Across the world, governments are beginning to treat AI infrastructure as a strategic national asset, essential for maintaining technological sovereignty.
Nov. 28, 2025 –
When Nvidia CEO Jensen Huang noted in an interview earlier this year that building sovereign AI infrastructure is “more important than developing the atomic bomb,” he wasn’t indulging in hyperbole; he was capturing a profound shift in how nations now define power and security in the digital age.
Artificial intelligence is rapidly becoming the backbone of economic productivity, industrial competitiveness, and national defense. Just as electricity and the internet once transformed societies, AI now demands its own dedicated infrastructure, including vast data centers, specialized chips, and localized compute capacity to train, deploy, and safeguard models critical to a country’s future.
Across the world, governments are beginning to treat AI infrastructure as a strategic national asset, essential for maintaining technological sovereignty. In this new era, sovereignty is no longer defined solely by territory or energy independence. It now extends to the ability to generate and control one’s own intelligence. This article explores how the U.S., the Middle East, and Europe are building sovereign AI infrastructure, outlining their respective roadmaps, broader implications, and the challenges that lie ahead.
The U.S. remains far ahead in the global race to expand AI infrastructure. Over the next five years, nearly 25 GW of data center capacity purpose-built for AI workloads are expected to come online. (Note: This estimate excludes recent announcements by OpenAI with Nvidia, AMD, and Broadcom, as those projects have not specified whether those chips will be used in facilities in the U.S.) This buildout represents more than $800 billion in cumulative investment and includes multi-gigawatt programs such as OpenAI’s Stargate initiative with Oracle, Nvidia, and SoftBank, alongside massive expansions by Amazon Web Services, Google, and Meta.
Rising demand for model training and inference explains only part of why those tech giants are investing heavily in their own data center infrastructure. At scale, compute becomes a strategic asset: Owning it enables purpose-built facilities, custom accelerators, higher utilization, and materially lower lifetime costs than rented cloud capacity. It also keeps model training and proprietary data under direct control, meeting security and data-sovereignty requirements. Beyond operational benefits, infrastructure ownership supports a wide range of competitive objectives, from reinforcing recurring, compute-as-a-service revenue streams to accelerating improvements in AI-driven products and capabilities. To sustain this scale, companies are locking in scarce resources, including power, land, and GPUs, through long-term energy contracts, early site acquisition, and direct chip-supplier partnerships.
At the policy level, this corporate buildout is reinforced by Washington’s industrial strategy. The CHIPS and Science Act and energy incentives aim to keep the next generation of AI compute anchored in U.S. soil. These measures encourage co-investment between hyperscalers, semiconductor manufacturers, and utilities, creating a cycle in which public policy and private capital amplify each other.