Jakub Pachocki, OpenAI's Chief Scientist, sits down with Jacob to cover the full arc of where AI research stands today and where it's headed. The conversation spans the explosive growth of coding agents and what it signals about near-term AI capability, the use of math and physics benchmarks as proxies for general intelligence, how reinforcement learning is being extended beyond easily-verified domains toward longer-horizon tasks, and what it means to run a research organization at the precise moment the models themselves are starting to accelerate the research. Jakub shares a candid take on the competitive landscape, why chain-of-thought monitoring is one of the most promising tools in the alignment toolkit, and — with unusual directness — why the concentration of power enabled by highly automated AI organizations is a societal problem that doesn't yet have an obvious solution.
(0:00) Intro
(1:53) Research Intern Capability Timelines
(4:59) Math Breakthroughs
(7:59) RL Beyond Verifiable Tasks
(12:32) RL vs In-Context
(19:01) Allocating Compute Internally
(28:18) AI for Science
(31:40) Pattern Matching
(33:23) Solving the Hardest Math Problems
(37:40) Chain of Thought Monitoring
(44:33) Generalization and Value Alignment in Models
(47:57) Inside OpenAI
(51:55) Quickfire
With your co-hosts:
@jacobeffron
- Partner at Redpoint, Former PM Flatiron Health
@patrickachase
- Partner at Redpoint, Former ML Engineer LinkedIn
@ericabrescia
- Former COO Github, Founder Bitnami (acq’d by VMWare)
@jordan_segall
- Partner at Redpoint
