(Disclaimer: I am not an anti-AI guy — I am just listing the common talking points I see in my feeds.)
My strong intuition at the moment is that the environmental impact is greatly exaggerated.
The energy cost of executing prompts has dropped enormously over the past two years - something that's reflected in this report when it says "Driven by increasingly capable small models, the inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024". I wrote a bit about that here: https://simonwillison.net/2024/Dec/31/llms-in-2024/#the-envi...
We still don't have great numbers on training costs for most of the larger labs, which are likely extremely high.
Llama 3.3 70B cost "39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware" which they calculated as 11,390 tons CO2eq. I tried to compare that to fully loaded passenger jet flights between London and New York and got a number of between 28 and 56 flights, but I then completely lost confidence in my ability to credibly run those calculations because I don't understand nearly enough about how CO2eq is calculated in different industries.
The "LLMs are an environmental catastrophe" messaging has become so firmly ingrained in our culture that I think it would benefit the AI labs themselves enormously if they were more transparent about the actual numbers.
Is there any separate analysis on AI resource usage?
For a few years now it has been frequently reported that building and running renewable energy is cheaper than running fossil fuel electricity generation.
I know some fossil fuel plants run to earn the subsidies that incentivised their construction. Is the main driver for fossil fuel electricity generation now mainly bureaucratic? If not why is it persisting? Were we misinformed as to the capability of renewables?
Where certain uses equate to significant jumps in power of manipulation.
That's not to pick on Palantir, it's just a class of software that enables AI for usecases that are quite scary.
It's not as if similar software isn't used by other countries for the same use cases employed by the US military.
Given this path, I doubt the environment will be the focus, again.
I think the biggest mistake liberals make (I am one) is that they expect disinformation to come against their beliefs when the most power disinformation comes bundled with their beliefs in the form of misdirection, exaggeration, or other subterfuge.
Is it? I don’t think I have ever seen it really brought up anywhere it would matter.
It would be quite rich in a country where energy production is pretty much carbon neutral but in character from EELV I guess.
It was even the subject of a popular network TV show (Person of Interest) with 103 episodes from 2011-2016.
1. Renewable energy, especially solar, is cheaper *sometimes*. How much sunlight is there in that area? The difference between New Mexico and Illinois for example is almost a factor of 2. That is a massive factor. Other key factors include cost of labor, and (often underestimated) beautacratic red tape. For example, in India it takes about 6 weeks to go from "I'll spend $70 million on a solar farm" to having a fully functional 10 MW solar farm. In the US, you'll need something like 30% more money, and it'll take 9-18 months. In some parts of Europe, it might take 4-5 years and cost double to triple.
All of those things matter a lot.
2. For the most part, capex is the dominant factor in the cost of energy. In the case of fossil fuels, we've already spent the capex, so while it's more expensive over a period of 20 years to keep using coal, if you are just trying to make the budget crunch for 2025 and 2026 it might make sense to stay on fossil fuels even if renewable energy is technically "cheaper".
3. Energy is just a hard problem to solve. Grid integrations, regulatory permission, regulatory capture, monopolies, base load versus peak power, duck curves, etc etc. If you have something that's working (fossil fuels), it might be difficult to justify switching to something that you don't know how it will work.
Solar is becoming dominant very quickly. Give it a little bit of time, and you'll see more and more people switching to solar over fossil fuels.
https://hai.stanford.edu/ai-index/2025-ai-index-report/resea...
A few more charts in the PDF (pp. 48-51)
https://hai-production.s3.amazonaws.com/files/hai_ai-index-r...
You get to upgrade them, kill them off, have them on demand
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.
"AI's Power Requirements Under Exponential Growth", Jan 28, 2025:
https://www.rand.org/pubs/research_reports/RRA3572-1.html
As a point of reference: The current demand in the UK is 31.2 GW (https://grid.iamkate.com/)
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.
A robotic police officer on every corner isn't at all far fetched at that point.
Despite their name I imagine the transportation costs of weights would be quite low.
Thank you for your reply by the way, I like being able to ask why something is so rather than adding another uninformed opinion to the thread.
Can you quantify how much less driving resulted from the increase of LLM usage? I doubt you can.
There is a lot of heated debate on the "correct" methodology for calculating CO2e in different industries. I calculate it in my job and I have to update the formulas and variables very often. Don't beat yourself over it. :)