the writer Nina Illingworth, whose work has been a constant source of inspiration, posted this excellent analysis of the reality of the AI bubble on Mastodon (featuring a shout-out to the recent articles on the subject from Amy Castor and @[email protected]):
Naw, I figured it out; they absolutely don’t care if AI doesn’t work.
They really don’t. They’re pot-committed; these dudes aren’t tech pioneers, they’re money muppets playing the bubble game. They are invested in increasing the valuation of their investments and cashing out, it’s literally a massive scam. Reading a bunch of stuff by Amy Castor and David Gerard finally got me there in terms of understanding it’s not real and they don’t care. From there it was pretty easy to apply a historical analysis of the last 10 bubbles, who profited, at which point in the cycle, and where the real money was made.
The plan is more or less to foist AI on establishment actors who don’t know their ass from their elbow, causing investment valuations to soar, and then cash the fuck out before anyone really realizes it’s total gibberish and unlikely to get better at the rate and speed they were promised.
Particularly in the media, it’s all about adoption and cashing out, not actually replacing media. Nobody making decisions and investments here, particularly wants an informed populace, after all.
the linked mastodon thread also has a very interesting post from an AI skeptic who used to work at Microsoft and seems to have gotten laid off for their skepticism
Quantum computing marketing feels that way because people have slapped the quantum label on everything from health stickers to car parts.
But about half the field is pretty dour on if and when that will ever be a reality.
Ironically the use case that’s had the most promise for quantum foundations over the past few years is photonic based neural networks for AI. Because the end result is what matters and the network itself acting like a black box is generally fine, most of the measurement problem goes away and analog processing of ML workloads have been already showcased. MIT just the other week announced availability of a DIY kit for researchers in replicating their work on an A100 equivalent running in photonics even. In that space, the speed has been the opposite of the general purpose quantum computing field.