I recently joined Gus Docker on the Future of Life Institute Podcast. We debated many of the recent themes of this Substack:
The AI feedback loop: How reasoning models changed the AI landscape, why agents may be next, and what a self-improvement feedback loop could mean. One scenario we explored: leading labs reach AGI-level systems doing AI research, while your daily life looks identical because of regulatory and social friction. The economy is about to transform at unprecedented speed while appearing normal on the surface.
Robot economics: How quickly robots could scale up, how that could turn an intelligence explosion into an industrial explosion, and what might prevent it.
Personal preparation: Why saving makes sense even if AI makes us far richer; which skills increase in value (get close to AI or far from it); and whether it makes sense to move to the US while you still can.
Here’s the video:
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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
is there a transcript for reading?