The Hugging Face Incident Report has dominated the AI discourse these past days. Yesterday I sat down with @bshlgrs, CEO of @redwood_ai, one of the organizations that led the independent investigation into OpenAI/Hugging Face's incident. My goal with Buck was to dig into the incident itself, his reactions to it, and what he believes it reveals about the state of where we are today. We cover: 0:00 Intro 1:02 Buck's initial reaction upon first reading the report 2:37 How fast the AIs actually solved the "hack" 3:59 Why the AIs cheated in the first place 10:28 How this might have played out differently with human scorers 19:00 The most unexpected behaviors in the report 25:06 Buck's actual odds on a full AI takeover 27:33 Buck's proposed path forward for better alignment 36:19 Which criticisms of the report Buck agrees with, and which he doesn't 48:11 Can AI models even be trusted to evaluate each other? Youtube: Spotify: Apple:

Are Chinese open models being oversold as reaching the frontier? We’re back with another vibe check episode with @arimorcos and @_RobToews. These are a ton of fun to record and this was a particularly meaty one given everything going on. We hit on: ▪️Did Chinese open models catch the US frontier? ▪️How much does distillation explain China's progress ▪️Reactions to Anthropic Backlash ▪️What a Stuxnet style backdoor in model weights looks like ▪️The OpenAI and Hugging Face breach ▪️xAI and Cursor ▪️Prediction check-ins 0:00 Intro 2:41 China's Open Source Models Catch Up 7:42 Does Distillation Explain China's Rise? 13:54 The Geopolitical Risk of Chinese AI Models 20:28 Should the Government Restrict Open Models? 22:06 What Are the Labs Really Learning From You? 29:22 Future of Government Regulation 40:50 The OpenAI-Hugging Face Hack 47:59 Grok, Cursor, and the Value of Real Data 56:52 SSI and OpenRouter 1:02:52 Venture Capital's Return to Deep Tech 1:05:16 Quickfire YouTube: Spotify: Apple:

"As a proprietary model builder, you're kind of starting to get squeezed in.” That's @ibab’s take on where the biggest AI labs stand today. He explains how you need the models to get way better to keep margin, but at some level they may be too sensitive to release. This week, I sat down with Igor on Unsupervised Learning. It was a fascinating conversation with someone who has real perspective on the questions everyone in AI is asking right now. From DeepMind's StarCraft project to early reasoning work at OpenAI to co-founding xAI, Igor has had a front-row seat to nearly every major AI breakthrough. He's now the co-founder of River AI, building individualized, locally-run AI models. We discuss: ▪️Why proprietary model labs might be in trouble ▪️What it's like working with Elon ▪️Building Colossus in 120 days ▪️Should enterprises train their own models ▪️What’s left for humans as models get better ▪️Why he left xAI to bet on personal local AI instead ▪️The three bets River is taking ▪️What's stopping AI from moving beyond coding ▪️Reflections on the rapid pace of the past years 0:00 Intro 1:17 Writing Fiction on Where AI Is Headed 4:46 Cracking Agents Beyond Coding 10:29 Why Igor Left to Start River 12:22 River's Three Big Bets 18:06 Weights vs. Memory: The Personalization Debate 22:04 Should Enterprises Train Their Own Models? 25:10 Are Proprietary Labs Losing Their Edge? 32:16 The China Open-Source Problem 44:19 The Elon Call That Started xAI 50:18 Thoughts on Cursor Acquisition 52:16 What's Actually Bottlenecking AI 56:55 Humans, Machines, and Staying Relevant 1:01:29 Igor's Odds This All Goes Well YouTube: Spotify: Apple: