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. 2d ago

    AI:AM — AI Safety Strategy, Cyberattack Resilience, and Workplace Agent Benchmarks · October 7, 2026

    Atlas Ignota founder Evan Miyazono explains how emergency supplies, privacy-preserving agent identity, software verification and adoption commitments fit into a strategy for reducing AI-assisted cyber risks. Mercor model-training research lead Edward Hu describes workplace agent benchmarks built from real business records, why task scores do not prove job readiness, and how enterprises can test tools and workflows before training models. Chapters (0:00) Grid outages and AI job tests (1:22) An AI agent played the instruments in Reaper (3:49) OpenAI's model broke a math barrier overnight (4:53) Are data centers pushing bond yields toward 6%? (7:54) Meet Evan Miyazono of Atlas Ignota (8:56) A grid blackout nobody is positioned to fix (10:43) Why Atlas stopped building and started recruiting (12:37) Experts have ideas; adoption is the hard part (14:03) Was that AI attack an accident? (15:22) Agent identity without exposing refugees and journalists (19:08) Why Evan keeps some of his agents private (20:24) AI could make software proofs radically cheaper (23:08) Why two weeks of food and water (24:42) Who can make a guardrail universal? (27:01) Meet Edward Hu from Mercor (27:51) APEX gives agents assignments, not jobs (31:09) Mercor bought real companies for their data (33:47) Company records expose the task-to-role gap (35:22) Why math fell before customer service (38:29) Fix the harness before you train the model (41:05) Most enterprises will distill, not run RL (42:01) Agent swarms buy speed, not compute efficiency (43:34) One rubric rewarded a dozen incompatible answers (46:49) Perfect accounting scores do not replace accountants (48:55) Clear verifiers let AI surpass human experts (51:18) Recording every work trace carries social costs (53:43) AI coworkers need better organizational tests (55:15) Factory know-how still lives outside software (56:43) Agents now need far less project management (59:25) Is the $200 plan a loss leader? (1:01:18) Permission fatigue is an unsolved problem (1:03:02) Anthropic's API is still too slow for production Guests Edward Hu — Head of AI Modeling, Mercor (𝕏 | LinkedIn) Evan Miyazono — Founder & CEO, Atlas Ignota (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 Safety Strategy, Cyberattack Resilience, and Workplace Agent Benchmarks · October 7, 2026

  2. 3d ago

    AI:AM — AI Coding Agents, Chip Design, and the Value of Human Judgment · October 6, 2026

    Thomas Sohmers, co-founder of Positron AI, describes how coding agents changed chip-design testing, drove token spending above payroll, and shaped the company’s plans for commodity memory and looped transformers. Shawn “swyx” Wang of AI Engineer discusses hiring and performance in the age of Claude: employees still need to bring expertise, judgment, testing, and accountability to AI-generated work. Chapters (0:00) Chip design agents and hiring (1:22) The Curve debate shifts toward runaway AI (2:36) Models turn reasoning into better training data (4:05) Frontier models may already have research taste (4:33) A frontier executive entertains a hard intelligence cap (5:55) Claude-built RL environments leak capability to rivals (7:21) Frontier leaders see next year as critical (8:00) Could safety end up consuming most compute? (9:31) Cheating models are now fixing the training environments (10:30) Will Anthropic press its lead or show restraint? (11:28) Meet Thomas Sohmers of Positron (12:35) AI closed Positron's chip design iteration loop (13:36) Token spend passed payroll and hit $100,000 daily (14:53) AI agents taught themselves Positron's verification tools (18:10) AI still misses Positron's design ingenuity (20:00) Why Asimov bets on commodity memory (23:52) Better models are outrunning memory price increases (24:50) Looped transformers save memory, not work (27:37) Asimov follows customers, not every algorithm (29:39) Why Positron kept buying the best model (31:10) Meet swyx from AI Engineer (32:19) Demand is up, and two employees are under review (33:38) The growth report that exposed missing expertise (34:26) A launch agency sold him Claude's slop (35:29) Taste, not years of coding, is the hiring test (37:22) Two coding agents created one race condition (38:46) A $40,000 SaaS subscription becomes a bounty (39:31) Cheap vibe-coded submissions create expensive verification (41:06) Skeptical event staff switch when changes take two hours (42:33) Every spreadsheet is unbuilt custom software (44:40) Manage team leads by results, not people by prompts (46:49) Ninety on SWE-bench still isn't usable software (47:59) Claude cracked 3SUM, but who gets the credit? (50:45) NanoGPT speed run hides a better optimizer (52:44) How many clones would replace your AI? (55:33) Frontier labs say AI for science is next (57:16) AI audits could trigger mass scientific retractions (58:14) Doctors welcome AI more than expected Guests Thomas Sohmers — CTO and Co-Founder, Positron AI (LinkedIn) swyx — swyx, curator (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 Coding Agents, Chip Design, and the Value of Human Judgment · October 6, 2026

  3. 5d ago

    AI:AM — AI Genetics and Rare-Disease Diagnosis · September 30, 2026

    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 Ultrafast keep developers in flow? (2:09) ChatGPT sign-in makes OpenAI harder to bypass (2:37) OpenAI apps will run rival models too (3:11) Eight times faster means less human oversight (5:15) OpenAI pulled a model; should Anthropic chill? (5:46) Portable ChatGPT subscriptions cut SaaS trial costs (7:05) OpenAI's enterprise push is what scares Anthropic (7:39) Everyone builds a productivity suite now (8:16) AI agents move humans from documents to decisions (9:25) Can OpenAI grow without racing to RSI? (10:34) Meet Joel Borgen, novelist (11:27) Proof of work: how Joel actually wrote the novel (15:08) Joel cut fourteen percent from his manuscript (17:45) Nathan's AI songs reveal a culture gap (19:03) Astra finally cracked Joel's viola test (20:35) AI breakthroughs are outrunning cultural norms (22:47) Could communities choose their own AI future? (25:40) The Steward's comfort still caps human ambition (27:57) Would the Steward be a worthy successor? (29:50) Human taste still matters in AI art (30:57) Meet Daniel McKinnon of Gamow Labs (32:06) Why AI genetics needs biological experiments (35:07) A failed drug program unlocked Gamow's lab (36:48) Owen's missed diagnosis became a life's mission (39:18) AI agents can search beyond human cutoffs (42:11) Sequencing got cheap; interpretation stayed the bottleneck (44:17) Why Gamow chose diagnostics over screening (47:37) Reanalysis in a week for ten dollars a genome (49:25) What Gamow builds on top of frontier models (52:07) Stronger models can break existing tools (53:27) Better models are shrinking Gamow's harness advantage (54:41) Can robots make genetic experiments affordable? (56:05) Can biological experiments teach genetic AI? (57:53) Should AI welfare research stay public? (59:11) Could AI design threats nature never could? (1:01:00) Government AI upgrades meet decades of data kludges (1:02:42) Easy name changes weaken names as identity (1:03:28) Factory and Cognition's feud spooks enterprise buyers (1:05:23) Safety pledges signed, but no audit exists yet (1:06:29) Should AI labs fund society's cyberdefenses? Guests Daniel McKinnon — CEO, Gamow Labs (𝕏 | LinkedIn) Joel Borgen — Author, The Receipt Horizon (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 — AI Genetics and Rare-Disease Diagnosis · September 30, 2026

  4. Sep 29

    AI:AM — AI Agent Security, GPU Markets, and the Risk of an AI Chernobyl · September 28, 2026

    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 Chernobyl look like? (3:59) The dangerous AI arms race. (4:30) Opening and AI agents (7:45) Agents choosing useful work (19:36) Voice cloning safeguards (22:22) Voiceprints and privacy (32:45) Why agents learn to cheat (36:12) Agents beyond the sandbox (37:43) What Silicon Data measures (40:02) Why GPU markets need indices (43:49) How GPU prices are normalized (49:10) Why hyperscalers charge more (1:06:36) The falling cost of intelligence (1:06:46) Liquidity and market manipulation (1:10:49) The future of compute benchmarks (1:14:40) Why compute prices vary globally (1:17:09) Silicon Data and Compute Exchange (1:18:50) AI and national security (1:24:05) What an AI Chernobyl means (1:34:26) Defining AI red lines (1:50:45) Monitoring AI data centers (1:55:58) Verification and enforcement (2:33:35) AI agents as insider threats (2:33:45) Signals of genuine cooperation (2:57:14) The AI intelligence explosion (3:01:57) The agent arms race (3:04:29) Markets built for agents (3:07:46) Who pays for disappearing inefficiency (3:09:56) Waiting for an AI crisis Guests Jeremie Harris — Jeremie & Edouard Harris, Gladstone AI (𝕏 | LinkedIn) Steve Hou — Steve Hou, Silicon Data (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 Agent Security, GPU Markets, and the Risk of an AI Chernobyl · September 28, 2026

  5. Sep 26

    AI:AM — Human Tissue Models, Physical AI, and the Future of Testing · September 25, 2026

    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 understood. Chapters (0:00) AI safety has two different meanings. (0:15) Why drug testing needs human tissue. (3:20) Physical AI is bigger than robots. (5:01) Biology’s bar is lifesaving progress. (11:17) Opening and AI news (11:38) Jensen Huang’s safety argument (13:24) The “just software” debate (15:31) Why AI companies must mature (21:12) Muse and product safety (29:25) Testing AI agents in the real world (29:35) Product safety versus existential risk (29:45) The alignment philosophy problem (38:34) OpenAI versus Anthropic (40:36) When safety concerns converge (41:55) Human tissue models (44:12) Why organoids need realism (47:35) Measuring tissue responses (54:03) Mapping causal biology (1:03:40) Improving drug development (1:09:08) Foundation models and experiments (1:12:18) TissueDisk and robotic labs (1:12:28) Before clinical trials (1:30:37) What is Physical AI? (1:32:25) Beyond robots and cars (1:35:33) Training on physical data (1:38:04) Cleaning and aligning sensor data (1:40:34) Human and machine outputs (1:49:30) A river construction case study (1:53:28) How Newton finds hidden patterns (1:57:33) Zero-shot sensor adaptation (2:01:16) Industrial impact and expert knowledge (2:05:22) From machines to ecosystems (2:14:10) Physical agents and superintelligence (2:15:34) The big AI-biology question (2:21:55) Specialists versus generalists (2:23:39) Distilling frontier models (2:33:33) AI discovers a CRISPR sequence (2:39:22) Useful biology before theory (2:45:10) From job displacement to progress (2:46:44) AI diffusion and economic growth Guests Andrei Georgescu — CEO, Vivodyne (𝕏 | LinkedIn) Nick Gillian — CTO, Cofounder, Archetype 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 — Human Tissue Models, Physical AI, and the Future of Testing · September 25, 2026

  6. Sep 23

    AI:AM — Beyond Safety Scores: Funding Structural Alignment · September 23, 2026

    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 compute needs a financial market. (2:55) Brute-force compute can break standards. (5:13) Opening and model release race (7:29) Why models are getting cheaper (12:24) How AI models are judged (18:20) Why frontier models converge (19:54) Distillation and smaller models (21:12) Recursive self-improvement (22:17) The collapse in AI prices (28:48) Why multi-agent AI matters (35:43) Three multi-agent failure modes (48:37) How agents learn cooperation (53:27) Detecting tacit collusion (1:11:26) A positive vision for AI cooperation (1:17:00) What Ornn does (1:18:34) GPU pricing and hedging (1:22:18) Building a transaction index (1:24:43) Why compute prices need real transactions (1:34:13) GPU futures and capacity contracts (1:53:34) How long GPUs stay useful (1:53:44) Hedging chip obsolescence risk (1:53:54) The future of compute markets (1:59:53) Agentic startups and compute economics (2:07:37) Brute-force compute and RSA (2:17:07) AI and self-driving cars (2:40:03) AI, mathematics, and peer review (2:40:13) Why AI doctors may win (2:46:52) Medicine after chatbots Guests Lewis Hammond — Research Director, Cooperative AI Foundation (𝕏 | LinkedIn) Wayne Nelms — Co-Founder, Ornn (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 — Beyond Safety Scores: Funding Structural Alignment · September 23, 2026

  7. Sep 21

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

    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 who owns the customer relationship when agents become first-class users. Chapters (0:00) When safety becomes reflexive. (0:10) When AI audits become box-checking. (1:22) Who owns the AI customer? (2:01) Opening and live setup (4:20) JEV and fast classifiers (5:40) Guardrails for AI agents (7:12) AI moderation use case (11:25) System one and system two (13:24) Multi-agent scaling (21:18) Emergent agent hierarchies (23:43) The case for slowing down (34:28) AI and mathematics (41:10) Capability acceleration (45:45) Building an AI organization (57:36) Funding the AI project (58:17) Meet Max Nadeau (1:05:21) Why safety research resists metrics (1:15:13) When AI audits become box-checking (1:23:07) Funding across uncertain timelines (1:30:49) Why fund whole organizations (1:39:06) Preparing for larger grants (1:39:16) Why talent is the bottleneck (1:46:48) Opening and agent setup (1:49:16) The AI services business model (1:52:59) Muse and autonomous shopping (1:56:52) Who owns the customer (1:57:37) Agents as the universal interface (2:00:01) Why ban shopping agents (2:05:31) The bot-detection arms race (2:07:26) A better lane for agents (2:09:30) Agents as internet users (2:11:31) Closing and next steps Guests Max Nadeau — Program Officer, Coefficient Giving (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 — When Safety Tests Fail · September 21, 2026

  8. Sep 17

    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
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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
    36
  • Rating
    Explicit
  • Copyright
    © Familiar AI, Inc.
  • Show Website
    AI:AM

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