Gartner analysis warns that more than $3.5 trillion in AI infrastructure spending is at risk by 2030.

Gartner Inc. analysis indicates that more than $3.5 trillion in AI infrastructure investments could be at risk of impairment by 2030. The projection stems from the rapid pace of investment in AI technologies outstripping the revenue needed to justify it.

According to Gartner, AI data center operators must generate a total of $8.5 trillion in revenue by 2030 to cover their capital expenditures and financing costs and to earn a 12% return on invested capital. Gartner forecasts, however, that attributable revenue from enterprise and consumer AI will not exceed $3.6 trillion during this period, less than half of what is necessary.

This widening gap between required investment and expected revenue raises serious concerns about the sustainability of the current AI infrastructure boom. The rapid depreciation of high-performance computing assets compounds the problem, as these assets risk becoming obsolete before they can generate adequate returns.

For niche AI infrastructure providers such as CoreWeave Inc. and Nebius Group NV, the stakes are especially high. Their business models are closely tied to demand for computing capacity, leaving them more vulnerable than diversified technology giants such as Microsoft Corp., Alphabet Inc., and Amazon.com Inc.

Gartner forecasts that economic pressures and the lack of differentiation among more than 80% of neocloud providers will likely lead these companies to cease independent operations by 2030. Will Sommer, a senior director analyst at Gartner, notes that many of these providers are attempting to pivot toward higher-margin software and services similar to those offered by established cloud providers. He cautions, however, that success is uncertain.

“All of them want to be the full-stack. They can’t all be,” Sommer said. He highlighted that while a significant amount is spent on computing, only a small fraction of that budget is captured by the owners of the computing resources.

As the landscape evolves, the industry will need to navigate these economic challenges carefully to secure a viable future in AI infrastructure development.