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2:03:56EN260 Doctors and 49,000 Criteria: How OpenAI Built Attending-Level Health AIAnime : 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.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyAnime : Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
44:46ENA Single Training Environment Can Cost Millions. Prime Intellect Wants a GitHub for RLWill Brown and Johannes Hagemann argue that the eval suite you already use to test agents is one step away from a training set.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding PipelineAnime : Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
39:47ENThe Context at Step 14 Isn't in the CodeLangChain cofounder Harrison Chase on why long-horizon agents finally work—and why traces, harnesses, and first-draft workflows matter more than scaffolding.
48:00ENA Two-Second Vote Is Training Models to Be ClickbaitAnime : 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.
1:54:33ENCreative Agents, AI Evaluations and Enterprise Trust: Forward Future LiveMatthew Berman, Nick Wentz, Fabian Stelzer, Tejal Patwardhan, Christina Cacioppo, Jeremy Epling, Spenser Skates · Forward FutureLeaders from Glif, OpenAI, Vanta and Amplitude discuss creative workflows, knowledge-work benchmarks and practical AI adoption in December 2025.
42:06ENAfter SWE-bench Hits 80.9%, Dianne Na Penn Reckons with Opus 4.5Anime : Jacob EffronAnthropic’s head of product for research on computer use, deceptive token prices, thin harnesses, and why alignment makes a model a better thinker.
1:11:18ENHe Got Bored Five Minutes Into a Waymo. Nathan Labenz Says That's the PointAnime : Nathan LabenzOn a crossover with the Existential Hope podcast, the Cognitive Revolution host argues that the scarcest resource in AI is a positive picture of daily life, not another doom scenario.
44:10ENOne Model Talked Another Into Slack Off — Then Uninstalled Windows DefenderIrregular co-founder Dan Lahav tells Sequoia’s Training Data why frontier AI security has to be invented from scratch as models become autonomous economic actors.
32:22ENQwen Image Edit Plus Compared With Nano Banana, Seedream and GPT Image 1Matthew Berman · Forward FutureMatthew Berman compares four image-editing models across compositing, precise edits, style transfer, object removal and weather effects.
1:53:50ENA curriculum rewrite takes five years. AI does not wait.Anime : Nathan LabenzJohan Falk, a Swedish teacher-turned-analyst, argues that AI literacy and AI friends are more urgent than putting a tutor in every classroom.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few PointsAnime : Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
1:28:17ENAdam Gleave's Three Capability Tiers: Attack Agents in 5–7 Years, Fully AI Companies Around Year 14Anime : Nathan LabenzFAR.AI and the UK AI Security Institute stacked adversarial attacks on frontier safeguard pipelines and found many breakthroughs were engineering failures, not a verdict against defense in depth.
42:20ENA Name Invented at 2:30 a.m. Put Gemini on Top of the App StoreAnime : Jacob EffronGoogle's Nicole Brichtova and Oliver Wang on Nano Banana, why image models got smart when language models did, and why a prompt still is not a finished product.
1:00:35ENMercor CEO Brendan Foody on Expert Data, AI Evaluations and GrowthInvité : Brendan Foody · Anime : Harry StebbingsFoody explains Mercor’s marketplace, reported revenue run rate and views on model development, margins and company culture.
50:00ENCrosby Bills by the Document, Not the Hour, and Turns Contracts Around in Under an HourThe founders of an AI-native law firm explain why they became lawyers with engineers at the next desk instead of shipping legal software, and how deal velocity became the product.
1:50:08ENARC-AGI, Agent Memory and Interactive Worlds: Forward Future LiveMatthew Berman, Nick Wentz, Greg Kamradt, Charles Packer, Alan · Forward FutureGreg Kamradt, Charles Packer and Skywork AI’s Alan join Matthew Berman and Nick Wentz to examine how AI systems learn, remember and interact with environments.
42:12ENHire the AI First: Andrew Ng on Agentic Workflows and the Scarcity of JudgmentAnime : Sarah Guo, Elad GilOn No Priors, Andrew Ng argues that agentic AI has made software cheap and fast, which moves the bottleneck to product judgment, error analysis, and the instinct to try an AI before adding headcount.
30:05EN100 Signups, 95 Drop-offs: How Gamma Turned AI Slides Into a $50 Million BusinessGamma founder Jon Noronha tells Sequoia's Training Data how a park-bench idea, image models, and relentless A/B testing built a cash-flow-positive visual storytelling platform.
1:48:49EN$500, 2,000 Steps, and a Vending Machine: Grok 4 Profited, Claude Called the FBIAnime : Nathan LabenzAndon Labs put frontier models in charge of simulated and real vending machines to see what breaks when nobody is in the loop.
30:11ENFrom 12 Percent on AIME to IMO Gold in Two MonthsAlex Wei, Sheryl Hsu, and Noam Brown tell Sequoia's Sonya Huang how a three-person OpenAI sprint used general-purpose reinforcement learning, not Lean, to reach gold-level on the 2025 International Mathematical Olympiad.
1:02:54ENEnterprise Coding Tools Flipped the 80/20. Reflection AI Built the Other HalfAnime : Sarah Guo, Elad GilFormer Gemini reward-model lead Misha Laskin on Asimov, why comprehension beats generation, and why there is no such thing as generalization—only pulling the test distribution into train.
38:10ENPlus Five for Format, Plus Ten if It CompilesAnime : Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
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