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1:18:05EN
Unsupervised Learning: With Jacob Effron· 2025년 12월 18일팟캐스트The 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.
48:00ENUnsupervised Learning: With Jacob Effron· 2025년 12월 15일팟캐스트A Two-Second Vote Is Training Models to Be Clickbait진행자 Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.
1:16:53ENUnsupervised Learning: With Jacob Effron· 2025년 10월 24일팟캐스트Karpathy Called Today's Models Slop. His Decade Timeline Still Sounds Bullish진행자 Jacob EffronDatology AI's Ari Morcos and Radical Ventures' Rob Toews walk through Karpathy's cold water, OpenAI's dealmaking, and who actually pays for the data-center boom.
57:52ENUnsupervised Learning: With Jacob Effron· 2025년 8월 13일팟캐스트They Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.진행자 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.
44:21ENUnsupervised Learning: With Jacob Effron· 2025년 6월 4일팟캐스트Mercor's CEO: Knowledge Work's Next Job Is Writing Evals진행자 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.
56:10ENUnsupervised Learning: With Jacob Effron· 2024년 10월 29일팟캐스트DeepL Beat Google at Translation by Refusing to Be a General Model진행자 Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.
1:10:19ENUnsupervised Learning: With Jacob Effron팟캐스트The Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers진행자 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.