When companies make ESG claims, sensible measurement and open traceability should always be the first proof they must provide. Without these, and validation from a credible independent entity such as a non-profit or government agency, all ESG claims from companies are merely PR puff pieces to keep the public at bay (especially in "AI").

The brain uses 20% of the human body's energy.

I wouldn't be surprised if mankind will evolve similar to an organism and use 20% of all energy it produces on AI. Which is about 10x of what we use for software at the moment.

But then more AI also means more physical activity. When robots drive cars, we will have more cars driving around. When robots build houses, we will have more houses being built, etc. So energy usage will probably go up exponentially.

At the moment, the sun sends more energy to earth in an hour than humans use in a year. So the sun alone will be able to power this for the foreseeable future.

The important part remains internalizing emission costs into the price of electricity. Fussing over individual users seems like a distraction to me. Rapid decarbonization of electricity is necessary regardless of who uses it. Demand will soar anyway as we electrify transportation, heating, and industry.

> When you ask an AI model to write you a joke or generate a video of a puppy, that query comes with a small but measurable energy toll and an associated amount of emissions spewed into the atmosphere. Given that each individual request often uses less energy than running a kitchen appliance for a few moments, it may seem insignificant.

> But as more of us turn to AI tools, these impacts start to add up. And increasingly, you don’t need to go looking to use AI: It’s being integrated into every corner of our digital lives.

Forward looking, I imagine this will be the biggest factor in increasing energy demands for AI: companies shoving it into products that nobody wants or needs.

The book “AI Atlas” covers the energy and other costs of AI.

What’s the net energy footprint of an employee working in an office whose job was made redundant by AI? Of course that human will likely have another job, but what’s the math of a person who was doing tedium solved by AI and now can do something more productive that AI can’t necessarily do. In other words, let’s calculate the “economic output per energy unit expended.”

On that note, what’s the energy footprint of the return to office initiatives that many companies have initiated?

I wonder how the energy requirements are distributed between training and inference. Training should be extremely flexible, so one can only train when the sun shines and nobody uses the huge amount of solar power, or only when the wind turbines turn.

>you might think it’s like measuring a car’s fuel economy or a dishwasher’s energy rating: a knowable value with a shared methodology for calculating it. You’d be wrong.

But everyone knows fuel economy is everything but a knowable value. Everything from if it has rained in the past four hours to temperature to loading of the vehicle to the chemical composition of the fuel (HVO vs traditional), how worn are your tires? Are they installed the right way? Are your brakes lagging? The possibilities are endless. You could end up with twice the consumption.

By the way, copy-pasting from the website is terrible on desktop firefox, the site just lags every second, for a second.

I would like to see more data centers make use of large-scale Oil Immersion-Cooling. I feel like the fresh water use for cooling is a huge issue.

https://par.nsf.gov/servlets/purl/10101126

With all the issues and inefficiencies listed, there is a lot of room for improvement. I'm hopeful that just as the stat they give for data center energy not rising from 2005-2017, so to will the AI energy needs flatten in a few years. GPUs are not very efficient. Switching to more task specific hardware will provide more efficiency eventually. This is already happening a little with stuff like TPUs.

The point that stood out to me as concerning was

"The carbon intensity of electricity used by data centers was 48% higher than the US average."

I'd be fine with as many data centers as they want if they stimulated production of clean energy to run them.

But that quote links to another article by the same author. Which says

"Notably, the sources for all this power are particularly “dirty.” Since so many data centers are located in coal-producing regions, like Virginia, the “carbon intensity” of the energy they use is 48% higher than the national average. The paper, which was published on arXiv and has not yet been peer-reviewed, found that 95% of data centers in the US are built in places with sources of electricity that are dirtier than the national average. "

Which in turn links to https://arxiv.org/abs/2411.09786

Which puts the bulk of that 48% higher claim on

"The average carbon intensity of the US data centers in our study (weighted by the energy they consumed) was 548 grams of CO2e per kilowatt hour (kWh), approximately 48% higher than the US national average of 369 gCO2e / kWh (26)."

Which points to https://ourworldindata.org/grapher/carbon-intensity-electric...

For the average of 369g/KWh. That's close enough to the figure in the table at https://www.epa.gov/system/files/documents/2024-01/egrid2022...

which shows 375g/KWh (after converting from lb/MWh)

But the table they compare against shows.

    VA 576g/KWh
    TX 509g/KWh
    CA 374g/KWh

and the EPA table shows

    VA 268g/KWh
    TX 372g/KWh
    CA 207g/KWh

Which seem more likely to be true. The paper has California at only marginally better than the national average for renewables (Which I guess they needed to support their argument given the number of data centers there)

I like arxiv, It's a great place to see new ideas, the fields I look at have things that I can test myself to see if the idea actually works. I would not recommend it as a source of truth. Peer review still has a place.

If they were gathering emissions data from states themselves, they should have caclulated the average from that data, not pulled the average from another potentially completely different measure. Then their conclusions would have been valid regardless what weird scaling factor they bought in to their state calculations. The numbers might have been wrong but the proportion would have been accurate, and it is the proportion that is being highlighted.