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32:57ENA 7-billion-parameter model on a MacBook: Mistral's CEO reruns the cost of AIAnime : Sarah Guo, Elad GilArthur Mensch tells No Priors why Chinchilla made small models valuable, why open-sourcing Mistral 7B is not a bioweapon risk, and why guardrails belong on the application.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5Anime : Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
52:48ENOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListAnime : Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.
1:01:26ENAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.Anime : Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.
1:20:49ENA Wafer-Scale Chip So AI Stops Waiting on AmdahlAnime : Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.
1:00:36ENForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lessonAnime : Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.
26:02ENMoore’s Law Is Not Dead—It Is a Cascade of S-CurvesAnime : Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.
23:57ENChips, Agents and a Reckoning: Sarah Guo and Elad Gil on the 2023 AI BoomAnime : Sarah Guo, Elad GilThe hosts answer listener questions on the GPU crunch, how soon enterprises will ship AI products, how to build agents, and what 2024 holds for tech valuations.
1:18:48ENData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesAnime : Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.
57:21ENGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build NextAnime : Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.
31:46ENWhy Rivian Threw Out Its First Self-Driving Stack and Built Its Own BrainAnime : Sarah Guo, Elad GilRivian founder RJ Scaringe explains the neural-net reset, in-house chips, software-defined architecture and the mass-market R2 bet.







