For the record my stance triggering this large comment chain was based off what OP wrote about AI.
AI is a crutch for dumb people.
I never said you had to like it or not liking it makes you an idiot.
If you want to say I was calling people who say
AI is a crutch for dumb people.
Are idiots, I’ll accept that accusation.
He wasn’t competent enough to pull off the whacky things they wanted to do.
I doubt they’ll run someone more to the center. They’ll be even further to the right is my guess, but they’ll try to find someone who’s not a dumbass at the same time.
I still can’t believe how close we came to this Trump wannabe running the province.
What does Samsung’s memory division think is going to happen to their phone division if they won’t sell them ram, wow.
I wonder if they realized there weren’t enough GPUs, so they decided lets just build a massive ram cpu/farm to do the job at 1/100th the speed and waste money on the inefficiency.
Are you fucking kidding me? Holy fucking hell.
I just wanted to add one other thing on the hardware side.
These H200’s are power hogs, no doubt about it. But the next generation H300 or whatever it is, will be more efficient as the node process (or whatever its called) gets smaller and the hardware is optimized and can run things faster. I could still see NVIDIA coming out and charging more $/flop or whatever the comparison would be though even if it is more efficient power wise.
But that could mean that the electricity costs to run these models starts to drop if they truly are plateaued. We might not be following moores law on this anymore (I don’t actually know), but were not completely stagnant either.
So IF we are plateaued on this one aspect, then costs should start coming down in future years.
Edit: but they are locking in a lot of overhead costs at today’s prices which could ruin them.
My point is it’s not early anymore. We are near or past the peak of LLM development.
I think we’re just going to have to agree to disagree on this part.
I’ll agree though that IF what you’re saying is true, then they won’t succeed.
You > the trends show that very few want to pay for this service.
Me > These companies have BILLIONS in revenue and millions of customers, and you’re saying very few want to pay
Me > … but you’re making it sound no one wants it
You > … That’s all in your head, mate. I never said that nor did I imply it.
Pretty sure it’s not all in my head.
The heat example was just one small example of things these large data centers (not just AI ones) can do to help lower costs, and they are a real thing that are being considered. It’s not a solution to their power hungry needs, but it is a small step forward on how we can do things better.
https://www.bbc.com/news/articles/cew4080092eo
1Energy said 100 gigawatt hours of energy would be generated through the network each year, equivalent to the heat needed for 20,000 homes.
Edit: Another that is in use: https://www.itbrew.com/stories/2024/07/17/inside-the-data-center-that-heats-up-a-hospital-in-vienna-austria
This system “allows us to cover between 50% and 70% of the hospital’s heating demand, and save up to 4,000 tons of CO2 per year,” he said, also noting that “there are virtually no heat losses” since “the connecting pipe is quite short.”
If your stance is
AI is a crutch for dumb people.
You’re right on track to be that 70 year old raising his cane in the air ranting about the useless AI stuff going on and now you can’t figure out how to get our social security check because it uses that new AI based system.
Why on earth do you think things can’t be optimized on the LLM level?
There are constant improvements being made there, they are not in any way shape or form fully optimized yet. Go follow the /r/LocalLlama sub for example and there’s constant breakthroughs happening, and then a few months later you see a LLM utilizing them come out, and they’re suddenly smaller, or you can run a larger model on smaller memory footprint, or you can get a larger context on the same hardware etc.
This is all so fucking early, to be so naive or ignorant to think that they’re as optimized as they can get is hilarious.
These companies have BILLIONS in revenue and millions of customers, and you’re saying very few want to pay…
The money is there, they just need to optimize the LLMs to run more efficiently (this is continually progressing), and the hardware side work on reducing hardware costs as well (including electricity usage / heat generation). If OpenAI can build a datacenter that re-uses all it’s heat for example to heat a hospital nearby, that’s another step towards reaching profitability.
I’m not saying this is an easy problem to solve, but you’re making it sound no one wants it and they can never do it.
rely on input data, which is running out.
Thats part of the equation, but there is still a lot of work that can be done to optimize the usage of the llms themselves, and the more optimized and refined they are, the cheaper it becomes to run, and you can also use even bigger datasets that weren’t feasible before.
I think there’s also a lot of room to still optimize the data in the data set. Ingesting the entire worlds information doesn’t lead to the best output, especially if you’re going into something more factual vs creative like a LLM trained to assist with programming in a specific language.
And people ARE paying for it today, OpenAI has billions in revenue, the problem is the hardware is so expensive, the data centeres needed to run it are also expensive. They need to continue optimizing things to narrow that gap. Open AI charges $20 USD/month for their base paid plan. They have millions of paying customers, but millions isn’t enough to offset their costs.
So they can
This is so early that they have room to both improve 1 and 2.
But like I said, they (and others like them) need to figure that out before they run out of money and everything falls apart and needs to be built back up in a more sustainable way.
We won’t know if they can or can’t until they do it, or it pops.
But the profit absolutely can materialize because it is useful.
Right now the problem is hardware / data center costs, but those can come down at a per user level.
They just need to make it useful enough within those cost constants which is 100% without a doubt possible, it’s just a matter of can they do it before they run out of money.
Edit: for example, nvidia giving OpenAI hardware for ownership helps bring down their costs, which gives them a longer runway to find that sweet spot.
Ah, yes those people definitely don’t care, they’ll make their money either way.
Even if that doesn’t exist yet in the USA, it’s definitely in the UK with all their CCTV stuff.
And we know US law enforcement can use things like Ring doorbells.
Ya, I don’t want it shoved in my face. I want to choose how and where I use it without them trying to compromise my entire device in the process. Fuck what windows is doing for example.
You can tell who’s going to grow up into the current generations tech illiterate elderly based on how people talk about AI today.
Australia isn’t the greatest spot to run a data centre in general in terms of heat, but I do understand the need for sovereign data centres, so this obviously can’t work everywhere.
What makes you think $3.5 million can’t be profitable? A mid sized hospitals heating bill can get into the many hundreds of thousands or into the millions even. Especially if it’s in a colder environment. A 5-6 year payback on that wouldn’t be terrible and would be worth an upfront investment. Even a 10 year payback isn’t terrible.
These colder locations are the ideal locations for the data centres in the first place because they generally want a cooler climate to begin with, so they will gravitate to them when possible.
Edit: And if you build a data centre with this ability to recoup heat, you could start building further commercial things in the area and keep the heat redistribution very close. You don’t need to travel very long distances. You do need to put some thought into where they go through and whats around or will be built around.