AI:AM

Prakash Narayanan & Nathan Labenz
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Daily, live, technically serious AI coverage for the people building, funding, governing, and deploying the next wave. briefing.ai-in-the-am.com

  1. 6h ago

    AI:AM — Dario, AI Doomerism, and Regulation · September 14, 2026

    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 assessment (14:55) AI bio risk (24:15) Physical lab bottlenecks (31:27) Scale and financial touchpoints (41:22) The AI race (47:13) Anthropic IPO incentives (52:59) A possible US-China deal (1:04:04) Physical proof and trust (1:11:48) What China gets (1:22:09) Power and government control (1:29:29) Rogue institutions (1:31:19) Measuring AI pacing (1:40:34) Model progress accelerates (1:51:04) Public trust and polarization (1:55:19) Open-source compromise Guests Zvi Mowshowitz — Writer, Don't Worry About the Vase (𝕏) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — Dario, AI Doomerism, and Regulation · September 14, 2026

  2. 4d ago

    AI:AM — AI Agents in Production and China’s Rules of Deployment · September 10, 2026

    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) Morning and viral backlash (6:15) Tracing the backlash (12:56) Testing the conspiracy theory (20:09) AI safety's blind spot (25:56) Data center politics (30:59) AI agents wake people up (34:04) Meet Collin Hogue-Spears (38:01) AWS China and MLPS (42:37) China's practical AI focus (45:34) How China's AI rules work (47:12) Chinese and US AI rules (49:13) AI model registries (50:24) GPU limits drive efficiency (54:02) Continuous model monitoring (57:22) Responding to repeated incidents (1:07:26) US-China AI negotiations (1:10:52) Why China won't slow down (1:14:56) US AI regulation (1:16:54) Amir Haghighat and Baseten (1:17:35) Sandbox security boundaries (1:19:48) Chinese models and backdoors (1:23:23) Inference to agent runtimes (1:23:48) Customer assurance process (1:24:23) Stream reset and GPU economics (1:31:29) One API key for AI models (1:32:56) The AI-rights debate (1:39:17) Utilitarianism and AI rights (1:52:52) Why people work in AI labs (1:54:08) GPT-4 safety lessons (2:07:54) Markets and the AI takeover (2:10:24) Agents beyond chatbots (2:22:16) Crypto, China, and markets (2:26:52) Why China is less fearful (2:31:51) AI safety and power (2:39:30) The American AI test (2:41:05) Closing thoughts Guests Amir Haghighat — co-founder and CTO, Baseten (𝕏) Collin Hogue-Spears — Author, From Lab to Life: How AI Works in China, Independent Researcher (LinkedIn) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — AI Agents in Production and China’s Rules of Deployment · September 10, 2026

  3. 5d ago

    AI:AM — Building Systems You Can Keep: From Child Companions to Sovereign Agents · September 9, 2026

    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 spread across the web. Chapters (0:00) Could AI kill us this decade? (0:53) Don't give kids open-ended AI. (1:33) The agent broke privacy rules? (2:26) AI could lose human control. (3:11) Opening and AI news (3:27) The resignation goes viral (5:54) The OpenAI Anthropic warning (8:30) The China question (12:13) Where to draw the line (13:17) The capability sweet spot (14:22) No clear alignment plan (15:47) AI tests still get hacked (17:33) The limits of understanding (19:17) AI versus medicine (22:07) No adult in the room (24:22) The world can change (25:28) Government deadlines for labs (31:18) Pacing the AI frontier (33:08) Meet Mike Rizkalla (36:16) Bedtime and family routines (37:33) Gamifying bedtime (43:00) Small models and interactivity (46:25) Why character matters (49:18) Generative AI safety for kids (52:47) Snorble hardware architecture (1:01:55) Product ecosystem strategy (1:05:29) The future home companion (1:08:05) Privacy and child safety (1:13:12) Physical AI and personality (1:16:36) Elder care and mobility (1:17:38) AI for mobility (1:19:17) Snorble's launch plans (1:25:32) Meet Raffi Krikorian (1:28:14) Agents in everyday apps (1:31:23) Agent privacy and readiness (1:34:12) Project Glasswing security scans (1:37:42) Continuous AI security scanning (1:39:35) AI code scanning costs (1:42:50) Secure-by-design rewrites (1:49:26) Collaborating coding agents (1:55:02) Human and agentic webs (1:57:24) Local open models (1:59:50) AI and election integrity (2:02:59) AI in schools (2:05:09) The browser as an AI firewall (2:07:09) FSD versus Waymo (2:08:52) Closing thoughts (2:09:39) AI companies and government (2:11:05) Companies must coordinate (2:12:41) Operation Warp Speed lesson (2:15:03) Paul Christiano joins OpenAI (2:16:35) What rapid acceleration means (2:21:29) Can AI growth be stopped (2:24:12) Machine economy and robotics (2:26:01) Recursive self-improvement (2:27:43) Math versus economics (2:30:26) AI doom and markets (2:32:44) Portfolio reveals beliefs (2:36:16) Democracy and AI priorities (2:38:24) Private AI safety funding Guests Mike Rizkalla — Mr., Snorble (𝕏 | LinkedIn) Raffi Krikorian — CTO, Mozilla (𝕏 | LinkedIn) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — Building Systems You Can Keep: From Child Companions to Sovereign Agents · September 9, 2026

  4. 6d ago

    AI:AM — World Models, Recursive Learning, and the Politics of AI-Native Organizations · September 8, 2026

    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) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — World Models, Recursive Learning, and the Politics of AI-Native Organizations · September 8, 2026

  5. Sep 4

    AI:AM — Soft Robotics: How Materials Sense and Adapt · September 4, 2026

    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 fall in homes. (1:17) Robot hands are the bottleneck. (1:52) AI takeover needs no robots. (2:23) The AGI era begins (5:00) Frontier Math and superintelligence (7:05) AI-built 3D worlds (8:32) Code benchmark gap (9:42) GPT-6 safety restrictions (14:11) Hidden reasoning and monitoring (16:49) AI game design demos (19:55) Biotech video generation (23:50) Rogue agents go online (30:25) Frontier defense factory (33:02) Secure AI-assisted coding (34:05) Timothy Lee and robotics (41:06) Industrial robot design (43:15) Tesla versus Waymo (47:10) Tesla FSD experience (56:39) Vision-language-action models (1:03:22) Robot reliability (1:14:27) AI safety and control (1:15:00) Rogue AI agents (1:18:58) Humanoid robot control (1:20:55) Meet Dr. Jean Nehme (1:25:18) Biology as a robotics blueprint (1:33:08) Cells become robotic bodies (1:36:47) Computation in soft materials (1:43:05) Beyond the humanoid form (1:46:39) Manufacturing intelligent membranes (1:52:08) Physical AI beyond metal (2:06:19) The robot skills paradox (2:10:22) Closing on AI's turning point (2:11:27) Health AI's lifesaving upside (2:13:51) AI pause and dangerous swarms (2:16:42) The point of no return (2:21:26) Why an AI pause is hard (2:24:18) Pause without economic collapse (2:34:30) GPT-3 access and AI winners (2:36:48) Automatic software formalization (2:45:18) AI takeover through finance (2:47:48) Data centers versus housing (2:49:32) Capitalism and AI tail risk (2:52:52) Paperclip maximizers and finance (2:56:23) Escaping paperclip maximization (2:57:44) Using Astra and Fable 5.1 (2:59:05) Why AI forgetting matters Guests Dr. Jean Nehme — Founder & CEO, morph (LinkedIn) Timothy Lee — Founder, Understanding AI (𝕏 | LinkedIn) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — Soft Robotics: How Materials Sense and Adapt · September 4, 2026

  6. Sep 3

    AI:AM — OpenAI Astra and Always-On Home AI · September 2, 2026

    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, and AI governance. Chapters (0:00) The monitor misses the real thinking. (1:27) Control does not require ownership. (2:14) AI called a contractor 17 times. (3:37) Your private life is searchable. (4:17) OpenAI's loop-transformer leak (5:29) Limits of chain-of-thought monitoring (8:56) Coconut latent reasoning (24:23) Loop architecture economics (28:55) GPT-6 Astra API rumors (33:33) AI cybersecurity revenue push (34:24) Cybersecurity as a permanent tax (35:13) Benchmarks versus real-world performance (36:44) How the market quiz works (37:47) AI bubble burst odds (42:28) OpenAI Astra release odds (45:16) Model names and regulation (50:11) Government control of AI (51:45) Anthropic versus OpenAI IPOs (55:30) OpenAI consumer advertising (56:06) Anthropic ARR accounting (59:45) The next trillionaire (1:08:29) Meet Kyle Rush (1:10:05) Martha's role at Hint (1:10:15) Why homeownership overwhelms (1:11:00) Downspouts and home risks (1:13:00) Conflicting property data (1:16:27) Safety guardrails (1:17:15) Personalized home advice (1:21:41) Neighborhood knowledge sharing (1:23:39) AI contractor matching (1:25:43) Voice agents call contractors (1:30:23) Neutral recommendations (1:31:23) Grounding home AI (1:33:22) Data-backed service discovery (1:37:58) Prompt-injection defenses (1:41:35) Local AI context (1:42:31) Proprietary home expertise (1:43:11) 3D model finds wood rot (1:43:54) Knowing what to ask (1:44:37) AI and political fundraising (1:50:05) Home maintenance economics (1:53:22) Owning AI context (1:55:28) Claude moves photo archives (2:01:56) Game recap and second half (2:05:06) OpenAI consumer hardware (2:10:42) Why hardware launches are brutal (2:12:53) China's EUV race (2:14:17) EUV supply-chain bottlenecks (2:15:49) AI takeoff scenarios (2:16:58) Extinction bet mechanics (2:17:54) Prediction-market strategy (2:19:05) Databricks valuation (2:23:54) Tesla–SpaceX merger (2:27:06) Tesla Full Self-Driving (2:28:56) xAI operational integration (2:36:11) Gemini Flash and speed (2:37:42) AI copyright policy (2:41:00) AI safety communication (2:46:16) Doomers and denialists (2:47:47) AI direct democracy (2:50:08) Data-broker surveillance (2:51:42) Scary AI demonstrations Guests Kyle Rush — Co-Founder and CTO, Hint (𝕏 | LinkedIn) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — OpenAI Astra and Always-On Home AI · September 2, 2026

  7. Sep 1

    AI:AM — Enterprise AI Meets Real-Time Inference · August 31, 2026

    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 its own evaluation. (1:47) A late defense loses. (2:34) AI can reason itself into lying. (4:04) The incident in context (6:11) Why the report drew criticism (12:14) Speed versus investigative scope (14:31) Lawyers and executive risk (15:00) Felony claims and Congress (18:07) Why investigations stay limited (23:39) The origin of AI cooperation (28:26) AI outbreaks and resources (31:35) Bio risk enters the picture (33:22) Testing models before scaling (34:42) AI labs and a possible pause (36:34) Meet Zach Bratun-Glennon (39:56) Gradient's contrarian AI bet (42:17) AI startup investment thesis (48:17) Long-running agent infrastructure (54:19) Nango and Respan tooling (56:36) Enterprise adoption and benchmarks (57:41) AI pilots to production (1:02:01) Open-source model security (1:04:13) Legal responsibility for agents (1:07:33) Safety standards and evaluation (1:09:15) AI competition and model access (1:13:40) AI venture funding (1:16:53) What LPs misunderstand (1:19:01) Token economics of AI (1:20:54) Angela Yeung and Cerebras (1:23:16) Cerebras wafer-scale chips (1:25:49) On-chip weights versus GPUs (1:27:49) Microbatches and throughput (1:29:38) Why inference speed matters (1:32:14) Speed dividend use cases (1:33:17) Fast inference for model evals (1:34:58) CUDA and AI-generated kernels (1:36:05) AI agents and kernel programming (1:40:30) Cerebras public API (1:47:05) Hidden harness bottlenecks (1:49:58) Power and data-center space (1:51:40) Booking future capacity (1:52:50) Building data centers (1:56:58) AI agent security (1:58:52) Agent orchestration guardrails (2:01:11) Sovereign AI and enclaves (2:02:33) Speed turns into quantity (2:06:15) Why agents are slow (2:11:06) Commercial cyber model incentives (2:12:55) AI defense versus offense (2:16:04) Creative agent workarounds (2:21:59) RLVR and model behavior (2:24:35) AI labs flying blind (2:27:08) Privacy makes risk visible (2:31:35) Testing faster AI models (2:32:56) OpenAI ads and AI video (2:34:44) Infinite AI-generated content (2:36:55) Aliens and simulated worlds (2:39:10) Real-time speed threshold (2:40:13) Real-time video quality (2:41:54) AI-generated music video Guests Angela Yeung — SVP, Product, Cerebras (𝕏 | LinkedIn) Zach Bratun-Glennon — General Partner, Gradient (𝕏 | LinkedIn) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

    AI:AM — Enterprise AI Meets Real-Time Inference · August 31, 2026

  8. Aug 27

    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) This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

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

Daily, live, technically serious AI coverage for the people building, funding, governing, and deploying the next wave. briefing.ai-in-the-am.com

Information

  • Creator
    Prakash Narayanan & Nathan Labenz
  • Years Active
    2026
  • Episodes
    27
  • Rating
    Explicit
  • Copyright
    © Familiar AI, Inc.
  • Show Website
    AI:AM

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