Shay Elkin

An often quoted number is that each ChatGPT query uses about 3 Watt-hours of electricity to answer, and I got nerd-sniped to find how that value was computed. But before sharing what I found, I'd like to answer a different question:

Should We Care?

Not today, I think.

Most of the incentives, and hence efforts, were directed at having an AI that produces better answers. So less brainpower was dedicated to minimizing the energy consumption of running the models. As more resources are dedicated to the issue, we'd see significant gains in energy utilization. We already got a glimpse of what is possible with DeepSeek-R1, where a strong incentive to reduce the compute required for building and running the model resulted in impressive gains, that directly translate to a reduction in the energy used.

Public attention is a scarce resource, and heated arguments on energy use distracts from discussing things that can't be solved by throwing more engineering effort at. What we need to care about today is the significant impact AI already has on our lives.

The 3Wh Estimate

Tracking the references for the oft-quoted 3Wh figure all end up leading to the same paper: The growing energy footprint of artificial intelligence (de Vries, 2023), which performs the following calculation:

The implict assumption that OpenAI's servers are always running at their peak power consumption is probably incorrect, as they'd melt if it was the case. But I am even more uncomfrotable with the provenance of the fleet size and usage numbers: they are sourced from an analyst report, which has no publicly published sourcing for those numbers, and from what I can tell, they were made up as resonable assupmtions for the model.

In other words, the estimate of 3 Watt-hours per query isn't backed by real world data or measurements.