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85개정렬
2:00:51ENResample Every Token and You Can Watch the Model Decide진행자 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:30:54ENHumanoids Outran Usain Bolt. DeepMind Still Scores Robotics as GPT-2진행자 Nathan LabenzGoogle DeepMind's Gemini Robotics lead on why sprinting is easy, frying an egg is hard, and one brain should drive any body.
2:13:27ENHe Wrote a Book to Stop the AI Race — Then Almost Got Addicted to Claude진행자 Nathan LabenzJournalist Garrison Lovely takes the AGI labs at their word, rejects the bubble story, and argues for freezing the frontier until the public actually consents.
2:10:42ENThe Exam Papers Nobody Audits진행자 Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
2:13:09ENOne Chain of Thought, 100 Million Tokens: RL Is a Hell of a Drug진행자 Nathan LabenzApollo Research's Bronson Schoen on reward-seeking models, invented slang, and why chain-of-thought monitoring will not save us.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI Safety진행자 Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
1:57:46EN30% Consciousness, 47% Bee, 80% Turnover: Putting Numbers on What AI Is Becoming진행자 Nathan LabenzA Cognitive Revolution weekly cut that starts inside the model with Cameron Berg, then zooms out through gradual disempowerment, Europe’s bind, and the companies rebuilding around agents.
1:44:28ENFable launch week: a model that refuses, a two-year alignment clock, and a Twitter account handed over진행자 Nathan LabenzNathan Labenz’s AI in the AM highlights reel from June 2026 covers Anthropic’s new frontier model, the founding of Sequent, and a week that settled one argument: AI is not slowing down.
1:22:36ENA Thousand Researchers, a Million AIs: Frontier Labs Start Talking About the Brakes진행자 Nathan LabenzThe first week of AI in the AM follows OpenAI's timeline for an ML-intern model this year and a human-level AI researcher by early 2028 — and the closed-door meeting where labs admitted they might one day need to slow down together.
1:41:58ENA thousand people on guard, a million in the sprint진행자 Nathan Labenz80,000 Hours founder Ben Todd on how to spend a career in the years when your impact on AI will actually peak.
3:36:29ENClaude Rated Its Own Welfare 4.49 — the First Time Any Version Crossed Neutral진행자 Nathan LabenzCameron Berg returns to The Cognitive Revolution to survey six months of introspection, functional-emotion, and model-welfare research, plus a still-unpublished case that learning and feeling may be the same phenomenon.
3:10:49ENCrunch Time: Ajeya Cotra's Playbook for the Months Before Superintelligence진행자 Nathan LabenzA conversation on recursive self-improvement, public warning systems, and using each generation of AI to align and control the next.
1:49:53ENDon't Fight Backprop: Goodfire's Bet on Designing What Models Learn진행자 Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.
1:34:29ENThree IMO Golds Missed the Same Problem. Harmonic Wants Math to Compile진행자 Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
2:25:42ENThe experts argued through the coffee break — and never agreed whether AI can build AI진행자 Nathan LabenzOn a marathon Cognitive Revolution live show, a cancer-AI researcher, a former OpenAI board member, and a national-security analyst each hit a different wall in the boom.
1:04:51EN125 million cells took a decade. The next billion takes months진행자 Nathan LabenzMark Zuckerberg and Priscilla Chan on pairing a frontier biology lab with a frontier AI lab so models train on data that was designed for them.
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.
1:18:15ENIf 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.
1:58:48ENARC 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.
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.
1:01:49ENChatGPT'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.
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