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 Introduction

02:03 Meet the Co-Founders

03:01 The Birth of Liquid Neural Networks

06:28 Applications and Impact of Liquid AI

09:38 The Worm Model and Its Significance

16:05 Mathematical Foundations and Breakthroughs

24:29 Scaling AI for Real-World Applications

28:05 Edge Computing and AI

29:58 Future Prospects and Use Cases

31:36 The Commoditization of AI Models

32:22 Liquid AI: Reducing Intelligence Costs to Zero

32:53 Diverse Applications of AI

33:43 Case Studies and Real-World Examples

38:05 The Impact of Deep Seek on the AI Ecosystem

44:13 Advice for Aspiring AI Founders

45:40 The Importance of Technical and Non-Technical Collaboration

51:03 Ramin's Journey from Scientist to Entrepreneur

53:31 Liquid AI's Vision for 2025

55:10 Conclusion and Future Prospects

Direitos Civis nos EUA

História em Meia Hora

Intro

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