Ramin Hasani, Liquid AI | theCUBE +NYSE Wired: AI Factories - Data Centers of the Future

In this conversation from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future, Liquid AI co-founder and Chief Executive Officer Ramin Hasani joins theCUBE’s John Furrier to unpack how efficient, adaptable AI is reshaping the edge – and why that matters for next-gen enterprise infrastructure. Hasani traces Liquid AI’s roots out of MIT CSAIL and explains “liquid neural networks,” a biologically inspired class of models designed for adaptability after learning. The discussion explores why small, capable models at the edge complement hyperscale training, how hybrid device–cloud orchestration will emerge, and why networking and memory (think long-context/KV cache) are central to AI factory design and operations.

Hasani details Liquid AI’s approach to AI that designs AI to search mathematical operators and discover architectures that run across Qualcomm, AMD and NVIDIA hardware. He shares how the company is building foundation models for devices – from in-car intelligence to privacy-sensitive, on-device use cases – with multi-modal capabilities spanning text, audio, vision and other signals. Viewers will hear claims around the firm’s “nanos” (small foundation models), including sub-10 ms activations and specialized performance with tiny parameter counts, plus the broader vision for physical AI where agents and robotics intersect at the intelligent edge. The conversation connects these advances to AI factories’ broader impact: distributed compute, GPU-dense designs and rethinking data centers as the cornerstone of digital strategy in a hybrid world.

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00:00 - Intro
00:06 - Exploring theCUBE: Origins and Innovations in Liquid AI
02:32 - Advancements and Developments in AI and Robotics
05:56 - Building Tomorrow: The Future and Efficiency of AI Infrastructure and Architecture
09:54 - Advancements in Adaptive Computing for AI: Innovations in Memory and Networking
13:58 - Momentum and Market Reach
16:28 - The Future of AI and Hardware Integration

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