Bigger AI models dominate the headlines—but what if the real breakthrough lies in making AI smaller?In this episode, I sit down with Ramin Hasani, Alexander Amini, and Daniela Rus—who are at the helm of Liquid AI—to discuss how their approach challenges conventional architectures and unlocks new frontiers for AI deployment.Also on the docket:• Ramin explains how insights from a microscopic worm led to a novel AI model.• Alexander breaks down why Liquid AI operates efficiently on local devices—without compromising modality.• Daniela exposes a critical flaw in today’s AI incumbents and why efficiency is the next major battleground.We also unpack all things DeepSeek—its implications for OpenAI, Meta, and enterprises scaling private AI. 00:00 Introduction02:03 Meet the Co-Founders03:01 The Birth of Liquid Neural Networks06:28 Applications and Impact of Liquid AI09:38 The Worm Model and Its Significance16:05 Mathematical Foundations and Breakthroughs24:29 Scaling AI for Real-World Applications28:05 Edge Computing and AI29:58 Future Prospects and Use Cases31:36 The Commoditization of AI Models32:22 Liquid AI: Reducing Intelligence Costs to Zero32:53 Diverse Applications of AI33:43 Case Studies and Real-World Examples38:05 The Impact of Deep Seek on the AI Ecosystem44:13 Advice for Aspiring AI Founders45:40 The Importance of Technical and Non-Technical Collaboration51:03 Ramin's Journey from Scientist to Entrepreneur53:31 Liquid AI's Vision for 202555:10 Conclusion and Future Prospects

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