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Utility Governance Is Democracy’s Entry Point Into the AI Race

AI’s physical expansion depends on electricity, water, land, and grid access. That gives utilities, regulators, and local governments a crucial opening to shape who pays, who benefits, and whether the public retains a meaningful say in the AI race.

Mark Howard

— 7 min read
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The AI race looks almost weightless from a screen. Its infrastructure is anything but.

Behind every frontier model sits an expanding physical system: power plants, transmission lines, substations, transformers, water supplies, land, roads, and a grid that was not designed around the sudden arrival of customers asking for hundreds of megawatts at a time.

That physical dependence changes the politics of AI.

Technology companies can build models, buy chips, and organize computing systems through largely private decisions. They cannot, by themselves, allocate scarce grid capacity, approve land uses, assign water, or decide which customers will pay for the infrastructure their projects require.

Those decisions pass through utilities, regulators, state and local governments, courts, and public processes. Sometimes the public role is substantial. Sometimes it is little more than a procedural cameo. But it exists.

That makes utility governance one of democracy’s most important entry points into the AI race.

Not a guarantee. An entry point.

When Private Strategy Becomes Public Policy

A large AI data center is not simply another commercial building that happens to use a great deal of electricity. Its demand may require new generation, substations, transmission upgrades, water systems, and grid equipment ordered years before the first server is switched on.

Those commitments can shape utility rates, public capital plans, land use, water allocation, and the availability of infrastructure for everyone else. A project may be privately owned, but many of its consequences arrive through systems the public already governs.

This is where the AI race becomes unusually contestable.

At the model layer, the public often encounters decisions after companies have made them. At the infrastructure layer, expansion must pass through institutions with legal duties, formal procedures, public records, rate cases, permitting standards, and avenues for appeal.

None of those mechanisms is automatically democratic. Utility proceedings can be highly technical. Local approvals can move through administrative channels that attract little notice. Agreements may remain confidential. Regulators can be overmatched, captured, or simply hurried.

Still, the grid creates something scarce elsewhere in the AI economy: a place where private ambition must be translated into public obligations.

The Covenant Inside a Utility Connection

A utility connection is not merely permission to plug in. For a very large customer, it becomes a long-term allocation of capacity, cost, and risk.

Who pays for new infrastructure? How much power must the customer commit to purchase? What happens if projected demand never materializes? Can the customer leave while other ratepayers remain responsible for equipment built on its behalf?

The answers can be written into tariffs, interconnection agreements, special rate structures, minimum-payment commitments, cost-allocation rules, and conditions attached to public incentives.

That is the hidden covenant inside the utility relationship: access to public infrastructure in exchange for enforceable commitments about who bears the cost and risk.

The covenant can be weak, opaque, and tilted toward the largest customer in the room. It can also be made visible, reviewable, and durable enough to protect the broader system.

Public institutions are beginning to test both possibilities.

Public Institutions Are Starting to Write the Terms

In June, the Federal Energy Regulatory Commission opened proceedings requiring all six regional grid operators under its jurisdiction to justify or reform the tariffs governing how data centers and other large electricity users connect to the transmission system.

The action is important, but it should be described carefully. FERC has not imposed one national rule for large-load connections. It issued tailored show-cause orders directing each regional operator to defend its current approach or propose changes. The proceedings put cost allocation, transparency, reliability, and consumer protection formally on the table.

That is already a meaningful shift. AI-scale electricity demand is no longer being treated simply as a series of private service requests. It is becoming a grid-governance problem.

The White House Ratepayer Protection Pledge advances a similar principle from a different direction. Participating technology companies pledge to build, buy, or bring the new power their data centers require; pay for associated delivery infrastructure; and negotiate rates requiring them to keep paying for capacity built on their behalf whether they ultimately use it or not.

The principle is sound: ordinary customers should not be left financing speculative infrastructure for some of the wealthiest companies in the world.

The limitation is equally clear. The pledge is voluntary. Its value will depend on whether the commitments become utility tariffs, contracts, regulatory orders, and enforceable state rules. A press event can announce a principle. Utilities and regulators still have to make it operational.

Senator Ed Markey has proposed a more compulsory approach. His discussion draft of the Protecting Communities from Data Center Impacts Act would require covered data centers to obtain a federal certificate before permitting and construction. The proposal would also require them to pay for necessary grid infrastructure, fund renewable energy and storage to meet their capacity needs, reduce demand during grid stress, and meet labor standards.

The proposal remains a discussion draft, not enacted law. It nevertheless marks a conceptual step: a large data center would be treated less like an ordinary private development and more like infrastructure whose scale creates public obligations before construction begins.

Pennsylvania offers the most developed current example of attaching those obligations to state support. Its Governor’s Responsible Infrastructure Development, or GRID, Standards condition expedited permitting, data-center tax benefits, and access to preferential tax programs on commitments in four areas: energy affordability, transparency and community engagement, workforce and economic development, and environmental protection.

The standards do not automatically govern every data center in Pennsylvania. They define the terms on which a project can receive the Commonwealth’s assistance.

That distinction matters. Pennsylvania is using something government already controls—the allocation of public support—to bargain for stronger public commitments.

A developer seeking GRID certification must explain how it will obtain incremental power without imposing additional costs on other ratepayers, pay costs attributable to its interconnection and service, disclose projected electricity and water use, engage affected communities early enough to influence design decisions, and report on compliance before and after operations begin.

This is what enabling bureaucracy can look like. The state is neither prohibiting development nor waving it through. It is trying to make speed conditional on responsibility.

Whether the standards will be enforced consistently remains to be seen. But the design is important: faster approval is exchanged for clearer obligations, continuing disclosure, and a public record of compliance.

Scarcity Is Also a Governance System

AI infrastructure is also intensifying competition for the equipment needed to move electricity.

Reuters reported in July that lead times for some generator step-up transformers had exceeded 160 weeks by early 2026. High-voltage circuit breakers and switchgear are also under pressure. Utilities and developers are reserving production capacity years in advance, making upfront payments, and widening their supplier networks.

Transformers, it turns out, do not move at software speed.

This turns procurement into another layer of capacity governance. Which projects can reserve scarce equipment five years ahead? Which utilities have enough purchasing power to secure favorable production slots? Which communities wait longer for the equipment needed to replace aging systems, connect housing, or improve resilience?

The market will answer some of those questions. But a market in which the largest firms can lock up critical equipment years in advance is also allocating public capacity. Scale can reproduce itself quietly: the actors with the most capital gain the best access to the equipment required to create even more capacity.

Local Conflicts Show What Contestability Looks Like

The democratic stakes become clearest when infrastructure decisions move from abstract forecasts into particular communities.

In Colorado Springs, residents appealed the administrative approval of Project Taurus, a proposed AI data center in an existing building. Their concerns include noise, water use, and possible effects on utility bills. The appeal moved the matter into a public hearing before the planning commission, with a further appeal to the City Council possible.

The institutional fact is not simply that opposition appeared. It is that an administrative decision remained reviewable before the project became irreversible.

In California’s Imperial Valley, a proposed hyperscale data center has generated disputes over environmental review, land-use procedures, electric service, and Colorado River water. The developer sued the Imperial Irrigation District after the public utility rejected its water-service application. The City of Imperial filed a separate lawsuit challenging the environmental review, and local voters began gathering signatures for a countywide ban, as CalMatters reported.

The dispute is messy. That is not necessarily evidence that governance has failed. It is evidence that the project’s assumptions about water, land, environmental review, and public consent did not remain private assumptions.

Virginia’s Digital Gateway project shows the procedural stakes even more starkly. The plan would have allowed as many as 37 data centers near Manassas National Battlefield. Courts voided the rezoning after finding that Prince William County had not provided the public notice required by law. When the last developer withdrew its appeal in July, the project ended.

It would be easy to read these cases as a morality play in which communities stop data centers and democracy wins. That would be too easy.

A project approved promptly under clear standards, full disclosure, fair cost allocation, and enforceable conditions can also demonstrate democratic government working well. Delay is not accountability by definition. Nor is rejection the only legitimate public outcome.

The better test is whether major commitments are visible, reviewable, and contestable before they become too expensive—politically or financially—to reconsider.

Can the public see who will pay? Can regulators test the demand forecast? Can communities assess water, noise, traffic, land-use, and emergency-service effects? Can conditions be enforced after the ribbon cutting?

Those questions are becoming part of the AI race’s institutional architecture.

An Entry Point, Not a Guarantee

The physical dependence of AI on electricity, water, land, and public infrastructure does not give democratic institutions control over AI. It gives them leverage over the conditions under which AI expands.

That leverage can be squandered. Governments can offer subsidies before understanding the costs. Utilities can negotiate special arrangements that remain opaque to other customers. Local governments can approve projects without adequate technical capacity. Public processes can become so slow and unpredictable that only the largest companies can survive them.

Governance can disperse power, but badly designed governance can concentrate it too.

The opportunity is to preserve contestable scale: large enough to support the infrastructure AI requires, but not so opaque or one-sided that only hyperscalers, major utilities, and well-financed developers can participate meaningfully in the decisions.

That means transparent load forecasts. Clear cost-allocation rules. Take-or-pay commitments when infrastructure is built for a specific customer. Public disclosure of power and water demands. Community engagement early enough to change a project rather than merely react to it. Periodic reporting after operations begin. And institutions with enough technical capacity to distinguish a serious project from an optimistic queue position wearing a very expensive suit.

Chips and models will help determine what AI can do. Utility governance will help determine who pays for its expansion, who receives scarce capacity, who carries the downside risk, and whether the public retains a meaningful voice before the system hardens around private decisions.

Infrastructure-scale AI requires infrastructure-scale governance.

That may be democracy’s most practical way into the race.

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