulations would add only 1-2% to training costs - trivial compared to America's compute lead. The real threats to US advantage are chip export policies and smuggling, where NVIDIA lobbies to sell advanced chips to China, potentially reducing the US advantage from 30x to 1.7x. Scott notes the irony that many people opposing safety regulation on China grounds simultaneously support chip exports, and argues safety regulation might actually help the US by improving security, enabling compute governance, and preventing future overreactions to AI incidents. 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