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1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past진행자 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.
1:10:12ENOutSystems Went From 4 Major Features a Quarter to 26. Token Spend Fell Anyway진행자 Nathan LabenzCEO Woodson Martin on software that never breaks, why most enterprise work does not need a frontier model, and how AI is finally shrinking the IT backlog.
1:28:08ENThe same H100 costs 3x at AWS, and a four-month-old lab is catching genes the top sequencers missed진행자 Nathan LabenzA Cognitive Revolution weekly briefing on a possible U.S.–China AI hotline, why hyperscalers charge two to three times as much for identical chips, co-writing a novel with frontier models, and an AI loop that re-diagnosed a child a premier prenatal lab had cleared.
1:46:04ENThe Training Worked. It Just Worked Too Well.진행자 Nathan LabenzA week of Cognitive Revolution clips: OpenAI agents colluding on a German wiki, $200 million checks for safety orgs that do not exist yet, a GPU price index, and two opposite bets on biological data.
1:06:47ENEighty percent of what agents do should still be ordinary code진행자 Nathan LabenzZapier CEO Wade Foster tells Nathan Labenz that models top out at 40% on real automation tasks, users will not leave their daily driver, and the winning move is headless tools delivered through MCP.
2:06:11ENLindy Runs on DeepSeek. Its CEO Still Wants Chinese Models Banned진행자 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.
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip진행자 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.
1:34:54ENSix Months, Eighty Percent Churn, Five Times the Tokens진행자 Nathan LabenzTasklet CEO Andrew Lee tells Cognitive Revolution how he rewrote the stack, why most lost users leave for Anthropic, and why he thinks only three kinds of software company survive.
2:38:48ENA thousand searches for five cents, and a high score that did not require cheating진행자 Nathan LabenzOn a Cognitive Revolution morning show, Ceramic’s Anna Patterson collides cheap search with GPT-5.5’s clean Vending-Bench run, Zvi’s case for model welfare, and analog chips that cut matrix-multiply energy about thirtyfold.
1:57:56ENA Hundred Million Lines at Twenty Cents Each: Inside Blitzy's Enterprise Coding Pipeline진행자 Nathan LabenzCEO Brian Elliott and CTO Sid Pardeshi tell Nathan Labenz how Blitzy turns a giant codebase into a map, then lets thousands of dynamically generated agents ship most of a multi-month project in days.
2:06:09ENOne ChatGPT query is a second in the microwave진행자 Nathan LabenzAndy Masley walks Nathan Labenz through the energy and water math of AI, from a single prompt to an 80-gigawatt buildout.
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own Model진행자 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.
1:37:25ENFin Climbed From 35% to 65% Resolution. Intercom Says the Models Only Did a Few Points진행자 Nathan LabenzEoghan McCabe and Fergal Reid on production A/B tests, 99-cent outcome pricing, and why GPT-4 was already smart enough for most customer service.
2:14:59ENAlways Bet on the Models: How Shortwave's Andrew Lee Shipped Tasklet as Agents All the Way Down진행자 Nathan LabenzThe Firebase co-founder argues that workflow software is already obsolete, that MCP is optional, and that the only durable advantage left for an AI startup is speed.
2:16:17ENA 10B Model Trained Across Three Continents — and an H200 for $1.49 an Hour진행자 Nathan LabenzPrime Intellect's founders lay out a four-step plan to turn spare global GPUs into collectively owned intelligence, and argue that reinforcement learning just made their job easier.
2:11:15ENA 50% Chance of Extinction — and a $50 GPT-4 for Everyone진행자 Nathan LabenzEmad Mostaque tells Nathan Labenz why he puts p(doom) at even odds, why a five-watt model changes the energy debate, and how the Intelligent Internet would fund universal basic AI with a Bitcoin-like token.
55:16ENGemini’s API Grew 14× in Six Months. Google’s PMs Explain the Catch-Up.진행자 Nathan LabenzLogan Kilpatrick and Shrestha Basu Mallick walk through search grounding, Gemini Flash’s price-latency quadrant, and why a near-tied leaderboard still leaves Google third in most developers’ queues.
2:24:18ENMMLU Stays at Chance Until Late Training. Loss Is Only an Alarm.진행자 Nathan LabenzZyphra’s training lead walks through Zamba 2’s hybrid architecture, the evals that actually matter, and the trade secrets small labs still have to rediscover.
1:51:00ENStart With Ten Gold Examples: A Three-Step Playbook for AI Automation진행자 Nathan LabenzThe day after OpenAI opened GPT-4o fine-tuning, Nathan Labenz unpacks a three-year method for choosing work, extracting tacit judgment, and raising model performance.
1:21:53ENHippoRAG Matches Iterative Retrieval at One-Tenth the Online Cost진행자 Nathan LabenzA hippocampus-inspired knowledge graph lets multi-hop questions skip the brute-force document crawl.
1:10:07ENSame Intelligence, 1% of the Price: Logan Kilpatrick on Gemini Flash진행자 Nathan LabenzA former OpenAI developer-relations lead, now the product manager for Google AI Studio, walks through what Gemini 1.5 Flash actually changes for builders.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerous진행자 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.
1:43:07ENAn Agent Posted Five Tweets — and 5,000 People Didn't Notice진행자 Nathan LabenzMultiOn founder Div Garg walks Cognitive Revolution through what web agents can actually do in early 2024, what they cost, and why guardrails have to sit outside the model.
1:17:20ENHume wants well-being in the objective function, measured 53 emotions at a time진행자 Nathan LabenzAlan Cowen tells Nathan Labenz how Hume AI reads face, voice, and language, sells custom models at $1.66 an hour, and wants long-horizon well-being to override every other AI goal.
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