AI:AM — Web Infrastructure and Superintelligence · August 26, 2026

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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