AI:AM

Prakash Narayanan & Nathan Labenz

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

1h ago

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
1d ago

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
1d ago

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
Jul 1

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
Jun 30

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
Jun 28

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
Jun 17

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
Jun 17

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

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

Creator

Prakash Narayanan & Nathan Labenz
Years Active

2026
Episodes

16
Rating

Explicit
Show Website

AI:AM

AI:AM

Episodes

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

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

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

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

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

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

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

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

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