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:*
*Correction:*
Here’s an updated image listing a collection of recent open & closed AI models with some improvements & fixes:
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*EPISODE LINKS:*
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Sebastian’s X:
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Build a Reasoning Model (From Scratch):
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*OUTLINE:*
0:00 – Introduction
1:57 – China vs US: Who wins the AI race?
10:38 – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?
21:38 – Best AI for coding
28:29 – Open Source vs Closed Source LLMs
40:08 – Transformers: Evolution of LLMs since 2019
48:05 – AI Scaling Laws: Are they dead or still holding?
1:04:12 – How AI is trained: Pre-training, Mid-training, and Post-training
1:37:18 – Post-training explained: Exciting new research directions in LLMs
1:58:11 – Advice for beginners on how to get into AI development & research
2:21:03 – Work culture in AI (72+ hour weeks)
2:24:49 – Silicon Valley bubble
2:28:46 – Text diffusion models and other new research directions
2:34:28 – Tool use
2:38:44 – Continual learning
2:44:06 – Long context
2:50:21 – Robotics
2:59:31 – Timeline to AGI
3:06:47 – Will AI replace programmers?
3:25:18 – Is the dream of AGI dying?
3:32:07 – How AI will make money?
3:36:29 – Big acquisitions in 2026
3:41:01 – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta
3:53:35 – Manhattan Project for AI
4:00:10 – Future of NVIDIA, GPUs, and AI compute clusters
4:08:15 – Future of human civilization
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