#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
Source
lexfridman.com
Date
Why it matters
Two ML researchers debate the 2026 state of AI, from scaling to AGI
Practical takes on best coding AI, open vs closed models, post-training
Career advice, work culture, and predictions on OpenAI, Anthropic, NVIDIA
Chapters
00:00Introduction
01:39Sponsors, Comments, and Reflections
16:29China vs US: Who wins the AI race?
25:11ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
36:11Best AI for coding
43:02Open Source vs Closed Source LLMs
54:41Transformers: Evolution of LLMs since 2019
1:02:38AI Scaling Laws: Are they dead or still holding?
1:18:45How AI is trained: Pre-training, Mid-training, and Post-training
1:51:51Post-training explained: Exciting new research directions in LLMs
2:12:43Advice for beginners on how to get into AI development & research
2:35:36Work culture in AI (72+ hour weeks)
2:39:22Silicon Valley bubble
2:43:19Text diffusion models and other new research directions
2:49:01Tool use
2:53:17Continual learning
2:58:39Long context
3:04:54Robotics
3:14:04Timeline to AGI
3:21:20Will AI replace programmers?
3:39:51Is the dream of AGI dying?
3:46:40How AI will make money?
3:51:02Big acquisitions in 2026
3:55:34Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
4:08:08Manhattan Project for AI
4:14:42Future of NVIDIA, GPUs, and AI compute clusters
4:22:48Future of human civilization
Transcript
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-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/ai-sota-2026-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact SPONSORS: To support this podcast, check out our sponsors & get discounts: Box: Intelligent content management platform. Go to https://box.com/ai Quo: Phone system (calls, texts, contacts) for businesses. Go to https://quo.com/lex UPLIFT Desk: Standing desks and office ergonomics. Go to https://upliftdesk.com/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Shopify: Sell stuff online. Go to https://shopify.com/lex CodeRabbit: AI-powered code reviews. Go to https://coderabbit.ai/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex Perplexity: AI-powered answer engine. Go to https://perplexity.ai/ OUTLINE: (00:00) – Introduction (01:39) – Sponsors, Comments, and Reflections (16:29) – China vs US: Who wins the AI race? (25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning? (36:11) – Best AI for coding (43:02) – Open Source vs Closed Source LLMs (54:41) – Transformers: Evolution of LLMs since 2019 (1:02:38) – AI Scaling Laws: Are they dead or still holding? (1:18:45) – How AI is trained: Pre-training, Mid-training, and Post-training (1:51:51) – Post-training explained: Exciting new research directions in LLMs (2:12:43) – Advice for beginners on how to get into AI development & research (2:35:36) – Work culture in AI (72+ hour weeks) (2:39:22) – Silicon Valley bubble (2:43:19) – Text diffusion models and other new research directions (2:49:01) – Tool use (2:53:17) – Continual learning (2:58:39) – Long context (3:04:54) – Robotics (3:14:04) – Timeline to AGI (3:21:20) – Will AI replace programmers? (3:39:51) – Is the dream of AGI dying? (3:46:40) – How AI will make money? (3:51:02) – Big acquisitions in 2026 (3:55:34) – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta (4:08:08) – Manhattan Project for AI (4:14:42) – Future of NVIDIA, GPUs, and AI compute clusters (4:22:48) – Future of human civilization
