How much electricity does AI consume?
theverge.com
theverge.com
The estimates are between 1% and 5% worldwide depending on the used definition AI/ML and the definition of world energy use (for example global electricity versus global energy).
[1] Integrative design for radical energy efficiency - Amory Lovins https://youtu.be/Na3qhrMHWuY?t=1026
[2] "Energy. Estimated global data centre electricity consumption in 2022 was 240-340 TWh, or around 1-1.3% of global final electricity demand. This excludes energy used for cryptocurrency mining, which was estimated to be around 110 TWh in 2022, accounting for 0.4% of annual global electricity demand."
https://www.iea.org/energy-system/buildings/data-centres-and...
[3] Stanford Seminar - Saving energy and increasing density in information processing using photonics - David B. Miller https://www.youtube.com/watch?v=7hWWyuesmhs
[1] https://www.nytimes.com/2023/10/13/us/bitcoin-mines-china-un...
[2] Energy, Following the Numbers - Saul Griffith Stanford talk https://www.youtube.com/watch?v=1ewEaTlGz4s
If history is anything to go by energy consumption will remain the same or increase, we’ll just have more output
further increasing the efficiency of the process will only increase total consumption even further.
the entirety of the AI boom has been a perfect demonstration of Jevons Paradox in action. Previously these intractable problems simply went unsolved. Now it is possible to solve them with a plausible degree of efficiency, and that increases the demand for solved problems hugely because the price per solved problem has declined. If you push energy even lower it will only fuel even further demand for more solved problems because wow, it’s cheap now!
Computers slower than my pencil arithmetic used to run on valves and now here I am typing for half a day and blazing on the internet in my cordless, battery powered MacBook Air.
Day 1 of cars were infinitely less polluting than current day, the rebound effect is a killer
Could you please elaborate on that?
If I had the time I could add numbers from Google, Microsoft and other hyperscaler analyses on software efficiencies in their datacenter papers.
I'm sure Alan Kay has some insights on software efficiencies [2].
A small part of hardware inefficiencies are the energy use per transistor of computer chips. It has gone up since the 28nm node. Our datacenters have primarily smaller node chips (16nm, 7nm, 5nm) and have therefore gone up in energy use.
[1] Integrative design for radical energy efficiency - Amory Lovins https://youtu.be/Na3qhrMHWuY?t=1026
[2] Is it really "Complex"? Or did we just make it "Complicated"? - Alan Kay https://www.youtube.com/watch?v=ubaX1Smg6pY&t=2541s
The real question is when AI will use 100% of electricity ;)
1. Data centers & Bitcoin are using about the same amount of electricity, about 2% of the US energy production each
2. AI is a subset of data center usage, but expect this portion to drastically rise and drive up the energy usage
3. Data centers & AI are continuingly becoming more efficient while Bitcoin becomes less so by design, L2 crypto is an efficiency play for crypto more generally
It's not really about efficiency as it's about computing power and available miners.
If it's not economically beneficial to mine (costs more than reward) miners will stop mining leading to the difficulty decreasing which in turns makes it profitable to mine again.
one of the major key principles behind bitcoin is that the hashing becomes more difficult over time.
hardware can't constantly keep parity with difficulty.
Now, there has been a strong positive correlation between time and total network hashrate, so for all practical purposes, difficulty has (and likely will continue to) increase over time.
That said, if half of all miners went offline overnight, block times would approximately double, and the next difficulty rebalabce would go dramatically lower in an effort to maintain 20 minute block times.
e.g. you can't bullshit your way into finding this number, 000000000000000000024394a1f3cb1a0c16e601a2bd5910635bb2468d2ba316, without expending a huge amount of energy, while you can verify very quickly how many guesses it statistically took to find it.
An attacker can't just use words to spin a manipulative narrative, or cut of the head of an organization with a targeted attack. They actually have to commit massive numbers of joules and bit flips. And if an attacker actually acquires that much control over mining power, suddenly they realize they're too heavily invested in the network to want to harm it.
In the age of increasing generative AI, proof that you have some tie to real world cost is an increasingly valuable trait.
It looks like estimates for global energy production are over 30 TWh last year, so that would mean bitcoin is around 1/3rd of one percent of total global power usage.
Note that’s just bitcoin, not all of crypto.
That means the 2% claim is an order of magnitude lie.
Ironically, this nature can actually fortify the electric grid in some areas, such as Texas. Bitcoin mining businesses have discovered that the unreliability of the existing grid can be mitigated through vertical integration - they create renewable power generation facilities, and when the cost of electricity on the grid is low (because demand is low and supply is high), they use their own renewably-sourced energy for next to nothing.
When grid conditions deteriorate in Texas' deregulated energy market, wholesale electricity prices surge, as those are times when demand approaches or exceeds supply.
When that happens, the electricity being generated by these vertically integrated companies is worth more being sold to the grid than it's worth being used to mine bitcoin, so the miners all shut off (within milliseconds, as this is all automated), and the power that location generates starts getting sold to the grid, which increases supply, helping to lower the electricity prices, and to keep the lights on for everyday people.
It's not a magic bullet that fixes the entire grid, but there is a growing body of evidence saying that it helps grid reliability in Texas more than it hurts, and these vertical integrations are overwhelmingly done with renewable energy sources.
I'm sure the location-agnostic aspect of Bitcoin mining does lend itself to deployment in places where power is plentiful, but where there is little local demand, and the cost of transporting that power far away is cost prohibitive, though I don't have specific example of that.
> I'm sure the location-agnostic aspect of Bitcoin mining does lend itself to deployment in places where power is plentiful, but where there is little local demand, and the cost of transporting that power far away is cost prohibitive, though I don't have specific example of that.
https://www.coinmint.one/ is a specific example of that. Power is delivered directly from the Moses-Saunders dam to a shuttered aluminum smelting plant. Sending the power anywhere else, is cost prohibitive due to the remote location and low local power needs. Connecting it to the grid would overwhelm what is there, so new construction would be needed.
If their numbers and methodology are correct, bitcoin mining energy use in the US is somewhere between 0.8-3.8% of US electric generation. However, we don't know how much of this mining is actually connected to the grid. There are some off grid operations, like waste methane harvesters. The US government is starting to collect this data, so we might have more precise public information soonish.
If the price of bitcoin were to double, the (collective) rewards to mining double, too, and so does the environmental damage. In about 4 years, the reward for mining a block is scheduled to halve, and the environmental damage will at that time be about half of what it is now (a few months after the previous halving) provided the price of bitcoin does not change. All the miners know exactly when the reward is going to halve, so as the halving-date approaches, about 4 years from now, miners will invest less and less in mining hardware and other capital improvements, which "smooths out" the damage so that it decreases somewhat smoothly between now and then instead of suddenly halving on the day the reward halves.
And, of course, let's not discuss:
- tourism + air travel
- increasingly large cars far bigger than required from a utilitarian perspective
- luxury good production
- theme parks/fireworks displays
- cruise ships
Etc.
Bitcoin/crypto opposition is 95% pushed by embedded financial interests that will use any lever to protect their control over money and the power it gives them.
So there is more energy in a tanker truck full of gasoline than it takes to train an AI model. And a person uses more energy to go to the grocery store than then will reasonably use generating things with AI all week.
This is the part where people say Crypto is only for fraud, scams and just making money so it is different. I think AI will open all new avenues of fraud that will make the ransomware mess look pleasant in comparison. AI may destroy the very concepts of truth and trust even for those looking for it, and it ALSO will waste lots of energy doing it.
You obviously disagree but opening with "A common HN bugbear is all the energy crypto wastes" is no more disingenuous than my reply.
Oh, hell nah!
Dark visions: AI's mere application will be solving problems AI created in the first place - and, incidentally, the erosion of trust brought up a use case for crypto, at last.
I will be in my hut eating moss.
Anyway, given previous examples of the netflix arch, I’d expect most of the cost of streaming is mostly TLS session management.
I approve of this unit of measure
For example the 'entire tech economy' produces more efficient engines, devices that save on fertilizer planting crops, machines that sequence genes, etc. Attempting to handwave AI as a cost of this, unless you can otherwise explain, seems like you're saying "Climbing down from the trees was a mistake"
I'm not familiar with what is required but I do believe that the larger the dataset the better it is, and that specialized GPUs have already been released.
The difference is that the AI field is
- rapidly producing better algos and hardware, L2 scaling in crypto can be seen similarly, but nowhere near the same pace of innovation
- AI is producing business & consumer value now, there is a sustainable business model, crypto still seems like a separated economic system
There is a finite amount of fabs that can produce GPUs which sets an upper limit on the production. I doubt we are going to see _that_ many more fabs being built.
I also think everyone is hoarding as many GPUs as possible, which makes sense for meta/openai/google but probably much less sense for other players with time - nevermind random corporates that are just jumping on the bandwagon. I really think we'll see a small number of players (more than just openai, meta and google) produce most of the foundational models, then everyone will finetune and infer off them (which are many orders of magnitude less). That's not to say there won't be huge demand for GPUs, but I don't think it's going to be that every fortune 500 needs a 10k GPU cluster.
The key differentiator IMO with crypto is crypto by its nature has exponentially more computer resource requirement built into (most) of it. I don't know if AI does after a certain point, and I think there are enormous efficiency gains happening which simply doesn't happen in crypto (which you point out).
In the UK which has virtually no bandwidth caps on fixed line broadband bandwidth use isn't growing particularly fast: https://www.linx.net/news/capacity-planning-a-priority-for-l..., despite a huge increase in FTTH availability.
You can see the same in Amsterdam: https://www.ams-ix.net/ams/documentation/total-stats which is basically flat Y/Y.
This may be because of more private peering away from IXs but if there was massive growth you'd see it.
This is wrong. Crypto does not need a massive amount of GPUs in a data-center to function and waste lots of resources unlike generative AI and deep learning which for any serious model to be used for inference; it needs tons of data centers and GPUs to serve millions.
AI has always required hundreds of millions of dollars a year in inference costs alone as the data scales whilst also requiring lots of energy to output a working model including the risk of overfitting and garbage results.
Compare this to crypto where it increases with time.
Im not too worried about this. Maybe we'll have accelerators for inference, but only if they can be cheap and efficient alternatives to GPUs (again, for inference).
Pretty much all activity in modern society is going to consume electricity, and overall demand is not going to be decreasing in the first place, so it seems a bit silly to look at this from the demand side: we're always going to need more and more power, and the focus is properly on how to generate power in a clean and scalable way regardless of what it's being used for.
Consumer market impact is an interesting topic, though, if there are massive spikes in demand that could drive prices up for other users of electricity. It will be important to ensure to minimize artificial impediments to the expansion of the supply side to mitigate that risk.
At least, cryptocurrencies have managed to combat this criticism with alternatives to proof-of-work and even Ethereum made it possible for a wasteful PoW blockchain to migrate to proof-of-stake which is an energy efficient alternative consensus mechanism [0][1] and have reduced their consumption by 99%.
The field of Deep Learning has made little efficient alternatives with any measurable impact and have always needed tons of GPUs in data-centers and the demand is made even worse with generative AI whilst continuing to green-wash the public with faux green proposals for years.
Not much progress in these so-called practical alternatives to this waste that AI has produced or any reduction of energy usage. As it data and model scales, the energy consumption and costs will only just get worse even by 2027.
[0] https://digiconomist.net/ethereum-energy-consumption
[1] https://www.cell.com/patterns/fulltext/S2666-3899(22)00265-3
> Moreover, the organizations best placed to produce a bill — companies like Meta, Microsoft, and OpenAI — simply aren’t sharing the relevant information.
To me this shows both unfamiliarity with large corporate structures and unfamiliarity with AI research. The former because, if you want an exact accounting at a company where there are several teams running dozens or hundreds of models, it becomes someone's job essentially to compile this information because it takes a lot of work. So, you are surprised that a corporation doesn't outlay $200k/year or more getting these figures to decorate your article with? The corporations do know, on a month-to-month basis, what they're spending on this stuff - since they settle the invoices. But doing the work to get these figures into a simple, digestible form is a lot of effort, and I think that quite frankly, that effort should fall on the journalist, since it's the journalist who gets the benefit from having those numbers...
As to unfamiliarity with AI research, many papers I have been reading lately are very interested in measuring and minimizing the compute cost of models, and they often compare different methods and are often extremely precise about their training process and equipment. I feel like this article wants to sell the story that these faceless corpos don't care about energy consumption, but the researchers definitely do. Granted certain specific products / models such as ChatGPT 4 do not disclose exactly their process, but I feel like it would not be difficult to come up with a good estimate using similar models (mostly documented right out in the open in scientific papers).
Tracking the energy consumption of AI is an important and emerging issue, but this article feels too partisan to be useful.
Since we should all be concerned about protecting our resources and thus the planet - and that means reducing unnecessary energy consumption - this figure should be clearly included in the general costs of AI. And by that I don't mean in the electricity bills of corporations. Of course, it is in the interest of researchers to increase the efficiency of modelling and application, but for other reasons. As long as the big, well-known companies are in charge, the target figure towards which everything is optimised and maximised is commercial profit.
To cut a long story short, the only way to gain some ground here is through independent regulation, for example to get more transparency into this issue.
To my knowledge, most of the researchers and users of AI are using their own resources, not "ours", so I'm not sure there's much to be worried about.
> And by that I don't mean in the electricity bills of corporations.
What else would it be accounted for in? The users of electricity are purchasing it from the producers, who in turn purchase equipment and services from vendors, etc. It's all a chain of specific transactions among specific parties all the way down. There's no point at which some fuzzy collection of arbitrarily aggregated people is involved as one of the parties.
> As long as the big, well-known companies are in charge, the target figure towards which everything is optimised and maximised is commercial profit.
And they make that profit by delivering value to their customers -- what's the problem there?
> To cut a long story short, the only way to gain some ground here is through independent regulation, for example to get more transparency into this issue.
What is "independent regulation"? Who is conducting it, what makes them "independent" and what is their incentive to be involved in the first place?
Plus, there are weird things with the figures. It lists the 0.012kwh figure required to charge a smartphone, but the source it cites for that number explicitly says that the correct number is 0.022kwh. Was this article hallucinated by AI?
Overall though, frankly I found it comforting that AI would “only” use 0.5% of global electricity, as I expected much higher. Google and several other peers are committed to being 100% renewable anyway.
Still, credit to the author for starting discussion about this.
It's worth knowing this is a very recent phenomena. Before ~2020, it was extremely rare to see any such paper, with most of the focus in publishing being on maximizing metrics like accuracy. The biggest force pushing people toward smaller models were researchers at smaller institutions who lacked access to big GPU clusters.
> So, you are surprised that a corporation doesn't outlay $200k/year or more getting these figures to decorate your article with?
I think it's incorrect to suggest these figures would only serve to make for a better "The Verge" article. One of the externalized costs here is climate change.
Actually my point is that to specifically isolate costs that are from AI is a lot of work at a company of this scale. These companies are absolutely accountable for their overall energy usage and externalities!
* how many resources will they still consume after being replaced by AI,
* is AI just more resource consumption on top,
* will the people replaced be deported to "elsewhere", converted to Soylent Green, or supported by UBI funded by an AI tax?
Never go ankle deep in these questions.
Sam Altman says we need to invent nuclear fusion to meet the AI power consumption.
Did anyone stop to think maybe reducing power usage is a better solution than a moon shot invention?
This is fundamental research and if you kill research because you think it's frivolous you will kill many things you didn't know you wanted. Frivolous research is the backbone of scientific progress.
Even generative AI - it may be called generative AI because that's flashy, but its real power is as classifiers, and classifiers are incredibly useful.
Imagine robots that could perfectly sort recycling/garbage/compost, what do you think that is worth?
Imagine robots that can mechanically remove weeds so that zero herbicides are needed, what do you think that is worth?
The possibilities really are endless and I am excited to see how these technologies evolve.
AI can also aid in optimizing energy grids that adapts to supply and demand [1], aid farmers in optimizing fertilizer and water use [2], logistics companies to optimize routes and fuel use [3], etc.
It's not at all only about the consumer-facing products and these are areas often in need of urgent attention and optimization to boot.
[1] https://www.iea.org/commentaries/why-ai-and-energy-are-the-n...
[2] https://dl.acm.org/doi/10.1145/3587716.3587742
[3] https://www.reuters.com/sustainability/climate-energy/logist...