Midjourney founder David Holz estimates that a frontier model thinking at human speed uses about 50 W, close to the brain's 20 W
Let's say a frontier model is a 10T MoE on GB300 NVL72. That's ~5 Joules/token or ~50 W at human thinking speed vs our brain at 20 W. The energy grid is $0.08/kWh and eating rice $0.50/kWh. So on an energy-per-thought standpoint rn we're surprisingly similar to frontier models!
Context
Holz's napkin math suggests the energy cost of machine thought is already in the same range as human thought, a useful frame for debates about AI power demand.
David Holz founded the image generation lab Midjourney. The human brain is commonly estimated to run on about 20 W. Holz assumed a frontier model is a 10 trillion parameter mixture of experts served on Nvidia's GB300 NVL72 rack system, a configuration he described hypothetically.
Deep dive
On October 11, David Holz posted a back of the envelope comparison. He supposed a frontier model is a 10T parameter mixture of experts running on GB300 NVL72, and estimated that works out to about 5 joules per token, or about 50 W at human thinking speed, versus about 20 W for the brain.
He then compared the price of the fuel: grid electricity at $0.08 per kWh against food energy from eating rice at $0.50 per kWh. His conclusion was that, on an energy per thought basis, humans and frontier models are surprisingly similar right now.
Holz did not publish his full assumptions, and his figures are estimates rather than measurements. An August analysis on the Code and Life blog put measured serving energy for 2026 models at roughly 0.6 to 4 joules per output token at the wall, which at a human pace of about 5 tokens per second works out to about 3 to 20 W. That puts Holz's 5 joules per token at the high end, consistent with his assumption of a very large model.
The same analysis noted that agents doing sustained work produce tokens far faster than people think, so their total power draw can be much higher than a brain even when the energy per token is similar.
What to expect next
Watch for more rigorous published measurements of energy per token on the newest hardware, which Nvidia and independent groups such as ML.Energy track, and whether efficiency gains in racks like Vera Rubin push machine thought clearly below the brain's 20 W.
Also watch how comparisons like this feed into public debates about data center power use, where total demand matters more than efficiency per thought.