Mapping Out America’s AI Data Center Boom: 4,000 Sites and 442 GW in Active Queue
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The AI boom is rapidly redrawing the map of U.S. data center infrastructure — but the most important signal may lie not just in where facilities exist today, but where power is being requested next.
A dataset compiled from public records by Datacenter.fyi shows there are 3,995 existing data centers across the United States, with roughly 2,900 operational. Virginia and Texas dominate that footprint, with 575 and 410 sites respectively, forming the backbone of the country’s digital infrastructure.
But the next phase of growth is already taking shape.
Texas leads the nation with 336 planned data center projects, outpacing every other state and signaling its emergence as the primary expansion frontier. Virginia follows with 203 projects, while secondary markets such as Ohio (68 planned) and Indiana (37 planned) are gaining traction as developers look beyond traditional hubs. In total, the dataset counts 932 data centers that have been planned and proposed.
At the same time, historically dense markets like California show a more limited pipeline, with just 21 projects planned — a reflection of tightening power availability and permitting constraints in legacy tech corridors.
The geographic spread points to a clear shift: proximity to users is no longer the defining factor. Power is.
That shift becomes even more apparent when looking beyond physical sites to the grid itself.
Data from Interconnect.fyi further shows active interconnection requests surged to 1,687 projects in 2025, representing a record 442 gigawatts of active requested capacity. Just five years earlier, that figure stood at roughly 100 gigawatts.
The growth has been nonlinear. From fewer than 1 gigawatt in 2012, interconnection demand has scaled into the hundreds of gigawatts, with a sharp acceleration beginning around 2021 and intensifying through 2024 and 2025.
Together, the two datasets — one mapping physical infrastructure, the other mapping power demand — reveal the full scope of the AI buildout. What is visible on the ground is only a fraction of what is queued at the grid level.
Meanwhile, the capital behind this expansion is increasingly concentrated among a handful of technology giants.
Companies including Amazon, Google, Microsoft and Meta are not only among the largest spenders on AI infrastructure, but also rank among the most prolific data center operators in the U.S., according to Datacenter.fyi’s breakdown of facility ownership.
Amazon alone accounts for 479 data centers, making it the largest operator by count, followed by CyrusOne (221), QTS (186) and Digital Realty (170). Microsoft (153), Equinix (129) and Google (79) also rank prominently, underscoring how hyperscalers and large colocation providers dominate the physical footprint of compute.
That ownership concentration is being reinforced by an unprecedented surge in capital spending.
In the first quarter alone, Amazon, Google, Microsoft and Meta collectively spent more than $130 billion on capital expenditures, largely directed toward AI data centers and supporting infrastructure. The figure marked another record and represented a sharp increase from a year earlier.
The spending trajectory shows little sign of slowing. Combined outlays from the four companies are expected to approach $700 billion this year, as each races to secure capacity for increasingly compute-intensive AI workloads.
Taken together, the data center map, the interconnection queue, and the capex surge point to the same conclusion: the current buildout is not just broad-based, but highly capital-intensive and led by a small group of firms with the balance sheets to sustain multi-year infrastructure investments.
That dynamic also helps explain the pressure building in interconnection queues. As hyperscalers commit to gigawatt-scale deployments, their demand is translating directly into large-load requests that now dominate grid pipelines across multiple regions.
The pattern feels familiar
Long before AI drove hyperscale demand, Bitcoin miners were already optimizing for the same constraint. Following the China’s Bitcoin mining ban, mining infrastructure rapidly migrated to the U.S., with Texas emerging as a global hub that effectively replaced regions like Sichuan and Xinjiang.
That migration triggered an earlier wave of power and infrastructure development — from substations to large-load interconnections — built around energy-intensive compute. In hindsight, it offered a preview of the dynamics now playing out at a much larger scale.
Industry executives have increasingly drawn that connection. At a recent panel discussion at Bitcoin 2026, featuring MARA and CleanSpark, participants described Bitcoin miners as having “plowed the road” for AI, establishing the operational and infrastructure blueprint that data center developers are now expanding upon.
The implications are becoming clearer as interconnection queues swell.
Grid access — not land or hardware — is emerging as the primary bottleneck. Projects are measured not just by capital invested, but by their position in increasingly crowded queues, where timelines can stretch years.
For the mining sector, the convergence is both validating and competitive. The same regions that once attracted hashpower are now being targeted by AI developers with deeper balance sheets and longer-term contracts, intensifying the race for power.
In that sense, mapping the U.S. data center landscape today is less about counting facilities and more about understanding the underlying energy dynamics.
The physical footprint — nearly 4,000 sites — tells the story of where computing has been. The 442 gigawatts sitting in interconnection queues tells the story of where it’s going.
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