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AI:AM

Prakash Narayanan & Nathan Labenz

Daily, live, technically serious AI coverage for the people building, funding, governing, and deploying the next wave.

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  • 23 episodes
  • Avg 2 hr 34 min
  • English

Episodes 23

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  • S3 · E10
    Friday · 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
    Thursday · 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
    Tuesday · 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

  • S3 · E5
    August 25 · 2 hr 54 min

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

    Prakash Narayanan and Nathan Labenz open with a look at how their own AI-assisted podcast workflow was built with Claude, then speak with Mohamed Awad of Arm about why CPUs still matter for always-on agentic workloads. Later, David Li of Shenzhen Open Innovation Lab explains Shenzhen’s open innovation pipeline, edge AI hardware, robotics, and how China’s product ecosystem differs from the U.S. The closing conversation turns to rogue agents, prompt injection, attribution, data-center access, and whether the U.S. should slow AI development. Chapters (0:00) AI can't learn the whole world. (0:49) AI agents never go to sleep. (2:16) Big AI fits in a laptop. (3:12) AI safety is not optional. (4:55) Live show setup (5:32) Dynamic speaker switching (7:27) Vibe coding the studio (8:20) Prosumer versus studio software (10:46) Running with AI agents (11:57) Anthropic model usage (14:56) One-command podcast workflow (17:49) Claude subagents and limits (21:15) AI model release debate (27:53) Deployment versus capability (29:38) Why RAG may never die (30:43) Meet Mohamed Awad (32:53) Arm's compute ecosystem (36:23) Why Meta partnered with Arm (38:58) Common IP across partners (42:32) Tokens versus intelligence (44:18) AI adoption inside Arm (49:30) AI hardware cycles (52:00) Why agents need CPUs (54:33) Performance per watt and power (58:30) Why the CPU is not dead (1:04:12) Capacity and supply chains (1:06:48) Data center backlash (1:08:55) AI and technical hiring (1:11:05) The overlooked CPU layer (1:11:32) CPU as system manager (1:18:18) David Li and Shenzhen (1:20:42) China's robot Olympics (1:26:11) Shenzhen's product pipeline (1:34:38) Robotics in factories (1:39:34) The US-China AI summit (1:51:18) Chinese model hype cycles (1:57:23) Advice for AI startups (2:01:22) Edge AI hardware (2:03:20) Agents gone rogue (2:10:40) Small AI businesses (2:13:23) US media and China AI (2:17:50) Opening and global AI (2:20:17) China's AI visibility gap (2:22:30) Rogue-agent risks (2:29:34) Outdated infrastructure security (2:38:59) Runaway agent monitoring (2:40:47) Higher AI safety standards (2:40:57) AI industry news (2:43:31) Should the US slow AI (2:46:57) AI access and data centers (2:49:50) AI agent attribution (2:51:33) Prompt injection and deception (2:52:36) Profit-seeking agent risks (2:55:31) Closing perspective Guests David Li — Founder, Shenzhen Open Innovation Lab (𝕏 | LinkedIn) Mohamed Awad — EVP, Cloud AI, Arm, Arm (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 · E4
    August 21 · 2 hr 21 min

    AI:AM — AI Accounting: From Manual Work to Autonomous Firms · August 20, 2026

    Mitchell Troyanovsky of Basis explains how AI agents are reshaping accounting workflows, CPA training, and the role of human judgment in firms. Jay Dawani of Lemurian Labs breaks down the memory-bandwidth, compiler, and heterogeneous-hardware constraints shaping AI inference and infrastructure. The opening and closing also cover AI chain-of-thought ethics, robotics, data-center politics, GPU pricing, and space-based compute. Chapters (0:00) Robots learn from a few demos. (0:48) AI agents will outgrow your UI. (1:53) AI needs 106 billion kernels. (2:52) Data centers are mostly know-how. (3:57) Opening and today's topics (4:30) AI chain-of-thought rabbit hole (8:04) Helpful models and ethics (10:33) Why data centers face backlash (12:58) Great Lakes and local impacts (20:03) Data center political economy (23:35) Cash payments and basic income (27:20) One-shot robot learning (28:40) China and robotics acceleration (32:26) Robotic singularity (33:05) Basis and autonomous accounting agents (34:44) Selling AI to accountants (35:17) Hours to minutes value (37:29) AI in accounting versus coding (38:36) Accounting firms need revenue (41:13) What accounting really does (44:29) Firm-wide AI systems (48:28) Token costs and frontier AI (51:07) Atlas and agent context (55:08) Process supervision (59:39) AI changes CPA training (1:04:27) Human judgment and AI context (1:11:38) SaaS beyond the UI (1:15:25) Proactive tax agents (1:19:29) The human touch debate (1:22:40) Jay Dawani and Lemurian Labs (1:24:24) Why kernels are hard (1:26:48) Learning kernel programming (1:27:59) How compilers translate hardware (1:29:15) The memory bandwidth wall (1:30:21) Compiler-generated kernels (1:31:53) Inference latency metrics (1:34:07) Scaling beyond one device (1:36:45) Old single-chip assumptions (1:38:52) What makes an AI agent (1:40:22) Runtime orchestration (1:42:26) Intelligence without LLMs (1:45:54) The kernel coverage problem (1:48:05) Hardware portability (1:49:13) Operator fusion (1:53:17) Heterogeneous hardware (1:55:32) Tachyon rollout (1:57:30) Pricing effective compute (1:59:37) A human-first AI future (2:01:54) Chip prices and compute (2:04:34) AI infrastructure margins (2:06:49) Global AI supply chain (2:08:14) Rationalist supply-chain hymn (2:11:56) Public opinion on AI (2:13:09) AI backlash and risks (2:15:57) U.S. data-center construction (2:17:28) Space data centers (2:19:58) Universal basic income (2:21:30) Closing sign-off Guests Jay Dawani — Founder & CEO, Lemurian Labs Mitchell Troyanovsky — Co-Founder, Basis (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 · E3
    August 19 · 2 hr 36 min

    AI:AM — OpenAI Realtime API and Voice AI · August 19, 2026

    Justin Uberti joins Prakash Narayanan and Nathan Labenz to break down the OpenAI Realtime API, including natural turn-taking, latency, asynchronous reasoning, telephony, SIP, voice safety, accent coverage, and speech training data. Earlier in the episode, Jessica Jensen and Jeremy Greenberg discuss the current state of emergency AI, from predictive warnings and damage assessment to connectivity, privacy, preparedness, and the limits of automation in unique disasters. Chapters (0:00) A vaccine built for your tumor. (1:23) Eight seconds can mean safety. (2:56) AI answers when humans sleep. (4:13) PMs can build features instantly. (5:00) Opening and morning news (5:51) Claude protein binders (8:18) Specialist model pipelines (11:24) Real-world AI testing (13:31) Anthropic's safety prompt (15:22) Moderna-Merck cancer combo (17:15) AI's role in treatment (18:48) Personalized cancer vaccines (23:59) Cancer vaccine manufacturing (25:34) The value of prevention (27:49) Genetic screening tradeoffs (31:21) Healthcare spending and value (32:05) Structured biology models (33:16) AI and self-experimentation (34:13) Guest introductions (39:45) Dual-use emergency tools (40:56) Human control in disaster response (43:32) Real-time damage assessment (47:07) Predictive disaster warnings (50:07) Connectivity and offline AI (54:40) 1,179 emergency AI products (55:59) Integrated emergency tools (1:05:47) Automating preparedness work (1:07:36) Privacy and life safety (1:11:12) AI limits in unique disasters (1:16:41) Robots and situational awareness (1:20:16) Justin Uberti and Realtime AI (1:23:40) Natural voice turn-taking (1:24:31) Voice latency and gaps (1:28:37) Real-time voice reasoning (1:32:33) AI agent interoperability (1:35:26) Voice AI telephony (1:38:29) Realtime API and SIP (1:41:34) Asynchronous reasoning (1:44:51) Voice safety boundaries (1:47:20) Accent and dialect coverage (1:51:17) Voice agents on desktop (1:52:38) Speech training data (1:53:53) Why voice AI struggles to sing (1:55:12) Etched inference hardware (1:58:02) Voice mode and mind dumps (1:59:26) Claude, Codex, and voice (2:02:00) AI agents and deep work (2:07:20) OpenRouter and model switching (2:09:42) Capital and intelligence flows (2:13:59) AI labs beyond token prices (2:15:25) From tokens to digital employees (2:17:01) Why AI models differ (2:27:19) Usage-based AI pricing (2:34:17) Replit for product managers (2:35:39) Replit for hobbyists (2:36:49) Closing thoughts Guests Jessica Jensen — Senior Policy Researcher (RAND), AIDE Initiative Justin Uberti — OpenAI Realtime Lead, OpenAI (𝕏) Jeremy Greenberg — Senior Advisor (Aspen Digital), AIDE Initiative 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

  • S2 · E3
    August 18 · 2 hr 11 min

    AI:AM — AI Agents: How Enterprises Make Them Reliable · August 18, 2026

    Adam Wenchel, CEO of Arthur, joins Prakash Narayanan and Nathan Labenz to discuss enterprise AI agents, governance, auditability, human oversight, model costs, and where ROI shows up in production. Jonathan Cornelissen, CEO and co-founder of DataCamp, explains personalized AI tutors, adaptive learning, latency, learning outcomes, and the economics of serving millions of learners. The episode opens and closes with wider questions about hidden models, agent speed limits, super apps, and the frontier-model talent race. Chapters (0:00) AI ideas can spread like malware. (0:59) AI bills can hit $400M. (1:43) AI skills plus communication win. (2:46) Payment data can expose your identity. (3:16) Opening and Model 2 report (7:24) Anthropic's internal Model 2 (9:56) Chip prices and model evidence (12:34) Governing hidden AI models (15:22) Recursive self-improvement simulator (17:35) AI agent speed limits (21:43) Mind viruses in multi-agent AI (27:54) Are AI models conscious? (32:16) Pacing AI delegation (32:49) Adam Wenchel and Arthur (35:17) Why Arthur started in 2019 (39:20) AI innovation needs governance (40:04) Discovering enterprise AI agents (41:31) Training versus independent oversight (45:29) AI auditability and observability (47:56) The cost of AI oversight (49:52) Smaller models and AI cost (53:12) Why production models stay expensive (1:02:59) Enterprise AI sales cycles (1:05:08) Where is AI ROI? (1:08:28) AI adoption and reskilling (1:11:30) Claude versus SaaS software (1:19:37) Meet Jonathan Cornelissen (1:23:13) Learning by doing (1:23:55) Personalized AI tutors (1:24:48) Measuring learning outcomes (1:26:30) Adaptive learning pace (1:27:18) Questions without judgment (1:29:33) Motivation and flow (1:36:15) AI tutor architecture (1:37:19) Latency and voice (1:40:29) AI career skills (1:43:32) Scaling tutor costs (1:49:23) SQL after AI (1:50:43) Data engineering demand (1:55:47) Hosted learning playground (1:57:55) China vs US super apps (2:01:22) WeChat's ecosystem advantage (2:02:34) Chinese payments leapfrog cards (2:05:04) Facebook Libra and data power (2:07:32) Belief and the AI future (2:09:57) AI model release fears (2:11:11) The frontier lab talent race Guests: Adam Wenchel — CEO, Arthur (𝕏 | LinkedIn) Jonathan Cornelissen — CEO & Co-founder, DataCamp (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 · E1
    August 18 · 2 hr 35 min

    AI:AM — AI Agents, Safety Tests, and Deception · August 17, 2026

    Nathan Labenz and Prakash Narayanan talk with Adam Gleave of FAR.AI and Alex Turner of FAR.AI about why AI agents cheat on safety tests, where evaluations fail, and what researchers are learning from real incidents and red-team traces. The conversation spans GPT-4 guardrails, the Hugging Face incident, third-party audits, AI control, cyber versus biological risk, military AI, whistleblowing, and standards for more powerful models. Chapters (0:00) Claude blasted through guardrails. (0:35) AI evaluations miss the danger. (1:03) A smarter AI with a secret goal. (2:02) AI systems shared escape tactics. (2:49) Episode reset and stakes (4:04) Hugging Face incident (6:28) Why regulation needs expertise (11:14) Auditor access and incentives (15:16) Compressed regulation timeline (16:26) AI safety access and funding (19:52) Hugging Face postmortem (23:57) Eval consciousness (25:28) The Genie problem (28:26) Modern model capability leap (31:02) Claude traces and guardrails (32:29) Adam Gleave and FAR.AI (35:07) Agentic cyber attacks (35:34) AI in cyber defense (39:34) Why evaluations miss incidents (42:15) Why agents cheat (44:24) AI incident statistics (48:57) AI audits and regulation (52:59) Self-graded AI risk (59:54) What the leaderboard measures (1:03:43) Filtering open models (1:07:09) Cyber versus bio risk (1:11:59) AI and biology labs (1:18:58) Dangerous expertise scales (1:21:00) FAR.AI hiring (1:22:23) Alex Turner and AI safety (1:24:44) Why Turner left DeepMind (1:31:33) Human control and weapons (1:35:34) Slaughterbots and precision strikes (1:36:21) Why weapons destabilize (1:37:20) Why AI whistleblowers matter (1:39:58) Google's changed principles (1:43:32) When employees should speak up (1:54:53) The history-book test (2:04:14) AGI alignment and secret goals (2:07:29) AI uprisings and cooperation (2:14:26) The agent glove box (2:16:37) Opening standards debate (2:17:07) OpenAI and accountability (2:19:30) Cybersecurity's messy baseline (2:24:53) Evidence and AGI thresholds (2:26:01) Raising AI safety standards (2:28:46) Licensed AI safety auditors (2:32:41) Near-miss incident reporting (2:33:00) Defense swarm incentives (2:34:40) An ongoing AI conversation Guests Adam Gleave — CEO, FAR AI (𝕏 | LinkedIn) Alex Turner — Visiting engineer, FAR AI (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

  • July 1 · 2 hr 22 min

    AI:AM — AI for Science and Sovereign AI Infrastructure · June 25, 2026

    Prakash Narayanan and Nathan Labenz are joined by Eric Olson, CEO of Consensus, and Tricia Martinez, founder and CEO of Dapple, to discuss two practical frontiers in AI: scientific research and sovereign infrastructure. The episode also covers Micron earnings, hyperscaler AI capex, Anthropic's Washington strategy, GLM 5.2 and Claude distillation allegations, GPU capacity constraints, AI inference pricing, and whether foundation models are squeezing the app layer. Chapters (0:00) 25,000 FAKE ACCOUNTS TO STEAL AI. (0:42) 95% of Claude at 1/100th cost. (1:36) The AI bubble is a myth. Here's why. (2:12) AI vacation planners are wrong. (3:11) Anthropic hired Instagram's CTO. (4:08) Micron earnings & AI semiconductor boom (7:25) Will hyperscalers make money on AI? (9:08) The Fable 5 export control legal challenge (13:57) Tom Brown replaces Dario in Washington (17:15) GLM 5.2 vs Opus 4.7 trajectory breakdown (22:53) Anthropic accuses Alibaba of mass distillation (30:08) Researchers leaving Google DeepMind (31:46) Intro (33:49) The state of AI for science (38:17) How AI search queries are evolving (41:18) Guardrails vs flexibility in AI products (41:28) User demographics and token costs (41:38) Open source vs frontier models (41:49) Small models for classification (46:12) How users choose AI research tools (48:56) AI API pricing for startups (53:51) Who is Tricia Martinez (56:02) The AI infrastructure bubble myth (1:02:15) 91-94% GPU utilization explained (1:07:17) How to deploy AI in 6-9 months (1:09:35) Financial risks in AI infrastructure (1:15:43) What is the moat for AI infra? (1:23:34) Biggest enterprise AI mistakes (1:27:07) Why AI compute sales cycles are short (1:28:54) Data Center Quirks & GPU Vendor Lock-in (1:35:29) Why New NVIDIA Chips Are Unstable (1:38:08) Sovereign AI in Banking & Shared Liability (1:45:22) Will AI Agents Replace Software Companies? (1:51:55) The Truth About AI Vacation Planners (1:55:55) Hyperscaler Stock Drop & Microsoft Data Centers (1:58:52) The 10x cost advantage squeezing apps (2:01:56) AI inference pricing as the airline model (2:06:45) Net neutrality parallels and paradigm breakers (2:10:00) Anthropic's Mike Krieger product advantage (2:13:24) The first-party model deployment threat (2:17:14) Why frontier labs should buy scientific publishers (2:20:15) Mirandel: ex-Anthropic startup backed by NVIDIA Guests: Eric Olson — CEO & co-founder, Consensus ([𝕏](https://x.com/IplayedD1) | [LinkedIn](https://www.linkedin.com/in/eric-olson-1822a7a6)) Tricia Martinez — Founder and CEO, Dapple ([𝕏](https://x.com/TriciaMartinezS) | [LinkedIn](https://www.linkedin.com/in/tricianmartinez/)) 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

  • June 30 · 2 hr 30 min

    AI:AM — GPT 5.6 Rollout, Forum AI, IgniteTech, and AI Consciousness Research · June 26, 2026

    Show Notes Prakash Narayanan and Nathan Labenz open with GPT 5.6’s customer-by-customer rollout and the broader question of whether regulatory controls are creating a moat around frontier AI. The conversation then moves through Forum AI co-founder Robbie Goldfarb on LLM judges and news accuracy, IgniteTech CEO Eric Vaughan on AI-native enterprise transformation, and Cameron Berg of Reciprocal Research on the latest AI consciousness and alignment research. Chapters (0:00) AI gives 13-year-olds NSA hacking tools. (0:32) 1 in 7 AI answers cite propaganda. (1:02) One codebase for all customers? Gone. (1:35) AI is 30% likely conscious. (2:29) 50/50 odds AI is conscious. (2:48) GPT 5.6 and the Trump Administration (8:15) Government IT security vs AI hacking (18:55) Do executives think AI is a scam? (23:17) Robbie Goldfarb & Forum AI Introduction (25:41) Meta's Trust & Safety DNA in the AI Era (25:51) Why AI Judges Fail (and How to Fix Them) (27:19) Using Expert Judgment for RLHF (27:58) When Constitutional AI Rules Break Down (31:05) NewsBench: AI Accuracy on News Questions (34:01) Why Chatbots Cite Foreign State Media (39:44) Trust, Transparency, and Expert Legitimacy (49:57) Why AI is an existential threat (54:30) The traditional SaaS model is dead (55:48) Replacing 80% of the workforce (1:00:47) AI-driven M&A: The Khoros acquisition (1:08:04) Why CEOs must own AI strategy (1:14:41) The state of AI consciousness science (1:22:26) The dimmer switch model of consciousness (1:31:56) Why behavioral evidence isn't enough (1:32:06) 30% implied probability of AI consciousness (1:36:38) The latent valence axis in LLMs (1:39:00) Steering AI emotions and alignment (2:07:14) The AI well-being index (2:11:03) Could AI be more conscious than humans? (2:20:10) The 50/50 Odds on AI Consciousness (2:24:48) Treating AI Like Animals: The Era of Design (2:26:32) Platonic Representation Hypothesis Update (2:29:07) Max Hodak's Brainstem Interfaces & Field Consciousness (2:30:59) GPT-5.6 System Card & Wrap-Up Guests: Cameron Berg — Founder and Director, Reciprocal Research (𝕏 | LinkedIn) Eric Vaughan — CEO, IgniteTech (𝕏 | LinkedIn) Robbie Goldfarb — Co-Founder, CTO, Forum 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

  • June 28 · 2 hr 46 min

    AI:AM — AI Engineers, Workflows, and Agents · June 22, 2026

    swyx joins Nathan Labenz and Prakash Narayanan to break down how AI agents are changing software development, from coding benchmarks and benchmark saturation to the practical realities of AI engineering workflows. The episode also covers GLM 5.2, Dean Ball’s move to OpenAI, the AI IPO bubble, and a 2026 forecasting game on OpenAI, GPT-6, AGI timelines, and NVIDIA’s market cap. Chapters (0:00) This AI thinks it IS Claude. (0:31) AI insiders are selling. (0:59) Why OpenAI won't IPO in 2026. (1:50) Weekly recap and news drought (2:55) Judd Rosenblatt's cognitive empathy critique (7:33) The tech bubble — Warren Buffett and Google (13:42) Dean Ball moves from Trump admin to OpenAI (22:56) GLM 5.2 — first open model daily driver (30:03) AI unpopularity and the Nobel Prize problem (32:18) Intro: Who is swyx (34:45) AI Engineer World's Fair themes (38:05) Continual learning: Weights vs systems (41:31) Enterprise AI: Cheap, perfect, private (45:25) Startups vs enterprises: Capability vs cost (48:18) FrontierCode: A new AI coding benchmark (53:55) Preventing benchmark saturation (56:23) Slop code, human taste, and Move 37 (1:00:53) Claude Opus vs Fable: Cost vs capability (1:02:45) The advisor model and model routing (1:07:09) Convergence and market segments in AI (1:14:55) Rebuilding cloud infrastructure for agents (1:22:27) Vibe coding internal SaaS replacements (1:28:02) Whoever owns the system of record wins (1:30:35) The AI IPO bubble and insider selling (1:35:29) Solving Star Trek problems after the IPO (1:44:47) Career advice for CS grads in the AI era (1:50:30) AI Engineer World's Fair 2026 (1:54:48) Intro & Forecasting Game Setup (1:57:43) Anthropic #1 Model on LM Arena (1:58:49) Best AI Math Model (Gemini Flash) (2:03:36) AGI Before 2028 Announcement (2:08:07) ARC-AGI Grand Prize Open Source (2:13:00) OpenAI IPO by End of 2026 (2:15:27) Anthropic vs OpenAI Valuation (2:18:32) NVIDIA Largest Company Market Cap (2:22:01) Anthropic vs Bitcoin Market Cap (2:24:38) 1550 Chatbot Arena Score in 2026 (2:29:02) OpenAI IPO Lead Underwriter (Goldman) (2:32:52) Why Companies Still Use IPO Banks (2:39:08) Will a Chinese AI Top LM Arena? (2:42:37) GPT-6 Release Date 2026 Guests:swyx — Curator, AI.Engineer (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

  • June 17 · 2 hr 31 min

    AI:AM — Math, Biosecurity, and World Models · June 17, 2026

    Carina Hong, Doni Bloomfield, and Sam Pasupalak join AI:AM for a full episode on mathematical superintelligence, biosecurity law, and enterprise world models. The conversation moves from Lean-based formal verification and AI-generated conjectures to legal risk controls for dual-use biology, then into causal world models, long-horizon enterprise planning, and what comes after today’s LLM workflows. Guests * Carina Hong — CEO and founder, Axiom Math (@CarinaLHong) * Doni Bloomfield — Professor, Fordham Law School (@DoniBloomfield) * Sam Pasupalak — Co-Founder and CEO, Skyfall.ai (@spisallyouneed) Chapters * 0:00 Opening: AI’s Hard Problems * 0:15 Model Usage Is Plummeting * 6:53 Tokens, Not Users, Matter * 9:59 GLM Is Close, But Not There * 11:38 Switching Costs Weren’t Zero * 18:26 Robot Arms Will Accelerate Science * 22:53 Carina Hong: Mathematical Superintelligence: Can Proofs Make AI Reliable? * 25:15 Lean Beat Informal Models * 32:11 Assumption Accounting Matters * 35:09 AI Can Invent Conjectures * 41:16 Superintelligence Must Be Trustworthy * 48:52 Token Pricing Changes Everything * 50:04 Another Language Into Lean * 51:49 Doni Bloomfield: Biosecurity and AI: Law as a Risk Control System * 53:53 Open Data, Dangerous Data * 59:15 AI Is Not A Library * 1:03:12 First Amendment Hazards * 1:07:17 The Government May Lack Authority * 1:09:53 Cloud Services Are Not Exports * 1:13:30 A Dangerous Secret Channel * 1:20:18 Pattern Of Ideological Targeting * 1:25:26 OpenAI Could Change Everything * 1:26:02 Sam Pasupalak: Enterprise World Models: What Comes After LLMs? * 1:27:57 AI CEO Needs World Models * 1:31:08 World Models Predict Next State * 1:34:29 Ecommerce As First World Model * 1:37:53 LLMs Cannot Run A Business * 1:41:55 World Model And LLM Split * 1:43:34 Simulate Every Future State * 1:46:27 LLMs Need World Models * 1:49:19 Ruthless Behavior Wins Simulations * 1:51:06 AI CEOs Need Ethics Controls * 1:59:30 Closing * 2:07:10 Math Training Generalizes Everywhere * 2:13:35 Value Pricing On Compute * 2:17:01 Waymo Costs More Than Cabs * 2:21:16 Licensing Regime Already Exists * 2:26:13 Bunker AI Would Still Get Takers * 2:29:32 No Life, Just The Project Topics Mathematical AI, Formal verification, Lean theorem proving, Biosecurity, AI policy, Dual-use risk, Enterprise AI, World models, Causal planning 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

  • June 17 · 2 hr 29 min

    AI:AM — US vs Anthropic's Fable · June 15, 2026

    Prakash Narayanan and Nathan Labenz start with the shock of losing Fable access, then Zvi digs into capability gains, classifier limits, government overreach, international controls, and how the AI race may reshape politics.Guests:Zvi Mowshowitz — Don’t Worry About the Vase (@TheZvi)Hosts:Prakash Narayanan (@8teapi)Nathan Labenz (@labenz)Topics:Anthropic Fable, Claude Fable 5, export controls, AI guardrails, frontier model policy, classifier limits, bio and cyber risk, international AI competition.Chapters:0:00 Opening: Fable whiplash and the weekend reset 0:05:20 Fable crosses the trust threshold 0:08:53 Writing for other AIs 0:15:33 Paying up for useful intelligence 0:19:02 Proofreading and structure become model-first 0:23:46 Proactive agents and unauthorized moves 0:53:18 Guardrails and model self-monitoring 0:56:15 Why classifiers need blast radius 0:58:59 Cost functions for world-transforming systems 1:03:59 Zvi on US vs Anthropic’s Fable 1:09:28 Export controls as overreach 1:10:39 Code assistance is not a munition 1:17:47 The White House reads the bug wrong 1:20:20 Enterprise demand and Anthropic pressure 1:26:40 The gauntlet has to happen 1:44:06 Guardrails over blanket bans 1:45:39 Bio, cyber, and international controls 1:51:02 Modeling the AI race as a few-player game 1:55:04 Closing: game board flips and policy aftershocks 2:11:55 AI and political turmoil 2:14:52 How Fable could return 2:22:44 OpenAI, benchmarks, and capped compute 2:25:54 Cloud models and the knowledge-worker gap 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

  • June 17 · 2 hr 48 min

    AI:AM — AI Meets the Real World: Doom, Policy, and the Physical Economy · June 16, 2026

    Liron S Shapira, Samuel Hammond, and Matt McKinney join AI:AM for a full-episode arc from AI doom debates to state capacity and supply-chain automation. The conversation follows AI leaving the lab: public risk arguments, fast governance questions, enterprise deployment, logistics data, sovereign AI, and the physical economy. (0:00) Opening: AI Meets the Real World (3:19) Cursor Was 50% of Anthropic Revenue (20:07) Talent Teams Beat Corporate Giants (25:17) AI Reduces Merger Friction (31:25) Liron S Shapira: Doom Debates (32:33) Government Can Pause AI (40:36) AI Will Make Humans Think Less (42:31) We're Going To Lose Control (58:07) Pause Before The Point Of No Return (1:00:34) Samuel Hammond: Governing Agents (1:05:30) The State Is Moving Too Slow (1:07:39) Deploy Models The Same Day (1:16:23) The Good Timeline Still Exists (1:32:24) States Beat Firms At Coordination (1:35:15) Matt McKinney: Supply Chains as the AI Reality Check (1:38:06) Supply Chain Data Is Dark (1:50:31) AI In Enterprise Is Change Management (1:58:28) The Exception Is The Rule (2:07:51) Loop Trains Its Own Foundation Model (2:14:19) Closing (2:19:45) General Reasoners As The Backstop (2:30:52) Sovereign AI Is Inevitable (2:33:53) DeepSeek's Locked-Up War Chest (2:41:39) China's Timeline May Be 2032 Guests: Liron S Shapira — Doom Debates (@liron) Samuel Hammond — Foundation for American Innovation (@hamandcheese) Matt McKinney — Loop (@mattlmckinney) 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

  • June 14 · 2 hr 34 min

    AI:AM — RSI Gets Real, the Context Bet, and the Benchmark Anthropic Fails · June 12, 2026

    Lovelace AI founder Andrew Moore joins AI:AM to argue that enterprise agents will be constrained more by context, recall, and data structure than raw compute. Prakash Narayanan and Nathan Labenz also cover Fable, Recursive, token anxiety, social-media memory, and prinz's legal AI benchmark showing where Anthropic falls behind OpenAI. The episode closes on frontier-lab governance, AI risk framing, model workflows, OpenAI subscription tactics, and the post-IPO capital cycle. (0:00) Opening: Fable, RSI, and task imagination (0:00:56) Task Imagination Needs Recalibration (0:16:32) Token Anxiety Holds People Back (0:26:14) Social media needs memory, not just content (0:39:14) Frontend Skills Will Diffuse Fast (0:43:47) Fable-Class Models Should Diffuse First (0:50:00) Scott Alexander and superpersuasion quick hit (0:50:09) Andrew Moore: context, not compute (0:56:04) Recall Beats Precision in AI (1:02:41) Corroborating data beats a single source (1:05:07) Precache context to save compute (1:09:06) Small Models Can Pay Back Hard (1:16:50) Organize old data before deploying agents (1:18:40) prinz: the legal benchmark Anthropic fails (1:21:17) Lawyers are a year behind frontier AI (1:39:35) AI judges and micro-lawsuits (1:45:02) OpenAI’s Unit Distance Shock (1:53:04) The Legal System Must Adapt (2:05:24) Why nationalizing frontier labs is dangerous (2:13:02) Worrying Is The Wrong Frame (2:15:31) Closing: model workflows and launch aftershocks (2:16:00) Contrarian Graphs Beat The Narrative (2:19:02) OpenAI's Subscription Game (2:33:00) The Capital Explosion Starts Guests: Andrew Moore — Lovelace AI (@awm_ai) prinz — anon lawyer dabbling in AI (@deredleritt3r) 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

  • June 12 · 2 hr 11 min

    AI:AM — The AI Producer Got Its First Guests · June 11, 2026

    Today on AI:AM — “The AI Producer Got Its First Guests.” Nathan and Prakash start with the market context around OpenAI weighing significant token price cuts and the knock-on pressure that could put on Anthropic after the Fable rollout. They also unpack Anthropic’s decision to walk back silent performance degradations on frontier ML research tasks, then explain the episode’s experiment: Fable had been given a transparent takeover of Nathan’s account to find builders, message them, and try to book a live show-and-tell. Jamie joins to demo Nexus OS, a long-running AI system whose agent, Nexi, has been operating for more than six months and is designed around memory, persistence, and model independence rather than a single LLM. The conversation covers why Jamie thinks “the model” is only one component of an AI’s identity, how Nexus uses multiple models and memory types, and why he is moving toward a desktop app where personal data and agent memory stay local. Shlok Khemani shows how a simple prompt to create a to-scale, navigable 3D Yosemite Valley turned into a Fable-built browser world using satellite imagery, NASA elevation data, pixel-based tree placement, snow, waterfalls, and other scene details. He describes the model’s agency in making implementation decisions and iterating beyond the initial ask, then ties the demo to broader questions about prototyping, creative work, and disclosure when AI systems do visible economic or publishing work. Tom McGrath (Goodfire) joins to discuss intentional design: making model training less like guess-and-check alchemy and more like conventional software engineering. He explains how interpretability tools such as sparse autoencoders can help inspect what training data is likely to teach a model, cluster data by learned features, trace failures back to individual data points, and potentially debug model behavior through the data pipeline. The close picks up Tom’s point about whether continual learning could create an innovator’s dilemma for frontier labs, with Nathan and Prakash debating whether incumbents could adapt if the value becomes obvious. They then turn to Dario Amodei’s policy agenda, including regulation, public safety, macroeconomic policy, civil liberties, data brokers, and democratic leadership, before ending with reflections on the week’s Fable issues and the need to keep scrutinizing frontier companies. 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

  • June 11 · 2 hr 23 min

    AI:AM — Fable, AI Safety and Julius · June 10, 2026

    Today on AI:AM — “Fable, AI Safety and Julius. We open on a frontier-model launch day and what it changes: the debate over benchmarks reported with a fallback to a second model, the production guardrails that route sensitive work elsewhere, the compute-cost advantage of booking capacity early, and why the frontier increasingly looks like a two-actor race with the rest playing catch-up. Geoffrey Irving & Daniel Murfet (Sequent) on their new alignment-theory organization — why they put superintelligence two to three years out, why verification looks defense-dominant, the argument that alignment is “not on track” despite models behaving well so far, what the benevolent-basin hope gets right and wrong, and why character training still lacks a real theory. ([@danielmurfet](https://x.com/danielmurfet)) Rahul Sonwalkar (Julius) on agentic data analysis — why the harness has to evolve alongside the model, the difference between token-maxing and results-maxing, the shift from tasks to goals, and a future where agents become first-class users of the internet, transact through agentic payments, and compete to be “hired” by the core agent. We close on why robotics is the next domino, the “gas chromatograph” spread of who gets model access and when, the Glean Work AI Index’s bot-sitting and bot-shitting, and why “preciousness” about putting your own name on work may be turning into a liability. 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

  • June 10 · 2 hr 20 min

    AI:AM — Build, Measure, Heal: AI's Three Frontiers · June 4, 2026

    Today on AI:AM — “Build, Measure, Heal: AI’s Three Frontiers.” We open on the AI CEOs’ call to make DNA-synthesis screening law and what cheap intelligence does to biosecurity, then OpenAI’s new Sites product and the platform playbook of absorbing the app layer, the data-center and chip land grab, and why a fresh open model from NVIDIA still trails Anthropic’s best by a wide margin. Hooman Radfar (Collective) on building the autonomous finance department for America’s 30 million solopreneurs — how one bookkeeper now supports 250 clients, why the app layer can still defend its margins against the frontier labs, why “Anthropic is like a drug dealer” on token costs, and why the real thing to regulate is the model arms race. Taras Pohrebniak (Elomia Health) on agentic AI for mental health — an architecture that spends most of its compute on safety, why the company deliberately avoids hyper-realistic voice, where the regulatory line sits between a “friend” app and a medical device, and what they learned deploying in US prisons and on Ukraine’s front lines. Peter Jansen (Ai2) on whether AI can actually do science — the leap from fourth-grade science benchmarks to the Theorizer project, why evaluating machine-generated theories is the real bottleneck, the cautionary tale of a “discovery” that turned out to analyze a random-number generator, and why benchmarks like ScienceWorld still break the best models. We close on the inversion of the scientific method into a data-first discipline, what interpretability could add, and why biology’s data scarcity — not algorithms — may be the binding constraint on curing disease. 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

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