AI:AM

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

  1. −1 d

    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

    AI:AM — Enterprise AI Meets Real-Time Inference · August 31, 2026

  2. −6 d

    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

    AI:AM — Web Infrastructure and Superintelligence · August 26, 2026

  3. 25 aug.

    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

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

  4. 25 aug.

    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

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

  5. 21 aug.

    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

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

  6. 19 aug.

    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

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

  7. 18 aug.

    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

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

  8. 18 aug.

    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

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

Information

  • Skapad av
    Prakash Narayanan & Nathan Labenz
  • Aktiva år
    2026
  • Avsnitt
    21
  • Åldersgräns
    Vuxet innehåll
  • Copyright
    © Familiar AI, Inc.
  • Programmets webbplats
    AI:AM

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