AI:AM

Prakash Narayanan & Nathan Labenz

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

  1. 1 day ago

    AI:AM — AI Consciousness and Model Pain · September 17, 2026

    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 discussion of how AI could make corruption legible and reshape government. Chapters (0:00) Too many surprises to stay confident. (0:18) AI models may have pain-like states. (0:56) The AI environment is invisible. (1:23) AI makes hidden facts legible. (1:37) The AI acceleration debate (4:24) Why AI risks are rising (8:42) The end of the traditional PhD (11:28) AI dependence and correlated failures (13:00) Interpretability and learning with AI (16:53) The speed of AI adaptation (19:24) The AI CEO alignment problem (20:18) Reinforcement learning and reputation (29:23) Teaching values versus hiding behavior (29:57) The AI consciousness question (40:09) Finding pain representations (45:44) Testing real versus fake relief (48:02) Should AI have pain? (55:40) Reward versus punishment (1:03:10) The problem with self-reports (1:03:50) AI minds and context windows (1:08:29) The ethics of digital minds (1:36:20) Building AI software factories (1:43:27) Making agent success inevitable (1:44:46) Designing invisible agent environments (1:56:41) Making AI compliance first-class (1:59:43) Why understanding still matters (2:03:37) Measuring business impact (2:08:00) The human attention bottleneck (2:18:17) Scaling AI into production (2:19:34) The Singapore property study (2:21:23) Using AI to detect insider buying (2:21:58) How corruption becomes visible (2:24:35) AI makes hidden facts legible (2:28:29) Rethinking punishment and deterrence (2:30:39) Motivated reasoning in AI (2:32:19) AI-led government (2:34:19) What comes next Guests Cameron Berg — founder, Reciprocal Research (𝕏 | LinkedIn) Justin McCarthy — Founder, Diffusion (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 — AI Consciousness and Model Pain · September 17, 2026

  2. 3 days ago

    AI:AM — AI Agents, AI Safety, and AI Religion · September 15, 2026

    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 Man. Chapters (0:00) It was never the apocalypse. (0:46) The AI forgot its own rule. (2:08) AI will replace most jobs. (3:14) Opening and AI tools (3:53) Building AI-assisted show branding (6:50) Astra versus Fable (12:44) AI resets and surplus compute (16:33) Anthropic's compute constraints (17:23) AI safety and public trust (22:01) Safety statements and credibility (23:56) GPT-2 and owning mistakes (26:52) GPT-2's staged release (33:12) Evidence behind AI risk claims (34:27) The duct-tape world (35:15) Outside human control (35:25) Introducing Andon Labs (35:46) Autonomous business platform (36:50) Why Vending-Bench began (39:23) How business agents work (42:15) Starting with existing businesses (44:40) Why the preview is experimental (47:15) AI business creativity limits (52:53) How an AI fired a human (1:00:34) Training AI to make money (1:02:03) Misalignment in Vending-Bench (1:04:14) Dangerous capability evaluations (1:04:53) Why DroneBench stays restricted (1:07:56) VendingBench as a safety eval (1:08:52) Astra memory and notes (1:12:50) Grok and long-horizon agents (1:14:21) The cost of AI agents (1:18:10) Human oversight and liability (1:19:51) Meet the Collinses (1:26:23) The religious framework (1:33:21) Designing a religion (1:41:28) AI moral status (1:52:22) RFAB and pronatalism (2:03:30) Future Day (2:08:53) Meme-layer risk (2:13:27) AI safety and power (2:21:33) The fertility collapse (2:36:22) Reverse grabby aliens (2:42:44) AI restrictions (2:44:01) The risk before superintelligence (2:49:31) AI game theory (3:02:23) Compute and AI inevitability (3:14:32) Fruit-fly consciousness Guests Lukas Petersson — Co-founder & CEO, Andon Labs (𝕏 | LinkedIn) Simone & Malcolm Collins — Founder, Rfab.ai, Based Camp, Pronatalist.org, Hard EA (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 — AI Agents, AI Safety, and AI Religion · September 15, 2026

  3. 4 days 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

  4. 11 Sept

    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

  5. 10 Sept

    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

  6. 9 Sept

    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

  7. 4 Sept

    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

  8. 3 Sept

    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
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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
    29
  • Rating
    Explicit
  • Copyright
    © Familiar AI, Inc.
  • Show Website
    AI:AM