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It’s worth remembering that every major technological shift has gone through a speculative boom that looked like a scam in the moment. The dot-com bubble didn’t mean the internet was a fraud - it just meant people overestimated how quickly its impact would pay off. The same was true for the housing market after the subprime crash: people still need places to live. What’s happening with AI now feels similar. There’s definitely hype and inflated valuations, but that doesn’t mean the underlying technology will vanish once the bubble bursts. The noise will fade, and what’s genuinely useful will become infrastructure - quietly integrated into everyday life, just like the internet did.
Curiously, actual scams also go through “a speculative boom that looked like a scam in the moment”, and then they turn out to actually be an overhyped scam that doesn’t in fact change the World.
Crypto currencies are a good example.
Your “don’t throw the baby out with the bath water” statement makes a lot of sense in the early stages, when we don’t really know yet if what’s being overhyped might or not be just the beginning of something big, hence one shouldn’t just discount a tech because there’s a massive hype train on it. The thing is, this was maybe 1 or 2 years ago for things like LLMs, but by now it’s becoming obvious that it’s a dead end since the speed of improvement and cost relative to improvement ratio have become very bad.
Whilst broader Machine Learning tech is useful, as it was useful already since when it started (back in the 90s Neural Networks were already used to recognized postal codes on mail envelopes for automated sorting), this bubble was never about the broader domain of Machine Learning, it was about a handful of very specific NN architectures with massive numbers of neurons and huge training datasets (generally scrapped from the Internet), and it’s those architectures and associated approaches to try and create a machine intelligence that are turning out to not at all deliver what was promised and as they’ve already reached a point very low incremental returns, seem to be a dead-end in the quest to reach that objective. What they do deliver - an unimaginative text fluff generator - turns out to be mainly useless.
So yeah, if you’re betting on the kind of huge neural networks with huge datasets used in the subsection of ML which has been overhyped in this bubble and the kind of things they require such as lots of GPU power, you’re going to get burned because that specific Tech pathway isn’t going to deliver what was promised, ever.
Does this mean that MLs will stop being useful for things like mail sorting or other forms of image recognition? Of course not, those are completelly different applications of that broad technique which have very little to do with what people now think of as being AI and the bubble around it.
Machine Learning has a bright future, it’s just that what was pushed in this bubble wasn’t Machine Learning in general but rather very specific architectures within it - just like when the “Revolution in Transportation” which turned out to be the Segway and kind crap thus quickly fizzled didn’t destroy the entire concept of transportation, so the blowing up of the LLMs bubble isn’t going to destroy the concept of Machine Learning, but in both cases if you went all in into that specific expression a technology (or the artifacts around it, such as massive amounts GPU power for LLMs), that the broader domain will keep going one isn’t going to be much comfort to you.
But assuming crypto is a bubble, I don’t think it has burst yet. So we’re not really at the point, post bubble bursting, where we can look back and determine if it was a total scam or not.
And no one at wall street bothered to ask actual engineers of the actual likelihood of all of these crazy promises being made. Ask engineers and they’d give an optimistic and realistic approach and could guide investors on what is possible and what isn’t. But nooo, gotta love the hype cycle instead
Because capitalism thrives on VC hype.
As someone who’s been through the Bay Area/ Silicon Valley Startup gauntlet, I can assure you that there are plenty of engineers who are deep in the AI koolaid.
I think the other commenter was implying that independent engineers be consulted.
"Microsoft “invests” in Openai by giving the company free access to its servers. Openai reports this as a ten billion dollar investment, then redeems these “tokens” at Microsoft’s data-centers. Microsoft then books this as ten billion in revenue.
That’s par for the course in AI, where it’s normal for Nvidia to “invest” tens of billions in a data-center company, which then spends that investment buying Nvidia chips. It’s the same chunk of money is being energetically passed back and forth between these closely related companies, all of which claim it as investment, as an asset, or as revenue (or all three)."
- Doctor Doctorow
Hmm, the graph given is sus. The trend starts before the AI sector was really a thing, like literally 2010.
If I just look at the extra degree to which it came back after covid, it’s maybe double the dotcom bubble and a lot smaller than 2008.
Edit: To explain a bit more,
they’re basically assuming that any growth past the corporate interest rate plus 2% is bullshit. If they’ve drawn the graph correctly that actually predicts the 'oughts recessions pretty well, but past 2010 looks a lot like it has meaningless drift.
The big question, when it comes to whether to buy into this, is if it works across the last century. Since it’s a simple, old idea and it’s not everywhere I’m guessing no, and they did some strategic cropping.
“This is fine.”
So if your system is oscillating wildly, your P term is too large, right?
P = social media, 24h news cycle I = education D = regulation
?
Yep. We’re all cooked.
Except the billionaires - who will obviously retreat to their cozy luxury bunkers in New Zealand.
