The catalysts for a crash are already laid out, and it can happen sooner than most expect. AI is here to stay. If used right, chances are it will make us all more productive. That, on the other hand, does not mean it will be a good investment. Big tech doesn’t need to win, just outspend Magnificent 7 companies are increasing capex to their biggest ever to differentiate their tech from each other and the big AI labs, but the key realization is that they don’t have to spend it to win. It’s a defensive move for them, if they commit $50B, OpenAI and Anthropic need to go raise $100B each to stay competitive, which makes them reliant on investors’ money. As the numbers get bigger, the amount of funds that can write checks of the size required to fill such amounts gets smaller. And many of them are now getting bombed in the Gulf. This is the reason there’s a push for IPOs, it’s because it’s the only option left to keep the funding coming. Taking this into account, Google is extremely well positioned to weather the storm. When they announce capex expenditure, they don’t spend it overnight. They can simply deploy month by month until their competitors struggle to raise and get forced to capitulate. At that point they can just ramp down the spending and declare victory in a cornered market. They don’t need capex, they just need to make it very clear for everyone that nobody can outspend them. It is hard to picture as numbers get so big, but Alphabet (Google’s parent) is ten times more valuable than the biggest military company 1. This also has a great implication for the Mag 7, especially Google: their capex will be a lot smaller in practice than projected, and as investors hate to see high capex in tech, the market will probably reward that if it materializes. As of March 2026, Alphabet’s market cap is ~$2T while Lockheed Martin’s is ~$120B. ↩

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.

  • wizblizz@lemmy.worldEnglish
    21·
    1 month ago

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

    • Fubarberry@sopuli.xyzOPMEnglish
      1·
      1 month ago

      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.

    • skibidi@lemmy.worldEnglish
      1·
      1 month ago

      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.