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1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go PastModeration: 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.
2:19:30ENThe investigators were allowed six days. The word protein appeared once.Moderation: 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 OneModeration: 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.
1:26:16ENMilliseconds to Match: How Criteo Bids on a Billion Anonymous ProfilesModeration: 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.
1:57:29ENFrontier vision models score 12.5%: Joseph Nelson on the real waterline of computer visionModeration: 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.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Moderation: 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 ownsModeration: 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.
38:10ENPlus Five for Format, Plus Ten if It CompilesModeration: 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 TokensModeration: Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
1:42:16EN267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionModeration: Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.
1:42:17ENThe Cheapest Inference Provider Is the Most DangerousModeration: Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
1:22:43ENFive billion minutes of calls: why Dialpad trained its own business LLMModeration: Nathan LabenzDan O’Connell on real-time speech, verticalizing the stack, and why he and Nathan Labenz disagree about when AI employees show up.
52:48ENOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListModeration: 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.Moderation: 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 AmdahlModeration: 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.
57:21ENGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build NextModeration: Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.








