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  • With 2,500 GW Waiting to Connect, OpenGrid Bets AI’s Power Delay Is Partly a Data Problem

    With 2,500 GW Waiting to Connect, OpenGrid Bets AI’s Power Delay Is Partly a Data Problem

    TL;DR · 30-second read

    The Short Version

    Before a new power plant or a big electricity user like a data center can plug into the grid, engineers must model what the connection would do to the network. Worldwide, projects equal to roughly 2,500 large power plants are waiting in line for that approval.

    The Linux Foundation, the nonprofit behind much of the free software the internet runs on, launched OpenGrid to help. Its argument: part of the wait comes from planning software that cannot share data, not only from a shortage of power lines.

    The Linux Foundation announced OpenGrid on October 7, 2026, at Open Source Summit Europe in Prague. OpenGrid is an open source initiative that builds a shared data and interoperability layer for electric grid planning. It includes open standards and schemas, programmatic APIs and a central hub of validated, model-ready datasets, so that the commercial and open source tools planners already use can work together. Its first flagship application, the OpenGrid Translator, is being developed for release in 2027 and is meant to move data and models between widely used commercial platforms and open source tools.

    According to the Linux Foundation’s announcement, distributed via PR Newswire, more than 2,500 gigawatts of generation, storage and large-load projects are waiting in grid connection queues worldwide. The initiative’s stated goal is for every country to have access to high-quality grid planning tools by 2030. Backers are Breakthrough Energy GRIDS, Global Grid Catalyst, Connected Grid Initiative and Google.org. Technical delivery partners are Open Energy Transition, Tapestry, TransitionZero and QXT Energy.

    Executive Summary

    OpenGrid does not build a new grid planning program. It builds the plumbing between existing ones: common data formats, connectors and a validated data repository. The Linux Foundation describes grid planning as a critical bottleneck as electricity demand rises, new industrial and data center loads come online, and more generation and storage projects request connection. It says that planning is constrained by fragmented data, incompatible software, and modeling capabilities that many institutions and countries cannot access.

    This matters for data center infrastructure because a large new load cannot energize until the utility or grid operator has studied it. Those studies run on network models. If the models are slow to assemble, hard to exchange or impossible for outside parties to check, every project in the queue waits longer. OpenGrid is betting that a shared, neutral data layer can reduce that friction. The announcement does not quantify how much time it expects to save, and software alone does not build the transmission lines the grid still needs.

    Why a Data Center’s Power Date Runs Through Someone Else’s Model

    The 2,500-gigawatt figure explains the mechanism behind this launch. It counts generation, storage and large loads such as data centers waiting for permission to connect. Each of those requests ultimately has to be represented in a grid model: a digital description of lines, transformers, substations and power flows that engineers use to test whether the network can absorb the new project safely. When a developer asks for hundreds of megawatts for an AI campus, the answer depends on that modeling work, and so does any list of upgrades the developer may be asked to fund.

    The release locates part of the delay in the modeling workflow itself. TransitionZero chief executive Matt Gray says that building the company’s own modeling software showed it “how much time is lost” moving data and models between formats. ESIG executive director Debbie Lew points to the need to integrate economic planning tools with reliability tools, and transmission planning (high-voltage, long-distance lines) with distribution planning (local networks). Each handoff between incompatible tools means manual conversion, re-validation and the risk of error. That is the friction OpenGrid’s Translator, schemas and data hub are designed to remove.

    The people affected sit on both sides of the queue. Utilities and system operators would get faster model exchange. Regulators and other stakeholders, in Alice Yake’s words, would gain the ability to examine “data that others cannot verify” today. Developers of data centers and generation would benefit indirectly if studies move faster or are disputed less. The claim is a modest one, and fairly stated: data friction is one contributor to the wait, not its single cause.

    A Translator, Not a Replacement

    The most consequential design choice is what OpenGrid says it will not do. It does not try to replace the commercial grid planning software that utilities and operators depend on. It offers a neutral layer that lets commercial and open source tools interoperate. That lowers the adoption barrier considerably. A planning department does not have to abandon validated workflows, retrain staff or re-certify methods to benefit. It can export a model, translate it and test it elsewhere.

    That approach mirrors how the Linux Foundation’s better-known projects gained traction: by standardising the layer underneath competing products rather than competing with them. The analogy has limits, though. Grid planning tools are a narrower, more specialised market than cloud software, and the value of a translator depends on how faithfully it handles proprietary formats. Karin Wadsack of the Global Power System Transformation Consortium frames neutral governance as a guard against vendor lock-in, meaning dependence on a single supplier. Whether incumbent vendors treat that as an opportunity or a threat will shape how useful the Translator becomes.

    The timeline is also worth noting. The Translator is targeted for 2027, and later additions are framed as future expansion: open solvers (the computational engines that run the simulations), no-code graphical interfaces, standardised planning workflows and AI-assisted data preparation. The present launch is a governance structure and a roadmap, not a shipping product.

    What Software Cannot Shorten

    The release’s own figures show the limits of a data-layer fix. It says annual grid infrastructure investment needs to roughly double to more than $600 billion by 2030, and that 80 million kilometres of power lines must be added or replaced by 2040. Faster, more transparent planning can help decide which lines to build and where, but it does not manufacture transformers, secure rights-of-way or finance construction. For a data center developer, a quicker study that concludes a major transmission upgrade is required still leaves a long wait for that upgrade.

    The global framing points to where OpenGrid may matter most. The release says emerging economies are expected to account for nearly 80 percent of global electricity demand growth through 2030, and that access to modern planning capabilities is especially uneven in fast-growing markets. In places where planning teams lack expensive licensed software or clean network data, open tools and validated datasets could change what is possible, not just make it a little faster. For operators siting AI and cloud capacity in those markets, the credibility of national grid plans directly affects where power can be promised.

    Background

    The Linux Foundation is a nonprofit that hosts open source projects including Linux, Kubernetes, PyTorch, RISC-V and the Model Context Protocol. Its model is to provide neutral governance so that competitors can jointly maintain shared technology. OpenGrid applies that model to electric grid planning, where utilities, grid operators and regulators rely on specialised software to simulate how networks behave as new generation and demand are added.

    Grid planning has become a pressure point as electrification, industrial growth and data center construction raise demand while large volumes of solar, wind and storage projects seek connection. Because planning tools often use different, sometimes proprietary data formats, moving a network model between organisations or programs can require significant manual work. That is the interoperability gap OpenGrid is designed to close.

    Sources

    Source: Linux Foundation lanza OpenGrid para crear una infraestructura para la red eléctrica mundial, the Linux Foundation’s announcement of the OpenGrid open source grid-planning initiative, also issued in English as Linux Foundation Launches OpenGrid to Build Open, Shared Infrastructure for the World’s Electric Grid.

  • 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.

  • 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.

  • EIA: Data Center Server Energy Use Grows Across US Commercial Buildings

    EIA: Data Center Server Energy Use Grows Across US Commercial Buildings

    On May 19, 2026, the U.S. Energy Information Administration (EIA) — the federal government’s independent energy statistics agency — published new commercial-buildings data showing that energy consumed by data center servers is growing across the nationwide commercial building stock. The finding lands in the middle of an intense public debate over how much electricity the AI build-out actually consumes.

    The release matters less for any single number than for its source: this is federal survey data, not a vendor forecast, quantifying how server energy use has expanded within America’s offices, dedicated data centers, and the server rooms tucked inside ordinary commercial buildings.

    Executive Summary

    EIA’s announcement extends its commercial-buildings statistical program — best known through the Commercial Buildings Energy Consumption Survey (CBECS), the government’s long-running census-style study of how U.S. commercial buildings use energy — to document rising server energy consumption across the building stock. In plain terms: the computers doing the computing inside commercial buildings are drawing a growing share of those buildings’ electricity.

    Why it matters: nearly every claim about the ‘AI power crunch’ to date has rested on private-sector estimates from consultancies, utilities, and technology vendors, each with its own methodology and, in some cases, its own commercial interest in the answer. A federal statistical agency measuring the same trend from building-level survey data gives regulators, utilities, and investors a common, disinterested baseline — the kind of number that ends up cited in rate cases, siting decisions, and congressional testimony.

    For infrastructure operators, the direction of the data is unsurprising. The significance is that the growth is now visible across the commercial building stock — not only in purpose-built hyperscale campuses, but in the broader population of buildings that house servers.

    Federal Numbers Change the Power Debate

    Until now, the data center energy conversation has been dominated by projections — analyst decks, utility interconnection queues, and corporate sustainability reports. Projections are arguments; survey data is evidence. EIA’s commercial-buildings program measures what buildings actually consumed, which makes it the closest thing the industry has to a scoreboard. When a .gov dataset says server energy use is growing across the building stock, it becomes much harder for any side of the debate — boosters or critics — to dismiss the trend as hype or alarmism.

    That cuts both ways. Utilities seeking rate recovery for grid upgrades, developers seeking permits, and efficiency advocates seeking standards will all now cite the same federal source. Expect this data to surface in state utility commission filings and local zoning fights, where the credibility of the underlying numbers is often the whole battle.

    The Hidden Data Center Problem

    The phrase ‘commercial building stock’ is doing important work in EIA’s framing. Public attention fixates on gigawatt-scale AI campuses, but a substantial slice of America’s server fleet has historically lived in less visible places: server rooms in office buildings, hospital basements, university closets, and small enterprise data centers. These embedded loads are dispersed, often inefficient, and poorly captured by headline hyperscale statistics.

    Growth measured across the whole stock suggests the compute boom is not just a story of a few hundred giant facilities — it is diffused through the built environment. For the efficiency industry, that is a market signal: dispersed, aging server rooms are prime candidates for consolidation into professionally run colocation facilities, which typically achieve far better power usage effectiveness (PUE — the ratio of total facility power to the power that actually reaches computing equipment).

    Winners, Losers, and the Grid in Between

    The beneficiaries of officially documented demand growth are the companies positioned to serve it: colocation and cloud operators with contracted power in hand, transmission developers, and equipment suppliers across the cooling and electrical chain. Utilities gain justification for capital programs, though they also inherit the political risk of rising rates being blamed on data centers.

    The exposed parties are energy buyers competing for the same electrons — manufacturers, electrified transport, and ordinary ratepayers — and any data center developer whose business case assumes cheap, quickly available power. Federal confirmation of demand growth strengthens the hand of grid planners who argue for building ahead of load, but it equally strengthens critics who ask whether that growth should pay its own way. The honest reading of EIA’s data is that it quantifies the trend without settling the policy argument.

    Background

    EIA has surveyed U.S. commercial buildings for decades through CBECS, producing the government’s authoritative picture of how offices, schools, hospitals, and other non-residential buildings consume energy. Data centers historically registered as a small but disproportionately energy-intensive slice of that stock — buildings that consume many times more electricity per square foot than a typical office.

    The context shifted sharply after 2023, when large-scale AI training and inference drove a wave of data center construction and record utility interconnection requests, making data center electricity demand a national policy issue. Against that backdrop, federal measurement of server energy use across the building stock arrives as a reference point both industry and its critics have lacked.

    Source: Data center server energy use grows across the commercial building stock — U.S. Energy Information Administration announcement of new commercial-buildings energy data, published May 19, 2026.

  • MISO Forecasts 35% Load Growth by 2035 as Data Centers Reshape the Grid

    MISO Forecasts 35% Load Growth by 2035 as Data Centers Reshape the Grid

    The Midcontinent Independent System Operator (MISO) — the grid operator coordinating electricity across a footprint spanning 15 U.S. states and the Canadian province of Manitoba — expects electric load to jump roughly 35% by 2035, according to an April 2026 report from Utility Dive. The primary driver named in the forecast is data center growth.

    A 35% increase over roughly a decade represents a dramatic break from the era of essentially flat U.S. electricity demand that prevailed from the late 2000s through the early 2020s, and it puts one of the largest grid operators in North America on record quantifying the scale of the AI-and-cloud buildout.

    Executive Summary

    MISO’s forecast is a planning document, not a press release from a company selling something — which makes it one of the more consequential data points in the ongoing debate over how much electricity the data center boom will actually consume. Regional transmission organizations (RTOs) like MISO exist to keep supply and demand balanced in real time and to plan the wires and generation needed years ahead. When an RTO raises its ten-year demand outlook by more than a third, that number flows directly into transmission planning, capacity auctions, and the resource plans of dozens of utilities.

    The significance is twofold. First, it validates what individual utilities across the Midwest and Gulf South have been reporting piecemeal: hyperscale data center projects are arriving in interconnection queues at a pace with no modern precedent. Second, it sets up a decade of hard trade-offs. Meeting 35% growth requires new generation, new transmission, and new large-load interconnection rules — all on timelines that historically run slower than the two-to-three-year construction schedule of a data center campus.

    For the infrastructure industry, the headline number is both an opportunity signal and a warning: the grid is now the binding constraint on digital infrastructure growth, and the regions that solve power delivery fastest will win the next wave of siting decisions.

    The End of Flat Demand Is Now Official Planning Doctrine

    For roughly fifteen years, U.S. grid planners could assume that efficiency gains — LED lighting, better HVAC, industrial offshoring — would offset economic growth, keeping total electricity demand nearly flat. That assumption underpinned everything from utility rate cases to power plant retirement schedules. A 35% load-growth forecast from MISO formally retires it for one of the largest grid footprints in North America.

    What makes an RTO forecast different from a consultant’s projection is accountability: MISO must plan transmission and resource adequacy against this number. If the forecast is right and the buildout lags, the result is capacity shortfalls and price spikes. If the forecast is wrong and infrastructure is overbuilt, ratepayers carry stranded costs. Either error is expensive, which is why the assumptions behind the number — how much announced data center load actually materializes — deserve as much scrutiny as the number itself.

    Data Centers as the Marginal Buyer of Power

    A data center is, from the grid’s perspective, an unusual customer: it demands large blocks of power (often hundreds of megawatts per campus), runs at high utilization around the clock, and wants to connect years faster than traditional industrial load. When such customers become the dominant source of demand growth, they effectively set the terms of grid expansion — and grid operators, utilities, and regulators are still working out who pays for the upgrades those connections require.

    The economics cut in several directions. Utilities in MISO territory gain a growth story they have not had in a generation, which supports investment in wires and generation. Existing ratepayers face the risk of subsidizing infrastructure built for loads that may not fully arrive — a concern regulators in several states are already addressing through special large-load tariffs and financial-commitment requirements. Data center developers, meanwhile, face the reality that power availability, not land or fiber, now determines where and when they can build.

    Winners, Losers, and the Speed Mismatch

    The core tension in a 35%-by-2035 scenario is timing. Gas turbines face multi-year order backlogs, new nuclear operates on decade-plus horizons, and large transmission projects routinely take seven to ten years from planning to energization. Data center campuses go from groundbreaking to load in two or three. That mismatch favors whoever can bridge it: developers with early interconnection positions, utilities with spare capacity or fast-track large-load processes, suppliers of grid equipment, and operators pursuing on-site or co-located generation.

    It also raises competitive stakes between regions. MISO’s footprint — stretching from the upper Midwest to the Gulf Coast — competes with PJM, ERCOT, and the Southeast for hyperscale siting. A credible, well-executed plan to serve 35% more load is itself an economic-development asset; a forecast without matching buildout is a queue of frustrated customers who will site elsewhere.

    Forecast Versus Reality: The Phantom Load Question

    Every load forecast in the current environment must grapple with duplicate and speculative requests. Developers commonly file interconnection requests in multiple jurisdictions for the same project, and some announced campuses will never be built. Grid operators know this and apply screening assumptions, but the industry has little historical data on what fraction of AI-era announced load converts to actual consumption. The honest read of any 35% figure is that it is a planning scenario with meaningful uncertainty in both directions — actual growth could undershoot if projects evaporate, or overshoot if AI demand keeps compounding.

    That uncertainty is not a reason to dismiss the forecast; it is a reason to watch how MISO and its member utilities structure commitments. Mechanisms that require large customers to put capital at risk — minimum-take contracts, collateral requirements, contribution to network upgrades — are the market’s way of separating real load from phantom load, and their adoption across the footprint will be a better indicator of true demand than any single projection.

    Background

    MISO was founded in 1998 and became the first FERC-approved regional transmission organization in the United States in 2001. It coordinates generation and high-voltage transmission across a footprint stretching from the upper Midwest down through the Gulf South, serving tens of millions of people through its member utilities. Like other RTOs, it does not own power plants or lines; it operates markets and plans the system that its members build.

    The forecast arrives amid a broader U.S. re-acceleration of electricity demand after more than a decade of stagnation, driven by AI and cloud data center construction, manufacturing reshoring, and electrification. Grid operators across the country have been revising load outlooks upward repeatedly since the early 2020s, and interconnection queues for both large loads and new generation have swelled to historic levels — making forecasts like this one central to the industry debate over how much of the announced boom is real.

    Source: MISO expects load to jump 35% by 2035 on data center growth — Utility Dive report, April 21, 2026, on MISO’s ten-year load forecast.