AI 正在重繪美國數據中心版圖

Power access is shaping where new campuses rise, when they come online, and the networks connecting them.

October 18, 2026

In June 2024, construction began on the Stargate campus in Abilene, Texas. Within a year, its first two buildings had been energized. By September 2025, Crusoe said the site was running early training and inference workloads on Oracle Cloud Infrastructure. The planned campus would eventually contain eight buildings. Crusoe’s account captures both the speed of the AI infrastructure boom and its defining complication: large campuses become useful in stages.

That progression is changing the geography of computing. Developers need sites where land, electricity and equipment can come together on a workable schedule. As established markets fill up, new locations become attractive, and connecting distant facilities becomes an engineering problem of its own.

The spending curve shows how quickly this has become a national construction story. In November 2022, private US data center construction was running at an annualized pace of $13.9 billion. By August 2026, that rate had reached $85.0 billion, roughly six times as much. These Census Bureau figures cover data centers broadly and measure construction work, including fixed electrical and mechanical systems. They exclude racks and servers, whose costs sit on top of the building investment.

Figure 1. Monthly construction spending expressed as a seasonally adjusted annual rate, in current dollars. The August figure is a spending pace, rather than money already spent during 2026. Source: US Census Bureau.

Construction spending was already increasing before ChatGPT’s launch. Because the figures are in current dollars, the curve combines changes in construction prices with changes in the amount being built. It establishes the growing scale of spending without measuring computing capacity.

The next question is where that work can happen. In its August report, JLL placed 77% of North American data center capacity under construction in frontier markets, highlighting West Texas, Ohio, Louisiana, Indiana and the Carolinas. Its market analysis describes a buildout extending well beyond the industry’s established hubs.

Texas illustrates the shift. JLL reported 26 gigawatts of existing and under-construction capacity across the state, compared with 13 GW in Virginia. Northern Virginia remained the largest individual market, with nearly 7 GW of inventory. The state totals show the direction of investment while including projects that still have to become operating facilities.

Figure 2. State totals combine existing capacity and capacity under construction, including hyperscalers’ own facilities. They do not measure electricity currently being consumed. Source: JLL, Midyear 2026.

A large pipeline creates a planning problem for utilities. They must decide how much infrastructure to build before they know which projects will arrive, when their servers will be installed, or how quickly customer workloads will fill them. A speculative request can therefore influence planning long before it becomes a substantial electricity load.

AEP Ohio’s experience shows how that pipeline changes as commitments become binding. Its new tariff required developers to advance through paid studies and financial commitments. About one-fifth of the initial requested capacity reached signed contracts. AEP attributed the narrowing to the filtering of speculative or uncertain projects. The utility still had 17.9 GW of total contracted data center projects, including earlier contracts, scheduled to come online progressively through 2035. AEP’s February update distinguishes those future obligations from operating demand.

Figure 3. Successive stages of one AEP Ohio intake cohort, as reported in February 2026. The final bar excludes contracts signed before the tariff. Source: AEP Ohio.

Texas faces its own version of the credibility problem. ERCOT’s September board update documented a pause in energization approvals for relevant new data center and cryptocurrency facilities of at least 75 MW while verification and community-impact reviews proceeded. Projects received provisional classifications; those classifications did not constitute final permission to connect. The operator expected to publish its community-impact report by December 10.

For developers, that makes the location of available power consequential to computing design. Microsoft offers a concrete example. Its Fairwater facilities in Wisconsin and the Atlanta area are linked through a dedicated network intended to let sites cooperate on large AI workloads. Microsoft explicitly identifies land and power availability as reasons to spread work across locations. Its description of the architecture connects the choice of geography to the need for new networking infrastructure.

Distance introduces costs of its own. Large training jobs require frequent communication among processors; slow exchanges can leave expensive hardware waiting. Microsoft describes dedicated fiber and changes to network protocols designed to reduce bottlenecks between sites. Finding land and power therefore creates another task: making the resulting facilities operate efficiently together.

There are also ways to change the terms on which a facility receives electricity. In a report published October 14, the International Energy Agency examines connection agreements that allow consumption to be curtailed, phased in or adjusted to grid conditions. Such arrangements can help projects connect sooner by changing the reliability or timing of their access.

The tradeoff is practical. Interrupting expensive AI hardware can be costly, and a facility operator may have limited control over customers’ workloads. The IEA’s analysis identifies coordination among computing customers, facility operators and electricity networks as a condition for greater flexibility. Onsite generation also brings equipment lead times and engineering requirements of its own.

The buildout is consequently creating an architecture that extends beyond each campus. Transmission studies, substations, fiber routes and electricity agreements increasingly influence where AI work can run. Abilene’s first buildings became useful when several of those pieces came together. Repeating that achievement across a much larger pipeline will determine how quickly the new map translates into computing capacity.

Data: Figures use the research snapshot checked October 18, 2026. Each chart retains its source’s geography, period and definition. Charts are original graphics based on the cited figures.