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!

Sebastian Raschka's Blog offers insights, tutorials, and research updates on machine learning, deep learning, and artificial intelligence. Covering topics such as neural networks, data science, and Python programming, Sebastian Raschka's Blog provides resources for students, researchers, and practitioners in the field of AI. Developers can learn about  algorithms, research methodologies, and practical applications of machine learning through Raschka's blog posts and publications.

Sebastian Raschka

A comprehensive 4.5-hour podcast discussion covering the current state of AI in 2026, including geopolitics of the AI race, comparisons of leading LLMs (ChatGPT, Claude, Gemini, Grok), training methodologies (pre-training, mid-training, post-training), scaling laws, open vs closed models, coding assistants, emerging research directions (text diffusion, tool use, continual learning, long context, robotics), AGI timelines, compute infrastructure, and long-term implications for industry and society.

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