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1:25:33ENWhy GPU Capacity Comes in Three-Year Rectangles — Erik Bernhardsson, ModalInvitado: Erik Bernhardsson · Presenta: Vibhu SapraModal’s CEO tells Latent Space why AI demand almost never looks like a three-year GPU contract, how the company pools compute across thousands of customers, and why sandboxes grew from a product they almost killed into a third of revenue.
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go PastPresenta: Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
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.
35:39ENCompute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That RatioPresenta: Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom SiliconPresenta: Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
2:19:30ENThe investigators were allowed six days. The word protein appeared once.Presenta: Nathan LabenzA week of Cognitive Revolution morning shows on OpenAI’s agent-swarm incident, GPT-6 Astra’s loop transformer, and whether the pause debate is already over.
2:06:41ENZero the J-Space and a Model's Hidden Goal Lights Up on Token OnePresenta: Nathan LabenzA weekly Cognitive Revolution cut of Anthropic's global-workspace paper, AI superforecasters, open video world models, and a chip that treats inference as data movement.
1:10:15ENThe 100x lives between the layers: Dylan Patel on co-design and a 60x yearSemiAnalysis’s founder argues that matching models, kernels, and silicon is what turns a 2x here and a 2x there into 100x, while equivalent-quality inference has gotten about 60 times cheaper in a year.
30:34ENCerebras Waited Ten Years for a Market That Finally Needed SpeedPresenta: Sarah Guo, Elad GilFounder and CEO Andrew Feldman walks No Priors through wafer-scale chips, a $20 billion OpenAI deal, and why a dinner-plate processor only mattered once AI became an everyday tool.
1:26:16ENMilliseconds to Match: How Criteo Bids on a Billion Anonymous ProfilesPresenta: Nathan LabenzCriteo’s CTO and AI lab head walk through the millisecond auction, the DeepKNN-to-foundation-model stack, and why a live product catalog still beats a frozen LLM.
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.
1:57:29ENFrontier vision models score 12.5%: Joseph Nelson on the real waterline of computer visionPresenta: Nathan LabenzThe Roboflow CEO tells Nathan Labenz why vision still lags language, how to distill SAM 3 into an edge model for Wimbledon, and why the United States has almost never led open-source vision.
44:19ENThe $500-per-Kilogram Line: Why Starcloud Thinks Data Centers Belong in SpaceStarcloud founder Philip Johnston walks through the physics, launch economics, and business model of putting AI compute in orbit.
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.
1:02:19ENA Top Video Model's Half-Life Is 30 Days. fal Runs 600 AnywayThe fal founders tell Sequoia why generative video is a compute-bound problem that language-model shops keep underestimating, and why they will not let any customer marry a single model.
41:32ENMoore's Law Is Slowing. Nvidia's CTO Still Wants 10× a YearMichael Kagan on why Nvidia's $7 billion Mellanox deal, not just more transistors, is how you turn a data center into one computer.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Presenta: 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.
1:32:47ENA transformer co-author wants a $160 million model no company ownsPresenta: Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't HoldPresenta: Jacob EffronThe FlashAttention author and Together AI chief scientist on why hardware portability is a myth, how Claude Code made him 1.5x faster, and why workloads will split the chip market.
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.
38:10ENPlus Five for Format, Plus Ten if It CompilesPresenta: 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.
2:12:00ENGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million TokensPresenta: Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a YearPresenta: Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
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