This is just a draft, best refrain from linking. (I hope we’ll get this up tomorrow or Monday. edit: probably this week? edit 2: it’s up!!) The [bracketed] stuff is links to cites.

Please critique!

A vision came to us in a dream — and certainly not from any nameable person — on the current state of the venture capital fueled AI and machine learning industry. We asked around and several in the field concurred.

AIs are famous for “hallucinating” made-up answers with wrong facts. The hallucinations are not decreasing. In fact, the hallucinations are getting worse.

If you know how large language models work, you will understand that all output from a LLM is a “hallucination” — it’s generated from the latent space and the training data. But if your input contains mostly facts, then the output has a better chance of not being nonsense.

Unfortunately, the VC-funded AI industry runs on the promise of replacing humans with a very large shell script. If the output is just generated nonsense, that’s a problem. There is a slight panic among AI company leadership about this.

Even more unfortunately, the AI industry has run out of untainted training data. So they’re seriously considering doing the stupidest thing possible: training AIs on the output of other AIs. This is already known to make the models collapse into gibberish. [WSJ, archive]

There is enough money floating around in tech VC to fuel this nonsense for another couple of years — there are hundreds of billions of dollars (family offices, sovereign wealth funds) desperate to find an investment. If ever there was an argument for swingeing taxation followed by massive government spending programs, this would be it.

Ed Zitron gives it three more quarters (nine months). The gossip concurs with Ed on this being likely to last for another three quarters. There should be at least one more wave of massive overhiring. [Ed Zitron]

The current workaround is to hire fresh Ph.Ds to fix the hallucinations and try to underpay them on the promise of future wealth. If you have a degree with machine learning in it, gouge them for every penny you can while the gouging is good.

AI is holding up the S&P 500. This means that when the AI VC bubble pops, tech will drop. Whenever the NASDAQ catches a cold, bitcoin catches COVID — so expect crypto to go through the floor in turn.

  • Mii@awful.systems
    5·
    8 months ago

    From my uneducated perspective, LLM hype seems to me more like any other tech bubble than Bitcoin. It is actually built on the promise of return on investment. But somehow the whole industry seems to burn way more money than it can rake in, and this has to, at some point, raise some eyebrows with the investors. Normally, they prop up dozens of startups, calculating with a high failure rate because one successful venture would cover the losses plus turn a profit. AI companies however burn so much money and still have no way to make that back, so this concept doesn’t work.

    I don’t think you can keep this alive just by convincing the next idiot to pump in more money than you did, like you can with Bitcoin.

      • mountainriver@awful.systems
        5·
        8 months ago

        I don’t have a dog in the race and I always think the bubbles will burst before they do. But with that caveat, shouldn’t the interest rates be a factor?

        My reasoning is that part of a bubble is that as long as line goes up there are assets which can be used for collateral for loans for new money to push the line up. With a low interest rate the new money is cheaper, with high interest it’s more expensive. So all else equal, the boom should burst quicker with higher interest rates.