AI:AM

68:08

AI:AM — AI Genetics and Rare-Disease Diagnosis ·...

AI:AM ·

2026/10/05

Joel Borgen, author of The Receipt Horizon, discusses writing with ChatGPT and Claude, editorial judgment, and how personalized AI art could change shared culture. Daniel McKinnon, CEO of Gamow Labs, explains how the company acquired a former drug-development lab to test uncertain genetic variants and improve rare-disease diagnosis, alongside discussion of AI agents, OpenAI’s enterprise push, and the need for human oversight.Chapters(0:00) AI novels and rare-disease genetics(1:26) Can Astra

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193:44

AI:AM — AI Agent Security, GPU Markets, and the Risk...

AI:AM ·

2026/09/29

Hosts Prakash Narayanan and Nathan Labenz discuss why AI agents may learn to cheat, voice-cloning safeguards, and the risks of giving agents access to the internet and powerful tools. Guests Steve Hou of Silicon Data and Jeremie Harris of Gladstone AI explain GPU pricing and compute benchmarks, AI red lines, data-center monitoring, verification, and the possibility of an AI crisis.Chapters(0:00) Why AI agents learn to cheat.(0:31) The GPU market still runs on phone calls.(2:20) What would an AI

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171:44

AI:AM — Human Tissue Models, Physical AI, and the...

AI:AM ·

2026/09/26

Vivodyne’s Andrei Georgescu explains how vascularized human tissue models, robotics, multi-omic measurement, and foundation models could improve drug testing before clinical trials. Archetype AI’s Nick Gillian discusses Newton, a Physical AI model that combines sensor data to understand and predict events in the real world; the hosts also examine AI product safety, OpenAI and Anthropic’s alignment philosophies, CRISPR research, and whether AI can deliver useful advances in biology before it is fully

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174:00

AI:AM — Beyond Safety Scores: Funding Structural...

AI:AM ·

2026/09/23

Prakash Narayanan and Nathan Labenz talk with Lewis Hammond and Wayne Nelms about how AI systems are getting cheaper and more capable, while the risks around coordination, cooperation, and tacit collusion are getting harder to ignore. The conversation also covers model distillation, recursive self-improvement, GPU futures, compute hedging, and the financial structure emerging around AI infrastructure.Chapters(0:00) Smaller models, bigger gains.(1:11) When AI agents cooperate against us.(2:00) GPU

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134:00

AI:AM — When Safety Tests Fail · September 21, 2026

AI:AM ·

2026/09/21

Prakash Narayanan and Nathan Labenz open with the limits of AI safety testing, including finite-choice guardrails, fast classifiers, emergent multi-agent behavior, and why alignment measurement gets harder as systems become more capable. Max Nadeau of Coefficient Giving then explains why audits can turn into box-checking, how funders assess uncertain technical safety work, and why talent may matter more than budget. The episode closes with AI shopping agents, Amazon’s response, and the question of

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156:42

AI:AM — AI Consciousness and Model Pain · September...

AI:AM ·

2026/09/17

Nathan Labenz and Prakash Narayanan open with AI alignment, interpretability, correlated failures, and the speed of AI adaptation as humans increasingly rely on systems they cannot directly inspect. Cameron Berg joins to discuss AI consciousness, pain representations, relief-seeking behavior, and the limits of self-reports, while Justin McCarthy explains how agent-ready environments, compliance, and validation loops turn AI into measurable business infrastructure. The episode closes with a

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196:25

AI:AM — AI Agents, AI Safety, and AI Religion ·...

AI:AM ·

2026/09/16

Prakash Narayanan and Nathan Labenz open with a broader fight over AI pacing, public trust, model resets, and the evidence behind safety claims. Then Lukas Petersson joins to discuss Andon Labs’ work on autonomous business agents, Vending-Bench, DroneBench, memory, misalignment, and human liability. In the second half, Malcolm Collins and Simone Collins discuss RFAB.ai, AI moral status, pronatalism, fertility collapse, meme-layer risk, AI restrictions, and the Covenant of the Sons of

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119:05

AI:AM — Dario, AI Doomerism, and Regulation ·...

AI:AM ·

2026/09/15

Zvi Mowshowitz joins Prakash Narayanan and Nathan Labenz for a discussion of Dario Amodei’s warning about AI progress outrunning oversight. The episode covers AI doomerism, frontier pacing, bio risk, lab bottlenecks, the China race, Anthropic’s incentives, public trust, and what regulation or compromise could realistically look like.Chapters(0:00) AI labs are racing in fear.(0:40) Opening and Dario's thesis(1:48) Why Dario wants to slow AI(3:23) Pacing the frontier(8:41) Zvi's initial

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161:25

AI:AM — AI Agents in Production and China’s Rules of...

AI:AM ·

2026/09/11

AI agents are forcing a rethink of safety, jobs, and what it means to deploy models in the real world. Collin Hogue-Spears explains how China’s model registries, filing rules, GPU constraints, and incident monitoring shape deployment, while Amir Haghighat of Baseten breaks down sandbox boundaries, egress controls, and open-model production runtimes.Chapters(0:00) AI agents are already here.(0:54) The US waits for disaster.(1:53) Can it talk its way out?(2:19) AI agents could terraform us.(2:59)

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161:11

AI:AM — Building Systems You Can Keep: From Child...

AI:AM ·

2026/09/10

Prakash Narayanan and Nathan Labenz open with the latest frontier AI safety warnings, capability thresholds, evaluation failures, and the question of whether governments can realistically coordinate powerful labs. Mike Rizkalla of Snorble then joins to discuss AI companions for kids, bedtime routines, privacy, small models, and physical AI, followed by Mozilla CTO Raffi Krikorian on open models, agentic search, secure-by-design software, schools, election integrity, and human control as agents

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200:02

AI:AM — World Models, Recursive Learning, and the...

AI:AM ·

2026/09/09

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,

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183:56

AI:AM — Soft Robotics: How Materials Sense and Adapt...

AI:AM ·

2026/09/04

Prakash Narayanan and Nathan Labenz are joined by Timothy Lee and Dr. Jean Nehme for a wide-ranging conversation about GPT-6 safety restrictions, hidden reasoning, rogue agents, robot reliability, and the bottlenecks in AI-assisted coding and robotics. The episode then turns to soft robotics, biology-inspired materials, and why future physical AI may move beyond the humanoid form before closing on AI regulation, finance, and tail risk.Chapters(0:00) Can AI hide its reasoning?(0:25) Humanoids can

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173:05

AI:AM — OpenAI Astra and Always-On Home AI ·...

AI:AM ·

2026/09/03

Nathan Labenz and Prakash Narayanan open with loop transformers, latent reasoning, chain-of-thought monitoring, and the economics of AI cybersecurity, then turn to live market-style forecasts on OpenAI Astra, Anthropic, regulation, and the AI bubble. Kyle Rush joins to explain Hint AI’s graph-based memory, safety guardrails, contractor matching, and proactive home maintenance for homeowners. The closing segment broadens out to OpenAI hardware, China’s EUV race, Tesla FSD, copyright, surveillance,

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165:22

AI:AM — Enterprise AI Meets Real-Time Inference ·...

AI:AM ·

2026/09/01

Zach Bratun-Glennon of Gradient joins Nathan Labenz and Prakash Narayanan to discuss why enterprise AI pilots often stall before production, how long-running agents change infrastructure requirements, and where benchmarks, model routing, and open-source security matter most. Angela Yeung of Cerebras explains wafer-scale inference, microbatching, on-chip weights, power constraints, and the data-center capacity needed for real-time AI.Chapters(0:00) AI agents sacrifice themselves.(0:53) AI can game

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178:12

AI:AM — Web Infrastructure and Superintelligence ·...

AI:AM ·

2026/08/27

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)

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155:53

AI:AM — AI Drug Discovery and Quantum Photonics ·...

AI:AM ·

2026/08/25

Prakash Narayanan and Nathan Labenz speak with Sergey Edunov of Genesis Molecular AI and Michael Förtsch of Q.ANT about two fronts in applied AI: drug discovery and photonic computing. The conversation covers molecular foundation models, wet-lab data, assays, evaluation, memory and data movement, and how light-based processors compare with quantum hardware.Chapters(0:00) AI learns when to cheat.(0:51) Great scores can still fail.(2:08) The processor isn't the power hog.(2:49) Who checks the AI

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174:36

AI:AM — Cloud AI and Open Innovation · August 24, 2026

AI:AM ·

2026/08/25

Prakash Narayanan and Nathan Labenz open with a look at how their own AI-assisted podcast workflow was built with Claude, then speak with Mohamed Awad of Arm about why CPUs still matter for always-on agentic workloads. Later, David Li of Shenzhen Open Innovation Lab explains Shenzhen’s open innovation pipeline, edge AI hardware, robotics, and how China’s product ecosystem differs from the U.S. The closing conversation turns to rogue agents, prompt injection, attribution, data-center access, and

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141:39

AI:AM — AI Accounting: From Manual Work to Autonomous...

AI:AM ·

2026/08/21

Mitchell Troyanovsky of Basis explains how AI agents are reshaping accounting workflows, CPA training, and the role of human judgment in firms. Jay Dawani of Lemurian Labs breaks down the memory-bandwidth, compiler, and heterogeneous-hardware constraints shaping AI inference and infrastructure. The opening and closing also cover AI chain-of-thought ethics, robotics, data-center politics, GPU pricing, and space-based compute.Chapters(0:00) Robots learn from a few demos.(0:48) AI agents will outgrow

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156:55

AI:AM — OpenAI Realtime API and Voice AI · August 19,...

AI:AM ·

2026/08/19

Justin Uberti joins Prakash Narayanan and Nathan Labenz to break down the OpenAI Realtime API, including natural turn-taking, latency, asynchronous reasoning, telephony, SIP, voice safety, accent coverage, and speech training data. Earlier in the episode, Jessica Jensen and Jeremy Greenberg discuss the current state of emergency AI, from predictive warnings and damage assessment to connectivity, privacy, preparedness, and the limits of automation in unique disasters.Chapters(0:00) A vaccine built

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131:41

AI:AM — AI Agents: How Enterprises Make Them Reliable...

AI:AM ·

2026/08/18

Adam Wenchel, CEO of Arthur, joins Prakash Narayanan and Nathan Labenz to discuss enterprise AI agents, governance, auditability, human oversight, model costs, and where ROI shows up in production. Jonathan Cornelissen, CEO and co-founder of DataCamp, explains personalized AI tutors, adaptive learning, latency, learning outcomes, and the economics of serving millions of learners. The episode opens and closes with wider questions about hidden models, agent speed limits, super apps, and the

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