alt Hacker News> We might have just shot our most valuable non-AI tech products in the foot.
Counterpoint: the fiber buildout during the dotcom boost. That crashed the economy pretty hard when the bubble burst, but we are still benefitting from all the dark fiber that was arranged for and built out back in that era. A lot of today's ISPs were able to grab up that fiber after the bust for cents on the dollar.
Assume that OpenAI and Anthropic go bust, which at least one of them likely will, and possibly a fair few of the datacenters that are under construction will also collapse. Someone will be able to snatch these physical assets again for cents on the dollar and run open-weight models on them or train new ones.
The problem isn't (and no, this is not an AI tell, everything I write here got typed on a 2022 M2 MBA by hand) the assets, they will be put up for productive usage, just as with any other large bankruptcy or bubble in history. The problem is the "IOU" that is being passed from one hand to the next like a hot potato. Assuming a recovery of, maybe, 20% after the collapse, at 1.6 trillion dollars of assets under management by some kind of private investment/debt we're looking at about 1.3 trillion dollars in valuation that is going to be wiped out.
And given that a lot of the investment market is actually backed by pension funds... this is going to be a bloodbath. Not only will there be a lot of people laid off in addition to the layoffs we already saw "due to AI", but when the pension funds and thus their payouts collapse? We'll see retirees flooding the employment markets who just try to make a living, rendering the situation for everyone else even worse. Flipping burgers used to be a gig for students, these days students compete with people of all ages desperate to survive - and thus desperate to undercut others in wages.
Another problem will be the capacity buildout in the semiconductor industry. It's already heading toward an oligopoly after numerous boom-bust cycles: you only have two and a half GPU chip vendors (NV, AMD, Intel), two vendors of general-purpose CPU vendors (Intel and AMD - I exclude Apple because they do not sell their CPUs to any third party and ARM because 99% of non-Apple ARM chips do not go towards servers, desktops and laptops), three RAM manufacturers (Samsung, SKhynix, Micron) and two and a half physical chip manufacturers (TSMC, Samsung, Intel). When the AI bubble bursts, it will be one of a hell of an effort to prevent at least one actor from going bankrupt.
[1] https://prospect.org/2025/11/19/ai-bubble-bigger-than-you-th...
You're expecting that there's going to be a supply collapse only, but there's a real risk the collapse hits both supply and demand.
A lot of the current AI business is FOMO and vanity metrics. Nobody really wants to acknowledge the support tickets where the first three responses are the customer cursing because they didn't appreciate being handed off to a chatbot, or the reworks, or the compliance/policy/privacy concerns, or the internal friction and brand damage it's causing.
Right now, a lot of that is being dazzled away by how "cheap" the alternative is, since it's built on an unsustainable cost base. It's like someone opened a "restaurant" where the food was actually supplied by making a bazillion new DoorDash accounts to claim promotional credits and having them drop the food at the "kitchen". During the initial phase, the customers will forgive that the burger was cold because it was $1.79.
Once the funny money runs out and services start shuttering or pricing for actual profitability, people are going to ask about actual quality and return on investment. There will be a demand rollback.
Even if you can do it cheaper with an open-model running on fire-sale hardware, we probably don't need 500 "chatbot listens and transcribes your meeting" services that weren't that much better than dictation software running locally on a Pentium III. We probably don't need AI-powered support experiences that manage to be worse than actually keyword-searching your company's Confluence. We probably don't need to be spinning up coding agents to spend 15 minutes discombobulating and bibblewabbling and re-reading 82 billion tokens of context before making a two-line change that an actual developer with learned experience in the code would make in 15 seconds.