this post was submitted on 28 Aug 2025
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Really? Companies are going to keep building datacenters that need entire nuclear reactors to themselves without any of that converting into revenue? This is going to keep going forever in your mind?
Definitely a bubble to be burst at some point unless we are able to harness energy and reduce waste substantially better than now.
A lot will fail, sure, but that happens in literally every single developing industry. There are plenty of industries out there that aren't profitable, but are still going. Tesla wasn't profitable between 2003 and 2020, yet here we are, where they not only make profit, but they've kickstarted the electric cars industry. And that's despite that they sell shitty cars and their CEO is a nazi.
What AI companies are profitable? Besides the one selling shovels in a gold rush.
They will be profitable in ten years after everything crashes and only a few are left
The power usage is massively overstated, and a meme perpetuated by Altman so he’ll get more more money for ‘scaling’. And he’s lying through his teeth: there literally isn’t enough silicon capacity in the world for that stupid idea.
GPT-5 is already proof scaling with no innovation doesn’t work. So are open source models trained/running on peanuts nipping at its heels.
And tech in the pipe like bitnet is coming to disrupt that even more; the future is small, specialized, augmented models, mostly running locally on your phone/PC because it’s so cheap and low power.
There’s tons of stuff to worry about over LLMs and other generative ML, but future power usage isn’t one.
Except none of these companies are making money. Like almost literally none. We're about three years into the LLM craze, and nobody has figured out how to turn a profit. Hell, forget profit, not bleeding through prodigious piles of cash would be a big deal.
Nods vigorously.
The future of LLMs basically unprofitable for the actual AI companies. We are in a hell of a bubble, which I can’t wait to pop so I can pick up a liquidation GPU (or at least rent one for cheap).
That doesn’t mean power usage is an existential issue. In fact, it seems like the sheer inefficiency of OpenAI/Grok and such are nails in their coffins.
Power usage is what's sucking the cash. What else could it be? Not all of these companies are building out lots of datacenters the way OpenAI is. They built what they have, and are now trying to make money on it.
The companies that are charging for AI are charging about as much as buyers are willing to pay, but it's orders of magnitude too small to cover their costs. The big cost is power usage.
On the training side, it’s mostly:
Paying devs to prepare the training runs with data, software architecture, frameworks, smaller scale experiments, things like that.
Paying other devs to get the training to scale across 800+ nodes.
Building the data centers, where the construction and GPU hardware costs kind of dwarf power usage in the short term.
On the inference side:
Sometimes optimized deployment frameworks like Deepseek uses, though many seem to use something off the shelf like sglang
Renting or deploying GPU servers individually. They don’t need to be networked at scale like for training, with the highest end I’ve heard (Deepseek's optimized framework) being like 18 servers or so. And again, the sticker price of the GPUs is the big cost here.
Developing tool use frameworks.
On both sides, the biggest players burn billions on Tech Bro “superstar” developers that, frankly, seem to Tweet more than developing interesting things.
Microsoft talks up nuclear power and such just because they want to cut out the middleman from the grid, reduce power costs, reduce the risk of power outages and such, not because there’s physically not enough power from the grid. It’s just corporate cheapness, not an existential need.