What makes a data center an 'AI data center' vs other kinds? I am sure that certain workloads are better suited for a particular server rack vs another; but can't a data center built for other computing needs also do AI and vice-versa?

Data center mech eng here - from our perspective it's higher rack densities typically due to GPUs. It's certainly possible to have high densities due to CPUs as well but I've seen a significant spike in rack densities in the last couple of years which has caused a switch from air cooling to liquid to chip.

One side effect of higher density is less footprint on the building to exhaust the heat, which is one reason (the main one being efficiency) that cooling towers and indirect evaporative cooling are favoured over air cooled condensers which leads to large amounts of water consumption.

Cooling towers are also much quieter than air cooled condensers which is a significant factor near any residential areas. It would be great to see more use of data center waste heat for process or district heating to save on water consumption.

Another issue with AI training in particular is huge (multi-MW) swings in power consumption at the start and end of each training run which must be a nightmare for the sparkies.

The distinction is scale. "AI Datacenters" are a new level of scale with new levels of power consumption and heat generation. Sure you could run regular compute and w/e in them but it's not practical to build these mega sites for regular compute. GPU Compute / AI workloads require network/interconnect bandwidth and latencies where distance matters so you're forced to solve problems you wouldn't otherwise have to. Those problems are mostly solved with money.

Different I/O, power and cooling requirements for majority GPU workloads?