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1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missed진행자 Nathan LabenzA Cognitive Revolution weekly briefing on a possible U.S.–China AI hotline, why hyperscalers charge two to three times as much for identical chips, co-writing a novel with frontier models, and an AI loop that re-diagnosed a child a premier prenatal lab had cleared.
31:39ENThe Skull Is Already the Vat: Max Hodak on Restoring Sight진행자 Sarah Guo, Elad GilPatients blinded by macular degeneration put on laser-projector glasses, read books, and fill in Sudoku — because a chip under the retina is sending vision back into the brain.
47:22ENFrom One in a Thousand to 15 Percent: Chai Discovery Treats Drug Design as a Scaling ProblemJoshua Meier and Matthew McPartlon tell Sequoia’s Training Data why antibody design is an engineering problem, not a bespoke science, and why they sell a molecular CAD suite instead of their own drugs.
55:19ENThe drone is 15% of Zipline: 140 million miles, zero incidentsKeller Rinaudo Cliffton and Eric Watson explain why the aircraft is the small part of an automated logistics system now cheaper than a car.
1:44:15ENNathan Labenz's p(doom) Is 10 to 90 Percent. He Is More Optimistic Anyway진행자 Nathan LabenzFive years ago, expecting AGI by 2035 made you an extremist. On The Intelligence Horizon, the Cognitive Revolution host explains why the same forecast now sounds bearish, and why he still thinks a defense-in-depth strategy might keep society on the rails.
2:03:56EN260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AI진행자 Nathan LabenzKaran Singhal tells Nathan Labenz how ChatGPT Health was evaluated with physicians, walled off from model training, and aimed at raising both the floor and the ceiling of human health.
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.
48:14ENA New Drug Burns $2 Billion and 10 Years. Benchling's CEO Wants AI to Cut That to Three진행자 Sarah Guo, Elad GilMost medicines fail in years seven to ten, after hundreds of millions have already gone into the clinic—and Sajith Wickramasekara thinks sending things to space may already be easier.
1:29:30EN2,500 Compounds and a 51 Percent Hit Rate: How a Small Graph Net Found New Antibiotics진행자 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.
44:49ENHow OpenEvidence Became the Clinical OS for 40% of U.S. Doctors진행자 Sarah Guo, Elad GilFounder Daniel Nadler tells No Priors how a free search tool for physicians, not an answer engine for patients, took high-stakes clinical knowledge in 18 months.
1:28:48ENDoctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.진행자 Nathan LabenzAmbience Healthcare and Cleveland Clinic walk through the AI scribe that cut pajama time, climbed ICD-10 with reinforcement fine-tuning, and won 75% voluntary use.
49:27ENFrom 0.1% to 20%: Chai-2 Turns Antibody Design Into Prompt Engineering진행자 Sarah Guo, Elad GilChai Discovery co-founders Joshua Meier and Jack Dent tell Sarah Guo how a generative model now designs antibodies that bind on the first 24-well plate.
1:26:21ENFor under $10, Google's AI independently recovered a lab result nobody had published진행자 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.
38:43ENTwo in Five Doctors Want Out. An On-Call Cardiologist Built the Note That Lets Them Stay진행자 Sarah Guo, Elad GilAbridge CEO Shiv Rao on treating the doctor-patient conversation as healthcare's source of truth, and why the company ran straight at the hardest health systems.
1:31:35ENThirty Years of Research, Collapsed into One Sentence진행자 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.
1:04:52ENThe Consumer Playbook That Put Medical AI in 100,000 Exam RoomsOpenEvidence CEO Daniel Nadler explains how a free App Store product, trained only on peer-reviewed literature, reached as much as a quarter of U.S. physicians in a year.
1:33:01ENRare 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.
1:32:45ENHe doubled a company with AI — then asked the labs to pause hyperscaling진행자 Nathan LabenzOn the Nick Halaris Show, Cognitive Revolution host Nathan Labenz argues for adopting today's models fast and slowing the race to systems more powerful than we are.
1:37:07ENA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.진행자 Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.
1:01:55ENA $9 AI nurse, an 86 on the medical boards, and a 10% extinction line진행자 Nathan LabenzNathan Labenz’s solo scouting report walks from medical state of the art through how models actually learn, then to live players, scaling trends, and a control plan that still does not exist.
1:59:15ENA Protein Backbone in a Minute: The Real Progress, Bottlenecks, and Risks of AI Biology진행자 Nathan LabenzThe White House set a thousand-fold lower reporting bar for biological models than for language models—and this conversation shows why that gap already looks prescient.
3:04:52ENDoctors Chose AI on Eight of Nine Dimensions: A GPT-4 Red-Teamer's Scouting Report진행자 Nathan LabenzNathan Labenz tells 80,000 Hours what late-2023 systems can already do, which thresholds still matter, and why a polarized policy fight is a gift to heavy-handed regulation.
39:02ENAI’s product advantage is consistency. Its real weakness is getting conned.진행자 Nathan LabenzNathan Labenz updates the Cognitive Revolution scouting report: jailbreaks, resume automation, Med-PaLM’s 40% radiology win, and what might follow Transformers.
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