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  • A 6,831 MW Shortfall at PJM’s $325 Cap Shows Data Center Power Is a Build Problem

    A 6,831 MW Shortfall at PJM’s $325 Cap Shows Data Center Power Is a Build Problem

    TL;DR · 30-second read

    The Short Version

    The organization that runs the electric grid for 67 million people across 13 states, including Pennsylvania, held its auction to line up power plants for 2028 and 2029. The price hit the maximum allowed, again. Even at that price, it could not secure as much power as it wanted.

    Data centers, giant buildings full of computers, are one big reason demand is climbing fast. A Pennsylvania consumer advocate says higher power costs already add $220 to $320 a year to a typical household’s bill. Paying top dollar is not fixing it, because new power plants cannot be built and connected fast enough.

    PJM Interconnection, the Valley Forge, Pennsylvania-based nonprofit that operates the largest electricity grid in the United States, announced on July 14, 2026 that its latest capacity auction cleared at $325 per megawatt-day for the 2028-2029 delivery year. That is the maximum allowed under a price cap first negotiated by Pennsylvania Governor Josh Shapiro’s administration after the 2024 auction set a record of $269.92, and later extended at the request of PJM-state governors and the Trump administration. The Pennsylvania Capital-Star reported that the auction also fell 6,831 megawatts short of PJM’s capacity goal, a larger deficit than in last year’s auction.

    PJM President and CEO David Mills said the results show “demand for electricity continues to grow faster than electricity supply.” PJM’s peak demand forecast has risen by 66,000 megawatts through 2036, driven by retiring fossil fuel plants, electrification of transportation and industry, and proposed data center development.

    Executive Summary

    PJM’s capacity auction, the mechanism it uses to pay power plants to be available on the hottest and coldest days of the year, hit its price ceiling again. At $325 per megawatt-day, the 13-state grid still came up 6,831 megawatts short of its target. The cap will also apply to the next auction, scheduled for December and covering 2030 and 2031.

    This matters beyond the price. When an auction clears at its maximum and still falls short, the shortfall shows how much supply the highest permitted price could not attract. Consumer advocates and the generators’ own trade association disagree on a lot, but both point to the same near-term obstacle: getting new plants built and connected to the grid. The main limit on power for data centers in the largest US grid is construction and interconnection, not willingness to pay.

    For households, the cost is already visible. Pennsylvania’s former consumer advocate estimates the last two auctions added about $16 billion in reliability costs, and $220 to $320 a year to the average Pennsylvania residential bill.

    At the Ceiling, the Price Signal Runs Out of Room

    A capacity auction is supposed to work like any market. If the grid needs more standby power, the price rises until enough generators commit to being available. The price is quoted per megawatt-day, the payment for one megawatt of capacity held ready for one day. In this auction that mechanism hit its limit. The clearing price of $325 is the cap itself, and even at that price PJM ended up 6,831 megawatts below its capacity goal, a wider gap than last year’s.

    This is the key point for anyone planning around PJM power. A market that clears below its cap tells you what supply costs. A market that clears at its cap and still comes up short only tells you that the maximum permitted price was not enough. Two years ago the record was $269.92. Prices are now pinned at the ceiling, and the ceiling has been extended through the December auction for 2030 and 2031. Patrick Cicero, the former Pennsylvania consumer advocate now with the Pennsylvania Utility Law Project, described what that looks like: “These are flat prices that are going to remain high. It’s not necessarily another spike, but it’s really, really high prices remaining.”

    Why the Constraint Is Construction, Not Capital

    Robert Routh of the Natural Resources Defense Council blames data center load for the price pressure, and Todd Snitchler of the Electric Power Supply Association, which represents competitive generators, defends the price as a healthy investment signal. The two still converge on where the bottleneck sits. Routh said a lack of money is not among the problems: “Tech companies, data center customers that have a need for power have deep pockets. Any power plant that can get built these days should have no problem finding buyers and arranging financing.” He named the real obstacles as supply chain delays, long construction timelines, and siting and permitting hurdles. Snitchler said “new supply is coming; the issue now is to address getting it connected to the system as quickly as possible.”

    That connection step is PJM’s responsibility. The grid operator vets proposed power plants and transmission lines before approving them to connect, and it has faced intense criticism for a years-long backlog of projects waiting in that line, often called the interconnection queue. Some projects dropped out while they waited. PJM says it has made significant progress by prioritizing shovel-ready projects.

    There is one honest caveat. Because the cap is binding, this auction cannot show how much supply an uncapped price might have drawn in, and EPSA’s argument that years of record-low prices discouraged investment deserves weight. Still, even the generators’ trade group names connection rather than compensation as the near-term limit. For data center developers, the implication is plain: a higher bid does not produce a megawatt that has not been built. The scarce resources are deliverable, connected capacity and the time it takes to create it.

    Who Carries the Cost, and the Proposal to Separate It

    The link between auction prices and retail bills is indirect, but it is real. Cicero said the last two auctions added about $16 billion to the cost of ensuring reliability. Some Pennsylvania utilities have raised default rates, the rate paid by customers who do not shop for an electricity supplier, by up to 20%. He put the added cost to the average Pennsylvania residential customer at $220 to $320 per year.

    The policy response under discussion is a separate auction, sought by PJM-state governors and the Trump administration, in which data centers and other large-load customers would buy long-term power commitments that fund new capacity. Routh described it as a matchmaking venue to build new supply “without raising prices for everyone else.” By his own account, though, that process faces the same supply chain, timeline and permitting hurdles as everything else. It could change who pays for new generation. On its own, it does not shorten the time it takes to build and connect that generation, which is what closes a 6,831-megawatt gap.

    Weighing the Competing Explanations

    Each camp’s framing captures part of the picture. NRDC’s Routh attributes the pressure to data centers directly: “Data center load growth is degrading grid reliability, and it’s raising prices to the Governor Shapiro-negotiated cap.” PJM’s 66,000-megawatt forecast increase through 2036, nearly 80 times the output of the Three Mile Island nuclear plant, bundles three drivers into one number: retiring fossil plants, electrification, and proposed data centers. How much of that number belongs to data centers is the question that decides whether large-load cost allocation addresses most of the problem or only part of it.

    The Three Mile Island example shows how the pieces fit together. That plant is set to reopen next year to serve data centers exclusively, a sign that large tech buyers are already securing dedicated supply outside the shared pool. Snitchler’s point that new supply is coming is plausible given that buying interest. Until it is connected, however, the region is running on the capped price and a growing shortfall, and the costs fall largely on customers who did not create the new load.

    Background

    PJM Interconnection coordinates electricity supply across 13 states, including Pennsylvania, for 67 million customers. Alongside real-time grid operations, it runs capacity auctions years in advance to ensure enough generators are on standby for peak demand. It also reviews and approves new power plants and transmission lines seeking to connect, a process that has drawn criticism for a years-long backlog.

    Capacity prices in PJM were low for years, which the generators’ trade association says discouraged investment. The 2024 auction broke that pattern with a record $269.92 per megawatt-day. Pennsylvania’s governor then negotiated a price cap, and PJM later extended it at the request of state governors and the Trump administration. Demand forecasts have risen sharply since, as fossil plants retire, transportation and industry electrify, and data center proposals multiply.

    Sources

    Source: PJM Interconnection electricity price hits cap again in latest auction (Pennsylvania Capital-Star): PJM’s 2028-2029 capacity auction clears at the $325 cap and falls 6,831 megawatts short of its goal.

  • FERC Aims to Cut Data Center Grid Queues and Electricity Bills: What It Means

    FERC Aims to Cut Data Center Grid Queues and Electricity Bills: What It Means

    IEEE Spectrum reported on June 25, 2026, that the Federal Energy Regulatory Commission (FERC) — the U.S. agency that oversees the interstate power grid and wholesale electricity markets — aims to cut the queues that data centers face when seeking grid connections, while also containing electricity bills. The syndicated item carries only the headline, so the specific mechanism, docket, and timeline are not detailed in the material available here.

    The framing itself is significant: the regulator is treating slow grid interconnection and rising consumer power costs as a single, linked problem — the two pressures the AI data center boom has placed on the U.S. electric system.

    Executive Summary

    According to the report, FERC is moving to shorten the waits that large new loads — chiefly AI data centers — endure before they can connect to the grid, and to do so in a way that limits the impact on ordinary electricity bills. Interconnection is the process by which a new generator or major customer is studied, assigned any needed grid-upgrade costs, and physically wired into the transmission system; the backlog of these requests is widely regarded as one of the tightest bottlenecks on U.S. data center growth.

    Why it matters: hyperscale operators can erect a building in 18 to 24 months, but securing hundreds of megawatts of firm grid power can take far longer, and utilities in several regions have quoted multi-year waits. At the same time, household and business electricity prices have become politically charged in data-center-heavy regions, with debates over how much of the grid buildout ordinary ratepayers should fund. A federal move that credibly addresses both — speed and cost — would be the single biggest regulatory lever on how fast AI infrastructure can actually energize.

    What is and is not substantiated: the available source confirms the regulator’s stated aim but not the instrument. Whether this is a formal rulemaking, a policy statement, or guidance to grid operators — and whether it is binding — cannot be determined from the headline alone, and readers should weight it accordingly until the underlying FERC documents are public.

    Why the Interconnection Queue Is the Real Bottleneck

    Every large project that wants to plug into the high-voltage grid — a solar farm, a gas plant, or increasingly a gigawatt-scale data center campus — must file an interconnection request and wait for engineering studies that determine what upgrades the grid needs and who pays for them. By the end of 2023, Lawrence Berkeley National Laboratory counted roughly 2,600 gigawatts of generation and storage capacity waiting in U.S. queues — more than double the nation’s entire installed generating fleet — with typical waits stretching toward five years from request to operation.

    Data centers sit on the demand side of this equation, and large-load interconnection has historically been even less standardized than the generator process, handled utility by utility and state by state. For AI operators, the queue — not chips, land, or capital — is frequently the schedule-defining constraint. That is why a federal regulator signaling it wants to compress these timelines matters more to data center delivery dates than most technology announcements.

    Two Goals in Tension: Faster Hookups and Lower Bills

    Cutting queues and cutting bills pull in different directions, and the report’s pairing of them is the most analytically interesting element. Connecting multi-hundred-megawatt loads quickly often requires transmission upgrades whose costs, under traditional utility ratemaking, are spread across all customers. Consumer advocates in several data-center-heavy states have argued that households are subsidizing the grid expansion that serves hyperscale computing; utilities and data center operators counter that large, steady loads can spread fixed grid costs over more sales and put downward pressure on rates.

    Both claims can be true depending on how cost allocation is structured — which is precisely the kind of question FERC decides. Mechanisms observers have debated in recent years include dedicated large-load rate classes, requirements that data centers fund their own upgrades or bring their own generation, and co-location arrangements that place computing directly at power plants. Which of these, if any, the regulator is now advancing is not specified in the available source.

    What a Federal Regulator Can — and Cannot — Fix

    FERC has a track record here: its Order 2023 overhauled the generator interconnection process, replacing first-come-first-served study lines with clustered, first-ready-first-served batches, backed by deposits and readiness requirements to flush speculative projects from the queue. Extending comparable discipline to large loads would be a logical next step, and FERC has also been drawn into the co-location debate through disputes over data centers sited at existing power plants.

    But the agency’s jurisdiction has hard edges. States control retail rates, generation siting, and most permitting; regional grid operators run their own study processes; and no order can conjure the transformers, turbines, and skilled crews that are in genuinely short supply worldwide. A FERC action can remove procedural delay — often years of it — but the physical buildout still moves at the pace of supply chains and state approvals. Expectations should be calibrated to that split.

    Winners, Losers, and What to Watch

    If queue reform for large loads materializes and works, the clearest beneficiaries are hyperscalers and data center developers with projects stalled behind study backlogs, along with the transmission engineering firms and equipment suppliers that would see demand pulled forward. Utilities face a mixed outcome: faster load growth boosts their invested capital base, but tighter federal timelines and cost-assignment rules constrain how they manage it. Generation developers could gain if load and supply requests are studied more coherently together.

    The unresolved variable is the ratepayer. If the regulator pairs faster interconnection with cost rules that make large loads bear the upgrades they cause, the political friction around data center power could ease; if speed comes without that discipline, bill impacts could intensify the local backlash that has already slowed projects in several markets. The details — still unpublished in the material available here — will determine which scenario unfolds.

    Background

    FERC is the century-old independent agency that governs the U.S. interstate grid, and interconnection reform has been its defining workstream of the 2020s. After two decades of essentially flat electricity demand, AI data centers, manufacturing, and electrification pushed load growth back onto utility planning maps around 2023–2024, colliding with queue backlogs that Lawrence Berkeley National Laboratory measured at roughly 2,600 gigawatts of waiting capacity by the end of 2023. Order 2023 tackled the generator side of the problem; large loads — the data centers themselves — remained governed by a patchwork of utility and state processes.

    Through 2024 and 2025, disputes over co-locating data centers at power plants and over who pays for grid expansion made large-load policy one of the most watched dockets in U.S. energy. The June 2026 report places FERC’s next move squarely in that lineage: an attempt to standardize and speed how the grid absorbs its biggest new customers without letting the cost land on everyone else’s bill.

    Source: U.S. Regulator Aims to Cut Data Center Queues and Electricity Bills — IEEE Spectrum report, June 25, 2026, on FERC’s effort to speed data center grid interconnection while containing consumer electricity costs.

  • PJM’s Market Monitor Says AI Data Centers Are Reshaping America’s Largest Grid

    PJM’s Market Monitor Says AI Data Centers Are Reshaping America’s Largest Grid

    PJM Interconnection’s independent market monitor has concluded that AI-driven data center growth is reshaping the power markets it oversees, according to a June 2026 report from Data Center Knowledge. PJM operates the largest wholesale electricity market in the United States, coordinating the grid across 13 states and the District of Columbia for roughly 65 million people.

    The finding matters because it comes from the market’s designated referee rather than from a vendor or developer: the monitor exists precisely to assess, without commercial interest, whether the market is functioning competitively — and it is now attributing a fundamental shift in that market to data center load.

    Executive Summary

    The headline is short but consequential: PJM’s market monitor — the independent body charged with policing competition in the nation’s largest electricity market — has identified AI data center growth as a force actively reshaping that market. For two decades, US grid planners worked in a world of essentially flat electricity demand, where efficiency gains offset economic growth. That assumption has broken, and PJM, whose footprint includes Northern Virginia’s Data Center Alley, is where it broke first and hardest.

    When the market monitor says demand growth is ‘reshaping’ the market, it is signaling that data center load is no longer a forecasting footnote but a structural driver of prices, planning, and investment decisions. PJM’s recent capacity auctions — the mechanism that pays generators to be available years in advance — have produced record-setting results widely attributed in part to surging demand forecasts, and those costs flow through utility bills to every customer class.

    For the industry, an independent confirmation of this shift cuts both ways. It validates the scale of the AI infrastructure build-out that developers have been describing. It also raises the stakes for how that growth is managed: who pays for new transmission and generation, how speculative interconnection requests are filtered from real ones, and whether supply can be added fast enough to keep reliability and affordability intact.

    From Forecasting Footnote to Structural Force

    The most important word in this story is ‘reshaping.’ Grid operators revise load forecasts constantly; what they rarely do is declare that the character of the market itself has changed. PJM’s service territory covers all or part of 13 states and DC, and it includes the densest concentration of data centers on the planet in Northern Virginia. When demand there grows, it does not simply add megawatts — it changes which power plants run, where transmission congestion appears, and how much capacity the market must procure years ahead.

    An assessment from the independent market monitor carries different weight than one from PJM itself or from data center developers. The monitor’s role — in PJM’s case performed by an outside firm — is to evaluate market competitiveness and flag structural problems without a commercial stake in the outcome. Its reports are read closely by federal and state regulators. Framing AI data center growth as market-reshaping effectively puts the issue on the regulatory agenda, not just the industry conference circuit.

    Capacity Markets, and Who Ends Up Paying

    PJM runs a capacity market: generators are paid not only for the electricity they produce but for committing to be available during future peak periods. When demand forecasts rise sharply — as data center growth has caused them to — the market must procure more capacity against a supply base that has been shrinking as older coal and gas plants retire. Basic economics follows: tighter supply against higher demand means higher clearing prices, and PJM’s recent auctions have set records that state officials and consumer advocates have publicly protested.

    Capacity costs are socialized across ratepayers, which is where the political friction originates. Households and small businesses in PJM states are seeing bill increases driven partly by demand they did not create. Expect the policy debate to center on cost allocation: large-load tariffs that require data centers to underwrite the infrastructure they trigger, minimum take-or-pay commitments, and rules for co-located or behind-the-meter arrangements where a data center pairs directly with a power plant. How those rules land will materially affect data center project economics in the region.

    Winners, Losers, and the Speculation Problem

    The near-term winners are clear: owners of existing generation in PJM, whose assets have been revalued by scarcity, and transmission developers with projects in flight. Data center operators with secured power — signed interconnection agreements and energized substations — hold an asset that is increasingly the scarcest input in the industry. The squeezed parties are late-arriving developers facing multi-year waits for grid connection, and energy-intensive industries competing for the same electrons.

    The unresolved analytical problem is demand-forecast quality. It is widely acknowledged in the industry that developers file interconnection requests with multiple utilities for the same prospective project, meaning some portion of announced demand is duplicative or speculative. If markets procure capacity against inflated forecasts, ratepayers overpay; if forecasts are discounted too aggressively and the load shows up, reliability suffers. Distinguishing real load from phantom load is arguably the central technical challenge the monitor’s finding implies — and one the industry itself has an interest in helping solve, since credibility with regulators depends on it.

    The Supply Response Is the Whole Game

    High prices are a symptom; the cure is new supply, and here timelines diverge badly. A hyperscale data center can be built in roughly two to three years. New gas turbines face multi-year equipment backlogs, nuclear operates on decade scales, and renewables plus storage — often the fastest option — face their own interconnection queues and siting fights. Transmission, the connective tissue, is slower still.

    That mismatch, more than any single auction result, is what ‘reshaping the market’ means in practice. It pushes data center operators toward creative structures: siting near existing generation, contracting directly for new-build power, investing in on-site generation, and accepting flexibility obligations — curtailing or shifting load during grid stress — in exchange for faster connection. For infrastructure providers, grid access has moved from a line item in site selection to the decisive variable.

    Background

    PJM traces its roots to a 1927 power pool between Pennsylvania and New Jersey utilities and has grown into the largest regional transmission organization in the US, dispatching power across 13 states and DC. An independent market monitor oversees its wholesale markets and publishes regular assessments of their competitiveness and health. For most of the 2000s and 2010s, PJM — like the rest of the US grid — planned around flat demand, as efficiency gains offset economic growth.

    That era ended as cloud and then AI data center construction accelerated, concentrated in PJM territory around Northern Virginia. The region’s recent capacity auctions have produced record-setting prices that drew objections from state officials and consumer advocates, putting data center load growth at the center of an escalating debate over grid reliability, cost allocation, and how fast new generation and transmission can be built.

    Source: PJM Monitor: AI Data Center Growth Reshaping Power Markets — Data Center Knowledge report on the PJM independent market monitor’s assessment of AI-driven load growth, June 3, 2026.

  • One Op, 2 Audiences: OpenAI Case Shows AI Power Bills Are Now an Influence Target

    One Op, 2 Audiences: OpenAI Case Shows AI Power Bills Are Now an Influence Target

    TL;DR · 30-second read

    The Short Version

    OpenAI, the company behind ChatGPT, says it shut down a group of accounts, likely run from China, that used its chatbot to write fake American social media posts.

    The posts claimed that data centers, the giant computer warehouses that power artificial intelligence, are pushing up ordinary families’ electricity bills.

    Worries about power bills are real. But this case shows that the fight over these buildings now draws foreign propaganda, not just debate at local town halls.

    OpenAI said on June 1, 2026 that it had banned a cluster of ChatGPT accounts that likely originated in China and used the chatbot to produce content for a covert influence operation it calls “Data Center Bandwagon.” The accounts posed as Americans from a range of backgrounds and posted English-language comments and images on X arguing that data centers and AI applications are driving up electricity demand and passing higher costs to ordinary households. The case study is part of OpenAI’s June 2026 report on PRC-linked influence operations targeting AI debates in the US.

    OpenAI assessed that the operators were likely a social media operations team at a private Chinese technology company working for provincial-level government clients. The same cluster also targeted overseas Chinese audiences, including attempts to generate insulting comments about dissident Li Ying. OpenAI says its models refused those requests.

    Executive Summary

    The takedown is small in scale but specific in subject. According to OpenAI, the operators asked ChatGPT for comic strips about a power grid operator’s capacity auction prices, based on reporting from a legitimate regional newspaper. They wanted the rising prices framed as the result of new peak demand from data centers and AI, with the costs ultimately landing on households. The content was then posted by likely inauthentic accounts on X, next to links to real news stories.

    This matters for the infrastructure industry because it moves the data center power-cost debate out of the purely local arena. Until now, the dispute over who pays for the grid upgrades behind AI campuses has mostly played out in utility commission dockets, county zoning hearings and regional press. OpenAI’s case shows that at least one foreign-directed operation treated that same argument as useful material. It sat on the operators’ task list alongside harassing Chinese dissidents.

    The case does not show that concern about data center electricity costs is manufactured. OpenAI describes an operation that amplified legitimate reporting while hiding who was doing the talking. It does not describe one that invented the underlying issue. That distinction should shape how developers, utilities and community groups respond.

    How the Operation Worked

    OpenAI’s account describes a workflow rather than a one-off stunt. The operators prompted ChatGPT in Simplified Chinese and requested outputs in English and Chinese. They reached the service through VPNs, because OpenAI does not allow access from China. Their outputs included short comments, comic strips and edited stock images. For the edits, they added text to generic electricity-market pictures to support a narrative about ordinary people subsidizing AI infrastructure. Posts carried hashtags such as #capacityauction, #datacentersuccess and #datacenters.

    The team also used the model as back-office tooling. It asked for code to automate logins and to manage interactions across multiple platforms. It used ChatGPT to extract usernames, prepend X or YouTube links, strip hyperlinks and format data for spreadsheets. It also had ChatGPT polish internal work reports. According to OpenAI, those reports set out objectives such as building persistent, credible accounts and anticipating platform enforcement. In practice, the AI was not doing the persuading. It was lowering the labor cost of running many fake voices at once.

    Why Power Bills Were the Chosen Wedge

    The operators did not pick an abstract talking point. Capacity auctions are how some regional grid operators pay power plants to be available during the highest-demand hours. Clearing prices in those auctions depend heavily on forecasts of peak load. When forecasts rise, partly because of large new data center loads, the cost of that guaranteed capacity rises too. Those costs generally flow through to retail electricity bills. That is a real, documented mechanism, and it is exactly the chain of cause and effect the operators asked ChatGPT to dramatize: data centers raise peak demand, peak demand raises capacity prices, and households pay.

    This is the core of the operational point. The argument over who pays for AI’s grid footprint was already one of the most sensitive issues in data center siting. It turns on cost allocation between large new loads and existing ratepayers, which is complex and easy to reduce to a slogan. That made it a ready-made wedge. The same cluster that pushed this narrative to US audiences was also, per OpenAI, aiming content at overseas Chinese audiences in line with Party-state priorities. So the power-cost debate was treated as one line item in a broader influence effort, not as a niche energy topic.

    Everyone who builds, powers or permits AI infrastructure is affected: developers seeking local approvals, utilities filing large-load tariffs, grid operators publishing auction results, and state regulators weighing who should bear upgrade costs. Each now operates in a debate that can be deliberately amplified, and each will find it harder to tell organic sentiment from manufactured sentiment by counting social media volume alone.

    Real Concern, Borrowed Voice

    It would be a misreading to treat this case as evidence that opposition to data centers is foreign-driven. OpenAI’s own description has the operators linking to legitimate news coverage of capacity auctions and data center demand. The deception lay in who was speaking, fabricated Americans, not in whether rising capacity prices exist. Residents, consumer advocates and local officials raising cost questions are part of a genuine policy debate and deserve answers on the merits. Labeling them as a foreign campaign would be both unfair and unsupported by anything in this report.

    The same even-handedness applies to the report itself. OpenAI is one of the largest sponsors of new AI data center capacity, so it has a stake in how this debate goes. Its attribution rests on behavioral evidence: prompts in Simplified Chinese, VPN access, uploaded work reports describing detection evasion, and coordinated persona use. That evidence speaks to coordination and inauthenticity, not to whether the cost argument is right. OpenAI also does not say how far the content spread. The case shows intent and method. It does not show impact.

    What Changes for Builders and Platforms

    For the infrastructure industry, the practical lesson is that vague reassurance is a weak defense in a debate that can be deliberately amplified. Projects that publish verifiable figures give neutral observers something to check a viral claim against: contracted load, tariff structure, who funds transmission and substation upgrades, and how capacity costs are allocated. The more concrete the public record, the less room a fabricated narrative has to work in.

    For platforms, the most revealing material is the planning document. OpenAI says one report focused on Facebook and laid out a plan to build lifestyle and commentary personas, amplify narratives through cross-account interaction while preserving the look of organic engagement, and combine organic posting with paid ads. It also called for backup accounts and separated account activity to avoid detection. That is a playbook for long-term presence, not a burst of spam, and it suggests detection will increasingly depend on cross-company signal sharing rather than on any one platform’s view.

    Background

    OpenAI publishes periodic threat reports describing accounts it has banned for using its models in influence operations, scams and cyber activity. The Data Center Bandwagon case is one entry in its June 2026 report on PRC-linked operations targeting US AI debates, and it follows an earlier report in which OpenAI documented harassment of the same dissident, Li Ying.

    The target topic is one of the most contested in AI infrastructure. Large AI data centers can draw as much electricity as a sizable town. Their growth has fed into higher peak-demand forecasts, and in some regions into higher capacity auction prices, which in turn reach household bills. Utilities, grid operators, regulators and communities have been debating how those costs should be shared between new large loads and existing customers.

    Sources

    Source: “Data Center Bandwagon” Campaign: US-targeted influence activity – OpenAI, OpenAI’s case study on a banned ChatGPT account cluster that generated social media content about data center electricity costs.

  • Data Centers Drive a 76% Surge in PJM Capacity Prices: AI Load Meets the Grid

    Data Centers Drive a 76% Surge in PJM Capacity Prices: AI Load Meets the Grid

    Capacity prices in PJM Interconnection — the regional transmission organization that operates the largest wholesale electricity market in the United States — have surged 76%, and reporting by E&E News (POLITICO) on May 16, 2026 identifies data center demand as the principal driver. PJM coordinates power across 13 states and the District of Columbia, serving roughly 65 million people, so a price move of this size in its capacity market ripples directly into the electric bills of a substantial share of the American population.

    Capacity prices are not the price of energy itself; they are what the market pays generators simply to be available during the hours of highest demand. A 76% jump in that availability premium is the market’s way of saying that spare headroom on the grid is getting scarce — and the reporting attributes that scarcity chiefly to the wave of AI-driven data center construction concentrated in PJM’s footprint.

    Executive Summary

    The reported 76% surge in PJM capacity prices is arguably the most concrete, dollar-denominated evidence to date that AI infrastructure buildout is stressing the US power system. Forecasts of data center load growth have circulated for two years; a capacity auction result is different. It is a binding market outcome — real money that electricity suppliers must pay, and ultimately recover from customers, because demand is growing faster than dependable supply.

    The mechanism matters. PJM procures capacity through auctions held in advance of each delivery year: generators offer their availability, and the auction clears at the price needed to cover forecast peak demand plus a reserve margin. When large new loads such as hyperscale data centers enter the forecast while older power plants retire and new ones queue slowly for interconnection, the supply-demand balance tightens and the clearing price rises. A 76% increase indicates that tightening is now severe, not incremental.

    For the infrastructure industry, the signal cuts both ways. It validates the scale of AI demand that data center operators have been describing — but it also raises the operating cost of every facility in the region, hands utilities and consumer advocates a concrete number to organize around, and increases the likelihood of regulatory intervention in how large loads connect to and pay for the grid.

    What a Capacity Price Actually Measures

    Capacity markets are insurance markets for the grid. Separate from the energy market, where power is bought and sold as it is consumed, a capacity auction pays generators a fixed amount — typically quoted per megawatt-day — to guarantee they will be available when the system hits its peak. The clearing price is therefore a pure scarcity signal: it reflects how much spare, dependable generating capacity exists relative to forecast peak demand, years before that peak arrives.

    That is what makes a 76% surge more telling than any demand forecast. Forecasts can be revised; auction results are settled commitments backed by penalties for non-performance. When the availability premium jumps this sharply, it means the market — with real capital at stake — has concluded that the cushion between peak demand and dependable supply in PJM is thinning quickly. Attribution of the surge to data centers puts a name on the demand side of that squeeze.

    Why AI Load Lands So Hard on PJM

    PJM’s territory includes Northern Virginia, the densest concentration of data centers on Earth, along with fast-growing markets in Ohio, Pennsylvania, and the Chicago area. Data center load has characteristics that stress a capacity market more than most growth: facilities are large — a single AI campus can draw as much power as a mid-sized city — they run near-continuously rather than peaking with the weather, and they arrive in clusters on compressed construction timelines measured in a couple of years.

    Supply cannot respond at that speed. New gas turbines face multi-year equipment backlogs, renewable and storage projects sit in long interconnection queues, and coal units continue to retire on schedules set years ago. Capacity auctions exist precisely to signal when this mismatch is forming, and the reported surge suggests the signal has moved from amber to red. In that sense the price is doing its job — the open question is whether investment in new generation can respond before the cost of scarcity compounds.

    Who Pays, and Who Benefits

    Capacity costs flow through electricity suppliers to virtually all retail customers, spread across households, businesses, and industry regardless of who caused the demand growth. That socialization of costs is the political flashpoint: a homeowner in Baltimore or Columbus pays part of the premium created, in large part, by hyperscale computing facilities they may never see. Expect this number to feature in rate cases, state legislative hearings, and the ongoing debate over whether large loads should face special tariffs or bring-your-own-generation requirements.

    On the other side of the ledger, existing generators — particularly gas, nuclear, and other dispatchable plants that can pledge dependable capacity — are clear beneficiaries, and higher capacity revenue is exactly the incentive the market design uses to attract new entry and keep existing plants online. Data center developers face a more nuanced picture: higher power costs raise operating expenses, but a market that rewards firm capacity also strengthens the case for the on-site generation, storage, and long-term supply deals that many operators are already pursuing.

    A Price Signal With Policy Consequences

    Sharp capacity price increases rarely stay contained within market design circles. When the driver is identifiable — here, data centers — regulators and politicians gain a specific target for cost-allocation reform. Proposals already circulating across US grid regions include dedicated rate classes for very large loads, requirements that new data centers fund transmission upgrades, and co-location arrangements that pair facilities directly with power plants. A 76% surge gives all of those efforts fresh momentum in PJM’s 13 states.

    For the broader AI infrastructure economy, the strategic takeaway is that power availability — not land, fiber, or chips — is consolidating as the binding constraint on growth in established markets. Operators that secured capacity, interconnection positions, or generation partnerships early hold an appreciating asset. Those planning new facilities in PJM territory now face higher costs, longer utility timelines, and a more contentious public environment — pressures that are already redirecting some development toward regions with more available headroom.

    Background

    PJM Interconnection began as a power pool of Pennsylvania, New Jersey, and Maryland utilities and grew into the largest grid operator in the United States, running wholesale energy and capacity markets across 13 states and the District of Columbia. Its capacity construct, the Reliability Pricing Model, procures guaranteed generating capacity through auctions held in advance of each delivery year — a design meant to keep enough dependable supply online as the generation fleet changes.

    For most of the 2010s, flat demand and cheap shale gas kept PJM capacity prices low. That era ended as AI and cloud growth transformed data centers into the region’s dominant new load — anchored by Northern Virginia, the world’s largest data center market — while coal retirements and slow interconnection queues constrained supply. Capacity auctions in the mid-2020s began registering that squeeze with sharply higher clearing prices, of which the 76% surge reported in May 2026 is the latest and among the starkest examples.

    Source: Data centers drive 76% surge in PJM power prices — E&E News by POLITICO, reporting published May 16, 2026 on data center demand driving capacity price increases in the PJM grid region.