#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI
February 1, 2026
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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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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