Naveen Rao

Naveen Rao publicly unveiled what he called the “first physical dynamical computer ever built”: Unconventional AI taped out the chip June 1, five months after the company “started in earnest in January,” and it is already generating images in the lab. The headline number: ~500 nanojoules per image versus millijoules on a GPU — “many orders of magnitude more efficient than a standard computer, and it’s because it just doesn’t move information around.”

Rao’s prior track record frames the bet: he founded Nervana Systems in 2014, sold it to Intel (saying he sold it “way too early”), ran Intel’s AI group, then built GPU-scaling infrastructure with his team before joining forces with Databricks in 2023.

The company’s goal is 1,000× power efficiency versus existing hardware, and Rao has pulled the timeline in from five years to three and a half — “we’ve actually solved very deep scientific problems more quickly because of AI, interestingly enough.” The stated endgame is bolder still: “the overarching goal of this company is to beat biology.”

The energy math driving the thesis: Google alone crosses 3.2 quadrillion tokens per month; at 10 joules/token that’s 12 GW — against ~40 GW of total US data-center power and under 100 GW worldwide. Rao’s estimate: “we’re going to run out of energy pretty fast, in about 3 years or so,” and since ~50% of the cost of serving a token is energy, the business case is “we’re going to monetize that 1,000× better than existing hardware.”

Biology is the existence proof: a human brain runs on 20 watts, a monkey brain on one watt — the same as your phone — and a squirrel’s on 8 milliwatts. The mechanism gap is data movement: the human cortex moves ~16 billion bits/second while a GPU moves ~30 trillion bits in and out of memory, “10–100× more than that” inside the chip.

The architecture moves beyond conventional von Neumann designs — “compute and memory in one thing, we don’t have a memory interface” — branded “4D computing”: three physical dimensions via die stacking plus time as the fourth. A supporting breakthrough on sparsity turned n-squared connection scaling into a “holy grail” result: throwing away connections made systems more efficient, more scalable, and more trainable at once.

Under Chamath’s questioning, Rao said a full product is “within two years”: a rack-scale data-center system, “tokens in, tokens out through a network cable, but the inner guts are completely different.” Existing models will work — porting happens “at the model layer,” not the operations layer — but matmul as such doesn’t exist in the hardware, and the software stack is Python libraries, not CUDA.

The investor kicker is Jevons’s paradox: Rao says halving the price can lead to more than 2× consumption, while making something 1/1,000th the price can lead to consuming more than 1/1,000th as much. He thinks a 1,000× disruption of a roughly $1 trillion 2030 AI market — “maybe it’s bigger than that” — will create “the largest market that humanity’s ever seen,” with the topology shifting from gigawatt data centers to “many small data centers all over the place” and eventually “billions of robots.”

🔗 Original source & video: Naveen Rao: 4D Computing, AI’s Energy Wall & Beating Biology