#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).

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Transcript:

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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