Benjamin Todd joins the podcast to discuss how reasoning models changed AI, why agents may be next, where progress could stall, and what a self-improvement feedback loop in AI might mean for the economy and society. We explore concrete timelines (through 2030), compute and power bottlenecks, and the odds of an industrial explosion. We end by discussing how people can personally prepare for AGI: networks, skills, saving/investing, resilience, citizenship, and information hygiene. Follow Benjamin's work at: https://benjamintodd.substack.com Timestamps: 00:00 What are reasoning models? 04:04 Reinforcement learning supercharges reasoning 05:06 Reasoning models vs. agents 10:04 Economic impact of automated math/code 12:14 Compute as a bottleneck 15:20 Shift from giant pre-training to post-training/agents 17:02 Three feedback loops: algorithms, chips, robots 20:33 How fast could an algorithmic loop run? 22:03 Chip design and production acceleration 23:42 Industrial/robotics loop and growth dynamics 29:52 Society’s slow reaction; “warning shots” 33:03 Robotics: software and hardware bottlenecks 35:05 Scaling robot production 38:12 Robots at ~$0.20/hour? 43:13 Regulation and humans-in-the-loop 49:06 Personal prep: why it still matters 52:04 Build an information network 55:01 Save more money 58:58 Land, real estate, and scarcity in an AI world 01:02:15 Valuable skills: get close to AI, or far from it 01:06:49 Fame, relationships, citizenship 01:10:01 Redistribution, welfare, and politics under AI 01:12:04 Try to become more resilient 01:14:36 Information hygiene 01:22:16 Seven-year horizon and scaling limits by ~2030

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