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OpenAI made $13 billion in 2025 and lost $21 billion doing it (www.techspot.com)
submitted 3 months ago by to c/technology@lemmy.world
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[–] 3 points 3 months ago (10 children)

YouTube would lose a billion annually early on as they expanded infrastructure to keep up with massive demand 🤷‍♀️

They figured it out eventually, all they had to do was enshittify everything.

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  • [–] 8 points 3 months ago (4 children)

    That is entirely different from llm models. People like youtube, it has utility. Llm models don't have enough utility to pay for their data centers and we all know it. Why are you simping for them? You believe their hype? That discredits you.

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  • [–] 0 points 3 months ago*

    "I don't like them therefore no one does" -you

    People had the same conversations when YouTube was new and unproven, before it was the household name it is now. Many people thought it would never be profitable and watched them burn billion after billion.

    I do think LLMs are overhyped, I'm with you there, but I do think they also provide utility that many use. Is it a bubble that will pop? 100%. But just like the web after the dotcom bubble, it'll never go away.

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  • [–] -2 points 3 months ago (2 children)

    Lmfao, look at this righteous edge lord.

    Who needs to think things through, when you can just condemn someone who says anything "black" when you're on team "white".

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  • [–] 2 points 3 months ago (4 children)

    The difference being they had a plan to monetize YouTube which offset the operating costs and even could make a profit, in addition to the stuff they over invested in having long term benefits. Aka they roughly could spent a lot to save money later off of what they built.

    LLMs are not the same. They cost a shitload of money to run. Actual token based usage without being subsidized by investors would make any LLM cost users so much money that the actual value of it would immediately become major problem. Sure one can, currently, get decent code output by using hundreds or thousands of tokens, or using multiple LLMs / loops, or having agents go burn however many on iteration! But if we paid actual costs this shit would rapidly be shut down.

    The AI infrastructure is also not saving money long term. Training is unbelievably expensive. Compute costs are all about gigantic data servers with video cards running, and the economics of all that is way tighter than anyone gives credit for. Those cards last like 3 years. The cooling costs are crazy. You have to have constant use to be efficient. None of these things are able to be covered by AI economics nor does it even make sense to be.

    It costs too much, it's just being covered by your money being pissed away by tech investors for a technology that cannot survive.

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  • [–] 3 points 3 months ago

    Sure one can, currently, get decent code output by using hundreds or thousands of tokens, or using multiple LLMs / loops, or having agents go burn however many on iteration!

    This always stumps me. Because if that was true, Anthropic products (API, Cache, Claude Cowork, Claude Code) would not be shitty.

    They are shitty. They are coded shittily, and Anthropic is unable to solve some bugs for years now. E.g. there is the console flickering bug that they tried to fix 3 times and rollbacked or failed all of them.

    Or maybe we define decent differently.

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  • [–] 1 point 2 months ago* (last edited 2 months ago) (2 children)

    In theory costs could come down with each new hardware generation if the we dont keep pushing models the to max extent of what the hardware can do while pushing size.

    E.g Claude Opus today, only trained in a similar size and manner as today, will be cheaper to run on whatever the next GPU that comes out with higher speeds and processing capabilities, unless of course NVidia raises the cost substantially. Given the current situation I think nvidia might do that which would hamper this lowering of costs, but it should possible, if not slower.

    E.g 10 years from now it will be cheaper to run a opus similar model. But 10 years from now everyone will want the mythos of today, then. That wont be cheaper.

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  • [–] 1 point 2 months ago (1 child)

    This has been stated since ChatGPT was released and has not happened. The video cards released specifically for LLM usage do not benchmark particularly better than the previous generation. And it's still unbelievably expensive to run these cards and maintain the facility and, again, you only get like 3 or 5 years out of them! That's a crazy investment lol

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  • [–] 1 point 2 months ago* (last edited 2 months ago)

    But the new GPUs absolutely can run the GPTs from back then better. We just dont want that anymore, we want the better bigger models that continue to be as or more expensive as what it was back then.

    When you replace the cards in 5 years it'll run it even better. We just wont want that then.

    Edit: and gains dont have to be huge, even 5-10% between generations, but take that to 10 years like I said and it can be substantial.

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