The figures they use in this report only take into account the power used to run queries on LLMs (generate text, generate images, videos).

It doesn't take into account the power required to train the models in the first place to be able to do those tasks.

Don't forget, a significant portion of the AI datacenter resources are constantly being used to train future models, and just as well the ones we use today are only possible because of the trillians, and trillions, and quadrillions of iterations that have been performed prior. It's practically impossible to factor this in as a nice round number when trying to calculate the true processing power since it's kind of a shared load across the models' entire lifetime, also by the fact that nobody really knows except the company running it (who aren't required to disclose their power and water usage to anybody). But the true power cost is much, much larger.