From Land Use to Capacity Governance: The AI Infrastructure Problem Coming Into View

AI data centers are still commonly governed as buildings on parcels of land, large utility customers, or private capital projects. At the scale now coming into view, they are becoming something larger: claims on regional capacity.

That claim crosses systems that government usually manages separately. Electricity demand can shape generation and transmission. Those choices can affect water use, emissions, and rates. Local siting decisions can create costs and constraints well beyond the jurisdiction that approved the project.

No single institution is necessarily ignoring the problem. The harder issue is that each may ask a reasonable question about one part of it while no public process asks whether the region can absorb the whole.

That is the emerging shift from land-use governance to capacity governance.

The mismatch

The United States is beginning to build AI infrastructure at a scale usually associated with national projects. Much of it is still governed as ordinary private development.

Some of that infrastructure will be necessary if AI is going to support medicine, science, and public services. The issue is not whether data centers should be built. It is whether the institutions around them are asking a large enough question.

A local government asks whether a project belongs on a particular site. A utility asks whether it can serve the load and recover its costs. A water provider asks whether supply is available.

All three questions are legitimate. None is sufficient by itself.

The larger question is:

Can this region absorb the linked demand for power, water, transmission, land, and public cost—in this place, at this time, and under these conditions?

By capacity governance, I mean the public rules and institutions used to decide how much regional capacity can be committed to AI infrastructure, on what terms, and at whose risk.

AI data centers are not just buildings. They are claims on systems that other people also depend upon.

The scale is forcing the issue

For years, data centers sat in the background of the digital economy. AI is moving them toward the center of infrastructure planning.

The International Energy Agency projects that global data-center electricity use will more than double by 2030 in its base case, growing more than four times as fast as electricity demand from other sectors. In the United States, Lawrence Berkeley National Laboratory estimates that data centers could consume between 6.7 and 12 percent of the nation’s electricity by 2028, up from about 4.4 percent in 2023.

The forecasts will not be exactly right. Some announced projects will be delayed, reduced, or abandoned. But uncertainty at this scale is itself a governance problem. Utilities and communities must make long-lived decisions today for loads that may—or may not—reshape regional systems tomorrow.

Private spending points in the same direction. A Bridgewater analysis reported by Reuters estimated that Alphabet, Amazon, Meta, and Microsoft could invest about $650 billion in AI-related infrastructure in 2026 alone.

A Wall Street Journal analysis found that, over the previous three years, leading technology companies had committed more to AI data centers, chips, and energy than the inflation-adjusted cost of building the Interstate Highway System over four decades.

This is private investment, not a public works program. Yet once investment reaches this scale, its consequences do not remain inside corporate balance sheets. They arrive in electricity plans, water commitments, and utility rates.

“Tech capex” becomes a public-capacity question.

The project creates a whole; government reviews pieces

Public authority is divided for understandable reasons. Land-use departments know land use. Utilities know their systems. Regulatory commissions know rates and service obligations. The problem is that a large data-center project does not divide its effects so neatly.

Start with electricity and water. A data center requires enormous amounts of power, but the water implications depend on how that power is produced and how the facility is cooled. Water-based cooling can reduce electricity use while increasing direct water consumption. Air cooling can conserve water while requiring more power. A choice made to protect one system can therefore place additional pressure on another.

That tradeoff becomes more consequential when a local development decision commits regional resources. In 2019, Mesa, Arizona, agreed to make 1,120 acre-feet of water available annually for a proposed Google data center. The amount could rise to 4,480 acre-feet—roughly four million gallons a day—if Google met specified development milestones. (TIME)

Mesa was negotiating a project within its boundaries, but the agreement did more than decide what could be built on a parcel. It conditionally reserved a substantial amount of capacity within a municipal water system that draws upon shared regional supplies in an already water-stressed state. Repeated across several cities, such agreements can determine how much regional water remains available for other development or for responding to future shortages—without any single decision appearing to make that larger allocation.

Google later shifted the Mesa facility to air cooling, so the authorized maximum did not become its actual operating demand. That was a meaningful response to local water conditions. It also moved part of the capacity claim from water to electricity. The example is therefore not evidence that Mesa made the wrong decision. It shows how a nominally local agreement can implicate several regional systems, and how solving one part of the problem can change another. (City of Mesa)

The same pattern appears in utility finance. A utility may conclude that it can serve a large customer by building new generation, transmission, or substations. But if the projected load arrives late—or never arrives—the infrastructure remains. Whether the data-center operator or existing customers bear that cost depends on collateral requirements, minimum-payment commitments, and exit fees. What begins as a service agreement with one customer can become a long-term ratepayer obligation.

The existing governance system of distributed decisions is hardly idle. Local governments review sites and development agreements. Utilities and state commissions decide how service will be provided and costs allocated. Grid operators study reliability and transmission. Water providers assess supply.

Each institution can be active and competent while still seeing only one part of the claim.

The Federal Energy Regulatory Commission’s recent large-load proceedings illustrate both the progress and the limitation. FERC has pressed regional grid operators to clarify how data centers connect to the transmission system while protecting reliability and ratepayers. (FERC, 2025; FERC, 2026)

That work matters. It can make the electricity portion of the decision more transparent and fair. But FERC does not decide whether a region should commit scarce water to the same project, whether a local tax agreement justifies the public costs, or whether several projects together exceed the capacity a metropolitan area can comfortably support.

The project creates a whole. The existing system distributes responsibility for its pieces. What remains uncertain is who, if anyone, is responsible for putting those pieces back together before the commitments become difficult to reverse.

Bad outcomes do not require bad actors

It is tempting to tell this story as a conflict between technology companies and the public. Sometimes that framing fits. Secrecy, rushed approvals, and weak disclosure can make public oversight little more than a procedural cameo.

But the deeper problem does not require a villain.

A city can reasonably approve a project that fits its plan. A utility can reasonably agree to serve a new customer. A regulator can reasonably approve a tariff that appears to protect existing ratepayers.

The overall result can still be poorly governed.

Ratepayers may inherit costs built around an optimistic demand forecast. Water commitments may accumulate before their regional effect is visible. Local approvals may lock in assumptions before wider grid consequences are understood.

None of this requires institutional incompetence. It requires only a mismatch between the scale of the capacity claim and the structure of the decision-making system.

Private capital will not wait for coherence

Private capital is one reason the United States can build quickly. It can finance facilities, secure equipment, and take risks that government would struggle to take directly.

It also seeks speed and certainty. When public systems are fragmented or slow, companies will route around the bottlenecks. They may co-locate with power plants, build generation behind the meter, or offer to reduce demand during grid emergencies in exchange for faster service.

Those arrangements may be sensible. A company that adds power, pays its own costs, and can curtail demand may be easier to serve than one that simply asks the grid for immediate full service.

But every workaround raises the same question: does it solve the regional capacity problem, or move the pressure somewhere less visible?

A private power agreement may shorten the interconnection wait while increasing emissions. Air cooling may conserve water while increasing electricity demand. A confidential development agreement may speed land assembly while weakening public trust.

Private routing decisions will shape the public system whether public institutions are ready or not. If government cannot ask the full capacity question early enough, the important commitments will be made before the public can see how they fit together.

From managed fragmentation to capacity governance

The practical near-term path is to strengthen the institutions we already have.

Three improvements matter most. Large projects should disclose their expected demands clearly enough for the public and regulators to evaluate them. Customers should bear the costs and risks of infrastructure built primarily for their benefit. Projects with regional effects should trigger notice and coordination beyond the jurisdiction issuing the land-use approval.

That would be managed fragmentation: a divided system made more transparent, connected, and fair.

It may be enough in some regions. In others, cumulative pressure may require a new coordinating layer—not a national agency that replaces local governments and utility regulators, but a process capable of putting the full regional claim into view before the pieces are committed one at a time.

The form should follow the problem. A water-stressed basin may need one model. A grid-constrained data-center corridor may need another. A fast-growing metropolitan region facing simultaneous land, power, and water pressures may need a third.

Uniformity is not the point. Matching the scale of governance to the scale of the capacity claim is.

Naming the problem while the choices are still open

The United States is still early enough in the AI infrastructure buildout to shape its rules. The window will not remain open indefinitely.

If each project is treated as an isolated land-use case or utility request, larger capacity decisions will be made by default through private deals, one-off approvals, and emergency responses. By the time the cumulative effects are obvious, many choices may already be expensive to reverse.

This is not an argument against building AI infrastructure. It is an argument for governing the buildout at the scale at which its consequences arrive.

The first step is to name the problem correctly.

AI data centers are claims on regional capacity.

The next step is to decide whether our distributed institutions can manage those claims—or whether some regions will need a new public layer capable of seeing the whole.

That is where this series will go next.