The New York Times and Siena released another batch of polls this morning across a swath of red states. The general picture is the same as its been for weeks. Many of these races are close. But Democrats are ahead in almost all of them. The exception in this batch is Iowa where Ashley Hinson is up by a single point over Democrat Josh Turek. That’s essentially a tie and it compares to Sherrod Brown +3, Mary Peltola +7 (first round) and James Talarico +6.
Some have posited at least the possibility that Democrats are benefiting from poll bias in the reddest states. But I think the real story here is another we’ve discussed: candidate quality. Hinson has run in and won a number of competitive races over the last decade. And she’s objectively a solid if not necessarily a terribly candidate. She’s a former newscaster and those always benefit from some level of familiarity and trust based on many voters feeling they know them. The others range from meh (John Husted in Ohio) to catastrophic (Ken Paxton). Again and again we’re seeing cases where Republicans have been able to run passable and even sub-standard candidates in red states. And there’s a good chance that will continue to be the case when there’s no Cat-5 Hurricane year. But it’s when a big storm comes that you find out the levee ain’t very strong.
We’ve been following this conversation here in The Editors’ Blog about what AI is, what it can do and whether it can possibly live up to its hype. It’s worth considering the question from what we might loosely call not only the supply side but also the demand side. From a very broad perspective, the history of the last three decades has been one defined by the fact that you have too much capital chasing too few productive investments. That’s the driver of the recent history of cycles of booms, bubbles, over-investment and busts: the Asian Financial Crisis, the Dot Com Bust, the Global Financial Crisis, perhaps soon the AI Bubble Collapse, along with many smaller of regional ones.
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.
