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Category: Cooling Infrastructure

  • Johnson Controls’ Up-to-97MW Guide Shows On-Site Power’s Waste Heat Is AI Capacity

    Johnson Controls’ Up-to-97MW Guide Shows On-Site Power’s Waste Heat Is AI Capacity

    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.

  • 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.

  • LG’s 600kW Nvidia-Certified CDU Shows Why AI Cooling Is a Vetted-Vendor Market

    LG’s 600kW Nvidia-Certified CDU Shows Why AI Cooling Is a Vetted-Vendor Market

    TL;DR · 30-second read

    The Short Version

    LG, best known for televisions and fridges, just won approval from Nvidia, the company whose chips run most artificial intelligence systems, to supply equipment that keeps those chips cool.

    One cabinet of Nvidia’s newest chips uses roughly as much electricity as a hundred homes. That makes it far too hot for fans, so liquid has to be pumped right up against the chips.

    LG is the first Korean company approved for this. The approval matters because buyers rarely risk a leak next to millions of dollars of hardware.

    Startup Fortune reported that LG Electronics’ 600kW-class coolant distribution unit (CDU) has passed Nvidia’s final quality certification. That makes LG the first Korean company approved in this category of liquid cooling equipment for Nvidia-based AI server racks. LG announced the approval in late July 2026.

    LG says it will next seek certification for 1MW, 2.5MW and 4MW CDU models. It sells the unit as part of a ‘Chip to Chiller’ package spanning server-level cold plates through building-scale chillers.

    Executive Summary

    A coolant distribution unit is the pump-and-heat-exchanger that moves liquid between a data center’s cooling plant and the cold plates mounted on GPUs and CPUs. LG’s 600kW unit is now on Nvidia’s approved list. LG says it holds coolant temperature within ±0.25°C, runs redundant pumps and power supplies, and includes virtual sensors and leak detection.

    The approval matters because of rack density. Nvidia’s DGX GB200 NVL72 system draws about 120kW per rack, roughly four times the level at which air cooling alone stops working. At that density, liquid cooling is mandatory. It also sits millimetres from very expensive silicon, so buyers screen suppliers before any price is discussed.

    For LG, the certification also serves its shift toward business customers. It gets LG into the running for Nvidia-based deployments. It does not yet deliver orders: LG has named affiliate projects and earlier regional references, but no large external cloud contract.

    At 120kW a Rack, Air Is Out of the Picture

    Data Center Engineering puts the practical limit for air handling at roughly 30kW per rack. Above that, heat removal has to move closer to the chip. Nvidia’s own DGX GB200 system guide describes the NVL72 rack at approximately 120kW, with 72 Blackwell GPUs and 36 Grace CPUs in a single rack-scale design. For operators, liquid cooling at that density is a requirement, not a design choice.

    The CDU is the working heart of that liquid loop. It takes heat from coolant returning off the chips’ cold plates and passes it to the facility’s water system, while holding flow and temperature steady. On paper, a 600kW unit covers the heat of about five 120kW racks. That is a row-level building block, not a whole-hall system.

    Why Approval Comes Before Price

    This is the mechanism behind the headline. When the equipment being cooled costs millions of dollars per rack, a coolant leak or pump failure is a business interruption plus a large hardware bill, not a routine repair. Buyers therefore lean on Nvidia’s qualification as a first screen. The approved list narrows the field before bidding starts, so a cooling vendor’s first hurdle is getting qualified, and competing on price comes after.

    LG’s stated specifications match what that screen looks for: tight temperature control (±0.25°C), redundant pumps and power so cooling continues through a component failure, and leak detection. None of this is novel on its own. Together, with Nvidia’s approval attached, it lets LG compete for business it previously could not bid on.

    The effects run in several directions. Established cooling vendors gain a large, well-capitalised new competitor. Hyperscalers, the largest cloud operators, and other data center builders gain another qualified source at a time when liquid cooling demand is rising fast. Uncertified suppliers face a clearer barrier. One certification does not prove the whole market works this way, but it shows plainly where the entry gate for Nvidia-based cooling sits.

    From 600kW to 4MW: The Roadmap Is the Real Test

    LG plans to seek certification for 1MW, 2.5MW and 4MW CDUs next. The scale jump is significant: a 4MW unit could, in principle, serve the heat of more than thirty NVL72-class racks. That is the size at which large AI campuses buy cooling. The 600kW approval gets LG into the running. The megawatt-class approvals will decide whether LG can supply those campuses.

    LG frames the opportunity with a market forecast of about 26 percent annual growth, reaching roughly $133.5 billion (about 185 trillion won) by 2034. This is LG’s own projection, and it has not published the underlying methodology. Readers should treat it as the company’s view of market size, not an independent estimate. The direction is less in doubt than the number: Nvidia’s published rack power figures already require liquid cooling at the top end.

    An Appliance Maker’s Business-Customer Arithmetic

    The certification also fits LG’s broader repositioning. At a January 7 briefing, LG said business lines including B2B, webOS and direct-to-consumer grew from about 29 percent of revenue in 2021 to 45 percent in the second half of 2025. Their share of operating profit rose from 21 percent to about 90 percent. Data center cooling draws on work LG already does: HVAC, heat transfer and precision temperature control.

    The execution risk is real. LG’s stated pipeline includes AI data centers for group affiliate LG CNS, along with earlier cooling references in North America and Indonesia. These are credible starting points. They do not show that global cloud buyers, who usually demand worldwide service coverage and proven delivery at volume, will commit to LG for large deployments.

    Background

    LG Electronics built its brand on televisions, refrigerators and washing machines. For several years it has been shifting its growth strategy toward business customers, including commercial HVAC, and this year it said those business lines generate most of its operating profit. Precision cooling for data centers is a natural extension of that heat-transfer engineering.

    Nvidia’s Blackwell-generation rack-scale systems have made liquid cooling routine at the high end of AI infrastructure. The GB200 NVL72 combines 72 GPUs and 36 CPUs in one rack drawing about 120kW, far above the roughly 30kW at which air cooling reaches its limit. That has created fast-growing demand for direct-to-chip cooling components such as cold plates and coolant distribution units. Nvidia’s supplier qualification acts as the main gate into that supply chain.

    Sources

    Source: LG Electronics wins Korea’s first Nvidia liquid cooling certification as AI data centers hit a heat wall (Startup Fortune): LG’s 600kW coolant distribution unit clears Nvidia’s quality certification.

  • Air-Cooling 1 of 3 Botetourt Buildings Leaves Google’s 8M-Gallon Question Open

    Air-Cooling 1 of 3 Botetourt Buildings Leaves Google’s 8M-Gallon Question Open

    TL;DR · 30-second read

    The Short Version

    Google is planning a $3 billion computer-server complex in rural southwest Virginia. A water deal signed last year allowed it to eventually draw up to 8 million gallons a day, roughly a dozen Olympic swimming pools, while the region faces drought.

    After local pushback, Google says its first building will be cooled with air instead of water. But that covers only one of three buildings, and air cooling usually burns more electricity. Residents now want to know how much, and where that power comes from.

    Google confirmed on Friday, July 24, 2026, that the first building of its proposed $3 billion data center campus at the Botetourt Center at Greenfield in Botetourt County, Virginia, will use air cooling rather than water to keep its equipment cool, Virginia Business reported. Cooling for the campus’s two other planned buildings “remains under active evaluation,” according to an update on botetourtcountydatacenter.com.

    The decision follows community concern over Google’s October 2025 utility services funding agreement with the Western Virginia Water Authority, which anticipated up to 8 million gallons of water per day at full buildout and no more than 2 million gallons per day initially.

    Executive Summary

    Google has chosen air cooling for the first of three buildings on its Botetourt County campus. A Google spokesperson said the company “determined that air cooling was the appropriate solution based on the facility’s needs and our focus on operating efficiently and responsibly,” and the project website described the choice as “driven by business demand and infrastructure requirements.”

    The announcement is a meaningful concession to local water concerns, arriving as the neighboring Roanoke drought region sits at emergency status. But it is scoped narrowly. It settles one building, not the campus, and Google has not published a revised water estimate or said whether the 8-million-gallon full-buildout figure in its water agreement still reflects its plans.

    It also shifts the debate rather than ending it. Cooling a data center without water generally means spending more electricity, and a local group, the Southwest Virginia Data Center Transparency Alliance, is now asking how much power the facility will need and where it will come from.

    One Building of Three Doesn’t Move the Ceiling

    The number that alarmed residents was 8 million gallons a day, the full-buildout figure in Google’s October 2025 agreement with the Western Virginia Water Authority. That figure describes the whole campus. The July 24 decision covers only the first building, and the project website says the other two remain under evaluation. Nothing in Google’s statement revises the agreement’s terms or offers a new campus-wide estimate.

    That matters because of how utility planning works. A water authority sizing pipes, treatment and supply commitments plans against the maximum a customer is entitled to draw, not the amount the first phase happens to use. As long as the agreement anticipates up to 8 million gallons at buildout, the authority, local officials and neighboring communities are still planning around that ceiling. The first building likely draws well under the agreement’s 2-million-gallon initial limit, but Google has not said by how much.

    So the practical effect is a deferral. The first phase gets lighter on water; the larger question of what the finished campus will consume moves to the decision on buildings two and three, which has no announced timeline.

    Water Saved Is Power Spent

    Data centers turn nearly all the electricity they use into heat, and that heat has to go somewhere. Evaporative, or water-based, cooling removes it efficiently by evaporating water, much as sweat cools skin. Air-based systems avoid that water loss but generally rely more heavily on fans and mechanical chillers, which consume electricity. Operators track the two sides with separate yardsticks: water usage effectiveness (litres per unit of computing energy) and power usage effectiveness (total facility energy divided by what the servers use). Improving one often worsens the other.

    That is the trade-off the Transparency Alliance flagged. Co-founder Julie Bivins asked how much power the facility will now use and whether it will come from fossil fuels, natural gas or nuclear. Those are fair questions, and they apply symmetrically: Google has not quantified the added energy draw, and the alliance has not quantified it either. Whether the swap is a net environmental gain depends on the local grid’s generation mix and on how water-stressed the region is, and neither side has put those numbers on the table.

    The density of the hardware inside also shapes the choice. Air cooling suits lower-density server halls; the most power-hungry equipment, including the accelerators used for AI workloads, increasingly pushes operators toward liquid cooling. Google has not said what the Botetourt buildings will host, but the website’s reference to “business demand and infrastructure requirements” suggests the cooling decision for later buildings will turn on what they are built to run, not only on water.

    A Cooling Decision Made in a Drought

    The timing is hard to separate from the local climate. On July 15, Gov. Abigail Spanberger announced that the Roanoke Drought Evaluation Region, which includes Roanoke city and Roanoke County next to Botetourt, had reached emergency status, while most of Virginia sat under drought warnings or watches.

    Local governance has been strained as well. Botetourt County said on July 3 that its planning commission lacks legal authority to independently convene public hearings, and The Roanoke Times described tense proceedings at the commission’s July 13 meeting, where members voted to recommend that the Board of Supervisors direct them to organize a meeting on water supply.

    For developers elsewhere, the lesson is that cooling architecture is no longer purely an engineering decision made late in design. Water commitments negotiated early, and disclosed later through public-records requests, can become the defining public fact about a project. Publishing water and power estimates together, per building and at buildout, is the clearest way to show what a cooling trade-off actually saves.

    Background

    Google’s planned campus sits at the Botetourt Center at Greenfield, an industrial park in Botetourt County, just north of Roanoke in southwest Virginia. The company has proposed a $3 billion investment across three buildings, part of a broader push of large data center projects beyond Virginia’s established hub in the northern part of the state.

    Data centers house the servers behind cloud services, search and AI, and they need continuous cooling. Many large facilities use evaporative systems that consume significant water; others use air-based or closed-loop designs that use less water but more electricity. That choice has become a central point of negotiation between developers and host communities, particularly in areas facing drought.

    Sources

    Source: Google to use air-cooling tech for first Botetourt data center, reducing water use (Virginia Business): Google confirms air cooling for the first building of its Botetourt County campus as water and power concerns grow.

  • 55 Oklahoma Data Centers, Zero Water Permits: AI Cooling Runs on Utility Contracts

    55 Oklahoma Data Centers, Zero Water Permits: AI Cooling Runs on Utility Contracts

    TL;DR · 30-second read

    The Short Version

    Oklahoma has about 55 data centers, the giant warehouses of computers behind apps, websites and artificial intelligence. Not one has applied for a state permit to draw water.

    They buy water from local utilities like any other customer, so the state does not track how much they use. One Google site uses as much water each year as 10,000 homes, and most of it evaporates.

    With a $15 billion campus proposed near Yukon in a state hit by years of drought, neighbors are asking questions developers are not yet required to answer.

    None of the 55 data centers that advisory firm Baxtel lists in Oklahoma has applied for a state water permit, according to reporting by Investigate Midwest published in The Oklahoman. They buy water as customers of municipal utilities, rural water districts or industrial parks that already hold water rights, so their consumption sits largely outside state oversight. Senate Bill 259, signed by Gov. Kevin Stitt in May, allows groundwater permits for data centers only if they use water-conserving, recycling cooling systems, but it does not reach facilities that buy water instead of seeking permits.

    The gap is drawing attention as Atlanta-based Beltline Energy proposes a $15 billion, 1,000-megawatt campus of 15 buildings on 480 acres straddling Oklahoma City and Yukon. Yukon is considering selling the project up to 2.5 million gallons of wastewater per day, while Beltline has requested extensions on the water agreement and has not said which cooling system it would use.

    Executive Summary

    Oklahoma’s newest data center water rule targets the one route data centers do not use. State permits govern taking water directly from aquifers and rivers; data centers instead contract with local suppliers. “So far, we have not had any permits issued specifically to data centers,” said Julie Cunningham, director of the Oklahoma Water Resources Board.

    The practical consequence is that the decisions that determine how much water an AI campus consumes, including its volume, its source and whether it evaporates or recycles cooling water, are being made in municipal supply agreements rather than state permits. Yukon’s pending contract with Beltline is a live example.

    Closing the permitting gap would not by itself produce usage data. The state relies on self-reported figures, metering requirements have failed twice, and the water board reported fewer than 20 staff in 2025. Rep. Carl Newton, the author of SB 259, says a new bill next year will address data centers drawing groundwater through a secondary source.

    Why the Permit Rule Misses the Meter

    SB 259 attaches a sensible condition to groundwater permits: data centers can get one only if their cooling systems are designed to conserve and recycle water. But a permit is only required when a user withdraws water itself. A data center that buys from a city, a rural water district or an industrial park holding industrial water rights is treated as an ordinary customer of that system, and the state never sees an application. That is why a statewide count of zero permits sits alongside 55 listed facilities.

    The real regulatory instrument is therefore the supply contract. Google’s Pryor data center, the state’s largest, buys water from the state-owned MidAmerica Industrial Park, which draws and treats it from the Neosho River. Beltline’s Yukon purchase agreement hinges on a separate water agreement with the city. Whoever negotiates those contracts, usually a municipal utility or city council, effectively decides volume, source and whether the water is potable or treated wastewater. The state’s conservation standard never enters the room.

    This matters for everyone on the supply side. City water utilities are becoming the de facto gatekeepers for AI cooling, often with small staffs and without statewide guidance. Developers face a patchwork of local terms rather than one state rulebook. And residents who want answers, like Yukon-area neighbor Pencia Truhan, who said developers “don’t have to tell us what they’re going to do about water,” find no state filing to consult.

    The Cooling Choice Is Also a Power Choice

    Cooling design is where water and electricity trade off. Open-loop, or evaporative, systems like Pryor’s lose water to the atmosphere: Google reports the site consumes 1.1 billion gallons a year, about the annual use of 10,000 American households, and returns roughly 275 million gallons to the Neosho River. That leaves about 825 million gallons evaporating each year. Closed-loop systems recirculate water, and air-cooled systems use little water but significantly more electricity.

    For Beltline’s proposal, that trade-off lands on an unusually large base. A 1,000-megawatt campus would, at full capacity, draw roughly as much electricity as Oklahoma City’s average use and more than double the demand of Google’s Pryor facility. Choosing air cooling to spare local water would push that electricity figure higher; choosing evaporative cooling would do the reverse. Because the cooling system has not been disclosed, neither the water nor the full power burden can yet be estimated.

    Yukon’s Math: Wastewater, Wells and a 2035 Deadline

    Yukon’s planning memo describes selling Beltline up to 2.5 million gallons of wastewater a day, and officials have promised the project will not rely on drinking water. At full volume that is about 900 million gallons a year, comparable in scale to what Pryor evaporates annually. Using treated wastewater is a common way to spare potable supply, but the sale still commits a resource the city may want for other purposes.

    The city’s supply position is already tight. Yukon relies on 15 leased wells in the Garber-Wellington Aquifer plus purchases from Oklahoma City, and those leases expire in 2035. A city-commissioned study projects its 27,300 residents will grow by about 23% over 25 years, in Canadian County, the state’s fastest-growing county. Against that, Beltline-commissioned studies project 450 permanent jobs and $543.12 million in tax revenue over 25 years, figures that potential tax abatements would significantly reduce.

    Measurement Is the Harder Fix

    Even a closed loophole would leave a data gap. The Oklahoma Water Resources Board relies on self-reported water use. Newton’s original bill would have required commercial groundwater users to install meters; that provision was removed after opposition from Panhandle irrigation groups, and Stitt vetoed similar legislation in 2023 after the Oklahoma Farm Bureau and others argued it infringed on private property rights.

    Capacity is the other constraint. In 2025 the board reported fewer than 20 staff handling an average of 250 permit applications and amendments a year, plus investigations and fieldwork, and its 140-page Water Demand Forecast from last October does not mention data centers. Cunningham said the agency is working to build expertise. Until measured data exists, planners in a drought-stressed state are forecasting water demand without one of its fastest-growing users in the model.

    Background

    Data centers house the servers behind everyday online services, and the newest hyperscale facilities built for artificial intelligence concentrate enough computing to generate heat on an industrial scale. Removing that heat often uses large volumes of water, which is why cooling design has become a siting issue alongside land and power. Oklahoma already hosts one of Google’s largest facilities in Pryor, where the company outlined plans addressing power and water for new sites, and the state is drawing further proposals.

    Beltline Energy, based in Atlanta, began as a renewable energy developer that secured land and developed solar projects before selling them to investors, and it is applying a similar model to data centers. It has developed more than a dozen solar farms but lists no completed data center projects. Its Oklahoma proposals, in Yukon and Oklahoma City, Piedmont and Luther, arrive as the state’s water regulator, which relies on self-reported usage data, works to develop expertise in the sector.

    Sources

    Source: Loophole leaves data center water consumption in Oklahoma largely untracked (The Oklahoman), on how Oklahoma data centers buy water from utilities outside state permitting, and the proposed Beltline Energy campus near Yukon.

  • 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.

  • Meta AI Data Center Linked to Rare Bacteria in a City Water System

    Meta AI Data Center Linked to Rare Bacteria in a City Water System

    Forbes reported on July 10, 2026 that a Meta AI data center has been linked to rare bacteria detected in a city’s water system — a striking escalation of the long-running debate over how much water AI data centers consume, into a question about what they may put back. The headline alone frames the story; the publicly circulated material does not name the city, identify the bacteria, or explain the mechanism of the alleged link.

    The report lands as Meta and its hyperscale peers are in the middle of the largest data center construction wave in history, much of it cooled — directly or indirectly — with municipal water.

    Executive Summary

    According to Forbes, a Meta data center built to serve the company’s artificial-intelligence workloads has been connected to the presence of a rare bacteria in the water system of a nearby city. If substantiated, this would mark a significant shift in the data center water debate: for years the argument has centered on quantity — how many millions of gallons evaporative cooling draws from local supplies — while this story raises a quality and public-health dimension.

    Why it matters: water is the quiet dependency of the AI buildout. Many large data centers use evaporative cooling, in which water absorbs server heat and is partially evaporated away, because it is dramatically more energy-efficient than pure air-based cooling. That efficiency comes with entanglement — data centers become major customers of, and in some configurations discharge back into, the same municipal systems that serve residents.

    Important caveat up front: ‘linked’ is doing heavy lifting in this headline. The available material does not establish causation, name a health authority’s finding, or describe Meta’s response. This article analyzes the stakes while flagging exactly what remains unverified.

    When the Water Debate Becomes a Public-Health Story

    Data center water use has been a community flashpoint for several years, but the framing has been almost entirely volumetric: how many gallons per day, whether aquifers or reservoirs can sustain it, and whether households pay more as a result. A bacteria-in-the-water-system story changes the emotional and regulatory register entirely. Volume disputes are negotiated in rate cases and zoning hearings; contamination questions summon health departments, environmental regulators, and — fairly or not — a much deeper reservoir of public anxiety.

    Mechanically, there are plausible pathways for a large industrial water user to interact with a municipal system’s water quality: heavy draws can change pressure and flow patterns in distribution pipes, warm discharge or blowdown water (the mineral-concentrated water periodically flushed from cooling systems) must be treated and returned somewhere, and large open-loop cooling towers are themselves known habitats for waterborne bacteria such as Legionella. To be clear, none of these mechanisms is confirmed in this case — the source material does not say which, if any, applies. But they explain why a ‘link’ claim is at least technically conceivable rather than absurd on its face.

    What ‘Linked’ Does and Does Not Establish

    The scrutiny has to run in every direction. For the reporting: what evidence supports the link — sampling data, a utility investigation, a health-department finding, or expert inference? Correlation between a new industrial water customer and a new detection is not causation; municipal systems detect unusual organisms for many reasons, including aging pipes, source-water changes, and improved testing. For Meta: what water does the facility draw, what does it discharge, under what permit, and what monitoring does it publish? For the utility and local officials: what does the testing history show before and after the facility came online, and has anyone actually been harmed?

    The honest answer, based on what has circulated publicly, is that we cannot yet distinguish between three very different stories: a genuine contamination pathway traced to the facility, a coincidental detection amplified by the data center’s high profile, or something in between — for example, system stress that made an existing problem visible. Each has radically different implications, and readers should hold all three open until primary documents surface.

    The Economics of Water in the AI Buildout

    Hyperscalers use water because physics and economics reward it. Evaporative cooling can cut a facility’s cooling energy dramatically compared with mechanical chillers, lowering both operating cost and the grid capacity a site must secure — often the binding constraint on AI campuses measured in hundreds of megawatts. The industry’s own metric, water usage effectiveness (WUE), exists precisely because operators know the trade-off is real: save electrons, spend water.

    That calculus is shifting. Direct-to-chip liquid cooling and closed-loop systems — which recirculate a fixed volume of water or coolant rather than continuously evaporating fresh supply — are increasingly standard for dense AI hardware, and several operators have announced designs that consume little or no water for cooling. A public-health controversy, even an ultimately unproven one, accelerates that shift by adding reputational and permitting risk to the cost side of the evaporative-cooling ledger. Communities negotiating with data center developers now have one more reason to demand closed-loop designs, discharge transparency, and independent water-quality monitoring as conditions of approval.

    Winners, Losers, and the Precedent That Matters

    If the link is substantiated, the losers are obvious: the affected community first, then Meta’s siting pipeline, and then every operator whose pending permits get re-examined through a public-health lens. The beneficiaries would be vendors of waterless and closed-loop cooling, water-treatment and monitoring firms, and jurisdictions that wrote strong discharge and reporting requirements into their agreements and can now point to them.

    If the link is not substantiated, the story still matters, because permitting battles run on narrative as much as data. The industry has often been slow to publish site-level water data, treating it as competitively sensitive; that opacity leaves a vacuum that headlines fill. The durable lesson either way is that transparency is cheaper than suspicion: operators who publish withdrawal, discharge, and monitoring data before a controversy get to argue from their own numbers rather than someone else’s framing.

    Background

    Meta operates one of the world’s largest data center fleets and has been expanding it aggressively to support its artificial-intelligence ambitions, with new campuses whose power demands are measured in the hundreds of megawatts and beyond. Like its hyperscale peers, the company has faced recurring community scrutiny over local resource impacts — power, land, and especially water — and, like those peers, has publicized water-restoration commitments intended to offset consumption.

    Until now, the water controversy around AI infrastructure has been overwhelmingly about scarcity: whether local systems can supply large evaporative-cooling loads without straining households and agriculture. A report tying a facility to bacteria in a municipal system — whatever its ultimate substantiation — moves the debate from resource competition to public health, a categorically more sensitive terrain for operators, regulators, and residents alike.

    Source: Meta AI Data Center Linked To Rare Bacteria In City’s Water System — Forbes report, July 10, 2026, connecting a Meta AI data center to a rare bacteria detection in a municipal water system.

  • MHI Reports Field-Verified Efficiency Gains From AI Cooling Optimization

    MHI Reports Field-Verified Efficiency Gains From AI Cooling Optimization

    Mitsubishi Heavy Industries (MHI) announced on July 9, 2026 that it has demonstrated energy-efficiency improvements through cooling optimization in an operational data center. Rather than a lab simulation or a controlled test bed, the demonstration ran in a live facility — the setting where cooling systems must respond to real, fluctuating IT loads.

    Executive Summary

    MHI, the Japanese heavy-industry group whose portfolio spans power generation, HVAC and thermal systems, says it has shown measurable energy-efficiency improvements by optimizing cooling in a data center that was actively serving production workloads. The approach centers on smarter control of cooling equipment — adjusting how chillers, air handlers and airflow respond to actual conditions rather than running at conservative fixed settings.

    The announcement matters for a simple reason: cooling is one of the largest non-IT consumers of electricity in a data center, and it is one of the few places where efficiency gains can be captured without touching the servers themselves. With AI workloads pushing rack power densities sharply higher, operators are looking hard at control-layer optimization as a way to cut operating costs and free up power capacity. A field demonstration in a live facility — as opposed to vendor modeling — is the kind of evidence buyers increasingly demand, though the syndicated version of this release does not carry the underlying figures, which readers should verify against MHI’s full publication.

    Why a Live-Facility Demonstration Matters

    Cooling-optimization claims are easy to make in simulation and hard to prove in production. A real data center has messy thermal behavior: IT load rises and falls with customer demand, outside temperatures swing by season and hour, and no operator will tolerate a control experiment that risks overheating servers. Demonstrating gains in an operational facility means the system had to deliver savings while respecting those constraints — which is why field verification is the credibility bar for this product category.

    That said, a single-site demonstration is evidence, not proof of general applicability. Results depend heavily on the baseline: a facility with poorly tuned cooling will show dramatic improvement from almost any optimization, while a well-run site will show far less. The commercial question is not whether MHI improved one building, but how transferable the method is across climates, cooling architectures and load profiles — something only multi-site data can answer.

    Cooling Is the Biggest Efficiency Lever Left

    In most data centers, cooling is the largest energy consumer after the IT equipment itself, which is why the industry’s standard efficiency metric — PUE, or power usage effectiveness, the ratio of total facility power to IT power — is largely a measure of cooling overhead. Servers get more efficient with every silicon generation, but the facility side improves only when operators invest in it. Control-layer optimization is attractive because it can often be applied to existing equipment: the chillers stay, the software running them gets smarter.

    The economics have sharpened as AI infrastructure scales. Grid connections are constrained in many markets, so every kilowatt not spent on cooling is a kilowatt available for revenue-generating compute. For operators facing multi-year waits for new power capacity, efficiency gains at the cooling layer function as found capacity — frequently at a fraction of the cost of new construction.

    MHI Enters a Crowding Field

    MHI is not alone here. AI-assisted cooling control has been pursued by hyperscalers internally and by facility-equipment and building-management vendors for several years, and the space now includes established cooling manufacturers, controls specialists and software startups. MHI’s differentiation, if it holds, comes from owning the equipment side: a company that builds chillers and thermal systems can integrate control optimization more deeply than a software-only vendor, and can stand behind the combined result.

    For MHI, the strategic logic is also defensive. As liquid cooling, heat reuse and AI-driven operations reshape data center thermal design, equipment makers that offer only hardware risk being commoditized while the value migrates to the control and services layer. A demonstrated optimization capability positions MHI to sell outcomes — efficiency, capacity headroom — rather than just machines. Whether that translates into a commercial product with published pricing and guarantees is the next thing to watch.

    Background

    Mitsubishi Heavy Industries is a diversified Japanese engineering group whose thermal-systems businesses build chillers, HVAC and industrial cooling equipment — the physical machinery that data center cooling optimization software ultimately controls. Like other established equipment makers, MHI has been extending from hardware into the control and services layer as data center operators demand measurable efficiency outcomes rather than standalone machines.

    The push comes amid a broader industry squeeze: AI-driven demand has data center construction booming while grid power in major markets is scarce, making energy efficiency both a cost issue and a capacity issue. Cooling, as the largest non-IT energy consumer in most facilities, has become the primary battleground, with hyperscalers, controls vendors and equipment manufacturers all pursuing AI-assisted optimization of the thermal plant.

    Source: MHI Demonstrates Energy Efficiency Improvements through Cooling Optimization in Operational Data Center — Mitsubishi Heavy Industries announcement, July 9, 2026, via Google News.

  • Ecolab Closes $4.75B CoolIT Deal for AI Cooling

    Ecolab Closes $4.75B CoolIT Deal for AI Cooling

    Ecolab, the Minnesota-based water, hygiene and industrial services company, has closed its $4.75 billion acquisition of CoolIT Systems, a Calgary-based specialist in liquid cooling for high-density computing. The deal, reported by Electronics360 on July 7, 2026, gives Ecolab a foothold in direct-to-chip cooling technology used in AI training clusters.

    Executive Summary

    The acquisition places Ecolab, historically known for cleaning chemicals and water treatment, squarely inside one of the fastest-growing subsegments of data center infrastructure: liquid cooling for AI workloads. CoolIT’s direct-to-chip (DTC) systems circulate coolant across cold plates mounted on processors, removing heat that increasingly cannot be shed with air alone.

    At $4.75 billion, the price signals that Ecolab views AI-driven thermal management as a durable industrial category rather than a cyclical bet. It also consolidates a market that, until recently, was populated largely by specialist engineering firms. For buyers of AI infrastructure, the transaction raises questions about supplier concentration; for competitors, it raises the bar for the scale of balance sheet needed to serve hyperscale customers.

    Why Liquid Cooling, and Why Now

    Modern AI accelerators, such as the GPUs used to train large language models, dissipate hundreds to over a thousand watts per chip. Once rack densities exceed roughly 30-50 kilowatts, forced-air cooling becomes impractical: fans cannot move enough air, and the room-level heat load overwhelms conventional CRAC (computer room air conditioning) units. Direct-to-chip liquid cooling, which CoolIT sells, moves a fluid across a cold plate bolted to each chip and carries heat out of the rack via a coolant distribution unit. It is more efficient than air, but demands new plumbing, materials expertise, and long-term service contracts — precisely the kind of recurring industrial work Ecolab is built to sell.

    The timing reflects a broader shift. Hyperscale operators and colocation providers are retrofitting existing halls and designing new campuses around liquid-ready racks. That transition creates a decade-long tail of installation, chemistry, monitoring and maintenance revenue, which fits Ecolab’s route-based service model more naturally than one-off equipment sales.

    Industrial Services Meets Silicon

    Ecolab’s core competency is delivering water, cleaning and process chemistry to industrial customers at scale, with technicians on site and consumables on subscription. CoolIT’s core competency is engineering cold plates, manifolds and coolant distribution units for demanding compute environments. The strategic thesis is that these are complementary: CoolIT gets access to a global services organization and enterprise procurement relationships; Ecolab gets a defensible product line in a growth market where its existing water-treatment expertise — corrosion, biofouling, fluid chemistry — is directly relevant.

    The risk in that thesis is cultural and technical integration. Data center customers demand tight change control, rapid engineering iteration, and validated compatibility with each new generation of chip. Industrial-services firms historically operate on slower cycles. Whether Ecolab preserves CoolIT’s engineering cadence, or slows it in pursuit of scale efficiencies, will shape the deal’s outcome.

    Market Structure and Competitive Response

    Liquid cooling has been an active acquisition target across the infrastructure industry, with mechanical, electrical and chemical majors all seeking exposure. Ecolab’s $4.75 billion outlay is large enough to reset valuation expectations for remaining independent cooling specialists, and to encourage rival strategics to accelerate their own moves. For hyperscalers standardizing on multi-vendor supply chains, further consolidation could narrow sourcing options and increase reliance on a small number of large suppliers.

    Competitors — including established thermal management vendors and newer entrants building rear-door heat exchangers or immersion systems — now face a rival with a global service footprint they cannot easily replicate. Immersion cooling, which submerges entire servers in dielectric fluid, remains a parallel approach that this deal does not directly address, leaving room for differentiated bets.

    Background

    Ecolab has spent decades building a global route-based industrial services business, selling water treatment, cleaning chemistry and related engineering to manufacturers, hospitals, food processors and utilities. CoolIT Systems, founded in Calgary, grew from PC cooling into an established supplier of liquid cooling hardware for enterprise and high-performance computing, expanding sharply as AI training clusters drove rack power densities beyond the limits of air cooling.

    Liquid cooling itself is not new — mainframes used it decades ago — but the surge in AI-driven demand has turned a niche into a strategic infrastructure category. Direct-to-chip systems are now standard in new hyperscale AI builds, and retrofits of existing data halls are underway across the industry.

    Source: Ecolab closes $4.75B CoolIT acquisition to corner AI data center cooling – Electronics360 reports the closing of Ecolab’s acquisition of liquid cooling specialist CoolIT Systems.