Rebuilding the Computer for the AI Age: Unconventional AI's Naveen Rao
In a Nutshell
Naveen Rao, CEO of Unconventional AI, argues that current computers are vastly inefficient for AI (gigawatts per model vs. human brain's 20 watts), facing imminent energy limits, and must shift from 80-year-old digital paradigms to brain-inspired nonlinear dynamics using physics for computation. Biology proves efficiency near thermodynamic limits via stochastic oscillators and trainable couplings, enabling rich state-space navigation without von Neumann bottlenecks. Their prototype chip, taping out this summer, demos generative models that "kick and run" dynamics to morph images (e.g., horses), promising orders-of-magnitude gains in intelligence per watt.
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Naveen Rao is a pioneer in the AI space with a PhD in neuroscience. He started one of the first AI chip companies, ran MosaicML (one of the first AI training companies), and built all of Databricks AI. He left to start Unconventional AI as CEO, aiming to redefine the future of computing by bringing neuroscience back.
Naveen Rao: "Afternoon everyone. Super excited to be here. I'm Naveen Rao and CEO of Unconventional AI. We're unconventional because maybe it's actually the wrong word to use cuz I think we're going to change the name to conventional. It's a great time to be a startup. Having no baggage is a true competitive advantage. We can do things faster than traditional chip companies and full stack companies. We can get to tape outs in months instead of years."
Guarantee some audience members are trying to prove wrong the claim about ASI. Need much greater computer efficiency at the fundamental substrate level—how information processing happens at the physics level—not algorithmic or data efficiency.
Computers work the way they did 80 years ago. Digital abstraction and floating point numbers date to the 1940s for machines built on different substrates for different purposes. Now building machines for intelligence.
AI makes us more efficient (coding, running a thousand agents on a phone), but energy efficiency is the real question. Already using many gigawatts for AI inference and training. In 2-4 years, no more energy available for AI.
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