• After the bubble pops how much would our lives be impacted?

  • Would AI vanish or still be there?

  • How exactly do you think the bubble will pop? Will AI companies simply run out of money? Or will it be because of the environmental effects?

  • When do you think the “pop” will take place?

  • After the bubble pops, in future there will be companies/people who will try the AI thing again? What will that be like?

    • tal@lemmy.todayEnglish
      10·
      4 days ago

      While I agree that AI is here to stay, he did say the bubble popping. I could believe that there could be a reduced level of investment for a while. In the past, we did have periods where we thought that various AI tasks would be easier to solve than they were, and investment fell back as we discovered that there were more hard problems to solve before we could accomplish a particular feat. Didn’t go away, but did see a decrease in work on it for a while.

      https://en.wikipedia.org/wiki/AI_winter

      In the history of artificial intelligence (AI), an AI winter is a period of reduced funding and interest in AI research.[1] The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or even decades later.

      The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the “American Association of Artificial Intelligence”).[2] Roger Schank and Marvin Minsky—two leading AI researchers who experienced the “winter” of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. They described a chain reaction, similar to a “nuclear winter”, that would begin with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] Three years later the billion-dollar AI industry began to collapse.

      There were two major “winters” approximately 1974–1980 and 1987–2000,[3] and several smaller episodes, including the following:

      • 1966: failure of machine translation

      • 1969: criticism of perceptrons (early, single-layer artificial neural networks)

      • 1971–75: DARPA’s frustration with the Speech Understanding Research program at Carnegie Mellon University

      • 1973: large decrease in AI research in the United Kingdom in response to the Lighthill report

      • 1973–74: DARPA’s cutbacks to academic AI research in general

      • 1987: collapse of the LISP machine market

      • 1988: cancellation of new spending on AI by the Strategic Computing Initiative

      • 1990s: many expert systems were abandoned

      • 1990s: end of the Fifth Generation computer project’s original goals

      Enthusiasm and optimism about AI has generally increased since its low point in the early 1990s. Beginning about 2012, interest in artificial intelligence (and especially the sub-field of machine learning) from the research and corporate communities led to a dramatic increase in funding and investment, leading to the current (as of 2026) AI boom.

      Obviously, we did achieve a number of those — like, we have pretty solid machine translation of human language today. I remember, pre-Brexit, a senior EU translator for the UK talking about EU translation work. One thing he mentioned was that he did all of his first drafts via Google Translate and then just did manual cleanup by hand — and I’d call that a fairly prestigious translation position. But it took more time and research than we initially expected.

    • pizza_the_hutt@sh.itjust.worksEnglish
      2·
      3 days ago

      It will if it is too expensive to generate a positive return on investment. That’s not to say that there aren’t (very) specific use cases where running an LLM makes sense, but the general use case of attempting to replace human workers with AI will likely be recognized for the pipe dream that it is. Companies are already realizing this with the AI bills coming due now, and this attitude will compound once they start having major failures and no one is still around who can fix the problems.

      • venusaur@lemmy.world
        11·
        3 days ago

        Are you saying that you think there is a possibility that AI technology, especially LLMs, would disappear from usage? Even as search tools? Even used locally?

        • pizza_the_hutt@sh.itjust.worksEnglish
          1·
          2 days ago

          Like I said above, I think there will be some very specific cases where running an LLM makes sense. Think along the lines of querying lakes of scientific data. This won’t be something of value to the average Joe or the average company. My experience with using LLMs for general search has been very hit or miss. I always end up using a general search engine, even for internal data sources.

          • venusaur@lemmy.world
            11·
            2 days ago

            Maybe that’s just you but millions of people are interfacing with LLMs every day as a replacement to a traditional search engine. I don’t see that changing. Either way, sounds like you are agreeing that AI will not go away entirely. That would be a silly thing to say.

            It’ll also go dormant as LLMs are only one AI tech. Other AI tech will be developing while supporting infrastructure grows until we have another boom. World models for example and JEPA. That’s why Meta is pushing AI glasses. They need training data.

            • pizza_the_hutt@sh.itjust.worksEnglish
              1·
              2 days ago

              Those millions of people using AI for general search are currently getting it for free, which is going to end when the bubble bursts and the companies providing it go under, if not sooner when said companies try to make a profit. This is exactly what’s happening to corporate AI customers now. They are realizing there isn’t a good ROI now that OpenAI and other AI providers are being forced to charge proper costs for queries and tokens, rather than the teaser or “adoption” rates they charged before.

              • venusaur@lemmy.world
                1·
                1 day ago

                Interesting theory. I’d be surprised if Google started charging for their AI results. Too much free training data and they can afford to keep models running for the masses. They’re not solely an AI company. At the worst, these AI companies get swallowed up by bigger companies that can keep running even if AI is not profitable, which it is in some cases.