State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490

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).
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*Transcript:*

Transcript for State of AI in 2026: LLMs, Scaling Laws, China, Coding, Agents, GPUs, AGI | Lex Fridman Podcast #490

*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

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