Artificial Intelligence’s Gigantic Financials, and the Effects They are Having on Others

A great deal has recently been announced about the often-staggering sums of money associated with AI.  What have we heard, and what does it mean?

On August 27^th, Tripp Mickle saw “Nvidia Sales Jump 56%, a Sign the A.I. Boom Isn’t Slowing Down” (The New York Times).  What was, at least as of that date, “the most valuable public company in the world,” and had the month before reached $4 trillion market value, sold $46.74 billion from May through July with “profit” of $26.42 billion, the latter up 59%. 

On the same date and in the same publication, we read that “The A.I. Spending Frenzy Is Propping Up the Real Economy, Too” (Lydia DePillis).  “Companies will spend $375 billion globally in 2025 on A.I. infrastructure, the investment bank UBS estimates,” and “that is projected to rise to $500 billion next year.”  Per the Commerce Department, “investment in software and computer equipment, not counting the data center buildings which have “overtaken office construction,” accounted for a quarter of all economic growth this past quarter.”  The boom has especially helped building materials companies, along with “electricians, engineers and heavy-equipment operators.”  The sector is so strong that “the most significant constraint on data center growth is more likely to be supply:  The energy, water, workers and technical equipment required to construct and run them are all getting more expensive.”

The Economist, on September 13^th, took a warning stance with “The $3trn bet on AI,” saying that “even if the technology achieves its potential, some people will lose their shirts.”  The piece, though, did not focus on the near-certainty of some companies losing their market share or their perceived potential, but asserted that even “in the rosiest scenario,” many shareholders “would face big losses,”; if worse, “the flow of capital could slow; some startups, struggling under the weight of losses, could fold altogether,” and “a lot of today’s spending could prove worthless,” as “more than half the capex splurge has been on servers and specialized chips that become obsolete in a few years.”

One area less considered is “How Wall Street’s Big Bets on A.I. Are Driving Interest in Huge Parking Lots” (Patrick Sisson, The New York Times, September 16^th).  A company, Gray, “building 22 data centers,” reports that each one requires “a space nearby that’s large enough to store millions of dollars’ worth of tools, generators, and tractors and trailers, in other words “a couple of acres of gravel or asphalt near highways, ports and other shipping infrastructure.”  That is nothing completely new, as e-commerce already “requires huge spaces to park inventory, shipping containers and the vehicles used in last-mile delivery services,” meaning that rents for suitable industrial outdoor storage facilities are up, an average of 123% in the past five years, and “owners of anything that resembles” such properties, such as “old truck stops and auto repair yards,” “have been hearing more from brokers.”

In response to differing perceptions, Cade Metz and Karen Weise told us, in the New York Times on September 16^th, “What Exactly Are A.I. Companies Trying to Build?  Here’s a Guide.”  Those mentioned in the article, which prints out to 13 pages, are “A Better Search Engine,” “Tools That Make Office Workers More Productive (and Maybe Replace Them),” “An Everything Assistant,” “A.I. Friends,” “Scientific Breakthroughs,” and “A.I. That’s as Smart as a Human, or Smarter.”  As I have documented, instances of these objectives have already succeeded, and companies expect many more.

Have you wondered “What Wall Street Sees in the Data Center Boom” (Ian Frisch, September 20^th, additionally in the Times)?  Analysts, right or wrong, consider that “data center capacity has become a barometer for both the health of the tech market and the risk of an A.I. bubble,” so they have been enthused about AI for most of this year.  Yet there are causes for concern, namely that “even if A.I. proliferates, demand for processing power may not,” “some worry that costs will always be too high for profits,” “it’s not just Silicon Valley with skin in the game,” and, as we will see, “the stakes extend beyond finance.”

As Schumpeter revealed in “AI’s $4trn accounting puzzle” (The Economist, September 20^th), depreciation rates have an enormous impact on industry profitability.  Not all companies agree on “the longevity of all those fancy AI chips they are installing,” especially now that Nvidia has “said it would unveil a fresh AI chip every year rather than every couple of years,” and the useful lives of servers have proven controversial.  Using reasonable assumptions, the author estimated that if “the entire AI big five” set server depreciation at three years, “their combined annual pre-tax profit would fall by $26bn, or 8% of last year’s total,” which could “amount to a $780bn knock to their combined value.”  Serious business those accountants are doing.

Since the most effective way for businesses to assure that their customer relationships will stay good is to buy those they sell to, it was no surprise that we can expect “Nvidia to Invest $100 Billion in OpenAI” (Tripp Mickle and Cade Metz, The New York Times, September 22^nd).  That will be “part of a wider effort among tech companies to spend hundreds of billions of dollars on A.I. data centers around the world.”  Yet that raised the question “Is A.I. Investment Getting Too Circular?” (Andrew Ross Sorkin et al., The New York Times, October 7^th).  Do such deals
“raise questions about the robustness of the artificial intelligence boom”?  Possibly, if, as a “prominent short seller” put it, “Don’t you think it’s a bit odd that when the narrative is ‘demand for compute is infinite,’ the sellers keep subsidizing the buyers?”  That is a good question, and the answer could be that nobody else, even in 2025, has enough money.  After all, when “deals with AMD, Nvidia, Oracle, CoreWeave and others promise to give the ChatGPT maker more than 20 gigawatts of computing power over the next decade, roughly equivalent to that of 20 nuclear reactors,” and “the electricity needed to support that compute could cost about $1 trillion,” they’re getting way up there.

An idea to consider from a Yale Law School professor and Budget Lab president there is if “There are Two Economies: A.I. and Everything Else” (Natasha Sarin, October 6^th, also in the Times).  In a year when the American population has been bifurcated into rich and not rich, and split ever more sharply into pro-Donald Trump and anti-Trump sections, it is nothing too strange to suggest that related economic forces have done the same.  Money spent on AI-benefiting capital “may reach 2 percent of the gross domestic product in 2025,” a 20-fold three-year increase, and it alone may be pushing national economic growth from 1% to “almost twice that.”  “There are signs that the non-A.I. economy is under duress,” shown by problems with inflation and jobs, and “it’s possible that other parts of the economy are being held back by A.I.’s dominance,” as opportunities in that area may be hogging capital.  If indeed “the A.I. boom is masking Trump’s policy blunders,” we may need it to succeed even more than we think.

Overall, artificial intelligence is cutting new channels in our economy wider than any before.  The courses they take will be different.  Its outcomes and major impacts inside and outside that field are still very much unknown.  Sometimes, all we can do in such circumstances is to hang on for the ride and stay away from the banks as much as we can.  That is where we are now.