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[–] 82 points 1 week ago (15 children)

How long has AI been “teetering on the brink“?

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  • [–] 36 points 1 week ago* (last edited 1 week ago) (4 children)

    Most of the historical boondoggles took about 5 years to work their way out. We are on year 3. If that's any guide, we got a whole lot more electric boogaloo left to play.

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  • [–] 24 points 1 week ago (3 children)

    Most boondoggles don't burn trillions of dollars. AI is starting to hit a wall already because there just isn't any more money. It's why there's this desperate push to go public even though their financials are a dumpster fire.

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  • [–] 8 points 1 week ago* (last edited 6 days ago) (1 child)

    As a percentage of Capex to GDP it's about a third of the way to where the railway mania was at during its peak. There's room to run. The difference between then and now is that all the spending is concentrated in a handful of companies.

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  • [–] 21 points 1 week ago* (last edited 1 week ago) (5 children)

    Depends if you're asking people who are informed or news headlines. The informed people have been saying the AI bubble is set to burst around Q2-Q3 2027. That's when loan obligations come due and the majority of investors are set for payouts.

    The news will say it's happening everyday, just like Trump is just about to die everyday, or MAGA is "turning against him" everyday according to the articles.

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  • [–] 2 points 6 days ago (2 children)

    Ai can crash before then, if people stop investing money or loaning money. It doesn't mean OpenAI run out of money straight away, but it means the projects they are involved in don't happen, so the mountain of debt piles up and the sources of revenue dry up. So, it all happens faster and the smaller neo-clouds go belly up first.

    Already an Aussie one just pulled its launch IPO due to lack of interest and were considering dropping their price significantly.

    If the IPO prices of other companies drop significantly, it means the early investors make very little profit, for a lot of risk, so they start to look elsewhere too.

    AI companies need huge amounts of investment over extended periods, so either scenario is problematic for them.

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