Ramin Hasani, Liquid Ai | theCUBE + NYSE Wired: Robotics & AI Infra Leaders
In this interview from theCUBE + NYSE Wired: Robotics & AI Infra Leaders 2026, Ramin Hasani, co-founder and chief executive officer of Liquid AI, joins theCUBE's John Furrier and Howie Xu to discuss how specialized, efficient AI models are powering intelligence at the edge. Hasani explains how Liquid Foundation Models bring specialized intelligence outside the data center, running on devices as small as a Raspberry Pi. He details a new production deal with Mercedes-Benz, where a 600-megabyte multimodal model powers in-car voice intelligence on low-cost automotive processors. Hasani argues that intelligent routing across cloud, sovereign and device-based models is key to cutting inference costs, while small models thrive on fine-tuned specialization rather than general-purpose reasoning.
The conversation explores why Liquid AI moved beyond the transformer architecture, addressing the exponential compute and memory costs created by growing context windows and KV cache demands. Hasani notes that frontier labs, including Moonshot's Kimi models, are now adopting similar efficiency-first design principles. He and Xu debate the networking challenges of distributed, on-device intelligence, and outline emerging opportunities in AI for science, materials discovery and robotics. The discussion also touches on the risks of AI-generated content saturation and the industry's shift toward continual learning as models move from static systems to adaptive ones. Hasani closes by pointing to cybersecurity and edge privacy as fertile ground for the next wave of AI entrepreneurship, offering a grounded view of where efficient, specialized intelligence is headed next.
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00:00 - Foundations and Frontiers of AI: Innovations, Efficiency, and Ethics
04:08 - AI Innovation Amid Economic and Automotive Dynamics
06:42 - Optimizing and Specializing Small AI Models: Challenges and Solutions
08:59 - Reimagining AI: Alternatives and Edge Considerations
12:56 - Balancing AI: Exploring Opportunities and Addressing Content Saturation Concerns
18:42 - Advancing AI: Continuous Learning and Future Trajectories
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