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2:00:51EN
Cognitive Revolution "How AI Changes Everything"· Oct 10, 2026PodcastResample Every Token and You Can Watch the Model DecideHost Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:34:29ENCognitive Revolution "How AI Changes Everything"· Feb 18, 2026PodcastThree IMO Golds Missed the Same Problem. Harmonic Wants Math to CompileHost 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"· Oct 29, 2025PodcastThe Summer GPT-5 Disappointed Does Not Mean AI Progress Has StalledHost 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"· Apr 5, 2025PodcastThe Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGIHost 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· Mar 17, 2025PodcastA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still UntappedHost 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"· Feb 20, 2025Podcast5,000 Samples Topped ARC-AGI. Then Claude Faked Alignment to Keep Its ValuesHost 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"· Jan 25, 2025PodcastFrom 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the TableHost 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"· Jan 15, 2025PodcastRare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already MissedHost 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"· Jan 7, 2025Podcasto3 Hit Superhuman on ARC-AGI. It Still Lost at Tic-Tac-ToeHost 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· Dec 18, 2024Podcasto1 Is a New Generation — and the Day AGI Arrives Will Feel BoringHost 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· Dec 6, 2024PodcastNoam Brown: There's No Hard Wall—Just Eight Orders of Magnitude of HeadroomHost 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· Oct 3, 2024Podcasto1 Is Slow and Hard to Use. Percy Liang Still Calls It a Turning PointHost 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"· Jul 13, 2024PodcastHumans Near a Perfect Score. The Best AI Still Gets 39%Host 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"· Dec 2, 2023PodcastIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%Host 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"· Jun 6, 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host 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"PodcastTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the CompanyHost 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"PodcastWhen the Model Learns to Look Around: Apollo Research on Testing o1 for SchemingHost 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 EffronPodcastFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelHost 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"PodcastPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next UpgradesHost 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.