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

Scott introduces a new AI forecasting project predicting rapid AI development and potential superintelligence by 2028, led by Daniel Kokotajlo, whose previous 2021 predictions proved remarkably accurate. Longer summary

Scott Alexander introduces a new AI forecasting project led by Daniel Kokotajlo and a team of experts, which predicts rapid AI developments leading to superintelligence by 2028. The post begins by noting how accurate Kokotajlo's 2021 predictions were, then presents the team's forecast which includes an intelligence explosion in 2027, government involvement in AI companies, and potential scenarios ranging from misaligned AI to technofeudalism. Scott notes that while team members have varying timelines, they consider this an 80th percentile fast scenario that shouldn't be ruled out. Shorter summary

Scott Alexander analyzes a paper suggesting scientific progress is slowing relative to researcher numbers, arguing this trend is expected and possibly beneficial. Longer summary

Scott Alexander discusses a paper by Bloom, Jones, Reenen & Webb (2018) that suggests scientific progress is slowing down relative to the number of researchers. The paper shows that while progress in various fields (e.g., transistor density, crop yields) remains constant, the number of researchers has increased exponentially. Scott argues that this constant progress despite exponential increase in inputs should be our null hypothesis, as expecting proportional increases would lead to unrealistic outcomes. He suggests that the 'low-hanging fruit' explanation is most plausible, where early discoveries were easier to make. Scott also warns against trying to 'fix' this trend, as it could lead to dangerous consequences if scientific progress accelerated too quickly. Shorter summary