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95% of Companies See ‘Zero Return’ on $30 Billion Generative AI Spend, MIT Report Finds
(thedailyadda.com)
this post was submitted on 21 Aug 2025
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Emerging technology always loses money in the first few years. Sometimes for a decade or so. This isn’t new.
AI isn't "emerging." The industry is new, but we've had neural networks for decades. They've been regularly in use for things like autocorrect and image classification since before the iPhone. Google upgraded Google Translate to use a GPT in 2016 (9 years ago). What's "emerging" now is just marketing and branding, and trying to shove it into form factors and workloads that it's not well suited to. Maybe some slightly quicker iteration due to the unreasonable amount of money being thrown at it.
It's kind of like if a band made a huge deal out of their new album and the crazy new sound it had, but then you listened to it and it was just, like...disco? And disco is fine, but...by itself it's definitely not anything to write home about in 2025. And then a whole bunch of other bands were like, "yeah, we do disco too!" And some of them were ok at it, and most were definitely not, but they were all trying to fit disco into songs that really shouldn't have been disco. And every time someone was like, "I kinda don't want to listen to disco right now," a band manager said "shut up yes you do."
If you really want to be reductionist, it’s just electricity being fed through silicon. Everything is. Just 1’s and 0’s repackaged over & over!
But it shows a significant lack of insight and understanding. Guess you can make a ton of money with puts on all these companies, with that kinda confidence.
Please let me know what major breakthrough has happened recently in the machine leaning field, since you're such an expert. Throwing more GPUs at it? Throwing even more GPUs at it? About the best thing I can come up with is "using approximately the full text of the Internet as training data," but that's not a technical advancement, it's a financial one.
Applying tensors to ML happened in 2001. Switching to GPUs for deep learning happened in 2004. RNNs/CNNs was 2010-ish. Seq2seq and GAN were in 2014. "Attention is All You Need" came out in 2017; that's the absolute closest to a breakthrough that I can think of, but even that was just an architecture from 2014 with some comparatively minor tweaks.
No, the only major new breakthrough I can see over the past decade or so has been the influx of money.
Then sell your services as a consultant to these businesses, and let them know it’s not actually doing anything different. Let the researchers know that Ai cant possibly be finding cancer at better rates than humans, because nothing’s changed.
Let the world know they fell for it, setup puts against the companies, and make bank.
Are you trying to claim that the fact that there's lots of money flowing to these AI companies is proof that AI isn't just a bubble caused by money flowing to these AI companies?
I’m saying, if you’re so confident it’s a bubble, why don’t you bet your life savings on it?
First of all, because it doesn't matter whether it's actually real or not, investment doesn't actually follow innovation. The actual value of a company or idea has almost nothing to do with its valuation.
But more importantly, why do you think that's the important part of this conversation? I'm not talking about its long term viability. Neither were you. You were just saying that it was a new innovation and still had to mature. I was saying that it was actually a much older technology that already matured, and which is being given an artificial new round of funding because of good marketing.
So are you alleging the studies, like the ones showing AI is vastly superior at identifying cancer in patients, are fabricated?
I'm saying they can only do it because the big innovation was "throw more money at it." Yes, given a functionally infinite amount of hardware, electricity, legal free reign, and publicity, I could invent a machine that does at least one (1) impressive thing, too.
Remember, these models weren't created to identify cancer in patients better than humans. They were created to do everything better than humans. And the fact that they are mediocre at everything except identifying cancer in patients (and a handful of other things) means that they're failing at 99.997% of their goal.
That doesn't mean that it's innovative, or a breakthrough technology that deserves time to mature. It just means that you get more swings at the law of averages if you have a lot of money.