So, if I understand this correctly, a few individuals in the USA are buying up all the RAM and GPUs being manufactured in the world, with huge loans that they are hoping they will be able to pay off someday, to put into some of the biggest data centers ever constructed, all over cheap, peaceful villages, for which the electric grid has to be substantially upgraded, massively increasing our carbon emissions, and a large portion of the available fresh water supply has to be redirected, so that we can consume:
- Generative AI: A Large Language Model (LLM) that has gobbled up most of humanity’s literature and content on the Internet, so that it can predict the next word in a sentence.
- Agentic AI: A list of traditional IF THEN statements that do things based on the output of the magical LLM and then create a new prompt that they feed back into the LLM in a never ending loop, with the aim of consuming as many tokens as possible.
- Ontology: A long list of relationships between real-world objects and concepts to constantly remind the LLM not to make up impossible stuff.
Correct?
I suspect that they have run out of ideas how to make us buy new smartphones.
So basically Silibandia bought GPUs before finding a purpose.
They have no plans to pay back the loans. Those are corporate loans and the individuals doing this are already pocketing the money themselves.
Remember, it’s a bubble. It’s a massive con job. The main point is to get rich on the side before the bubble bursts.
(Of course tech will continue to evolve. We all know that.)
They are currently working on ways to make us avoid smartphones altogether, no idea if that is deliberate or not though, since humanity is completely absurd.
- Agentic AI: A list of traditional IF THEN statements that do things based on the output of the magical LLM and then create a new prompt that they feed back into the LLM in a never ending loop, with the aim of consuming as many tokens as possible.
It’s so much worse than that. We use the pleasant answer predictor to predict what function calls and arguments passed to those functions might be the most pleasing.
It can provide all of the capabilities of piping
curlintosudowith none of the warm fuzzy feeling of knowing I’ve turned over control of my computer’s fate to a stranger on the Internet.Instead, I’ve turned my computer’s fate over to a statistical approximation of the slurry from mixing together the outputs of every stranger from the Internet.
“Do I understand correctly?” Presents perspective with openly slanted language.
Try using a steelman next time.
It’s not about us. It never was. It’s primarily about misleading investors into believing it can replace workers. But they have no path to profitability. They keep raising the prices, and at this point it costs more than people and does a much worse job.
Exactly. And they need to make back the biggest up-front loss in the history of investment, and probably - even adjusted for inflation - in the history of currency.
And all that on a product that people don’t want.
The data centers will eventually slam the electric grid hard, but currently they are doibg short term power generation with pollution spewing fossil fuel methods.
At least one has been busted polluting local waterways as well. They are rushing everything and ruining the environment wherever they go.
Alao massive amoubts of sound pollution near residential neighborhoods.
Why are lots of people down-voting my post? Is my understanding wrong - granted, I am still learning? Or do they think that AI is only good for us?
Five’s not many downvotes, your most inaccurate guesses aren’t even offending to anyone.
On Lemmy you’ve just got a couple ai Stans who won’t tolerate anything that sounds like it could be even backhandedly critiquing ai. Then there’s a few lemmy users who just browse by new and downvote posts for no reason at all. Give it a few hours, it will even out
Thank you!
you are not understanding correctly, you are parroting click bait titles.
Please elaborate and educate me. I would love to learn.
The fresh water thing is wrong. The study that came out showing AI using shittons of water was including the cooling pond water at power plants. It was… 76% of the water use? I forget the % but it was so much that it completely changed the statistic from “holy shit” to “oh, who cares, then?”
Also, most of the mega huge data centers under construction are everywhere. Not “villages”. Meta’s big one is actually in the middle of nowhere.
Agentic AI isn’t just “if else” stuff. It’s way TF more complicated than that. It’s so fucking complicated, they’re terrified of implementing it at my work because they fear they won’t be able to understand what went wrong when something inevitably goes wrong (LOL).
The ontology thing isn’t really a thing. That’s just what outsiders are calling some internal programming they’re adding to LLMs so they don’t hallucinate “obvious shit”. The actual issue there is that, yeah: You can feed the LLM output back into itself four fucking times over to double-check it but that uses 4x as many tokens!
The thing you’re missing is that token usage is exploding. It’s like the world of AI has collectively decided that it needs that 4x token usage but they can’t figure out a way to do that economically, so they’re just sort of whistling while looking away from their balance sheets while at the same time getting seemingly endless loads from private equity idiots who think “AGI is just around the corner.”
ok. well in memphis elon is using 5 million gallons a day of fresh aquifer drinking water and then dumping it in the mississippi river.
he told the city that he would build a water treatment plant for his own use to curb the waste of drinking water.
he then ‘paused’ the building of the plant (still in its plannning phase) and instead is building more computer warehouses for grok.
please do not use grok.
I don’t know that story, but if the data center isn’t built yet, that’s just the normal water consumption that would be required for any building construction.
Building big buildings uses a lot of water. It’s necessary for dust management, soil compaction, etc. They literally just spray it everywhere during construction and that’s actually very important! LOL.
It always amuses me when I see people complaining about data centers that are still under construction using “millions of gallons” of water. I’m thinking, “yeah dude, that’s how construction works.”
They never complained about the construction water usage of all the other buildings in the area which make the “huge data center” look like a drop in the ocean for that kind of thing.
well setting your patronizing tone aside, it is not under construction. it’s the multiple colossus sites in memphis. elon has been running diesel turbines to power it for over a year.
and he’s using aquifer water to cool it.
maybe stop being an ass and read up on these things before correcting people who live next to this bullshit.
Thanks, interesting thoughts. I am trying to understand agentic AI and its potential uses and security risks better. I am also a bit hesitant to let it loose on my laptop, even if you sandbox the thing. I was also amazed how all our executives were pushing us to use AI for everything, without really trying to figure out legitimate use cases that will move the needle and without considering the massive potential costs. Then some of our agents suddenly got switched off, when token budgets were used up faster than expected.
The S in “agentic” stands for security!
I almost forgot: The reason why “business leaders” want to see AI everywhere is because they think it’ll be just like the adoption of every other technology to this point: It gets cheaper over time (not always better, but usually so).
The assumption is that if they “beat their competitors” to be the first ones using AI efficiently, they’ll utterly destroy them (economically; they won’t be able to compete). It’s a very bad assumption.
So far, “Big AI” is getting better but at costs that scale geometrically with the amount of “better”. That is: You can improve reliability (e.g. reduce hallucinations, increase accuracy, improve outputs in various ways, etc) but only by drastically inflating the cost and at reduced speed and efficiency.
We’re starting to learn that LLMs need about two generations of hardware advances before they’re going to be cost efficient for the types of “human productivity enhancement” that business leaders want. It’s only affordable now because “Big AI” is subsidizing the costs, trying to get customers hooked. The assumption being that if they’re hooked on AI, they’ll be able to raise prices to reflect actual costs. Just like the business leaders, this is a very bad assumption.
Instead, what’s going on is that the open weights AI models are starting to get “good enough” for most tasks (that you’d want to use them for, e.g. coding or agentic automation stuff). That means that all the billions and billions being spent by Big AI is just building up debt that will never be repaid and having this side effect of using up all the chip/memory capacity in the entire world.
It’s an absurd situation and the world will eventually look back on this time like we do the dotcom era.
capitalism is the most efficient system
