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3,570개정렬
14:04ENQwen3: Open Weights, Adjustable Reasoning and Tool UseMatthew Berman · Forward FutureMatthew Berman reviews the April 2025 Qwen3 family, its benchmark claims, training pipeline and local demonstration.
55:40ENTo Solve All Diseases, Isomorphic Labs Needs a Half-Dozen AlphaFoldsMax Jaderberg on turning DeepMind's game-playing research into a general AI engine for drug design—and why prediction alone cannot search a 10^60 molecular universe.
1:02:51ENTaavet Hinrikus on Venture Incentives and Europe’s Technology Ambitions게스트 Taavet Hinrikus · 진행자 Harry StebbingsThe Wise co-founder and Plural partner explains his approach to early-stage investing, fund alignment and building European capabilities.
1:41:29ENFiverr’s CEO Sent a Wake-Up Memo: AI Won’t Take Your Job. A Peer Who Uses It Will.진행자 Nathan LabenzMicha Kaufman tells Nathan Labenz how a 15-year freelance marketplace is trying to give creators their own models instead of letting public AI eat their work for free.
34:07ENOla Källenius on Mercedes-Benz’s Next Transformation: EVs, AI, and Modern Luxury게스트 Ola Källenius · 진행자 张小珺Mercedes-Benz CEO Ola Källenius describes a century-scale shift driven by zero-emission powertrains, software, and AI. He explains why the company is expanding electric models while protecting the craftsmanship, safety, and desirability that define its luxury position.
5:13ENHow Jane Street’s Accounting Team WorksCarmen, Ava, Hannah · Jane StreetAccounting staff describe shared responsibilities, automation, early involvement in decisions and growth in a flat organization.
19:06ENSleep-Time Compute: Preparing AI Context Before a QuestionMatthew Berman · Forward FutureMatthew Berman reviews a 2025 paper on moving some reasoning ahead of a user query, and explains where its experiments found benefits and limits.
30:46ENDeep Research Turned Web Browsing into a Trainable Problem진행자 Sarah Guo, Elad GilOpenAI researcher Isa Fulford explains why a side project on read-only synthesis became an agent, when reinforcement fine-tuning is worth it, and why models will hack the search engine you gave them.
1:26:55ENVenture Liquidity, Endowments and AI Investing in 2025게스트 Rory O’Driscoll, Jason Lemkin · 진행자 Harry StebbingsHarry Stebbings, Rory O’Driscoll and Jason Lemkin discuss private-market liquidity, fund construction and the economics of AI businesses.
3:04:01ENXi Jinping Revived China's Personality Cult. He Did Not Revive Mao's Chaos.게스트 Jeffrey Wasserstrom · 진행자 Lex FridmanHistorian Jeffrey Wasserstrom on Confucius, Tiananmen, censorship, the 2025 trade war, Hong Kong's warning to Taiwan, and why history has no straight road.
2:10:07ENA $30,000 Operation Could Knock a $10 Billion Superintelligence Data Center Offline for a Year진행자 Nathan LabenzGladstone AI’s Jeremy and Edouard Harris inventory what an American Manhattan Project for superintelligence would actually have to secure — and what it would cost in concrete, supply chains, and civil liberties.
26:35ENTesting Gemini 2.5 Flash on Coding and Video UnderstandingMatthew Berman · Forward FutureMatthew Berman compares Google’s faster model with Gemini 2.5 Pro through interactive simulations, games and questions about a long video.
45:30ENA $20,000 AI Agent Replacing a $90,000 SDR: Manny Medina on Pricing in the Agent EraA fully loaded human SDR costs $70,000 to $90,000 a year; one AI SDR pitch is $20,000 per agent for 80% of the work, plus a bonus when targets are hit.
3:08:35ENo3 isn't AGI. Zvi Mowshowitz still put p(doom) at 70 percent진행자 Nathan LabenzOn his ninth visit to The Cognitive Revolution, Zvi Mowshowitz argues that OpenAI's o3 is a tool-use leap rather than AGI, then explains why he raised the probability of AI catastrophe anyway.
1:01:21ENJason Wilk on Dave’s Turnaround, Credit and Digital Banking게스트 Jason Wilk · 진행자 Harry StebbingsThe Dave founder discusses going public, AI underwriting, customer acquisition and competing to serve consumers with short-term credit.
2:27:59ENA 10-to-90% P(doom) Host Gets Asked If His Show Sounds Like It진행자 Nathan LabenzNathan Labenz and Liron Shapira sit on the same side of the table and test a week of AI news against the question of how likely extinction actually is.
15:15ENEarly Reactions to OpenAI o3 and o4-mini: Tool Use, Math and MistakesMatthew Berman · Forward FutureMatthew Berman reviews 2025 demos and benchmark reports, from location guessing and coding to simple failures that challenge the hype.
15:45ENAI News: Gemini 2.5 Flash, OpenAI Models, Claude Research and Kling 2.0Matthew Berman · Forward FutureMatthew Berman reviews a week of 2025 model releases, research tools and agent announcements, including historical price and benchmark comparisons.
3:29:02ENRobert Rodriguez: Sift the Ashes, Keep the Pipe Open, Don't Blink게스트 Robert Rodriguez · 진행자 Lex FridmanThe director of El Mariachi, Desperado, Sin City, and Alita: Battle Angel tells Lex Fridman how limitation, failure, sound, and identity became his method.
40:54ENDiscovery rates collapsed tenfold. KoBold still found a 5% copper deposit진행자 Sarah Guo, Elad GilJosh Goldman explains why KoBold Metals treats mineral exploration as an information problem, pairing AI with century-old maps to hunt lithium and copper.
1:35:02ENa16z, Founders Fund and the Risks Behind Venture’s Biggest Funds게스트 Rory O’Driscoll, Jason Lemkin · 진행자 Harry StebbingsHarry Stebbings, Rory O’Driscoll and Jason Lemkin debate fund size, late-stage returns, AI uncertainty and delayed liquidity.
1:28:01ENThe Q* Letter Never Happened: Helen Toner on OpenAI, Adaptation Buffers, and AI at War진행자 Nathan LabenzThe former OpenAI director explains why a famous Reuters story was false, why locking down AI will not work, and how military “battle buddy” demos skip the hard questions.
15:46ENOpenAI o3 and o4-mini: Tool Use, Benchmarks and the First Codex CLIMatthew Berman · Forward FutureMatthew Berman reviews OpenAI’s April 2025 reasoning-model release, a scientific image-analysis demonstration and the debut of its terminal coding agent.
58:11ENPatrick Hsu Wants an App Store for Biology, Not Just Better DrugsThe Arc Institute co-founder argues that Evo 2, trained on genomes rather than proteins, should underpin tools for every step of the scientific method.
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