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  • 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.

  • 3M and Microsoft Partner on AI Data Center Materials

    3M and Microsoft Partner on AI Data Center Materials

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

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

    Executive Summary

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

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

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

    Why Materials Suddenly Matter to Hyperscalers

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

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

    What a Strategic Partnership Actually Buys

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

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

    Enterprise Transformation: The Ambiguous Second Leg

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

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

    Risks and Open Questions on Both Sides

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

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

    Background

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

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

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

  • 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.

  • Wafr Technologies’ Reported $100M Raise Shows Investors Chasing the Cooling Bottleneck

    Wafr Technologies’ Reported $100M Raise Shows Investors Chasing the Cooling Bottleneck

    Cooling vendor Wafr Technologies has raised $100 million, according to a report carried by Data Center Dynamics on July 7, 2026. The publication characterized the raise as a report rather than a company announcement, and the item available to us does not name the investors, the round structure, or the intended use of proceeds.

    Executive Summary

    According to the Data Center Dynamics item, Wafr Technologies — identified simply as a cooling vendor — has reportedly secured $100 million in new funding. That is the extent of what the source substantiates: a company name, a sector, a dollar figure, and the qualifier “report,” which signals the news has not been confirmed in detail by the company itself.

    Even in that skeletal form, the story matters because of what it represents. Cooling — the unglamorous business of moving heat away from computer chips — has become one of the tightest constraints on data center construction in the AI era. A nine-figure round for a cooling specialist, if confirmed, would be another data point in a clear pattern: capital that once flowed almost exclusively to chips, land, and power is now chasing thermal management, because without it the rest of the AI buildout stalls.

    Why Heat Became the Industry’s Chokepoint

    For most of the data center industry’s history, cooling was a solved problem: blow chilled air across servers, exhaust the hot air, repeat. That model works up to roughly the power density of a traditional enterprise rack. AI training hardware broke the equation. Modern accelerated-computing racks draw many times what air can economically remove, which is why the industry is shifting to liquid cooling — circulating fluid directly to cold plates on the chips, or immersing hardware in dielectric fluid — to carry heat away far more efficiently than air ever could.

    That transition is not optional for AI-class facilities, and it is happening faster than the supply chain matured. Cold plates, coolant distribution units, rear-door heat exchangers, and the engineering talent to deploy them have all been in tight supply. When a component becomes the binding constraint on a trillion-dollar buildout, capital follows. A reported $100 million round for a cooling vendor fits that logic precisely.

    What a Nine-Figure Round Signals About the Market

    Cooling has historically been the domain of large industrial incumbents — the Vertivs and Schneider Electrics of the world — for whom thermal management is one product line among many. Venture-scale money flowing to independent cooling specialists suggests investors believe the liquid-cooling transition is big enough, and moving fast enough, to support new entrants rather than simply enlarging incumbents’ order books.

    It also says something about where returns are perceived to be. Building data centers is capital-intensive and increasingly commoditized; supplying the critical components that gate construction can carry better margins and faster growth. Investors who missed the GPU wave or the land-and-power wave may see thermal management as the remaining underpriced layer of the AI infrastructure stack. Whether that thesis pays off depends on execution questions this report cannot answer — but the direction of the money is itself informative.

    Winners, Losers, and the Scaling Test Ahead

    If the raise is confirmed, the most immediate beneficiaries are data center operators and their customers: more capitalized suppliers mean more manufacturing capacity, shorter lead times, and more competitive pricing in a segment where demand has outrun supply. Chipmakers benefit indirectly, since every rack that can be cooled is a rack that can be sold.

    The harder question is whether a funded challenger can convert capital into share. Cooling is a trust business — operators are conservative about anything that puts liquid near multi-million-dollar hardware — and incumbents hold deep service networks and long-standing customer relationships. History in this industry suggests that well-funded specialists either scale into meaningful suppliers, get acquired by incumbents seeking their technology, or burn capital competing on price. A $100 million war chest buys time to find out which path applies; it does not guarantee the answer.

    Reading a Report, Not a Press Release

    It is worth being precise about the evidentiary status here. The source is a trade-press item flagged as a report — not a company announcement, not a regulatory filing. The figure could ultimately prove different in size, structure (equity versus debt), or timing. Trade reporting on private raises is often directionally right and precisely wrong. Until Wafr Technologies or its investors confirm the details, the responsible reading is: a credible industry publication believes a cooling vendor has attracted roughly $100 million, and that belief is consistent with everything else happening in the thermal-management market.

    Background

    For decades, data center cooling meant air: chillers, raised floors, and hot-aisle containment, handled largely by big industrial suppliers as one product line among many. The AI era upended that. Racks built around modern accelerators draw several times the power of traditional enterprise racks, pushing the industry toward direct-to-chip liquid cooling and immersion systems that can remove heat air cannot. That transition turned a mature, sleepy segment into one of the most supply-constrained corners of the infrastructure market, and capital has followed — into incumbents’ expansion and, increasingly, into independent specialists.

    Wafr Technologies enters the public record here with little published history: the report available to us identifies it only as a cooling vendor. That thinness is itself common in this cycle, where private thermal-management companies often surface in trade press via funding reports before making detailed public disclosures.

    Source: Cooling vendor Wafr Technologies raises $100m – report, Data Center Dynamics, July 7, 2026 — a trade-press report of the funding round, unconfirmed by the company at publication.

  • China Switches On the First Commercial Underwater Data Center

    China Switches On the First Commercial Underwater Data Center

    China has brought online what is being described as the world’s first commercial underwater data center, according to a report published July 4, 2026 by the Spanish outlet OkDiario. The facility submerges sealed server modules in the ocean and uses the surrounding seawater as its cooling medium, an approach the report says sharply reduces the energy the facility consumes.

    The report frames the launch as a template other coastal regions could adopt, naming Cartagena, Spain as the kind of Mediterranean port city where the model might be replicated. It does not disclose the operator, the facility’s capacity, or its precise location.

    Executive Summary

    The announcement matters because it moves underwater data centers from experiment to product. Submerging servers has been tested before — most famously by Microsoft — but a commercial deployment means paying customers are expected to run real workloads on seabed infrastructure, and that changes the questions from “does it work?” to “does it pencil out?”

    The core appeal is cooling. Keeping servers from overheating is one of the largest energy costs in any data center, and the deep ocean offers a vast, stable heat sink at no mechanical-chilling cost. If seawater cooling delivers the efficiency the concept promises at commercial scale, it would arrive at a moment when AI-driven demand has made power and cooling the industry’s tightest constraints.

    That said, the source report is brief and light on specifics. It attributes no capacity figures, energy metrics, customer names, or operator details. The launch is a genuine milestone in cooling infrastructure if the commercial framing holds — but the evidence available in this report is a claim of a first, not a documented performance record.

    Why Put Servers on the Seabed?

    Data centers spend an enormous share of their electricity not on computing but on removing the heat that computing generates. The industry measures this with PUE — power usage effectiveness, the ratio of total facility power to the power that actually reaches IT equipment. Conventional air-cooled facilities need chillers, fans, and often large volumes of water to hold safe temperatures, and in hot climates that overhead climbs steeply.

    The ocean solves the problem passively. Below the surface, water temperature is low and remarkably stable year-round, and water conducts heat far better than air. A sealed capsule on the seabed can reject heat directly into an effectively unlimited sink, eliminating most mechanical cooling. Subsea deployment also removes evaporative water consumption — a growing point of friction between data centers and the communities that host them — and seabed real estate near dense coastal cities is not competing with housing or industry the way urban land is.

    From Microsoft’s Experiment to Chinese Commercialization

    The concept is not new; the commercial claim is. Microsoft’s Project Natick sank a sealed server vessel off Scotland’s Orkney Islands from 2018 to 2020 and reported that the submerged servers failed at a fraction of the rate of an equivalent land-based control group — likely because the nitrogen-filled, human-free capsule eliminated oxygen corrosion, humidity swings, and accidental knocks. Microsoft judged the experiment a technical success but never turned it into a product. China, meanwhile, has been running underwater data center pilots off its own coast for several years, so a progression from pilot to commercial service there is consistent with the trajectory — even though this report does not name the company involved.

    If the commercial characterization is accurate, China would be first to market with a technology a US hyperscaler proved and shelved. That is a familiar pattern in infrastructure: the economics that don’t fit one company’s portfolio can fit another market’s constraints, particularly where coastal land, grid capacity, and water for cooling are all scarce at once.

    The Hard Economics of Subsea Capacity

    The obstacles are as real as the appeal. A submerged module cannot be serviced by a technician; a failed component stays failed until the entire vessel is raised, which pushes operators toward redundant hardware and infrequent, expensive retrieval cycles. Marine engineering, corrosion-resistant housings, subsea power and fiber connections, and specialized deployment vessels all add capital cost that the cooling savings must repay. Insurance, uptime guarantees, and repair logistics for seabed assets are largely uncharted territory for enterprise customers used to walking their auditors through a facility.

    Environmental questions also need honest accounting. Rejecting heat into the ocean is thermodynamically unavoidable here, and while small-scale trials such as Natick reported minimal localized warming, the effect of dense clusters of commercial modules on marine ecosystems is site-specific and largely unstudied. Coastal permitting regimes — fisheries, shipping lanes, protected habitats — will shape where this model can actually go, and the report offers no detail on how the Chinese deployment cleared those hurdles.

    Could Cartagena Be Next?

    The report’s suggestion that coastal cities like Cartagena could follow is speculation, not an announced project, and it is worth being clear about that distinction. Still, the logic of the shortlist is sound: Mediterranean port cities combine dense populations that want low-latency services, constrained urban land and grids, warm climates that make conventional cooling expensive, and immediate deep water. Those are precisely the conditions under which subsea capacity is most competitive against land-based builds.

    For European adoption, the gating factors would be EU environmental review, marine-spatial-planning approvals, and — not least — the geopolitics of importing a Chinese-proven infrastructure model into European digital sovereignty debates. Any operator pursuing it would more likely license the concept or develop it independently than deploy Chinese-operated modules in EU waters.

    Background

    Underwater data centers trace to Microsoft’s Project Natick, which began with a proof-of-concept in 2015 and culminated in a sealed vessel of several hundred servers operating off Scotland from 2018 to 2020. The retrieved servers had failed at a small fraction of the rate of an identical land-based group, validating the reliability case — but Microsoft ended the program without a commercial product. China picked up the thread with coastal pilot deployments in the years that followed, pursuing subsea capacity as an answer to scarce coastal land, strained grids, and the water consumption of conventional cooling.

    The timing is not incidental. By 2026, explosive AI demand had made electricity and cooling the data center industry’s defining bottlenecks worldwide, pushing operators toward liquid cooling, novel sites, and any design that cuts overhead energy. A commercial subsea launch is China staking a claim to one of those frontiers first.

    Source: China just switched on the first underwater data center, cooling servers with the ocean to slash energy use, and coastal cities like Cartagena could be next — OkDiario report, July 4, 2026, on China’s launch of the first commercial seawater-cooled underwater data center.

  • Aquifer ‘Thermal Batteries’ Could Cut AI Data Center Cooling Energy and Water Use

    Aquifer ‘Thermal Batteries’ Could Cut AI Data Center Cooling Energy and Water Use

    Research publicized June 29, 2026 via Tech Xplore suggests that aquifers — naturally occurring layers of water-bearing rock underground — could serve as ‘thermal batteries’ for data centers, storing heat and cold across seasons. According to the report, the approach may reduce the cooling energy AI data centers consume and cut their water use, two of the industry’s fastest-growing environmental pressure points.

    Executive Summary

    The announcement is a research finding, not a product launch: scientists propose using aquifer thermal energy storage — pumping water underground to bank cold in one season and withdraw it in another — as a way to offset the enormous cooling loads created by AI computing. The headline claim is twofold: lower cooling energy demand and reduced water consumption compared with conventional approaches such as evaporative cooling, which loses large volumes of water to the atmosphere by design.

    Why it matters: cooling is one of the largest non-compute energy costs in a data center, and water use has become a siting and permitting flashpoint in drought-prone regions. AI accelerators run hotter and denser than traditional servers, magnifying both problems. A storage-based approach that shifts cooling work to underground reservoirs — rather than burning electricity on chillers or evaporating potable water in real time — would attack both constraints at once. The open question, which the source headline’s own careful ‘may cut’ phrasing acknowledges, is whether the technique scales from research findings to the round-the-clock, high-density heat loads of production AI facilities.

    Why Cooling Is the Quiet Crisis of the AI Buildout

    Every watt a server consumes becomes heat that must be removed, and AI hardware has pushed rack power densities far beyond what legacy air-cooling systems were built for. Operators today choose among imperfect options: mechanical chillers, which are reliable but electricity-hungry; evaporative cooling, which trades electricity for significant water consumption; and liquid cooling, which moves heat efficiently at the rack but still needs somewhere to reject it. Cooling efficiency is captured in metrics like PUE (power usage effectiveness — total facility power divided by computing power), and shaving it has direct economic value at AI campus scale.

    Water has arguably become the more politically sensitive constraint. Data center water consumption has drawn scrutiny from communities and regulators in water-stressed regions, and several jurisdictions now weigh water impact in permitting decisions. A cooling architecture that credibly reduces both energy and water use addresses the industry’s two most visible externalities simultaneously — which explains why a research result, rather than a commercial deployment, is drawing attention.

    How an Aquifer Becomes a Battery

    Aquifer thermal energy storage, often abbreviated ATES, is conceptually simple: use paired wells to circulate groundwater, storing thermal energy in the aquifer itself. In winter, cheap ambient cold is banked underground; in summer, that stored cold is withdrawn to absorb data center heat, with the warmed water returned to a separate zone of the aquifer for later use or dissipation. The ‘battery’ framing is apt — the aquifer shifts cooling capacity across time, much as an electrical battery shifts energy from cheap hours to expensive ones.

    The underlying technique is not new. ATES has been deployed for decades in district heating and cooling systems, particularly in the Netherlands, where favorable geology and supportive regulation made it routine for buildings. What the new research explores is its application to a much harder customer: data centers, whose heat output is continuous, dense, and growing. Because the water circulates in a closed loop underground rather than evaporating into the air, the approach could sidestep the consumptive water losses that make evaporative cooling controversial.

    Who Wins If It Works — and What Stands in the Way

    The clearest beneficiaries would be operators in regions with suitable aquifer geology and strong seasonal temperature swings, where winter cold can be banked cheaply. Utilities and grid planners would welcome anything that flattens data center cooling load, since peak cooling demand coincides with summer grid stress. Drilling, geothermal, and groundwater engineering firms would gain a new market adjacent to the booming data center construction sector.

    The obstacles are equally concrete. ATES only works where the geology cooperates — the right aquifer depth, permeability, and low natural groundwater flow — which makes it a siting-dependent solution, not a universal one. Groundwater is heavily regulated nearly everywhere, and injecting warmed water underground raises legitimate environmental review questions about thermal plumes and water chemistry. And AI’s heat load is continuous rather than seasonal, so an aquifer system would likely supplement, not replace, conventional cooling. None of these hurdles is disqualifying, but each stands between a promising research finding and a bankable design that a hyperscaler would commit to.

    Background

    Data center cooling has evolved through waves of pressure: from raised-floor air cooling, to hot/cold aisle containment, to economizers and evaporative systems, and most recently to direct liquid cooling as AI accelerators pushed rack densities beyond what air can handle. Each wave traded among the same three currencies — electricity, water, and capital — and the AI buildout has sharpened all three constraints at once, with water use in particular becoming a community and permitting issue in water-stressed markets.

    Aquifer thermal energy storage sits within a broader family of underground thermal techniques, alongside borehole storage and geothermal heat pumps. ATES matured in northern Europe over several decades as a building heating-and-cooling technology; the research reported here represents an attempt to carry that mature concept into the much more demanding environment of AI computing infrastructure.

    Source: Aquifer ‘thermal batteries’ may cut AI data center cooling demand and save water — Tech Xplore report, June 29, 2026, on research into using aquifer thermal energy storage to reduce data center cooling energy and water consumption.

  • Microsoft Claims Water-Positive Data Center Operations: What the Claim Really Covers

    Microsoft Claims Water-Positive Data Center Operations: What the Claim Really Covers

    Microsoft is claiming water positivity across its data center operations, according to a June 27, 2026 report from Data Center Dynamics. Water positivity means an operator replenishes more water to stressed watersheds than its facilities consume — a milestone Microsoft first committed to reaching by 2030 when it announced its water-positive pledge in 2020.

    The claim spans one of the world’s largest cloud footprints, and it arrives at a moment when AI-driven capacity growth has put data center water consumption under intense public and regulatory scrutiny. The available report is headline-level, so the scope, accounting method, and verification behind the claim remain to be detailed.

    Executive Summary

    Microsoft has publicly positioned its data center operations as water positive — consuming less water, net of replenishment projects, than it returns to the watersheds where it operates. If the claim holds up under scrutiny, it would represent the first time a hyperscale cloud operator has asserted that its fleet, as a whole, has crossed that line, and it would land years ahead of the company’s stated 2030 target.

    Why it matters: water has become the second front, after power, in the fight over data center siting. Communities from Arizona to the Netherlands have pushed back on facilities that draw millions of gallons for evaporative cooling, and regulators increasingly ask for water commitments alongside grid commitments. A credible water-positive benchmark from the market’s second-largest cloud provider would reset expectations for every operator negotiating a site — including colocation and wholesale providers who compete for the same land, power, and permits.

    The operative word is credible. Water positivity is an accounting construct, not a physical description of any single site, and its value depends entirely on scope, measurement, and where the replenishment actually happens. The source reporting available at publication does not yet answer those questions, and they are the right ones to ask of any operator making a similar claim.

    What “Water Positive” Actually Means — and What It Doesn’t

    Water positivity is a ledger claim: over a defined period, the volume of water an operator restores — through wetland restoration, leak-repair programs, irrigation efficiency projects, aquifer recharge, and similar investments — exceeds the volume its operations consume. Consumption here typically means water evaporated or otherwise not returned to the source, which for data centers is dominated by evaporative cooling, the technique of cooling air or water by letting some of it evaporate, trading water for large electricity savings.

    What the construct does not mean is that any individual data center stopped drawing water. A facility in a drought-stressed basin can keep consuming while the corporate ledger balances with a restoration project elsewhere. That is not inherently bad-faith accounting — carbon markets work on a similar logic — but water is far more local than carbon. A gallon replenished in one river basin does nothing for the aquifer under a different one. The strongest version of a water-positive claim is basin-matched: replenishment in the same watersheds where consumption happens, weighted toward the most stressed ones. Whether Microsoft’s claim is basin-matched is exactly the kind of detail the headline-level reporting leaves open, and it is the difference between a milestone and a marketing line.

    The Cooling Economics Behind the Claim

    Data centers face a three-way trade among water, energy, and capital. Evaporative cooling is cheap and energy-efficient but water-hungry. Closed-loop and air-cooled designs eliminate most on-site water consumption but raise electricity use or capital cost, and in hot climates they can strain the power budget that operators are already fighting to secure. Microsoft has spent several years publicizing designs that move toward zero-water cooling for new builds, alongside efficiency metrics like WUE — water usage effectiveness, the liters of water consumed per kilowatt-hour of IT load.

    A fleet-level water-positive result, if achieved early, most plausibly reflects three levers working together: newer builds consuming less per megawatt, replenishment portfolios scaling faster than consumption, and — the uncomfortable variable — how fast AI capacity growth adds consumption to the denominator. The AI buildout cuts both ways here. High-density AI halls increasingly use direct liquid cooling, which circulates coolant in a closed loop and can actually reduce on-site water consumption per unit of compute, but the sheer volume of new capacity can swamp per-unit gains. Any operator’s water math in 2026 is a race between those two curves.

    A Benchmark With Teeth — If the Methodology Is Public

    The industry consequence of this claim depends less on Microsoft than on procurement. Enterprise cloud buyers and public-sector tenders already ask for carbon disclosures; a hyperscaler asserting water positivity gives sustainability teams a new line item to demand from every provider. Google and Amazon have announced their own 2030-era water goals, so competitive pressure to demonstrate progress — not just pledge it — will rise. Colocation operators, who often lack the balance sheet for large replenishment portfolios, may feel the squeeze most: their water story is largely their cooling design, not an offsetting ledger.

    For communities and regulators, the useful move is to treat the claim as an invitation to standardize. Today there is no universally accepted audit standard for water positivity comparable to the frameworks maturing around carbon. Claims are only comparable across operators if consumption scope (owned versus leased capacity, construction water, upstream power-generation water), replenishment crediting rules, and basin matching are disclosed. An early, well-documented claim from a market leader could seed that standard. A thinly documented one would invite the same greenwashing skepticism that has dogged renewable energy certificates — and would make life harder for operators doing the work rigorously.

    Background

    Microsoft is one of the world’s largest data center operators, running cloud infrastructure across dozens of countries to serve its Azure, Microsoft 365, and AI businesses. In 2020 the company pledged to become water positive by 2030 as part of a broader sustainability program that also targets carbon-negative operations, and it has since promoted lower-water cooling designs for new facilities alongside a portfolio of watershed replenishment projects.

    The claim lands in an industry racing to build AI capacity while facing growing scrutiny over resource consumption. Water has joined electricity as a gating factor for new data center permits, and no common audit standard yet exists for corporate water-positivity claims — which makes the methodology behind any such announcement as consequential as the announcement itself.

    Source: Microsoft claims water positivity across data center operations — Data Center Dynamics report, June 27, 2026, on Microsoft’s claim of water-positive data center operations.

  • Rising Heat and Humidity Are Shrinking the Free-Cooling Window for Data Centers

    Rising Heat and Humidity Are Shrinking the Free-Cooling Window for Data Centers

    Research highlighted by Phys.org on June 26, 2026 warns that rising global temperatures and humidity are undermining one of the data center industry’s most important energy-efficiency strategies: free cooling, the practice of using cool outside air or water to remove server heat instead of running energy-hungry mechanical chillers. As more hours of the year become too hot or too humid for outside air to do the job, facilities worldwide face growing cooling energy demand.

    The finding lands at a sensitive moment. Data center construction is accelerating to serve AI workloads, and cooling is typically the largest energy consumer in a facility after the IT equipment itself — so any climate-driven loss of free-cooling hours compounds an already steep power challenge.

    Executive Summary

    The core claim is straightforward: free cooling only works when the outside environment is cooler and drier than the conditions servers require, and climate change is steadily reducing the number of hours per year when that is true. Heat is only half the story — humidity matters just as much, because evaporative cooling systems, which cool air by evaporating water, lose effectiveness as the air becomes more saturated. Regions that were designed around thousands of free-cooling hours a year are watching that budget shrink.

    Why it matters: efficiency assumptions made at design time are baked into a data center for decades. A facility engineered in a climate that no longer exists will either consume more energy than its models promised, lean harder on water, or require retrofit investment. For an industry under scrutiny over electricity and water consumption, the research reframes climate not as a sustainability talking point but as an engineering input — one that belongs in site selection, cooling-system choice, and capacity planning from day one.

    Free Cooling Was the Industry’s Efficiency Workhorse

    For the past fifteen years, the biggest gains in data center efficiency — reflected in falling PUE, the ratio of total facility power to IT power — came largely from using the outdoors as a heat sink. Air-side economizers pull in filtered outside air; water-side economizers and evaporative systems use cooling towers to shed heat with modest energy input. Hyperscale operators famously sited facilities in cool climates precisely to maximize these hours.

    The research reported by Phys.org attacks the durability of that playbook. If the number of hours cool and dry enough for economization declines, chillers run more, and the efficiency gap between a well-sited facility and a poorly sited one narrows in the wrong direction. The gains of the last decade were real, but they were partly a loan from a stable climate — and the terms of that loan are changing.

    Humidity Is the Underappreciated Variable

    Public discussion of data center cooling fixates on temperature, but wet-bulb temperature — a combined measure of heat and humidity that sets the floor for evaporative cooling — is the more binding constraint. When wet-bulb temperatures rise, evaporative systems must work harder and consume more water for less cooling effect, and in extreme conditions they cannot reach the setpoints servers need at all. That pushes operators back toward mechanical refrigeration exactly when grid demand for air conditioning also peaks.

    This has a second-order consequence: the trade-off between energy and water gets sharper. Evaporative cooling saves electricity but consumes water; dry coolers and chillers save water but consume electricity. Rising humidity degrades the attractiveness of the water-based option in many regions, forcing a choice between two increasingly expensive resources — often in communities already contesting data center water use.

    Winners: Liquid Cooling, Cool Geographies, and Honest Modeling

    If outside air can carry less of the load, the premium shifts to technologies that tolerate warmer heat rejection. Direct-to-chip liquid cooling and immersion cooling move heat in water or fluid rather than air, allowing higher operating temperatures and, in many designs, year-round heat rejection without compressors even in warm climates. The AI build-out was already pushing the industry toward liquid cooling for density reasons; climate trends add an efficiency rationale.

    Geography gains value too. Sites in cool, dry, or high-latitude regions — the Nordics, parts of Canada, high-altitude locations — become relatively more attractive, though they bring their own constraints in connectivity, latency, and power availability. And engineering firms that model cooling against forward-looking climate projections rather than historical weather files gain a real advantage: a 25-year asset should be designed for the climate of 2040, not 2010.

    Risks: Stranded Efficiency and Rising Operating Costs

    The losers in this shift are facilities whose economics depend on free-cooling assumptions that no longer hold — particularly older air-cooled sites in regions warming fastest. Their operating costs drift upward without any change in workload, and their sustainability reporting deteriorates through no operational fault. For colocation providers, whose customers increasingly scrutinize PUE and water metrics in procurement, that drift is a competitive problem, not just an engineering one.

    There is also a grid-level risk. The hours when data centers lose free cooling are the same hot hours when regional grids are most stressed. Climate-driven cooling demand is therefore correlated demand — it arrives when power is scarcest and most carbon-intensive, which is precisely the scenario utilities and regulators planning for data center growth need to model.

    Background

    Data center cooling has evolved through distinct eras. Early facilities ran cold and relied almost entirely on mechanical chillers. From roughly 2010 onward, hyperscale operators drove a revolution in economization — siting in cool climates, using outside air and evaporative systems, and widening acceptable server temperature ranges — which pushed the best facilities’ PUE from around 2.0 toward 1.1. That efficiency story became central to the industry’s answer to critics of its energy footprint.

    The current AI build-out is testing every part of that model: rack power densities have jumped severalfold, cooling loads are climbing, and communities are scrutinizing both electricity and water consumption. Research showing that climate change is eroding free cooling adds a structural pressure on top of a cyclical boom — and helps explain the industry’s accelerating shift toward liquid cooling and climate-aware site selection.

    Source: Rising heat and humidity challenge energy-efficient data center cooling worldwide — Phys.org report, June 26, 2026, on research into climate-driven erosion of data center free-cooling potential.

  • NIST’s 3 Water Blueprints Show AI Cooling’s Cyber Risk Starts at Utility Remote Access

    NIST’s 3 Water Blueprints Show AI Cooling’s Cyber Risk Starts at Utility Remote Access

    TL;DR · 30-second read

    The Short Version

    A US government standards agency has published a guide telling water and sewer utilities that their riskiest weak spot is remote access: the ways staff and outside contractors log in from elsewhere to run pumps and treatment equipment.

    More than half of American water systems serve 500 people or fewer and have few staff to defend them.

    This matters beyond your tap. Many of the giant computer buildings behind artificial intelligence use water to keep their machines cool, often supplied by those same local utilities.

    The US National Institute of Standards and Technology (NIST) has published SP 1800-45, Cybersecurity for the Water and Wastewater Sector: Build Architecture, which identifies secure remote access as the immediate cybersecurity priority for water and wastewater utilities, Industrial Cyber reported on June 26. The project, run by NIST’s National Cybersecurity Center of Excellence (NCCoE), offers high-level reference architectures and three example implementations built from commercially available products.

    The three designs cover a traditional on-premises gateway (demonstrated with TDI ConsoleWorks), a cloud-managed access model (StrongDM with Cisco Duo), and encrypted machine-to-machine links between remote sites (Q-Net Security hardware). NIST describes them as product-agnostic demonstrations of capability, not vendor endorsements.

    Executive Summary

    NIST’s new practice guide gives water utilities a concrete starting point for what it calls the most pressing gap in their operational technology (OT), the networks of controllers and sensors that physically run pumps, valves and treatment processes. Utilities have moved from manual, on-site operation to automated and remotely managed systems. That shift improved efficiency but opened two-way paths into equipment that, if misused, could let an attacker manipulate processes or create unsafe conditions.

    The guidance matters to the data center industry because water is an upstream dependency for many facilities that use water-based cooling. A data center can harden its own networks and still depend on a municipal control system it neither owns nor sees into. NIST’s focus on remote access marks where that external dependency is most exposed, and its emphasis on staffing and funding signals that many utilities will struggle to close the gap quickly.

    The guide is also a snapshot of a market decision facing thousands of utilities: build remote-access security in-house, rent it from a cloud provider, or encrypt the links between sites in hardware. Each choice moves risk to a different place, and at least one vendor is already arguing that none of these designs goes far enough.

    Why a Water Security Guide Is a Data Center Story

    A water-cooled data center, especially one that relies on evaporative cooling towers, depends on a steady supply of water at usable pressure and quality. That supply is managed by the local utility’s SCADA (supervisory control and data acquisition) system: servers, programmable logic controllers (PLCs, the small industrial computers that switch pumps and valves) and operator screens spread across a main control station, regional centers and remote pump stations. NIST’s point is that these pieces are held together by remote access, both people logging in from outside and machines at separate pumping stations talking to each other over telecom networks. The guide warns that enabling two-way remote access to operational or safety systems widens the attack surface and could allow malicious actors to manipulate processes or create unsafe conditions.

    Follow that chain downstream and the conclusion in our headline follows. For a facility whose cooling depends on municipal water, the earliest point at which a cyber incident could reach its cooling is not its own chiller plant. It is the utility’s remote-access layer: the vendor login to an operator screen, the cloud-brokered session, or the unencrypted controller traffic between pump stations. NIST’s three blueprints map directly onto those three entry points, which is why they read as a map of inherited risk for anyone siting or operating water-cooled capacity.

    The exposure is uneven. Facilities using air cooling or closed-loop liquid systems draw far less from the municipal supply than those running evaporative towers, and any disruption at a utility would hit hospitals, homes and industry first and just as hard. The practical point is narrower and more useful: for water-dependent sites, the utility’s cyber posture belongs in site selection, resilience planning and supply conversations, alongside drought risk and capacity allocation.

    Utility Size Decides Which Blueprint Is Realistic

    NIST’s own figures show why no single design fits the sector. Very small community water systems, serving between 25 and 500 people, make up more than half of all systems but serve a small fraction of the population. Fewer than 1 percent of utilities are classified as very large, serving more than 100,000 people, yet they supply nearly half of the US population. Larger utilities tend to run dispersed infrastructure with complex SCADA networks and dedicated IT and OT staff. Smaller ones often rely on simpler or fully manual systems, aging hardware, few staff and tight budgets.

    That split drives the choice of architecture. NIST expects larger systems to support in-house deployments, while smaller utilities are likelier to lean on vendor-managed or cloud-based remote access. The consequence is that for a large share of the country’s water systems, security will rest heavily on the terms of a service contract. NIST explicitly warns utilities to check those contracts for encryption, access controls, intrusion detection and vulnerability management. The guide also stresses that adequate staffing and funding are essential to configure and maintain any of these environments, a quiet acknowledgment that the architecture is the easy part.

    Three Architectures, Three Places to Put the Risk

    The first design is the classic industrial pattern: a demilitarized zone (DMZ, a buffer network between the outside world and the control system) guarded by paired firewalls, with a remote access server inside it. Users authenticate with multi-factor authentication over HTTPS, role-based rules limit which SCADA consoles, operator screens and PLCs they can reach, and every session is logged. NIST demonstrated it with TDI ConsoleWorks. It offers the utility the most control and demands the most in-house skill.

    The second moves authentication, authorization and session monitoring to cloud-managed infrastructure. Users sign in with multi-factor authentication, receive an access token, and a security gateway inside the operational network checks that token before allowing a connection. NIST demonstrated this with StrongDM and Cisco Duo. It lowers the management burden, which is why NIST pitches it at smaller utilities, but it shifts part of the trust to the provider.

    The third addresses traffic that no person touches: PLCs and remote terminal units exchanging process data between sites. Here NIST recommends encryption appliances at each network edge that authenticate one another and encrypt data before it leaves. The demonstration used Q-Net Security’s Q-Box hardware to protect Modbus traffic between PLCs. Modbus is a long-established industrial protocol that was not designed with built-in encryption, so this design closes a gap that access controls on human users do not address.

    The Hardware-Isolation Critique, Weighed Fairly

    Mike Carr, field CTO of Xona Systems, agreed that remote access is the water sector’s biggest cybersecurity weakness, but argued that the named products are all software sitting on the same attack surface adversaries already exploit. He called for hardware-enforced protocol isolation that physically separates the user from the critical network, summarised as “You can’t hack what you can’t reach.”

    The critique makes a real point: remote-access gateways are themselves internet-facing targets, and adding controls to an exposed path is not the same as removing the path. Several parts of the claim need testing against the guide itself, though. The third implementation is hardware-based. NIST presents all three as product-agnostic examples rather than recommended products. And NIST treats remote diagnostics and vendor maintenance as operational necessities, while noting that some utilities already choose one-way alarming or on-site work for sensitive tasks. The statement that attacks are seen “in the field every day” comes without incident data. Utilities weighing the argument should ask any vendor, including this one, for evidence that its model preserves the maintenance access they actually need.

    Background

    NIST, part of the US Department of Commerce, publishes widely used cybersecurity frameworks and guidance. Its National Cybersecurity Center of Excellence produces the SP 1800 series of practice guides, which show how commercially available products can be combined to solve specific security problems in a given sector. SP 1800-45 applies that approach to water and wastewater utilities, a critical infrastructure sector made up of thousands of systems that range from tiny rural operators to large metropolitan authorities.

    For the data center industry, water has become a central siting and operating issue as capacity grows for AI and cloud workloads. Facilities that cool with water depend on the reliability of local utilities, which puts utility resilience, including the cybersecurity of utility control systems, alongside power availability and land in data center planning.

    Sources

    Source: NIST SP 1800-45 outlines remote access security as key priority for water, wastewater sector amid expanding OT cyber risks (Industrial Cyber): coverage of NIST’s practice guide on securing remote access to water and wastewater operational technology.

  • Why Liquid Cooling Is Non-Negotiable for High-Density AI Factories

    Why Liquid Cooling Is Non-Negotiable for High-Density AI Factories

    Data Center Dynamics published an analysis on 25 June 2026 contrasting the cooling demands of AI factories — facilities purpose-built for dense GPU training and inference clusters — with those of conventional cloud data centers, arguing that liquid cooling is now essential for high-density AI workloads rather than an optional upgrade.

    The piece lands amid an industry-wide retooling: operators worldwide are redesigning halls, mechanical plants, and supply chains around direct-to-chip and other liquid cooling approaches as accelerated computing outgrows the air-cooled designs that served the cloud era.

    Executive Summary

    The core claim is straightforward: the data center designs that carried the cloud computing era are hitting a physical ceiling. Conventional cloud halls were engineered around air cooling — moving chilled air through racks drawing power in the single-digit-to-low-double-digit kilowatt range. AI training clusters concentrate far more power in each rack, because modern GPU systems pack accelerators tightly together to keep them on fast, short interconnects. At those densities, air simply cannot carry heat away fast enough, and liquid — which is far denser and holds vastly more heat per unit volume than air — becomes the only practical medium.

    Why it matters: cooling is no longer a back-of-house mechanical detail but a gating factor for who can host AI workloads at all. Operators with liquid-ready facilities can court the highest-value tenants; operators with legacy air-cooled halls face expensive retrofits or a narrowing addressable market. For enterprises buying AI capacity, a provider’s cooling architecture is now a proxy for whether it can actually deliver current-generation GPU infrastructure.

    The analysis frames this as a structural divide — ‘AI factory’ versus ‘cloud hall’ — rather than a spectrum, which is a useful lens even if real-world facilities often blend both.

    The Physics Sets the Deadline, Not the Marketing

    Air cooling works by blowing large volumes of conditioned air through servers, and it has a well-understood practical ceiling: as rack power climbs, the airflow, fan energy, and temperature gradients required become unmanageable. Liquid cooling — most commonly direct-to-chip cold plates, where coolant flows across a metal plate bonded to the processor, or immersion, where hardware is submerged in a dielectric (electrically non-conductive) fluid — removes heat at the source with far greater efficiency. This is not a vendor preference; it is thermodynamics. Water-based coolants can absorb on the order of thousands of times more heat per unit volume than air, which is why every leading accelerated-computing platform roadmap now assumes liquid at the high end.

    The important nuance is that the ceiling is not a single number. Well-engineered air systems with hot-aisle containment can stretch surprisingly far, and many inference and enterprise workloads will remain comfortably air-coolable for years. The ‘non-negotiable’ framing applies specifically to dense training clusters, where chips must sit physically close together for interconnect performance. Density is a networking decision as much as a thermal one — and that is precisely why it cannot be relaxed just to make cooling easier.

    Economics: Liquid Costs More Up Front and Less to Run

    Liquid cooling shifts spending from operations to capital. Cold plates, coolant distribution units, manifolds, leak detection, and plumbing add up-front cost and engineering complexity that air systems avoid. In exchange, operators typically get lower fan energy, better power usage effectiveness (PUE — the ratio of total facility power to IT power, where closer to 1.0 is better), and the ability to run warmer coolant loops that reduce or eliminate energy-hungry chillers. Heat captured in liquid at useful temperatures is also far easier to reuse — for district heating or industrial processes — than diffuse warm air.

    The strategic consequence is that cooling architecture now shapes site selection and facility economics together. A liquid-cooled AI factory can put more revenue-generating compute on the same power envelope, which matters enormously when grid connections — not land or capital — are the scarcest input in the industry. That said, buyers should treat sweeping efficiency claims with care: realized PUE depends on climate, design discipline, and utilization, and figures quoted for flagship builds do not automatically transfer to retrofits.

    Winners, Losers, and the Retrofit Question

    The clearest winners are operators and builders that committed early to liquid-ready designs — reinforced floors for heavier racks, space for coolant distribution, higher-capacity power delivery — along with the supply chain behind them: cold-plate and CDU manufacturers, fluid suppliers, and mechanical contractors with liquid experience. Chipmakers benefit too, since liquid cooling removes a constraint on how much power their next generations can draw.

    The harder story is the installed base. Thousands of existing air-cooled halls cannot be casually converted: adding liquid means new piping, floor loading analysis, leak-management protocols, and often a rethink of the entire mechanical plant. Some facilities will be retrofitted profitably, some will serve the still-large market for air-coolable workloads, and some will be stranded relative to AI demand. For colocation providers, the honest question customers should ask is not ‘do you support liquid cooling?’ but ‘how many megawatts of it can you deliver, at what density, and by when?’

    Operational Risk: New Skills, New Failure Modes

    Bringing liquid into the white space introduces failure modes the air-cooled era rarely faced: leaks near live electronics, coolant chemistry maintenance, and the coordination of facility water loops with IT equipment loops. None of these are exotic — mainframes were water-cooled decades ago, and modern systems are engineered with negative-pressure loops and leak detection — but they demand skills that many data center operations teams are still building. Expect certification programs, standardized quick-disconnect fittings, and reference designs to matter as much as raw technology in determining who executes this transition smoothly. The industry’s real constraint may be trained people, not parts.

    Background

    Data center cooling has followed computing density for decades: water-cooled mainframes gave way to air-cooled commodity servers in the client-server and cloud eras, when racks drawing modest power made air the cheap, simple choice. The generative AI boom reversed the trend — modern accelerator systems concentrate unprecedented power in single racks, and leading GPU platform roadmaps now assume liquid cooling at the high end, pulling the entire industry’s mechanical design along with them.

    Data Center Dynamics, the publication behind this analysis, is a long-established trade outlet covering data center design and operations. Its framing of ‘AI factories’ versus conventional cloud facilities echoes terminology popularized by the accelerated-computing industry to describe purpose-built AI infrastructure — a sign of how thoroughly that vocabulary has permeated the sector.

    Source: AI factory cooling vs cloud data centers: Why liquid cooling is essential for high-density AI workloads — a Data Center Dynamics analysis, published 25 June 2026, on why liquid cooling has become a baseline requirement for dense AI infrastructure.