[mentalgear]

To assess the env impact, I think we need to look a bit further:

While the single query might have become more efficient, we would also have to relate this to the increased volume of overall queries. E.g in the last few years, how many more users, and queries per user were requested.

My feeling is that it's Jevons paradox all over.

[fc417fc802]

The training costs are amortized over inference. More lifetime queries means better efficiency.

Individual inferences are extremely low impact. Additionally it will be almost impossible to assess the net effect due to the complexity of the downstream interactions.

At 40M 700W GPU hours 160 million queries gets you 175Wh per query. That's less than the energy required to boil a pot of pasta. This is merely an upper bound - it's near certain that many times more queries will be run over the life of the model.

[signatoremo]

LLM usage increase may be offset by the decrease of search or other use of phone/computer.

Can you quantify how much less driving resulted from the increase of LLM usage? I doubt you can.