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At a glance
WHAT IT’S REALLY ABOUT
Analog-dynamics chips aim to cut AI energy and approach AGI
- Unconventional AI is framed less as a “chip company” and more as a first-principles effort to understand how learning and intelligence could be implemented directly in physical systems.
- Rao argues the dominant digital paradigm (precise, deterministic arithmetic) is mismatched to AI’s stochastic, distributed nature, and that analog/dynamical substrates could compute certain AI workloads more efficiently by leveraging physics.
- The main forcing function is energy: AI data centers are becoming grid-scale loads, making efficiency—not just faster chips—the limiting constraint on AI’s growth and ubiquity.
- Unconventional expects early fit for models with explicit dynamics (diffusion/flow/energy-based models) while still acknowledging transformers’ practical success and the need to bridge between parameterizations.
- Rao suggests time-evolving dynamical systems may better capture causality—one ingredient he believes is missing from today’s AI—potentially moving systems closer to “AGI,” while emphasizing the claim is still speculative.
IDEAS WORTH REMEMBERING
5 ideasEnergy, not compute, is becoming the binding constraint for AI scaling.
Rao points to data centers consuming a meaningful share of the grid and forecasts major new generation needs (hundreds of gigawatts) to meet AI demand, arguing efficiency breakthroughs are required alongside new power infrastructure.
Analog computing’s advantage is using physics directly, not simulating physics with numbers.
Where digital systems represent quantities via finite-bit numerics, analog approaches can embody the quantity in a physical medium, potentially yielding large efficiency gains for the right classes of problems.
Digital won historically because it scaled reliably; analog struggled with variability.
Rao explains early analog machines were efficient but hard to scale due to manufacturing variability, while digital abstraction (high/low states) enabled reliable scaling—an echo of today’s “scaling up GPUs” challenges.
Intelligence may be better served by stochastic, dynamical substrates than deterministic arithmetic.
Because neural networks and brains operate as noisy, distributed dynamical systems, Rao argues it’s plausible to find an electrical-circuit “isomorphism” that implements learning/inference more naturally than matrix-multiply-centric architectures.
Models with explicit dynamics (diffusion/flow/energy-based) are a prime target for new substrates.
He highlights diffusion/flow/energy-based models as naturally expressed via differential equations, making them candidates for mapping onto time-evolving physical systems for efficient computation.
WORDS WORTH SAVING
5 quotesMost of what we're doing is, at the beginning, it's theory and, uh, really kind of looking at first principles of how learning works in a, in a, in a, in a physical system.
— Naveen Rao
We've been building largely the same kind of computer for 80 years. We went digital back in the 1940s.
— Naveen Rao
Intelligence is the physics. They're one and the same. There's no, you know, OS and, you know, some sort of API and this and that.
— Naveen Rao
I, I'm the opposite of an AI doomer. I think AI is the next evolution of humanity. I think it takes us to a new level, allows us to collaborate, understand each other, and understand the world in much deeper ways.
— Naveen Rao
If we are successful here, the world will not forget this for a very long time, right? This will be written in history books.
— Naveen Rao
SPEAKERS
Matt Bornstein
hostPartner at Andreessen Horowitz (a16z) who hosts and interviews founders about AI and infrastructure.
High quality AI-generated summary created from speaker-labeled transcript.
