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29:44ENHarry Wang: AI, Two-Dimensional Semiconductors, and the Future of Chip ManufacturingA semiconductor researcher explains materials, yield, AI-controlled experiments, and a proposed feedback loop between models and chips.
0:53ENSam Altman on AI and the Discovery of New KnowledgeSam Altman · OpenAIAltman expects progress in mathematical discovery to extend to biology, materials and energy.
0:35ENSebastian Bubeck Responds to a Math Authorship DisputeMatthew Berman · Forward FutureMatthew Berman recounts OpenAI researcher Sebastian Bubeck’s account of communications with a mathematician identified as Levent.
15:37ENMatthew Berman on Qwen3.8-Max, AI Costs and Open-Weight CompetitionMatthew Berman · Forward FutureA 2026 review of reported benchmark results and research demonstrations leads to questions about deployment economics and geopolitical dependence.
1:43:39ENFive Mutations Could Make a 60% Fatal Bird Flu Spread Between PeopleModeration: Nathan LabenzJohns Hopkins assistant professor Jassi Pannu argues for locking down only the sliver of functional data that teaches models how to make pandemic viruses — and keeping the rest of biology open.
2:25:42ENThe experts argued through the coffee break — and never agreed whether AI can build AIModeration: 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.
1:34:42ENA $500 Kernel, a Ten-Month Village, and the Temper of AgentsModeration: Nathan LabenzJames Zou, Sam Hammond, and Shoshannah Tekofsky on science agents, America's knowledge-work bet, and what models actually do when you leave them alone.
0:47ENGPT-5 and an Automated Laboratory: A Scientific WorkflowMatthew Berman · Forward FutureMatthew Berman describes an OpenAI and Ginkgo Bioworks demonstration connecting an AI model to laboratory automation.
1:04:51EN125 million cells took a decade. The next billion takes monthsModeration: 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.
1:29:30EN2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New AntibioticsModeration: Nathan LabenzMIT's Jim Collins tells Nathan Labenz how a bootlegged 2018 project, a binary growth-inhibition label, and Chemprop graph networks pulled novel antibiotics out of chemical space that labs could never screen by hand.
1:01:49ENChatGPT's Co-Creator Left to Put Nature in the Reward LoopModeration: 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.
1:55:02ENA hundred experiments a day, and the valley of death in materialsModeration: Nathan LabenzRadical AI’s co-founders tell Nathan Labenz how a closed loop of frontier models and a self-driving lab is meant to collapse a forty-year materials cycle into less than a decade.
57:52ENThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.Moderation: Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.
1:12:29ENGoogle's AI Co-Scientist Solved a Phage Puzzle Humans Missed for YearsModeration: Nathan LabenzImperial College scientists José Penadés and Tiago Costa told the Podovirus podcast how a still-unreleased Google system recovered their unpublished answer: tailless capsids that pick up stray phage tails.
1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had publishedModeration: Nathan LabenzVivek Natarajan and Anil Palepu tell Nathan Labenz how AMIE beats primary-care doctors on treatment plans, and how Co-Scientist matched Imperial College's unpublished bacterial-resistance mechanism.
1:43:54ENA Year to Grow a Brain Organoid, an Hour to Simulate ItModeration: Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.
1:31:35ENThirty Years of Research, Collapsed into One SentenceModeration: Nathan LabenzImmunologist Derya Unutmaz on writing a medical patent in half an hour, treating o1 Pro as a professor-level collaborator, and why he no longer trusts his own expertise until he asks the model.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeatModeration: Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.
1:01:53ENHumans Wrote 1.3 Percent of the Tokens. The AI Lab Still Designed Working Nanobodies.Moderation: Nathan LabenzStanford's James Zou describes a GPT-4o research group that invented a protein-design pipeline for under $100 — and a sparse autoencoder that pulled an undocumented motif out of ESM-2.
1:55:53ENAI Beats Humans at Reading Papers. It Still Cannot Count Atoms.Moderation: Nathan LabenzAndrew White, Head of Science at Future House, maps what language models can already do in chemistry and biology—and the bottlenecks they do not dissolve.
1:23:37ENNine of the top ten research ideas were Claude'sModeration: Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.
1:13:23ENAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.Moderation: Nathan LabenzMichael Levin and Leo Pio Lopez on a multilayer embedding that predicted melanoma, a cancer with no mutations, and why biology's real interface is cognitive.
1:18:32ENA DNA Model That Can Tell Which Genes You Cannot TouchModeration: Nathan LabenzStanford's Brian Hie walks through hybrid architectures, a genome-scale language model trained only on DNA, and an antibody that binds 25 times tighter than nature.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never ShowedModeration: Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.
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