There's a misconception regarding the "consumption" of water, also a bit of a bias towards AI data centers whereas most used water is actually from energy production (via carbon, fuel or even hydroelectric) which is actually a factor to be considered when calculating the actual water use and consumption.
Regarding energy production and water "consumption" I read some papers and as far as I could understand numbers flactuate wildly. 5-40% of the water that runs through the system ends up being consumed via evaporation (so from potentially drinkable/usable for agriculture water to mostly water that ends up in the sea).
What I'm trying to say is that, yes, we should be very aware of the water that we consume in our big data centers but should also put a great focus on the water used by the energy that fuels the data center itself, much of the discourse ends up being "haha use water for email silly" when it should be a catalyst for a more informed approach to water consumption.
Basically I fear that the ai industry can make use of our ignorance and eappease with some "net zero" bs completely ignoring where most of the water is consumed and how.
And yes there are solutions to avoid using fresh water for energy production: solar/wind, using sea water, using polluted water, more sophisticated systems that actually "consume" as little water as possible. These methods have drawbacks that our governments and industry refuse to face and would rather consume and abuse our resources, I really want people to focus on that.
Mathematicians are taught to be elastic with notation, because they tend to be taught many different interpretations of the same theory.
On the other hand engineers use more strict and consistent notation, their classes have a more practical approach.
Using the same notation makes it faster to read and apply math, a more agile approach helps with learning new theories and approaches and with being creative.