Why The Future of AI May Be Smaller Than You Think, w/ Jeffrey Li
Liquid AI's COO on why the next phase of AI may run on small, specialized on-device models rather than giant cloud models — and the economics behind the shift.
Guest
Jeffrey Li
Chief Operating Officer, Liquid AI
Jeffrey Li is chief operating officer at Liquid AI, an MIT-linked company developing compact, efficient models for deployment on edge devices. His work involves translating research on liquid neural networks and specialized AI into products and partnerships spanning devices, vehicles, and enterprise applications.
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What if the future of AI is not bigger models in bigger data centers, but smaller ones running quietly on the devices you already use every day?
In this episode of AI-Curious , we talk with Jeffrey Li, COO of Liquid AI , about why the next phase of AI may depend less on giant cloud models and more on small , specialized models that run directly on phones, laptops, cars, and other edge devices.
We explore the case for on-device AI, why large models are only part of the story, and how companies should think about speed, privacy, cost, and real-world deployment as AI moves from experimentation to everyday products.
We also dig into the economics behind this shift. Along the way, we discuss why cloud-based AI can break down when every query has to travel to a data center, why enterprise ROI gets harder as AI subsidies fade, and why many real-world use cases do not need a giant model capable of doing everything.
Instead, they may need a smaller, more tailored system built for a specific task, domain, or device.
We also get into Liquid AI’s research roots at MIT, the origins of liquid neural networks, and what it looks like to bring production-quality AI into places like Mercedes vehicles and e-commerce systems.
This is a practical conversation about the future of edge AI , specialized models , privacy-preserving AI , and what happens when intelligence moves closer to the user.