Morgan Stanley Drops a $50 Billion Bombshell — Can Big Tech Still Afford to Build the AI Factories of the Future?

The fundamental problem with AI is that it breaks the principle of seeking economy of scale that's been fueling tech for decades.

AI simply doesn't scale: in the best case scenario, a 2x performance requires 2x the resources to run the models. In practice, it's even worse than that. It means like unlike previous techs that created a bubble, the massive capital investments don't translate into cheaper operating costs, which don't translate into more customers or increased profits.

AI as it's done today is a fucking dead end until someone invests in the fundamental research needed to run AI efficiently. But nobody does that because all the VC money is funneled into scaling up shit that doesn't scale well.

Who the heck does really need a frontier model.

Almost nobody. In my experience, there are basically two good ways to use AI:

1. When you have a well-defined problem for which a potential answer can be validated programmatically. Basically, “I need to get here; I don’t care how, just let me skip the friction of getting there”.
2. When you have a poorly-defined problem and need help looking at the problem from multiple new angles. Basically, adding friction so that you can understand something better. Crucially, this is about improving your understanding, not producing a final output.

The workflows that AI companies are trying to sell will supposedly go from poorly-defined problem to provably valid solution. But that’s a really bad idea, for so many reasons. It takes a ton of power, it’s unreliable, and it can fail spectacularly.

Hank Green had a quip a while ago, something like “I have a hope that we’ll look back at this time period and be like: remember when the big AI companies had everyone basically driving Formula 1 cars everywhere, when they really just needed a Honda Civic?”