Verifiers Can Be Built From Rubrics

Irene Zhang raises long-horizon verifiability, and Nathan says dense rubric-based rewards, expert consensus, and taste communities can train broader agentic behavior.

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Episode notes

This special cross-post from The Intelligence Horizon features Nathan Labenz in a wide-ranging conversation on compressed AI timelines, expert disagreement, and why he believes the singularity is near. They discuss interpretability, RL scaling, and the balance between extraordinary upside, like curing major diseases, and serious existential risks. Nathan explains his evolving p(doom), why he’s slightly more optimistic about robustly good AI, and how defense-in-depth strategies might keep society on track. The episode also explores US-China rivalry, AI governance, and why human cooperation may matter more than technical control alone.

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CHAPTERS:

(00:00) About the Episode

(03:27) Special Sponsor

(05:12) Opening and AGI framing

(12:08) Scaling RL and paradigms (Part 1)

(21:31) Sponsors: Tasklet | VCX

(24:24) Scaling RL and paradigms (Part 2)

(28:56) Verifiability and long horizons

(41:13) LLMs and world models (Part 1)

(41:19) Sponsor: Claude

(43:32) LLMs and world models (Part 2)

(54:17) Energy, hardware, and chips