AI Is A Money Trap
(www.wheresyoured.at)
from Powderhorn@beehaw.org to technology@beehaw.org on 11 Aug 01:24
https://beehaw.org/post/21562436
from Powderhorn@beehaw.org to technology@beehaw.org on 11 Aug 01:24
https://beehaw.org/post/21562436
As always with Zitron, grab a beverage before settling in.
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Oh god, another AI hot take 🙄
Yes, OpenAI and Cursor both are waaaaayyyy overhyped & overvalued.
So were pets.com and yahoo.com back in 1999. But that didn’t stop FAANG from becoming honestly trllion-dollar valuation because while there was breathless Internet hype, the Internet was about to completely change the way the world works.
AI today is like the Internet in 1999.
I was at a startup in 1999 … in Seattle. I actually ducked out because it was clear that about all they could do was arrange outings for the staff.
People immediately knew how internet could help us even during the dot com bubble. Anyone who had used Google (or before that, Yahoo) would immediately fall in love with them with how they help their live. AI (LLM)? Not so.
The Internet boom didn’t have the weird you’re-holding-it-wrong vibe too. Legit “It doesn’t help with my use case concerns” seem to all too often get answered with choruses of “but have you tried this week’s model? Have you spent enough time trying to play with it and tweak it to get something more like you want?” Don’t admit limits to the tech, just keep hitting the gacha.
I’ve had people say I’m not approaching AI in “good faith”. I say that you didn’t need “good faith” to see that Lotus 1-2-3 was more flexible and faster than tallying up inventory on paper, or that AltaVista was faster than browsing a card catalog.
Like Zitron says in the article, we’re 3 years into the AI era and there is not a single actually profitable company. For comparison, the dot-com bubble was About 5-6 years from start to bust. It’s all smoke and mirrors and sketchy accounting.
Even if/when the AI hype settles and perhaps the tech finds its true (profitable) calling, the tech itself is still insanely expensive to run and train. It’s going to boil down to Microsoft and/or X owning nuclear power plants, and everyone else renting usage from them.
People are making money in AI, but like always, it’s the founders and C-suite, while the staff are kicked to the curb. It’s all a shell game and everyone that has integrated AI into their lives and company workflows, is gonna get the rug pulled out from under them.
I don’t think it’s going to come down to these absurd datacentres. We’re only a few years off from platform-agnostic local inference at mass-market prices. Could I get a 5090? Yes. Legally? No.
I have to think that most people won’t want to do local training.
It’s like Gentoo Linux. Yeah, you can compile everything with the exact optimal set of options for your kit, but at huge inefficiency when most use cases might be mostly served by two or three pre built options.
If you’re just running pre-made models, plenty of them will run on a 6900XT or whatever.
I don’t expect anyone other than … I don’t even know what the current term is … geeks? batshit billionaires? to be doing training.
I’m very much of the belief that our next big leap in LLMs is local processing. Once my interactions stay on my device, I’ll jump in.
This is a little misleading, because obviously FAANG (and others) are all building AI systems, and are all profitable. There are also tons of companies applying machine learning to various areas that are doing well from a profitability standpoint (mostly B2B SaaS that are enhancing extant tools). This statement is really only true for the glut of “AI companies” that do nothing but produce LLMs to plug into stuff.
My personal take is that this is just revealing how disconnected from the tech industry VCs are, who are the ones buying into this hype and burning billions of dollars on (as you said) smoke and mirrors companies like Anthropic and OpenAI.
This is an interesting take in that only doing one thing but doing it well has been, historically, how businesses thrived. This vertical integration thing and startups looking to be bought out instead of trying to make it on their own (obviously, VCs play a role in this) has led to jacks of all trades.