AI September 26, 2026

Industry: AI Data Centers for Utilities: What’s Different About Grid Constraints

Pick almost any week in 2026 and some company announces an AI campus measured in hundreds of megawatts, sometimes gigawatts. Read the press release twice and you notice what it leaves out: the name of the substation, the delivery date for the transformers, and who pays if the building never fills with GPUs. Those omissions are where the grid constraint lives. AI data centers differ from ordinary large customers because they stack enormous, uncertain demand onto a handful of specific grid nodes. The binding problem for a utility is usually getting power delivered to one site, through transmission lines, substations and large power transformers, and deciding who carries the cost if the project shrinks, moves or disappears. Whether the country has enough electricity overall is a separate question, and often the easier one.

I cover AI model launches, chip earnings and market moves every day for Veritya Daily. Over the past two years, the power question has moved from a footnote in hyperscaler earnings calls to the first thing I check when a new campus is announced. This guide covers what is structurally different about AI load, why announcements overstate real demand, how utilities are rewriting their rate books in response, and which signals tell you a project will get energized.

The short version

AI data centers strain the grid locally before they strain it nationally. A region can have surplus generation and still be unable to serve a 300 MW campus at a particular node because the lines, transformers or substation there are already full. Utilities are responding with data-center-specific tariffs that require collateral, minimum payments and curtailment rights. Grid regulators are starting to treat these customers as a category of their own.

Why AI data centers are a different kind of load

AI data centers differ from conventional industrial customers on five traits utilities plan around. They concentrate demand at a single substation or transmission node. They arrive on uncertain timelines. Their demand can change fast or be interrupted. And the companies behind them can move a project, or a workload, to another state. Each trait breaks an assumption built into decades of utility planning.

Think about how a utility plans for a steel mill or a chemical plant. The customer picks a site for its raw materials, rail access and labor pool. It builds to a known engineering schedule, stays for decades, and runs a load profile the utility can estimate from similar plants elsewhere. Forecast error is modest, and the customer has no cheap way to move once the concrete is poured.

An AI campus inverts most of that. The developer picks a site largely because power might be available there, so it often files requests with several utilities at once and keeps the one that answers fastest. Phases get energized in stages, and a hyperscaler can cancel a lease or slow a build when chip supply or financing changes. Some AI workloads, such as batch training jobs, can pause or shift to another facility. That flexibility is useful to a grid operator, but it also means demand can drop sharply without warning.

The scale is what turns these traits into a planning problem. Data centers already draw about 5% of U.S. electricity, and the Electric Power Research Institute expects that share could triple by 2035, AP reported. A national share that size would be manageable if it spread evenly. It doesn't. In PJM, the grid operator covering much of the mid-Atlantic and Midwest, data centers make up 94% of projected peak-load growth, per Latitude Intelligence's 2026 analysis. When one customer type explains nearly all of the growth in the country's largest power market, forecasting errors about that customer become forecasting errors about the whole system.

Regulators have noticed. NERC, the body that sets reliability standards for the North American bulk power system, is developing a Computational Load Entity category with related standards. The practical meaning: large AI and computing loads may end up with their own reliability obligations instead of being classified alongside ordinary industrial customers.

Trait	Conventional industrial load	AI data center
What drives site choice	Raw materials, logistics, labor	Available grid capacity, land, fiber
Concentration	Large at one site, sized to a known process	Hundreds of MW at one node, often clustered with other campuses
Timeline certainty	Follows a fixed engineering schedule	Phased; often delayed, downsized, relocated or canceled
Ramp behavior	Steady or predictable shift patterns	Can change quickly as workloads start, stop or move
Operational mobility	Tied to physical inputs	Workloads and future phases can shift between sites
Curtailment potential	Hard without halting production	Some workloads can pause or relocate, if contracts allow
Duplicate requests	Rare	Common; developers shop several utilities at once

Is the U.S. running out of electricity, or out of places to deliver it?

Mostly the second. National generation keeps growing, and the tighter limit is deliverability: whether transmission lines, transformers, substations and the local network can carry power to one particular site. The clearest way to see this is to split "grid supply" into five separate questions. An AI data center can fail any one of them while passing the rest.

Start with supply. NERC's 2026 Summer Reliability Assessment counted more than 58 GW of bulk-system resources added before summer 2026, about 16 GW of it solar, 15 GW storage and 7 GW gas. Now set that against demand. The same assessment puts this summer's peak near 865 GW, up from roughly 842 GW in 2023. On paper, one year of additions is more than twice the growth in peak demand over three years, although solar and storage don't count megawatt-for-megawatt at the hour of peak.

So why does anyone talk about scarcity? Because power that exists in one place often can't reach another. Day-ahead congestion costs in six U.S. markets outside California hit $11.1 billion in 2025, roughly a third higher than the year before (S&P Global Market Intelligence, 2026). Congestion cost is what the market pays when the cheapest power can't flow to where it's needed because lines are full. The other side of that coin shows up as wasted renewable output: the S&P analysis tracked 23.8 million MWh of curtailed renewable generation across six ISO/RTO markets through July 2026, up 17.5% on the same stretch of 2025. Wind and solar farms are being told to stop producing in one place while buyers elsewhere pay premiums.

That is the deliverability gap in two numbers. It is why I use what I call the five-lock test when I read about a new campus. Power has to clear all five locks:

Energy adequacy. Does the region produce enough megawatt-hours over a year to cover the new load?
Capacity adequacy. Is there enough firm supply available at the single worst hour, usually a summer or winter peak?
Deliverability. Can transmission move that supply to the specific node where the campus connects?
Local grid strength. Can the nearby substation, transformers and distribution network handle hundreds of megawatts, including sudden swings, without voltage or stability problems?
Cost recovery. If the utility builds upgrades, who pays, and what happens if the load never arrives?

National headlines mostly argue about the first two locks. Projects mostly stall at the third and fourth. The fifth is where the politics are.

Local grid strength is also where the physical bottleneck gets concrete. Large power transformers are custom-built, heavy, and slow to procure. Commenters on r/energy describe hyperscalers and utilities competing for the same units, with procurement timelines stretching to roughly two to three years. A developer can sign a land deal in a quarter and still wait longer for the transformer than for the building. On r/Lineman, line workers and grid technicians have been asking what these loads mean for reliability on an aging system, a reminder that the people who maintain the local network have questions the press releases don't answer.

The Western Interconnection shows how this plays out regionally. According to WECC's State of the Interconnection 2026, the West used 932,901 GWh in 2025 and peaked at 163 GW, and most of the large loads in the regional forecast are data centers, both conventional and AI. A few hundred megawatts is a rounding error against 163 GW of regional peak. At a single substation, it can exceed everything the equipment was designed for.

Why interconnection requests overstate real AI demand

Interconnection requests overstate AI demand because filing is cheap and finishing is expensive. Developers submit requests at several utilities, then delay, downsize, relocate or cancel. A utility that treats every request as firm demand overbuilds and risks stranded costs. One that discounts too aggressively underbuilds and loses the customers that were real.

I call the unrealized portion paper megawatts: demand that exists in a queue or an announcement but will never draw current. It is the single most misread number in AI infrastructure coverage. When I see a 1 GW announcement, the first thing I look for is which phase has a signed electric service agreement and a date. Usually the answer is one phase, and far smaller than the headline figure.

Texas gave the market a clean demonstration. ERCOT cut its summer peak-load forecast to a range of 90.5 to 98 GW, down from a previous estimate of 112 GW. That revision removed between 14 and 21.5 GW from the expected peak, a gap larger than the entire peak demand of many U.S. states. The earlier figure leaned heavily on large-load requests; the revised range reflects a harder look at which of those loads would show up on schedule. NERC has also warned that data center interconnection delays make demand forecasting harder, as Utility Dive has reported.

Paper megawatts exist on the supply side too. Lawrence Berkeley National Laboratory's Queued Up: 2026 Edition found roughly 8,200 projects waiting in U.S. interconnection queues at the end of 2025, carrying 1,312 GW of generation and 749 GW of storage. Most of that will never be built. Active queue volume fell 10% year over year, largely because projects withdrew.

Put those together and you get the two-sided uncertainty utilities face. Both the demand being requested and the supply being proposed are inflated, and neither inflates by a predictable amount. A planner can't match a speculative data center against a speculative solar farm and call the problem solved.

Tip: When you read an AI campus announcement, separate three numbers: the headline capacity, the capacity with a signed utility service agreement, and the capacity with an energization date. Only the third one should move your expectations about near-term power demand or chip orders.

This matters for anyone modeling AI infrastructure spending. If you're estimating GPU demand from announced data center megawatts, you're working from paper megawatts. The more useful inputs are the ones utilities themselves now require: posted collateral, contracted demand, and ramp schedules. I made a similar argument about hyperscaler budgets in our piece on AI capex signals that reveal real monetization; the power side follows the same logic.

The Grid Has Five Locks: 58 GW resources added before summer 2026, 16 GW solar additions, 15 GW storage additions, 7 GW gas a

Who pays if a data center never shows up?

Increasingly, the data center pays, by contract. Stranded-cost risk arises when a utility builds substations, lines, transformers or generation for a project that doesn't materialize, then recovers the cost from everyone else's bills. Data-center-specific tariffs push that risk back onto the large customer through collateral, minimum bills, demand ratchets and exit fees.

The tariff wave is recent. Latitude Intelligence identified 25 utilities in 19 states that had filed data-center-specific tariffs, and 18 of those 25 filings landed in 2024 and 2025. Two years ago, most utilities served data centers under general large-commercial rates. That's no longer the case.

The tools in these tariffs vary, but a common set keeps appearing:

Minimum-load or firm-load commitments, where the customer pays for a contracted level of demand whether it uses it or not.
Demand ratchets, which set future billing demand as a percentage of a past peak or contracted level.
Collateral, such as letters of credit or deposits, sized per megawatt.
Construction-cost recovery for dedicated substations and lines.
Ramp schedules that commit the customer to reaching specified load levels by specified dates.
Curtailment provisions that let the utility reduce service during emergencies or peaks.
Rules for behind-the-meter generation, meaning power plants on the customer's side of the meter.

Collateral is where the numbers get large. Dominion Energy Virginia's GS-5 tariff cites a maximum of $1.5 million per MW, with reductions available for financially strong customers. For a 200 MW phase, that ceiling works out to $300 million in security before any discount. A company with an investment-grade balance sheet gets relief; a newer developer financing a speculative campus does not. That asymmetry is by design, and it filters paper megawatts better than any forecasting model.

Ratchets do similar work on the billing side. The average demand ratchet across the tariffs Latitude analyzed came to 79%. In plain terms, once a customer sets a high peak or contracted level, its bills stay anchored near that level even if usage falls. A campus that expected to run 100 MW and ends up at 50 MW keeps paying for something close to 79 MW under a typical structure.

Public sentiment is pushing in the same direction. On r/technology, recurring threads argue that large data centers should provide or fund their own power instead of getting priority grid access while households absorb the reliability and upgrade costs, and many commenters back legislation requiring AI facilities to pay for the grid upgrades they trigger.

My view: the tariff approach is the right one, and strict collateral is fair. A utility commits capital for 30 to 40 years based on a customer's promise. Asking that customer to put money behind the promise protects everyone else on the system, and it forces developers to reveal how confident they are. The tradeoff is real, since stricter terms favor the largest, best-capitalized hyperscalers over smaller entrants. That is a competition issue worth watching, but it is a smaller harm than spreading stranded costs across residential bills.

Warning: A "flexible" data center under a high demand ratchet may have less financial flexibility than its operational flexibility suggests. If a campus can curtail load but still pays for most of its contracted peak, the savings from curtailing are smaller than the headline suggests. Read the ratchet terms before assuming flexibility lowers costs.

A worked example: the same 25 MW load in two states

Tariff design alone can move a data center's power bill by roughly half. E3 modeled an identical 25 MW data center load under five U.S. utilities' large-load tariffs and found annualized costs ranging from about 6.4 to 9.6 cents per kWh. That gap changes site economics before any question of land, fiber or tax incentives comes up.

The two ends of E3's comparison are Dominion Energy Virginia's GS-4 tariff, at about 6.4 to 6.5 cents per kWh, and Georgia Power's PLL-18, at about 9.4 to 9.6 cents per kWh (E3, Comparing Large Load Tariffs for Data Centers). Same load, same hardware, different rate book.

Here is my own illustration of what that spread means in dollars. Assume the campus runs at a steady 90% of its 25 MW capacity all year, and use the midpoint of each E3 range:

Annual energy: 25 MW × 8,760 hours × 0.90 = 197,100 MWh, or 197.1 million kWh.
Under the Dominion GS-4 midpoint of 6.45 cents: about $12.7 million a year.
Under the Georgia Power PLL-18 midpoint of 9.5 cents: about $18.7 million a year.
Difference: roughly $6 million a year, or about $60 million over a 10-year contract.

That is for 25 MW. Scale to a 250 MW campus and the same spread approaches $60 million a year. These are illustrative figures built on a load-factor assumption, not E3's own totals, but the direction holds: tariff structure is a first-order input to where AI capacity gets built.

Now layer in the risk terms from the previous section. Suppose the same developer plans to grow the site to 100 MW. At the $1.5 million per MW collateral ceiling cited in Dominion's GS-5 tariff, full security for 100 MW would be $150 million before creditworthiness reductions. If the tariff carried a ratchet near the 79% average Latitude found, and the developer contracted for 100 MW but only energized 50 MW, billing demand would stay close to 79 MW. The cheap energy rate still applies. The developer pays for capacity it isn't using.

The lesson for anyone comparing sites or companies: the headline cents-per-kWh figure is only part of the cost. Collateral ties up capital, ratchets lock in payments, and ramp schedules create penalties for delay. A lower rate paired with heavy risk terms can end up costing a slow-moving developer more than a higher rate with lighter terms.

Can flexibility and on-site power ease grid constraints?

Partly, and more than most coverage assumes. Curtailing large loads on a small number of peak days frees surprising amounts of capacity, and on-site generation lets some projects skip parts of the transmission queue. Neither is free. AI customers pay for uptime, and on-site gas plants bring their own fuel, permitting and emissions constraints.

The capacity math on curtailment is striking. Latitude's estimate: targeted curtailment of large loads on about 15 peak days a year could release 6% to 17% of total system capacity. The reason is that grids are sized for their worst few hours. If a data center agrees to drop load during those hours, the utility can connect it sooner without building for a peak it will never add to.

Crypto readers have seen this before. In Texas, bitcoin miners were among the first large loads to treat curtailment as a business model, powering down during grid stress in exchange for lower costs or payments. AI is a harder fit. A mining rig can stop mid-hash with little loss. An interrupted training run can lose hours of progress unless it checkpoints often, and inference serving live users is latency-sensitive. Still, a portion of AI compute, such as batch training, fine-tuning and offline processing, can tolerate interruption or move to another site. Operators that separate interruptible from firm workloads can offer utilities curtailment on part of their load.

On-site power is the other response, and it is growing fast. About 56 GW of behind-the-meter generation is under development for data centers, roughly three-quarters of it fueled by natural gas, the Latitude research shows. Behind-the-meter means the plant sits on the customer's side of the utility meter, so the campus can run partly or wholly on its own supply. Batteries play a supporting role too; our coverage of Energy Vault's $600M Texas AI data center power deal shows how storage is being packaged with data center projects.

Grid operators are starting to reward co-location formally. SPP's High Impact Large Load process promises a 90-day study and approval timeline for qualifying loads paired with generation: at least 10 MW connecting at 69 kV or below, or at least 50 MW above 69 kV. Ninety days is fast by interconnection standards, where studies commonly run far longer. The condition is that the customer brings supply with it, which relieves the capacity and deliverability locks at the same time.

Where do I land? I would favor a straightforward trade: faster interconnection in exchange for enforceable curtailment rights and paired generation, with the curtailment terms written into the tariff instead of left to voluntary programs. SPP's approach points in that direction. The risk is that behind-the-meter gas becomes a way to avoid grid rules altogether, which is why the tariff provisions governing on-site generation matter as much as the curtailment clauses.

What to watch if you follow AI infrastructure and power markets

Track evidence of energization, not announcements. The useful signals are signed service agreements, tariff class, posted collateral, transformer delivery dates and grid operator forecast revisions. These show which AI capacity will draw power and when, which matters for chipmakers, cloud providers, utilities and anyone pricing power-market exposure.

When a company announces an AI campus, I work through a short checklist:

Which utility and which tariff? A named data-center tariff with collateral and ratchets means the developer has accepted real financial commitments.
What's the energization date for phase one? A date tied to a signed agreement carries weight. "Up to 1 GW by 2030" carries almost none.
Where are the transformers? Given the two-to-three-year procurement timelines people in the power industry describe, a project without secured equipment is unlikely to hit a near-term date.
Is there paired or behind-the-meter generation? Co-located supply can shorten timelines, especially under fast-track processes like SPP's.
What are the grid operators saying? Forecast revisions like ERCOT's are the best real-time check on paper megawatts.

The same filter applies to equity stories. Nvidia's chip sales depend on data centers that can plug in; see our analysis of Nvidia's $500B AI infrastructure push. CoreWeave's contracted demand only becomes revenue when capacity is energized, a point that sits behind our coverage of the CoreWeave $100B backlog. Power is becoming the same kind of physical choke point that memory already is, as we argued in Samsung's 400+ layer BV-NAND and the AI memory bottleneck. And the broader case for judging AI companies on delivered revenue instead of announced plans is in AI stocks demand revenue reality over product hype.

Most of these signals come from regulatory filings, operator forecasts and earnings calls that are scattered and slow to surface on social feeds. If you'd prefer a morning summary of the day's AI, crypto and finance news, including grid and power stories as they break, that's what our newsletter The Daily Brief is for.

Bottom line

The grid constraint facing AI data centers is local, financial and uncertain in ways a national electricity headline can't capture. The U.S. added more than 58 GW of resources ahead of summer 2026, yet congestion costs and curtailed renewables both rose, because power that exists can't always reach the node where a campus wants to connect. Requests overstate real demand, as ERCOT's 14 to 21.5 GW forecast cut showed. Utilities are answering with tariffs that make developers post collateral and commit to paying for what they request.

If you follow this sector, stop asking whether there's enough electricity and start asking whether a specific site clears all five locks: energy, capacity, deliverability, local grid strength and cost recovery. The projects that clear them will have a tariff, a transformer and a date. The rest are paper megawatts.

:::faq

Why are AI data centers harder for utilities to plan for than factories?

AI data centers concentrate huge demand at a single grid node and arrive on uncertain timelines. Developers often file with several utilities at once, then delay, downsize, relocate or cancel projects, and some AI workloads can shift between sites. A factory's location, schedule and load profile are easier to predict. That uncertainty makes it hard for utilities to size substations, transformers and transmission without risking overbuilding or leaving real customers unserved.

Does the U.S. have enough electricity for AI data centers?

Nationally, supply is growing: NERC counted more than 58 GW of new resources before summer 2026, while summer peak demand rose from about 842 GW in 2023 to roughly 865 GW. The tighter constraint is deliverability, meaning whether transmission lines, transformers and substations can carry power to a specific site. Rising congestion costs and renewable curtailment show that available power often can't reach where it's needed.

What is a demand ratchet in a data center tariff?

A demand ratchet sets a customer's future billing demand as a percentage of a past peak or contracted level, even if actual usage falls. Latitude Intelligence found an average ratchet of 79% across the data center tariffs it analyzed. So a campus contracting for 100 MW but running only 50 MW would typically keep paying for something close to 79 MW, which protects other ratepayers from stranded costs.