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

Summary

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). Thank you for listening ❤ Check out…

Key takeaways

• The AI model landscape has fragmented into a multi-winner ecosystem where no single company holds a durable technology monopoly—differentiation now hinges on compute resources, post-training techniques, and product culture rather than novel architectures.
• Transformer architectures have remained fundamentally unchanged since GPT-2; the real performance gains are coming from post-training stages (RLHF, reasoning fine-tuning), systems-level optimizations (FP8/FP4 training, KV cache compression), and inference-time scaling like extended thinking.
• Chinese open-weight models (DeepSeek, Kimi, MiniMax, Qwen) are strategically released under permissive licenses to capture global developer mindshare and enterprise GPU spend that geopolitical concerns prevent them from winning through direct API sales.

Why this matters

For B2B technology leaders, the commoditization of frontier model architectures means competitive advantage will increasingly be determined by post-training specialization, infrastructure efficiency, and ecosystem lock-in rather than raw model capability—making vendor selection and open-weight adoption strategy a board-level decision in 2026.

Entities

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). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sc See below for timestamps, transcript, and to give feedback, submit questions, contact L

Themes

• llm
• machine-learning
• safety-alignment
• society
• culture