How much electricity does AI consume?

https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption

by doener • 2 years ago

106 119 2 years ago

As I read this article I felt myself being extremely critical of it. Part of this may be that I'm enamored with AI and so I feel defensive about it, but I can't help the feeling that this article needed more work. For example:

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.

zx8080• 2 years ago

Is parent comment AI generated?

smartmic• 2 years ago

At the very least, the article has brought to the forefront the awareness that, in addition to all the other effects of the hype, the research and use of AI on a large scale also consumes an extreme amount of energy.

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.

sdenton4• 2 years ago

To be sure, we only care about electricity usage because of externalities, most importantly CO2 emissions. Many of the big players have gone to great lengths to build out renewables capacity to power their data centers, which tends not to be considered in these articles.

Gormo4• 2 years ago

Since we should all be concerned about protecting our resources and thus the planet

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?

mepiethree• 2 years ago

I work in the sustainability industry so this is obviously important to me, but I agree the article needed more work. Comparing the energy required to train GPT3 to watching Netflix is nonsensical. Training is a global task. A better comparison would be the cost to train GPT vs that to upload all Netflix movies.

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.

Fripplebubby3• 2 years ago

Yes, I ultimately I guess it does not hurt anything to have this article in circulation even if I have some critiques, better to discuss the elephant in the room.

lynndotpy• 2 years ago

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

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.

Fripplebubby3• 2 years ago

Fair point about recency.

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!

loeg• 2 years ago

Yeah. Meta has internally widely shared the expected size of a new very large cluster they are building in 2024 and from the number of H100s or whatever you can probably calculate a reasonable low estimate on power draw (there will be other machines to support the compute, e.g., storage). I don't know that it's public yet so I won't say the exact size but I expect it to be leaked or spoken about publicly shortly anyway. It's a lot of MW. They are semi-joking talking about building nuclear reactors to power future DCs.

rossdavidh• 2 years ago

It is difficult to say for sure, but one thing is definite, and that is that it consumes more than it is worth, at this point anyway.

x86x875• 2 years ago

More than the entire country of Argentina?

infecto6• 2 years ago

Not definite at all. That is only your world view.

monkaiju2• 2 years ago

Seems like a pretty worldview to say generating garbage text and 5 legged horse images isn't worth a measurable amount of our electricity, especially in a time where we so clearly need to be reducing consumption.

INTPenis2• 2 years ago

I was just thinking a few weeks ago that there is a risk LLMs will bring on a wave of big computer builds similar to crypto mining. For storing all the data, and for calculating all the queries from their users.

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.

verdverm2• 2 years ago

More data & compute still produces noticeably better results, and probably always will (?)

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

martinald2• 2 years ago

I agree. This reminds me of ~20 years ago when bandwidth usage was growing incredibly exponentially and everyone thought people in 2024 would be consuming 20TB/day. In reality bandwidth growth slowed down massively, to the point now where it is growing at something like 10-50%/yr and everyone went under because there was far too much capacity.

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).

jszymborski• 2 years ago

Inference and fine-tuning are only going to get more efficient. Training likewise.

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).

Gormo4• 2 years ago

It's odd to characterize that as a "risk". It seems to follow that increases in demand for computing use cases would in turn generate more demand for the hardware required to implement them.

INTPenis2• 2 years ago

The article was about electricity consumption, so in the context of environmental impact, or consumer market impact, it is a risk.

verdverm2• 2 years ago

From looking at a variety of sources, my best, rough understanding is that

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

x86x875• 2 years ago

Not sure where bitcoin got dragged into the conversation. Also not sure how BTC becomes less efficient by design. The hardware BTC is mined on has improved exponentially over time moving from general purpose cpus to gpus to custom asics.

ta988• 2 years ago

But what's the relation between that efficiency and the number of hashes computed? My understanding was that difficulty is increasing with efficiency so you end up having to try harder to get a block mined successfully.

serf• 2 years ago

Also not sure how BTC becomes less efficient by design.

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.

[0] https://www.blockchain.com/explorer/charts/difficulty

AxelLuktarGott• 2 years ago

The point of mining is that you don't want new blocks too often because it would create too many forks in your chain. The "difficulty" in mining is by design, if we make better hardware that can generate hashes more quickly then we need to make the valid hashes more rare to keep the property that new blocks are reasonably spaced out.

anonporridge2• 2 years ago

The entire innovation of Proof of Work (that made bitcoin a success where predecessors failed) is that it is costly in real world energy to corrupt the network.

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.

mistrial93• 2 years ago

are these Botcoin numbers verified at all? that is two percent of some common electrical grid? or remote hydro-power sites or something.. seems like a really large number, and lots of reasons to exaggerate/misrepresent it

jakeinspace• 2 years ago

The current bitcoin total hash rate is obviously public information (currently about 6E20 hash/second). I was also able to find a report on average network-wide hashing efficiency, which is obviously self-reported, but is at least in line with logic (just slightly trails the current highest efficiency ASIC miners). That gives around 13 GW of total instantaneous power consumption. The US produced around 4000 TWh last year, which is an average of about 460 GW of power. That would make global bitcoin power consumption equal to about 2.8% of US energy production.

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.

anonym292• 2 years ago

I can't speak to the 2% figure, but as for the power sources, Bitcoin is somewhat unique, as it is ostensibly the most price-sensitive, most location-agnostic, and most interruptible instance of large-scale power consumption. Mining is done strictly for profit, so it only performed at any scale strictly where it is profitable.

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.

anonporridge2• 2 years ago

This Cambridge group is the best source I know of that estimates bitcoin mining energy usage and location. There are wide error bars because we don't know exactly what mining hardware is being used, even though we know the approximate hashrate. https://ccaf.io/cbnsi/cbeci

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.

jdmoreira2• 2 years ago

This is day one. This is the least efficiency it will ever be. All the software optimisations are still ahead of us and even hardware will get more optimised.

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.

x86x875• 2 years ago

Or another way to look at it: this is day one. Usage will just continue to growth.

jdmoreira2• 2 years ago

Yes, of course but not per unit of output. It will become more efficient and more widespread. I don't really see a long-term problem in that. Maybe a short-term problem yes

itishappy• 2 years ago

Will efficiencies ever catch up to our voracious apatite for "better"? Current top-of-the-line models seem to have gotten bigger much faster than they've gotten more efficient, and I don't see that trend stopping anytime soon.

Gormo4• 2 years ago

Either the value proposition of AI will incentivize investment that successfully improves AI efficiency as operating it becomes more expensive, or AI development will plateau around whatever equilibrium point is defined by the best attainable efficiency.

surajrmal2• 2 years ago

AI has been pushed into products for a few years now. We've had accelerated hardware for it for at least 6 years. It's hardly day 1. Surely things will continue to improve, but it's perhaps more poignant to say it's still early in the evolution cycle for AI technology.

lm284692• 2 years ago

This is day one. This is the least efficiency it will ever be

Day 1 of cars were infinitely less polluting than current day, the rebound effect is a killer

ithkuil• 2 years ago

I find it hard to believe that the first cars were less polluting (per unit of power) compared to modern cars.

Could you please elaborate on that?

morphle3• 2 years ago

I disagree. Software and hardware optimisations have not happened since the Rocky Mountain Institute did this data centre efficiency analysis [1].

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

moffkalast3• 2 years ago

The more efficient it gets, the more practical it'll be and the more we'll use it. Jevons paradox tends to hold for this sort of thing.

The real question is when AI will use 100% of electricity ;)

rvz2• 2 years ago

Yet AI still cannot even find a sustainable alternative to their inferencing, training and fine-tuning processes to combat their mass consumption of electricity and water for years.

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

CollinEMac• 2 years ago

From the article: “The tl;dr is we just don’t know.”

megaman821• 2 years ago

At numbers this small on the global scale, is this something that merits mass concern?

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.

bonton892• 2 years ago

A common HN bugbear is all the energy crypto wastes so it seems reasonable that AI energy usage would be of interest as well.

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.

andybak• 2 years ago

AI will be a mixed bag. Crypto is probably a net negative.

You obviously disagree but opening with "A common HN bugbear is all the energy crypto wastes" is no more disingenuous than my reply.

jijijijij• 2 years ago

AI may destroy the very concepts of truth and trust

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.

myaccountonhn• 2 years ago

1% is huuuuuuuuge on the global scale. Especially since it’s questionable how useful it is. Are we really allocating our limited resources well?

prpl• 2 years ago

700W per customer dedicated for Netflix seems incredibly high - basically saying a 1u is dedicated per customer (no TV power consumption) or half of a 1U factoring in a 52” TV (admittedly I’m omitting network power costs, transcoding, etc…)

Anyway, given previous examples of the netflix arch, I’d expect most of the cost of streaming is mostly TLS session management.

1-6• 2 years ago

With today’s AI, we’re taking a big hammer to the problem using unoptimized but vastly flexible machines like GPUs. Once the code settles down, expect ASICs to run much of the show and lower energy consumption.

mwhitfield• 2 years ago

You're maybe not wrong, but I'm pretty sure I was reading this same comment 10 years ago, when we were just calling it deep learning.

bongodongobob2• 2 years ago

Yeah and it actually works now. It's not just weird dog faces hallucinated with Starry Night.

quonn• 2 years ago

This is really unlikely, because the operations are pretty basic math and the GPUs do these about as efficiently as possible. Surely those GPUs will be improved further, but a custom ASIC probably won‘t help much with training. And NVIDIA has been producing GPUs that are specifically targeted at deep learning and they control both the software library and the hardware. I don‘t quite see how they couldn‘t optimize whatever they want to optimize.

xadhominemx• 2 years ago

The chips won't be that much more efficient on a like for like basis but the models will be much smaller so the chips can be smaller or run much larger batches

pixl974• 2 years ago

Being that us meatbags operate on a pretty limited power budget I'm guessing there is still a lot of algorithmic optimization to be done, and those algorithms will need hardware to run on.

londons_explore• 2 years ago

For inference, there is the option to produce ASIC's with the weights hard coded into the chip. They'll be super fast and power efficient, with the huge downside they aren't reprogrammable.

Cacti• 2 years ago

The models will simply fill the unused compute with additional training or more expensive models.

UncleOxidant• 2 years ago

There are already some custom chips out there such as groq's[1]. And isn't Google using a lot of TPUs for this sort of thing now? Microsoft also said to be working on custom AI chips.

[1]https://groq.com/

kcb• 2 years ago

The H100 at this point includes just the bare minimum hardware to be considered a GPU. As long as AI is primarily matrix multiplication, GPUs are going to be near ideal.

Dylan16807• 2 years ago

It's still spending most of its space on generic floating point units. And there's no hardware support for compressed weights as far as I know.

ultra_nick• 2 years ago

Agreed, using neuron layers as vectors seems like a waste. Moving to a graph-like compute model seems like it'd be more efficient.

krunck2• 2 years ago

... and then the energy saved by using more efficient hardware and software will be used to power more hardware to increase total AI power. There is no "enough" AI yet. There may never be.

bonton892• 2 years ago

Jevon's paradox basically. AI use cases that did not have RoI will open up as it becomes more efficient. The enough is always as much as it can.

iamacyborg• 2 years ago

Once the code settles down, expect ASICs to run much of the show and lower energy consumption.

If history is anything to go by energy consumption will remain the same or increase, we’ll just have more output

paulmd• 2 years ago

expect ASICs to run much of the show and lower energy consumption.

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!

pxeger1• 2 years ago

Only 130 US homes’ annual power consumption to train GPT-3 is surprisingly small to me, considering it only needs to be done once.

moffkalast3• 2 years ago

Tbf, GPT-3 was made before the Chinchilla paper and was only trained on 0.3T tokens which is basically nothing for its size or for any current model (Mistral 7B, a 25x smaller model was trained on 8T). Doing it properly would require much more power.

morphle3• 2 years ago

We don't know exactly how much electricity the AI/ML part of global energy use is, but it is being counted as part of the global datacenter energy use.

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

hristov• 2 years ago

You have a citation for 2022 which is right before use of AI exploded and NVIDIA started selling AI machines like hotcakes. I expect energy use to be much larger this year.

morphle3• 2 years ago

I'm sure you are right but the numbers for 2023 will be published next year. We can also estimate [2] and extrapolate from the recent shift of 30% (?) of bitcoin mining from China to the US [1]. ARPA-E funded the tabulation of all US energy uses in Sankey Diagrams, which included datacenter use [2] but also does not split out NVDIA's, Bitcoin mining or AI's parts.

[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

mmoskal• 2 years ago

FWIW a recent The Economist report [0] lists data center usage at 1-2% of global electricity consumption (which is 1/3 of energy IIRC) and AI at 8% of that in 2023 projected to grow to 15-20% by 2028. Given how careful the hyper scalers are about using renewables and offsets (edit: compared to other big energy users that is) I refuse to feel bad about it... [0] https://www.economist.com/technology-quarterly/2024/01/29/da...

Havoc• 2 years ago

a single Balenciaga pope

I approve of this unit of measure

verticalscaler• 2 years ago

What if instead of all the gnashing of teeth about the "co2 output of AI" we let it go wild and have it solve fusion for us.

bgnn2• 2 years ago

They don't even consider the energy consumed to produce the chips AI/ML is running on at all. The biggest chip producer TSMC ysed 22000 gigawatt hour last year. This doesn't include oder foundries, memory etc.. AI is now morebthan 15% of TSMC revenue. AI/GPU chips ate often in advanced nodes consuming orders of magnitude more energy than older nodes. So one can assume energy consumption share is much more than 15% of that 22000 gigawatt hour. Let's say 25%. That would be 5500 gigawatt hour. That's like 0.15% of whole US energy consumption of 2020. So 1-2% during operation is totally fathomable.

littlestymaar• 2 years ago

Does TSMC produce the pure silicon themselves from silica or is the refinery done by a supplier? I suspect it's the latter, but idk. It's a very energy intensive process, and huge source of CO2 emissions as well because you use coal to provide carbon in order to reduce the silica and get silicon + CO2.

bgnn2• 2 years ago

They get raw wafers from other suppliers AFAIK. It comes pre-doped even.

Engineering-MD• 2 years ago

Realistically the hidden costs of AI are huge. Microchips are the very peak end of manufacturing complexity and require an entire global supply chain in order to produce. While there are other uses for microchips, automation is the ultimate use, and AI is the current pursuit. I would argue you could consider the entire global tech economy as being part of the cost of AI. The real costs are huge.

pixl974• 2 years ago

So, let me counter with, what is the cost of efficiency?

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"

Ferret7446• 2 years ago

The question no one wants to ask; how much resources is consumed by the people whose economic output would be replaced by AI?

defrost• 2 years ago

Don't stop there;

* 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.

danjc• 2 years ago

Anyone else think that the Netflix estimate of 0.8kWh seems far too high?

How much electricity does AI consume?

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