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  • Infinium’s 1 MW in 250 Sq Ft Shows Waterless AI Cooling’s Real Test Is Heat Rejection

    Infinium’s 1 MW in 250 Sq Ft Shows Waterless AI Cooling’s Real Test Is Heat Rejection

    TL;DR · 30-second read

    The Short Version

    A Sacramento company best known for making clean fuel has launched a cooling system for the computer buildings behind artificial intelligence. Its selling point is that it uses no water.

    Instead of blowing cold air over computers, it sinks them in a liquid that does not conduct electricity. The company says one unit can handle up to a megawatt of computing, roughly the electricity several hundred homes use, in a space about the size of a large bedroom.

    The catch is that the heat still has to go somewhere, and the company has not said how the system copes on a hot day.

    Infinium, a Sacramento clean-fuel company, has unveiled the EdgeSites Vector ONE, a self-contained data center cooling system that uses no water, the Sacramento Business Journal reported on July 27, 2026. The system submerges servers in dielectric fluid, a liquid that does not conduct electricity, rather than cooling them with air conditioning.

    According to the report, Vector ONE supports up to one megawatt of computing power in 150 to 250 square feet, and the company says it allows computing in 70% less space than air-cooled equipment. No pricing, customers, shipment dates or energy-efficiency figures were given.

    Executive Summary

    Infinium is entering data center cooling with an immersion system, a design in which servers sit in tanks of non-conductive liquid that carries heat away from chips far better than air does. The product, sold under the company’s EdgeSites and Infinium Edge names, is pitched on two claims: it uses no water, and it packs up to a megawatt of computing into a footprint the size of a large room.

    Both claims land on live issues in AI infrastructure. AI servers run hotter and denser than conventional servers, pushing air cooling to its limits, and data center water consumption has become a flashpoint with local communities and regulators. A product that addresses density and water at once is well aimed.

    What the announcement does not yet show is the part that decides whether waterless cooling works at scale: how the system rejects a megawatt of heat to the outdoors, how much electricity that takes, and how its capacity holds up in hot weather, which is exactly when evaporative cooling earns its keep.

    A Megawatt in a Large Room Is the Real Headline

    The most concrete number in the announcement is density. One megawatt in 150 to 250 square feet works out to roughly 4,000 to 6,700 watts of computing per square foot of floor. That is far beyond what traditional raised-floor, air-cooled data halls are built to handle, and it is the kind of concentration modern AI hardware demands. Graphics-processor servers used to train and run AI models pack far more power into each rack than the general-purpose servers most facilities were designed around.

    Immersion cooling makes that density possible because liquid absorbs and moves heat far more effectively than air. The claim of 70% less space follows naturally: without the aisles, air handlers and spacing air cooling requires, servers can sit much closer together. For operators short on floor space, or trying to fit AI capacity into an existing building or a small edge site, that is a genuine advantage.

    Waterless Moves the Hard Problem Outdoors

    Immersion solves the problem of getting heat off the chip. It does not make the heat disappear. Every watt a server draws ends up as heat in the fluid, so a fully loaded Vector ONE has to shed up to a megawatt of heat continuously, and it has to do so from a very small footprint. That step, known as heat rejection, is where the water question is actually decided.

    Many large data centers use evaporative cooling because evaporating water is an extremely effective way to dump heat, especially in hot, dry weather. A system that uses no water must reject heat by dry means, typically fans pushing outside air across heat exchangers. Dry systems avoid water consumption entirely, but their capacity depends on outdoor air temperature: the hotter the day, the harder they work and the more electricity their fans draw. Immersion helps here, because the fluid can run warmer than the air in a conventional data hall, which widens the gap between fluid and outdoor air and makes dry rejection more practical. How much it helps is a question of engineering data Infinium has not published.

    This is why the density figure and the water claim are linked. Packing a megawatt into 250 square feet concentrates the heat-rejection burden, and the outdoor equipment, its size, noise, power draw and hot-day rating, becomes the component that determines whether the megawatt figure holds in July in Sacramento or Phoenix. Site developers in water-stressed regions, utilities sizing grid connections, and buyers comparing total energy costs are the people for whom that answer matters most.

    Water Is the Pressure Point, Energy Is the Trade-Off

    Positioning around zero water use is commercially astute. Data center water consumption has drawn scrutiny in drought-prone areas, and a cooling approach that removes water from the conversation can ease permitting and community relations. That value is real even before any efficiency numbers are considered.

    The efficiency claim as reported, that immersion cools servers more efficiently than air conditioning, is a comparison with air cooling, a fairly low bar for liquid systems. The more relevant comparison for AI-scale sites is against water-assisted cooling, where operators trade water for lower electricity use. The industry-standard way to express that trade is power usage effectiveness, or PUE, the ratio of total facility power to computing power, alongside water usage effectiveness. Neither figure accompanied the announcement, so the claim that Vector ONE is both waterless and efficient remains to be demonstrated.

    An Edge Product From an Unexpected Supplier

    Infinium’s core business is electrofuels, synthetic fuels made from renewable electricity, and the company has recently landed an airline fuel deal and begun work on what it calls the world’s largest electrofuel plant. A cooling product is a notable step sideways, though both lines of business sit at the intersection of energy and heavy industrial engineering.

    The EdgeSites and Infinium Edge branding, and the one-megawatt unit size, point toward modular and edge deployments rather than the hundred-megawatt campuses hyperscalers build. That is a sensible niche: edge sites are often in places where water hookups, space and cooling infrastructure are hardest to come by, and a self-contained unit that arrives ready to run fits those constraints. It also means Vector ONE competes with established immersion and modular data center vendors, where track record, server-maker support and service networks weigh heavily in purchasing decisions.

    Background

    Infinium is a Sacramento-based clean-fuel company that develops electrofuels, synthetic fuels produced with renewable electricity. It has announced an airline fuel supply deal and started construction on what it describes as the world’s largest electrofuel plant. The EdgeSites Vector ONE, marketed under its Infinium Edge name, marks its move into data center cooling.

    Immersion cooling is an established technique that has moved from niche uses toward mainstream interest as AI hardware pushes power density beyond what air cooling handles comfortably. At the same time, data center water consumption has become a siting and permitting issue in water-stressed regions, increasing demand for cooling approaches that avoid evaporative water use.

    Sources

    Source: Infinium unveils self-contained data center cooling system that uses no water (Sacramento Business Journal, July 27, 2026), reporting on Infinium’s EdgeSites Vector ONE waterless immersion cooling system.

  • Fort Bliss’s 1,384 Acres Show Military AI’s Hard Limit Is Power, Not Land

    Fort Bliss’s 1,384 Acres Show Military AI’s Hard Limit Is Power, Not Land

    TL;DR · 30-second read

    The Short Version

    The United States military is letting private companies build giant computer centers for artificial intelligence on its bases. One in Texas will cover more than two square miles of Fort Bliss. Another is planned for a Utah testing range.

    The catch is electricity. A local newsroom estimated the Texas site alone could use more power than all 460,000 homes and businesses on El Paso’s power system combined. Members of Congress from both parties now want the Pentagon to explain where that power and water will come from before building starts.

    Lawmakers from both parties are trying to attach conditions to the Defense Department’s program of leasing military land to private companies for AI data centers, Politico reported on July 25, 2026. The Army has signed two conditional agreements: Carlyle Group will build on 1,384 acres at Fort Bliss in El Paso, Texas, by 2027, and CyrusOne will build on 1,201 acres at Utah’s Dugway Proving Ground by 2029, according to an Army fact sheet. The Air Force has offered unused land on five installations.

    Most of the proposals would require briefings, studies or planning rather than stopping construction. A provision from Rep. Ken Calvert (R-Calif.) requiring DOD to brief Congress on community impacts before construction was adopted in the House Defense spending bill, and a provision from Rep. Cory Mills (R-Fla.) barring land deals for data centers with significant components made by U.S. adversaries is in the House-passed NDAA. Broader Democratic amendments, including one from Rep. John Garamendi (D-Calif.), were rejected.

    Executive Summary

    The Pentagon’s plan to host commercial hyperscale data centers on military bases, set in motion by President Trump’s executive order last year directing the department to identify suitable sites, has moved from policy to signed deals. The Army describes its Fort Bliss and Dugway projects as coming at “no upfront cost to taxpayers” and including “comprehensive behind-the-meter power and water solutions”, meaning the sites would generate or source their own electricity and water rather than draw on the local utility.

    That promise is now the center of the congressional debate. The measures gaining traction do not question whether the military can offer land; they ask the Defense Department to show, before construction, how these facilities will be powered and cooled without straining base infrastructure, local grids, or water supplies. For developers, that turns power and water planning from a project detail into a condition of doing business on federal land.

    The House and Senate still have to reconcile their versions of the defense authorization and spending bills, possibly after the November elections, so the final terms remain open.

    Land Is the Part the Military Can Give Away

    The Army’s pitch to developers is built on land: large, secured, already federally controlled parcels offered without an upfront cost to taxpayers. Fort Bliss alone puts 1,384 acres on the table, and Dugway another 1,201. Even supporters in Congress frame the benefit in land terms. Rep. Derrick Van Orden (R-Wis.) said he favors base siting if it means fewer data centers on farmland that could otherwise grow crops.

    But acreage is not what limits an AI data center. The binding constraint is electricity, and then water for cooling. El Paso Matters has estimated that the Fort Bliss facility could require more power than all of El Paso’s 460,000 electricity customers combined. Whether or not that estimate holds at full build-out, it sets the scale: a single campus of this kind can rival a mid-sized city’s demand. No amount of available land changes that arithmetic.

    That is why the Army’s promise of “behind-the-meter” power carries so much weight. Behind-the-meter means generation on the customer’s side of the utility connection, so the data center would not simply pull from the local grid. If it works, the developer absorbs the cost and risk of building a power plant. If it falls short, the load lands on the regional utility and its ratepayers, which is precisely the outcome lawmakers from both parties say they want the Pentagon to rule out in advance.

    Congress Is Asking for Proof, Not Stopping the Build-Out

    The legislative scorecard shows where the consensus sits. Proposals to halt or ban construction failed: Rep. Rashida Tlaib’s (D-Mich.) amendment to prohibit AI data centers on federal lands was rejected, as was a Senate Democratic amendment to block leasing until DOD finalizes a data center strategy. Garamendi’s pre-deal certification requirement, covering energy, water, noise, light, security and supply chains, was voted down in House Armed Services.

    What advanced is disclosure. Calvert’s provision, written into the House Defense spending bill, acknowledges that data centers “place significant strain on energy and water resources” and requires a pre-construction briefing on community impacts. A bipartisan Energy-Water package asks the Department of Energy to mitigate data centers’ effects on grid reliability and ratepayers. Even critics of Garamendi’s approach, such as Rep. Pat Harrigan (R-N.C.), suggested annual impact reports as an alternative. The live disagreement is not whether the Pentagon must account for power and water, but whether that accounting comes before a deal is signed or after.

    DOD officials argue the department has already accounted for these impacts and warn that added requirements could slow the military in a competitive AI race. That concern is legitimate; so is lawmakers’ complaint, voiced by Rep. Mike Levin (D-Calif.), that they have not received a briefing. Both claims could be tested quickly if the department published the power and water figures behind its agreements.

    The Supply Chain Clause May Bite Harder Than the Studies

    The provision with the most direct commercial consequence may be the one from Mills, now in the House-passed NDAA. It would bar DOD from providing land to data centers containing “significant components” manufactured by U.S. adversaries. An unnamed Army official told the Federal News Network that this would create an “unworkable standard” that could drive commercial partners away from Army land.

    Unlike a briefing requirement, a component rule reaches into the equipment bill of materials: servers, networking gear, power and cooling systems. How much it bites depends on how “significant” is defined and enforced, and on whether it applies to behind-the-meter generation equipment as well as computing hardware. For developers weighing base sites against private land, a sourcing rule that is hard to certify could matter more to the economics than any study requirement.

    Who Carries the Risk

    For Carlyle and CyrusOne, the emerging terms shift risk onto the developer: finance the facility, supply its own power and water, and, if Mills’ language survives conference, document the origin of key components. For base commanders, the question is whether on-site generation and water use interact with installation readiness. For neighboring communities such as El Paso, the stakes are grid reliability and electricity bills, the same concern driving the House Energy and Commerce Committee’s Ratepayer Protection Act, H.R. 9340.

    The broader lesson for anyone siting AI infrastructure is that federal land does not bypass the power question. It relocates it, and adds a layer of public accountability that private sites do not face.

    Background

    AI data centers pack large numbers of power-hungry processors into a single site, and their electricity and cooling needs have become a flashpoint for utilities, regulators and communities across the United States. Hyperscale facilities, the largest class of data center built for cloud and AI workloads, can draw power on the scale of a small city and often use significant volumes of water for cooling.

    The Defense Department entered this market after a presidential executive order last year directed it to identify military sites suitable for AI infrastructure. The Army’s model leases base land to private developers, who build and operate the facilities, and the Air Force has offered unused land at five installations. Carlyle Group is an investment firm; CyrusOne is a data center developer and operator.

    Sources

    Source: The Pentagon wants to build data centers. Congress would like a word. (Politico) — bipartisan congressional scrutiny of Defense Department land leases for AI data centers at Fort Bliss, Dugway and Air Force installations.

  • PJM’s 3.1 GW Data Center Dropout Shows AI’s Other Grid Risk: Load That Leaves

    PJM’s 3.1 GW Data Center Dropout Shows AI’s Other Grid Risk: Load That Leaves

    TL;DR · 30-second read

    The Short Version

    When one power line failed near Washington, DC, a wave of data centers (the warehouse-sized buildings full of computers that run the internet and AI) unplugged from the power grid within seconds and switched to their own backup power.

    Together they had been using about 3% of the electricity on a grid that serves 67 million people. Losing that much demand at once threw the system off balance. Lights flickered from Virginia to Chicago, and it took more than 10 minutes to settle.

    It was twice as big as a similar scare in 2024, and data centers keep multiplying. The proposed fix is to make them stay connected through brief hiccups, often by using giant batteries.

    A single power line failure outside Washington, DC, this past week caused a wave of Northern Virginia data centers to switch to backup power at nearly the same moment, TechCrunch reported. According to PJM data, about 3.1 gigawatts of demand disappeared from the PJM Interconnection grid in roughly 30 seconds. After a partial recovery, more facilities dropped off, and the grid’s surplus peaked at 3.49 gigawatts. Stabilizing took another 11 minutes. A line fault like this one normally takes only a few seconds to recover from.

    According to Reuters, the disconnected load was about 3% of PJM’s demand at the time. Sensor data from the startup Ting Labs showed voltage spiking across the grid from Northern Virginia to Chicago. There was no blackout, but lights flickered across the region. The event was roughly twice the size of a 2024 PJM incident in which 60 data centers disconnected at once and removed 1.5 gigawatts.

    Executive Summary

    Most discussion of data centers and the power grid asks whether there is enough electricity to feed them. This event shows the opposite failure: a cluster of very large customers that all leave the grid at once when they sense trouble. Each facility’s protective logic behaved as designed and switched to on-site backup power when voltage dipped. Together, those individually sensible decisions turned a routine line fault into a regional disturbance that lasted more than ten minutes.

    Two remedies are being proposed. The first is coordination. Ali Zain Banatwala, a senior market models specialist at the Independent Electricity System Operator, argues that neighboring loads need a way to disconnect and reconnect one after another rather than all together. The second is ride-through, which means keeping facilities connected through brief disturbances. Ricardo de Azevedo, CTO of ON.Energy, whose company sells campus-scale battery systems built for that purpose, said ERCOT, the Texas grid operator, will require large loads to ride through disruptions.

    The stakes rise with the load. Synapse Energy Economics put data centers at about 6% of PJM’s load in 2024 and projects 24% by 2040. The potential size of a synchronized dropout grows with that share unless the facilities are built to behave differently.

    AI’s Other Grid Problem Is Load That Leaves

    An electric grid has to keep supply and demand in near-perfect balance from moment to moment. When a line goes down, the grid normally absorbs the loss and rebalances within seconds. This time, the voltage dip from the fault reached many data centers sitting close together. Their protection systems are designed to make split-second decisions to safeguard the computers inside, and they moved the facilities onto backup power within a few seconds of each other. The grid lost about 3.1 gigawatts of demand in roughly 30 seconds. Generators were suddenly producing more electricity than customers were using, and voltage jumped.

    The second wave is the detail grid planners should focus on. PJM’s grid appeared to recover partway before more loads dropped off, pushing the surplus to a peak of 3.49 gigawatts. In that pattern, the disturbance triggers disconnections, and the disconnections deepen the disturbance. That feedback is what stretched a seconds-long event into an 11-minute one. A gigawatt is roughly the output of a large power plant. In effect, the grid had to absorb the sudden departure of several power plants’ worth of customers.

    That is why the grid risk from the data center buildout is not only a question of supply. Operators are used to planning around the sudden loss of a large generator. Clustered data centers create a mirror-image contingency: a large block of customers lost all at once because of a single triggering event. Everyone on the grid is exposed to that. Households saw flickering lights, generators must ramp down fast, and the operator must hold capability in reserve for a contingency that grows as data center load grows.

    Concentration Turns a Local Fault Into a Regional Event

    Northern Virginia has the highest concentration of data centers in the world. That density is what made the disconnection synchronized, because one voltage dip reached many facilities almost at once. The effect traveled well beyond Virginia. Ting Labs runs a sensor network through household electrical outlets, and it recorded the voltage spike as far away as Chicago. PJM covers the territory from New Jersey to Illinois, serves 67 million customers, and is the largest grid operator in the United States.

    The trend line gives the event its weight. In 2024, 60 data centers disconnected together and removed 1.5 gigawatts. This time the figure was about 3.1 gigawatts, roughly double. Data centers were about 6% of PJM’s load then, and Synapse Energy Economics projects 24% by 2040. Two events are not a statistical series, and nothing here shows the next one will double again. The arithmetic still matters: if protective behavior stays the same while the share of load it governs roughly quadruples, the ceiling on a synchronized dropout rises with it.

    De Azevedo called the event “the canary in the coal mine” and said incidents involving large loads are “happening more and more.” That claim comes from a vendor of the remedy. The two PJM incidents, with their measured sizes, are the documented reference points.

    Two Fixes: Choreograph the Exit, or Don’t Leave

    The first approach accepts that data centers will disconnect but asks them to do it in order. “We need to figure a way for these loads that are located next to each other to sequentially either disconnect or reconnect,” Banatwala said. Staggered behavior would let grid operators write procedures in advance instead of reacting to a cliff. The hard part is coordination. The facilities belong to different owners, each has settings tuned to protect its own uptime, and they would need shared thresholds or a shared signal.

    The second approach is ride-through, meaning the facility stays connected through brief disturbances. ON.Energy’s version is an uninterruptible power supply, the battery backup that keeps servers running through outages, sized for an entire campus, including chillers and other equipment as well as servers. Batteries and power-conversion equipment sit between the grid and the facility, so the grid sees one steady, predictable load. The company says the system:

    • charges its batteries when the grid has surplus power;
    • discharges to the servers when supply dips;
    • follows the grid within milliseconds;
    • smooths the swings of AI training workloads that ramp power up and down.

    It says it is installing 3 gigawatts of these systems across four campuses.

    Those claims are specific enough to test. The test is whether a campus behind such a system stays connected and grid-friendly during a real fault like this one. The economics matter too. Sizing batteries and power electronics for a whole campus adds capital cost that the developer bears, while most of the benefit goes to the grid. Mandates may be what shifts that calculation, such as ERCOT’s ride-through requirement, which de Azevedo described.

    What Developers and Buyers Should Expect

    From a tenant’s point of view, the July event worked: the data centers switched to backup, and the computers presumably kept running. The cost of that decision fell on the grid and everyone else connected to it. This asymmetry is what makes the event a policy question rather than just an engineering one. Individual facilities have little reason to change behavior that protects their own uptime unless interconnection rules require it.

    For developers, the likely direction is toward grid connection terms that specify how a facility behaves during a disturbance, not just how much power it draws. ERCOT is moving that way, according to de Azevedo. PJM, where both incidents occurred, is where the question is now most visible. For suppliers of storage, power conversion and switchgear, the grid-facing layer of a campus becomes part of the design requirement. For colocation buyers, it is reasonable to ask providers how their facilities respond to voltage dips and whether ride-through requirements could change facility design or pricing.

    Background

    PJM Interconnection is a regional transmission organization, the independent body that runs the high-voltage grid and wholesale electricity market for an area stretching from New Jersey to Illinois. It serves 67 million customers and is the largest grid operator in the United States. Its territory includes Northern Virginia, which has the highest concentration of data centers in the world.

    Data centers are built never to lose power. When grid voltage wavers, they switch to on-site backup, such as uninterruptible power supplies and generators, in a split second. That design protects the computers inside. When many facilities share the same stretch of grid, however, it also means they can react to the same disturbance at the same moment. PJM saw this in 2024, when 60 data centers disconnected simultaneously, and saw it again at twice the scale this July. ERCOT, the operator of most of Texas’s grid, is among the grid managers moving to require large loads to ride through disturbances.

    Sources

    Source: One fallen power line exposed a growing AI data center problem — here’s how to fix it (TechCrunch): a report on the roughly 3-gigawatt data center disconnection on the PJM grid and proposed ride-through fixes.

  • 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.

  • GPU Liquid Cooling Cuts Fans, Not Heat: Ventiva’s 1-2 CFM Bet on the Back of the Rack

    GPU Liquid Cooling Cuts Fans, Not Heat: Ventiva’s 1-2 CFM Bet on the Back of the Rack

    TL;DR · 30-second read

    The Short Version

    Data centers are full of computers that run hot, and fans have long blown that heat away. Fans alone use about 15% of a data center’s electricity, according to one company’s chief executive.

    Newer servers pipe liquid directly over their hottest chips, so many fans can be removed. The catch: smaller parts nearby, like network and memory cards, can then overheat and slow the whole machine down.

    Ventiva makes a tiny, silent air mover with no moving parts that uses electrically charged air to cool those parts. It is still being tested, and laptops get it before data centers do.

    Ventiva, a cooling technology company founded about 15 years ago as Thorn Micro Technologies, is qualifying a fanless “ionic air mover” for use inside data center servers. CEO Carl Schlachte told Facilities Dive that the device is aimed at a specific problem created by direct-to-plate liquid cooling. When operators remove bulk fans after moving processors onto liquid-cooled plates, lower-value components such as network interface cards (NICs), boot-optimized storage solution (BOSS) cards and memory modules (DIMMs) can overheat and throttle server performance.

    According to Schlachte, fans consume about 15% of the energy flowing into data centers, and the component needs only one or two cubic feet per minute (CFM) of airflow to cool those parts. Ventiva is qualifying the device with server manufacturers for the rest of 2026. First shipments go into laptops and mini PCs next year, and data center deployments follow.

    Executive Summary

    The announcement is less about a new way to cool a data center and more about a gap in an existing one. Direct-to-plate liquid cooling runs coolant through metal plates mounted on the hottest chips, the CPUs and GPUs, and it lets operators strip out many of the bulk fans that used to push air through a server. Removing those fans saves energy. It also removes the incidental airflow that kept everything else in the chassis cool. Ventiva is selling a product for that residual heat.

    This matters because liquid cooling adoption is being driven by dense GPU servers, and every liquid-cooled server still contains air-cooled parts. If those parts throttle, operators lose some of the performance they paid for. Ventiva’s pitch is that a solid-state air mover built into the card can recover it without bringing the fans back.

    The 15% figure attached to the story needs careful reading. It describes fans’ share of data center energy as Schlachte characterised it. It is not a measured saving from Ventiva’s device, which targets component temperature and performance rather than facility power.

    Liquid Cooling Moves the Heat Problem Rather Than Ending It

    A cold plate cools only the chip it touches. In a conventional air-cooled server, bulk fans pull a large volume of air front to back, and that stream cools the processors as well as everything downstream of them: network cards, boot drives, memory modules and power components. When direct-to-plate cooling takes over the processors, operators can reduce or remove those fans. The processors are fine. The components that were riding along in the airflow lose their cooling.

    This is the mechanism behind the headline. Schlachte described servers that were “throttling due to overheating at the back”, meaning the chips automatically slowed down to protect themselves. He said the fix does not require much air: one or two CFM moving between DIMMs is enough for things to “start to get better almost immediately.” For comparison, a single conventional server fan is designed to move far more air than that. The operational point is that a liquid-cooled server needs very little airflow for its residual components, but it still needs some, and it needs that airflow in the right place. A device small enough to sit on the card itself could supply it.

    The people affected are server manufacturers designing liquid-cooled platforms and operators retrofitting air-cooled halls with direct-to-plate systems. In both cases, the residual air-cooled load is a design problem. It does not disappear when the coolant loop goes in.

    Reading the 15% Correctly

    Coverage of the launch attached a 15% energy efficiency figure to the device. As Schlachte described it, 15% is the approximate share of data center energy consumed by fans. That figure is the reason operators want to remove fans, and removing them is a benefit of liquid cooling. Ventiva’s product does not produce that saving. At most, it makes the saving easier to keep by solving the thermal side effect.

    The company’s own performance claims are softer and not yet quantified in public. Schlachte said upgraded servers would run “some double-digit percentage better” and suggested operators would get “15% more uptime.” Neither figure came with a test configuration, workload or baseline. Uptime is normally measured as the share of time a system is available, so a 15% improvement implies a starting point that few production data centers would accept. The claim may mean fewer throttling events or fewer thermal faults, but it has not been defined. These are plausible directions of benefit, but they are not substantiated results, and buyers should treat them as hypotheses until qualification data is published.

    An OEM Channel, Not a Facilities Purchase

    Ventiva’s route to data centers runs through server manufacturers. Schlachte described OEMs approaching facilities managers to propose an off-cycle upgrade: technicians would swap existing BOSS cards, NICs or DIMMs for versions with the air mover built in. He framed this as “no hit to you in terms of bottom line.” That framing leaves open who absorbs the cost of the new cards and the technician time.

    This channel has advantages. The unit of adoption is a card, not a cooling plant, so an operator can trial it in a handful of servers without facility engineering work. It also depends on OEMs qualifying and warrantying the parts, which is why Ventiva is spending the rest of 2026 on qualification and thermal simulation. The sequencing reflects that dependency: laptops and mini PCs first next year, and data centers after that.

    Old Physics, New Operating Conditions

    The underlying principle, electrohydrodynamic flow, is well established. A charged wire creates a small plasma field that ionises nitrogen and oxygen molecules in the air. The ions drift toward a negatively charged collector and drag neutral air along with them, producing a breeze with no blades or motor. Schlachte noted that people have observed the effect for about 300 years. Removing moving parts removes a common failure point, and small size allows placement directly on a component.

    The engineering question is whether that principle holds up for years inside a server, in a hall with its own dust, humidity and maintenance regimes. Ventiva has said it has cards running and simulations under way. It has not yet published the reliability, efficiency and service-life data that operators and OEMs use to decide whether a new component belongs in production racks.

    Background

    Data centers have traditionally been cooled with air: room-level systems chill the air, and fans inside each server pull it across the components. As processors, especially the GPUs used for AI workloads, have grown denser and hotter, operators have adopted direct-to-plate liquid cooling, which carries heat away from the hottest chips through coolant-filled plates. That shift lets operators reduce the number of bulk fans, which Ventiva’s CEO says account for roughly 15% of data center energy use.

    Ventiva, originally Thorn Micro Technologies, has spent about 15 years developing solid-state air movers based on electrohydrodynamic flow, the airflow produced when charged ions travel between a wire and a collector. The company is positioning the technology first in compact consumer devices and then in servers. There, it targets components that are not on the liquid cooling loop but still need a small, steady supply of air.

    Sources

    Source: Ventiva touts fanless cooler for 15% data center energy efficiency gains — Facilities Dive’s interview with Ventiva CEO Carl Schlachte on the company’s ionic air mover for liquid-cooled servers.

  • Supermicro’s 120kW Rear Doors Shift the AI Retrofit Limit From Airflow to Heat Rejection

    Supermicro’s 120kW Rear Doors Shift the AI Retrofit Limit From Airflow to Heat Rejection

    TL;DR · 30-second read

    The Short Version

    Computer chips that run artificial intelligence throw off enormous heat. Supermicro, a major server maker, now sells ten models of a water-cooled door that bolts onto the back of a server cabinet and soaks up that heat before it reaches the room.

    The largest door handles about as much heat as 80 household space heaters running at once. That lets older buildings, designed around fans and air conditioning, host far hotter equipment without major rebuilding.

    The catch: the heat still has to leave the building, and Supermicro has not fully explained how.

    Supermicro announced in a July 15, 2026 press release that it has expanded its rear door heat exchanger (RDHx) portfolio to ten models, with cooling capacities from 10kW to 120kW per door and up to 240kW at the rack level. The doors are part of its Data Center Building Block Solutions (DCBBS) program, which packages servers, racks, power, cooling, management software and deployment services into validated, pre-integrated systems.

    Supermicro says the doors mount to standard EIA, ORv3 and MGX racks, can serve as a primary liquid cooling method or be paired with its direct-to-chip cold plates, and can be installed in new or existing data centers without major facility modifications or dedicated facility chilled water.

    Executive Summary

    Supermicro is broadening the least invasive rung of its liquid cooling ladder. A rear door heat exchanger replaces the back door of a server cabinet with a liquid-filled coil and fans; hot exhaust passes through the coil and leaves the cabinet cooled. The new lineup spans ten models from 10kW to 120kW per door, which lets operators size the door to anything from a single dense server stack to a rack-scale AI system.

    The pitch is aimed squarely at the large installed base of air-cooled data halls that were never designed for AI hardware. If a door can capture heat at the cabinet, an operator can place a high-density GPU rack in an older room without plumbing liquid to every server or rebuilding the hall’s airflow.

    That solves one constraint and exposes others. A door that captures 120kW of heat still has to hand that heat to something that carries it outdoors, and a rack that needs 120kW of cooling needs roughly that much power delivered to it. For operators, the doors move the retrofit question from the room to the building’s heat rejection plant and electrical distribution.

    A Door That Turns an Air-Cooled Hall Into a Liquid-Assisted One

    Traditional data halls cool servers the way an office cools people: chilled air is pushed into the room, servers draw it through their chassis, and hot exhaust is returned to air handlers. That works until the heat in a single cabinet becomes so concentrated that the room cannot move enough air to carry it away. GPU servers built for AI training and inference routinely reach that point, which is why the industry has been moving toward liquid cooling.

    A rear door heat exchanger is the gentlest version of that move. Instead of routing coolant into each server, which is what direct-to-chip cold plates do, it intercepts the exhaust at the back of the cabinet. The servers stay air-cooled internally; the room simply never sees most of their heat. Supermicro says its doors fit standard EIA racks (the long-established 19-inch format), ORv3 (the Open Compute Project’s Open Rack version 3) and MGX (Nvidia’s modular server reference architecture), which covers most of what operators already have on the floor or are buying for AI.

    The ten-model range is the practical news. A 10kW door and a 120kW door solve different problems, and offering both lets an operator match cooling to the rack rather than overbuilding. Supermicro’s stated features include intelligent fan control, N+1 redundancy (one more fan or component than strictly needed, so a single failure does not stop cooling) and anti-condensation protection, which matters because a cold coil in a humid room can drip onto equipment.

    The Retrofit Limit Moves From Airflow to Heat Rejection

    A rear door changes where heat is captured, not how much heat exists. Nearly every watt a server draws ends up as heat, so a door absorbing 120kW is handing 120kW of thermal energy to its coolant, and at Supermicro’s stated rack-level maximum of 240kW, twice that. In an air-cooled hall, the traditional ceiling on rack density is airflow: how much cold air the room can deliver and how much hot exhaust it can return before it recirculates into server intakes. By neutralizing exhaust at the cabinet, a rear door largely removes that ceiling.

    What remains is the plant behind the door. Captured heat has to reach the outdoors through chillers, dry coolers or cooling towers, and a building’s heat rejection capacity was sized for the hall it was originally designed to be. Supermicro says the doors deploy without the need for dedicated facility chilled water or extra hardware, but it has not said what the coil connects to instead. If the heat is dumped back into the room by a liquid-to-air unit, the existing air conditioning absorbs it and the old limit returns in another form. If the doors tie into an existing water loop, that loop’s spare capacity becomes the constraint.

    The same logic applies to electricity. A rack that needs 120kW of cooling is drawing roughly that much power, and older halls were generally not built to deliver that at every position. Supermicro’s option of DC-powered doors fed from rack busbars simplifies powering the door itself, not the upstream feed to the rack. For colocation providers and enterprises weighing an AI retrofit, the door shifts the practical question from whether a rack can be cooled to how many such racks the building’s cooling plant and switchgear can actually support.

    Rear Doors and Cold Plates Are Complements, Not Rivals

    Supermicro positions the doors both as a standalone option and as a companion to its direct-to-chip (D2C) liquid cooling. That pairing reflects how dense AI systems are actually cooled. Cold plates mounted on processors and GPUs remove the heat from the hottest chips directly into liquid, but memory, power supplies, storage and networking gear in the same rack still shed heat into the air. A rear door can capture that residual heat so the room does not have to.

    For operators, the hybrid approach offers a staged path: start with doors on air-cooled GPU servers in an existing hall, then add cold-plate systems as liquid distribution is built out, with the doors continuing to handle what the plates miss. Supermicro has not published how it splits heat between the two methods in a combined deployment, which is the figure facility engineers would need to size the loops.

    Bundling Is the Strategy; the Economics Are Still Claims

    The broader move is DCBBS itself. Supermicro wants to sell not just servers but the rack, power, cooling, management software and installation as one validated package, and says that approach reduces integration risk, lowers total cost of ownership and shortens time-to-online. Real-time monitoring of temperature, pressure, flow rate and pump status through Redfish (an industry standard interface for managing hardware), SNMP (a long-standing network monitoring protocol) and Supermicro’s SuperCloud Composer software is meant to fold cooling into the same management plane as the servers.

    Single-vendor integration has genuine appeal when AI hardware lead times are tight and every week a facility sits unfinished is lost revenue. The trade-off buyers weigh is flexibility: a tightly validated stack is simpler to deploy but ties more of the facility’s design to one supplier’s roadmap. Supermicro’s cost and speed benefits are stated rather than quantified, so buyers will need site-specific figures before treating them as planning assumptions.

    Background

    Super Micro Computer (NASDAQ: SMCI) is a San Jose, California server and storage maker that designs and manufactures its systems in the United States, Taiwan and the Netherlands. It has become one of the principal suppliers of GPU servers for AI workloads and, through its Data Center Building Block Solutions program, increasingly sells complete rack-scale and data center-level systems rather than individual machines.

    Liquid cooling has moved from niche to mainstream as AI hardware has pushed per-rack power far beyond what traditional air-cooled halls were built to handle. Operators generally choose among rear door heat exchangers, direct-to-chip cold plates and immersion cooling, often combining methods. Rear doors are typically the least disruptive to install, which makes them a common first step for existing facilities.

    Sources

    Source: Supermicro Expands End-to-End DCBBS Liquid Cooling Portfolio with Rear Door Heat Exchangers for High-Density AI and HPC Infrastructure, Supermicro’s July 15, 2026 announcement of a ten-model rear door heat exchanger line rated 10kW to 120kW per door.

  • Crusoe and Lancium Plan 1.0 GW AI Data Center in Childress, Texas

    Crusoe and Lancium Plan 1.0 GW AI Data Center in Childress, Texas

    Crusoe and Lancium announced plans for a 1.0 gigawatt (GW) artificial-intelligence data center campus in Childress, Texas, a small city in the state’s panhandle region served by the ERCOT power grid.

    The joint announcement, dated July 14, 2026, positions the site as a hyperscale-class AI compute campus, though the release itself provides only a headline-level description of the project.

    Executive Summary

    The Crusoe-Lancium announcement adds another gigawatt-scale AI campus to a Texas pipeline that has become the epicenter of North American data center growth. A 1.0 GW site is roughly the electrical footprint of a mid-sized city, and building one for AI training and inference workloads reflects the scale at which frontier model operators and their infrastructure partners are now planning.

    The pairing is notable on its own terms. Crusoe operates AI cloud infrastructure and has historically emphasized co-locating compute with abundant or otherwise stranded energy. Lancium specializes in “controllable load” data center designs intended to flex consumption in response to grid conditions. Together, the two companies are marketing a Childress campus that, at least conceptually, blends AI-optimized halls with a grid-friendly load profile.

    What the announcement does not resolve is arguably more important than what it discloses: capital structure, anchor tenants, interconnection queue position, water use, and construction phasing are all absent from the public headline.

    Why Childress, and Why Now

    Childress sits in the Texas panhandle, a region rich in wind generation and, increasingly, solar — but historically light on data center load. Developers have been pushing west and north out of the traditional Dallas-Fort Worth and Austin corridors in search of two things: available transmission capacity and land at prices that pencil for gigawatt campuses. A 1.0 GW footprint is difficult to interconnect anywhere on ERCOT quickly, but the panhandle’s generation surplus and long-distance transmission lines make it a plausible venue for large loads that can tolerate some siting distance from major metros.

    The timing tracks with a broader industry pattern. Hyperscale AI announcements in 2025 and 2026 have shifted from megawatt-scale expansions to gigawatt-scale campuses, reflecting both the power density of modern AI accelerators and the strategic value of securing capacity years ahead of demand.

    Controllable Load Meets AI Compute

    Lancium’s core pitch has been that data centers can be designed as “controllable load resources” — facilities that ramp consumption up or down to help balance a renewables-heavy grid, in exchange for lower effective power costs and faster interconnection. Historically, that model has been an easier fit for cryptocurrency mining than for latency-sensitive cloud workloads. Applying it to AI compute is more nuanced: training runs are batch-like and can, in principle, tolerate curtailment windows, while inference is closer to real-time and typically cannot.

    Neither company has publicly detailed how the Childress campus will split those workload types, or how curtailment obligations would flow through to tenants. That is a material question. If the campus behaves like a conventional 24/7 hyperscale load, the interconnection story is one thing; if it genuinely flexes, it is a different — and potentially more grid-constructive — proposition.

    Winners, Losers, and What Is Actually Substantiated

    The announcement, as issued, substantiates two things: that Crusoe and Lancium have publicly committed to the project’s existence and its nameplate scale, and that Childress has been chosen as the location. It does not substantiate a construction start date, a power-on date, an anchor customer, a capital partner, or a specific mix of on-site versus grid-supplied generation. Readers should treat 1.0 GW as a stated design intent, not a delivered capacity.

    If the project proceeds as announced, the near-term beneficiaries are the local tax base, regional construction trades, and equipment vendors ranging from switchgear manufacturers to liquid-cooling suppliers. Longer term, incumbent Texas colocation operators face increased competition for transmission upgrades and skilled labor. Ratepayers and grid operators face a familiar set of questions about who pays for interconnection upgrades and how quickly load can be absorbed without stressing reliability margins.

    Background

    Crusoe began as an operator known for using otherwise-flared natural gas to power computing, and has since repositioned around AI cloud infrastructure and large-scale training campuses. Lancium, founded in Texas, has focused on designing data centers as flexible grid participants — an approach shaped by the state’s high share of variable renewable generation and its independent grid operator, ERCOT.

    The broader context is a multi-year surge in AI compute demand that has pushed data center announcements from tens of megawatts to hundreds and now over a thousand. Texas, and the panhandle in particular, has emerged as a preferred venue because of transmission-connected wind and solar surpluses, available land, and comparatively fast large-load interconnection processes.

    Source: Crusoe and Lancium Announce 1.0 Gigawatt AI Data Center Campus in Childress, Texas — joint corporate announcement of a planned hyperscale AI campus in the Texas panhandle.

  • New York Enacts First Statewide Hyperscale Data Center Moratorium

    New York Enacts First Statewide Hyperscale Data Center Moratorium

    On July 14, 2026, New York Governor Kathy Hochul announced what her office describes as the first statewide moratorium on new hyperscale data centers, pausing approvals for the largest class of AI and cloud campuses across the state.

    The announcement, made through the Governor’s official channels, frames the action as a siting policy intervention rather than a permanent ban, though the source material does not detail duration, thresholds, or exemptions.

    Executive Summary

    New York has become the first U.S. state to impose a statewide freeze specifically targeting hyperscale data centers — the campus-scale facilities, typically hundreds of megawatts and up, that host the workloads of the largest cloud and AI companies. Coming from the governor of a top-five state economy with meaningful grid, tax, and permitting leverage, the move sets a precedent other states will study closely.

    Why it matters: hyperscale siting has become the single most contested piece of digital infrastructure policy in the United States, colliding with electricity availability, water use, ratepayer equity, noise, and local land use. A statewide pause reframes what has been a patchwork of town-hall fights into a top-down policy question — and shifts near-term development attention toward states with clearer rules of the road.

    What we do not yet know from the release is nearly as important as what we do: the megawatt threshold that triggers the moratorium, its duration, whether projects already in queue are grandfathered, and what standards a lifted moratorium would impose. Until those details land, both celebration and alarm are premature.

    Why New York, and Why Now

    Hyperscale data centers — single campuses that can draw as much electricity as a mid-sized city — have moved from a niche real-estate category to a first-order infrastructure story in roughly three years, driven by generative AI training and inference demand. States that welcomed them early, notably Virginia, Texas, and Georgia, are now confronting transmission constraints, rising residential power bills, and organized community opposition. New York, which combines a constrained downstate grid with abundant upstate land and hydro, is a natural next frontier — and a natural place for a policy pause. A statewide moratorium, if that is what this ultimately is, is a signal that the state wants to define the terms of entry before, not after, a build-out.

    Precedent-Setting, but the Details Will Decide Everything

    The label “first statewide moratorium” is doing a lot of work in this announcement, and the substantive impact depends on parameters the release does not specify. A moratorium that applies only to facilities above, say, 500 MW and lasts six months while a siting framework is drafted is very different from an open-ended pause on anything over 50 MW. Similarly, whether the freeze covers utility interconnection queues, state environmental review, or only certain incentive programs will determine whether developers see this as a speed bump or a redirect. Reasonable observers on all sides should press for those specifics before drawing conclusions.

    Winners, Losers, and Second-Order Effects

    In the short run, incumbent New York operators with facilities already energized gain scarcity value; hyperscale tenants with existing leases become harder to displace. Developers holding land but not yet permits face the most uncertainty. Neighboring states with power headroom — parts of Pennsylvania, Ohio, and the Midwest — may see accelerated inbound interest, though transmission and gas-turbine lead times cap how quickly they can absorb it. Utilities, ratepayer advocates, and organized labor each have legitimate but different stakes in how a successor framework is written, and it would be a mistake to treat any one of those constituencies as speaking for “the community.”

    The Harder Question: What Comes After the Pause

    Moratoriums are easier to announce than to lift. The productive version of this policy ends with a clear standard: megawatt-tiered review, transparent grid-impact studies, water and noise limits, community-benefit expectations, and predictable timelines. The unproductive version leaves developers guessing and simply exports the load — and its emissions — across a state line. Both outcomes are on the table, and the release does not yet tell us which the administration is aiming for.

    Background

    New York has long been a major digital-infrastructure market, anchored by dense fiber and financial-services demand in the New York City metro and by cheaper power and land upstate. As artificial intelligence has driven a step-change in data center power requirements, states across the country have wrestled with how to review projects that can each request hundreds of megawatts of grid capacity — loads that historically took years or decades of organic growth to accumulate.

    Governor Kathy Hochul, in office since 2021, has repeatedly emphasized both climate targets under New York’s Climate Leadership and Community Protection Act and the state’s ambitions in advanced industries. A statewide moratorium on hyperscale siting sits squarely at the intersection of those two agendas, and it lands in a national environment where data center policy has moved from a specialist concern to a mainstream one.

    Source: First Statewide Moratorium on New Hyperscale Data Centers Launched by Governor Kathy Hochul — Official announcement from the Office of New York Governor Kathy Hochul, July 14, 2026.

  • 3M and Microsoft Partner on AI Data Center Materials

    3M and Microsoft Partner on AI Data Center Materials

    On July 14, 2026, 3M and Microsoft announced a strategic partnership focused on advancing AI data center infrastructure and enterprise transformation. The announcement was carried on Microsoft’s own newsroom (Microsoft Source).

    The headline positions the collaboration around AI-era infrastructure — a domain where 3M has historically supplied materials, adhesives, films and thermal management products, and where Microsoft is one of the world’s largest hyperscale operators.

    Executive Summary

    The release frames a tie-up between an industrial materials incumbent and a hyperscale cloud operator at a moment when AI compute is straining the physical envelope of data centers. Power density per rack, heat rejection, and materials that can survive higher junction and coolant temperatures have all become gating factors for GPU deployments.

    What is substantiated in the headline is intent: a strategic partnership, AI data center infrastructure as the target, and enterprise transformation as a secondary theme. What is not yet substantiated — at least in the excerpt available to us — is scope: which 3M product lines, which Microsoft facilities, on what timeline, and under what commercial structure.

    For readers evaluating the announcement, the useful posture is neither dismissal nor hype. Materials science is a genuine bottleneck for AI infrastructure, and 3M has relevant portfolios. Whether this specific partnership delivers meaningful capacity or is primarily a marketing framing will depend on details the release, as published, does not spell out.

    Why Materials Suddenly Matter to Hyperscalers

    For most of the cloud era, hyperscale data centers were an integration problem: racks of commodity servers, air cooling, and steady incremental efficiency gains. AI training and inference clusters have changed the physics. Modern GPU accelerators dissipate hundreds to over a thousand watts each, and racks are moving from the 10–20 kW range typical of general-purpose cloud toward 50–100 kW and beyond. At those densities, the materials in contact with silicon — thermal interface materials, dielectric fluids for immersion cooling, cold-plate seals, and vapor-barrier films — become first-order engineering constraints rather than commodity inputs.

    3M’s historical relevance here is real: the company has long supplied fluorinated dielectric fluids used in two-phase immersion cooling, thermal interface products, and specialty films and tapes used inside servers and networking gear. Microsoft, for its part, has publicly experimented with immersion cooling in prior years. A partnership badged as targeting AI data center infrastructure sits squarely in this well-established technical overlap, even if the announcement itself does not enumerate specific product families.

    What a Strategic Partnership Actually Buys

    "Strategic partnership" is one of the more elastic phrases in corporate communications. In practice, such arrangements range from joint marketing and preferred-supplier status at the light end, to co-development agreements, capacity reservations, and equity or offtake commitments at the heavy end. The release headline as available does not disclose where on that spectrum this deal sits.

    For 3M, a formal alignment with a top-three hyperscaler is commercially valuable regardless of the exact contract structure: it validates its materials portfolio for AI workloads at a moment when the company has been repositioning after divesting parts of its business and navigating environmental litigation around per- and polyfluoroalkyl substances (PFAS). For Microsoft, tying a materials supplier more closely into its infrastructure roadmap is consistent with a broader hyperscaler trend of pushing further down the stack — into custom silicon, custom racks, and now, plausibly, custom materials specifications.

    Enterprise Transformation: The Ambiguous Second Leg

    The headline also references enterprise transformation, a phrase that in Microsoft’s usage typically implies Azure adoption, Microsoft 365, and Copilot-branded AI products. Read literally, it suggests 3M is also a customer — modernizing its own IT and manufacturing operations on Microsoft’s stack — not only a supplier.

    Two-way arrangements of this kind are common in hyperscaler deal-making: the supplier commits materials or capacity, and in return standardizes on the buyer’s cloud and AI platforms. Whether that reciprocity is present here, and on what scale, is not stated in the available excerpt. Buyers and investors should treat the enterprise-transformation framing as a signal to look for future disclosures around Azure commitments or Copilot deployments at 3M.

    Risks and Open Questions on Both Sides

    Any materials-heavy AI infrastructure story now runs into the PFAS question. Several of the dielectric and thermal fluids historically associated with immersion cooling belong to fluorochemical families that are under increasing regulatory scrutiny in the United States and European Union. 3M has publicly stated it intends to exit PFAS manufacturing by the end of 2025. A partnership announced in mid-2026 targeting AI infrastructure therefore raises a legitimate, non-inflammatory question: what chemistries are in scope, and how does the roadmap reconcile with that exit commitment? The release excerpt does not answer this.

    On Microsoft’s side, the risk is narrative. Hyperscalers have announced many AI-era infrastructure partnerships in the past two years — with utilities, nuclear developers, chipmakers, and cooling specialists. Each individually is plausible; collectively, they can create an impression of capacity certainty that specific contracts may not yet support. The measured read is that this announcement adds one more supplier relationship to that mosaic, and its weight will be visible only when product-level or facility-level detail follows.

    Background

    3M is a diversified U.S. industrial company whose materials science portfolio has long included products used inside data centers — thermal interface materials, films, adhesives, filtration and, historically, dielectric fluids associated with immersion cooling. The company has been repositioning in recent years, including a stated intent to exit PFAS manufacturing by the end of 2025 amid regulatory and litigation pressure.

    Microsoft is among the top three hyperscale cloud operators globally and has publicly committed to a large multi-year build-out to support AI training and inference workloads. That build-out has surfaced physical constraints — power, cooling, and materials — that were secondary concerns in the pre-AI cloud era, prompting a wave of supplier and infrastructure partnerships across the industry.

    Source: 3M and Microsoft announce strategic partnership to advance AI data center infrastructure and enterprise transformation — Microsoft Source, July 14, 2026.

  • New York Pauses New Hyperscale Data Centers Over 50 MW

    New York Pauses New Hyperscale Data Centers Over 50 MW

    New York has become the first U.S. state to pause new hyperscale data center approvals above a 50-megawatt (MW) threshold, according to a July 13, 2026 report from Inside Climate News. The action targets the largest facilities — the class typically used for cloud and AI training workloads — rather than smaller enterprise or edge sites.

    The reporting frames the move as a state-level response to rapid growth in data center power demand. The underlying article is the sole dated source available to us; specifics on scope, duration, exemptions, and enforcement are not restated here beyond what the headline confirms.

    Executive Summary

    A hyperscale data center is a very large facility — commonly tens to hundreds of megawatts of IT load — operated by or for cloud and AI providers. A 50 MW site can draw roughly the power of a small city. New York’s decision to pause approvals above that line puts a hard ceiling on the class of build that has driven most of the industry’s recent capacity growth.

    The significance is less about one state’s queue and more about precedent. Utilities across the country are absorbing multi-gigawatt interconnection requests, and several governors and public service commissions are actively rewriting siting, tariff, and interconnection rules. If New York’s approach holds up politically and legally, other states facing similar grid stress may borrow the template.

    For operators, hyperscalers, and their real estate partners, the immediate question is routing: whether projects earmarked for New York shift to neighboring PJM and New England markets, to the Midwest, or to the Southeast — each of which has its own transmission and permitting constraints.

    Why 50 Megawatts, and Why Now

    Fifty megawatts is a meaningful line. It is well above a typical enterprise data hall and squarely in the range where a single customer campus starts to look like a large industrial load to a utility. Regulators drawing the line there are, in effect, saying that facilities of this size deserve a different review than a warehouse or office park — even if the underlying zoning treats them alike. The threshold also captures the vast majority of AI training and cloud region builds announced over the last two years, which is presumably the point.

    The timing tracks with a broader shift. Grid operators from ERCOT to PJM have published sharply revised load forecasts driven by data center interconnection queues, and several utilities have asked commissions to rewrite the rules for how large new loads are studied, priced, and prioritized against existing customers. A statewide pause is a blunter instrument than tariff reform, but it buys time to design the finer tools.

    Winners, Losers, and the Map of AI Capacity

    In the near term, the clearest beneficiaries are markets that can credibly offer power, land, water, and a permitting path in the next 18 to 36 months. That short list currently includes parts of Virginia (despite its own constraints), Ohio, Indiana, Georgia, Texas, and a handful of Midwestern and Mountain West locations with generation headroom. Operators who already control land and interconnection queue positions in those regions gain optionality; those who were counting on New York capacity face a re-plan.

    The losers are more nuanced. New York loses some tax base, construction spend, and long-term operations jobs, but keeps grid capacity for other uses — including electrification of heat and transport, which the state has committed to under its climate law. Hyperscalers lose a latency-advantaged East Coast site option, though metro New York’s colocation footprint for latency-sensitive workloads is largely unaffected because those buildings are typically well under 50 MW.

    The Precedent Risk for the Industry

    The industry’s stated position for years has been that data centers are good grid citizens: predictable loads, willing to pay for infrastructure, and increasingly matched with clean generation. New York’s pause is a signal that at least one state is not persuaded that the current pace can be absorbed without displacing other public priorities. Whether that view spreads depends on how the pause is structured — a narrow, time-boxed study period reads very differently from an open-ended moratorium — and on how the industry responds.

    There is a real opportunity here for operators willing to negotiate: bring-your-own-generation deals, firm demand response commitments, waste-heat reuse, and transparent water reporting are all on the table in other jurisdictions and could shape what a post-pause approval regime in New York looks like. The alternative — treating the pause as a political problem to be waited out — invites more states to adopt similar caps before the industry has a seat at the design table.

    Background

    Data centers are the physical buildings that house the servers, storage, and networking equipment behind cloud services, streaming, enterprise software, and — most recently — generative AI. Hyperscale facilities are the largest tier, built by or for a small group of very large operators, and they have grown from tens to hundreds of megawatts per campus over the last decade. Their power draw has become large enough to reshape utility planning in several U.S. regions.

    New York has among the most ambitious state climate mandates in the country, with statutory targets for electrification and emissions reduction. The state also hosts the NYISO grid, dense metro loads, and a mix of nuclear, hydro, gas, and growing renewable generation. Reconciling large new industrial loads with those commitments is the policy backdrop for the reported pause.

    Source: New York Becomes First State in the Nation to Pause New Hyperscale Data Centers — Inside Climate News reporting on a statewide pause of new hyperscale data center approvals above 50 megawatts, published July 13, 2026.