Corporate guidance for capital expenditures keeps getting ratcheted higher and involves massive sums, largely focused on AI infrastructure.

By Wolf Richter for WOLF STREET.

The amount spent on the construction of data centers spiked by 7.0% month-over-month and by 46% year-over-year to a seasonally adjusted annual rate of $68 billion in June, according to construction data from the Census Bureau today. Since the beginning of 2022, monthly construction spending on data centers has spiked by over 500%.

But these amounts only reflect the construction costs of the building, the improvements around the building, and the equipment integrated into the building, such as HVAC systems. And this spending is growing on a near-exponential curve.

Once a data-center building is finished, the real spending commences to turn a concrete box into a functioning data center. The amounts here do not include the most expensive parts of a functioning data center: the immensely costly servers, the racks, the equipment to connect the servers to the internet, the electrical equipment to supply power to the servers, the power generators, the transmission lines, etc.

It is not often that corporate spending shoots up at this rate. Those kinds of the-sky-is-the-limit curves don’t last. They do hit a limit, and it’s a lot lower than the sky. But that moment hasn’t come yet.

The corporate announcements of capital expenditures keep getting ratcheted higher. In terms of the hyperscalers, the capital expenditures involve massive sums, largely focused on AI infrastructure.

For example, Alphabet increased its guidance for full-year 2026 capital expenditures to a range of $195-205 billion, up from $91 billion in last year. Meta increased its guidance for full-year 2026 capital expenditures to a range of $130-145 billion. Microsoft increased its guidance for 2026 to a range of $175-190 billion.

Hundreds of billions of dollars of cash flow that would have gone into share buybacks or Treasury securities is now going into AI infrastructure. In addition, companies have issued new shares at near record high prices, they have borrowed massive amounts, they have “committed” even more massive amounts, a big part of it off-balance sheet, all in order to push the AI infrastructure buildout forward at a blistering speed, come hell or high water.

This massive boom in data center construction is causing shortages of all kinds, including shortages of specialized labor, such as electricians. The electronic equipment needed for those data centers is causing shortages of semiconductors, including memory chips, and prices have spiked. The electricity needed to power the new data centers is straining the grid and pushing up electricity prices. There are shortages of power generation equipment. The list is long and reaches across the economy.

Since money doesn’t seem to matter in AI-related spending, with the priority being to just get it done, the boom is drawing resources away from other activities – and costs are ratcheting higher, and some of those higher costs have started to filter into consumer prices.

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