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18:50ENGemini 3.1 Pro and the Limits of AI BenchmarksA release-day review examines specialization, benchmark shortcuts, hallucinations and why one score cannot rank every use.
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.
1:48ENMatthew Berman on OpenClaw, AI Agent Networks and the Limits of the HypeMatthew Berman · Forward FutureBerman sees new possibilities in agent tools and social platforms while questioning sensational trading claims and assertions of AI sentience.
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.
1:24:44ENForward Future Live: Portable AI Infrastructure, Software and Formal ProofMatthew Berman, Nick Wentz, Tim Davis, Joseph Floyd, Carina Hong · Forward FutureTim Davis, Joseph Floyd and Carina Hong join Matthew Berman and Nick Wentz for a February 2026 discussion.
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.
1:03:00ENA Model That Solves New Math Problems Every Week Still Isn't AGI진행자 Jacob EffronFormer OpenAI research VP Jerry Tworek on why scaling still works, why models go hopeless when they fail, and why he left to chase continual learning.
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.
17:44ENRecursive Language Models: Extending Long-Input Work Beyond a Context WindowMatthew Berman · Forward FutureMatthew Berman examines an MIT research approach that lets language models inspect external text and make sub-calls, along with its benchmark and cost tradeoffs.
37:00ENThe AlphaChip Team That Laid Out Four TPUs Now Wants Every Company to Be DesignlessAnna Goldie and Azalia Mirhoseini tell Sequoia how an RL floorplanner survived TPU-team anger, learned to draw donut-shaped macros, and became a bet that AI will design chips that then feed AI.
1:30ENAn AI-Generated Proof for Erdős Problem 397Matthew Berman · Forward FutureMatthew Berman discusses Neel Somani's GPT-5.2 Pro-assisted proof and speculates about self-improving AI. Later research found that the problem already had a published human solution.
8:16ENAI and Erdős Problem 397: A Verified Proof, Prior Work and the Prospect of Self-ImprovementMatthew Berman · Forward FutureMatthew Berman discusses AI-assisted mathematics and argues that algorithmic discoveries could accelerate AI development, while the history of problem 397 adds important context.
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.
1:18:03ZH李广密:2025 年末的模型竞争、在线学习与 AI 创业게스트 李广密 · 진행자 张小珺从模型公司和算力竞争,到持续学习、机器人与全球创业,回看一位投资人在 2025 年末的判断。
33:27ENA 2025 AI Retrospective: Reasoning, Benchmarks and Uneven ProgressAI Explained reviews generated media, METR time horizons and competing views of general intelligence, then offers forecasts for 2026.
1:55:39ENAI Infrastructure, Gemini and Prediction Markets: Forward Future LiveMatthew Berman, Nick Wentz, Doug O’Laughlin, Koray Kavukcuoglu, Logan Kilpatrick, Abhishek Fatehpuria · Forward FutureSemiAnalysis, Google DeepMind and Robinhood guests join Matthew Berman and Nick Wentz to discuss model speed, chip supply, research, safety and financial products.
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.
1:18:05ENThe Real Bottleneck in AI Research Is Compute, Not Ideas진행자 Jacob EffronAfter NeurIPS, Ari Morcos and Rob Toews sit down with Jacob Effron to argue about plateauing models, SSI’s secrecy, OpenAI’s cash burn, and a set of spicy 2026 bets.
56:07ENDemis Hassabis on World Models, Scientific Discovery and the Path to AGIHannah Fry, Demis Hassabis · Google DeepMindIn a 2025 conversation, the Google DeepMind CEO discusses simulation, uneven model capabilities, safety and social change.
17:44ENInside Google DeepMind’s Robotics Lab: From Lunch Packing to LaundryHannah Fry, Kanishka Rao, Colin, Houston · Google DeepMindHannah Fry visits demonstrations of robot dexterity, unfamiliar objects and longer tasks.
48:24ENJohn Jumper on AlphaFold, Protein Design and Experimental ScienceHannah Fry, John Jumper · Google DeepMindThe Nobel laureate explains how structure predictions help researchers while retaining important limitations.
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.
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