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1:30:17ENHe Co-Wrote the Transformer Paper. Now He Wants You to Own Your AI.진행자 Nathan LabenzNEAR founder Illia Polosukhin maps a future of personal software, agent-run markets, and AI delegates that vote without a private agenda.
2:49:12ENWhy China's Master Plan for AI Never Mentions AGI진행자 Nathan LabenzBeijing-based Concordia AI CEO Brian Tse on China's safety rules, chip strategy, and why the two ecosystems look more alike than Washington assumes.
1:29:30EN2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics진행자 Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.진행자 Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few Points진행자 Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward Loop진행자 Nathan LabenzLiam Fedus and Ekin Dogus Cubuk on why language models still fail at physics, why literature data is too noisy to train on, and why Periodic Labs is aiming at a 200K superconductor.
2:20:35ENChatGPT Is a Thousand Days Old. Emad Mostaque Says We Have About a Thousand Left.진행자 Nathan LabenzThe Stability AI founder’s new economics argues that when intelligence is abundant, scarcity-based money and GDP stop making sense.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%진행자 Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14진행자 Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
1:32:47ENA transformer co-author wants a $160 million model no company owns진행자 Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
1:44:35ENJoe Hudson: $500 Billion Went Into AI, and Nobody Defined 'Good for Humanity'진행자 Nathan LabenzSam Altman says the OpenAI coach's superpower is emotional clarity — and that it will be one of the most critical skills in a post-AGI world.
3:14:49ENNothing This Summer Jumped the Curve: Why Zvi Lengthened His AGI Timelines진행자 Nathan LabenzOn his tenth visit to The Cognitive Revolution, Zvi Mowshowitz argues that GPT-5 and IMO gold stayed on trend, while chip policy, energy, and reinforcement learning nudged his p(doom) slightly up.
1:51:39ENA native ad engine for AI—and the questions it cannot dodge진행자 Nathan LabenzRyan Hudson, who sold Honey to PayPal for $4 billion, joins Nathan Labenz to explain ZeroClick’s bet on paid inference-time ads, and the societal incentives he has not yet had to confront.
1:55:02ENA hundred experiments a day, and the valley of death in materials진행자 Nathan LabenzRadical AI’s co-founders tell Nathan Labenz how a closed loop of frontier models and a self-driving lab is meant to collapse a forty-year materials cycle into less than a decade.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the Verbs진행자 Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
2:04:27ENA 10% Chance of an AI-Enabled Coup in the Next 30 Years진행자 Nathan LabenzTom Davidson maps three ways superintelligent systems could help a head of state or a lab CEO lock in power—and argues the window for building safeguards is short.
1:51:24ENFrontier AI Insiders Don't Trust Regulators. A Berlin Group Built Them an Anonymous Off-Ramp진행자 Nathan LabenzKarl Koch of the AI Whistleblower Initiative on gray-zone fears, retaliation, and a Tor-based way to ask for help before you decide anything.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBI진행자 Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
3:17:35ENTen Thousand Comments and 76 Spare Gigawatts: Four Months Writing America's AI Action Plan진행자 Nathan LabenzDean W. Ball spent four months as the White House's senior AI adviser, then left as the Action Plan landed — and explains how a to-do list got written inside a building that cannot run ChatGPT.
1:14:05ENSullivan boils US-China AI competition down to four buckets, four examples, and a path he calls boring진행자 Nathan LabenzThe former national security advisor treats transformative AI as a planning assumption, not a prophecy, and wants Washington to compete hard with Beijing without pretending a grand bargain will cure the rivalry.
1:45:00ENAmerica Holds 45% of the World's Data Centers. Everyone Else Must Build, Buy, or Ally진행자 Nathan LabenzAnjney Midha of a16z maps what “sovereign AI” actually means for companies and for countries that will never own the talent, data, and compute frontier.
1:27:01ENThe $16 Trillion Services Market Is Being Bought, Not Sold To진행자 Nathan LabenzMarc Bhargava of General Catalyst explains how AI-enabled rollups turn low-margin service firms into compounding, software-like businesses.
2:14:55ENFrom Character AI to a 1% Warning Shot: Gabriel Weil's Case for Liability as AI Governance진행자 Nathan LabenzA Touro law professor tells Nathan Labenz how negligence, strict liability, and punitive damages could make frontier labs treat public risk as a hit to their own balance sheet.
1:56:16ENSB 813 Would Hand AI Safety Checks to Licensed Private Firms — If Government Can Watch the Watchers진행자 Nathan LabenzGillian Hadfield and Andrew Freedman want California to set AI safety outcomes, license competing certifiers, and give companies a rebuttable presumption in court if they pass.
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