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

The following is a conversation all about the state of the art in artificial intelligence, including some of the exciting technical breakthroughs and developments in AI that happened over the past year, and some of the interesting things we think might happen this upcoming year. At times it does get super technical, but we do try to make sure that it remains accessible to folks outside the field, without ever dumbing it down. It is a great honor and pleasure to be able to do this kind of episode with two of my favorite people in the AI community, Sebastian Rashka and Nathan Lambert.

They are both widely respected machine learning researchers and engineers who also happen to be great communicators, educators, writers, and Twitterers, ex posters. Sebastian is the author of two books I highly recommend for beginners and experts alike. First is Build a Large Language Model, from scratch and build a reasoning model from scratch. I truly believe in the machine learning computer science world the best way to learn and understand something is to build it yourself from scratch.

Nathan is the post training lead at the Allen Institute for AI, and author of the definitive book on reinforcement learning from human feedback. Both of them have great ex accounts, great Substacks. Sebastian has courses on YouTube, Nathan has a podcast, and everyone should absolutely follow all of those. And now a quick few second mention of each sponsor, check them out in the description or at lexfriedman.com slash sponsors.

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