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2:00:51EN
Cognitive Revolution "How AI Changes Everything"· 2026년 10월 10일팟캐스트Resample 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.
51:55EN
3Blue1Brown· 2026년 9월 18일The last IMO problem AI could not solveGrant Sanderson · 3Blue1BrownGrant Sanderson walks through IMO 2025 Problem 6 — a tiling puzzle that frontier models missed — and the visual patience it takes to prove a construction is optimal.
1:34:29ENCognitive Revolution "How AI Changes Everything"· 2026년 2월 18일팟캐스트Three 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.
1:39:05ENCognitive Revolution "How AI Changes Everything"· 2025년 10월 29일팟캐스트The Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalled진행자 Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.
1:14:11ENCognitive Revolution "How AI Changes Everything"· 2025년 4월 5일팟캐스트The Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI진행자 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.
1:10:23EN
Unsupervised Learning: With Jacob Effron· 2025년 3월 17일팟캐스트A Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped진행자 Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.
3:18:45ENCognitive Revolution "How AI Changes Everything"· 2025년 2월 20일팟캐스트5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its Values진행자 Nathan LabenzRedwood Research chief scientist Ryan Greenblatt walks through inference scaling, alignment faking, and what it would mean to strike real deals with AIs.
1:48:20ENCognitive Revolution "How AI Changes Everything"· 2025년 1월 25일팟캐스트From 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table진행자 Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.
1:33:01ENCognitive Revolution "How AI Changes Everything"· 2025년 1월 15일팟캐스트Rare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed진행자 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
2:04:45ENCognitive Revolution "How AI Changes Everything"· 2025년 1월 7일팟캐스트o3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-Toe진행자 Nathan LabenzNathan Labenz walks through OpenAI's o3 scores, deliberative alignment, and why 2024's biggest surprise was beating PhDs while failing to book a calendar invite.
1:07:32ENUnsupervised Learning: With Jacob Effron· 2024년 12월 18일팟캐스트o1 Is a New Generation — and the Day AGI Arrives Will Feel Boring진행자 Jacob EffronOpenAI’s former chief research officer Bob McGrew on why o1 is a 100x leap, why agents stall on reliability, and why AGI may land as office tedium rather than a single dramatic moment.
48:18ENUnsupervised Learning: With Jacob Effron· 2024년 12월 6일팟캐스트Noam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of Headroom진행자 Jacob EffronA ChatGPT query costs about a penny. The problems society cares about most, he says, are worth a million dollars.
1:06:48ENUnsupervised Learning: With Jacob Effron· 2024년 10월 3일팟캐스트o1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning Point진행자 Jacob EffronStanford’s Percy Liang splits OpenAI’s o1 into a disappointing product and the most important research signal in years.
1:56:03ENCognitive Revolution "How AI Changes Everything"· 2024년 7월 13일팟캐스트Humans Near a Perfect Score. The Best AI Still Gets 39%진행자 Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.
1:22:59ENCognitive Revolution "How AI Changes Everything"· 2023년 12월 2일팟캐스트If Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%진행자 Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
2:00:19ENCognitive Revolution "How AI Changes Everything"· 2023년 6월 6일팟캐스트1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2진행자 Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
1:45:49ENCognitive Revolution "How AI Changes Everything"팟캐스트Trust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Company진행자 Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
58:57ENCognitive Revolution "How AI Changes Everything"팟캐스트When the Model Learns to Look Around: Apollo Research on Testing o1 for Scheming진행자 Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
57:25ENUnsupervised Learning: With Jacob Effron팟캐스트From RAG to Reasoning: Why the Future of AI Is a System, Not a Model진행자 Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.
1:03:10ENCognitive Revolution "How AI Changes Everything"팟캐스트Pause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgrades진행자 Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.