The University of Rhode Island’s AI lab estimates that GPT-5 averages just over 18 Wh per query, so putting all of ChatGPT’s reported 2.5 billion requests a day through the model could see energy usage as high as 45 GWh.
A daily energy use of 45 GWh is enormous. A typical modern nuclear power plant produces between 1 and 1.6 GW of electricity per reactor per hour, so data centers running OpenAI’s GPT-5 at 18 Wh per query could require the power equivalent of two to three nuclear power reactors, an amount that could be enough to power a small country.
I have an extreme dislike for OpenAI, Altman, and people like him, but the reasoning behind this article is just stuff some guy has pulled from his backside. There’s no facts here, it’s just “I believe XYX” with nothing to back it up.
We don’t need to make up nonsense about the LLM bubble. There’s plenty of valid enough criticisms as is.
By circulating a dumb figure like this, all you’re doing is granting OpenAI the power to come out and say “actually, it only uses X amount of power. We’re so great!”, where X is a figure that on its own would seem bad, but compared to this inflated figure sounds great. Don’t hand these shitty companies a marketing win.
I think AI power usage has an upside. No amount of hype can pay the light bill.
AI is either going to be the most valuable tech in history, or it’s going to be a giant pile of ash that used to be VC capital.
It will not go away at this point. Too many daily users already, who uses it for study, work, chatting, looking things up.
If not OpenAI, it will be another service.
Those same things were said about hundreds of other technologies that no longer exist in any meaningful sense. Current usage of a technology, which in this specific case I would argue is largely frivolous anyway, is not an accurate indicator of future usage.
Can you give some examples of those technologies? I’d be interested in how many weren’t replaced with something more efficient or convenient.
https://en.wikipedia.org/wiki/Dot-com_bubble
There were certainly companies that survived, because yes, the idea of websites being interactive rather than informational was huge, but everyone jumped on that bandwagon to build useless shit.
As an example, this is today’s ProductHunt
And yesterday’s was AI, and the day before that it was AI, but most of them are demonstrating little value with high valuations.
LLMs will survive, likely improve into coordinator models that request data from SLMs and connect through MCP, but the investment bubble can’t sustain
Technologies come and go, but often when a worldwide popular one vanishes, it’s because it got replaced with something else.
So lets say we need LLM’s to go away. What should that be? Impossible to answer, I know, but that’s what it would take.
We cant even get rid of Facebook and Twitter.
BUT that being said. LLMs will be 100x more efficient at some point - like any other new technology. We are just not there yet.
Those users are not paying a sustainable price, they’re using chatbots because they’re kept artificially cheap to increase use rates.
Force them to pay enough to make these bots profitable and I guarantee they’ll stop.
Or it will gate keep them from poor people. It will mean alot if the capabilities keep on improving.
That being said, open source models will be a thing always, and I think with that in mind, it will not go away, unless it’s replaced with something better.
I don’t think they can survive if they gatekeep and make it unaffordable to most people. There’s just not enough demand or revenue that can be generated from rich people asking for chatGPT to do their homework or pretend to be their friend. They need mass adoption to survive, which is why they’re trying to keep it artificially cheap in the first place.
Why do you think they haven’t raised prices yet? They’re trying to make everyone use it and become reliant on it.
And it’s not happening. The technology won’t “go away” per se, but these these expensive AI companies will fail.
Well, if they succeed, it’s because of efficiency and lowering costs. Second is how much the data and control is really worth.
The big companies is not just developing LLM’s, so they might justify it with other kinds of AI that actually makes them alot of money, either trough the market or government contracts.
But who knows. This is a very new technology. If they actually make a functioning personal assitant so good, that it’s inconvinient not to have it, it might work.
I can see government contracts making a lot of money regardless of how functional their technology actually is.
It’s more about who you know than what you can actually do when it comes to getting money from the government.
And most importantly the Pandora box has been opened for deep perfect scams and illegal usage. Nobody will put it in the box again, because even if everyone agreed to make it illegal everywhere it’s already too late.
That capital was ash earlier this year. The latest $40 Billion-with-a-B financing round is just a temporary holdover until they can raise more fuel. And they already burned through Microsoft, who apparently got what they wanted and are all “see ya”.
Bit of a clickbait. We can’t really say it without more info.
But it’s important to point out that the lab’s test methodology is far from ideal.
The team measured GPT-5’s power consumption by combining two key factors: how long the model took to respond to a given request, and the estimated average power draw of the hardware running it.
What we do know is that the price went down. So this could be a strong indication the model is, in fact, more energy efficient. At least a stronger indicator than response time.
That’s a terrible metric. By this providers that maximize hardware (and energy) use by having a queue of requests would be seen as having more energy use.
And an LLM that you could run local on a flash drive will do most of what it can do.
I mean no not at all, but local LLMs are a less energy reckless way to use AI
Why not… for the ignorant such as myself?
AI models require a LOT of VRAM to run. Failing that they need some serious CPU power but it’ll be dog slow.
A consumer model that is only a small fraction of the capability of the latest ChatGPT model would require at least a $2,000+ graphics card, if not more than one.
Like I run a local LLM with a etc 5070TI and the best model I can run with that thing is good for like ingesting some text to generate tags and such but not a whole lot else.
How slow?
Loading up a website with flash and GIF in the 90s dialup slow… Or worse?
Like make a query and then go make yourself a sandwich while it spits out a word every other second slow.
There are very small models that can run on mid range graphics cards and all, but it’s not something you’d look at and say “Yeah this does most of what chatGPT does”
I have a model running on a gtx 1660 and I use it with Hoarder to parse articles and create a handful a tags for them and it’s not… great at that.
Probably not a flash drive but you can get decent mileage out of 7b models that run on any old laptop for tasks like text generation, shortening or summarizing.
What do you use your usb drive llm for?
Can you give an example?
There’s such a huge gap between what I read about GPT-5 online, versus the overwhelmingly disappointing results I get from it for both coding and general questions.
I’m beginning to think we’re in the end stages of Dead Internet, where basically nothing you see online has any connection to reality.
People who fawn over generative AI haven’t tried to use it for more than 5 seconds. I wish it could run a ttrpg game for me or even just remember the details of its original prompt but its not even close.
The stock market is barely connected to reality and that is required to be updated every 3 months by every single company. Just imagine what the internet’s going to be like.
Well yeah, it’s a for-profit company. They exist solely to make money, that’s their entire goal.
It’s almost all marketing and has been for a while. ChatGPT peaked with 4o (and 4.5 if you used their API), 4.1 was a step backwards despite them calling it an improvement, and 5 was another step backwards.
They are not any closer to AGI, and we’re not going to see AGI from LLMs no matter how much they claim just how close we are to seeing AGI.
For reference, this is roughly equivalent to playing a PS5 game for 4 minutes (based on their estimate) to 10 minutes (their upper bound)
calulation
source https://www.ecoenergygeek.com/ps5-power-consumption/
Typical PS5 usage: 200 W
TV: 27 W - 134 W → call it 60 W
URI’s estimate: 18 Wh / 260 W → 4 minutes
URI’s upper bound: 48 Wh / 260 W →10 minutes
I love playing PS5 games!
How the hell are they going to sustain the expense to power that? Setting aside the environmental catastrophe that this kind of “AI” entails, they’re just not very profitable.
Look at all the layoffs they’ve been able to implement with the mere threat that AI has taken their jobs. It’s very profitable, just not in a sustainable way. But sustainability isn’t the goal. Feudal state mindset in the populace is.
Not just”not profitable”, they don’t make any money at all. Loss only.
I don’t buy the research paper at all. Of course we have no idea what OpenAI does because they aren’t open at all, but Deepseek’s publish papers suggest it’s much more complex than 1 model per node… I think they recommended like a 576 GPU cluster, with a scheme to split experts.
That, and going by the really small active parameter count of gpt-oss, I bet the model is sparse as heck.
There’s no way the effective batch size is 8, it has to be waaay higher than that.
Isn’t this the back plot of the game, Rain World? With the slug cats and the depressed robots stuck on a decaying world when the sapient, organic species all left?
The last 6 to 12 months of open models has pretty clearly shown you can substantially better results with the same model size or the same results with smaller model size. Eg Llama 3. 1 405B being basically equal to Llama 3.3 70B or R1-0528 being substantially better than R1. The little information available about GPT 5 suggests it uses mixture of experts and dynamic routing to different models, both of which can reduce computation cost dramatically. Additionally, simplifying the model catalogue from 9ish(?) to 3, when combined with their enormous traffic, will mean higher utilization of batch runs. Fuller batches run more efficiently on a per query basis.
Basically they can’t know for sure.
The team measured GPT-5’s power consumption by combining two key factors: how long the model took to respond to a given request, and the estimated average power draw of the hardware [they believe is] running it.
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that’s a lot. remember to add “-noai” to your google searches.
I’m just going to ignore the AI recommendations, let them burn money.
i don’t judge you for that. honestly it matters fuck all at this point
Or just use any other better search like Bing or duckduckgo. googol sucks and was never any good. Quit pushing ignorant garbage.
duckduckgo yes, but … bing?
googol sucks and was never any good.
Ha! Kids these days.
This is my weekly time to tell lemmings about Kagi, the search engine that does not shove LLM in your face (but still lets you use it when you explicitly want it) and that you pay for with your money, not your data.
Tech hasn’t improved that much in the last in the last decade. All that’s happened is that more cores have been added. The single-thread speed of a CPU is stagnant.
My home PC consumes more power than my Pentium 3 consumed 25 years ago. All efficiency gains are lost to scaling for more processing power. All improvements in processing power are lost to shitty, bloated code.
We don’t have the tech for AI. We’re just scaling up to the electrical senand demand of a small country and pretending we have the tech for AI.
This is nonsense, an M1 runs many multiples faster and at much lower wattage.
Not even the ai tech itself is enough for ai
It’s the muscle car era: can’t make things more efficient to compete with Asia? MAKE IT BIGGER AND CONSUME MORE
It takes less energy to dry a full load of clothes
A standard dryer is more like 2-5kWh for a load… Far more than 40Wh.
This bubble needs to pop, the sooner the better.
