When is harness optimization enough? When does post-training make sense? What will determine who becomes the new AI hyperscaler? @ypatil125, co-founder of @appliedcompute, was the perfect person to discuss this and more. Yash worked on Codex at OpenAI before leaving to co-found Applied Compute with fellow OpenAI researchers Rhythm Garg and Linden Li. Sixteen months in, the company is a unicorn, and is serving enterprises including DoorDash and Cognition. We cover: 0:00 Intro 1:42 The Case for Owning Your Intelligence 4:35 OpenAI x Baseten and the Multi-Model Future 7:19 Can Post-Training Beat Frontier Models? 9:03 What Data Stays Out of Distribution 11:51 Data Efficiency and Continual Learning 14:31 When Updating Model Weights Makes Sense 16:01 Online Training in Practice 18:16 Lowering the Cost of Post-Training 19:48 Does Every Company Need Its Own Model? 24:06 RL in Non-Verifiable Domains 26:59 How Applied Compute Picks Customers 28:46 Building an Inference Business 34:43 The Case for a New AI Hyperscaler 38:21 Building RL Environments 41:33 Quickfire YouTube: Spotify: Apple: