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  • A Utility’s First Quantum Customer Is Its Own Grid: EPB’s $4M Chattanooga Test

    A Utility’s First Quantum Customer Is Its Own Grid: EPB’s $4M Chattanooga Test

    TL;DR · 30-second read

    The Short Version

    • Chattanooga’s city-owned electric company, EPB, has switched on a quantum computer, an experimental machine that works very differently from a laptop, built by a company called IonQ.
    • Its first job is to help the power company run its own electric grid more cheaply and reliably, paid for by a $4 million federal science grant.
    • Local universities will start using it in early October.
    • It is unusual for a power company to host a computer like this, and the bigger promises about jobs and economic growth are still forecasts.

    EPB, the municipally owned electric and fiber provider in Chattanooga, Tennessee, said on Sept. 18, 2026 that it has launched an IonQ Forte Enterprise quantum computer at its EPB Quantum Center, in a release distributed via PR Newswire. The utility describes the site as America’s first commercial hub offering both quantum computing and quantum networking under one roof.

    The first users are eight EPB Quantum Computing Fellows, graduate researchers funded by a $4 million National Institute of Standards and Technology (NIST) grant, who are developing algorithms to optimize EPB’s power grid. EPB says its quantum computing service opens for business in early October, with the University of Tennessee at Chattanooga (UTC) as its first customer and Vanderbilt University lining up a first project.

    Executive Summary

    EPB is adding a quantum computer to a quantum network it launched in 2023, turning an electric utility into a host for specialized computing. The machine comes from IonQ, which separately is investing $15 million in a Tennessee Quantum Communications Research Center in Chattanooga with plans for about two dozen jobs. EPB, IonQ, Oak Ridge National Laboratory and NVIDIA have also installed a classical supercomputer inside the same facility for a hybrid project that combines the two kinds of computing to optimize parts of EPB’s automated grid.

    What matters most is the order of use. Before any outside business pays for time, the machine’s first workload is the utility’s own grid, followed by two universities. That gives the system a guaranteed, research-grade user base on day one, but it also means commercial demand from companies outside academia and EPB itself is not yet demonstrated in the announcement.

    The release also cites larger projections, including up to $1.1 billion in local economic gains over a decade and $2.7 trillion in potential economic value from quantum computing, which are forecasts rather than commitments.

    The Anchor Tenant Is the Grid Itself

    Any operator hosting expensive, scarce compute faces the same early problem: keeping the machine busy with work that justifies it. EPB’s answer is to be its own first customer. According to the utility, the EPB Quantum Computing Fellows, eight graduate researchers funded by a $4 million NIST grant, will be the first to run workloads, specifically algorithms aimed at optimizing local power grid circuits to reduce costs and improve resilience. The hybrid project with Oak Ridge National Laboratory, NVIDIA and IonQ points at the same target: portions of EPB’s automated electric grid.

    This is the mechanism behind the headline. A utility running a highly automated grid has a built-in optimization problem, such as how power flows through circuits and how to reconfigure them, that it can hand to a new computing platform without first finding a buyer. External demand comes next, and it is academic: UTC begins in early October with research it is already prepared to run, and Vanderbilt is still lining up its first project. For vendors like IonQ, a host that brings its own workload lowers the risk of an idle installation. For other utilities, the takeaway is narrower but real: owning the problem can be as important as owning the power or the fiber.

    The flip side is that the announcement names no paying commercial customer outside universities. The model is anchored in research, and its commercial proof point is still ahead.

    Why a Utility Can Host Compute at All

    Most compute hosts are data-center operators or cloud providers. EPB’s case rests on assets a typical utility does not have. It built a 100% fiber-to-the-home network starting in 2010, now offering service up to 25 gigabits per second, and launched the EPB Quantum Network in 2023. Quantum networking, which moves fragile quantum information over fiber, depends on exactly the kind of dark and lit fiber a telecom-savvy utility controls.

    The Board chair’s comparison to the 2010 gig-speed rollout is the strategic tell: EPB is repeating a playbook of building infrastructure ahead of demand and marketing the city around it. That worked for broadband. Whether it works for a technology still largely in the research phase is a different question, and the release is candid that the near-term users are researchers.

    Hybrid, Not Quantum Alone

    One of the more substantive details is easy to miss: a classical supercomputer now sits inside the EPB Quantum Center. Quantum processors today generally work alongside conventional computers rather than replacing them, with the classical machine handling most of the calculation and the quantum system tackling specific sub-problems. Co-locating both, with a national laboratory and NVIDIA as collaborators, is a practical design choice that reflects how quantum workloads are actually run.

    For facility operators, that means a quantum site is not just a single exotic box. It carries the power, cooling and networking requirements of conventional high-performance computing too, which is one more reason a utility with its own grid and fiber is a plausible host.

    Commitments Versus Projections

    The release mixes firm commitments with forecasts, and they deserve different weight. Concrete items include the $4 million NIST grant, eight fellows, IonQ’s $15 million research center with about two dozen planned jobs, the October service start, and an academy expected to serve roughly 400 high school students a year from 2028.

    Softer items include UTC’s plan to add 135 to 240 faculty, staff and students, Vanderbilt’s roughly 250-person institute, the Mayor’s figure of up to $1.1 billion in local economic gains over a decade, and a projected $2.7 trillion in near-term economic value from quantum computing. The release does not give the methodology behind the latter two figures. The “first” claims are also carefully qualified: first to offer commercially available quantum computing and quantum networking in one facility, not the first quantum computer available commercially in the country.

    Background

    EPB is a Chattanooga utility that provides both electricity and communications. In 2010 it began building a 100% fiber-to-the-home network that it describes as the world’s first community-wide gig-speed internet service, now offering up to 25 gigabits per second, alongside a highly automated electric grid. In 2023 it launched the EPB Quantum Network, and UTC became what the release calls the first American university connected to a commercial quantum network.

    IonQ is a quantum computing company whose Forte Enterprise system is now installed at the EPB Quantum Center. Quantum computers use the physics of very small particles to approach certain problems, such as optimization and simulation, differently from conventional machines, though most practical work today pairs them with classical computers.

    Sources

    Source: EPB Launches IonQ Forte Enterprise Quantum Computer in Chattanooga, EPB’s announcement of its quantum computing launch, first users and related local investments.

  • Utilities Turn to AI for Data-Center Load, but 84% Need 1+ Year to Scale a Pilot

    Utilities Turn to AI for Data-Center Load, but 84% Need 1+ Year to Scale a Pilot

    TL;DR · 30-second read

    The Short Version

    Power companies across America are racing to keep up with the giant computer warehouses that run artificial intelligence, which use enormous amounts of electricity.

    A new survey of 134 innovation chiefs at American power companies found that most now use artificial intelligence themselves to manage that demand. The catch: 84% say turning a small trial into something used across the whole company takes more than a year, and that delay is getting longer.

    Why it matters to you: 83% of those leaders say the cost of serving these warehouses is showing up in household electricity bills.

    National Grid Partners, the venture capital and innovation arm of UK and US energy company National Grid, released its third annual Utility Innovation Survey on September 18, 2026, at the NextGrid Alliance Summit in Boston. The survey of 134 innovation leaders at U.S. utilities, collected between May 20 and July 7, 2026, found that 78% are deploying or operationalizing at least one AI application to manage interconnection demand, while 74% say AI-driven data center load growth is affecting grid reliability.

    The survey also found that 84% of organizations take more than a year to move projects from pilot to full rollout, up from 66% in 2024, and that grid reliability has overtaken net zero as a top-three priority. National Grid Partners also announced investments in two startups, Terragrit and LineVision, and noted that National Grid has joined the newly formed AI Energy Management Alliance as a founding member.

    Executive Summary

    The headline number is adoption: nearly four in five U.S. utility innovation leaders say they are putting AI to work on the flood of requests to connect new load, much of it from AI data centers. That is the industry using the technology driving its demand problem as part of the answer to it.

    The more consequential numbers sit underneath. Innovation budgets rose at 72% of respondents, yet the share needing more than a year to scale a pilot has climbed for three consecutive surveys, from 66% to 78% to 84%. Most spending (59%) goes to incremental improvements, only 16% to transformational projects, and 87% say regulatory and rate-case frameworks written before the AI demand boom limit returns on innovation.

    Taken together, the survey describes an industry that has accepted AI as a grid-planning tool but cannot yet deploy it at the pace data-center load is arriving. For data-center developers, utilities, regulators and ratepayers, the practical constraint is increasingly how fast utilities can change the way they plan, not only how much they build.

    The Constraint Is Speed, Not Willingness

    The survey rules out the easy explanation that utilities are reluctant to adopt AI. Seventy-eight percent say they are deploying or operationalizing at least one AI application to manage interconnection demand, meaning the queue of customers waiting for permission and capacity to plug into the grid. Seventy-two percent report bigger innovation budgets this year, and only 1% say they have no plans to work with early-stage technology companies.

    What has not improved is the time it takes to turn an experiment into standard practice. Eighty-four percent of organizations now need more than a year to move a project from pilot to full rollout, up from 78% in 2025 and 66% in 2024. The trend has worsened for three straight surveys even as money has flowed in, which points to a structural bottleneck rather than a funding one. Respondents cite a shortage of workers skilled in AI and the industry’s traditionally cautious culture, and spending patterns reinforce the picture: 59% of innovation dollars go to incremental improvements against 16% for transformational initiatives.

    That matters downstream because 74% of the same leaders say AI data center load is already affecting grid reliability. The pressure is present now; the tools meant to relieve it face a year-plus path to scale at most utilities. Data-center developers waiting in interconnection queues, utility planners fielding large-load requests, and the software vendors selling into utilities all operate on that clock. A tool that could, for example, help a utility study or approve a connection faster only helps once it is embedded in how the utility actually works.

    Reliability Moves Ahead of Net Zero

    The sharpest year-over-year shift in the survey concerns priorities. Asked to rank organizational priorities, 73% of utility leaders placed reliability in their top three, up from 43% in 2025. Net-zero goals fell from 54% to 16% over the same period.

    This is a forced ranking of top-three priorities, so it measures relative emphasis rather than abandonment of climate commitments. Still, a swing of that size in one year is a clear signal about what is occupying planning attention. When a single data-center campus can represent a large new load on a regional system, keeping the lights on becomes the dominant planning question, and decarbonization targets compete for a smaller share of management focus.

    Rate Cases Written for a Slower Era

    Utilities in the U.S. generally recover investment through rate cases, the regulatory proceedings in which state commissions decide what a utility can charge customers and what return it may earn. Eighty-seven percent of respondents say those frameworks, developed before the current demand boom, are limiting returns on innovation investment. If a software tool that frees up grid capacity earns a utility less than building new poles, wires and substations, the incentive structure itself slows adoption, which is consistent with the lengthening pilot timelines.

    The cost question cuts the other way for households. Eighty-three percent of respondents say the cost of building infrastructure to serve AI data centers is being passed on to residential customers through higher bills. That is the view of the utility leaders surveyed rather than a measured cost allocation, and the survey does not quantify it, but it is a notable admission coming from inside the industry, and it frames why regulators are likely to scrutinise who pays for data-center grid upgrades.

    Flexible Data Centers as a Grid Resource

    National Grid’s answer, alongside its investment activity, is flexibility. The company has become a founding member of the AI Energy Management Alliance, whose announced members include Google, NVIDIA, Anthropic, Emerald AI, RWE, Constellation and NRG. The group says it will push for policies encouraging flexible data centers that automatically reduce power use during peak demand, buffering nearby communities from price spikes.

    The supporting evidence cited is a UK pilot last December involving Emerald AI, a National Grid Partners portfolio company, together with National Grid and NVIDIA, which found AI software could cut data center energy use by more than a third in under a minute during simulated grid strain while protecting critical workloads. That is a meaningful proof of concept, though it was a single pilot under simulated conditions. Whether flexibility becomes a dependable planning resource depends on data-center operators contractually committing to curtail, and on utilities trusting those commitments enough to connect load sooner, which brings the question back to how fast planning practices and rules can change.

    Background

    National Grid supplies electricity and natural gas to more than 80 million people in the UK, New York and Massachusetts, and is running major transmission programmes including the Great Grid Upgrade in the UK and the Upstate Upgrade in New York. Its venture arm, National Grid Partners, launched in 2018, is based in Silicon Valley with offices in Boston, London and New York, and describes itself as the utility industry’s only Silicon Valley-based corporate venture capital group.

    National Grid also founded the NextGrid Alliance, which says it includes more than 1,000 senior decision-makers from over 170 utilities in 25 countries and has facilitated more than 500 introductions between utilities and startups. The survey arrives as U.S. utilities face surging requests to connect large AI data centers, a demand shift that is pressing on grid planning, reliability and how infrastructure costs are allocated among customers.

    Sources

    Source: 2026 Utility Innovation Survey: Industry leaders turning more to AI as data-center boom reshapes grid planning, National Grid Partners’ announcement of its third annual survey of U.S. utility innovation leaders, via PR Newswire.

  • Quanta Services, High-Voltage Breakers and the Grid Bottleneck

    Quanta Services, High-Voltage Breakers and the Grid Bottleneck

    TL;DR · 30-second read

    The Short Version

    Building a data center is the easy part. Connecting it to electricity is not. That needs new power lines, new substations — the yards full of equipment that step electricity down to usable voltages — and the specialist crews who build them.

    Quanta Services is one of the largest of those construction firms. It has now tied itself to an equipment maker, Hyosung HICO, in a venture making high-voltage breakers: the giant switches that cut power in a fraction of a second when something goes wrong.

    Quanta’s shares have risen sharply over five years. The debate now is whether that price already assumes a decade of building.

    Simply Wall St reported on September 6, 2026 that Quanta Services (NYSE: PWR) is back in investor focus following updates on analyst sentiment, earnings estimate revisions and the HYOSUNG HICO BREAKER joint venture, a partnership tied to grid modernization and the manufacture of high-voltage circuit breakers.

    The shares closed at US$624.41. That price followed a 3.6% gain over seven days and a 7.1% decline over 30 days, set against a one-year total shareholder return of 67.8% and a five-year total shareholder return of 443.5%. The most-followed valuation narrative on the stock puts fair value at US$710.00, implying the shares are 12.1% undervalued, while Quanta’s trailing price-to-earnings multiple of 70.7x sits more than double the 32.5x construction industry average.

    Executive Summary

    Strip away the valuation debate and the operationally interesting item is the joint venture itself. Quanta Services is a contractor: it employs the linemen, the substation electricians and the heavy-equipment operators who physically build electric transmission and distribution systems. A venture in high-voltage breaker manufacturing moves it, at least partially, from installing grid hardware to having a stake in producing it.

    That direction of travel matters because the constraint on connecting new load to the grid — whether that load is a hyperscale data center, a reshored factory or a wind farm waiting in an interconnection queue — is increasingly physical rather than financial. Capital is abundant. Skilled crews, energized substations and long-lead switchgear are not. A contractor that controls both the labor to build a substation and a share of the equipment that goes inside it is positioned at the narrowest point in that chain.

    The market has already noticed. Quanta’s five-year return of 443.5% reflects an investor base that has repriced electrical contracting from a cyclical, low-margin trade into a scarce structural asset. The open question is not whether the demand is real — utility hardening, transmission expansion and AI-driven power demand are all visible — but how much of that decade is already in the price at 70.7x earnings.

    The Binding Constraint Is Construction, Not Capital

    The public conversation about artificial intelligence infrastructure has largely been about semiconductors and, more recently, about electricity generation. The less-discussed choke point sits between them: the wires, transformers, breakers and substations that carry power from where it is generated to where it is consumed, and the finite pool of qualified crews that install them. A gigawatt of contracted generation is worthless to a data center operator if the interconnection — the physical and contractual connection to the grid — cannot be built for several years.

    Quanta’s business is precisely that middle layer. Its stated markets span electric and gas utilities, power generation, load centers, manufacturing, communications and pipelines. That breadth is the point: the same craft labor pool serves grid replacement, utility storm hardening, renewable interconnection and industrial reshoring simultaneously. When several demand waves draw on one labor pool, the contractor’s scarcity value rises independently of any single end market. This is the structural case behind the stock’s re-rating, and it does not depend on any particular AI capital-expenditure forecast holding.

    The risk embedded in that same logic is symmetry. Diversified demand cushions a downturn in any one vertical, but it does not protect against a broad deferral of utility capital programs, nor against the execution risk that comes with very large, complex fixed-price programs. Simply Wall St’s own summary flags both: a slowdown in new grid or AI-related awards, and execution setbacks on large projects.

    Moving Upstream Into the Equipment It Installs

    A high-voltage circuit breaker is the protective switch at the heart of a substation. When a fault occurs — a downed line, a short circuit — the breaker interrupts a current that would otherwise destroy transformers and start fires, and it does so in milliseconds. It is unglamorous, safety-critical, heavily engineered equipment, and the number of qualified manufacturers is small.

    For a contractor, an interest in breaker manufacturing changes the negotiating posture on schedule. Contractors are typically at the mercy of equipment lead times: the crew is mobilized, the site is ready, and the project waits on switchgear. A manufacturing venture offers, in principle, visibility into that queue and a hedge against it. It also opens a second revenue line with a different margin profile from installation work — equipment manufacturing carries capital intensity and inventory risk that labor-led contracting does not.

    Whether this venture delivers those benefits at meaningful scale is genuinely unknown from what has been disclosed. The joint venture is described in terms of its purpose — grid modernization and high-voltage breaker manufacturing — rather than its economics. Investors should treat it, for now, as directional evidence about strategy rather than as a quantifiable earnings contributor.

    Two Valuation Frameworks, Opposite Conclusions

    The stock currently supports two defensible and contradictory readings, and the release presents both. On a forward-looking narrative basis, fair value is pegged at US$710.00 against a US$624.41 close, implying 12.1% upside; the company also trades at a discount to analyst price targets. On a ratio basis, the picture inverts: a 70.7x price-to-earnings multiple compares with 32.5x for the construction industry, 33.8x for named peers and a 38.6x “fair ratio” benchmark.

    These are not really disagreements about Quanta’s business. They are disagreements about classification. A multiple near the construction industry average assumes Quanta is a contractor whose earnings will eventually revert with the building cycle. A multiple near 70x assumes it is something closer to a bottleneck asset — a firm whose scarce labor base and customer relationships let it hold pricing power across a multi-decade grid rebuild. The peer set the ratio comparison uses, including Comfort Systems USA and EMCOR Group, are themselves specialty contractors that have re-rated on similar logic, which makes the “peer average” a moving target rather than a fixed anchor.

    The practical implication is that the compression risk is arithmetic, not speculative. If sentiment cools and the multiple drifts toward the 38.6x fair ratio without a compensating jump in earnings, the required earnings growth to defend the current price is substantial. The 7.1% decline over 30 days, against a 67.8% one-year return, suggests the market is already testing that question rather than settling it.

    Background

    Quanta Services is one of the largest specialty infrastructure contractors in North America, providing engineering and construction services across electric and gas utilities, power generation, load centers, manufacturing, communications, pipelines and energy. Its core asset is not equipment but people: a large, trained and licensed craft workforce whose skills — high-voltage line work, substation construction — take years to develop and cannot be scaled quickly in response to demand.

    That workforce has become unusually valuable. Utility capital spending has risen simultaneously across grid replacement, storm hardening, renewable interconnection and, most recently, the electrical infrastructure required to serve large computing loads. The result has been a sustained re-rating of listed specialty contractors, Quanta among them, from cyclical builders toward companies valued for structural scarcity — a shift reflected in Quanta’s 443.5% five-year total shareholder return and in the valuation debate that now surrounds the stock.

    Sources

    Source: Quanta Services (PWR) Gets A Grid Venture Boost, Is The Stock Too Expensive? — Simply Wall St, September 6, 2026, reporting on Quanta Services’ HYOSUNG HICO BREAKER joint venture, recent share price performance and the competing valuation cases for the stock.

  • 70% Oppose AI Data Centers Nearby. Their Power Lines Hinge on In-State Benefit

    70% Oppose AI Data Centers Nearby. Their Power Lines Hinge on In-State Benefit

    TL;DR · 30-second read

    The Short Version

    Power companies building electricity lines to feed new data centers, the warehouses of computers that run tools like ChatGPT, are using the government’s power to force landowners to sell.

    That power is only supposed to be used when the public benefits. Courts have usually allowed it when a line makes local electricity more dependable. One state’s top court blocked a line that crossed the state without serving anyone who lived there.

    With 7 in 10 Americans against data centers near them, expect more fights over farms and backyards.

    Power companies in states including Georgia and Pennsylvania have turned to eminent domain, the government power to take private property without the owner’s consent, to secure land for transmission lines serving the data center buildout. The practice is drawing opposition as polling shows 7 in 10 Americans oppose AI data centers in their own communities, University of Dayton law professor Aaron Walayat wrote in an essay published by The Conversation and republished by Fortune on July 19, 2026.

    The United States has more than 3,000 data centers, with another 1,500 in development, according to a Pew Research Center analysis. Data centers accounted for more than 4% of national electricity use in 2024. The legal question now in play is whether a line built to serve a private data center meets the Constitution’s “public use” requirement for taking land.

    Executive Summary

    Local opposition to data centers has mostly focused on the buildings themselves: utility bills, noise, pollution, water use and lost green space. It is now spreading to the transmission lines that carry power to those buildings. Transmission lines often have to cross private land, and when owners refuse to sell an easement, which is a right to use a strip of their property, utilities can ask a court to force the sale through condemnation.

    This matters because transmission is already one of the slowest parts of bringing new data center capacity online. A line that faces a credible legal challenge on public-use grounds adds legal risk to a project’s schedule. Precedent points to a consistent dividing line. Courts have upheld takings when a line delivers power or improves grid reliability for customers inside the state. They have been more skeptical when a line serves nobody in the state it crosses.

    For developers, utilities and regulators, how a line is justified in the record, and whom it demonstrably serves, is becoming as consequential as where it is routed.

    The Siting Fight Moves From the Building to the Wire

    A data center can be sited on a single parcel bought from a willing seller. The power it needs usually cannot. High-voltage transmission lines run for miles across many owners’ land, and every parcel on the route has to be secured. With more than 3,000 US data centers operating and another 1,500 in development, and with data centers already using more than 4% of the country’s electricity in 2024, utilities are planning new lines at a pace that makes holdout landowners a practical problem, not a rare one.

    The standard sequence is negotiation first and condemnation second. A utility offers to buy an easement. If the landowner declines, the utility, exercising power delegated by the state, asks a court to compel the sale at a court-determined “just compensation.” That is how power companies in Georgia and Pennsylvania have approached land acquisition for new lines. The 7-in-10 opposition figure measures attitudes toward data centers rather than transmission specifically, but the objections voters cite, higher bills and loss of green space, apply directly to a line crossing someone’s farm.

    Why In-State Benefit Is the Test That Decides These Cases

    The federal “public use” standard is permissive, so the contest shifts to state courts and state law, where a clear pattern emerges. The supreme courts of South Dakota and Vermont upheld utility takings because the lines provided at least some energy and improved grid reliability for in-state customers. In 1984, the Mississippi Supreme Court rejected a condemnation for a line that would have run from Mississippi into Louisiana without serving any Mississippi customers. In Texas, the state Supreme Court has held that a common carrier’s project must “serve the public” and “cannot be built only for the builder’s exclusive use.”

    Put together, those rulings describe the mechanism. A line documented as reinforcing reliability for a state’s own ratepayers fits well-established precedent, and the growing strain data centers place on the grid strengthens that case. A line whose practical function is to feed one private campus, or to carry power across a state border to load elsewhere, gives landowners a specific legal argument. Walayat’s reading is that reliability-driven takings will likely qualify as public use, while disputes over whether a line actually serves in-state customers remain the opening for challenges.

    The people most affected are the ones who write the justification. Utilities and their regulators decide how a line’s purpose is described in planning and certificate proceedings. Developers decide whether to take power from a dedicated feeder or from broader network upgrades. Landowners’ lawyers will look for evidence in that record that the public benefit is incidental. Projects that can show real benefit to in-state customers stand on firmer ground. Projects that cannot are carrying legal risk that belongs in their schedules.

    Kelo’s Long Shadow Makes State Lines Matter

    The backdrop is Kelo v. City of New London, the 2005 decision in which the US Supreme Court allowed a city to take homes for private redevelopment near a Pfizer facility on economic-development grounds. The redevelopment never happened, and Pfizer later left the city. The backlash was lasting: 45 states enacted eminent domain reform laws, and the supreme courts of Michigan, Ohio and Oklahoma have barred taking private property to hand it to another private party purely for economic development.

    For data center infrastructure, that history means a landowner’s odds depend heavily on the state. Challenges under a restrictive state constitution may succeed where a federal claim would not. Courts still routinely permit utilities to condemn land, so this is not a veto over transmission. It does mean that justifying a line as good for local economic growth, the argument Kelo accepted, is a weaker footing in many states than justifying it as necessary for reliable service to local customers.

    Weighing the Claims on Both Sides

    Each side’s argument deserves scrutiny. The industry’s position, that new lines are needed to keep the grid reliable as demand rises, is supported by the demand figures. It is also the same argument courts reward, which gives utilities an incentive to frame data center lines in reliability terms whether or not that is their main purpose. The opposition’s position, that private companies are borrowing a public power for private gain, echoes Kelo, where the promised public benefit never arrived. It has to be tested line by line, though, because many transmission projects do serve broad customer bases.

    The 70% figure is a strong signal of political headwind. It is not a legal standard. Public use is decided on the facts of each project, not by polling. The practical question for each contested line is the one courts have already asked: who in this state actually gets power from it?

    Background

    Eminent domain rests on the Fifth Amendment, which allows the government to take private property for public use if it pays just compensation. Most condemnations are carried out by state and local governments, which can delegate the power to utilities such as power and water companies. The US Supreme Court’s 2005 Kelo v. City of New London ruling read “public use” broadly to include economic development. The resulting backlash led 45 states to pass reform laws, and several state supreme courts, including those of Michigan, Ohio and Oklahoma, interpret their own constitutions more strictly.

    The AI boom has brought a matching surge in data center construction, to more than 3,000 facilities in operation and about 1,500 in development. Those facilities used more than 4% of US electricity in 2024, which pushes utilities to build new transmission. Those lines increasingly cross land whose owners do not want them.

    Sources

    Source: Power companies are using eminent domain to seize land for data centers as 70% of Americans say not in my backyard (Fortune, republished from The Conversation). An essay by University of Dayton law professor Aaron Walayat on the legal limits of taking land for data center transmission lines.

  • White House Seeks AI Power Cost Pledge From Utilities and Data Centers

    White House Seeks AI Power Cost Pledge From Utilities and Data Centers

    Reuters reported on July 12, 2026, citing sources, that the White House intends to rally electric utilities and data center operators behind a pledge addressing the power costs associated with artificial intelligence. The report frames the effort as a response to growing concern that the AI build-out is putting upward pressure on electricity bills.

    No official announcement accompanied the report, and the text, participants, and timing of any pledge had not been made public at the time of writing.

    Executive Summary

    According to the Reuters report, the administration is convening two industries whose interests increasingly collide on the electric grid: the utilities that must build generation and transmission to serve surging demand, and the hyperscale data center operators whose AI workloads are driving much of that demand. A “power cost pledge” — the report’s shorthand — suggests a voluntary commitment aimed at reassuring the public that households will not shoulder the cost of AI’s electricity appetite.

    The move matters because it signals that data center power demand has fully crossed from an industry planning question into a national political one. When the White House feels compelled to broker a public commitment on electricity costs, it reflects pressure from ratepayers, state regulators, and elected officials who are hearing about rising bills from constituents.

    It also matters for what it is not: a report based on unnamed sources, describing a voluntary pledge whose contents are unknown. Whether this becomes a substantive cost-allocation framework or a reputational exercise depends entirely on details that had not yet been disclosed.

    Why Electricity Bills Became an AI Problem

    The AI boom has made data centers one of the fastest-growing sources of new electricity demand in the United States, reversing roughly two decades in which overall power consumption was largely flat. Serving that growth requires new power plants, new transmission lines, and grid upgrades — and under traditional utility regulation, those costs are spread across all customers through rates approved by state commissions. That is the mechanism at the heart of the ratepayer backlash: households can end up helping pay for infrastructure built primarily to serve a handful of very large industrial customers.

    Utilities and data center operators counter that large customers typically sign long-term contracts, often pay for dedicated interconnection upgrades, and can anchor investments that benefit the whole grid. Both framings contain truth, and which one dominates in a given state depends on tariff design — the specific rate structures regulators approve. A federal pledge would be entering a debate that is normally fought state by state, utility by utility.

    What a Voluntary Pledge Can — and Cannot — Do

    Voluntary pledges are a familiar Washington instrument: they move quickly, require no legislation, and give all parties a public commitment to point to. If the pledge commits data center operators to pay the full incremental cost of serving their load — through special tariff classes, minimum-take contracts, or funding their own generation — it could genuinely shift cost risk away from households. Several utilities and states have already been moving in this direction through large-load tariffs, so a pledge could standardize and accelerate an existing trend.

    The limits are equally clear. A pledge cannot override state ratemaking authority; electricity rates are set by state public utility commissions, not the White House. It carries no enforcement mechanism unless one is built in. And “power cost” commitments are only as strong as their accounting: transmission, capacity, and reliability costs are notoriously difficult to attribute to a single customer class, which gives every party room to claim compliance. Analysts and consumer advocates will reasonably ask who verifies the math.

    Winners, Losers, and the Politics of Grid Cost Allocation

    For hyperscalers, a pledge is likely a price worth paying. Their binding constraint is speed of interconnection — how fast new facilities can get grid connections and power. A public commitment on costs could defuse local opposition and regulatory friction that currently slow projects. For utilities, the calculus is similar: demand growth is the best earnings story the sector has had in decades, and anything that keeps the political environment permissive protects that story.

    The open question is what ratepayer advocates get. If the pledge produces binding tariff structures and transparent cost attribution, consumers benefit. If it produces language without accounting, the underlying dispute simply resurfaces in the next rate case. Smaller data center operators and AI startups also warrant attention: cost-allocation rules designed around hyperscalers can inadvertently raise barriers for firms without the balance sheet to fund their own substations or sign decade-long power contracts.

    Background

    Since the generative AI boom began in late 2022, hyperscale cloud providers and AI companies have raced to build data center capacity across the United States, turning electricity availability into the industry’s defining constraint. After decades of roughly flat national power demand, utilities now face sustained load growth, and the question of who pays for the required generation and transmission has become a flashpoint in state rate cases and local permitting fights.

    Both federal and state policymakers have increasingly engaged with the issue — from grid interconnection reform to utility proposals for special large-load tariffs — as electricity affordability has risen on the political agenda. The reported White House pledge effort sits squarely in that context: an attempt to get ahead of ratepayer backlash without new legislation.

    Source: White House to rally utilities, data centers for AI power cost pledge, sources say — Reuters report, July 12, 2026, on a planned White House effort to secure a voluntary commitment on AI-related electricity costs.

  • Utilities Scramble for Transformers as Data Center Demand Strains the Grid Supply Chain

    Utilities Scramble for Transformers as Data Center Demand Strains the Grid Supply Chain

    Reuters reported on July 8, 2026 that US power companies are scrambling to secure electrical equipment — the transformers, switchgear, and related grid hardware that move electricity from generators to customers — as surging demand from data centers strains available supplies. The report frames a nationwide procurement crunch: utilities that once ordered this equipment on routine replacement cycles are now competing for constrained manufacturing capacity against a wave of new large-load projects.

    Executive Summary

    The headline is not about a single deal or data center campus; it is about the industrial base underneath all of them. Transformers step electrical voltage up for long-distance transmission and back down for delivery, and switchgear is the apparatus that switches, protects, and isolates circuits. Neither is optional: every new data center interconnection, substation upgrade, and grid expansion needs both. Reuters’ reporting indicates that US utilities can no longer take timely delivery of this equipment for granted.

    Why it matters: for the first time in decades, US electricity demand is growing meaningfully, and data centers — particularly AI-driven facilities — are a leading cause. When the equipment supply chain becomes the pacing item, it stops being a utility procurement problem and becomes a constraint on data center delivery schedules, grid reliability investment, and ultimately on how fast the AI buildout can proceed. Power availability has already emerged as the industry’s defining bottleneck; this report locates part of that bottleneck one layer deeper, in the factories that make grid components.

    Why Transformers Became the Grid’s Chokepoint

    Large power transformers are among the least glamorous and most consequential machines in the economy. They are heavy, highly engineered, often custom-built to a specific substation’s requirements, and produced by a relatively small number of manufacturers worldwide. Capacity to build them cannot be added quickly: it requires specialized factories, scarce materials such as grain-oriented electrical steel, and skilled workers who take years to train.

    The US grid spent roughly two decades with flat electricity demand, and the supply chain sized itself accordingly — tuned for steady replacement of aging units, not for a demand shock. When data center load growth, electrification, and grid-hardening programs all began pulling on that thin manufacturing base at once, order backlogs stretched and utilities found themselves queuing for hardware. The scramble Reuters describes is the predictable result of a just-in-time supply chain meeting a step change in demand.

    When Equipment Lead Times Set the Data Center Schedule

    For data center developers, this crunch changes what “time to power” means. A site can have land, fiber, permits, and even a utility willing to serve it, and still wait on a transformer delivery slot. Interconnection — the process of physically and contractually tying a new load into the grid — increasingly depends less on paperwork and more on whether the required substation equipment physically exists.

    That reality is reshaping behavior on both sides of the meter. Utilities are reported to be securing equipment earlier and more aggressively, which effectively shifts them from reactive procurement to strategic stockpiling. Large data center operators, for their part, have strong incentives to lock in capacity years ahead, pre-order long-lead equipment themselves, or favor sites where grid infrastructure already exists — one reason established carrier hotels and campuses with existing substation capacity have gained strategic value relative to greenfield sites.

    The Economics of Scarcity: Who Absorbs the Cost

    Scarcity moves pricing power toward manufacturers. Electrical-equipment makers with transformer and switchgear capacity are in an unusually strong position, and the open question is how much they will invest in expansion — factories are decade-scale bets, and executives remember the last long stretch of flat demand. Utilities, meanwhile, typically recover equipment costs through regulated rates, which means sustained price inflation in grid hardware eventually reaches ratepayers and invites regulatory scrutiny over how much of the buildout data center customers should fund directly.

    Among data center players, scarcity favors scale and incumbency. Hyperscale operators can pre-purchase equipment, sign long-term supply agreements, and absorb schedule risk in ways smaller developers cannot. If the crunch persists, expect it to act as a filter: well-capitalized projects with early equipment commitments proceed, while speculative projects — announced capacity without secured power and hardware — quietly slip or die. That could rationalize an overheated development pipeline, but it also raises barriers to entry across the industry.

    What Could Break the Bottleneck

    Several paths out exist, none fast. Manufacturers can and do add capacity, but new production lines take years to reach output. Standardizing transformer designs — reducing the custom engineering in each order — could raise effective throughput. Utilities can extend the life of existing units, share spares, and prioritize deployments. On the demand side, data centers that bring their own generation or agree to flexible operation reduce the immediate grid equipment burden.

    The honest assessment is that this is a multi-year imbalance. Equipment supply is a lagging system responding to a leading demand signal, and the gap between them is where project delays, price escalation, and strategic maneuvering will play out. For infrastructure operators, the practical takeaway is that secured power and in-hand electrical equipment are now assets in their own right, worth nearly as much as the buildings around them.

    Background

    For most of the 2000s and 2010s, US electricity demand barely grew, thanks to efficiency gains offsetting economic expansion. That era ended as data centers — driven most recently by AI training and inference workloads — joined manufacturing reshoring and electrification as major new sources of load. Utilities, regulators, and grid operators have spent the past several years revising demand forecasts upward and confronting the fact that generation, transmission, and the equipment supply chain were all sized for a slower world.

    Concerns about transformer supply predate the AI boom — the aging of the US transformer fleet and the concentration of manufacturing capacity have been discussed in grid-security circles for years — but data center growth has converted a slow-burning replacement problem into an acute procurement race. The July 2026 Reuters report captures that shift from the utilities’ side of the table.

    Source: US power companies scramble to secure equipment as surging data center demand strains supplies — Reuters reporting, July 8, 2026, on utilities competing for transformers and switchgear amid data-center-driven load growth.

  • National Grid’s $1.75B Joulent Deal: When Interconnect Delays Force Utilities to Buy

    National Grid’s $1.75B Joulent Deal: When Interconnect Delays Force Utilities to Buy

    National Grid has struck a $1.75 billion deal with Joulent, according to a Data Center Knowledge report published July 1, 2026. The report frames the transaction as a response to mounting interconnection delays driven by AI data center demand — utilities, unable to connect new load fast enough through conventional build-out, are increasingly spending to acquire capacity and capability rather than queue for it.

    Executive Summary

    The reported transaction pairs one of the world’s largest electricity transmission and distribution operators with Joulent in a deal valued at $1.75 billion. The headline framing is the important part: the deal is attributed not to routine portfolio strategy but to AI interconnect delays — the growing backlog of requests to connect large new loads and generation to the grid, a process that in many regions now takes years.

    Why it matters: if the reporting’s framing holds, this is a data point in a broader shift. Utilities have historically grown connection capacity by building — new substations, transformers, transmission lines — on regulated timelines. When AI-driven demand outruns those timelines, acquisition becomes the faster path. A $1.75 billion commitment suggests National Grid sees the capacity crunch as durable, not a passing spike. That said, the available source is a single news headline; the deal’s structure, scope, and closing conditions are not detailed in the material we can verify, and readers should treat specifics beyond the reported figure and parties with appropriate caution.

    Why Buying Beats Building When the Queue Is the Bottleneck

    Interconnection — the engineering and regulatory process of physically wiring a new data center, factory, or power plant into the grid — has become one of the defining constraints of the AI build-out. Studies, permitting, equipment procurement, and construction stack into multi-year waits in many markets, and lead times for critical hardware such as large power transformers and high-voltage switchgear have stretched dramatically since the early 2020s. In that environment, anything that already exists — installed equipment, an established delivery capability, a workforce, a manufacturing slot — carries a scarcity premium.

    A utility that spends $1.75 billion to acquire capacity or capability it would otherwise wait years to build is making a straightforward time-for-money trade. The economics can work because the cost of delay is now enormous on both sides of the meter: hyperscale customers measure the cost of a stranded, unpowered data center shell in the millions per month, and utilities that cannot connect large customers forgo years of revenue from their fastest-growing load class.

    National Grid’s Position in the AI Load Story

    National Grid sits at the center of this dynamic in two major markets. It operates the high-voltage transmission network in England and Wales — where grid connection queues became a widely acknowledged national bottleneck and the subject of regulatory reform efforts — and it owns large regulated electricity and gas utilities in New York and Massachusetts, in the demand path of the US Northeast’s data center and electrification growth. Few companies feel interconnection pressure from as many directions at once.

    That context makes the reported deal legible even without full details: a transmission-heavy utility facing connection backlogs on two continents has clear motives to secure capacity, equipment supply, or delivery capability by acquisition. It also carries risk. Large deals struck during a scarcity cycle can look expensive if the cycle turns — if AI load forecasts moderate or supply chains normalize, capacity bought at peak-crunch prices may earn a thinner return than capacity built patiently through the regulated process.

    What $1.75 Billion Signals — and What It Doesn’t

    The figure itself is the strongest signal in the reporting. Utilities are conservative, regulated businesses; a commitment of this size typically requires board conviction that the underlying driver — here, sustained AI-driven demand outpacing conventional grid expansion — will persist long enough to pay back the investment. In that sense the deal is a vote of confidence in continued data center growth, made by a party with unusually good visibility into actual connection requests rather than press-release pipelines.

    What the number does not tell us is the mechanism. “Buying your way to capacity” can mean acquiring a company outright, purchasing assets, locking up equipment manufacturing capacity, or securing services under a long-term contract — and each has very different implications for competitors, regulators, and customers. The single-source material available does not specify which of these the National Grid–Joulent transaction is, what Joulent brings to the arrangement, or how the spend will be recovered. Those distinctions matter: an acquisition that removes a supplier or contractor from the open market can tighten conditions for every other utility shopping in it, while a capacity contract merely reallocates near-term supply.

    Background

    National Grid built its position over decades as the operator of Great Britain’s electricity transmission backbone before expanding into the US Northeast, where it serves millions of electricity and gas customers in New York and Massachusetts. In both markets it entered the mid-2020s facing an unprecedented problem: connection requests from data centers, electrified transport, and new generation arriving faster than networks could be studied, permitted, and built, prompting queue-reform efforts by regulators on both sides of the Atlantic.

    The AI boom sharpened that squeeze into a defining industry constraint. Transformer and switchgear lead times stretched, hyperscale campuses began requesting connections measured in hundreds of megawatts, and ‘time to power’ displaced real estate as the data center industry’s scarcest resource — the backdrop against which a utility paying $1.75 billion to shortcut the queue becomes a rational, if notable, move.

    Source: AI Interconnect Delays Spur $1.75B National Grid-Joulent Deal — Data Center Knowledge report, July 1, 2026, on National Grid’s $1.75 billion deal with Joulent amid AI-driven grid interconnection backlogs.

  • Virginia Approves First Data Center Power Tax: A Precedent for AI-Era Grid Costs

    Virginia Approves First Data Center Power Tax: A Precedent for AI-Era Grid Costs

    Virginia has approved what is being described as the first-ever data center power tax, according to a June 23, 2026 report from Data Center Knowledge. The measure makes Virginia — home to the largest concentration of data centers in the world — the first U.S. state to attach a dedicated levy to data center power consumption.

    Details of the tax’s rate, structure, and effective date were not included in the initial report, but the “first-ever” framing marks a significant policy departure: rather than courting data centers exclusively with incentives, the state that hosts more of them than any other is now taxing the electricity they use.

    Executive Summary

    The significance of this measure lies less in its mechanics — which the initial reporting does not detail — than in its symbolism and its likely ripple effects. Virginia built its data center dominance in part on a generous sales-and-use tax exemption for data center equipment, a policy other states copied for two decades. A power tax moving in the opposite direction signals that the political economy of hosting data centers has shifted: the question in Richmond is no longer only how to attract capacity, but how to make that capacity pay for the grid strain it creates.

    For operators, hyperscalers, and their customers, the precedent matters more than the immediate cost. Utilities and regulators across the country have been wrestling with how to allocate the enormous transmission and generation investments driven by AI-era load growth — and whether ordinary ratepayers are subsidizing them. A dedicated tax on data center power is one answer to that question, and now the largest data center market on earth has adopted a version of it. Other states weighing similar debates will be watching closely.

    Because the available source is a headline-level report, the analysis below focuses on the policy context and the questions the measure raises, rather than on provisions that have not yet been publicly detailed.

    Why Virginia Was Always Going to Move First

    Northern Virginia — particularly Loudoun County’s “Data Center Alley” — hosts the densest cluster of data centers anywhere in the world, a position built on early internet-exchange infrastructure, proximity to federal customers, and a long-standing tax exemption on data center equipment. That concentration has made Virginia the place where the costs of the AI buildout show up first and loudest: transmission congestion, multi-year interconnection queues, land-use fights, and public concern that residential electricity bills are absorbing grid investments made largely to serve large industrial loads.

    Virginia’s own legislative auditors flagged these tensions in a December 2024 study of the industry’s fiscal and energy impacts, and the General Assembly has debated data center energy policy in every session since. Seen against that backdrop, a power tax is not a bolt from the blue — it is the next step in a multi-year negotiation between a state and an industry that has become its signature economic engine and its biggest new source of electricity demand.

    The Real Question: Who Pays for AI-Era Grid Growth?

    Electric grids recover their costs from customers through rates, and when one customer class grows explosively — as data centers have — regulators must decide whether the new transmission lines, substations, and generation get billed to that class or spread across everyone. Consumer advocates argue that spreading the cost amounts to households subsidizing some of the world’s wealthiest companies; utilities and operators counter that large, steady loads can actually lower average system costs by spreading fixed expenses over more kilowatt-hours. Both arguments have evidentiary support in different circumstances, which is precisely why the allocation fight has been so contentious.

    A tax is a blunter instrument than a rate class. Utility ratemaking assigns costs based on engineering studies of who causes them; a tax is a legislative judgment that a category of consumption should contribute more to public coffers, whatever the cost-causation math says. Whether Virginia’s measure funds grid infrastructure specifically, flows to the general fund, or offsets residential bills will determine whether it functions as genuine cost allocation or as a revenue measure wearing cost-allocation clothing. The initial reporting does not say — and that distinction is the single most important thing to watch as details emerge.

    What It Means for Operators, Tenants, and Competing States

    For data center operators, a per-unit levy on power lands directly on the largest line item in their operating budgets. Colocation providers will face the classic question of how much they can pass through to tenants under existing contracts; hyperscalers running their own facilities will absorb it as a marginal cost increase on Virginia capacity relative to other markets. The competitive effect depends entirely on magnitude: a modest levy on power in the market with the best fiber connectivity in the country changes few siting decisions, while a heavy one accelerates the diversification toward Ohio, Texas, Georgia, and the Carolinas that grid constraints were already driving.

    Competing states now face a strategic choice of their own. Some will advertise the absence of such a tax as a recruitment tool. Others — facing identical ratepayer politics as AI load arrives on their grids — may treat Virginia’s measure as proof of concept. It is worth remembering that Virginia’s data center equipment tax exemption was copied by more than thirty states. Policy that starts in the world’s data center capital has a history of traveling.

    A Precedent That Cuts Both Ways

    The industry has long argued, with some justification, that data centers are exceptional taxpayers — Loudoun County’s budget depends heavily on data center property tax revenue — and that layering new levies on top risks punishing a sector for succeeding. That argument deserves a fair hearing, and it will get one in the rate cases and legislative fights ahead. But the industry has also benefited from a bargain in which states competed to reduce its tax burden while the public bore growing grid costs, and Virginia’s move suggests that bargain is being renegotiated rather than abandoned.

    The measured takeaway: this is neither the end of Virginia’s data center industry nor a trivial development. It is the first formal acknowledgment, in statute, by the market that matters most, that data center power consumption is a distinct fiscal category. How the tax is structured — and whether it stabilizes the industry’s social license to operate or simply raises its costs — will determine whether operators come to see it as the price of durable acceptance or the start of an unwelcome trend.

    Background

    Virginia’s data center industry dates to the early internet era, when network interchange points in Northern Virginia made the region a natural home for hosting infrastructure. Over two decades, aided by a state sales-and-use tax exemption on data center equipment, Loudoun and neighboring counties grew into the world’s largest data center cluster, and data center property taxes became a pillar of local budgets. The AI boom then supercharged demand: utilities serving the region have projected sustained, historic load growth, and interconnection wait times stretched to years.

    That growth turned data centers into a live political issue in Richmond. A December 2024 state legislative audit examined the industry’s fiscal benefits and energy costs, and subsequent General Assembly sessions produced a stream of bills on data center siting, ratepayer protection, and tax treatment. The power tax reported in June 2026 is the most consequential product of that debate to date — the first time the industry’s electricity consumption itself has been made a taxable category.

    Source: Virginia Approves First-Ever Data Center Power Tax — Data Center Knowledge, June 23, 2026, reporting Virginia’s approval of the first U.S. tax targeting data center power consumption.

  • Bloom Report: AI Power Crunch Meets Community Pushback

    Bloom Report: AI Power Crunch Meets Community Pushback

    Bloom Energy has published a report arguing that continued expansion of AI data centers depends on operators addressing two intertwined constraints in parallel: electricity supply and local community acceptance. The report, released in June 2026, frames the two issues as inseparable rather than sequential.

    Executive Summary

    The fuel-cell maker’s central thesis is that the AI buildout cannot be solved by megawatts alone. Even where generation, transmission, or on-site power can be procured, projects increasingly stall on zoning, noise, water, and land-use objections from neighbors and municipalities. Conversely, community outreach without a credible power plan is equally insufficient.

    For an industry accustomed to treating power and permitting as separate workstreams, the framing is a nudge toward integrated planning. It also, unsurprisingly, positions Bloom’s distributed on-site generation product as a natural fit for that integrated approach — a commercial interest readers should weigh alongside the analysis.

    Why ‘Power And Community’ Is The Real Bottleneck

    For most of the cloud era, data center siting followed a familiar recipe: cheap land, fiber, tax incentives, and a utility willing to sign an interconnect. AI workloads have broken that recipe. A single hyperscale AI campus can now request hundreds of megawatts — comparable to a small city — on timelines that outpace utility planning cycles measured in years. Bloom’s report reframes this as a two-variable problem: neither raw generation nor social license alone is sufficient, and progress on one without the other tends to collapse the project.

    That framing matters because the industry has historically optimized for the technical variable and treated community relations as public affairs. When a substation upgrade takes five years and a rezoning fight can add two more, the bottleneck is whichever constraint binds first — and increasingly, both bind simultaneously.

    Winners, Losers, And The Distributed-Generation Pitch

    The report’s logic favors technologies that can be sited close to load, deployed quickly, and configured to reduce visible community impact — a description that fits Bloom’s solid-oxide fuel cells, but also natural-gas peakers, on-site solar-plus-storage, and eventually small modular reactors. Utilities that can offer flexible, phased interconnection may win share from those that cannot. Operators willing to co-locate generation with compute gain optionality against constrained grids.

    The losers, if the thesis holds, are projects that assume grid capacity will materialize on hyperscaler timelines, and jurisdictions that treat every large load as a windfall without offering a permitting path. It is worth noting that the report comes from a vendor whose products directly address the problem it describes; that does not make the diagnosis wrong, but readers should treat the prescription as one option among several.

    Community Concerns Are Not A Communications Problem

    The more substantive point in the report — to the extent the summary conveys it — is that community opposition is being driven by material impacts: water use for cooling, diesel backup emissions, noise from chillers and generators, truck traffic during construction, and property-value anxieties. These are engineering and siting questions, not messaging questions. Treating them as PR problems has, in several high-profile cases, hardened opposition rather than defused it.

    For buyers and investors, the implication is that due diligence on new capacity should include the permitting posture and neighbor relations of a site, not just its power and fiber. A campus with signed interconnects but an organized opposition can be as delayed as one with willing neighbors and no transformer.

    Background

    Bloom Energy, founded in 2001 and headquartered in San Jose, makes solid-oxide fuel cells that generate electricity on-site from natural gas, biogas, or hydrogen. Its customers include large enterprises and, increasingly, data center operators seeking alternatives to constrained grid interconnection.

    The wider context is a global surge in AI training and inference demand that has pushed data center power requests to levels utilities did not plan for. In the United States in particular, several regions have seen multi-year queues for large interconnects, prompting operators to explore on-site and behind-the-meter generation, direct utility partnerships, and, in some cases, relocation to more permissive jurisdictions.

    Source: AI Data Center Growth Hinges on Solving Both Power Constraints and Community Concerns, Bloom Energy Report Finds — Bloom Energy report frames power supply and community acceptance as inseparable constraints on AI data center expansion.

  • Gartner: Data Center Electricity Use to Grow 26% in 2026

    Gartner: Data Center Electricity Use to Grow 26% in 2026

    Research and advisory firm Gartner has published a forecast projecting that data-center electricity consumption will grow 26% in 2026. The figure, released in June 2026, puts a number on what utilities, grid operators, and data-center builders have been experiencing on the ground: power — not land, capital, or chips — has become the binding constraint on digital-infrastructure growth.

    Executive Summary

    Gartner’s headline claim is simple: the electricity consumed by data centers will rise 26% in 2026. For context, most mature electricity systems in developed economies have spent two decades planning around annual demand growth in the low single digits. A single customer class growing 26% in one year is the kind of step-change that utility resource plans — documents typically written on five-to-fifteen-year horizons — were not designed to absorb.

    The forecast matters less as a precise number than as a planning signal. If even a substantial fraction of that growth materializes, it shapes generation procurement, transmission buildout, interconnection queues, and electricity rates for every other customer sharing the grid. For data-center operators and their customers, it also signals that access to secured, deliverable power will continue to separate projects that get built from projects that wait.

    A 26% Jump Is a Planning Problem, Not Just a Number

    Electric utilities plan in decades. Building a new gas plant, a transmission line, or a large substation typically takes years of permitting, procurement, and construction. Demand that grows 26% in a single year — even within one customer segment — compresses those timelines past what traditional integrated resource planning can handle. The practical consequence is already visible across the industry: multi-year interconnection queues (the waiting list to connect large new loads or generators to the grid), utilities demanding long-term take-or-pay commitments from data-center customers, and regulators debating who bears the cost if forecast demand fails to show up.

    The forecast, in other words, is best read as a statement about mismatch: digital infrastructure now moves at software-industry speed, while the electricity system that feeds it still moves at heavy-civil-engineering speed. Closing that gap — through faster permitting, on-site generation, or demand flexibility — is the defining infrastructure challenge the number points to.

    AI Is Rewriting the Load Curve

    Growth of this magnitude is not organic expansion of traditional enterprise computing. Conventional data-center workloads — web serving, databases, storage — grew steadily for years while efficiency gains (better chips, better cooling, higher utilization) kept electricity demand roughly flat. What changed is accelerated computing: AI training and inference run on dense GPU racks that can draw several times the power of traditional server racks and tend to run at sustained high utilization rather than in daily peaks and troughs.

    That load profile is a mixed blessing for utilities. Flat, predictable, around-the-clock demand is easier to serve than spiky demand and can improve grid economics by spreading fixed costs over more kilowatt-hours. But it also removes slack: a grid serving large always-on loads has less headroom for extreme weather events and less tolerance for generation shortfalls. How much of Gartner’s projected growth is firm, flexible, or interruptible will matter as much as the total.

    Winners, Losers, and the Power Value Chain

    If the forecast is directionally right, the beneficiaries extend well beyond data-center operators. Makers of transformers, switchgear, generators, and cooling equipment — many already quoting extended lead times — see demand visibility measured in years. Generation developers, from gas turbines to nuclear restarts to utility-scale renewables paired with storage, gain a creditworthy customer class willing to sign long-dated contracts. Utilities in data-center-heavy regions gain load growth after decades of stagnation, though with real execution and rate-design risk.

    The squeezed parties are those competing for the same electrons and equipment: other large industrial loads, smaller colocation players without utility relationships, and — if cost allocation is handled poorly — residential ratepayers. For data-center operators themselves, the forecast reinforces an emerging hierarchy: companies holding contracted, deliverable power capacity own an appreciating asset, while those still in interconnection queues hold an option of uncertain value.

    Treat the Number as a Signal, Not a Certainty

    A forecast is a model, and this one — as syndicated — arrives without its assumptions attached. Projections of AI-driven power demand have varied widely across analysts, and history urges caution: early-2000s forecasts of runaway internet power consumption overshot badly because they underestimated efficiency gains. Chip-level performance-per-watt improvements, smarter model architectures, and rising inference efficiency could all bend the curve; conversely, faster-than-expected enterprise AI adoption could steepen it.

    The even-handed reading is that Gartner’s 26% figure is a credible-sounding midpoint from an established research house, but its value depends on methodology the public headline does not disclose — baseline year, geographic scope, and workload assumptions among them. Planners should treat it as one scenario input, not a settled fact.

    Background

    Data-center electricity demand was, for roughly a decade before the AI era, a story of successful restraint: workloads migrated into ever-more-efficient hyperscale facilities, and total consumption grew far more slowly than computing output. That equilibrium broke with the generative-AI buildout that began in earnest in 2023, as operators raced to deploy GPU clusters whose power density and utilization patterns overwhelmed the old efficiency offsets. Since then, power availability has displaced real estate as the industry’s primary constraint, and forecasts from analysts, utilities, and government agencies have been repeatedly revised upward.

    Gartner, a research and advisory firm whose projections are widely used in enterprise technology planning, publishes recurring forecasts on data-center spending and infrastructure. Its June 2026 electricity-consumption forecast lands amid active debate among utilities, regulators, and operators over how much of the projected AI load will actually materialize — and who should pay to serve it.

    Source: Gartner Says Data Center Electricity Consumption to Grow 26% in 2026 — Gartner’s June 2026 forecast announcement, as syndicated via Google News.