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pahlimur@lemmy.world 1 day agoWhen AI is actually invented I’ll call it AI. Right now we have a steroid juiced parrot that’s based on old school machine learning. Its great at summarizing simple data, but terrible at real tasks.
This is more people who aren’t dumb telling the marketing teams to stop hyping something that doesn’t exist. The dot com boom is echoing. The profit will never materialize.
NotMyOldRedditName@lemmy.world 1 day ago
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%wl without a doubt possible, it’s just a matter of can they do it before they run out of money.
pahlimur@lemmy.world 23 hours ago
The current machine learning models (AI for the stupid) rely on input data, which is running out.
Processing power per watt is stagnating. Moors law hasn’t been true for years.
Who will pay for these services? The dot com bubble destroyed everyone who invested in it. Those that “survived” sprouted off of the corpse of that recession. LLMs will probably survive, but not in the way you assume.
Nvidia helping openAI survive is a sign that the bubble is here and ready to blow.
NotMyOldRedditName@lemmy.world 23 hours ago
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 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 need 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.
pahlimur@lemmy.world 23 hours ago
None of this is true.
I’ve worked on data centers monitoring power consumption, we need to stop calling LLM power sinks the same thing as data centers. Its basically whitewashing the power sucking environmental disasters that they are.
Machine learning is what you are describing. LLMs being puppeted as AI is destructive marketing and nothing more.
LLMs are somewhat useful at dumb tasks and they do a pretty dumb job at it. They feel like when I was new at my job and for decades could produce mediocre bullshit, but I was to naive to know it sucked. You can’t see how much they suck yet because you lack experience in the areas you use them in.
Your two cost saving points are pulled from nowhere just like LLM inference works.
leftist_lawyer@lemmy.today 17 hours ago
Oh ffs … stfu.
redwattlebird@lemmings.world 23 hours ago
It is unlikely to turn a profit because the returns need to be greater than the investment for there to be any profit. The trends show that very few want to pay for this service. I mean, why would you pay for something that’s the equivalent of asking someone online or in person for free or very little cost by comparison?
Furthermore, it’s a corporation that steals from you and doesn’t want to be held accountable for anything. For example, the chat bot suicides and the fact that their business model would fall over if they actually had to pay for the data that they use to train their models.
The whole thing is extremely inefficient and makes us more dumb via atrophy. Why would anyone want to third party their thinking process? It’s like thinking everyone wants mobility scooters.
NotMyOldRedditName@lemmy.world 23 hours ago
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.
pahlimur@lemmy.world 23 hours ago
It’s not easy to solve because its not possible to solve. ML has been around since before computers, it’s not magically going to get efficient. The models are already optimized.
Revenue isn’t profit. These companies are the biggest cost sinks ever.
Heating a single building is a joke marketing tactic compared to the actual energy impact these LLM energy sinks have.
I’m an automation engineer, LLMs suck at anything cutting edge. Its basically a mainstream knowledge reproducer with no original outputs. Meaning it can’t do anything that isnt already done.
leftist_lawyer@lemmy.today 17 hours ago
Pretty obvious you’re not reading Zitron et al.
redwattlebird@lemmings.world 22 hours ago
Yep, I am. Just follow the money. Here’s an example:
https://www.theregister.com/2025/10/29/microsoft_earnings_q1_26_openai_loss/
… That’s all in your head, mate. I never said that nor did I imply it.
What I am implying is that the uptake is so small compared to the investment that it is unlikely to turn a profit.
😐
I’ve worked in the building industry for over 20 years. This is simply not feasible both from a material standpoint and physics standpoint.
I know it’s an example, but this kind of rhetoric is exactly the kind of wishful thinking that I see in so many people who want LLMs to be a main staple of our everyday lives. Scratch the surface and it’s all just fantasy.
leftist_lawyer@lemmy.today 17 hours ago
just need to optimize. Like they haven’t just been trying for years already with, again, incredibly marginal returns that continue to diminish with each optimization.
Derp.