There is so much that is unprecedented about the current AI capital expenditure wave that I'm going to break from my usual format and collate it, with some (mostly amazed) analysis. Consider this an update to my two recent pieces on this topic, here and here.
By way of preamble, the overarching point is that AI spending is eating everything, like a golden retriever left unsupervised in a room full of food bowls. You can shout LEAVE IT! all you want, and the food will still be gone before you can get the door open.
Why did the WSJ think the report so strange? Because imports collapsed (tariffs), exports picked up (tariffs), some capital spending went nowhere (rates smashed real estate), and other capital spending jumped (IT). It was a mess of conflicting forces that somehow worked out, like a tornado passing over a junk yard and whirling out a reasonable facsimile of the Kohinoor diamond. How did that happen?
I'm going to focus on one weird detail of the report, but ignore tariffs, which had a huge impact, but about which I wrote at length earlier this week. The key data point is IT spending, as the following table shows.
Paul Kedrosky
The topic of capital expenditure on AI infrastructure has emerged over the past month largely as a warning signal. In an earlier article, Paul Kedrosky notes that current AI datacenter spending is already larger than peak telecom spending (as a percentage of GDP) during the dot-com era, essentially acting as a massive private sector stimulus program, masking weaker sectors of the US economy (flat consumption, weak job growth, declining housebuilding). Other sources have pointed out that AI capex (information processing equipment plus software) has added more to GDP growth than consumers’ spending over the first two quarters of 2025. And Nvidia, arguably the largest beneficiary of this massive spending, now has the biggest weight in the S&P 500 of any individual stock since 1981 and the highest P/E as the index’s top stock since Microsoft in 1999.
We are in a historically anomalous moment. Regardless of what one thinks about the merits of AI or explosive datacenter expansion, the scale and pace of capital deployment into a rapidly depreciating technology is remarkable. These are not railroads—we aren’t building century-long infrastructure. AI datacenters are short-lived, asset-intensive facilities riding declining-cost technology curves, requiring frequent hardware replacement to preserve margins.
And this surge has unintended consequences. Capital is being aggressively reallocated—from venture funding to internal budgets—at the expense of other sectors. Entire categories are being starved of investment, and large-scale layoffs are already happening. The irony: AI is driving mass job losses well before it has been widely deployed.
Paul Kedrosky
In short, the gains in the US economy and stock market are increasingly concentrated among a handful of companies, and even these are growing only on the back of the overinflated expectations of the AI revolution and the massive expenditure that has accompanied it. This feels like a bubble ripening up for a nasty burst.
And signs of weakness and doubt have already started to creep in. After the disappointing launch of GPT-5, Sam Altman himself started talking about an AI bubble forming – although this might have more to do with OpenAI’s latest founding round, valuing the company at $300 billion; such comments might be meant to deter venture capitalists from backing their competitors. An MIT report found that 95% of generative AI pilots at companies deliver little to no measurable impact on P&L. After a hiring frenzy, Meta is downsizing and restructuring its AI division while putting a halt on further hiring – I honestly lost track of how many times Meta switched its AI strategy over the past couple of years. And there’s always the fun fact that OpenAI usage declines in the summer months when students aren’t using it to cheat on homework. The stock market fell on these news last week, but that might only be the beginning…
Post a Comment