While the stock market may love the term "AI," companies haven't figured out how to optimally monetize the services that go along with it.

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Silicon Valley has bet big on generative AI but it’s not totally clear whether that bet will pay off. A new report from the Wall Street Journal claims that, despite the endless hype around large language models and the automated platforms they power, tech companies are struggling to turn a profit when it comes to AI.

Microsoft, which has bet big on the generative AI boom with billions invested in its partner OpenAI, has been losing money on one of its major AI platforms. Github Copilot, which launched in 2021, was designed to automate some parts of a coder’s workflow and, while immensely popular with its user base, has been a huge “money loser,” the Journal reports. The problem is that users pay $10 a month subscription fee for Copilot but, according to a source interviewed by the Journal, Microsoft lost an average of $20 per user during the first few months of this year. Some users cost the company an average loss of over $80 per month, the source told the paper.

OpenAI’s ChatGPT, for instance, has seen an ever declining user base while its operating costs remain incredibly high. A report from the Washington Post in June claimed that chatbots like ChatGPT lose money pretty much every time a customer uses them.

AI platforms are notoriously expensive to operate. Platforms like ChatGPT and DALL-E burn through an enormous amount of computing power and companies are struggling to figure out how to reduce that footprint. At the same time, the infrastructure to run AI systems—like powerful, high-priced AI computer chips—can be quite expensive. The cloud capacity necessary to train algorithms and run AI systems, meanwhile, is also expanding at a frightening rate. All of this energy consumption also means that AI is about as environmentally unfriendly as you can get.

  • Fushuan [he/him]@lemm.eeEnglish
    2·
    1 year ago

    But ML is being used in the industry in tons of places, and it’s definitely cost effective. There’s simple models that take the input of machinery sensors and detect when something is faulty or needs repairing, not just malfunctioning parts but worn out parts too. It’s used heavily in image processing, tiktok is used by a lot of people and the silly AR thingies use image recognition and tracking in real time through your phone. I’m not saying that this features created revenue directly, but they do get viral and attract users, so yeah. Image processing is also used in almost any supermarket to control the amount of people in the store, at least since covid I see a dynamically updated counter in every supermarket I visit.

    It is also used in time estimations, how much traffic influences a trip is not manually set at all, it gets updated dynamically with real time data, through trained models.

    It is also used in language models, for real usages like translation, recommendation engines (search engines, store product recommendation…).

    The article is talking about generative models, more specifically, text prediction engines (ChatGPT, Copilot). ChatGPT is a chatbot, and I don’t see a good way to monetise it while keeping it free to use, and Copilot is a silly idea to me as a programmer since it feels very dangerous and not very practical. And again, not something I would pay for, so meh.

    • Lvxferre@lemmy.ml
      2·
      1 year ago

      But (+> contradiction) ML is being used in the industry in tons of places […] store product recommendation…).

      By context it’s rather clear which type of machine learning I’m talking about, given the OP. I’m not talking about the simple models that you’re talking about and that, as you said, already found economically viable applications.

      Past that “it’s generative models, not machine learning” is on the same level as “it’s a cat, not a mammal”. One is a subset of the other, and by calling it “machine learning” my point was to highlight that it is not a “toothed and furry chicken” as the term AI implies.

      The article is talking about generative models

      I’m aware, as footnote #1 shows.

      • Fushuan [he/him]@lemm.eeEnglish
        2·
        1 year ago

        By context it’s rather clear which type of machine learning I’m talking about

        Eh, it was to you and me, but we are not in a specialised community. This is a general one about technology, and since people tend to misunderstand stuff I prefer to specify. I get that you then wrote footnote #1, but why write statements like this one:

        So the text is saying that machine learning is costly. No surprise - it’s a relatively new tech, and even the state of art is still damn coarse²

        I know which branch of ML you are talking about, but in written form on a public forum that people might use as a reference, I’d prefer to be more specific. Yeah you then mention LLMs as an example, but the new ones are basically those, there’s several branches with plenty maturity.

        “it’s generative models, not machine learning”

        IDK why you are quoting me on that, I never said that. I’d just want people to specify more. I only mentioned several branches of machine learning, and generative models are one of them.

        Also, what’s that about contradiction? In the first paragraph I was mentioning the machinery industry, since I talk about machines. Then I talked about language models and some of their applications, I don’t get why that contradicts anything. Store product recommendations are done with supervised ML models that track your clicks, views, and past purchases to generate an interest model about you, and it’s combined with the purchases people with similar likes that you do do to generate a recommendation list. This is ML too.

        Dunno, you read as quite angry, misquoting me and all.