We’re seeing article after article now restating in slightly different ways the same basic point about the AI infrastructure boom: how will this level of investment ever be recouped? This new article in the Journal is entitled: Will America Spend 9% of Its GDP on AI? The Industry Is Counting on It. (It references the same Brookings study we discussed a week ago.) This is more or less the same story as I flagged in that Bain report in last night’s post. A market of $6 trillion for AI by 2031; 9% of GDP in 2032. The numbers may be based on slightly different global modelings — just how much investment, rates of return, etc. But they’re mostly different ways of stating the staggering growth that will be required to cover current levels of capital expenditure. The Journal notes that 9% of GDP in 2032 (one year later than the Bain & Co report focuses on) will mean Americans are spending as much on AI as food, half as much as they will spend on health care. The Journal article reminds us that this isn’t simply about adoption and new products. You have to factor in what will likely be rapidly falling prices as well as competition from new entrants with possibly far lower sunk costs and debt.
Getting into the particulars involves so many variables it’s hard make sense of it without an economic degree. But the overall story is the same as the one in the Bain study. Silicon Valley has committed the economy to unimaginable levels of investment in a very promising but still largely unproven technology. And now we have to go on a national crash course of maniacal innovation just to get out from under that spending overhang or the whole economy craters. Of course, if AI just does a million amazing things and makes all our lives happier and more fun … well, we’ll have to really thank them. But this seems uncertain.
A friend sent me this new Bain & Company study on the growth required to match the current levels of capital expenditure on AI, essentially breaking down the cost of the data center and other associated build outs. It’s an interesting document, not simply for the number-crunching and the predictions, but as a document in the more literary and analytic sense. Needless to say, Bain isn’t coming at this from any “boo capitalism!”/”this is insane lol” viewpoint. But the conclusions aren’t that far from the “this is insane lol” position. It’s very much, “hey! we can do this” but also, “um … wow, there’s quite a lot to do.”
The broad stroke numbers are these. The study says that to keep up with the current levels of capital expenditures AI will need to become a $6 trillion dollar market by 2031. (To give some perspective on that number, a recent Gartner study predicted that global IT spending will be just under $6.4 trillion this year. So devices, hardware, software, services, kind of everything.) It then posits that we can see the beginnings of, with some level of out-year predictability, something on the order of a $1.5 trillion market — which would be made up of a greatly expanded consumer market (Claude apps, ChatGPT, etc.) of between $200 and $400 billion and then an enterprise market (software, sales, marketing, business optimization) of between $1 and $1.4 trillion. So we’re left with about $4.5 trillion to go. That’s where the crash course in innovation comes in.
