I like that as well, thank you! Yeah, the “Daily AI Habit” in the MIT article was described as…
Let’s say you’re running a marathon as a charity runner and organizing a fundraiser to support your cause. You ask an AI model 15 questions about the best way to fundraise.
Then you make 10 attempts at an image for your flyer before you get one you are happy with, and three attempts at a five-second video to post on Instagram.
You’d use about 2.9 kilowatt-hours of electricity—enough to ride over 100 miles on an e-bike (or around 10 miles in the average electric vehicle) or run the microwave for over three and a half hours.
As a daily AI user, I almost never use image or video generation and it is basically all text (mostly in the form of code), so I think this daily habit likely wouldn’t fit for most people that use it on a daily basis, but that was their metric.
The MIT article also mentions that we shouldn’t try and reverse engineer energy usage numbers and that we should encourage companies to release data because the numbers are invariably going to be off. And Google’s technical report affirms this. It shows that non-production estimates for energy usage by AI are over-estimating because of the economies of scale that a production system is able to achieve.
Edit: more context: my daily AI usage, on the extremely, extremely high end, let’s say is 1,000 median text prompts from a production-level AI provider (code editor, chat window, document editing). That’s equivalent to watching TV for 36 minutes. The average daily consumption of TV in the US is around 3 hours per day.
Another way of looking at this: A “Daily AI Habit” on your table is about the same as driving a Tesla 10 miles, or a standard gas car about 3 miles.
Edit 4 AI videos, or detour and take the scenic route home from work… about the same impact.
I like that as well, thank you! Yeah, the “Daily AI Habit” in the MIT article was described as…
As a daily AI user, I almost never use image or video generation and it is basically all text (mostly in the form of code), so I think this daily habit likely wouldn’t fit for most people that use it on a daily basis, but that was their metric.
The MIT article also mentions that we shouldn’t try and reverse engineer energy usage numbers and that we should encourage companies to release data because the numbers are invariably going to be off. And Google’s technical report affirms this. It shows that non-production estimates for energy usage by AI are over-estimating because of the economies of scale that a production system is able to achieve.
Edit: more context: my daily AI usage, on the extremely, extremely high end, let’s say is 1,000 median text prompts from a production-level AI provider (code editor, chat window, document editing). That’s equivalent to watching TV for 36 minutes. The average daily consumption of TV in the US is around 3 hours per day.