This episode moves from the practical question of whether AI agents can run whole jobs to the deeper question of what kind of intelligence we are actually building. Prakash Narayanan and Nathan Labenz open with OpenAI Astra, long-running agent workflows, and the organizational changes that follow, then Ksenia Se of Turing Post joins for a wide-ranging conversation about world models, open-source access, privacy, alignment, and recursive self-improvement.
We also examine the 3D Navier-Stokes regularity problem and the recent AI-assisted solution claim that remains pending verification, along with the broader implications for mathematical work, engineering, and trust in frontier AI labs.
Show Notes
Prakash Narayanan and Nathan Labenz open with OpenAI Astra, persistent memory, long-running agents, and what AI automation could mean for work and organizations. Then Ksenia Se of Turing Post joins to compare LLMs with world models through prediction, physics, perception, action, multimodality, privacy, alignment, and recursive self-improvement. The episode closes with a careful look at the Navier-Stokes solution claim, AI-assisted mathematics, verification, and the politics of transparency, competition, and public accountability.
Chapters
(0:00) Some human tasks are gone.
(1:19) LLMs predict tokens. World models act.
(2:11) Can fluid equations stop predicting?
(2:49) The AI slowdown may be an illusion.
(3:10) Opening and Astra rollout
(4:25) AI-human division of labor
(6:54) AI music and Suno
(9:17) Astra’s 3D world generation
(11:26) Beyond traditional benchmarks
(13:26) Astra’s coding breakthrough
(17:43) AI replaces manual labeling
(19:49) 4D cardiac education
(24:11) Foundation models design hardware
(28:10) Why AI safety needs time
(42:50) Independent safety auditors
(48:51) Recursive self-improvement metrics
(51:59) Measuring long AI tasks
(54:52) Alpha Genome Atlas
(59:17) AI jobs and labor market
(1:00:25) Structural change and work
(1:07:26) Linear AI forecasts
(1:11:14) Persistent AI memory
(1:15:50) Ksenia Se and Turing Post
(1:18:18) AGI and capable models
(1:21:36) Open-source AI access
(1:25:20) Trust, privacy, and local models
(1:28:07) LLMs versus world models
(1:32:38) Multimodal AI and latent space
(1:34:46) Cross-domain superintelligence
(1:39:05) Theory of generalization
(1:44:15) Anthropomorphism and AI minds
(1:50:09) AI and peacebuilding
(1:53:17) AI for difficult conversations
(2:00:20) Working with AI
(2:01:35) AI writing and human voice
(2:07:21) Recursive self-improvement
(2:11:55) Reddit and model training
(2:14:53) AI, fear, and abundance
(2:17:43) Positive visions for AI
(2:21:28) Human versus US alignment
(2:23:52) Navier-Stokes enters the story
(2:24:39) The latest solution claim
(2:24:57) How the equation works
(2:25:31) The 3D regularity problem
(2:29:23) Vortex stretching
(2:30:06) Two routes to blow-up
(2:33:13) Physics-informed neural networks
(2:33:44) LMs versus physics
(2:34:26) Engineering applications
(2:35:08) AI-assisted results
(2:35:43) OpenAI’s methodology
(2:36:08) The authorship dispute
(2:41:47) Pending verification
(2:43:23) Opening trust and accountability
(2:44:40) Astra and the hidden model
(2:46:21) RL compute and the pause
(2:50:30) Why AI labs keep training
(2:52:13) Inference cannot stop
(2:55:20) GPT-4 red-team failures
(2:57:34) Mixed messages and trust
(2:59:09) Competition and skepticism
(3:00:49) Auditing without shared secrets
(3:02:19) Transparency or adversarial oversight
(3:03:11) The misleading RL baseline
(3:03:49) Where frontier models live
(3:05:41) Human genius as risk
(3:06:41) Researchers versus executives
(3:10:07) Third-party access and audits
(3:12:41) Compute and capital pressure
(3:15:13) Rivals set frontier speed
(3:17:18) The AI device roadmap
(3:18:03) Free AI and advertising
(3:19:01) Why public accountability matters
(3:19:32) The singularity and OpenAI stock
Guests:
Ksenia Se — AI Inferencer, Turing Post (𝕏 | LinkedIn)
