Scott Alexander argues that Plan A's proposed AI chip regulations are not a dystopian surveillance state, but rather comparable to existing regulations on controlled substances, and that most feared dystopian outcomes already exist in current banking and AI chat monitoring. Longer summary

Scott defends Plan A's AI chip regulation proposals against claims they would create an Orwellian surveillance state. He compares the proposed regulations (requiring factories, customers, and data centers to register and submit to inspections, plus cryptographic kill switches and transparency requirements) to existing regulations on controlled substances like Xanax, arguing they would simply make the AI chip industry more regulated without dystopian effects. He addresses specific concerns: consumer devices wouldn't need licenses (AI chips cost $40,000+ vs consumer hardware), open-weight models would be banned but replaced with open-algorithm requirements to prevent power concentration, and actual surveillance concerns are already worse in the status quo (banks monitor all transactions, OpenAI monitors chats). Scott argues the real costs are moving chip regulation from 50th to 95th percentile stringency, potentially taxing consumer hardware briefly, and banning new open-weight model training - substantial but not dystopian. Shorter summary

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 analyzes OpenAI's new deliberative alignment approach and explores different possibilities for who should ultimately control AI systems as they become more powerful. Longer summary

Scott discusses OpenAI's new paper on deliberative alignment, which combines constitutional AI with chain of thought reasoning to create more thoughtful AI responses. He explains how the process works by having AI models reflect on moral questions using a specification document. The post then explores different possible approaches to AI chains of command, including prioritizing companies, governments, specifications, moral law, average citizens, or humanity's coherent extrapolated volition. Scott expresses concern that we're heading toward either corporate or government control of AI systems, while acknowledging there may be better alternatives. Shorter summary

Scott Alexander summarizes and responds to comments on his book review about IRBs, covering various perspectives on research regulation. Longer summary

Scott Alexander summarizes key comments on his book review of 'From Oversight to Overkill' about IRBs (Institutional Review Boards). The post covers various perspectives on IRBs and research regulations, including stories from researchers, comparisons to other industries, discussions on regulation and liability, debates on act vs. omission distinctions, potential applications to AI governance, and other miscellaneous observations. Scott provides additional context and his own thoughts on many of the comments. 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 examines why the AI safety community isn't more actively opposing AI development, exploring the complex dynamics between AI capabilities and safety efforts. Longer summary

Scott Alexander discusses the complex relationship between AI capabilities research and AI safety efforts, exploring why the AI safety community is not more actively opposing AI development. He explains how major AI companies were founded by safety-conscious individuals, the risks of a 'race dynamic' in AI development, and the challenges of regulating AI globally. The post concludes that the current cooperation between AI capabilities companies and the alignment community may be the best strategy, despite its imperfections. Shorter summary

Scott Alexander discusses a new expert survey on long-term AI risks, highlighting the diverse scenarios considered and the lack of consensus on specific threats. Longer summary

Scott Alexander discusses a new expert survey on long-term AI risks, conducted by Carlier, Clarke, and Schuett. Unlike previous surveys, this one focuses on people already working in AI safety and governance. The survey found a median ~10% chance of AI-related catastrophe, with individual estimates ranging from 0.1% to 100%. The survey explored six different scenarios for how AI could go wrong, including superintelligence, influence-seeking behavior, Goodharting, AI-related war, misuse by bad actors, and other possibilities. Surprisingly, all scenarios were rated as roughly equally likely, with 'other' being slightly higher. Scott notes three key takeaways: the relatively low probability assigned to unaligned AI causing extinction, the diversification of concerns beyond just superintelligence, and the lack of a unified picture of what might go wrong among experts in the field. Shorter summary