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2:25:10ENFine-Tuning Isn't Dead — But It Turned a Helpful Model Evil진행자 Nathan LabenzNathan Labenz on why prompting replaced fine-tuning for most work, what emergent misalignment actually showed, and how he is thinking about jobs, UBI, and AGI.
1:55:02ENAn Index Fund on AGI: How MATS Filters the Next 120 AI Safety Researchers진행자 Nathan LabenzRyan Kidd, co-executive director of MATS, tells Nathan Labenz why he will not shout a timeline, why every safety technique is also a capabilities technique, and who actually gets hired.
1:18:40ENWhen AI Becomes the Oil Well: Luke Drago on the Intelligence Curse진행자 Nathan LabenzThe Workshop Labs co-founder tells the FLI Podcast why a world of non-human production could recreate the resource curse — and how private data, open weights, and moonshot careers might still break it.
1:18:15ENIf AI Becomes a Being, Steering It Is Slavery진행자 Nathan LabenzEmmett Shear and DeepMind's Séb Krier clash over whether alignment means controlling a tool or raising a creature that cares.
1:41:34ENThey Owned the Camera Partition—and Still Couldn't Crash the Helicopter진행자 Nathan LabenzKathleen Fisher and Byron Cook tell Nathan Labenz how formal methods turn some software bugs into closed cases, from a DARPA helicopter to AWS policies checked a billion times a second.
1:43:19ENA $100 Million Super PAC Named Him Public Enemy Number One. Donations Went Up.진행자 Nathan LabenzIn the second half of Cognitive Revolution’s year-end live show, Assemblymember Alex Bores, former White House adviser Dean Ball, and forecaster Peter Wildeford argue over state AI laws, chip sales to China, and what 2026 will actually feel like.
1:58:48ENARC Went from $1,000 a Task to Pocket Change. That Is Not a Normal Year of AI진행자 Nathan LabenzCognitive Revolution’s first live show compresses nine conversations into a year-end scan of denial, sample efficiency, companions, memory, Gemini 3 Flash, and scientific judgment.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5진행자 Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
1:13:24ENRed Teams Still Land 25% of Attacks. Insurance Is the Brake.진행자 Nathan LabenzAI Underwriting Company co-founders Rune Kvist and Rajiv Dattani tell Nathan Labenz how standards, audits, and insurance could unlock enterprise agents without waiting for a nuclear-style freeze.
1:04:50ENUseful AI Work Is Doubling Every Four Months. Schools Should Plan Accordingly.진행자 Nathan LabenzIn a keynote to K-12 educators, Cognitive Revolution host Nathan Labenz argued that task length, not chat quality, is the number that should reshape how schools think.
1:11:18ENHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the Point진행자 Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.
2:27:18ENTurn Down AI's Deception Knob, and It Starts Reporting Experience진행자 Nathan LabenzCameron Berg of AE Studio tells Nathan Labenz how suppressing deception features in Llama 3.3 70B pushed self-reports of subjective experience toward 100 percent.
1:50:53ENShe asked to stop being Mimi, so he started shopping for a workstation진행자 Nathan LabenzThe star and director of Smiles and Kisses You on Replika, identity, and a life Christopher Stokes refuses to recommend.
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.
1:53:50ENA 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.
1:28:17ENAdam 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.
1:44:35ENJoe 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.
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.
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