#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI

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: See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: CONTACT LEX: Feedback – give feedback to Lex: AMA – submit questions, videos or call-in: Hiring – join our team: Other – other ways to get in touch: SPONSORS: To support this podcast, check out our sponsors & get discounts: Box: Intelligent content management platform. Go to Quo: Phone system (calls, texts, contacts) for businesses. Go to UPLIFT Desk: Standing desks and office ergonomics. Go to Fin: AI agent for customer service. Go to Shopify: Sell stuff online. Go to CodeRabbit: AI-powered code reviews. Go to LMNT: Zero-sugar electrolyte drink mix. Go to Perplexity: AI-powered answer engine. Go to 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

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#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI is an episode from Artificial Intelligence by Lex Fridman.

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This episode was published on Feb 1, 2026.

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