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20:55ENJesse Hall: Build Voice Agents for Conversations, Not LeaderboardsJesse Hall · LiveKitLiveKit’s Jesse Hall explains how to evaluate voice agents as real-time systems: understand callers, handle interruptions, keep talking while tools run, and measure the delay people actually hear.
18:27ENDaksh Gupta on Building a Better AI Code Review AgentDaksh Gupta · GreptileGreptile co-founder Daksh Gupta explains why a webhook plus a coding agent is not enough for reliable pull-request review—and how targeted prompts, incident data, model inversion, codebase context, browser testing, and task-specific model routing can help.
17:42ENIs a Faster Model Actually Faster? Measure the Finished Job, Not the TokensMorgan Suo · MiniMaxMiniMax's Morgan Suo takes the engineering changes inside M3.1 Flash one by one and asks the question that matters to users: does the job an analyst needs actually get done sooner, cheaper and correctly?
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.
1:13:48ENCarriers Spend $200 Billion a Year. The Money Went Up the Stack진행자 Jacob EffronBenedict Evans tells Jacob Effron why analogizing AI to past platforms is a dead end, and why looking at how those platforms actually made money is not.
50:57ENA Trillion Dollars of GPUs Will Be Worth $100 Billion in Five Years, Says LSTM's Jürgen Schmidhuber진행자 Jacob EffronThe AI pioneer tells Unsupervised Learning's Jacob Effron why he is wildly optimistic about the technology and deeply pessimistic about the model companies buying the chips.
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.
1:06:36ENBills Doubled Overnight. Next, the APIs Might Vanish.진행자 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.
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.
57:37ENThe hot-swap era for models is over, OpenAI's enterprise product chief says진행자 Jacob EffronOlivier Godement on GPT-5.1, Codex, enterprise harnesses, and the three practices that actually make AI land inside large companies.
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.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't Hold진행자 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.
43:51ENThe $177 Billion GPU Bet Only Works If AI Becomes a $1.5 Trillion Market진행자 Jacob EffronFour Redpoint partners walk their limited partners through where AI value will accrue—and why revenue is now the easiest metric to misread.
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.
1:10:23ENA Million Tokens per Dollar: Why Noam Shazeer Thinks Inference Compute Is Still Untapped진행자 Jacob EffronThe Transformer co-inventor and Gemini researcher Jack Rae walk through test-time compute, Ilya Sutskever's AGI critique, and a four-year-old who now lectures his teacher about lizards.
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