If you want to know whether artificial intelligence is a gigantic economic bubble, don’t just take pundits’ word for it. Major investors are now moving their money to try to protect themselves from the AI bubble popping.

cross-posted from: https://lemmy.ml/post/54396848

If you want to know whether artificial intelligence is a gigantic economic bubble, don’t just take pundits’ word for it. Major investors are now moving their money to try to protect themselves from the AI bubble popping.

For an industry that we’re told is central to our collective future, the artificial intelligence (AI) industry increasingly seems to be built on a combination of artificially inflated hopes and bad accounting.

The industry’s critics have been raising concerns about its finances for a while: the fact that most of its flagship firms are grossly unprofitable, that they’ve had to jack up their prices to try break even (leading their business customers to pump the brakes on using their products), or that many of the eye-popping investment announcements that are meant to show demand for AI is strong have actually been circular financing — meaning different AI firms cycling money to and from each other.

But none of it has seemed to slow the industry’s boom or the relentless hype machine behind it. Until now, anyway. It used to be that you mostly heard concerns about the industry’s underlying economics in newspapers, blogs, social media posts, and podcasts, and that those concerns went completely unreflected in the markets. But these concerns seem now to have finally bled over into investors’ thinking.

  • Dispassionate@quokk.auEnglish
    431·
    3 days ago

    Imagine when that bubble pops and AI goes back down to “tool” level, and everyone goes back to work with a brand new dislike for work.

    • Ech@lemmy.ca
      55·
      3 days ago

      Even its tool status only exists because these companies subsidize the cost. In the event any survive the pop, realistic costs would price it out of being a frequent tool for typical workers.

      • Klear@piefed.worldEnglish
        32·
        3 days ago

        A lot of people are getting addicted, though. I have the sinking feeling they’re trying to keep this going for as long as they can so there’s enough addicts who will then sink their savings to get their fix once the bubble pops.

        • takeda@lemmy.dbzer0.com
          1·
          2 days ago

          That addiction made me wonder. Is it possible that AI was secretly used for disinformation on social media? I see some similarities.

        • Dispassionate@quokk.auEnglish
          91·
          3 days ago

          I think technology will catch up and AI models will improve to the point where we have whatever ChatGPT is using at the time of this comment running locally on regular hardware.

          That’s not saying the industry will die out of course, just that homegrown will be a real option.

          But that’s not solving any real problems.

          • NekoKoneko@lemmy.world
            8·
            2 days ago

            That isn’t really compatible with this timeline because these companies are spending trillions of dollars building data centers, which serendipitously is extinguishing any hope of buying local AI hardware by cornering the market for GPUs, RAM, CPUs and both solid state and platter hard drives.

            The implication of this all is that we will rent compute and run nothing locally. If hyperscalers succeed, that is the only way they make their money back. And if we will be able to afford local AI hardware, that also means they must fail.

            • Feathercrown@lemmy.worldEnglish
              3·
              2 days ago

              Yeah, schedenfreud is nice but it is not a filling meal. Seeing AI addicts get taken advantage of can make me smug but it cannot make me happy. People are still being exploited.

      • Feathercrown@lemmy.worldEnglish
        2·
        2 days ago

        Idk, the cat is kind of out of the bag here with local open-weights models. Even if all the providers shut down, someone who really wanted an AI could use one of those with not too much effort. A good model would require some impressive hardware, but a bad one is “good enough” for some people.

      • discocactus@lemmy.world
        71·
        3 days ago

        Only for the hyper scale centralized compute llms. Real machine learning tools predate and will persist past the bubble pop, but data center cloud based llms will go the way of the dodo one way or the other. Which is a disaster for the big companies that want to sell you compute as a utility, but really totally fine for most everyone else.

        • Ech@lemmy.ca
          4·
          3 days ago

          And none of those are “tool” level for the average company, so it’s not what’s being discussed.

        • grue@lemmy.world
          3·
          2 days ago

          Because the hyperscaler cartel is dumping cloud AI below cost, while simultaneously buying up essentially all the RAM, in order to deliberately and artificially destroy the local AI market.

        • Ech@lemmy.ca
          5·
          3 days ago

          I don’t think businesses want the overhead, but maybe some will. The capabilities will be much lower, though. The subsidized costs are high because of the hardware and power needed to generate the content it does. Companies willing to upgrade to comparable hardware and oversight would probably be just as willing to pay a higher subscription. Also, I would hope any company considering it would be able to see by then that the results aren’t worth the costs.

          • altkey (he\him)@lemmy.dbzer0.comEnglish
            2·
            3 days ago

            I am now wondering how would it be nicer to have a local scheduled solution, an LLM as an office printer. Slow moving common queue that makes people get more conservative in their LLM usage, plan it’s rent for themselves, send inputs to have outputs later that day. Some inconvinience to tone down hardware costs and establish a more mindful consumption.

            It could be fun to run experiments with that as one of test groups, and others unlimited or limited in some way, to compare how people adapt and how it affects workflows.

      • SorryQuick@lemmy.ca
        16·
        2 days ago

        They are profitable on inference (at least anthropic). Inference is cheap, it’s not susbsidized. It’s training that’s expensive, but if the bubble really does pop then current open source models (which are pretty much sold at inference rate + small margin) will actually go down in price since the cost of hardware will have gone down (assuming less demand for GPUs). Considering that the current best open model is roughly one month behind, we won’t revert to pre-ai anywhere. It’s just too cheap for that.

        Still, it’s lovely seeing people spreading misinformation based on something they saw on tiktok years ago. Keep up with the times or don’t spread outdated info.

        • Ech@lemmy.ca
          52·
          2 days ago

          Yeah. That $80 billion loss was all for training I guess 🙄

          • SorryQuick@lemmy.ca
            1·
            1 day ago

            Well that and the fact that they have a company to run and people to pay. Do you seriously believe inference is not profitable, despite evidence to the contrary being everywhere you look?

          • midribbon_action@lemmy.blahaj.zone
            21·
            2 days ago

            It’s not exactly $80 billion, $72 billion of that loss is accounting for the rising valuation giving early investors larger paybacks, aka Google and Amazon. The same people who give Anthropic its compute, at a very very healthy discount, are the ones who benefit most from a high valuation.

    • kshade@lemmy.worldEnglish
      4·
      3 days ago

      I dread to find out what the new thing will be that will distract everyone from the actual problems. Brain-computer-interfaces?