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42:18ENTen Percent of Search Is Health — and the Doctor Is Googling Too진행자 Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
1:28:43ENClinical Guidelines Are 11% Evidence. Curai’s Founder Still Wants GPT-4 in the Exam Room진행자 Nathan LabenzNeal Khosla of Curai Health argues that large language models already match median U.S. physicians on today’s tests — and that the real work is scaffolding, supervision, and a doctor’s signature.
58:24ENAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4진행자 Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.
6:53ENStableVicuna’s 2023 Release: An Early RLHF Chatbot Put to the TestMatthew Berman · Forward FutureMatthew Berman reviews the 13-billion-parameter model and tests poetry, simple code, reasoning and a resignation email.
1:21:06ENEveryone Talks About RLHF. Almost Nobody Is Doing It.진행자 Nathan LabenzHumanloop CEO Raza Habib on why an LLM API is maybe five percent of a working product, and what actually fills the rest.
6:24ENBloombergGPT: Mixing Financial and General Data for a Language ModelMatthew Berman · Forward FutureMatthew Berman examines the 2023 finance-focused research model, its training corpus, evaluation and decision not to release weights.
1:56:16ENThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.진행자 Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.
1:23:53ENGPT-4, 24 Hours Later: No Safety Locks, a Captcha Hire, and a U.S.–China Deadlock진행자 Nathan LabenzA day after OpenAI shipped GPT-4, ChinaTalk sat a red teamer, a pessimist, and a policy researcher down to argue over capability, safety, and a race nobody can brake.
11:53ENTesting GPT-4 Against GPT-3.5 at LaunchMatthew Berman · Forward FutureMatthew Berman reviews OpenAI's March 2023 GPT-4 announcement and compares the models on scheduling, reasoning, safety and travel prompts.
1:13:06ENLockheed Martin's CTO on Autonomy, the Moon, and the Culture of Not Getting It Wrong게스트 Keoki Jackson · 진행자 Lex FridmanKeoki Jackson tells Lex Fridman why going back to the Moon is the easy part—and why proving a learning system will still do the right thing is not.
1:45:49ENTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Company진행자 Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
1:25:13ENCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammate진행자 Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.
1:33:50ENEvals Are the New PRDs: Inside Anthropic's Research Product Team진행자 Lenny RachitskyDianne Penn, Anthropic's head of product for research and Labs, explains how the company found its identity, how her team turns user feedback into evals, and why human judgment still matters.
1:31:41ENThe Worst Model You'll Ever Use: Kevin Weil on Building Products at OpenAI's Pace게스트 Kevin Weil · 진행자 Lenny RachitskyOpenAI's chief product officer explains how evals, light planning, fine-tuned model ensembles and a small PM team shape the way the company ships.
1:22:36ENStop Chasing AI News: Chip Huyen on What Actually Makes AI Products Better게스트 Chip Huyen · 진행자 Lenny RachitskyAuthor of AI Engineering walks through pre-training, post-training, RL, evals and RAG, then explains what separates companies that get value from AI from those that stall.
1:07:08ENIf the Model Is the Product, the Eval Is the PRD: Inside Mercor's Rise게스트 Brendan Foody · 진행자 Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.
51:30ENLess Email, Better Thinking: Grammarly’s CEO on AI That Augments Communication진행자 Jacob EffronGrammarly CEO Rahul explains how the company is moving from grammar checks to end-to-end communication assistance—and why evals, user data, and measurable outcomes matter.
14:41ENTalking for 20 Minutes: How the Alexa Prize Turns Real Customers into a Testbed for Conversational AI게스트 Rohit Prasad · 진행자 Lex FridmanRohit Prasad, head scientist of Amazon Alexa, explains the Alexa Prize grand challenge, how it is judged and why true conversation still needs reasoning.
1:16:17ENFaster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI Era게스트 Nicole Forsgren · 진행자 Lenny RachitskyThe creator of DORA and SPACE explains why most productivity metrics mislead, how AI changes flow and trust, and how to build a developer experience program that proves its value.
1:36:57ENDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding Assistant진행자 Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.
1:24:01ENFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models Smarter진행자 Lenny RachitskyJason Droege explains what really happened with Meta's investment in Scale AI, how data labeling turned into expert work, and what Uber Eats taught him about building businesses.
57:21ENGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build Next진행자 Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.
1:25:34ENShipping in Days: How Claude Code's Head of Product Runs a Team at AI Speed진행자 Lenny RachitskyCat Wu, Head of Product for Claude Code and Cowork at Anthropic, explains how the PM role, hiring, tooling and culture change when features go from idea to users in a week or less.
58:57ENWhen the Model Learns to Look Around: Apollo Research on Testing o1 for Scheming진행자 Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
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