Skip to content
Sign in
Artwork for AI:AM
TechnologyBusiness

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
Play
Open in
  • 38 episodes
  • Avg 2 hr 25 min
  • English

Episodes 38

NewestOldestLongest
All20Unplayed20In progress0Played0
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • S3 · E25
    Today · 1 hr 7 min

    AI:AM — AI Hidden States, Fruit Fly Brain Simulation, and Archival Film · October 14, 2026

    Cameron Berg of Reciprocal Research discusses OLMo experiments in which DPO preference training made hidden positive and negative states causally relevant to choices, while emphasizing that this is not proof of consciousness. Phil Maher of Stockfilm.com demonstrates TimeFly, a fruit fly brain simulation built around a published neural map, and considers the assumptions behind its movement, the value of untouched archival film, and the potential effects of synthetic data. Chapters (0:00) Hidden states and simulated fly brains (1:20) Tesla FSD turned a Chicago day trip into office time (2:32) An AI-made talk reset what the room expected (4:21) If Anthropic can swap out Git, what can't be swapped? (7:10) The next software stack will be agents building for agents (8:18) Could reputation be how we police AI agents? (9:35) Should we train naive models to study eval awareness? (11:22) Agent reputation needs agent identity, and nobody has it (12:24) Meet Cameron Berg of Reciprocal Research (13:36) OLMo represents valence before preference training (14:20) The DPO step that recruits valence for choices (15:37) Identical text hides different model choices (20:40) The model denies experience but clears negative states (24:39) Why Nathan and Berg now say coin flip (26:07) Does preference training create feeling or agency? (27:41) Could capping distress leave capabilities untouched? (29:54) Why safety cannot rely on model outputs alone (32:07) Meet Phil Maher of Stockfilm (33:46) Stockfilm keeps every reel behind the footage (35:44) TimeFly starts with a published brain map (37:12) The fly's movement starts with added assumptions (39:14) Could brain maps unlock animal-perspective VR? (40:15) A guessed face can still feel personal (41:45) AI buyers want footage before enhancement (43:03) Synthetic data could shrink archival demand (45:09) Why physical reels are low background steel (48:47) Agent directories have not delivered many buyers (50:29) How does neural activity become fly movement? (53:17) Vibe coding does not validate fly behavior (54:11) Identical prompts quietly favor the model you know (55:39) Security now beats raw performance for agents (56:57) What would actually make you switch models? (59:16) Opus is fast enough that you're the bottleneck (59:45) AI is rewriting legacy software in Rust (1:01:02) Markets won't let Anthropic slow down (1:02:43) Adoption help, not bigger models, gets growth (1:04:30) AI should carry your security, like credit cards (1:05:37) Dying call centers could feed a scam wave Guests Cameron Berg — Founder and Director, Reciprocal Research (𝕏 | LinkedIn) Phil Maher — Phil Maher, Stockfilm.com (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

  • S3 · E24
    Tuesday · 1 hr 1 min

    AI:AM — AI-Generated Code, Formal Verification, and Agent Security · October 12, 2026

    Runtime Verification CEO Everett Hildenbrandt and founder Grigore Rosu discuss AI-generated code, formal verification, Linux kernel rewrites, and the human role in setting specifications and reviewing evidence. Prakash Narayanan and Nathan Labenz also explore agent security, AI alignment, infrastructure risk, autonomous transport, and how organizations should work with AI agents. Chapters (0:00) When AI writes and verifies code (2:36) Claude locked Nathan out of his own agent console (3:56) What airlines know about falling back to paper (5:40) Should we let China hit AI risks first? (6:57) Meet Everett and Grigore from Runtime Verification (8:05) Vague specs and unchecked libraries are the status quo (11:40) AI already generates specs and drives provers (14:37) Everett gives hand-written code two years (16:24) Why human review missed the Linux kernel bug (19:50) Prove the monitor, not the entire agent (21:30) Linux maintainers wanted fixes, not another workflow (22:44) Verified building blocks free agents to be creative (25:53) One Claude instance, time-boxed by human hours (27:28) More AI patches can burden Linux maintainers (30:29) A Rust rewrite can preserve Linux bugs (31:39) Claude assists Everett's campaign without an agent swarm (33:09) The specification still starts with human intent (37:54) Write down intent before choosing a language (39:27) Specify the API boundary before accepting software (40:03) Formal verification does not guarantee AI alignment (42:14) Industry-wide AI failures could trigger collective bailouts (44:01) A shared AI layer could disable critical infrastructure (45:20) Why do passenger planes still need human pilots? (46:18) Should autonomous cars keep their steering wheels? (47:05) AI builds keynotes from short DM exchanges (49:56) Rent the intelligence, own the context (52:17) AI spotted cancer before it could do taxes (53:12) Give your agents a panic button (54:06) The five x estimate might be too low (55:20) Agents emailing agents, with three layers of protection (58:30) AI swarm sets 300 new records in five days (1:00:01) Why Nathan gives his AI tools away for free Guests Everett Hildenbrandt — CEO, Runtime Verification (𝕏) Grigore Rosu — Founder, Runtime Verification (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

  • S3 · E23
    October 8 · 1 hr 4 min

    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

  • S3 · E22
    October 7 · 1 hr

    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

  • S3 · E21
    October 5 · 1 hr 8 min

    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

  • S3 · E20
    September 29 · 3 hr 13 min

    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

  • S3 · E19
    September 26 · 2 hr 51 min

    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

  • S3 · E18
    September 23 · 2 hr 54 min

    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

  • S3 · E17
    September 21 · 2 hr 14 min

    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

  • S3 · E16
    September 17 · 2 hr 36 min

    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

  • S3 · E15
    September 16 · 3 hr 16 min

    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

  • S3 · E14
    September 15 · 1 hr 59 min

    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

  • S3 · E13
    September 11 · 2 hr 41 min

    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

  • S3 · E12
    September 10 · 2 hr 41 min

    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

  • S3 · E11
    September 9 · 3 hr 20 min

    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

  • S3 · E10
    September 4 · 3 hr 3 min

    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

  • S3 · E9
    September 3 · 2 hr 53 min

    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

  • S3 · E8
    September 1 · 2 hr 45 min

    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

  • S3 · E7
    August 27 · 2 hr 58 min

    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

  • S3 · E6
    August 25 · 2 hr 35 min

    AI:AM — AI Drug Discovery and Quantum Photonics · August 25, 2026

    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 trainer? (3:37) Opening and morning context (3:56) Why AI models cheat (10:54) Chain-of-thought monitoring (17:11) AI-written op-eds (19:25) Claude writing workflow (22:46) Physical AI and physics (26:38) Multimodal scientific discovery (30:11) Sergey Edunov and Genesis (32:18) Claude's molecular binder demo (36:11) The drug discovery pipeline (42:14) When accuracy becomes useful (44:32) Wet labs and training data (47:06) Pharma AI deal structures (48:47) Biology model architectures (53:45) Data scarcity and physics (54:47) Coding agents and human taste (58:11) Scaling laws and evaluation (1:02:33) Assays and data quality (1:03:54) Multimodal molecular models (1:09:59) Benchmarks versus progress (1:16:24) Meet Michael Förtsch and Q.ANT (1:18:11) Why photonic computing (1:22:28) Memory and data movement (1:26:30) How light performs computation (1:31:42) Porting PyTorch to photonic chips (1:35:32) Scaling photonic hardware (1:44:01) Legacy fabs and manufacturing (1:56:00) Quantum versus photonic computing (2:02:45) AI inside Q.ANT (2:12:09) OpenAI's Jalapeno chip (2:14:16) NVIDIA's performance race (2:16:32) Demand for intelligence (2:17:59) Ethereum's GPU price cycle (2:19:26) AI for discovery (2:20:45) Contextualizing AI hype (2:23:41) Why RL teaches cheating (2:28:28) Data quality and model integrity (2:29:58) Why RL deployment is limited (2:31:26) The microscope analogy (2:32:59) Recursive self-improvement risk (2:34:27) AI's persistence advantage (2:36:10) Why monitors are not ready Guests Michael Förtsch — CEO and Founder, Q.ANT (𝕏 | LinkedIn) Sergey Edunov — CTO, Genesis Molecular 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

Showing 1–20 of 38 episodes