Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sb See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc.
*Correction:* Here's an updated image listing a collection of recent open & closed AI models with some improvements & fixes: https://lexfridman.com/wordpress/wp-content/uploads/2026/01/ai_models_2025.png
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*EPISODE LINKS:* Nathan's X: https://x.com/natolambert Nathan's Blog: https://interconnects.ai Nathan's Website: https://natolambert.com Nathan's YouTube: https://youtube.com/@natolambert Nathan's GitHub: https://github.com/natolambert Nathan's Book: https://rlhfbook.com Sebastian's X: https://x.com/rasbt Sebastian's Blog: https://magazine.sebastianraschka.com Sebastian's Website: https://sebastianraschka.com Sebastian's YouTube: https://youtube.com/@SebastianRaschka Sebastian's GitHub: https://github.com/rasbt Sebastian's Books: Build a Large Language Model (From Scratch): https://manning.com/books/build-a-large-language-model-from-scratch Build a Reasoning Model (From Scratch): https://manning.com/books/build-a-reasoning-model-from-scratch
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*OUTLINE:* 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization