On a “respond to an individual query” level, yeah it’s not that much. But prior to response the data center had to be constructed, the entire web had to be scraped, the models trained, the servers continually ran regardless of load. There’s also way too many “hidden” queries across the web in general from companies trying to summarize every email or product.

All of that adds to the energy costs. This equivocation is meant to make people feel less bad about the energy impact of using AI, when so much of the cost is in building AI.

Furthermore, that’s the median value–the one that falls right in the middle of the quantity of queries. There’s a limit to how much less energy a query to the left of the median can use; there’s a significantly higher runway to the right of the median for excess energy use. This also only accounted for text queries; images and video generation efforts are gonna use a lot more.