Why Can't AI Make Its Own Discoveries? — With Yann LeCun

Yann LeCun discusses the limitations of current autoregressive LLMs and argues that AI systems must build abstract world models to achieve true reasoning.

Yann LeCun is the chief AI scientist at Meta. He joins Big Technology Podcast to discuss the strengths and limitations of current AI models, weighing in on why they've been unable to invent new things despite possessing almost all the world's written knowledge. LeCun digs deep into AI science, explaining why AI systems must build an abstract knowledge of the way the world operates to truly advance. We also cover whether AI research will hit a wall, whether investors in AI will be disappointed, and the value of open source after DeepSeek. Tune in for a fascinating conversation with one of the world's leading AI pioneers. Chapters: 00:00 Introduction to Jan LeCun and AI's limitations 01:12 Why LLMs can't make scientific discoveries 05:40 Reasoning in AI systems: limitations of chain of thought 10:13 LLMs approaching diminishing returns and the need for a new paradigm 16:29 "A PhD next to you" vs. actual intelligent systems 21:36 Consumer AI adoption vs. enterprise implementation challenges 25:37 Historical parallels: expert systems and the risk of another AI winter 29:37 Four critical capabilities AI needs for true understanding 33:19 Testing AI's physics understanding with the paper test 37:24 Why video generation systems don't equal real comprehension 43:33 Self-supervised learning and its limitations for understanding 51:10 JEPA: Building abstract representations for reasoning and planning 54:33 Open source vs. proprietary AI development 58:57 Conclusion