this post was submitted on 03 Dec 2025
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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.
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 too 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 how LLM inference works.
lol