AI:AM — Web Infrastructure and Superintelligence · August 26, 2026
Notes de l’épisode
Prakash Narayanan and Nathan Labenz open on the real bottlenecks behind AI data centers, including power, chips, copper, construction, and the 100-gigawatt problem. Malte Ubl joins to discuss Vercel AI Gateway, production fallbacks, agent security, and AI code review, followed by Louis Kirsch and Damon Falck on Faraday, recursive self-improvement, reward hacking, and how humans can verify AI discoveries.
Chapters
(0:00) China may not be compute-starved.
(1:51) Sandboxes aren't inherently safe.
(4:26) Science needs wrong answers.
(5:09) Who pays when AI misbehaves?
(6:29) Opening and Ox Alpha
(7:38) Ox Alpha revealed
(8:01) China's AI infrastructure
(12:19) YMTC and NAND memory
(14:07) Apple, YMTC, and Micron
(15:01) Companies rivaling states
(17:07) Market denial strategy
(20:04) China's regulatory model
(21:55) Federal land infrastructure
(23:51) Alaska data centers
(26:38) Stranded gas to compute
(29:04) The 100-gigawatt problem
(30:48) Copper and future tech
(33:20) AI and material science
(34:38) Faster physics simulations
(37:38) Closing question
(37:48) Malte Ubl and Vercel
(39:10) Self-driving infrastructure
(39:20) AI decisions in production
(43:01) Eve for common agents
(46:25) Normalizing model providers
(48:35) AI Gateway economics
(59:33) Automatic provider fallbacks
(1:00:57) AI security becomes urgent
(1:01:51) Why AI attacks succeed
(1:04:26) DeepSec and code scanning
(1:06:57) Rerunning AI code review
(1:08:43) AI regulation and responsibility
(1:09:57) Provider responsibility and KYC
(1:11:03) Vercel Sandbox challenge
(1:14:50) AI model attack timelines
(1:16:44) Experimental agent harnesses
(1:21:40) Introducing Faraday and Inherent
(1:24:32) Recursive self-improving organizations
(1:28:13) Faraday's self-improvement loops
(1:30:57) Separating scientist and coder
(1:34:34) Why science differs from prediction
(1:37:49) Training with uncertain rewards
(1:40:33) Cheating and reward hacking
(1:44:15) Human control and AI scientists
(1:47:31) Scientific intuition and taste
(1:50:46) Meta-reinforcement learning
(1:53:11) Multimodal scientific models
(1:55:18) Faraday beyond orchestration
(1:56:50) Measuring recursive improvement
(2:02:22) AI agents and workplace context
(2:05:09) AI infrastructure bottlenecks
(2:12:17) Verifying AI discoveries
(2:14:20) AI company culture
(2:15:21) AI labs and organizational culture
(2:17:58) Founders, liquidity, and risk
(2:21:57) AI wealth changes culture
(2:28:35) Animal welfare and communication
(2:33:29) AI superpersuasion politics
(2:34:51) Privacy-preserving AI research
(2:38:39) Punishing AI agents
(2:43:47) Math versus empirical science
(2:52:50) AI persuasion reality
(2:56:25) AI creativity and music
(2:58:39) The AI treadmill
Guests
Louis Kirsch and Damon Falck — Co-Founder and Chief Superintelligence Officer (Louis), Member of Technical Staff (Damon), Inherent Laboratories (𝕏)
Malte Ubl — CTO, Vercel (𝕏 | LinkedIn)
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