Intelligence on the Edge: Liquid AI's Ramin Hasani on the Search for Device-Native Foundation Models

Liquid AI co-founder and CEO Ramin Hasani joins Nathan to make a technically grounded case against the idea that scale alone defines the future of AI. Drawing on Liquid’s path from MIT CSAIL work on liquid time-constant networks to Automated Foundation Model Design, he explains why efficient, hardware-aware architectures can look very different from frontier-scale attention models. The conversation centers on device-native foundation models for phones, laptops, cars, and wearables, including Liquid’s open-weight LFM family and production deployments at Shopify and Mercedes-Benz. The stakes are whether useful intelligence can move beyond the data center into local, privacy-sensitive, low-latency applications—and which model and chip companies will own that on-device intelligence layer.

For full show notes, links, and references, read the episode page:
https://www.cognitiverevolution.ai/intelligence-on-the-edge-liquid-ai-s-ramin-hasani-on-the-search-for-device-native-foundation-models/

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CHAPTERS:

(00:00) About the Episode

(03:53) Special Sponsor

(05:41) Liquid AI origins

(22:35) Neurons versus parameters (Part 1)

(22:40) Sponsor: Claude

(24:32) Neurons versus parameters (Part 2)

(30:51) Scaling liquid networks

(40:09) Automated model design

(52:04) Gating and input dependence

(01:01:16) Architecture bias spectrum

(01:09:17) Device foundation models

(01:18:16) Hardware intelligence layer

(01:30:01) Local agent setup

(01:36:20) Miniaturizing intelligence limits

(01:40:40) Curiosity driven AI future

(01:43:45) Episode Outro

(01:46:46) Outro

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Spotify:

foundation models

Ramin Hasani

Large, general-purpose AI models that can be adapted for many tasks.

attention models

Ramin Hasani

A common type of AI model architecture that focuses on specific parts of input data.

liquid time-constant networks

Ramin Hasani

A type of AI architecture designed for continuous-time data and efficient processing.