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 here.

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