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