Link: https://martinvol.pe/blog/2026/03/30/how-the-ai-bubble-bursts/

This is a look into how an AI bubble burst might play out. The biggest issue is money, companies like OpenAI and Anthropic are burning money with no good way to turn it profitable. Google can "win" the AI race just by continuing to spend money, and wait for the others to crash and burn.

OpenAIs recent attempts to turn a profit (shopping and sora) are both failing, and they've resulted to trying ads (which they had previously described as a last resort).

Anthropic is capturing the developer market, but some reports say their metered models cost 5x more than what people pay for them. It doesn't matter that they're having market success, if more users just means burning way more money.

If multiple major players crash or have to get bought out, we may see several major datacenters shutdown or underperform, which could have a strong ripple effect on the economy.

Noteworthy, this article doesn't include anything about the US government's involvement in AI. The US considers AI a race to reach AGI before china, and may attempt to prop up a failing AI market to achieve this.

datacenters failing would likely give positive surge to component economy, as all the wasted capacity would be freed.
Also companies would have to hurry to regain workers they replaced with ai when ai bubble bursts.

I can see nothing but positive effects from it, though they will likely be turned into bad ones by the rich.
Or maybe one of the companies survives it and gains complete dominance they exploit slowly.

Also companies would have to hurry to regain workers they replaced with ai when ai bubble bursts.

Pretty sure this won't happen, a lot of the tech companies are reliant on investors to keep running. Before AI, the investors were cutting off money, and it was causing mass layoffs across the tech sector.

AI hype for investors throwing money at companies again, which has largely stopped the layoffs. It's also why every company was throwing themselves into AI even if their company model didn't really benefit. Saying they were AI focused got the investor money flowing again.

So if AI bubble pops, not only will a lot of AI companies layoff people, but the venture investors will probably tighten up on their investments, causing all the not yet profitable tech companies to shut down or rapidly enshitify to try to turn a profit. Meanwhile we'll see far more layoffs across the tech industry.

And with how interconnected economies are, it will probably trigger a pretty large economy wide recession.

Overall I expect it to be a bad experience for everyone.

ffs, i guess its time to leave it field for good.

Strongly disagree it will make anyone more productive. Glorified autopredict will go the way of NFTs and 3D tvs, another bullshit pyramid tech scheme.

More productive is debatable, but even if it never improves I doubt it's going anywhere. This is anecdotal, but every housewife I know uses it religiously for recipes/meal planning/house project planning/etc.

Also a big part of it being unsustainable right now is training costs, if companies stop trying to make better models it's very feasible to continue offering the current models in financially sustainable way. But that only works until a rival company comes out with a bigger better model, so everyone is having to dump loads of cash into training constantly.

The productivity increases are real for office work. Absolutely not for 'generative' tasks - using them to do anything that takes a modicum of thought is a recipe for frustration and burned time - but they make pretty good search engines for unstructured information.

Now, you don't need a whole-ass model for that, a vector database would do just fine, but since everyone rushed to hook the models in to all their repos and internal wikis it is a convenient omni-search tool.

Now was that worth all the bullshit over the last 4 years as the bubble inflated? Not at all. But it is a real use case and has real value.

I even have leaders now pushing everyone to keep their docs up to date and to make sure tables have metadata fields and such so that agents can make best use of the information. This is particularly hilarious to me because they didn't care to make this investment when people would benefit, but doing it now makes my life easier anyway.