Scott uses Claude AI to help research California primary races and finds its tailored candidate analyses and recommendations align well with his eventual voting choices, suggesting AI advisors could improve democratic participation. Longer summary

Scott Alexander demonstrates how he used Claude AI to help research and make decisions for local California primary elections. He shares detailed examples of Claude's analysis of candidates and ballot measures, showing how the AI provided comprehensive summaries of candidates' positions, backgrounds, and endorsements tailored to his stated political preferences (centrist liberal, YIMBY, abundance-oriented). He tested Claude's recommendations against his own eventual choices across 10 races, finding strong agreement (5 perfect matches, 3 second-choices). Scott concludes that AI voting advisors could be valuable both for people who don't have time for deep research and for enhancing the research of those who do, and suggests this could be important for democratic decision-making in a post-AGI future. Shorter summary

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