TL;DR · 30-second read

The Short Version

A company linked to businessman Kevin O’Leary wants to build a huge computer campus in northern Utah, with fifteen gas power plants of its own on site.

The surprise is the water. The company says it needs about two gallons to make the electricity a typical home uses in a month. It also says the Great Salt Lake will get more water than it historically has.

One scientist says today’s best power plants are nowhere close to that. Utah regulators will now take public objections.

KSL reported that O’Leary Digital Utah DC Sub I, LLC, associated with businessman Kevin O’Leary, filed an application on Oct. 15, 2026 with the Utah Division of Water Rights to permanently change water use from irrigation to supply the proposed Stratos Project in Box Elder County.

The nearly 200-page filing describes 11 clusters of data center buildings, 7.5 gigawatts of on-site gas generation arranged as fifteen 500-megawatt blocks, and 4,850 operating employees. The application will be publicly advertised for two weeks, followed by a 20-day protest period.

Executive Summary

The Stratos Project application offers the most detailed public view so far of a very large proposed data center campus. It includes a water mass balance report by Beairsto & Associates and a hydrologic and depletion analysis by Loughlin Water Associates. The developers argue that the project would use less water than the land’s historical agricultural use and that more water would reach the Great Salt Lake from Salt Wells Spring Stream.

The most contested number is about two gallons of make-up water per megawatt-hour of electricity, the figure given by O’Leary Digital CEO Paul Palandjian. Make-up water is the fresh supply added to replace what a cooling system loses. Ben Abbott, an ecologist and executive director of the nonprofit Grow the Flow, says the proposed ratio would be 10 to 100 times better than today’s top-performing facilities if reciprocating engines are used, and has no precedent if combined-cycle turbines are used.

The dispute matters beyond one Utah campus. On-site generation can make cooling design central to whether a project is viable, because the power plant’s water needs become part of the data center’s own permit case.

Two Gallons Per Megawatt-Hour Carries the Water Case

The project’s water argument depends on one engineering claim. Exhibit B describes a “closed recycle loop with 90% recovery and 10% freshwater makeup,” and Palandjian translates that into about two gallons per megawatt-hour. A megawatt-hour is one megawatt of power delivered for an hour, and it is the standard unit for measuring electricity output.

Simple arithmetic shows the scale. If all 7,500 megawatts ran around the clock at that rate, the power plant would need about 15,000 gallons of make-up water an hour. That equals 360,000 gallons a day, or roughly 131 million gallons a year. If make-up is 10% of the flow, about 150,000 gallons an hour would circulate through the loop. These are our calculations from the developer’s stated figures, and they assume full output at all times, so they are an upper bound for the power loop alone at that rate.

The headline claim holds only if that rate is achievable at this scale. State reviewers, water users, Great Salt Lake advocates and any future tenants all have a stake in the answer. The report’s campus description also lists food service, showers, locker facilities and 154 acres of landscaping, so the two-gallon figure should not be read as a measure of the entire campus’s water use.

Why the Choice of Generator Changes the Argument

The plant has been described in two ways. One account of the documents refers to fifteen 500-megawatt combined-cycle natural gas plants, while the Exhibit B passage specifies a “reciprocating-engine generation facility.” The difference is material. A combined-cycle plant burns gas in a turbine and then uses the exhaust heat to make steam for a second turbine, and that steam must be condensed. Reciprocating engines are large piston engines with no equivalent steam cycle in their basic form, and their heat is typically removed through a coolant circuit.

Abbott’s critique reflects that split, calling the ratio far better than current top performers for engines and “not an example anywhere on the planet” for combined-cycle turbines. Palandjian replies that Abbott “is comparing us to plants that cool by evaporating water, and we do not.” Each position has a fair test. If the design does not evaporate water, comparisons with evaporative plants tell only part of the story. The developer’s strongest answer would then be operating data from non-evaporative plants of comparable size.

Moving away from evaporation also shifts the burden to other parts of the design. Heat still has to go somewhere, usually into the air through large radiators and fans, which can consume power and lose effectiveness on hot days. The 90% recovery figure raises a related question: where does the 10% that leaves the loop go? Those details will decide whether the two-gallon figure works as a year-round design number.

The Great Salt Lake Claim Is a Comparison With Farming

The application’s strongest language concerns the lake: “there can be no question that more water will reach the Great Salt Lake from Salt Wells Spring Stream than has reached it historically.” That is a claim about a baseline. The comparison is between the water historically consumed by irrigation and the water the project would consume in its place. This is why the filing includes a depletion analysis, which estimates how much water is actually used up and does not return to the system.

Abbott’s stated objection focuses mainly on whether the power output is achievable with so little water, which is a separate question from the farming baseline. The irrigation history could prove sound while the cooling design falls short, or the reverse. The developers’ conclusion needs both to hold, along with actual operations that match the plan over the life of the campus. The protest period is where those assumptions can be challenged formally.

On-Site Power Moves the Siting Question to Water

Building generation beside the data center changes which local resources a project leans on hardest. At this scale, a developer that makes its own electricity also takes on the water, fuel and heat-removal questions a distant power plant would otherwise carry. Stratos shows that dynamic clearly, though one Utah application is not evidence of an industry-wide pattern.

Developers considering similar campuses should note how quickly a cooling specification became the center of public debate. The other disclosed figures are more settled: 11 clusters at 400 people each plus 15 generation blocks at 30 each adds up to the stated 4,850 operating jobs. Palandjian also describes a footprint of about 2,500 acres inside a campus of roughly 10,000 acres, with the remaining 7,500 preserved. The two-gallon figure remains the number that needs the most proof.

Background

Data centers are buildings full of servers that run cloud services and other computing, and nearly all the electricity they use ends up as heat that must be removed. Large campuses therefore have to plan for power and cooling together, and those decisions can shape local water demand when evaporative systems are involved.

In Utah, changing how an existing water right is used requires an application to the Division of Water Rights. The Stratos filing covers Hansel Valley and Salt Wells Flat in Box Elder County, in a region where effects on the Great Salt Lake are a central public concern. O’Leary Digital was co-founded by Kevin O’Leary and Paul Palandjian, who serves as its chief executive officer.

Sources

Source: Stratos Project water application reveals new details on massive data center, KSL’s report on the water permit application for the proposed Box Elder County campus.

TL;DR · 30-second read

The Short Version

Oracle, the business-software giant, is building huge computer warehouses for artificial intelligence faster than the gas pipelines meant to power them can be finished. So it is filling trucks with natural gas and driving the fuel to the sites instead.

It works, but the gas costs roughly four times its normal price. It also does not go far. Running even a small slice of one planned site in New Mexico would need a fresh truckload about every 40 minutes, day and night.

The lesson is that building artificial intelligence now depends on pipes, permits and trucks, not just computer chips.

Bloomberg reported that Oracle has been trucking compressed natural gas (CNG) directly to several data center sites so that on-site generators can run before permanent gas pipelines are ready. According to people familiar with the work, truck deliveries kept a data center outside Salt Lake City moving for more than a year while Oracle waited for a pipeline. The same approach is supporting initial work at a campus Oracle is building for OpenAI in Shackelford County, Texas. Oracle is now considering it for Project Jupiter in New Mexico, where a delayed pipeline threatens the timeline of a site with an ultimate footprint of 2.45 gigawatts.

Certarus, a unit of Superior Plus, supplied the Utah deliveries, and VoltaGrid supplies Texas. Oracle publicly thanked VoltaGrid for “finding cost-effective power solutions to ensure our customers can access OCI capacity on schedule,” referring to Oracle Cloud Infrastructure. Last month Oracle sent the New Mexico project’s developer a force majeure notice. Oracle shares closed down 5.5% at $135.69, their steepest one-day drop since July 16.

Executive Summary

The announcement-free headline here is a supply chain story. Many large AI data centers have turned to on-site gas-fired power plants because grid connections take too long. Those plants still need fuel, and fuel normally arrives by pipeline, which can take months to design and build and can be held up by regulators. Oracle is filling that gap with “virtual pipelines”: fleets of trucks hauling compressed gas from a pipeline tap to the site, where it is decompressed and burned in generators.

The approach buys time, but it has limits. Jack Weixel of East Daley Analytics puts the delivered cost at roughly four times the price of gas at a major pipeline hub once labor, specialized equipment and truck fuel are included. Ellie Holbrook of SemiAnalysis estimates that if Project Jupiter ran just 100 megawatts on trucked gas, which is about 4% of its planned capacity, each large trailer would supply only about 40 minutes of electricity. Trucked gas can energize the first phases of a campus. The date the whole campus comes online is still set by the pipeline.

For the industry, the episode adds a new item to the list of things that can delay a data center. Developers already track turbines, transformers and interconnection queues. Gas logistics now belongs on that list too, along with the regulators who approve pipeline routes.

The Trailer Math Sets the Ceiling

Holbrook’s figures are the clearest way to see how far a virtual pipeline can stretch. At a 100-megawatt load, one large CNG trailer lasts about 40 minutes. A day has 1,440 minutes, so running that load around the clock means roughly 36 trailer loads every day, or one arrival every 40 minutes with no gaps for weather, traffic or a missed fill. Each load is a full cycle. Gas is drawn from a pipeline, compressed into the trailer, driven to the site (sometimes for several hours), then decompressed into the generators. A trip that long ties up several trailers on the road for every one unloading, so the fleet behind a steady stream of arrivals is much larger than the arrival count.

That 100 megawatts is only about 4% of Project Jupiter’s planned 2.45 gigawatts. As a rough illustration, if fuel demand scaled in a straight line with load, the full footprint would need close to 900 trailer loads a day. No one has proposed doing that, and the arithmetic shows why. Trucked gas is a bridge sized for early phases, such as commissioning, initial server halls and construction power. It cannot replace a pipeline at campus scale.

The consequence for people planning AI capacity is straightforward. A virtual pipeline can move the date a site first produces revenue. It does not move the date the site reaches full capacity. That second date still depends on Energy Transfer’s rerouted pipeline, which has slipped from this summer to next year.

Fuel Logistics Joins Turbines and Grid Queues on the Critical Path

On-site gas generation became popular because it let developers skip the years-long wait to connect to the electric grid. The Oracle sites show that the workaround brings its own bottleneck. A gas plant without a gas pipeline is idle equipment, and pipelines face many of the same constraints as power lines. In New Mexico, state regulators rejected Energy Transfer’s proposed route, forcing a reroute that pushed back the in-service date. The approval delay simply moved from the power line to the pipe.

This has practical effects on how sites are chosen and planned. When fuel may need to arrive by road for a year or more, as it did in Utah, factors that rarely appeared in data center site studies start to matter. These include the distance to a pipeline tap with available compression, road access able to handle constant heavy-truck traffic, and on-site space for decompression equipment and trailer staging. Developers comparing sites need to weigh the logistics of the interim fuel supply alongside land, water and fiber.

The suppliers stand to benefit. Until now, CNG trucking has mostly served remote mining and oil drilling operations far from the grid. Certarus and VoltaGrid are now marketing to data center builders as a transitional power source. Certarus said longer timelines for pipeline and grid connections “have created a growing need for rapidly deployable and scalable energy solutions.” Whether these services can scale is a separate question. The number of trucks, compressors and trailers available, along with the drivers to move them, sets a physical limit on how many large campuses can rely on trucked gas at the same time.

Paying Four Times for Speed

Oracle described VoltaGrid’s service as “cost-effective,” while East Daley’s estimate puts trucked gas at about four times hub prices. The two claims can both hold, because they measure different things. Per unit of fuel, trucked CNG is expensive. Compared with leaving finished data halls empty, missing delivery dates for customers such as OpenAI, or facing contractual exposure, a costly fuel bill can still be the cheaper choice. Oracle has not disclosed the numbers that would show which effect is larger.

Those numbers matter because of Oracle’s financial position. Its free cash flow is negative and is expected to stay that way until more of its AI data centers are completed, so each month a site comes online earlier brings revenue forward. The force majeure notice points to the same pressure from another direction. A force majeure clause excuses a party from certain obligations when events beyond its control occur, and Oracle’s notice may shield it from some payments to the New Mexico developer if delays continue. The trucks and the notice are two responses to the same pipeline delay. One keeps work moving, and the other limits the cost if work stalls.

The open question for investors and customers is where the extra cost lands. It could reduce Oracle’s margins in the early phases, be passed through to the tenant, or be absorbed through the developer agreement. None of these arrangements has been made public, and the answer will determine whether trucked gas is a minor bridging expense or a significant cost of speed.

Background

Oracle, long known for database and business software, has become a major builder of AI data centers through Oracle Cloud Infrastructure (OCI). It has committed billions of dollars and its reputation to delivering capacity quickly for customers including OpenAI, and its free cash flow is expected to remain negative until more of those facilities are complete. Its projects include a campus for OpenAI in Shackelford County, Texas, a data center near Salt Lake City, and Project Jupiter in New Mexico.

Power has become one of the main constraints on the AI data center buildout. Many large developments now generate their own electricity with on-site natural gas plants rather than waiting for grid connections. That approach depends on gas pipelines, which take months to design and build and can be delayed by regulators. Compressed natural gas trucking companies such as Certarus and VoltaGrid, which previously served mainly remote mining and drilling operations, are now offering their services to data center developers as a temporary fuel supply.

Sources

Source: Oracle Moves Gas by Trucks to Avoid Data Center Power Delays (SupplyChainBrain, via Bloomberg) on Oracle’s use of trucked compressed natural gas to keep data center projects in Utah, Texas and New Mexico on schedule.

TL;DR · 30-second read

The Short Version

Artificial intelligence data centers, the warehouse-sized buildings packed with computers, are running short of electricity. Many now build their own power plants on site.

Those generators give off a lot of leftover heat. Johnson Controls, a large maker of building cooling equipment, has published a design guide for machines that turn that heat into cooling, instead of powering the cooling with electricity.

The company says this can free up to 97 megawatts, enough electricity for tens of thousands of homes, to run more computers.

Johnson Controls on July 29, 2026 introduced an Absorption Chiller Reference Design Guide aimed at AI data centers that generate their own electricity on site. According to the company’s announcement, the design can unlock up to 97MW of additional AI capacity. It does this by using waste heat from on-site generation to drive the facility’s cooling, which frees electricity that conventional electric chillers would otherwise consume.

The guide is separate from the AI factory cooling reference design that Johnson Controls published earlier.

Executive Summary

A data center’s power supply is split between the computers themselves and everything that keeps them running, and cooling is one of the largest parts of that second share. Johnson Controls’ new guide targets that split. Absorption chillers produce chilled water using heat as their main energy input rather than an electric compressor. At a site that already burns fuel to make its own electricity, the exhaust and jacket heat from that generation can supply the chillers, so the electricity the cooling plant would have drawn can go to servers instead.

The headline figure is an upper bound: up to 97MW. Johnson Controls has not tied that number publicly to a specific site size, generator type or climate. The strategic point stands even so. As AI developers move generation behind the meter to avoid long grid-connection waits, the heat those plants produce becomes an asset to design around, not a byproduct to vent. By publishing a dedicated guide for that scenario, separate from its broader AI factory cooling design, Johnson Controls is treating on-site power as its own design category.

Why Waste Heat Becomes Compute Capacity

A power-constrained AI campus has a fixed electrical budget, and every megawatt spent on cooling is a megawatt not spent on GPUs. Conventional data center cooling relies on vapor-compression chillers, the same basic principle as a household refrigerator, which run large electric compressors. Absorption chillers work differently. They use a heat source, such as hot water, steam or engine exhaust, to drive a refrigeration cycle, and they need only modest electricity for pumps and controls.

Every fuel-burning generator, whether a gas turbine, a reciprocating engine or some fuel cell types, turns only part of its fuel into electricity. The rest leaves as heat. If that heat drives absorption chillers, much of the cooling load moves off the electrical budget and onto a thermal budget that was otherwise going to waste. The electricity released can power IT load. That is the mechanism behind Johnson Controls’ claim of up to 97MW of additional AI capacity. The pairing of generation with heat-driven cooling, long known as combined cooling, heat and power or trigeneration, is not new. What is new is framing it as a way to add AI compute capacity rather than as a way to save energy.

The people most affected are developers building campuses with behind-the-meter generation, and the investors financing them. For those projects, capacity is often capped by how much power the site can produce, not by floor space or chip supply. Recovering cooling power from waste heat raises the ceiling on revenue-generating compute without adding generators.

On-Site Power Changes the Cooling Question

Grid interconnection, the process of getting a utility to connect a large new load, has become one of the slowest steps in AI data center development. That has pushed developers toward generating their own electricity, at least as a bridge until grid power arrives. Once a site makes its own power, its cooling design no longer has to assume that all energy arrives as electricity. Heat is available too, and a reference design that shows how to use it addresses a question these projects now face.

This also explains why Johnson Controls kept the absorption guide separate from its earlier AI factory cooling reference design. A grid-fed facility and a self-powered facility start from different energy inputs, so a single template would fit one of them poorly. Splitting the guidance by power strategy lets engineers start from the case that matches their site.

What “Up To” Leaves Open

The benefit depends on several variables. It scales with how much recoverable heat the generation plant produces and at what temperature. It also scales with how much mechanical cooling the facility needs to begin with. Many new AI halls use direct liquid cooling with relatively warm water, which can reduce how many hours electric chillers must run. In those designs, the electricity an absorption system saves may be smaller than in a facility that relies heavily on chillers.

There are physical trade-offs too. Absorption chillers are typically larger and heavier than electric chillers of similar cooling output. They also reject more total heat, which usually means bigger cooling towers and possibly more water use. The cooling supply becomes tied to generator operation, so designers must plan for backup cooling when generators are down for maintenance. None of this undermines the concept. It does mean the up-to-97MW figure is a best case, and actual gains will vary from site to site.

Reference Designs as a Sales Channel

For an equipment maker, a reference design is both engineering guidance and a way to get specified early. Developers under schedule pressure tend to adopt pre-engineered architectures instead of designing mechanical plants from scratch, and the vendor behind the template benefits. Johnson Controls, which sells chillers under its York brand, is positioning itself at the point where cooling decisions become inseparable from power strategy. That link matters more as on-site generation spreads across AI projects.

Background

Johnson Controls is one of the largest suppliers of commercial heating, ventilation and air conditioning equipment. Its York-brand chillers are widely used in large buildings and data centers. As AI workloads have driven sharp increases in rack power density, cooling vendors have released AI-specific reference architectures. Johnson Controls had already published an AI factory cooling reference design before this absorption chiller guide.

Absorption cooling is a mature technology that has long been paired with on-site generation at universities, hospitals and industrial plants. Its relevance to data centers has grown as AI developers, facing slow grid connections, increasingly build their own generation and look for ways to get more compute out of every megawatt they produce.

Sources

Source: Johnson Controls introduces Absorption Chiller Reference Design Guide, unlocking up to 97MW of additional AI capacity from on-site power generation. Johnson Controls’ announcement of a reference design that uses on-site generation waste heat to cool AI data centers.

TL;DR · 30-second read

The Short Version

The US military is inviting private companies to build giant computer warehouses for artificial intelligence on at least 12 of its bases, from Texas to Alaska. Two projects, each estimated at about $2 billion, have been tentatively awarded.

The military pays nothing up front. In exchange for the land, it gets to use the computers. The catch is that each project must make its own electricity instead of plugging into local power lines, and must not drain local water. In effect, the builders are being asked to build their own power plant too.

Some proposed sites sit half a mile from family homes, and Congress is still arguing over the rules.

Military.com reported on July 27, 2026 that the Army and the Department of the Air Force have opened at least a dozen military installations to commercial data center developers over the past year and a half. Only two deals have been awarded, and both are conditional. In late March the Army selected Carlyle for roughly 1,384 acres at Fort Bliss, Texas. It selected CyrusOne, a data center operator jointly held by funds managed by KKR and BlackRock, for a roughly 1,201-acre parcel at Dugway Proving Ground, Utah. The Financial Times estimated each project at about $2 billion.

The other sites are still solicitations. The Army is considering Fort Hood, Texas, and Fort Bragg, North Carolina. The Air Force has sought bids at Arnold, Edwards, Joint Base McGuire-Dix-Lakehurst, Davis-Monthan and Robins, and more recently at Joint Base Elmendorf-Richardson, Eielson Air Force Base and Clear Space Force Station in Alaska. Proposals must include net-zero water usage and a power plan that does not draw on the local electrical grid. Initial operating capability is projected for fiscal 2027 at Fort Bliss and fiscal 2029 at Dugway.

Executive Summary

The Pentagon is turning spare base land into a currency for AI computing. Under the Army’s Enhanced Use Lease program, private developers finance, build, operate and eventually decommission hyperscale data centers on military land. Hyperscale refers to the warehouse-sized campuses built for the largest cloud and AI workloads. In return the service gets access to computing power, at what the Army calls “no upfront cost to taxpayers.” Army Secretary Dan Driscoll has described Fort Bliss as “the first hyper-scale data center that the Pentagon has ever done.”

The terms matter more than the land. Unlike commercial projects off base, these bids must bring a power plan that does not rely on the local grid and must achieve net-zero water use. For developers, that changes the job from building a data center to building a data center plus its own utility. For base commanders and the surrounding communities, siting and power have become part of the procurement itself.

The program is also politically unsettled. A House defense bill provision would bar leases unless developers exclude certain components from China, Russia, Iran and North Korea, and the Army has objected that the rule would penalize building on federal land.

The Land Is Free. The Power Plant Is Not.

In the commercial market, the scarcest input for a large AI campus is usually a grid connection, not land. A grid connection is the utility’s commitment to deliver large, steady amounts of electricity, and it often comes only after a long wait in an interconnection queue, the line of projects waiting for approval to plug in. The Army and Air Force solicitations rule that path out. Proposals must include a power plan that does not draw on the local electrical grid, a requirement that does not apply to identical projects built off base.

That changes what is actually being procured. The Army’s side of the bargain, land in exchange for computing access, is accurate in cash terms, but it moves the heaviest capital item onto the bidder. Whoever builds at Fort Bliss or Dugway is committing to finance and run generation as well as the data halls. The Army’s long-term vision, as described by Col. John Oliver, is a campus with a commercial side, a classified military data side and onsite power generation. The roughly $2 billion estimate for each project has not been broken down publicly, so nobody outside the negotiations can yet say how much of it is power plant and how much is computing.

The rule is likely to favor sponsors with deep capital and power-development experience, such as a global investment firm like Carlyle or a KKR- and BlackRock-backed operator like CyrusOne, over developers whose model depends on buying utility power. It also changes the utility relationship. El Paso Electric attended the Army’s spring listening session at Fort Bliss, and the Army says excess power generated on base could potentially be sold back to civilian grids. A would-be large customer could become a supplier instead. For residents, the rule speaks directly to the concern behind a Virginia state-funded study, which found monthly household energy bills there could rise by $14 to $37 by 2040 as data centers grow.

Water Is the Second Utility the Bidder Must Bring

Data centers use water to cool their servers, and the net-zero water requirement is hardest to meet at the Army’s flagship site. Defense Department procurement documents reported by Data Center Dynamics rate the water risk for the El Paso area as “Extremely High.” One option under discussion is having Carlyle drill a new well to feed El Paso’s desalination plant and offset what the data center uses. Oliver was candid that this is unproven: “We don’t know if that’s an engineering solution that we can actually get to yet.”

Water and power are linked. Cooling designs that use less water generally use more electricity. In a desert location, a strict water target therefore adds to the size of the on-site generation the developer must build and pay for. The two requirements compound rather than sit side by side.

A Dozen Sites, Two Awards, Very Different Neighbors

The sites vary widely. Dugway, where the military conducts its primary chemical and biological weapons testing, offers three parcels totaling about 3,466 acres, roughly an hour’s drive from the nearest community. Fort Hood has 207 acres on offer, and Fort Bliss has nearly seven times that. Contracting documents show a Fort Hood parcel within a half-mile of residential and commercial property, and several Fort Bragg locations within a mile of civilian areas and a half-mile of civilian housing. Smaller parcels close to homes leave less room for generation, and less buffer for its noise, than a remote range does.

Opposition near homes is not a hypothetical risk. Data Center Watch counts nearly $156 billion in projects nationwide delayed or canceled after local opposition. The Army has built community engagement into the bid: developers must describe outreach plans and assess “any risks or opposition.” Brookings senior fellow Darrell M. West said communities that accept these facilities tend to be the ones told the details up front, including energy source, water use, cost and noise.

Congress Is Still Writing the Rules

Rep. Cory Mills secured a provision in the House fiscal 2027 defense authorization bill barring data center leases unless developers avoid certain printed circuit boards, advanced semiconductors and chipsets made in China, Russia, Iran or North Korea. The Army objected that it would create a “federal land penalty,” imposing rules on installations that would not apply to identical projects anywhere else. Rep. John Garamendi’s proposal to require evaluation of energy, water, noise, light and security effects before any deal was rejected by House Armed Services Republicans. A Senate amendment to pause leasing until the Pentagon finalizes a data center strategy was also defeated.

Each condition adds to the same balance sheet. Free land has to pay for off-grid generation, net-zero water and possibly a restricted supply chain, and the Army’s own argument shows it knows the margin is not unlimited. Whether the economics still work at scale is the question the two conditional awards will answer first.

Background

AI workloads have pushed demand for hyperscale data centers faster than utilities can connect them, and local opposition has become a real constraint, with Data Center Watch counting nearly $156 billion in projects delayed or canceled. The Pentagon holds large tracts of underused land at remote and secure installations, and a Trump executive order directed agencies to open non-excess federal land to data center development. The Army’s Enhanced Use Lease program, with the U.S. Army Corps of Engineers handling lease negotiations and environmental review, is the vehicle.

Carlyle is a global investment firm. CyrusOne is a data center operator jointly held by funds managed by KKR and BlackRock. Dugway Proving Ground in Utah is where the U.S. military conducts its primary chemical and biological weapons testing, and Fort Bliss in El Paso is home to the Army’s 1st Armored Division.

Sources

Source: Pentagon Plans AI Data Centers at Military Bases Across Multiple Branches (Military.com), on Army and Air Force plans to lease base land to commercial AI data center developers.

TL;DR · 30-second read

The Short Version

The United States government is opening some of its own land to companies that want to build the huge computer warehouses that run artificial intelligence. One of the first chosen sites is in South Carolina, at a lab that supports the nation’s nuclear weapons work.

The twist: these projects are meant to come with their own power plants. The biggest artificial intelligence facilities can need as much electricity as a large power plant produces, and building power on-site is meant to keep that cost off ordinary households’ bills.

Engineering firm Amentum was picked to build it. No price, size or start date has been announced.

Microgrid Knowledge reported on July 20, 2026 that the National Nuclear Security Administration (NNSA), part of the U.S. Department of Energy (DOE), has selected Virginia-based engineering firm Amentum to develop a co-located artificial intelligence data center and on-site power generation project at the Savannah River National Laboratory site in South Carolina. The selection followed a competitive request for proposals that DOE began last year, after identifying 16 federal sites it judged could host AI data centers paired with their own energy infrastructure.

The effort, formally the Savannah River Site Artificial Intelligence Infrastructure and Energy Generation Project, has no announced timeline, generating technology, capacity or cost. DOE says the project could meet its own electricity needs without shifting large costs to utility customers and could also increase the power available to the grid.

Executive Summary

NNSA has named Amentum to lead construction of both an AI data center and the power plant that would feed it, on federal land at one of the country’s principal nuclear security sites. It is a concrete step in DOE’s program to put AI computing on government property, and the first selection at Savannah River under that program.

The significance lies less in the land than in the pairing. DOE did not simply offer acreage; it screened its sites for their ability to host data centers together with co-located generation, and it justified the Savannah River project on the grounds that it would not push large grid-upgrade costs onto utility customers. That makes federal sites a potential route around the slowest and most politically sensitive part of AI buildout: getting enough electricity to the building.

What has been announced is a selection and a scope, not a financed, sized project. The generating technology, megawatt capacity, tenant, financing and schedule are all still undisclosed, so the model’s claims about cost and grid benefits remain untested.

Sixteen Sites, One Condition: Bring the Power

DOE’s list of 16 federal sites was not a catalogue of empty lots. According to the department, it identified sites that could support AI data centers paired with co-located energy infrastructure. The Savannah River award makes that pairing explicit: generation is in the project’s official name, and Amentum is contracted to build the power supply alongside the data center rather than to plug into whatever the local utility can provide.

The reason is the grid connection itself. A large new electricity user normally joins the grid through an interconnection process, in which the utility studies whether its lines and power plants can serve the new load and what must be built if they cannot. At AI scale, those upgrades are substantial: industry projections cited in the coverage put future AI server racks at up to 1 megawatt each and the largest AI campuses at around 1 gigawatt, roughly the output of a large power plant. When a utility builds for a customer that size, the question of who pays tends to fall on its other customers. DOE’s stated case for Savannah River answers that directly, saying the project could generate its own needs “without shifting huge costs to utility customers.”

That is where federal land earns its place. A large, secured, government-controlled site offers space, a single landowner and an existing security perimeter for putting generation right next to the load, which many sites closer to population centers struggle to offer. The trade-off is engineering: AI training creates wide, fast swings in power demand, and a self-supplied site has less grid backing to absorb them. Whatever generation Amentum ultimately builds will need to be designed around that volatility, which makes the power system as central to the project as the data center it serves.

Why a Nuclear Security Site, and Why Amentum

NNSA Administrator Brandon Williams framed the award as a way to “move faster, apply innovative technologies, and make productive use of federal land while maintaining our commitment to mission delivery.” That last clause matters. Savannah River hosts sensitive defense work, and any private-sector partner operating there has to coexist with it. Amentum already does; it was selected earlier this year to provide design and engineering services for the site’s Tritium Finishing Facility, which supports the nuclear weapons program.

That existing footprint is a competitive advantage that is hard to replicate. Contractors that already understand federal security, procurement and site operations are positioned to win the first wave of these awards, while conventional data center developers may find their route onto federal land runs through partnerships with such firms. Amentum and NNSA say they will collaborate on expanding private-sector partnerships in the project, which leaves room for tenants, financiers or generation owners still to be named.

What Is Settled, and What Still Rests on “Could”

The announcement establishes a selection made through a competitive process, a defined scope covering both compute and generation, and a named site. Those are real commitments of intent from a federal agency. The headline benefits are conditional, however: the project could meet its own electricity needs, and it could increase power available to the grid. Williams himself called it a “proposed partnership.”

Until the parties disclose the generation type, capacity and commercial structure, the ratepayer-protection claim cannot be measured. Its value depends on details not yet public: whether the site stays connected to the grid for backup, who pays for that connection, and on what terms any surplus power is sold. The first projects under DOE’s program will set the template for the remaining sites, so those terms will matter well beyond South Carolina.

Background

The Savannah River Site in South Carolina is a Department of Energy complex overseen by the National Nuclear Security Administration, the DOE agency responsible for nuclear weapons and nuclear security. Its national laboratory supports defense programs including tritium production. Amentum, a Virginia-based firm spun out of AECOM about six years ago, ranks as the second-largest U.S. government services contractor behind Leidos and was already working at the site on the Tritium Finishing Facility.

The award sits within a wider federal push to use government land for AI infrastructure. DOE identified 16 of its sites as candidates for data centers paired with their own power, and Energy Secretary Chris Wright has described the AI race as “the next Manhattan project.” The pressure behind that push is electricity: an analysis by the Penn State Institute of Energy and the Environment projects AI’s share of global electricity use could rise from 4% to 20% by 2035.

Sources

Source: Amentum to Develop Co-Located AI Data Center and Power Infrastructure at Savannah River National Laboratory (Microgrid Knowledge, July 20, 2026): NNSA selects Amentum to build an AI data center with on-site power generation at the Savannah River Site.

TL;DR · 30-second read

The Short Version

The federal government’s main pollution regulator said on June 10 that it will not write national environmental rules for the giant computer warehouses that run artificial intelligence. States and local towns will decide instead.

That does not mean fewer fights. Some of these sites burn natural gas on their own property to get electricity quickly. In April, civil rights and environmental groups sued the company xAI, saying it ran 57 gas-fired generators near Memphis without the pollution permits the law requires. How that case ends will decide who gets to challenge these projects in court.

US Environmental Protection Agency (EPA) Administrator Lee Zeldin said at the Politico Energy Summit on June 10, 2026, that the agency will not pursue any nationwide environmental requirements or recommendations aimed at the AI industry, Crowell & Moring reported. Zeldin acknowledged that technologies exist to reduce data centers’ water consumption and air pollution, but said deciding the right practices for each facility is a matter for states and local communities, not EPA.

The announcement follows a year of EPA actions easing permitting for power generation and data centers, and it lands amid active litigation: an April 14 Clean Air Act citizen suit by the NAACP, the Southern Environmental Law Center and Earthjustice alleging that xAI and its subsidiary MZX Tech operated 57 unpermitted portable methane gas turbines to power the Colossus 2 data center in South Memphis, and a June 16 Department of Justice motion to intervene and have that suit dismissed.

Executive Summary

EPA has taken itself out of the business of writing AI-specific environmental rules. There will be no national standard for data center water use, air emissions or cooling practices, and no federal list of recommended best practices. That removes a layer of regulation developers had reason to expect, but it does not suspend the Clean Air Act or the Clean Water Act, and it does not change the fact that most day-to-day permitting decisions sit with state and local agencies.

The practical effect is to relocate risk rather than remove it. The part of an AI campus most exposed is on-site combustion — gas turbines and backup engines that make electricity on the property — because that equipment is what triggers air permits. With no federal template, those permits will be judged against differing state and local standards, and enforcement increasingly runs through private lawsuits, as the xAI case shows.

Whether that private enforcement route stays open is now itself in question. The Department of Justice has argued that private groups cannot pursue Clean Air Act suits over the federal government’s objection. The outcome of that argument will shape the legal risk attached to fast-tracked on-site power across the industry.

Stepping Back Is Not the Same as Fewer Permits

Zeldin’s June 10 statement rules out new federal requirements and recommendations aimed at AI. It does not rewrite existing law. Data centers that burn fuel on site still fall under the Clean Air Act, and those that discharge water still fall under the Clean Water Act. New Source Review (NSR) — the Clean Air Act program requiring a permit before a large new pollution source is built — still applies. Until EPA finalizes the rule it proposed in May 2026, a major NSR permit is still required before any construction begins.

Read in sequence, EPA’s recent actions share a pattern. In May 2025 it issued guidance allowing certain emergency engines to run up to 50 hours a year in non-emergency conditions to support grid reliability for data centers. In September 2025 it gave developers flexibility to start non-emissions-related building work before obtaining a Clean Air Act permit. In May 2026 it proposed redefining when construction legally ‘begins’ so non-emitting structures can go up earlier. Each step lets the concrete and steel move faster. None of them exempts the equipment that actually emits.

Why 57 Turbines Mark Where the Risk Now Sits

The server halls of an AI campus do not need an air permit; the generators feeding them can. That is the mechanism behind the title’s claim. When grid connections lag behind construction schedules, some developers bridge the gap with on-site gas generation, and that generation is precisely where Clean Air Act obligations bite. The citizen suit filed April 14 alleges that xAI and MZX Tech ran 57 portable methane gas turbines without required air permits to power Colossus 2 — a fleet the complaint treats as the data center’s power supply, not an ancillary backup. The allegations have not been proven in court, and the plaintiffs seek substantial penalties and cite health risks for families in South Memphis and North Mississippi.

With EPA declining to set a national standard, the questions that decide cases like this — whether equipment crosses major source thresholds (the emissions levels above which a facility needs the most demanding permits), what controls are required, how long ‘temporary’ generation may run — are answered by state air agencies and local boards, and tested by private plaintiffs in federal court. A citizen suit is a lawsuit a private party can bring to enforce a federal environmental law after giving 60 days’ notice. The people most affected are developers using on-site generation to beat grid timelines, the lenders and customers relying on those schedules, and the communities living next to the equipment.

The Justice Department’s Intervention Is the Swing Variable

On June 16, the Department of Justice moved to intervene in the xAI case and have it dismissed. Its filing argues that the suit threatens national security by seeking to cut power to AI infrastructure that supports the Department of War’s operations, and, more broadly, that the executive branch holds exclusive authority to enforce federal law, so private parties cannot pursue Clean Air Act citizen suits over the government’s objection.

If a court accepted that theory, it would sharply narrow a private right of action Congress wrote into the statute and raise separation-of-powers questions, since the executive branch would effectively decide which cases reach a judge. Crowell & Moring’s attorneys consider it unlikely the court will accept the argument. Either way, the ruling matters well beyond Memphis: it will tell developers whether federal non-enforcement actually lowers their exposure, or whether private enforcement fills the space EPA has left.

A Patchwork Rewards Early Paperwork

Without a national baseline, requirements will diverge by state and locality, and a multi-state developer will face different rules for the same turbine or cooling design. That favors operators who secure permits before breaking ground, audit whether their generation crosses major source thresholds, and treat compliance with federal law as protection against both citizen suits and a future administration that enforces more aggressively.

Zeldin’s own acknowledgment that pollution- and water-reducing technologies exist points to a hedge: voluntary adoption of measures such as closed-loop cooling, which recirculates water rather than evaporating it, may reduce the odds of state action and local opposition. The competitive edge in this environment belongs less to whoever builds fastest than to whoever can show a regulator, a neighbor or a judge that the power plant behind the data center was permitted from day one.

Background

The US Environmental Protection Agency sets national rules under laws such as the Clean Air Act and Clean Water Act, but state and local agencies issue and enforce many of the permits that apply to individual facilities. The rapid growth of AI data centers — and the electricity they need — has drawn activity from Congress, the executive branch, federal agencies and the courts, without yet producing a cohesive regulatory framework.

Since May 2025, EPA has issued a string of actions aimed at speeding data center and power plant construction, and in January 2026 it held a roundtable with the Data Center Coalition, the industry’s trade association. xAI’s Colossus 2 in South Memphis has become the most visible test of how environmental law applies to AI campuses that generate their own power on site.

Sources

Source: EPA Steps Back from AI Data Center Regulation: What Developers Must Know Now – Crowell & Moring LLP — EPA’s June 10, 2026 decision not to set national environmental standards for AI data centers, and the Clean Air Act litigation over xAI’s Colossus 2 turbines.