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18:31ENJanie Gu and Suyeol Yun: Why Frontier Models Fail as Buy-Side AnalystsJanie Gu, Suyeol Yun · Google DeepMindJanie Gu explains why benchmark scores can mislead, while Suyeol Yun presents MandateBench, which tests whether a model can turn a fund mandate into a defensible investment memo. The two talks show why real workflows, reproducible grading, and evidence-backed reasoning matter.
26:41ENAnekha Sokhal on Moshi, a Recommendation Engine for Commodity TradersGuest Anekha Sokhal · Host swyxThe Moshi founder tells swyx how she would rebuild a trader’s workflow: connect market data, private files and conflicting sources, then push the second-order effects people would otherwise miss. The hard part is not the dashboard. It is getting someone to actually use it.
52:27ENAfter Two Dozen Police Departments Said No, They Built an Anti-Network-Effect AI CompanyA Wisconsin county had 300 gigabytes sitting on a cold case—and Peregrine's founders still refuse to take the credit.
34:51ENGoogle Paid $10 Million for a Bankrupt Airline’s Data. That’s the Moat.Host Sarah Guo, Elad GilEon co-founders Ofir Ehrlich and Gonen Stein argue that models and compute are rented commodities — the only asset enterprises can actually keep is their data.
1:05:02ENJoon Sung Park on Simulating Human Behavior and Building SimileGuest Joon Sung Park · Host Harry StebbingsThe Simile co-founder explains his research on generative agents, the challenges of validating simulations, and his ambitions for a business built around modeling people.
1:02:42ENMercor’s Osvald Nitski on AI Training Data, Product and Customer ConcentrationGuest Osvald Nitski · Host Harry StebbingsMercor’s head of product discusses how the company supplies expert data to AI labs, designs its marketplace and plans to reach more enterprise customers.
1:14:12ENBrendan Foody on Mercor, AI Data and the Future of WorkGuest Brendan Foody · Host Harry StebbingsThe Mercor CEO discusses a security incident, data-provider economics, enterprise agents and the uncertain effects of AI on employment.
40:56ENFour of the Top Five Defense Primes Still Run Hardware Tests on PowerPointNominal’s cofounders argue that hardware never got the last twenty years of software infrastructure, and that physical AI will stall until test data lives in one place.
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.
1:18:05ENThe Real Bottleneck in AI Research Is Compute, Not IdeasHost 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.
48:00ENA Two-Second Vote Is Training Models to Be ClickbaitHost 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.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own ModelHost Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.
39:16EN500 Gloves, 10 Million Trajectories, and a $10,000 Home RobotHost Sarah Guo, Elad GilSunday Robotics co-founders Tony Zhao and Cheng Chi map the gap between a scalable imitation-learning recipe and a machine that actually does the dishes.
41:36ENFour Months to Drive Tokyo: How Wayve Bet the Company on One End-to-End NetworkWayve CEO Alex Kendall tells Sequoia’s Training Data why AV 2.0 ditches HD maps and hand-written stacks for a foundation model that can adapt to a new car and a new country in weeks.
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.
1:16:53ENKarpathy Called Today's Models Slop. His Decade Timeline Still Sounds BullishHost 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.
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.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Host Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.
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.
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.
32:59ENA Hundred People, a Billion in Revenue, and Zero Venture MoneyHost Sarah Guo, Elad GilSurge AI founder Edwin Chen on why quality human data is a poetry problem, not a bounding-box problem, and why leaderboards keep training models to write clickbait.
23:39ZH → EnglishLi Mu: Adding Ten Million Hours of Speech to a Text LLM, Open-Sourcing Higgs Audio李沐 · Boson AILi Mu explains how the team added a huge amount of speech data to text LLM training, built an AudioLLM that can listen and speak, and open-sourced Higgs Audio. He covers architecture trade-offs, token compression, where the data came from, and how to run it on a 3090.
1:42:06ENEvery Frontier Lab Is Spending a Billion a Year on Data. Here Is Where It GoesHost Nathan LabenzLabelbox CEO Manu Sharma on the $15 billion Scale deal, RL “gyms,” $250,000 trainers, and why post-training now eats the budget that used to belong to pre-training.
44:21ENMercor's CEO: Knowledge Work's Next Job Is Writing EvalsHost 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.
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