> analysts have estimated that it will take $2 trillion a year in revenue to pay for the infrastructure that has already been built

I doubt any credible analyst has claimed that. What's the total AI capex that's already been spent? About $1T? It's a pretty absurd idea that those DCs need to make $2T/year for 5-7 years -> $10T-14T over their lifetime to break even.

(Yes, this is nitpicking in the sense that there are probably analysts talking about how projected and sustained capex at >1T/year will require 2T/year in revenue, so patching the article won't be a biggie. But this article is cosplaying as financial analysis and leads off with such an obviously incorrect argument. What does that say about the credibility of the rest of the article?)

> Diseconomies of Scale.

Another very basic mistake here. The author starts talking about efficiency in the context of past technologies. That's lower unit costs as scale increases.

But for AI, they seem to switch from talking about unit costs to total costs. Or at least I can't explain what they say about models getting more expensive over time in any other way, because that is not true about unit costs.

We've never seen economies of scale as large as for AI. For a given quality level, the cost has been dropping at >10x per year, not increasing.