Erm ... that's a weird date considering this article came out yesterday. They actually pledge to triple the world's nuclear capacity by 2050[1]

There are a couple of weird things like that in this article, including the classic reference to "experts" for some of its data points. Still ... at least somebody's trying to quantify this.

[1] https://www.world-nuclear-news.org/articles/amazon-google-me...

Very quickly skimming, I have some trouble taking this post seriously when it omits that the larger DeepSeek one is a mixture-of-experts that will only use 12.5% (iirc) of its components for each token.

The best summary of text energy use I've seen is this (seemingly more rigorous, although its estimates are consistent with the final numbers made by the present post): epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use

Estimates for a given response widely for a "typical" query (0.3 Wh; 1080 joules) and a maximal-context query (40 Wh; 144k joules). Assuming most uses don't come close to maximizing the context, the energy use of text seems very small compared to the benefits. That being said, the energy use for video generation seems substantial

I would be interested in seeing the numbers corresponding to how LLMs are typically used for code generation

This program posts news to thousands of machines throughout the entire civilized world. Your message will cost the net hundreds if not thousands of dollars to send everywhere. Please be sure you know what you are doing. Are you absolutely sure that you want to do this? [ny]

Maybe we meed something similar in LLM clients. Could be phrased in terms of how many pounds of atmospheric carbon the request will produce.

I expect rapid progress in both model efficiency and hardware specialization. Local inference on edge devices, using chips designed specifically for AI workloads, will drastically reduce energy consumption for the majority of tasks. This shift will free up large-scale compute resources to focus on truly complex scientific problems, which seems like a worthwhile goal to me.

Impressive how Big Tech refuses to share data with society for collective decisions.

I'd also recommend the Data Vampires podcast series:

https://techwontsave.us/episode/241_data_vampires_going_hype...

https://techwontsave.us/episode/243_data_vampires_opposing_d...

https://techwontsave.us/episode/245_data_vampires_sacrificin...

https://techwontsave.us/episode/247_data_vampires_fighting_f...

I guess it becomes okay when the companies guzzling the energy are some of the biggest tech employers in the world, buttering your bread in some way.

- "by 2028 [...] AI alone could consume as much electricity annually as 22% of all US households."

What would the 22% be if compared against all US energy instead of just all US household?

> In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023.

As we all know, the generative AI boom only really kicked into high gear in November 2022 with ChatGPT. That's five years of "AI" growth between 2017 and 2022 which presumably was mostly not generative AI.

I found this article to be a little too one sided. For instance, it didn’t talk about the 10x reductions in power achieved this past year — essentially how gpt4 can now run on a laptop.

Viz, via sama “The cost to use a given level of AI falls about 10x every 12 months, and lower prices lead to much more use. You can see this in the token cost from GPT-4 in early 2023 to GPT-4o in mid-2024, where the price per token dropped about 150x in that time period. Moore’s law changed the world at 2x every 18 months; this is unbelievably stronger.” https://blog.samaltman.com/three-observations

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.

Yes, much of what is being promoted is slop. Yes, this bubble is driven by an overly financialized economy. That doesn't preclude the possibility of AI models precipitating meaningful advancements in the human condition.

From refrigeration to transportation, cheap and abundant energy has been one of the major driving forces in human advancement. Paradoxically, consuming cheap energy doesn't reduce the amount of energy available on the market. Instead it increases the size of the market.

Table A1 , PDF page 29:

https://www.epri.com/research/products/000000003002028905

> 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.

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.

Not sure about comprehensive claim here if end-to-end query chains were not considered.

For example the mobile wireless node (that're being used by the majority of the users) contribution to the energy consumption are totally ignored. The wireless power amplifier or PA for both sides of users and base-stations are notorious for their inefficiency being only less than than 50% in practice although in theory can be around 80%. Almost all of the current AI applications are cloud based not local-first thus the end users energy consumption and contribution are necessary.

> There is a significant caveat to this math. These numbers cannot serve as a proxy for how much energy is required to power something like ChatGPT 4o.

Otherwise this is an excellent article critiquing the very real problem that is opacity of these companies regarding model sizes and deployments. Not having an honest accounting of computing deployed worldwide is a problem, and while it's true that we didn't really do this in the past (early versions of Google searches were undoubtedly inefficient!), it's not an excuse today.

I also wish this article talked about the compute trends. That is, compute per token is going significantly down, but that also means use of that compute can spread more. Where does that lead us?

Which I already thought was odd, because London would need all that electricity to see through the giant mountain of poop piled up by all the horses the british use for transportation.

Even were it matches sorta (the 400 feet e-bike thing) that only works out for me because I use an AMD card. An NVIDIA card can have several times the generation speed at the same power draw so it all falls down again.

And the parameters they tried to standardize their figures with (the 1024x1024 thing) is also a bit meh because the SAME amount of pixels in a different aspect ratio can have huge variations in gen speed and thus power usage. for instance for most illustrious type checkpoints the speed is about 60% higher at aspect ratios other than 1024x1024. Its all a bit of a mess.

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

Build more nuclear, build more solar. Tax carbon.

The racks I am personally responsible for consume 17.2kW. That’s consistent across the year; sure things dip a bit when applications are shut down, but in general 17.2kW is the number. Presuming a national average of 1.2kW per home, each rack of equipment I oversee could potentially power 14 houses. I am responsible for hundreds of these racks, while my larger organization has many thousands of these racks in many locations worldwide.

I’ve found no other way to let the scale of this sink in. When put this way she is very clear: the price isn’t worth it to humanity. Being able to get, say, Door Dash, is pretty neat! But not at the cost of all our hoarded treasure and certainly not at the cost of the environment on the only planet we have access to.

The work done by AI will only ever benefit the people at the top. Because to be frank: they won’t share. Because the very wealthy have hoarding disorder.

Training SOTA models will, like steel mills or other large industrial projects, require a lot of environmental footprint to produce. But my prediction is that over time the vast majority of use cases in the hands of users will be essentially run on device and be basically zero impact, both in monetary cost and environment.

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

Finding the balance between innovation and long-term responsibility feels more important than ever.

This is outrageous. People still struggle to access fresh water (and power), but hey "sustainability is all to our company" is always promoted as if something nice is being done on from the behemoth's sides. BS. What a waste of resources.

I truly condemn all this. To this day I do still refuse to use any of this technology and hope that all this ends in the near future. It's madness. I see this as nothing more than next-gen restrictive lousy search engines, and as many have pointed out ads are going to roll soon. The more people adopt it the worse will be for everyone.

I always emphasize this: 10-15 years ago I could find everything through simple web searches. Everything. In many cases even landing on niche and unexpected but useful and interesting websites. Today that is a difficult/impossible task.

Perhaps there is still room for a well-done traditional search engine (haven't tried Kagi but people in general do say nice things about it) to surface and take the lead but I doubt it, when hype arrives especially in the tech industry people follow blindly. There are still flourishing "ai" startups and from night to day everyone has become a voice or expert on the subject. Again: BS.

Traditional web engines and searches were absolutely just fine and quite impressed with their outputs. I remember it. What the heck has happened?

... So don't? Explicitly shift the cost to the customer.

If I want to hook up to the energy grid with 3-phase power, I pay the utility to do it.

If a business wants more power and it isn't available, then the business can pay for it.

Then only businesses that really need it will be willing to step up to the plate.

No amount of "accounting" or "energy needs prediction" will guard against regulatory capture.

"Hard to test", but very obviously true if you make any attempt at guessing based on making a few assumptions... like they seem comfortable doing for all the closed source models they don't have access to being run in conditions they're not testing for. Especially considering they're presenting their numbers as definitive, and then just a couple paragraphs down admit that, yeah, they're just guessing.

Regardless, I know for a fact that a typical commercial shoot uses way more energy than driving across the TMZ in an e-bike (considering they're definitely using cars to transport gear, which gives you less than 4 miles for the same energy).

"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

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

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.

> AI is unavoidable

> We will speak to models in voice mode, chat with companions for 2 hours a day, and point our phone cameras at our surroundings in video mode

This is surely meant to be an objective assessment, not a fluff piece.