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) &#8211; Introduction
(01:39) &#8211; Sponsors, Comments, and Reflections
(16:29) &#8211; China vs US: Who wins the AI race?
(25:11) &#8211; ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
(36:11) &#8211; Best AI for coding
(43:02) &#8211; Open Source vs Closed Source LLMs
(54:41) &#8211; Transformers: Evolution of LLMs since 2019
(1:02:38) &#8211; AI Scaling Laws: Are they dead or still holding?
(1:18:45) &#8211; How AI is trained: Pre-training, Mid-training, and Post-training
(1:51:51) &#8211; Post-training explained: Exciting new research directions in LLMs
(2:12:43) &#8211; Advice for beginners on how to get into AI development &#38; research
(2:35:36) &#8211; Work culture in AI (72+ hour weeks)
(2:39:22) &#8211; Silicon Valley bubble
(2:43:19) &#8211; Text diffusion models and other new research directions
(2:49:01) &#8211; Tool use
(2:53:17) &#8211; Continual learning
(2:58:39) &#8211; Long context
(3:04:54) &#8211; Robotics
(3:14:04) &#8211; Timeline to AGI
(3:21:20) &#8211; Will AI replace programmers?
(3:39:51) &#8211; Is the dream of AGI dying?
(3:46:40) &#8211; How AI will make money?
(3:51:02) &#8211; Big acquisitions in 2026
(3:55:34) &#8211; Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
(4:08:08) &#8211; Manhattan Project for AI
(4:14:42) &#8211; Future of NVIDIA, GPUs, and AI compute clusters
(4:22:48) &#8211; Future of human civilization

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

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