Reasoning, Robots, and How to Prepare for AGI (with Benjamin Todd)

What this episode covers

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