AI's energy footprint

https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/

by pseudolus • 1 year ago

312 345 1 year ago

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").

stevage• 1 year ago

esg?

JohnFen• 1 year ago

Environmental/Social/Governance. From Wikipedia:

Environmental, social, and governance (ESG) is shorthand for an investing principle that prioritizes environmental issues, social issues, and corporate governance.

kkarakk• 1 year ago

when has ESG not been FUD or a way to bypass sanctions from poorly thought out climate change targets?

mg6• 1 year ago

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.

amelius3• 1 year ago

One problem: all this energy is eventually turned into heat ...

mg6• 1 year ago

Most of the sunlight that hits a roof is already turned into heat. Whether you use that for calculations or not does not make a difference.

Not sure about the exact numbers, but I guess that at the moment normal roofs and solar panels absorb very roughly about the same percentage of sunlight.

So if in the future solar panels become more efficient, then yes, the amount of sunlight turned into heat could double.

Maybe that can be offset by covering other parts of earth with reflective materials or finding a way to send the heat back into the universe more effectively.

vmg12• 1 year ago

When robots drive cars, we will have more cars driving around

This doesn't seem true. In SF, waymo with 300 cars does more rides than lyft with 45k drivers. If self driving cars interleave different tasks based on their routes I imagine they would be much more efficient per mile.

mg6• 1 year ago

Existing rides will be done more efficiently but since rides are so much cheaper without a driver, much more rides will be done.

A car driving from A to B will cost less than 50% of the current price. Which will unlock a huge amount of new rides.

0_____03• 1 year ago

Is it really only 300 cars? They feel like they're everywhere!

asdff5• 1 year ago

If waymo is doing more rides with 300 cars than 45k drivers on lyft, we can assume then that waymo cars are on the road serving customers at least 150x as long of time as a lyft driver. So yes it could really mean more cars are "around" even if the fleet is much smaller.

floxy5• 1 year ago

This doesn't seem true.

Seems like we are way too early in the adoption curve to tell. Currently the average number of passengers per trip is >1.0 across the whole fleet. Some day, I'd expect that to dip below 1.0, as people send an empty car to pick up the dog from the vet, or circle the block to avoid having to pay for parking, etc.

thelastgallon• 1 year ago

Thank you for this data point. It massively lowers the embodied carbon footprint (carbon from manufacturing, supply chain, transportation, etc.). Operational carbon is a solved problem; it is easy to measure and can be supplied from renewable sources.

fallingknife3• 1 year ago

That would mean that 5.5% of the SF population are lyft drivers

HelloUsername• 1 year ago

energy usage will probably go up exponentially

kindof sounds like Jevons paradox? https://wiki.froth.zone/wiki/Jevons_paradox

briandear2• 1 year ago

Why not nuclear?

mg6• 1 year ago

Building and running a nuclear reactor involves a lot of physical activity. And if the past is an indicator, we always move from physical activity to the flow of electrons.

The discussion about nuclear vs solar remind me of the discussions about spinning HDs versus solid state drives when they were new.

natmaka• 1 year ago

Because a mix of renewables deployed on a continent (most grids tend nowadays to extend on whole continents because it enhances continuity of service while offering many ways to optimize for emissions, costs...) is better (cheaper, less dependency towards any fuel, less risk related to accidents/neglects/mistakes/war/terrorism/hot waste/... ...).

GAFAM nuclear are mere announcements, intentions.

On the other front most already progress. https://www.reuters.com/sustainability/climate-energy/micros...

https://www.reuters.com/sustainability/climate-energy/micros...

Scarblac4• 1 year ago

But the article says that energy use by AI is 48% more carbon intensive than the US average. So talk of solar power is a red herring -- that's not what it is running on now.

mg6• 1 year ago

I am thinking about the future here.

I don't think there will be much carbon intensive energy creation in a few decades from now. It does not make sense economically.

newtonsmethod• 1 year ago

The energy use by AI probably is just as, if not more, carbon intensive, but the article never says that. It talks about the energy use of the general data center.

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

asdff5• 1 year ago

This assumes no technological adaptions towards efficiency. Consider yourself walking a mile and the energy expenditure. It isn't insignificant. Now imagine you have a bicycle. Some bicyclists will train and do century rides, a distance that were never possible merely walking for a day. But these are few bikers overall, most will not maximize capability to that extent but will still be taking advantage of the efficiency of the bike.

HPsquared4• 1 year ago

First-order effect is that quality of life will improve though as a result of all that work being done. People able to live more comfortably, relax more etc.

The main complaint about energy usage is it will damage the environment, which will (indirectly) reduce quality of life.

It's a question of which factor wins.

adrianN2• 1 year ago

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.

seb1204• 1 year ago

I agree but reducing consumption or increase of efficiency are still very important aspects of the energy transition. What is not consumed does not need to be generated.

adrianN2• 1 year ago

Yeah, but pricing signals are a good way of reaching those goals.

Scarblac4• 1 year ago

Businesses will only start doing that in significant amounts when carbon emissions are priced according to their environmental impact.

Lerc2• 1 year ago

I don't think it a given that reducing energy consumption is a required part of the transition.

Increasing demand can lead to stimulus of green energy production.

0_____03• 1 year ago

Sometimes I see chatter about using solar or nuclear or whatever power for data centers, thereby making them "clean," and it's frustrating that there isn't always the acknowledgement that the clean energy could displace other dirty generation.

Even with things like orphaned natural gas that gets flared otherwise - rescuing the energy is great but we could use it for many things, not just LLMs or bitcoin mining!

nostrademons• 1 year ago

If you internalize emissions costs into the price of electricity, reduced consumption will happen naturally. Precisely nobody likes higher energy bills, so there's a natural incentive to reduce consumption as long as you're paying for it.

oezi• 1 year ago

In some markets this might be right but in others it isn't. For instance, if you have CO2 certificates associated with a product then not buying it won't change emissions. It will make the price of certificates cheaper for everyone else and lead to other consumption elsewhere.

designerarvid3• 1 year ago

Pricing is a good way to steer in this direction. We would have more salad waste if salad was free to bring home from the supermarket I’m sure.

JKCalhoun• 1 year ago

True, but if data centers were exclusively built along places like the Columbia River, as an example, that energy, even excess energy, is practically free once you have the hydro-electric plant. I guess the same is true for solar, wind — any power generated that does not require consumables.

Rivers gotta run, suns gotta shine, winds gotta blow.

somewhereoutth• 1 year ago

Indeed. However the problem with LLMs is that vast amounts of VC money are being thrown at them, in the [misplaced] hope of great returns. This results in a resource mis-allocation of biblical proportions, of which unnecessary carbon emissions are a part.

hermitShell• 1 year ago

To do that we would need society to agree about what the emission cost is.

Making electricity so abundant and efficient is probably more solvable. You can’t solve stu… society

designerarvid3• 1 year ago

Agreed, that always been the key question for sustainability. Price is a fantastic mechanism, but negative externalities remain.

whimsicalism• 1 year ago

especially when you consider that most of these models are trained/running on contracts for exclusively renewable energy.

Proofread0592• 1 year ago

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.

Uehreka2• 1 year ago

In the short term perhaps, but even without carbon pricing the raw electricity prices will probably tamp down the enthusiasm. At someone point it’ll become cool for activist investors to demand to see ROI for AI features on earnings calls, and then the fat will get trimmed just like any other trend that goes too far.

I think the bigger underrated concern is if LLMs fall into an unfortunate bucket where they are in fact generally useful, but not in ways that help us decarbonize our energy supply (or that do, but not enough to offset their own energy usage).

keybored4• 1 year ago

Try to buy something that isn’t wrapped in three layers of plastic. Or that isn’t made of plastic itself. Then go to the checkout and see their “PSA” about how asking for a plastic bag to carry your plastic merchandise kills the planet.

I’m sorry. I’m being blocked by some mysterious force from understanding what “actual human” means. And I don’t know how to get you in contact with your car manufacturer. Would you like me to repeat my 20 step suggestion on how to troubleshoot “why does my shitty car put the A/C on freezer mode whenever “Indian Summer” tops the charts in Bulgaria”, but with more festive emojis?

Nition• 1 year ago

increasing energy demands for AI: companies shoving it into products that nobody wants or needs

I think of this a little every time Google gives me another result with the AI summary and no option for anyone to turn it off. Apparently worldwide there are 8+ billion searches every day.

xarope• 1 year ago

I have zero use for the AI summary that google chooses to prepend to my search summary. I decided to fact check a few, and they were totally wrong in subtle ways, either changing a word or phrase and thus inverting the meaning, or else completely summarizing from a site which had no relevance to my search.

So yes, I'd like to disable this completely. Even if it's just a single birthday candle worth of energy usage.

BewareTheYiga• 1 year ago

http://archive.today/mnHb8

mark_l_watson2• 1 year ago

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

emushack3• 1 year ago

Link?

simonw3• 1 year ago

You mean this one? https://en.m.wikipedia.org/wiki/Atlas_of_AI

It's from 2021 so won't cover the 2022-onwards generative AI boom.

From the Wikipedia summary it sounds like it's about machine learning algorithms like classification, AlphaGo and concerns about ethics of training and bias.

mark_l_watson2• 1 year ago

thank you! I got the name wrong. “Atlas of AI” by Kate Crawford.

briandear2• 1 year ago

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?

Scarblac4• 1 year ago

When human civilization crashes due to yearly climate change caused famines it won't matter how useful the work done by the AIs was.

folkrav• 1 year ago

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

That’s a lot of big assumptions - that the job getting replaced was tedious in the first place, that those other “more productive” job exists, that the thing AI can’t necessarily do will stay that way long enough for it not to be taken over by AI as well, that the tediousness was not part of the point (e.g. art)…

carunenjoyerlp5• 1 year ago

Net energy change of people doing work on their desk versus browsing the internet versus playing games, you will likely not see difference at all. They're all at rest, more or less thinking something. People at home sofa always have metabolic processes running regardless of whether it produces additional value to some corporation

lm28469• 1 year ago

a person who was doing tedium solved by AI and now can do something more productive that AI can’t necessarily do.

Like driving a uber or delivering food on a bicycle ? Amazing!

est31• 1 year ago

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.

jnsaff2• 1 year ago

AFAICT the energy cost of training is still fairly low compared to cost of GPU's themselves so especially during a land grab it's important to drive as near as possible full utilization of the GPU's, energy be damned.

I doubt this is going to change.

That said, the flip side of energy cost being not a big factor is that you could probably eat the increase of energy cost by a factor of say 2 and this could possibly enable installation of short term (say 12h) battery storage to enable you to use only intermittent clean energy AND drive 100% utilization.

carunenjoyerlp5• 1 year ago

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.

GuinansEyebrows3• 1 year ago

fuel economy, like blood glucose levels, is impacted by many factors, but you can measure it over time. you might not be able to prescribe a course of action but you can make corrections to the course you're already on.

emushack3• 1 year ago

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

carunenjoyerlp5• 1 year ago

Isn't water just the transfer medium between the server and the heat exchangers outside? How would changing that to oil help?

chneu• 1 year ago

It wouldn't really help.

Oil might be able to carry more heat but it's more expensive to use.

Oil immersion is something nerds like to think is amazing but it's just a pain in the ass for negligible benefits. Imagine the annoyance of doing maintenance.

emushack3• 1 year ago

True, but water, especially fresh water, is used in so many applications. I don't have hard numbers on this because I'm not THAT big of a nerd, but intuitively, I would think that if we can find alternatives to water, that would free up some fresh water resources, for I don't know, growing food? This is actually a major issue in my area - A data center is being built right now, and competing with municipalities for fresh water. Maybe they don't have to?

lxgr• 1 year ago

There are various data center cooling techniques, and not all of them use water. As a result, water usage, and water usage efficiency, vary wildly:

https://dgtlinfra.com/data-center-water-usage/

https://datacenters.microsoft.com/sustainability/efficiency/

giantg2• 1 year ago

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.

Lerc2• 1 year ago

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.

GuinansEyebrows3• 1 year ago

there are still negative externalities to high renewable-energy usage (heat and water usage, which itself requires energy to purify once returned to the sewer, plus the environmental impact of building an enormous heat island in places where there was little industry previously).

SoftTalker3• 1 year ago

If you are old enough you remember posting to Usenet and the warning that would accompany each new submission:

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.

wmf4• 1 year ago

Only if that same warning is attached to literally everything else you do. It's unfair to single out AI for using energy.

SoftTalker3• 1 year ago

But "asking ChatGPT" is something people do so casually without there being any apparent clues of the costs associated with that. I guess that is true of pretty much everything online though.

Even driving your car around you at least are somewhat aware of the gas you are burning.

GuinansEyebrows3• 1 year ago

fairness to one polluter over another isn't the real issue - look at prop 65 in california; or if you're not used to this in CA, think of any time you've been on-call. alert fatigue is real and diminishes the urgency of the underlying message.

reaperducer2• 1 year ago

It's unfair to single out AI for using energy.

Why? AI isn't a human being. We have no obligation to be "fair" to it.

yongjik• 1 year ago

A lot of us live in a country where "rolling coal" is a thing. I fear your prompt may have an opposite of the intended effect.

01HNNWZ0MV43FF2• 1 year ago

Taxing anything that can pollute (methane, gasoline, diesel) would let The Hand sort it out

UnreachableCode• 1 year ago

You say that as if rolling coal people are capable of using or understanding LLMs

nightski• 1 year ago

How does it make you feel that your computing carbon footprint may be higher than rolling coal? Are we the problem?

blkhawk2• 1 year ago

I find that message very curious because the message itself clearly does not cost much but the machines it is send on do. So the more messages that are send the less the messages will cost.

anon7000• 1 year ago

Well, Usenet is 45 years old, and the internet was not nearly as cheap and ubiquitous then

keybored4• 1 year ago

You don’t need it for any pragmatic benefit because it won’t work. It doesn’t work for eating meat. It won’t work for AI.

The only purpose is to scapegoat the possible environmental or economic fallout. Might as well put it on individuals. Like what’s always done.

I’ve already seen it on the national broadcast. There some supposed experts were wagging their fingers about using AI for “fun”. Making silly images.

Meanwhile we’re gonna put AI to good use in arms races: more spam (automated applications, ads, ads, ads, abuse of services) and anti-spam. There’s gonna be so much economic activity. Disruptive.

reginald78• 1 year ago

AI will probably increase GDP. In the same way shooting the windows out of everyone's houses would increase GDP. Then we can claim is grew the economy.

kmeisthax• 1 year ago

Individual LLM requests are vanishingly small in terms of environmental impact; inference providers use a lot of batching to do lots of work at once. Furthermore, LLMs and diffusion models are not the only ML workload. While generative AI tickles investors, most of the ML actually being deployed is more mundane things, like recommendation systems, classifiers, and the like; much of which is used for adtech purposes adversarial to that of users. If LLMs and diffusers were the only thing companies used ML for, but efficiency gains from new hardware remained constant, we'd still be at the 2017 baseline for environmental impact of data centers.

Likewise, I doubt that USENET warning was ever true beyond the first few years of the networks' lifetime. Certainly if everything was connected via dial-up, yes, a single message could incur hundreds of dollars of cost when you added the few seconds of line time it took to send up across the whole world. But that's accounting for a lot of Ma Bell markup. Most connections between sites and ISPs on USENET were done through private lines that ran at far faster speeds than what you could shove down copper phone wiring back then.

cj• 1 year ago

If what you're saying is true, why are we hearing about AI companies wanting to build nuclear power plants to power new data centers they think they need to build?

Are you saying all of that new capacity is needed to power non-LLM stuff like classifiers, adtech, etc? That seems unlikely.

Had you said that inference costs are tiny compared to the upfront cost of training the base model, I might have believed it. But even that isn't accurate -- there's a big upfront energy cost to train a model, but once it becomes popular like GPT-4, the inference energy cost over time is dramatically higher than the upfront training cost.

You mentioned batch computing as well, but how does that fit into the picture? I don't see how batching would reduce energy use. Does "doing lots of work at once" somehow reduce the total work / total energy expended?

troyvit3• 1 year ago

Individual LLM requests are vanishingly small in terms of environmental impact;

The article uses open source models to infer cost, because those are the only models you can measure since the organizations that manage them don't share that info. Here's what the article says:

The largest of our text-generation cohort, Llama 3.1 405B, [...] needed 3,353 joules, or an estimated 6,706 joules total, for each response. That’s enough to carry a person about 400 feet on an e-bike or run the microwave for eight seconds.

I just looked at the last chat conversation I had with an LLM. I got nine responses, about the equivalent of melting the cheese on my burrito if I'm in a rush (ignoring that I'd be turning the microwave on and off over the course of a few hours, making an awful burrito).

How many burritos is that if you multiply it by the number of people who have a similar chat with an LLM every day?

Now that I'm hungry, I just want to agree that LLMs and other client-facing models aren't the only ML workload and aren't even the most relevant ones. As you say adtech has been using classifiers, vector engines, etc. since (anecdotally) as early as 2007. Investing algorithms are another huge one.

Regarding your USENET point, yeah. I remember in 2000 some famous Linux guy freaking out that members of Linuxcare's sales team had a 5 line signature in their emails instead of the RFC-recommended 3 lines because it was wasting the internet or something. It's hard for me to imagine what things were like back then.

smusamashah• 1 year ago

Your quote is actually telling the opposite of your suggestion.

So they used to send this message, but then it stopped I assume. Costs lowered a lot or the benefits outweighed all associated costs. Same can happen here.

FollowingTheDao5• 1 year ago

Costs lowered a lot or the benefits outweighed all associated costs.

How is this even quantifiable?

How about this. Before using AI to make fake images and help kids cheat on their homework, we take it offline and use it to solve it's own problem of energy use.

You know what this does not happen? Because the goal is profit and the profit comes not from solving real important problem, but by making people think it is helping them solve made up problems.

kokanee• 1 year ago

Yeah, someone will probably make AI less damaging at some point. So let's not worry about it. That's the right mindset.

Edit: it's sad that I'm not sure if the downvotes are because people can't tell that this is sarcasm, or because they know that it is.

admiralrohan2• 1 year ago

We can't be pessimistic, that hinders flow. Should rather focus on creative ways to increase energy requirements. We will figure this out.

mirekrusin• 1 year ago

Or gigantic footer explaining you should consider impact before trying to print this email – which was always eating next page when printed.

teekert2• 1 year ago

I wonder what the carbon footprint of all those ads is.

amelius3• 1 year ago

Not just the ads, but also the overconsumption which they cause.

__MatrixMan__• 1 year ago

Not just overconsumption, but also waste due to supply chain fragility. If you can induce demand anywhere then supply has to do crazy things to keep up.

Ericson23143• 1 year ago

We really need to improve the power grid. I don't think about "A. I." very much, but I am glad that something is making us upgrade the grid.

lucb1e2• 1 year ago

I am glad that something is making us upgrade the grid

A few big consumers in centralized locations isn't changing the grid as much as the energy transition from fuels to electricity is

Ericson23143• 1 year ago

The transition to electric vehicles in the US has been disappointing, to say the least.

acidburnNSA• 1 year ago

I'm glad that the needs of AI and the sustainable capabilities of nuclear fission go well together.

simonw3• 1 year ago

I found this bit interesting:

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.

SoftTalker3• 1 year ago

People started using GPUs for ML at around that time.

mattnewton• 1 year ago

2017 is the year after AlphaGo beats Lee Sedol and is when the attention is all you need paper was published. The writing was on the wall. OpenAI just found product market fit in november 2022, but the industry wasn't wandering aimlessly until then.

NitpickLawyer• 1 year ago

Meta was in the process of AI-ing everything on their stacks, with search, similarity, graphs, recommendation, etc. everywhere on their properties. Incidentally that's why they already had tens/hundreds of thousands of GPUs when the LLM craze hit, and why they were in a good place to work on llama and other stuff.

phillipcarter5• 1 year ago

Well, this was disappointing:

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?

blkhawk2• 1 year ago

the numbers in the article are all over the place. I mean the article seems to try and some of the more general calculations on paper should work out but especially the image gen ones I can sorta disprove with my own experiences in local gen.

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.

bbor• 1 year ago

Interesting, thanks for sharing! I share some concerns others have about this piece, but I’m most shocked about their finding that image generation is cheaper than text. As someone who’s gone down this rabbit hole multiple times, this runs against every single paper I’ve ever cited on the topic. Anyone know why? Maybe this is a recent change? It also doesn’t help that multimodal transformers are now blurring the lines between image and text, of course… this article doesn’t even handle that though, treating all image models as diffusion models.

fred69• 1 year ago

Might have missed it but was disappointed to see no mention of externalized costs like the scraping burden imposed on every IP-connected server. From discussions on HN this sounds quite substantial. And again, why exactly should the few AI companies reap all the value when other companies and individuals are incurring costs for it?

Henchman212• 1 year ago

I work in DCO, thats Data Center Operations if you’re not aware. I’ve tried explaining the amount of power used to my elderly mom; it isn’t easy! But here’s my best take:

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.

jeffbee3• 1 year ago

It seems like you are having an emotional response to not understanding the general energy picture. For example, an A320 aloft uses the same energy as two thousand of your hypothetical racks (2.5 tons of kerosene per hour).

Each!

We are in no meaningful sense torching the biosphere to get AI.

globnomulous• 1 year ago

It seems like you are having an emotional response to not understanding the general energy picture.

This is condescending and rude. It also strikes me as obviously wrong. The 'response' isn't emotional in the slightest; it just explains the emotional and cognitive experience of acquiring understanding. It's a reasonable, well-reasoned explanation of the difficulty of intuitively grasping how much energy these data centers, and thus the companies that use them, consume -- and then of the shock many experience when it dawns on them.

For example, an A320 aloft uses the same energy as two thousand of your hypothetical racks (2.5 tons of kerosene per hour).
Each!
We are in no meaningful sense torching the biosphere to get AI.

What exactly is the reasoning here? That airplanes use a lot of energy, just like data centers or compared to data centers, and therefore that AI isn't ecologically damaging -- or isn't "meaningfully" damaging, whatever that means? That's not just wrong. It's a nonsequitur.

There's a simpler, easier, better way to wrap our heads around the data, one that doesn't require false dichotomies (or something like that): humans are torching the biosphere both to get AI and to travel.

Henchman212• 1 year ago

It seems like you’ve missed my point utterly. My explanation was targeted at my elderly mom, not at Hacker News readers. Her responses were genuinely hers, and I think they are reasonable given her understanding and my explanation. I apologize this didn’t come across and I’ll have to rethink how I phrase something like this in the future.

That said, your response was emotional as well: as if your words were cast down from on high, a gift from the gods! Your arrogance and rudeness should inspire some self-examination, but I am not hopeful on that front.

jahewson• 1 year ago

But it can’t really power “14 houses” because people in those houses are consuming external services such as those provided by your racks.

Unless your racks can only serve 14 customers.

fallingknife3• 1 year ago

But if you substitute the pre-doordash system of calling up a restaurant and ordering delivery or take out, the energy savings aren't even 1%. One gallon of gas contains 34 kWh of energy, so if one delivery takes 0.5 gallons of gas, it uses enough energy to power one of your racks for an hour. How many doordash orders can be processed by one of your racks in an hour? It's got to be in the millions.

floxy5• 1 year ago

40% of electricity consumption in Virgina will be data centers in 2030?

Table A1 , PDF page 29:

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

coolcase6• 1 year ago

Virginia, us-east-2, U.S.A.

(P.S. check your spelling!)

floxy5• 1 year ago

Whoops!

djoldman• 1 year ago

This leaves even those whose job it is to predict energy demands forced to assemble a puzzle with countless missing pieces, making it nearly impossible to plan for AI’s future impact on energy grids and emissions. Worse, the deals that utility companies make with the data centers will likely transfer the costs of the AI revolution to the rest of us, in the form of higher electricity bills.

... 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.

cco• 1 year ago

Today Google launched a model, Gemma 3n, that performs about as good as SOTA models from 1-2 years ago that runs locally on a cell phone.

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.

jeffbee3• 1 year ago

This series of articles is driving me insane. The authors or editors are using inappropriate units to shock readers: billions of gallons, millions of square feet. But they are not putting the figures into context that the reader can directly comprehend. Because if they said the Nevada data centers would use 2% as much water as the hay/alfalfa industry in Nevada then the entire article loses its shock value.

panstromek5• 1 year ago

I agree. Even first few paragraphs strike me as intentionaly misleading. The whole "AI energy saga" seems to be full of manipulative claims. I can't help to feel that the intent is just to color the AI as bad in whatever way possible. It feels like it's deriving the research from predetermined conclusions

protocolture3• 1 year ago

Weird I was assured that Bitcoin would be using all of the worlds electricity by now.

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.

scottcha• 1 year ago

Shameless plug . . . I run a startup who is working to help this https://neuralwatt.com We are starting with an os level (as in no model changes/no developer changes required) component which uses RL to run AI with a ~25% energy efficiency improvement w/out sacrificing UX. Feel free to dm me if you are interested in chatting either about problems you face with energy and ai or if you'd like to learn more.

mNovak• 1 year ago

This gives me the silly idea to go try to measure the power consumption of the local data center by measuring the magnetic field coming off the utility lines.

teleforce• 1 year ago

unprecedented and comprehensive look at how much energy the AI industry uses

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.

neves3• 1 year ago

Best article I've ever read about the energy needs of AI.

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...

johnny_________• 1 year ago

I ponder this a lot, but the interface of "MIT technology Review" is unbearably overdesigned, its got that annoying narrow smartphone format where you can't zoom out, and then all these fancy graphics. Can't we have crisp, easy-to-read HTML? The format annoyed me so much I didn't read the article because this kind of design makes me doubt the source. Alas

myaccountonhn2• 1 year ago

The article works fine with the browser's reader-mode for me.

paulcole3• 1 year ago

Are these the same people who claimed that crypto was going to use more energy than the entire world by 2030?

kleiba• 1 year ago

This reminds me of the quote: "Félix L'Herbier learns that there are more links in his brain than atoms in the universe."

LordDragonfang• 1 year ago

The new model uses [energy] equivalent to riding 38 miles on an e-bike... AI companies have defended these numbers saying that generative video has a smaller footprint than the film shoots and travel that go into typical video production. That claim is hard to test and doesn’t account for the surge in video generation that might follow if AI videos become cheap to produce.

"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).

thedevilslawyer2• 1 year ago

What's the energy imprint of the human doing the same work that AI is now going to do? If AI's imprint is less, what is the right thing for us to do?

ahtihn• 1 year ago

Kill the human?

If you don't want to go there, it doesn't really matter how much energy the human uses because the human will just use the same energy to do something else.

zavec• 1 year ago

Not necessarily. I think the point of comparison here is how much energy does AI use to e.g. generate a video, compared to the energy used not by the human themselves, but by running XYZ software on a computer with a beefy graphics card for however many hours it'd take a human to do the same work.

coolcase6• 1 year ago

The human is alive anyway so zero extra footprint.

wyre2• 1 year ago

I've been thinking about this. If the human-equivalent of training an LLM is sending hundreds/thousands of students through college for many years, I can't help but think that the energy needed for both outcomes is comparable.

mxfh• 1 year ago

My go-to example is when some EU initiatives proposed labeling mobile phones by energy use. It completely missed the forest for the trees, as a prime example of overoptimization if your goal is carbon emissions reduction.

Nearly any other daily activity of a consumer in the developed world uses orders of magnitude more energy and resources than scrolling TikTok on a phone.

Examples?

– Driving to work: commuting burns far more fuel in a week than your phone uses in a year.

– Gym sessions: heated, lit, air-conditioned spaces plus transit add up quickly.

– Gaming or watching TV: bigger screens, bigger compute easily 100x and higher power needs vs phone gaming.

– Casually cooking at home: using a metric ton of appliances (oven, stove, fridge, pans) powered like twice a week, replaced every ~10 years.

– Reading print media: a daily newspaper or weekly book involves pulp, ink, shipping, and disposal.

– Streaming on a laptop or smart TV: even this draws more power than your phone.

– Taking a shower: the hot water energy use alone dwarfs your daily phone charge.

Of couse not doing any sports or culture is also not what societies want, but energy wise a sedentary passive tiktok lifestyle is as eco friendly as it get's vs. any other real world example.

Phones are basically the least resource-intensive tool we use regularly. Externalities, context, and limited human time effects matter a lot more than what one phone uses vs the other.

Even e-readers already break even with books after 36 paper equivalents

https://www.npr.org/2024/05/25/1252930557/book-e-reader-kind...

xeox538• 1 year ago

I believe we're currently seeing AI in the "mainframe" era, much like the early days of computing, where a single machine occupied an entire room and consumed massive amounts of power, yet offered less compute than what now fits in a smartphone.

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.

panstromek5• 1 year ago

It sure seems like that to me. I was pretty impressed by how easily I could run small Gemma on 7 year old laptop and get a decent chat experience.

I can imagine that doing some clever offloading to a normal programs and using the LLM as a sort of "fuzzy glue" for the rest could improve the efficiency on many common tasks.

coolcase6• 1 year ago

Big tech ain't investing heavily so you can run local, what data does that leave them to sell, and what power and control does that give them. Zilch.

whatnow37373• 1 year ago

The CPU development curve is often thrown around but it very seldomly fits anything else in reality. It was a very rare and extraordinary set of coincidences that got it us here. Computation using silicon turned out to have massive growth potential for a variety of lucky reasons but say battery tech is not so lucky, nor is fusion nor is quantum computing.

The low hanging fruit has been plucked by said silicon development process and while remarkable improvement in AI efficiency is likely it is highly unlikely for that to follow a similar curve.

More likely is slow, incremental process taking decades. We cannot just wish away billions of parameters and the need for trillions of operations. It’s not like we have some open path of possible improvement like with silicon. We walked that path already.

Maybe photonics..

righthand• 1 year ago

I don’t understand the “chips designed for AI workloads” sentiment I hear all the time. Llms were designed using Gpus. The hardware already exists, so what will make it use less energy in a world where Gpus over the last decade have only become bigger, hotter, more power hungry hardware? If we could develop Llm on anything less we probably would have shifted back to Cpus already.

minimaxir• 1 year ago

Google's TPUs are an example of efficient chips designed for AI workloads, and were in development before the LLM boom.

It's just hard to replicate the power and efficiency of CUDA.

tempfile• 1 year ago

Jesus, who writes this stuff?

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.

dr_dshiv6• 1 year ago

Solving climate change will take a lot of energy.

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

Juliate3• 1 year ago

It's still _additional_ power usage that

1/ did not exist before

2/ does not replace/reduce previous/other power (some, very much more critical and essential) usages.

3/ a LOT of tasks are still way more energy/time-efficiently done with regular existing methods (dedicated software, or even by hand), but are still asked/improperly routed to AI chatbots that ... statistically guess the answer.

dr_dshiv6• 1 year ago

It is also additional value creation.

It also leads to automation and efficiency, even if it isn’t a fully linear path.

AI isnt a waste. We can’t let environmental consciousness get in the way of rather natural human development. Especially CO2. (I have different opinions about biodiversity because of the irreversible loss. I also believe that we have the technology to pause and reverse climate change — but don’t pursue it because of degrowth ideologies)

Fraterkes2• 1 year ago

The current AI-boom has been aroung for ~3 years. Implying that effeciency gains in AI will be like Moore's law (ie they will accrue at around the same high rate for decades) based on data from that time frame is pretty unresponsible in my opinion. Also, Altman is not in any way an expert in any of this and has an obvious financial interest in making this technology palatable. There's no good reason to cite his writing in these kinds of discussions.

dr_dshiv6• 1 year ago

Distillation seems like a pretty robust approach. There is no good reason to think that GPT5 or 6 won’t have massive rapid efficiency gains.

He’s one of the most knowledgeable persons in the world on the topic. It’s like saying that the ceo of BMW isn’t citable on conversations about expected cost decreases in electric cars.

Any environmental topic is susceptible to a huge amount of groupthink. The world isn’t so binary as people make it out to be. It is far from truth that LLMs=bad for environment, any more than computers=bad for the environment.

panstromek5• 1 year ago

There's a certain irony here in the fact that this page is maxing out my CPU on idle, doing some unclear work in javascript, while I'm just reading text.

guappa5• 1 year ago

I would like browsers to have very strict limits on the amount of cycles a page can use, and have some permission like for camera to use more CPU.

jve• 1 year ago

What a nice idea.

sligor• 1 year ago

I would like also that devs run their site/apps on downgraded machine/VM so that they can early detect performance issues.

coolcase6• 1 year ago

Run the browser in a cgroup?

vanschelven2• 1 year ago

I felt the same when reading this before lunch; it prompted this: https://www.bugsink.com/blog/tabdouse/

(cgroups, as per a sibbling comment, are addressed in this write-up as "not maximally satisfying")

panstromek5• 1 year ago

Oh, I love the maximum irony section. Sentry drives me nuts. Monitoring that somehow takes away half of your bundle size budget.

They could go even go further and report themselves as a perf issue, like when google's lighthouse reports google analytics.

liendolucas• 1 year ago

...and many buildings use millions of gallons of water (often fresh, potable water) per day in their cooling operations.

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?

bogtog2• 1 year ago

...and many buildings use millions of gallons of water (often fresh, potable water) per day in their cooling operations.

Of note, cooling is water evaporation, so the water will inevitably come back to us as good as new. This contrasts uses that will actually pollute water

bogtog2• 1 year ago

The largest model we tested has 405 billion parameters, but others, such as DeepSeek, have gone much further, with over 600 billion parameters.

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

James_K• 1 year ago

The fact that none of these companies want to tell you how much power they're using should be enough to reason that it's utterly horrible. If it were anywhere reasonable, they'd be boasting about how low the number is.

janaagaard• 1 year ago

- "4.4% of all the energy in the US now goes toward data centers"

- "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?

EdiX• 1 year ago

US households constitute 21% of all energy use [1], so 22% of this is 0.21 * 0.22, which is 4%.

[1] https://rpsc.energy.gov/energy-data-facts

southernplaces7• 1 year ago

The number of people in this comment thread defending this gargantuan energy footprint for a technology that currently in large measure is being used for a tremendous amount of dogshit things (and oceans of visual/text spam) is amusing considering the hysterics that this same energy use problem caused when it came to crypto.

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.

admiralrohan2• 1 year ago

Good that you connected it with crypto boom. But problem is it's hard to rewind these kind of trends once started. Human nature.

pjc502• 1 year ago

NFTs are dying and link rotting. ICOs are out of fashion.

VR is back in its niche.

3DTV .. I've not seen that marketed for quite a while now. Things that are fads will die out eventually.

Crypto meanwhile is in an odd space: everyone knows blockchains are too much of a hassle and that the volatility is too high, so they're using centralized exchanges and "stablecoins" (shadow dollars). There's still a huge amount of money there but not quite as much as the stadium ads / FTX peak.

vanschelven2• 1 year ago

At least in the case of AI the _theoretical_ benefits are there, whereas crypto is wasteful _by design_.

Whether the cost/benefit works out in the case of AI is another question.

phillipcarter5• 1 year ago

Yeah, it's a different compute/cost curve worth considering.

On one hand, the cost of compute per token has gone down a lot, and will continue to go down, because that's exactly the economic incentives at play. We had a little short-term nonsense where "the bigger the better" was all the rage, but inference was never this way, and now training is also pushing in this direction.

But on the other hand, less compute per token means it can be more broadly deployed. And so there is likely more energy use, not less, in the long run.

DavidSJ• 1 year ago

I agree with you that AI has much larger theoretical benefits than cryptocurrency, but I don’t think it’s fair to say cryptocurrency is wasteful by design. Bitcoin’s proof of work serves a vital function: securing the network from double-spending attacks. At the time of bitcoin’s invention, it was the only known solution to that problem, so it’s no more “wasteful by design” than a bank hiring a security guard. There are admittedly alternatives to proof of work today; I’m unsure how well they work by comparison, but even if they suffice for security, that only means that bitcoin is wasteful due to being more primitive technology, not by design.

drdrey• 1 year ago

Proof of work is wasteful by design

ddq• 1 year ago

Meanwhile the theoretical potential existential risks of advanced AI massively eclipse the capacity of crypto for catastrophic harm. The _good_ ending is if we merely waste a bunch of energy on slop machines to put millions of people out of work for the technofeudal overlords; an actual AGI breakthrough capable of exponential intelligence growth could go horribly wrong in ways already innumerably theorized and illustrated in science fiction and the mad scientists of silicon valley are enchanted by the siren's call, taking the warnings as instructions.

Economic chaos is one thing, Skynet is another. And then people with the power are stupid and evil. Joy.

ezfe2• 1 year ago

Crypto energy usage (bitcoin, proof of work) is literally a waste lol

DonaldFisk2• 1 year ago

Bitcoin, yes. But Ethereum uses proof of stake, which has around 0.1% of proof of work's energy requirements. If only there was a way of reducing AI's energy requirements by a similar amount.

skybrian• 1 year ago

Unlike Bitcoin, which burns more energy for the same usage as the price goes up, LLM inference prices are dropping rapidly [1] because AI companies have big incentives to cut costs.

Companies like Apple and Google are both building data centers and trying to make on-device AI a thing. Unfortunately, they also keep inventing new, more expensive algorithms.

It’s at least plausible that most LLM use will become cheap enough to run on battery-limited devices like laptops and phones, though it’s not what most people are betting on.

[1] https://epoch.ai/data-insights/llm-inference-price-trends

jrochkind1• 1 year ago

I remember when the tenor on HN comments toward crypto was very positive. It was not always negative.

polski-g• 1 year ago

Seems like a solved problem. We have an unlimited sources of clean power from nuclear and solar. China alone now has almost 1TW of solar capacity.

Build more nuclear, build more solar. Tax carbon.

troyvit3• 1 year ago

Tech companies like Meta, Amazon, and Google have responded to this fossil fuel issue by announcing goals to use more nuclear power. Those three have joined a pledge to triple the world’s nuclear capacity by 2025.

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...

neves3• 1 year ago

The weirdest thing in the article is the refusal of Big Tech to release this data. We wouldn't experts to guess. Society must have information to make decisions about what affects all the world.

WillPostForFood• 1 year ago

Do you think it is weird or just a typo?

troyvit3• 1 year ago

Heh, when I first started typing it I thought it was weird, then I discovered it was a typo, but didn't edit my first sentence. Weird huh?

palmfacehn• 1 year ago

From "The Limits to Growth" to peak oil and beyond: As a rule, scarcity doom and neo-malthusianism hasn't played out the way proponents claim. I've been very critical of the AI hype cycle here and elsewhere, but this isn't it. In the long run technological advancements increase our productivity and quality of life.

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.

thedudeabides5• 1 year ago

long silver for solar

pizzuh2• 1 year ago

Energy use is going to continue to climb.

I'm worried about the environmental impacts of this, but from everything I've seen society values model output more. Curious to watch this over the rest of the decade.

jes51992• 1 year ago

solar and battery costs are falling so fast that emissions are likely to go down, even with growth in energy use

edhelas• 1 year ago

Sure sure, you did the maths ?

How many batteries do you need to power-up a datacenter at night ?

pizzuh2• 1 year ago

Great point. Assuming we can adopt them at the correct pace and on a global scale, then energy doesn't have to be a limiting factor

some_random• 1 year ago

We shouldn't be hand wringing on energy usage, we should be generating energy too cheap to bother metering and too clean to bother thinking about. Nuclear baseline, renewables everywhere they make sense, we know how to do this we just need to do it.

airylizard• 1 year ago

As more and more people use brute force loops to make their AI agents more reliable, this hidden inference giant will only continue to grow. This is why I put my framework together, using just 2 passes as opposed to n+ can increase accuracy and reliability by a far greater amount than brute force loops while using significantly less resources. Supportive evidence and data can be found in my repo: https://github.com/AutomationOptimization/tsce_demo

benlivengood• 1 year ago

I find it weird that on the whole there wasn't as huge a pushback against AWS and Google and Facebook (by far the biggest fleets of data centers) for power and carbon emissions over the last couple decades. Maybe they managed propaganda better about paying for future renewable resources or how low their PUEs are.

Turning 4% of the U.S.'s electricity into cat videos and online shopping and advertisements and heat already sounds like a lot of use. Maybe the rapid rise of AI use is what's alarming people?

Contrast the 2016 study[0] of data center energy use where use was recently flat because of efficiency improvements in 2010-2020 but historically there was a ton of growth in energy consumption since ~1990; basically we have always been on a locally exponential growth curve in data center energy use but our constant factors were being optimized by the hyperscalers in that 2010-2020 period.

We also need to compare the efficiency of AI with other modes of computation/work. The article goes into detail on the supposed actual energy use but there's a simple metric; All the large companies provide costs per unit of inference which can put a hard ceiling on actual energy cost. Something like $20/1M tokens for the best models. METR used a 2M token budget. So you can currently price out N hours of work at $40 from whichever latest METR benchmarks come out and have a worst case cost for efficiency comparison.

Lastly, if we're not on a trend toward having Dyson swarms of compute in the long run then what are we even doing as a species? Of course energy spent on compute is going to grow quadratically into the distant future as we expand. People are complaining about compute for AI but compute is how we figure things out and get things done. AI is the latest tool.

[0] https://eta.lbl.gov/publications/united-states-data-center-e...

insane_dreamer• 1 year ago

It's hard to believe that as a society we are prioritizing AI queries (which are fine, nothing wrong with them in principle, and they do increase productivity in some cases) over the future wellbeing of our species (and planet). Winter is Coming, and these big companies and their investors couldn't care less.

I can't decide whether they think it won't be that bad, or the scientific forecasts are wrong, or that they just don't care because whatever turmoil results from serious climate change, they'll be able to rise above it, and well, fuck the rest of humanity.

I'm disgusted by our sense of priorities. (Though maybe I shouldn't be since we live in a country that values subsidizing the industrial-military complex over the health and education of its citizens.)

AI's energy footprint

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