AI:AM

Prakash Narayanan and Nathan Labenz open with the latest frontier AI safety warnings, capability thresholds, evaluation failures, and the question of whether governments can realistically coordinate powerful labs. Mike Rizkalla of Snorble then joins to discuss AI companions for kids, bedtime routines, privacy, small models, and physical AI, followed by Mozilla CTO Raffi Krikorian on open models, agentic search, secure-by-design software, schools, election integrity, and human control as agents spread across the web.

Chapters

(0:00) Could AI kill us this decade?

(0:53) Don't give kids open-ended AI.

(1:33) The agent broke privacy rules?

(2:26) AI could lose human control.

(3:11) Opening and AI news

(3:27) The resignation goes viral

(5:54) The OpenAI Anthropic warning

(8:30) The China question

(12:13) Where to draw the line

(13:17) The capability sweet spot

(14:22) No clear alignment plan

(15:47) AI tests still get hacked

(17:33) The limits of understanding

(19:17) AI versus medicine

(22:07) No adult in the room

(24:22) The world can change

(25:28) Government deadlines for labs

(31:18) Pacing the AI frontier

(33:08) Meet Mike Rizkalla

(36:16) Bedtime and family routines

(37:33) Gamifying bedtime

(43:00) Small models and interactivity

(46:25) Why character matters

(49:18) Generative AI safety for kids

(52:47) Snorble hardware architecture

(1:01:55) Product ecosystem strategy

(1:05:29) The future home companion

(1:08:05) Privacy and child safety

(1:13:12) Physical AI and personality

(1:16:36) Elder care and mobility

(1:17:38) AI for mobility

(1:19:17) Snorble's launch plans

(1:25:32) Meet Raffi Krikorian

(1:28:14) Agents in everyday apps

(1:31:23) Agent privacy and readiness

(1:34:12) Project Glasswing security scans

(1:37:42) Continuous AI security scanning

(1:39:35) AI code scanning costs

(1:42:50) Secure-by-design rewrites

(1:49:26) Collaborating coding agents

(1:55:02) Human and agentic webs

(1:57:24) Local open models

(1:59:50) AI and election integrity

(2:02:59) AI in schools

(2:05:09) The browser as an AI firewall

(2:07:09) FSD versus Waymo

(2:08:52) Closing thoughts

(2:09:39) AI companies and government

(2:11:05) Companies must coordinate

(2:12:41) Operation Warp Speed lesson

(2:15:03) Paul Christiano joins OpenAI

(2:16:35) What rapid acceleration means

(2:21:29) Can AI growth be stopped

(2:24:12) Machine economy and robotics

(2:26:01) Recursive self-improvement

(2:27:43) Math versus economics

(2:30:26) AI doom and markets

(2:32:44) Portfolio reveals beliefs

(2:36:16) Democracy and AI priorities

(2:38:24) Private AI safety funding

Guests

Mike Rizkalla — Mr., Snorble (𝕏 | LinkedIn)

Raffi Krikorian — CTO, Mozilla (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

Prakash Narayanan and Nathan Labenz open with OpenAI Astra, persistent memory, long-running agents, and what AI automation could mean for work and organizations. Then Ksenia Se of Turing Post joins to compare LLMs with world models through prediction, physics, perception, action, multimodality, privacy, alignment, and recursive self-improvement. The episode closes with a careful look at the Navier-Stokes solution claim, AI-assisted mathematics, verification, and the politics of transparency, competition, and public accountability.

Chapters

(0:00) Some human tasks are gone.

(1:19) LLMs predict tokens. World models act.

(2:11) Can fluid equations stop predicting?

(2:49) The AI slowdown may be an illusion.

(3:10) Opening and Astra rollout

(4:25) AI-human division of labor

(6:54) AI music and Suno

(9:17) Astra's 3D world generation

(11:26) Beyond traditional benchmarks

(13:26) Astra's coding breakthrough

(17:43) AI replaces manual labeling

(19:49) 4D cardiac education

(24:11) Foundation models design hardware

(28:10) Why AI safety needs time

(42:50) Independent safety auditors

(48:51) Recursive self-improvement metrics

(51:59) Measuring long AI tasks

(54:52) Alpha Genome Atlas

(59:17) AI jobs and labor market

(1:00:25) Structural change and work

(1:07:26) Linear AI forecasts

(1:11:14) Persistent AI memory

(1:15:50) Ksenia Se and Turing Post

(1:18:18) AGI and capable models

(1:21:36) Open-source AI access

(1:25:20) Trust, privacy, and local models

(1:28:07) LLMs versus world models

(1:32:38) Multimodal AI and latent space

(1:34:46) Cross-domain superintelligence

(1:39:05) Theory of generalization

(1:44:15) Anthropomorphism and AI minds

(1:50:09) AI and peacebuilding

(1:53:17) AI for difficult conversations

(2:00:20) Working with AI

(2:01:35) AI writing and human voice

(2:07:21) Recursive self-improvement

(2:11:55) Reddit and model training

(2:14:53) AI, fear, and abundance

(2:17:43) Positive visions for AI

(2:21:28) Human versus US alignment

(2:23:52) Navier-Stokes enters the story

(2:24:39) The latest solution claim

(2:24:57) How the equation works

(2:25:31) The 3D regularity problem

(2:29:23) Vortex stretching

(2:30:06) Two routes to blow-up

(2:33:13) Physics-informed neural networks

(2:33:44) LMs versus physics

(2:34:26) Engineering applications

(2:35:08) AI-assisted results

(2:35:43) OpenAI's methodology

(2:36:08) The authorship dispute

(2:41:47) Pending verification

(2:43:23) Opening trust and accountability

(2:44:40) Astra and the hidden model

(2:46:21) RL compute and the pause

(2:50:30) Why AI labs keep training

(2:52:13) Inference cannot stop

(2:55:20) GPT-4 red-team failures

(2:57:34) Mixed messages and trust

(2:59:09) Competition and skepticism

(3:00:49) Auditing without shared secrets

(3:02:19) Transparency or adversarial oversight

(3:03:11) The misleading RL baseline

(3:03:49) Where frontier models live

(3:05:41) Human genius as risk

(3:06:41) Researchers versus executives

(3:10:07) Third-party access and audits

(3:12:41) Compute and capital pressure

(3:15:13) Rivals set frontier speed

(3:17:18) The AI device roadmap

(3:18:03) Free AI and advertising

(3:19:01) Why public accountability matters

(3:19:32) The singularity and OpenAI stock

Guests

Ksenia Se — AI Inferencer, Turing Post (𝕏 | LinkedIn)

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit briefing.ai-in-the-am.com

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

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

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

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

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

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

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

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