Yes it's something I often see ignored as "common knowledge" dictates that in ML inference is way cheaper than training. But if you're running a model in production at google with loads of google searches hitting it every second. At what point does the inference costs start to outweigh the training costs?

I simply have no idea where the hinge point is. This could inform other questions like, could it be worth to scale up to get a more accurate model (pay up-front in training) to avoid further searches (inference)?

Green energy is often used in addition to the carbon-based energy. The total amount of used energy increases. Green energy should replace carbon-base energy.

Wind and solar are finite too. The places where they can be harvested are scarce. So if Google using up green energy for bells and whistles on the search page, less homes can use green energy for heating and transport.