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Global AI revenue exceeds data center depreciation costs for second consecutive quarter
Global AI revenue hit $25 billion in Q1 2026, clearing $21 billion in depreciation costs. Still, depreciation consumes two-thirds of income, leaving narrow profit margins.
Global AI revenue, excluding China, hit $25 billion in the first quarter of 2026, surpassing the industry's estimated $21 billion in depreciation costs tied to data-center and chip investments for the second consecutive quarter, according to a report from research firm Exponential View. The milestone suggests that the hundreds of billions of dollars tech companies are pouring into AI infrastructure may finally be generating enough income to cover the capital costs, though the margin for error is slim.
Depreciation charges still consume more than two-thirds of AI revenue, leaving a small buffer to cover power, labor, and financing. "For now, the economics are holding," the report states, but "the margin for error is narrow," with more financing risk shifting into capital markets through leases, debt, and equity, especially among so-called neoclouds.
Azeem Azhar, founder of Exponential View, told Bloomberg News: "It just about clears the depreciation hurdle, and roughly speaking, it's improving over time. At this stage of an investment in any kind of capital expenditure, you wouldn't expect to have dramatically jumped over that hurdle because if you had, you were probably leaving something on the table."
The biggest US tech companies - including Meta, Alphabet, Microsoft, and Amazon - plan to spend as much as $725 billion on capital expenditures this year, much of it on AI infrastructure. Generative AI revenue, excluding China, reached $110 billion over the past 12 months and is scaling three times faster than any previous information technology wave, including the internet, mobile applications, and the cloud, the report found.
The risk of faster asset obsolescence
The analysis assumes a six-year depreciation life for IT equipment, including GPUs. Some investors argue this is optimistic given the rapid pace of chip innovation, which can render older hardware less valuable within a few years. Michael Burry, the investor known for his bet against the US housing market before the 2008 financial crisis, has described understated depreciation as "one of the most common frauds of the modern era."
However, data in the report suggests older chip models are not collapsing in value. The rental price for an hour of access to Nvidia's H100 chip remains almost 80% of its launch level. "Even into its fourth year, it is completely in demand," Azhar said, noting it has become more expensive over the last year as demand for AI compute outstripped supply of Nvidia's new Blackwell chips.
Matt Garman, CEO of Amazon Web Services, said in February that the company had not retired six-year-old Nvidia A100 servers due to continuing demand.
Cheaper models shift the pricing power
More users are moving toward open-weight and Chinese AI models such as DeepSeek. Data from OpenRouter, a platform that gives developers access to multiple AI models, shows the share of tokens requested from Google, OpenAI, and Anthropic models fell to 33% in June 2026 from 72% a year earlier. "You don't always need a Nobel laureate to extract a number from your receipt to put into an expense spreadsheet," Azhar said.
That does not necessarily spell trouble for leading foundation-model companies, but it raises the bar for charging higher prices. They will need to compete with "additional services, with more lock-in, and with all of the things that allow you to charge a premium," he added.
Why this matters for finance professionals
The report quantifies a tension that has defined the AI investment cycle: whether the explosion in capital spending will ever be matched by user demand. While the revenue numbers are now large enough to cover depreciation, the narrow buffer leaves little room for writedowns if chip values decline faster than expected - a risk that could hit balance sheets as financing shifts from corporate cash flows to capital markets. For those tracking the durability of these investments, resources on AI for Finance can help assess the economic models behind the buildout and the signals that separate sustainable returns from a capital misallocation.