Scott provides detailed probabilistic timelines and beliefs about AGI development, AI safety, geopolitics, and future outcomes, with his median expectation being AGI in 2031 and 20% probability of existential catastrophe given current safety efforts. Longer summary

Scott lays out his detailed probabilistic beliefs about AI development across five main areas: timelines for AGI and superintelligence (25% chance of AGI by 2027, 50% by 2034), safety prospects (20% chance of doom given current safety efforts, down from 50% without them), geopolitics around AI pauses (40% chance of US-China pause agreement before point of no return), other outcomes like permanent underclass (only 20% likely to last more than a generation), and simulation hypothesis (66% chance the singularity is related to us being in a simulation). He provides arguments for optimism and pessimism on each point, with his modal scenario involving AGI in 2031, widespread automation by late 2030s, and Bostromian superintelligence making GDP go vertical in the early 2040s. The post is technical and probabilistic throughout, written in response to misinterpretations of his views. 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

Scott Alexander critically examines OpenAI's 'Planning For AGI And Beyond' statement, discussing its implications for AI safety and development. Longer summary

Scott Alexander analyzes OpenAI's recent statement 'Planning For AGI And Beyond', comparing it to a hypothetical ExxonMobil statement on climate change. He discusses why AI doomers are critical of OpenAI's research, explores potential arguments for OpenAI's approach, and considers cynical interpretations of their motives. Despite skepticism, Scott acknowledges that OpenAI's statement represents a step in the right direction for AI safety, but urges for more concrete commitments and follow-through. Shorter summary

Scott Alexander responds to comments on his AI risk post, discussing AI self-awareness, narrow vs. general AI, catastrophe probabilities, and research priorities. Longer summary

Scott Alexander responds to various comments on his original post about AI risk. He addresses topics such as the nature of self-awareness in AI, the distinction between narrow and general AI, probabilities of AI-related catastrophes, incentives for misinformation, arguments for AGI timelines, and the relationship between near-term and long-term AI research. Scott uses analogies and metaphors to illustrate complex ideas about AI development and potential risks. Shorter summary

Scott Alexander examines prediction markets, focusing on Metaculus forecasts for AI development and its potential impacts. Longer summary

Scott Alexander reviews several prediction markets, focusing on Polymarket, Kalshi, and Metaculus. He discusses the challenges of using Polymarket and the potential of Kalshi as a regulated futures exchange. The bulk of the post analyzes Metaculus predictions on AI-related topics, including the timeline for AGI development, human-machine intelligence parity, economic impacts of AI, and specific AI achievements. Scott notes the wide range of predictions and the interesting ways prediction markets can quantify expert opinions on complex topics. Shorter summary