Startseite
Podcasts
Lex Fridman Podcast Podcast
#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI

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

Lex Fridman Podcast vor 7 Monaten

0:00

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 Lex, etc.

Transcript:

https://lexfridman.com/ai-sota-2026-transcript

CONTACT LEX:

Feedback – give feedback to Lex:
https://lexfridman.com/survey

AMA – submit questions, videos or call-in:
https://lexfridman.com/ama

Hiring – join our team:
https://lexfridman.com/hiring

Other – other ways to get in touch:
https://lexfridman.com/contact

SPONSORS:

To support this podcast, check out our sponsors & get
discounts:

Box: Intelligent content management platform.

Go to https://box.com/ai

Quo: Phone system (calls, texts, contacts) for
businesses.

Go to https://quo.com/lex

UPLIFT Desk: Standing desks and office ergonomics.

Go to https://upliftdesk.com/lex

Fin: AI agent for customer service.

Go to https://fin.ai/lex

Shopify: Sell stuff online.

Go to https://shopify.com/lex

CodeRabbit: AI-powered code reviews.

Go to https://coderabbit.ai/lex

LMNT: Zero-sugar electrolyte drink mix.

Go to https://drinkLMNT.com/lex

Perplexity: AI-powered answer engine.

Go to https://perplexity.ai/

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