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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 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 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.
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.
@themurphy @rigatti There is one difference … LLM’s can’t be more efficient there is an inherent limitation to the technology.
https://blog.dshr.org/2021/03/internet-archive-storage.html
In 2021 they used 200PB and they for sure didn’t make a copy of the complete internet. Now ask yourself if all this information without loosing informations can fit into a 1TB Model ?? ( Sidenote deepseek r1 is 404GB so not even 1TB ) … local llm’s usually < 16GB …
This technology has been and will be never able to 100% replicate the original informations.
It has a certain use ( Machine Learning has been used much longer already ) but not what people want it to be (imho).
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”.