The environmental impact discussion is always incomplete because it only looks at AI in isolation. LLMs do exactly nothing by themselves, they are always used to facilitate some human endeavor, so we need to take a holistic view of the system.
I had previously done some napkin math (https://news.ycombinator.com/item?id=46984659) which found LLMs + humans are way more efficient than humans alone, but that analysis is by definition simplistic, and as some point out, does not account for outliers like coding. Fortunately, I just came across this source: https://gist.github.com/mdodkins/9b49624855cc41570c9d1012e0d...
That looks accurate and lines up with my previous numbers. A heavy day of Claude Code usage is equivalent to running 0.7 of a dishwasher cycle. Already that sounds pretty decent, but let's put that into context and refine the simplistic analysis a bit. Discussing footprints in terms of [electricity / water / CO2] tuples, that is approximately:
1.3 kWH / 2.6L / 540g
Compare that to a human's daily footprint (US averages, scaled down to an 8-hour workday; the numbers are actually higher if you look only at office use):
10 kWH / 102L / 4KG
As a % of the human footprint, that is ~ 13% / 2% / 13%.
Right off the bat water usage is negligible. But at a minimum if LLM usage saves a human 13% time at a task, we're at breakeven i.e. the LLM resource consumption is compensated by the savings in human consumption. Anything more than 13% and we actually start conserving resources on average!
Now I know the productivity impact of LLMs is a contentious topic, but my past comments include studies (from 2024, in the era of spicy autocomplete, long before coding agents!) showing ~30% boost in throughput and/or time savings. Interestingly the survey-based https://www.genaiadoptiontracker.com/ finds a consistent 30% time savings across industries. A human completing a task 30% faster consumes ~30% less resources i.e. we save ~3.3 kWH / 34L / 1.3KG per day.
This gives us a net savings of 2 kWH / 31L / 760g per human per day!
Even with 2024 estimates of productivity numbers we are conserving resources! I would wager that a full day's use of Claude Code today offers a much, much higher boost.
You could say coding is a small part of software engineering, but then resource consumption scales down correspondingly. The upshot remains that for a given task overall resource consumption goes down with increased LLM usage! In fact, if LLMs were adopted across all knowledge workers to do all possible tasks, we could recoup the enormous environmental cost of training models in a few business days! Subsequent ongoing savings can more than compensate for a lot of the slop produced elsewhere.
So -- and tongue only partly in cheek -- if you really want to make an environmental impact, use LLMs more.