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Artificial intelligence is reorganizing the physical world. Not metaphorically, but in the most literal sense.
For the first time in the history of digital infrastructure, the limiting factor in technological progress is not software, algorithms, or even semiconductor supply.
It is power and the ability to translate gigawatts of energy into actionable compute at previously unimaginable scale.
This shift has produced a new category of digital infrastructure.
The gigawatt-scale data center campus.
These are facilities designed to deliver at least 1 gigawatt (GW) of IT load, a number so large that it aligns more closely with utility-scale energy production than with any traditional data center footprint.
A single 1 GW build consumes the power equivalent of a major U.S. city or a full-scale nuclear reactor.
Nothing like this has existed before.
The sections that follow map the global buildout of these facilities across North America, Asia Pacific, the Middle East, and Europe, drawing from a dataset of more than fifty emerging gigawatt-class initiatives and a confirmed core of over twenty developments representing 37.1 GW.
Every one of these projects signals the same thing: power availability, not land, not fiber, not capital, is now the gating factor for AI progress.
Why the best developers are securing energy before they secure sites is examined in this analysis of why sophisticated investors are buying power before land.
Only regions capable of mobilizing multi-gigawatt generation, transmission infrastructure, and institutional capital will play a meaningful role in the next decade of AI deployment.
The Gigascale Inflection Point
The AI transition marks a clean break from the hyperscale era.
These new campuses do not resemble data centers as the industry has known them. They resemble energy assets, designed and financed with the same logic applied to large-scale power infrastructure. Three forces define this inflection point.
The first is scale and the capital aggregation required to achieve it.
A single gigawatt-class project demands between $9 billion and $15 billion once the integrated power systems (generation, transmission, substations, backup, cooling, and storage) are considered. No conventional real estate capital stack can shoulder that load. Instead, these projects demand sovereign wealth funds, global infrastructure private equity, and hyperscalers with the balance-sheet durability to lock in multi-decade commitments. This explains why the core sponsors of the Gigawatt Era are names like Blackstone, KKR/ECP, G42, PIF, Reliance, Meta, Oracle, Microsoft, and Amazon.
The second force is the reordering of geography.
Traditional factors such as fiber adjacency, metro proximity, and low-latency transit no longer dominate site selection. What matters now is the ability to secure gigawatt-scale power at predictable cost. Regions with significant energy surpluses, large tracts of developable land, and political willingness to approve massive power loads are the natural winners. This is why new gigawatt projects appear in places as varied as Amarillo in Texas, Richland Parish in Louisiana, Extremadura in Spain, Abu Dhabi in the UAE, Gujarat in India, and the NEOM corridor in Saudi Arabia. These locations reflect a single criterion: access to abundant, reliable power.
The third force is time.
Traditional hyperscale data centers could be delivered in one to three years. Gigawatt campuses require five to eight years, not because construction is slower, but because power interconnection, new generation, and transmission buildout sit on the critical path. These facilities must be built in lockstep with their energy supply, effectively merging the timelines of two historically separate industries.
How interconnection queues and grid delays are rewriting deployment timelines is examined in this analysis of the interconnection bottleneck reshaping the power map.
Global Dataset: The 1GW+ Foundation of the Gigawatt Era
The global pipeline of 1GW+ data center projects is no longer a loose constellation of experimental mega-campuses, it is now a substantiated, multi-regional development map stretching across the United States, Asia Pacific, the Middle East, and Europe.
These projects reveal a structural shift in digital infrastructure: gigawatt-class facilities are no longer anomalies. They are the new threshold for AI competitiveness across both frontier model training and national-scale inference.
This section contains the top confirmed gigawatt projects, sorted by region and capacity. Taken together, the dataset captures nearly 40 GW of verified development activity and more than 120 GW of global pipeline potential.
These numbers underscore a simple truth: leadership in the AI economy now requires physical control over energy at previously unthinkable magnitudes.
North America: The Dominant Force in the Gigawatt Era
North America has become the global anchor of gigawatt-scale AI infrastructure, with more than 27 GW of confirmed capacity. Texas is the epicenter, but the gravitational pull extends across the Midwest, Deep South, Mountain West, and Mid-Atlantic. The United States is the first country to demonstrate that gigawatt data centers are not theoretical constructs but deployable industrial systems.
Project Matador in Amarillo, Texas is the clearest expression of this new paradigm. Designed as an 11 GW hybrid energy-data complex, it spans 5,800 acres and integrates four 1-GW nuclear reactors, natural gas turbines, and solar fields. Fermi America, co-founded by former U.S. Energy Secretary Rick Perry, is orchestrating an energy footprint large enough to reroute regional grid flows. The initial 1 GW phase is targeting late-2026 completion, but the long-term roadmap stretches more than a decade as nuclear licensing, grid integration, and phased construction converge.
Stargate Abilene, a 1.2 GW campus, represents the world’s first operational gigawatt-scale AI facility. It opened its first phase in September 2025 with a build timeline of just 300 days, delivering 206 MW across two halls. The site is a full-stack AI manufacturing hub, home to 400,000 NVIDIA GB200 NVL72 GPUs distributed across eight buildings. The campus pulls 1.2 GW from ERCOT, integrates 360 MW of on-site gas generation, and draws power from West Texas wind and solar. Backed by $40 billion in total commitments (including $11.6 billion from Blue Owl Capital, Primary Digital Infrastructure, and JPMorgan) the project is fully leased to Oracle under a 15-year agreement.
Meta’s Richland Parish campus in Louisiana brings another dimension to gigawatt development. With 2 GW of capacity across 4 million square feet and a 2,250-acre land footprint, it is the largest AI training site in Meta’s portfolio and a cornerstone of its Llama model roadmap. The $10 billion project expands to $27 billion when accounting for Blue Owl–structured financing vehicles. Its power supply is anchored by Entergy’s Geaux Zero program, which will bring 1.5 GW of new renewables into service. Full completion is staged for 2030, positioning the site as one of the world’s dominant AI clusters.
Amazon’s Project Rainier, stretching across 1,200 acres in Indiana, reaches 2.2 GW of capacity and is dedicated entirely to Anthropic’s AI workloads. Seven buildings were operational by October 2025, hosting more than 500,000 AWS Trainium2 chips. When fully energized, Rainier will draw as much electricity as 1.6 million U.S. homes, illustrating the magnitude of demand that AI training can impose on regional grids.
Vantage’s Frontier campus in Shackelford County extends the Texas supercluster. With 1.4 GW planned across ten buildings and 3.7 million square feet, the site represents $25 billion in capital deployment. Designed for next-generation GPU density, the campus supports racks exceeding 250 kW using a closed-loop chiller system that drives water usage toward zero. Initial delivery is planned for the second half of 2026.
Provident and PowerHouse’s Grand Prairie development provides another major anchor for the Dallas–Fort Worth corridor. Its 1.8 GW switchyard sits on 768 acres and includes 24 buildings across three phases. ERCOT has already greenlit the first 500 MW, and the initial building is expected to energize in mid-2026.
The Stargate national network continues to expand. OpenAI, Oracle, and SoftBank added five new gigawatt-scale campuses in the second half of 2025, bringing the nationwide total to nearly 7 GW with $400 billion committed. Lordstown, Ohio, and Milam County, Texas, will contribute 1.5 GW combined within 18 months. The Related Digital campus in Saline Township, Michigan, Stargate’s first gigawatt facility in the Midwest, covers 250 acres and includes three 550,000-square-foot buildings backed by $7 billion in investment. A similar 1 GW campus in Port Washington, Wisconsin, is being co-developed by Oracle and Vantage, with completion planned for 2028.
Each of these developments reflects the same underlying reality: the United States is building the world’s first AI supergrid. No other region has yet demonstrated a comparable ability to build energy and compute together at gigawatt scale.
Asia Pacific: Rapid Acceleration Toward AI Sovereignty
Asia Pacific’s confirmed 6 GW of gigawatt-class projects reflect the rise of a second global compute pillar. The region’s growth is driven primarily by India’s national AI strategy and reinforced by Japan, South Korea, and Australia, all seeking to secure domestic control over high-density compute capacity.
Reliance Jamnagar in Gujarat anchors this transformation. At 3 GW, it is set to become the world’s largest AI data center by capacity. The site is embedded inside Reliance’s 7,500-acre energy complex, enabling direct integration with solar, wind, green hydrogen, and the world’s largest petcoke gasifier. The facility will run on a diversified and resilient energy portfolio designed specifically to stabilize AI training loads. NVIDIA is supplying advanced semiconductors through a strategic partnership with Reliance, ensuring the site remains competitive with U.S. hyperscalers. The project’s 24-month “Jamnagar style” construction cycle is unprecedented at this scale.
Google’s Visakhapatnam AI Hub is a national strategic asset for India, representing $15 billion in investment between 2026 and 2030. Its 1 GW target capacity is paired with a new subsea cable landing station that connects the east coast to Google’s global fiber backbone. Built with AdaniConneX and Airtel, the campus integrates renewable energy procurement, hyperscale fiber capacity, and AI-optimized data center architecture.
Goodman Tsukuba in Japan demonstrates how APAC markets with structural energy constraints can still participate in the Gigawatt Era through engineering sophistication. This 1 GW development occupies 111 acres in Tsukuba Science City, a location selected for relative seismic stability and proximity to Tokyo’s network while remaining outside the capital’s congested grid. Phase 1 delivers 50 MW in 2026, serving both domestic enterprises and global AI tenants.
South Korea’s Gangwon Hyperscale Cluster, delivering around 1 GW across Gangneung and Donghae, leverages surplus generation from nuclear and coal plants. By arranging direct-to-tenant power agreements, the cluster reduces transmission losses and accelerates deployment timelines. This strategy also decentralizes compute away from Seoul and aligns with Korea’s long-term AI industrial plan.
Australia’s ISPT Mamre Road campus illustrates a different dynamic. Spread across 128 acres in Sydney’s Kemps Creek district, the 1 GW development incorporates six four-story structures totaling 400,000 square meters. It includes nearly a thousand cooling units, more than 850 diesel generators, and over 7,000 lithium-ion battery cabinets. Construction is set to begin in early 2026, creating a significant new AI hub in the southern hemisphere.
Taken together, these projects show APAC transitioning rapidly from a collection of regional cloud markets into a coordinated, multi-country AI compute ecosystem. The region is no longer reacting to global AI trends, it is positioning itself to shape them.
Middle East: Gigawatt Infrastructure as Sovereign Strategy
The Middle East has embraced gigawatt data centers not as commercial assets but as instruments of national power. With 2.5 GW of confirmed builds, the region is constructing AI superclusters with the same intensity it once applied to oil and gas infrastructure.
Stargate UAE in Abu Dhabi is the most visible expression of this shift. The first gigawatt phase of a broader 5 GW UAE–US AI campus, the development spans ten square miles and is designed to host one of the largest concentrations of NVIDIA Grace Blackwell GB300 Ultra systems ever deployed. Led by G42’s Khazna Data Centers and backed by OpenAI, Oracle, NVIDIA, SoftBank, and Cisco, Stargate UAE reflects the UAE’s explicit ambition to build sovereign AI capacity. The first 200 MW will go online in 2026, with the full 1 GW phase targeted within three years. Its energy matrix blends nuclear, solar, and natural gas, ensuring the high availability required for frontier model training.
NEOM Oxagon DataVolt in Saudi Arabia reinforces the region’s leadership. This 1.5 GW campus, part of a $5 billion investment under Vision 2030, is engineered as a net-zero facility powered exclusively by solar and green hydrogen. Located along the Red Sea, the site integrates advanced cooling technologies with subsea fiber connectivity. The design is modular, allowing rapid scaling as AI demand accelerates. For Saudi Arabia, Oxagon is not merely a data center, it is a platform for developing an AI manufacturing and research ecosystem that the state intends to position at the center of global technological competition.
These projects reveal the geopolitical logic of the Middle East’s strategy. Compute is no longer a commodity. It is a sovereign asset that governments are willing to underwrite with hundreds of billions of dollars and integrated energy systems. The region is not building for cost efficiency; it is building for independence and influence.
Europe: Sines and the Sustainability-Driven Model
Europe’s 1.2 GW of confirmed gigawatt-class capacity may appear modest, but the region is pioneering the most advanced sustainability model in the global dataset. Energy constraints across Western Europe have made gigawatt development difficult, but they have also forced a level of design innovation unmatched elsewhere.
Start Campus SINES in Portugal provides the clearest example. The 1.2 GW development spans six buildings and represents €8.5 billion in capital deployment. SIN01, a 26 MW facility, became operational in late 2024, marking Portugal’s largest commissioned data center. The campus sits adjacent to major subsea cable routes that connect Europe to North America, Africa, and Latin America, making it one of the most strategically connected AI sites on the continent.
The project operates entirely on renewable energy and is engineered for a PUE of 1.1, with water usage reduced to zero through innovative seawater cooling drawn from the Atlantic. Grid supply is secured through the national transmission operator REN, and construction of SIN02, a 180 MW expansion, begins in 2025. Backed by Davidson Kempner Capital Management and Pioneer Point Partners, SINES is also aligned with Microsoft’s announced $10 billion investment in Portuguese AI infrastructure, which includes plans to lease capacity on the campus through nScale.
Europe’s model is defined by necessity. Energy scarcity and strict environmental standards limit raw gigawatt expansion, but they also incentivize the creation of the world’s most energy-efficient and environmentally resilient data centers. SINES demonstrates that Europe’s path to AI competitiveness will not be measured in gigawatts alone but in sustainability metrics that set global benchmarks.
Great article.
One thing I’ve been looking jnto is where does storage fit in these builds? Projects like Stargate Abilene with 360 MW on site gas, or Meta adding 1.5 GW of renewables at Richland Parish. These
Probably certainly need significant BESS for load balancing.
Fluence’s CEO recently mentioned 30+ GWh of AI data center projects in active discussions. Looks like the
optimal storage duration for AI facilities is 6 hours vs. 4 hours for typical grid storage, so battery requirements per site are meaningfully higher.
Curious if you’ve come across any disclosed storage capacity figures for these campuses.