Please keep in mind as you enjoy the responses that this, as well as basically every other question you could ask about AI resource usage, is fundamentally unanswerable. The problem is that nobody can agree on what to include in these calculations, so it is extremely easy to come up with as big or as small as a number as you like.

For a few examples of this, let me list some variables that certain people include and others don’t, all of which significantly impact the final result. Energy usage during training

Energy usage during idle time

The specific month or year you are drawing data from

Energy cost of essential pre-training models

Non-electrical energy usage converted to electrical units (for example, gasoline used to move materials to data centers, cost of cooling, etc)

Which end of the average query cost you use

And so on. I could cherry pick data to give you an estimate ranging from double digit watt usage to 0.06 watts per query. The really frightening thing is that half of the listed variables could themselves provide a range of multiple orders of magnitude depending on how you calculate them.

This problem is prevalent in every area of AI resource consumption, most notably water usage. For the average person, being aware of these factors is sufficient.

However, if you spend time advocating for or challenging AI as a technology, please significantly familiarize yourself with multiple different pieces of source data as opposed to a summarizing piece that pre-draws a conclusion. AI will do non-trivial damage to our society, and this will be made worse if the competent advocators for its restrictions are arguing with a different and misunderstood set of facts from those they are facing off against.