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

964 snips

Feb 1, 2026

Guest

Sebastian Raschka

Guest

Nathan Lambert

Sebastian Raschka, hands-on ML educator and author of practical LLM guides, and Nathan Lambert, post-training lead at AI2 and RLHF specialist, discuss China vs US competition, which chatbots excel at coding and long context, open vs closed model tradeoffs, architectural tweaks like MOE, where progress really comes from (systems, data, post-training), RL with verifiable rewards, scaling laws, tool use and agents, and timelines toward AGI.

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INSIGHT

Ideas Move Faster Than Exclusive Tech

Ideas flow freely across labs; the real bottleneck is budget, hardware, and organizational execution.
Sebastian Raschka argues no single company will hold exclusive technological advantage in 2026.

INSIGHT

Culture Trumps Raw Model Hype

Product success depends on culture, operational discipline, and focused bets, not only model quality.
Nathan Lambert credits Anthropic's product-focused culture for recent momentum in coding tools.

ADVICE

Match Model Mode To Task Urgency

Use fast, lower-cost models for quick tasks and switch to 'thinking' or pro models for research or high-stakes work.
Choose the model to fit latency, cost, and required reliability rather than always using the largest model.

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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:
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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