In Jensen's 5 layer cake analogy for AI (energy, chips, infrastructure, models, applications), the middle layer (esp the software infra part) is much less understood - and thats where @VAST_Data became a $30B company few people know about, powering @SpaceXAI, @CoreWeave, @nebiusai, @MistralAI, @nscale etc My conversation with Renen Hallak, CEO: 00:00 Intro 00:51 The hidden software layer in NVIDIA's AI stack 02:28 What actually makes an "AI factory"? 05:13 Should Walmart and Goldman build their own AI? 06:20 "We infer during the day, fine-tune at night" 13:16 Announcement: frontier models & sensitive data 15:32 From P vs. NP to founding VAST Data 17:32 OpenAI, Navier–Stokes and 10,000 agents 20:25 The pre-transformer insight behind VAST 21:55 DASE: VAST's "shared everything" architecture 25:18 "Storage was where startups go to die" 27:39 Trillions of vectors: why old databases break 29:01 Are S3, Snowflake and Databricks ready for AI 31:29 Data gravity, vendor lock-in and zero churn 33:13 Training vs. inference: why the infrastructure changes 34:46 Model routing, KV caches, RAG & agent memory 36:59 Identity, permissions and security for AI agents 40:27 Can multi-agent systems unlock scientific discovery? 41:55 DataEnclave: how confidential AI protects data and weights 45:16 Who should be AI's trust layer? 46:37 "Sometimes it scares me": 500 petabytes to 2 exabytes 50:08 Is circular AI financing creating systemic risk 51:32 Why VAST is profitable when AI infra isn't 53:24 What separates the winning neoclouds? 55:06 "Their lunch is being eaten": why hyperscalers lag 59:10 Where will the trillions accrue across the AI stack 1:01:18 NVIDIA: "There's no legal document between us" 1:03:59 What VAST learned from xAI and @elonmusk 1:05:36 "Bad things loudly and often": building at AI speed 1:06:53 More change in 10 years than the previous 1,000? 1:08:39 VAST's endgame: all the data in the world

When AI builds AI: my conversation with @RichardSocher about RSI and scientific progress. 00:00 Intro: AI That Improves Itself 00:55 Why Scientific Progress Is Slowing 03:08 The Labyrinth of Human Knowledge 05:59 Can AI Put Science Back Together? 07:57 How LLMs Learn Biology and Proteins 10:56 Next-Token Prediction as a World Model 16:44 Can AI Generate Truly Original Ideas? 17:32 Simulations, Verifiers and Superhuman AI 22:18 The Path to Recursive Self-Improvement 24:49 Why AI Hallucinations Can Drive Discovery 27:42 From Reading Biology to Writing It 31:31 Can AI Accelerate Drug Discovery? 33:31 Will AI Help Cure Cancer? 38:03 AI Breakthroughs in Biology, Energy and Materials 40:19 Will Some Societies Reject AI? 45:07 Building the AI Economist 52:22 The Scientific Data Bottleneck 53:41 The Four Pillars of the Eureka Machine 55:01 Teaching AI the Rules of Reality 57:44 Simulations and Virtual Cells 1:00:40 Self-Driving Robotic Laboratories 1:02:51 Agent Swarms and Open-Ended Discovery 1:04:30 The Compute Bottleneck 1:05:44 Inside @Recursive_SI 1:07:33 What Recursive Will Build First 1:10:10 How Do We Define Intelligence? 1:11:32 How Far Can Intelligence Go?