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

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

1 seconds Posted Feb 1, 2026 at 2:46 am.

) – Introduction

) – Sponsors, Comments, and Reflections

) – China vs US: Who wins the AI race?

) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?

) – Best AI for coding

) – Open Source vs Closed Source LLMs

) – Transformers: Evolution of LLMs since 2019

) – AI Scaling Laws: Are they dead or still holding?

) – How AI is trained: Pre-training, Mid-training, and Post-training

) – Post-training explained: Exciting new research directions in LLMs

) – Advice for beginners on how to get into AI development & research

) – Work culture in AI (72+ hour weeks)

) – Silicon Valley bubble

) – Text diffusion models and other new research directions

) – Tool use

) – Continual learning

) – Long context

) – Robotics

) – Timeline to AGI

) – Will AI replace programmers?

) – Is the dream of AGI dying?

) – How AI will make money?

) – Big acquisitions in 2026

) – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta

) – Manhattan Project for AI

) – Future of NVIDIA, GPUs, and AI compute clusters

) – Future of human civilization

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Show notes

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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