I recently sat down with Lex Fridman and Nathan Lambert for a comprehensive 4.5 h interview to discuss the current state of progress of AI, and what the progress in 2026 look like.

We cover the LLM landscape from multiple angles, starting with the geopolitics of the AI race and comparisons between today’s leading LLMs. Then we discuss how these systems are actually trained, scaled, and post-trained. Of course, we also discuss current open vs closed models, coding assistants, tooling, and emerging research directions, and closes with practical advice and a look at AGI timelines, compute, and the long-term implications for industry and society. Yes, there is a lot of ground we covered!

While this is an audio podcast, there are tons of figure overlays and animation in the video version on YouTube, which may help when things get a bit technical.

Happy watching!

State of AI 2026 with Sebastian Raschka, Nathan Lambert, and Lex Fridman

You can find the complete video here

Timestamps:

  • 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