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1:50:48ENWhy Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.
32:20ENWho Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.
2:00:51ENResample Every Token and You Can Watch the Model DecidePresenta: Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:09:44EN10,000 GB300s for Four Weeks: Reflection’s Open-Model LedgerPresenta: Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One LabPresenta: Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
1:20:28ENAI Valuations, Talent Wars and the Venture Capital Tradeoffs Behind ThemInvitado: Jack Altman, Jason Lemkin, Rory O’Driscoll · Presenta: Harry StebbingsHarry Stebbings, Jack Altman, Jason Lemkin and Rory O’Driscoll debate large early-stage bets, open-weight models and the changing market for exits.
1:10:08ENJerry Murdock on AI Financing, Open Models and Technology CyclesInvitado: Jerry Murdock · Presenta: Harry StebbingsInsight Partners' co-founder discusses credit risk, specialized models, security and how investors might navigate an uncertain AI cycle.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models BannedPresenta: Nathan LabenzFlo Crivello tells Nathan Labenz how Lindy Teammate lives in Slack, reaches two billion tokens of memory in two model calls, and why he would still outlaw the cheap Chinese models that currently power the product.
1:08:25ENAlex Atallah on OpenRouter, Model Competition and AI MarketplacesInvitado: Alex Atallah · Presenta: Harry StebbingsThe OpenRouter CEO discusses inference providers, open-weight models, enterprise safety and the economics of routing.
2:16:29ENFrom Two Papers a Month to Sixty: What Two Weeks in China Revealed About AI SafetyPresenta: Nathan LabenzNathan Labenz argues that the familiar 'China will never slow down' line does not survive contact with the actual Chinese AI safety ecosystem.
51:31ENThey Refunded Nearly $2 Million. Then Factory’s CEO Explained the Dark FactoryMatan Grinberg on being two years early, why a model-agnostic harness beats lab co-design, and why 90 percent of coding tokens will soon run with the lights off.
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipPresenta: Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.Presenta: Jacob EffronSix months after their last roundup, Ari Morcos and Rob Toews tell Jacob Efron why coding agents finally work, why near-frontier open weights may have peaked, and why a compute famine could pull the plug on public APIs.
42:57ENInference Is the Last Market: Baseten’s 30x YearPresenta: Sarah Guo, Elad GilCEO Tuhin Srivastava on a billion-dollar inference cloud, why the app layer survives, and a GPU crunch worse than the narrative.
54:26ENTen Billion Active Parameters, and the Unsexy Work of Making Them Agent-ReadyPresenta: 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.
4:25:13ENThe Architecture Barely Changed. Data, Cost, and Open Weights Did.Presenta: Lex FridmanNathan Lambert and Sebastian Raschka walk Lex Fridman through a year in which training methods and datasets moved more than model design, and 2026 looks like a fight over inference cost, agents, and who owns truly open models.
1:54:30ENFrom One in Ten Cancer Cells to One in a MillionPresenta: Nathan LabenzNathan Labenz's solo AMA updates his son's lymphoma treatment, calls Claude Opus 4.5 software AGI, and ranks the live players of early 2026.
1:16:21ENJensen Huang’s Year-End Case That Token Costs Can Fall a Billion-Fold Without Killing JobsPresenta: Sarah Guo, Elad GilOn No Priors, NVIDIA’s CEO uses radiology, open source, and a $2 trillion R&D budget to argue that 2025’s extremes missed the stack.
1:18:40ENWhen AI Becomes the Oil Well: Luke Drago on the Intelligence CursePresenta: Nathan LabenzThe Workshop Labs co-founder tells the FLI Podcast why a world of non-human production could recreate the resource curse — and how private data, open weights, and moonshot careers might still break it.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own ModelPresenta: 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.
1:09:48ENChina's AI Labs Went From 15 to Six. The Compute Gap Is 15-to-1.Presenta: Jacob EffronJordan Schneider tells Unsupervised Learning that Chinese models were a private-capital story, that Beijing still thinks in hardware, and that the West's real edge is chips it can serve — not a state master plan.
1:25:54ENHours after training ends, Z.ai puts the weights on the internetPresenta: Nathan LabenzZixuan Li of Z.ai tells ChinaTalk how a Chinese lab ships GLM, treats open weights as a ticket into Western ecosystems, and still sees itself as an upstart.
43:09ENA $100 Arm and a $300 Robot: Hugging Face Wants an App Store for Physical AIThomas Wolf tells Sequoia's Training Data why LeRobot, cheap hardware, and open datasets are Hugging Face's bet that software developers can become roboticists.
47:18ENThe Analyst Tracking 200 GPU Clouds Breaks Nvidia Into a Three-Headed DragonPresenta: Sarah Guo, Elad GilSemiAnalysis founder Dylan Patel on OpenAI’s open-weight model, a coming neocloud wipeout, and why a chip startup has to beat Nvidia on hardware, networking, and software at once.
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