Lex Fridman Podcast

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

Sonntag, 1. Februar 2026 · 4 h 40

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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:

lexfridman.com/ai-sota-2026-transcript

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OUTLINE:

() – 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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