In the ever-evolving landscape of technology and innovation, both blockchain and artificial intelligence (AI) have captured significant attention.

Let’s think about blockchain for a bit. Blockchain technology has been met with considerable hype, promising revolution across various industries. However, this enthusiasm has not translated into success for most ventures in this space. Research indicates that approximately 95% of blockchain startups fail within a year of operation. Contributing factors include market volatility, regulatory hurdles, and the lack of clear use cases.

A notable example is the collapse of Terra’s LUNA cryptocurrency in 2022. In just one week, $45 billion was lost, illustrating the inherent risks associated with blockchain projects.

AI startups are now experiencing their own wave of excitement and investment. However, they too encounter significant challenges. Over 80% of AI projects fail due to issues like insufficient market demand, operational difficulties, and ethical complexities.

Consider this: approximately 42% of AI startups fail because there is insufficient demand for their products or services. Not to mention, many AI ventures struggle with resource mismanagement, inadequate expertise, and scaling difficulties. You also have the additional challenge of navigating the evolving landscape of AI ethics and regulations adds layers of complexity that can impede progress. There’s not exactly decades of history to refer to regarding legal precedent with AI.

A lot of the hype and marketing I see today looks just like what I saw a few years ago, except instead of “blockchain” it says “AI” now. There are consulting firms, integration firms, everything. Is this just a sign the industry is just endless fads with no actual commercial usage?

Bitcoin was hyped as reinventing the world’s economy. Sure, it found a few usages, like replacing Western Union, or also by essentially becoming “digital gold” that people can just acquire and sit on, but last time I looked, VISA/Mastercard and the like were still doing 98% of the world’s commerce. In other words, Bitcoin fell far short of where many of its proponents said it would land years ago. Looking around at all these AI firms, I wonder how many of them will even exist in 3 years.

  • chicken@lemmy.dbzer0.com
    11·
    1 day ago

    My wife uses AI to write complex Excel spreadsheet formulas saving hours. She still has to double check them but it saves enormous time. My friend uses it to write proposals. Again it needs to be checked and again it saves hours of time.

    AI doesn’t replace people. It provides a productivity boost and it has been doing it for 2 years now.

    To me it’s obvious that AI is and will be really useful, but one of the great things about it is that it looks like a lot of that won’t be possible to gatekeep. Which seems like it would also mean that efforts to monetize it will fail.

    • Blue_Morpho@lemmy.world
      2·
      22 hours ago

      People can easily self host email, file backup, etc but pay for service anyway. AI will be prohibitively expensive to self host for a very very long time.

      • chicken@lemmy.dbzer0.com
        1·
        10 hours ago

        People can easily self host email, file backup, etc but pay for service anyway

        Who pays for email? Who pays subscriptions for file backup? Why would you when you can just use another companies service that is free? Self hosting AI is increasingly viable, but that isn’t even the problem for companies hoping to make billions on it, the problem is that as soon as they try to put the squeeze on their customers they will just go somewhere else that offers the same thing. Look at what happened with Deepseek; OpenAI can’t maintain dominance.

        AI will be prohibitively expensive to self host for a very very long time.

        It already isn’t, there are tiny models that are practical for some things that will run on basically anything, and there is a lot you can do with a mid to high end graphics card. Nvidia is artificially limiting vram but that’s not going to remain the limitation for long. But even if AI running on datacenter hardware maintains a big advantage, that’s not enough for these companies to make huge profits selling access.

        • Blue_Morpho@lemmy.world
          1·
          6 hours ago

          Who pays for email?

          That you don’t pay for gmail doesn’t mean it isn’t monetized. Google makes $220 Billion a year from ads.

          Who pays subscriptions for file backup?

          Google drive, Onedrive, and iCloud are forced down all users throats. Millions pay. A quick google says Apple makes $96 Billion a year from iCloud alone.

          the problem is that as soon as they try to put the squeeze on their customers they will just go somewhere else that offers the same thing

          If it were that easy Google wouldn’t make $350B a year. Google is just a search index and not very good.

          Look at what happened with Deepseek; OpenAI can’t maintain dominance.

          Yes, and Google displaced Yahoo. That doesn’t mean search stopped being monetized.

          Nvidia is artificially limiting vram but that’s not going to remain the limitation for long.

          It’s not VRAM, it’s ram in general. Deepseek needs ~700GB. Ram prices have been dropping about 30% every 5 years (and that’s ideal numbers. Moore’s law is dead. Current improvements are smaller than looking back 5 years). It’s about $6k to build your own Epyc for 6 tokens/sec. No average person is going to spend $6k when a service does it for $10 a month and is 10x faster. Do the math for how long it will be until $6k of ram costs $600.

          But the real killer that will keep AI out of consumer hands for forever is that it is reliant on new models to include updated information that was scraped from the Internet. If you ask Deep Seek about anything that’s happened in the past year, it can’t give any answers because the model was built with older data. So Google, Apple, Microsoft and anyone capable of indexing the entire web will always have an AI model that is updated with the latest information. Home users will be lucky to get models with information that is several years out of date. Building models requires 100x the resources as running the trained model and you need the data to train it.