A multi-year expansion cycle will drive worldwide data center capex to the $1.7 trillion mark by 2030, driven by hyperscalers as well as neo cloud service providers and sovereign AI initiatives, a new report from Dell’Oro Group indicates.
All three segments are entering a new phase of infrastructure expansion; key among them are Amazon, Google, Meta and Meta, which at the start of this year had raised their combined data center capital expenditures to nearly $600 billion.
Baron Fung, senior research director at Dell’Oro Group, said, “despite increased scrutiny around AI infrastructure returns, hyperscalers continue to invest aggressively, supported by large cash reserves and a long-term focus on market share. This growth is being driven by the deployment of larger and more complex AI clusters, which are increasing demand for high-performance networking, storage, inference capacity, and advanced power and cooling infrastructure.”
Findings in the report revealed that while the four US hyperscalers are expected to represent about half of global data center capex by 2030, emerging AI model builders and neo cloud service providers are also projected to grow at significant rates.
In addition, it predicts:
- Accelerated servers for AI training and domain specific workloads could end up accounting for upwards of two-thirds of the total data center infrastructure spend by 2030.
- Outside of hyperscale, enterprise data center investment is constrained as a result of the tariff situation, monetary policy, and the uncertainly about the type of returns AI can deliver.
Fung wrote in a report summary, “the breakout of capex related to the data center, which consumes the largest share of cloud service provider spending, is an important measure to watch each quarter, since a fluctuation by any of these companies will cause a major ripple through the entire supply chain.”
Asked on Thursday about the fact that capex is expected to approach the $1 trillion mark in 2026, he said it is somewhat surprising. “Last year, I thought it would take at least three years to get to that trillion dollar mark,” he said. “It seems increases are supported by the result of larger models needed for training infrastructure, and in turn, you need inference as well. You also need a supporting infrastructure in storage, networking, power, and cooling.”
AI, he said, has become “the tide that lift all boats, meaning that in addition to the core accelerated compute, AI also positively impacts complementary infrastructure, such as storage, networking, and physical infrastructure.”
Fung added that while much of the achievement of projected spending estimates will depend on whether or not this growth is sustainable, he pointed out, “it seems like the large hyperscalers have a lot of weight in optimizing cash flow and cost structures. They’re trying to get as creative as possible, generally moving towards a more vertical, integrated stack with their own custom networking and external financing, which would help [create] more sustainable deployments and operations.”
Enterprises thinking of expanding their own infrastructure can learn from this growth. In a recent article on the hyper spending of hyperscalers, Greyhound Research chief analyst Sanchit Vir Gogia said their capex spending levels can help pinpoint where the hyperscalers are expecting bottlenecks, which is useful information for enterprises planning their own cloud strategy across multiple geographies.
These and other factors can help enterprises plan their own execution timelines, he said.
Originally written by: Paul Barker
Source: CIO
Published on: 12 February 2026
Link to original article: Data center capex to hit $1.7 trillion by 2030 due to AI boom
