Results
81 articlesSort
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.
45:08ENSam Altman on AI Discovery, Consciousness, Compute and a Human-Centered FutureMatthew Berman, Sam Altman · Forward FutureIn a conversation with Matthew Berman, Altman discusses scientific progress, personal assistants, recursive self-improvement and the importance of human agency.
40:44ENGrant Sanderson: There's never been a better time to go into mathGrant Sanderson, Constantin Kogler · 3Blue1BrownStudents fear AI has solved mathematics. Grant Sanderson argues the opposite: even if machines prove theorems better than we do, the institution of math stays valuable—and this is a rare moment when newcomers can reshape it.
39:23ENThe Defenders' Window: Greg Brockman on AI-Generated Knowledge, Drug Discovery, and the New Physics of CybersecurityHost Sarah Guo, Elad GilOpenAI's president explains why solving open math problems is a proof point for science, how proactive agents change product form, and why security spending must exceed software spending.
38:10ENFrom deepfakes to DNA: how SynthID watermarks AIHannah Fry, Pushmeet Kohli, Jeremy Ratcliffe · Google DeepMindHannah Fry sits down with Pushmeet Kohli and Jeremy Ratcliffe to explain SynthID: a mathematical watermark now used on text, images and video, and being extended to protein sequences so DNA synthesis companies can tell AI-designed molecules from nature.
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.
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.
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.
1:15:19ENAnj Midha on Anthropic, Compute Bottlenecks and Building Frontier AIGuest Anjney Midha · Host Harry StebbingsThe AMP founder recounts Anthropic’s early fundraising and explains his investment thesis around scientific feedback, compute coordination and AI infrastructure.
32:22ENDemis Hassabis on AGI, Scientific Discovery and the Challenges AheadGuest Demis Hassabis · Host Harry StebbingsThe Google DeepMind CEO discusses compute, continual learning, safety standards and his hopes for AI in science, medicine and energy.
29:26ENChatGPT Almost Became a Meeting Bot. Its Co-Creator Is Now Rearranging AtomsHost Sarah Guo, Elad GilLiam Fedus tells No Priors how Periodic Labs is connecting language models to experiments so materials science can run in a closed loop.
58:27ENEven a dumber AI can win science if it never leaves the labGinkgo Bioworks CEO Jason Kelly tells Sequoia why autonomous labs, not smarter models, will change how experimental science gets done.
1:43:39ENFive Mutations Could Make a 60% Fatal Bird Flu Spread Between PeopleHost 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 AIHost 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 AgentsHost 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 monthsHost 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 ThreeHost 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 AntibioticsHost 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 LoopHost 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 materialsHost 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.
13:30ENHow AlphaGeometry Combines Logical Proof with Creative ConstructionAleph0Aleph0 explains how a symbolic geometry solver gets surprisingly far without AI, and how a language model supplies the auxiliary constructions that take it to Olympiad level.
57:52ENThey Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.Host 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.
Load moreShowing 24 of 81









