The largest builders of artificial intelligence data centres have doubled their debt load in the last five years, turning to borrowing to finance an unprecedented spending spree they claim is needed to transform the economy.
Alphabet, Amazon. com, Meta Platforms, Microsoft and Oracle, the five biggest spenders on new data centres in the US, collectively added some $350 billion to their debt obligations in the last five years, according to data compiled by Bloomberg.
Also Read: AI's biggest winners may be the engineers who don't write code
They're betting heavily that cutting-edge AI services will mean a flood of new revenue down the line. Investors have enthusiastically backed the companies, snapping up new bonds issued in a variety of currencies. But buyers gave an unusually chilly reception this week to a $25 billion issuance from Amazon, people familiar with the matter told Bloomberg, a sign that there's a limit to the amount of money available to back investment from the tech giants.
The cost of that borrowing is still relatively minor for most of the companies, which have been enormously profitable. Interest expense at the five topped $10 billion last year. That's more than double where it stood in 2019, but it pales in comparison to the free cash flow of just one of them. Google's cash from operations minus capital expenditure was $64 billion at the end of the March quarter.
Also Read: Are data centres uncool? Questions over AI's ecological trade-off are growing louder but solutions may be emerging
Other companies balance sheets are showing more strain. Amazon's free cash flow went negative in the quarter ending March 31. The cash burn at Oracle, whose debt stood at about 2.5 times its sales in 2025, is expected to accelerate. S&P Global Ratings on Thursday downgraded Oracle to the lowest investment-grade rating, citing the company's growing AI spending.
Software companies tend to be high-margin businesses that don't require much regular capital expenditure. For the industry's largest players, that started to change with the advent of cloud computing, which required big investment in server farms. AI data centers, which are usually larger than prior facilities and feature pricier chips, supercharged that outlay.
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Alphabet, Amazon. com, Meta Platforms, Microsoft and Oracle, the five biggest spenders on new data centres in the US, collectively added some $350 billion to their debt obligations in the last five years, according to data compiled by Bloomberg.
Also Read: AI's biggest winners may be the engineers who don't write code
They're betting heavily that cutting-edge AI services will mean a flood of new revenue down the line. Investors have enthusiastically backed the companies, snapping up new bonds issued in a variety of currencies. But buyers gave an unusually chilly reception this week to a $25 billion issuance from Amazon, people familiar with the matter told Bloomberg, a sign that there's a limit to the amount of money available to back investment from the tech giants.
The cost of that borrowing is still relatively minor for most of the companies, which have been enormously profitable. Interest expense at the five topped $10 billion last year. That's more than double where it stood in 2019, but it pales in comparison to the free cash flow of just one of them. Google's cash from operations minus capital expenditure was $64 billion at the end of the March quarter.
Also Read: Are data centres uncool? Questions over AI's ecological trade-off are growing louder but solutions may be emerging
Other companies balance sheets are showing more strain. Amazon's free cash flow went negative in the quarter ending March 31. The cash burn at Oracle, whose debt stood at about 2.5 times its sales in 2025, is expected to accelerate. S&P Global Ratings on Thursday downgraded Oracle to the lowest investment-grade rating, citing the company's growing AI spending.
Software companies tend to be high-margin businesses that don't require much regular capital expenditure. For the industry's largest players, that started to change with the advent of cloud computing, which required big investment in server farms. AI data centers, which are usually larger than prior facilities and feature pricier chips, supercharged that outlay.
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