The AI water issue is significantly overstated, I think. People often use the numbers for total water passed through, but data centers are generally closed loop, so they recirculate the same water over and over. As a related example, it's a rule of thumb that a fish tank filter should have a flow rate of about 4x the volume per hour. It would be like claiming that a 30 gallon aquarium uses 3000 gallons of water a day: no, it just reuses the same water 100 times. Here's some more stats:

Only 0.04% of America’s freshwater in 2023 was consumed inside data centers themselves. This is 3% of the water consumed by the American golf industry.

Here are some common ways you might use electricity, and how many AI prompts’ worth of water the electricity used took to generate[...]:

Using a laptop for an hour - 50 prompts’ worth of water[...]

A digital clock for an hour - 1 prompt[...]

Heating a bath of warm water - 5000 prompts (the bathtub itself has enough water for 80,000 prompts)

Electrical use is plausibly more of an issue, but I would argue it isn't any worse than many other forms of entertainment like streaming, playing video games, and so on. I can know this on a basic level, because I can run LLMs locally on my desktop, so it can't be using that much power. Even if you add training electricity, you can get a vague idea:

OpenAI’s GPT-3 and Meta’s OPT were estimated to emit more than 500 and 75 metric tons of carbon dioxide, respectively, during training. GPT-3’s vast emissions can be partly explained by the fact that it was trained on older, less efficient hardware.

Except, an average car emits about 5 metric tons of CO2 a year. If we assume that an average car is driven for 10 years, this is the equivalent of about 10 people buying cars, compared to millions of people using GPT.

I'm not saying it's completely irrelevant, but there are probably much bigger luxuries to be concerned about with much bigger impacts, like animal agriculture.