Quite a lot. Let's assume a hundred different LLMs of this scale are being trained at the same time. If you multiply the global use percentages by a hundred you'll get: 0.0026% of global greenhouse gas emissions and 0.00047% of freshwater use. Still a literal drop in the bucket.

> How many more when the transition from millions of jobs replaced by AI do you expect?

Dunno, but the argument is that I should feel bad about my current impact on the environment as I use my LLM to autocomplete my code or answer my questions. We have no idea what the future will hold. We can and of course should do everything to minimize the environmental impact of everything we do, but that's a different discussion. For example switching to clean energy sources will make a big positive impact on these numbers.

> What about inference across all of that?

The report speaks about that, the inference cost in marginal when compared to the training cost (~15% for CO2 and ~9% for water consumption).