The Globe and Mail has published a watchlist commentary on Coherent Corp (NYSE: COHR), the photonics and engineered-materials maker, arguing that the stock is “cooling off just as its AI thermal opportunity heats up.” The piece frames a recent share-price pullback against what it presents as a growing opportunity for Coherent in thermal management for AI computing infrastructure.
This is investor commentary rather than a company announcement: Coherent has not, in this item, disclosed new products, contracts, or financial targets. The interesting question the piece surfaces is a structural one — whether heat removal, rather than chip supply, is becoming the binding constraint on how densely operators can pack AI accelerators into a rack.
Executive Summary
The commentary positions Coherent as a beneficiary of a well-documented shift in data center engineering: as AI accelerators draw ever more power per chip and per rack, traditional air cooling runs out of headroom, pushing operators toward liquid and advanced thermal solutions. In that framing, companies that supply thermal components and materials sit on the critical path of AI buildout alongside — and in some respects ahead of — the chipmakers themselves.
Why it matters: Coherent is best known in AI infrastructure for optical transceivers, the laser-based modules that carry data between GPU servers. A credible second exposure in thermal management would broaden its AI story beyond optics. But readers should be clear-eyed about what this item is: a stock-watch article pairing a price decline with a thematic opportunity. The theme — thermal as a gating constraint — is real and widely corroborated across the industry. The company-specific claim — that Coherent is positioned to capture it in size — is asserted here rather than evidenced with disclosed design wins, revenue figures, or customer names.
Why Cooling Is Becoming the Binding Constraint
For most of data center history, air cooling was sufficient: fans and chilled airflow could remove the heat a rack of servers produced. AI accelerators have broken that model. Each generation of GPU draws substantially more power than the last, and operators want them packed tightly together because AI training performance depends on short, fast connections between chips. More power in less space means more heat in less space — and air, a poor conductor, simply cannot carry it away fast enough at the densities modern AI racks demand.
The industry’s answer is liquid cooling in its various forms — cold plates bolted directly to chips, rear-door heat exchangers, and immersion systems — along with the pumps, coolant distribution units, interface materials, and specialty components that make those systems work. The practical consequence is that a data center’s usable capacity is increasingly set by how much heat it can reject, not by how many chips it can procure. That is the structural insight behind the editorial framing here, and it is well supported by how hyperscalers and colocation providers are actually redesigning facilities.
Where Coherent Fits — and Where the Evidence Thins Out
Coherent’s clearest and best-documented AI exposure is optical: it is one of the major suppliers of the high-speed optical transceivers that link GPU clusters inside AI data centers, a business that scales directly with AI networking buildout. On thermal management specifically, Coherent’s heritage is in engineered materials and components — including thermoelectric cooling technology from its acquisition history and deep expertise in materials such as silicon carbide and diamond that are valued precisely for how they handle heat. That is a plausible foundation for a thermal-management business serving AI systems.
Plausible, however, is not the same as demonstrated. This commentary does not cite disclosed thermal-management revenue, named customers, or design wins in AI cooling, and none are announced in the source item. Investors evaluating the thesis should look for those specifics in Coherent’s own filings and earnings materials. It is equally worth noting that the thermal opportunity has many claimants: established cooling and power-infrastructure vendors, cold-plate and coolant-distribution specialists, and component makers are all converging on the same market, and the eventual split of value among them is far from settled.
Reading a Watchlist Piece for What It Is
The article’s hook — a stock “cooling off” while its opportunity “heats up” — is a valuation argument, not a news event. Such framing can be useful: markets do sometimes mark down a company’s shares for near-term reasons even as a long-cycle demand driver strengthens. But the same framing can dress up an ordinary pullback as a buying opportunity without establishing that the underlying business has changed. The honest read is that the macro thesis (thermal constraints on AI density) stands on broad industry evidence, while the micro thesis (Coherent as a distinct winner in thermal) rests, in this piece, on positioning rather than disclosed numbers.
For infrastructure operators and buyers, the takeaway is less about one stock and more about procurement reality: cooling capability is becoming a first-order selection criterion for sites, racks, and system vendors. Facilities designed only for air cooling face expensive retrofits, and supply of liquid-cooling components has become a schedule risk on AI deployments in its own right. Whoever the eventual share winners are, the direction of spend is not in serious dispute.
Background
Coherent Corp traces its lineage to II-VI Incorporated, a Pennsylvania-based engineered-materials and photonics company founded in 1971, which grew through decades of acquisitions — including thermoelectric-cooler maker Marlow Industries and optical-component businesses — before acquiring laser maker Coherent Inc. in 2022 and taking its name. Today the company supplies lasers, optical networking components, and specialty materials across telecom, industrial, and data center markets, with AI data center networking emerging as a headline growth driver.
The market backdrop is the rapid escalation of power density in AI computing. Each accelerator generation draws more power, and clustering them tightly is essential to training performance, pushing rack heat loads beyond what air cooling handles economically. That has turned liquid cooling and advanced thermal components from a niche into one of the fastest-moving segments of data center infrastructure spending.
Shares of Modine Manufacturing (NYSE: MOD) jumped after Hunterbrook published a report, based on what it describes as leaked files, claiming the thermal-management company has a roughly $4 billion deal tied to Google and a data center cooling demand pipeline of about $23 billion that also links Amazon as a customer. Multiple financial outlets, including Benzinga, Proactive, and Pluang, relayed the report on August 22, 2026.
Neither Modine, Google, nor Amazon has publicly confirmed the figures, which originate from the report rather than from any company disclosure.
Executive Summary
The claim at the center of the move is simple but large: a report by Hunterbrook, citing leaked documents, names Google and Amazon as customers behind a data center cooling pipeline it sizes at $23 billion, including a reported $4 billion arrangement connected to Google. For a company of Modine’s size — a century-old industrial thermal specialist rather than a hyperscale household name — numbers of that magnitude, if borne out, would represent a step-change in the scale of its data center business.
The market’s reaction is as informative as the claim itself. Investors bid the stock up on an unverified, third-party report — a signal of how hungry the market is for pure-play exposure to data center cooling. As artificial intelligence workloads push server racks toward power densities that air cooling alone cannot handle, the companies that move heat — through chillers, coolant distribution units, and liquid cooling systems — are being repriced as strategic AI infrastructure suppliers rather than cyclical industrial vendors.
What matters now is verification: whether the companies involved confirm, deny, or stay silent, and whether the reported pipeline reflects contracted backlog or aspirational opportunity. Those are very different things for a stock that just moved on the distinction being blurred.
Cooling Is Becoming the Buildout’s Next Bottleneck
For most of the data center industry’s history, cooling was a solved problem: blow enough cold air across the servers and manage the electric bill. AI has broken that model. Modern accelerator racks can draw many times the power of traditional server racks, concentrating heat beyond what air-based systems efficiently remove. The industry’s answer — liquid cooling, where coolant is piped directly to chips or to heat exchangers at the rack — requires specialized equipment, and the supplier base for that equipment is far smaller than the demand now chasing it.
That is the structural story that makes a report like this land so hard. Investors have already repriced power equipment makers, transformer suppliers, and generator manufacturers as AI bottleneck trades. Thermal management is the logical next link in that chain: every megawatt of new AI compute is also a megawatt of heat that must go somewhere. A report naming the two largest cloud builders as anchor customers of a mid-cap cooling specialist fits a narrative the market was already primed to believe.
What the Report Claims Versus What Is Confirmed
It is worth being precise about the evidentiary chain here. The $4 billion and $23 billion figures come from a media report citing leaked files — not from a Modine securities filing, an earnings call, or a customer announcement. Hyperscalers rarely confirm their suppliers, and suppliers are often contractually barred from naming hyperscaler customers, so silence from Google and Amazon would be unremarkable either way. As of the coverage cited, none of the three companies had substantiated the numbers.
The word “pipeline” also deserves scrutiny. In industrial sales, a pipeline is typically the total value of opportunities being pursued — not signed contracts, not backlog, and not revenue. If the $23 billion figure describes potential demand Modine is quoting against, the economic reality could differ substantially from what a headline reader might assume. The reports available do not make that distinction clear, and the distinction is worth billions.
The Messenger Matters: Reading a Hunterbrook Report
The source of the claim adds its own analytical wrinkle. Hunterbrook operates an unusual model in financial media: a newsroom paired with an affiliated investment fund that can trade on its reporting before publication. In this case the report is bullish — a departure from the short-seller-style exposés such outlets are better known for — but the incentive question cuts the same way in both directions. Readers and investors should ask of any market-moving report: who benefits from the move, and was the evidence strong enough to justify it?
None of that makes the reporting wrong. Leaked documents can be accurate, and Hunterbrook’s work has moved markets before precisely because it is often substantive. But the fair standard is symmetrical: the same skepticism this publication would apply to an unverified vendor press release applies to an unverified media report, however sophisticated the outlet. Until Modine addresses the figures directly — in a filing, an earnings call, or a formal statement — the $23 billion number is a claim, not a fact.
Concentration Risk Hides Inside the Opportunity
Suppose the report is directionally right. Even then, the economics carry a caveat familiar to anyone who supplies hyperscalers: customer concentration. A supplier whose growth story rests on two buyers — however creditworthy — inherits their capital-expenditure cycles, their pricing leverage, and their willingness to dual-source or bring capabilities in-house. Hyperscalers have a long record of commoditizing their supply chains once a technology matures, from servers to networking gear.
The competitive field is also crowding fast. Established HVAC and infrastructure giants, specialist liquid cooling firms, and well-funded startups are all racing into the same thermal market. A large pipeline today says little about margins three years from now if the bidding field triples. For buyers of cooling equipment, that competition is good news — more capacity and better pricing. For any single supplier’s shareholders, it is the risk that tempers the headline number.
Background
Modine Manufacturing, founded in 1916 and headquartered in Racine, Wisconsin, spent most of its history as a heat-transfer specialist serving automotive and industrial markets. In recent years it has pivoted deliberately toward higher-growth thermal businesses, with data center cooling — including chillers and precision cooling systems — becoming a centerpiece of its climate solutions segment. That repositioning has coincided with the AI-driven data center boom, which has turned formerly unglamorous supply categories like power distribution and heat rejection into some of the market’s most closely watched bottleneck trades.
Hunterbrook, the report’s source, represents a newer breed of financial media: an investigative newsroom paired with an affiliated fund that can trade on its findings. Its reports have moved stocks in both directions before, which is why a bullish claim about Modine’s customer pipeline traveled so quickly through financial media despite lacking company confirmation.
Financial media reports in August 2026 highlight that GE Vernova’s orders for AI data-center equipment in the first half of the year have already doubled the total it booked in all of 2025, according to coverage from The Motley Fool syndicated across Yahoo Finance and The Globe and Mail. In parallel, Yahoo Finance analysis asks whether Eaton Corporation and Trane Technologies — suppliers of electrical distribution gear and cooling systems, respectively — can emerge as major winners from the same AI data-center boom.
None of the items is a company press release; they are investor-focused analyses built around the order-growth headline. But taken together, they point at a consistent industry story: the equipment that powers and cools AI facilities, not the chips inside them, is where demand is now outrunning supply.
Executive Summary
The headline claim is striking: in one half-year, GE Vernova — the energy-equipment company spun out of General Electric — booked more AI data-center orders than in the entire previous year. The coverage frames this as evidence that hyperscalers and data-center developers are racing to lock in turbines, grid equipment, and electrical infrastructure years ahead of need. The companion piece extends the thesis to Eaton, which makes the switchgear, transformers, and power-distribution systems inside data centers, and Trane, whose chillers and thermal-management systems remove the enormous heat that AI server racks generate.
Why it matters: for the past two years, the constraint on AI capacity was widely assumed to be GPU supply. These reports suggest the constraint is migrating downstream — to megawatts and cooling tons. A data center without secured power generation, electrical distribution, and heat rejection cannot deploy a single chip, no matter how many accelerators its owner has purchased. If order books at the equipment makers are filling this fast, delivery lead times become a strategic variable for everyone building AI infrastructure.
A caveat up front: the source material is investment commentary, not audited disclosure. The doubling claim originates in stock-analysis coverage, and the articles supply no dollar figures, customer names, or delivery schedules that we can independently verify from the release text alone. The direction of the signal is consistent across outlets; the precision of it is not something this coverage establishes.
The Bottleneck Has Moved Downstream from Chips to Electrons
Every AI data center is, functionally, a machine for converting electricity into computation and heat. The industry spent 2023–2025 focused on the computation side — who could get GPUs, and how many. But GPUs are a fast-cycle product: fabs can expand output on a timescale of quarters. Heavy electrical equipment is not. Gas turbines, large power transformers, and high-capacity switchgear are engineered-to-order products with lead times measured in years, built in a small number of factories worldwide. When demand doubles, capacity cannot.
That asymmetry is what makes the reported GE Vernova order surge significant beyond one company’s income statement. If AI data-center orders in six months exceeded all of last year’s, buyers are effectively queueing — paying now for delivery slots later. In infrastructure markets, a lengthening queue is the classic signature of a bottleneck: the constraint on how fast the AI buildout can proceed stops being capital or chips and becomes the physical delivery calendar of the equipment supply chain.
Three Companies, Three Layers of the Same Stack
The coverage bundles GE Vernova, Eaton, and Trane together for a reason: they occupy successive layers of the same value chain. GE Vernova sits upstream, supplying power generation and grid-scale equipment — the megawatts themselves. Eaton sits in the middle, making the electrical distribution gear — switchgear, uninterruptible power supplies, transformers — that moves power safely from the substation to the server rack. Trane sits at the end of the energy journey, providing the chillers and cooling systems that reject the heat those racks produce. In a conventional data center, cooling can consume a substantial share of total power; AI racks, which run far denser than traditional IT loads, intensify that thermal problem.
The strategic implication is that AI demand does not create one winner but a chain of them — and a chain of potential choke points. An operator who secures generation but not switchgear, or switchgear but not chillers, still cannot open. That is why the market is asking the Trane-and-Eaton question at all: if the upstream layer (GE Vernova) is visibly capacity-constrained, the same dynamic plausibly applies to the layers behind it. Plausibly — the coverage poses the question about Eaton and Trane rather than documenting equivalent order data for them, and that distinction matters.
Reading Order Books Honestly: Signal, Not Revenue
Orders are a forward indicator, not money in the bank. An order becomes backlog, backlog becomes revenue only upon delivery, and the coverage here does not disclose the dollar value of the orders, their delivery timeline, cancellation terms, or margin profile. History counsels some humility: capital-equipment cycles have seen order books swell during booms and thin out when customers re-time projects. If AI capital spending decelerates — because model economics disappoint, power prices spike, or financing tightens — equipment orders placed years ahead of need are among the first things large buyers revisit.
There is also a framing question worth noting even-handedly. All three source articles are investor commentary keyed to stock tickers, published across financial outlets asking “is the stock still a buy?” That genre rewards dramatic framing of growth statistics. The underlying fact pattern — surging demand for power and cooling equipment from AI builders — is consistent with what the broader industry has been experiencing, and nothing in the coverage appears contrived. But readers should distinguish between the well-supported directional claim (demand is heavily outrunning historical levels) and the precise multiples in headlines, which the articles as syndicated do not source to specific filings in the material available here.
What This Means for Anyone Building or Buying Capacity
For data-center operators and enterprise buyers, the practical takeaway is that procurement of electrical and thermal equipment has become a competitive discipline, not a back-office function. When lead times stretch, operators who ordered early hold an asset — a delivery slot — that late movers cannot buy at any price in the short run. Expect that advantage to show up in which projects actually energize on schedule, and in the pricing power of colocation providers who already hold contracted power and installed cooling.
For the equipment makers, the boom is an opportunity wrapped in a capacity-planning dilemma: expand factories aggressively and risk overcapacity if AI spending normalizes, or expand cautiously and cede share. How GE Vernova, Eaton, and Trane each answer that question — none of which this coverage addresses — will shape the supply side of the AI buildout for the rest of the decade.
Background
GE Vernova became an independent company in 2024 when General Electric split into separate aviation, healthcare, and energy businesses, giving the energy unit a standalone identity spanning power generation, wind, and grid electrification. Eaton is a long-established power-management company whose electrical segment supplies the distribution and backup-power equipment inside commercial facilities and data centers. Trane Technologies, formed from the 2020 separation of Ingersoll-Rand’s climate businesses, is one of the world’s largest suppliers of commercial HVAC and chiller systems.
The market context is the AI infrastructure buildout that accelerated from 2023 onward, as hyperscale cloud providers and specialized developers began constructing data centers of unprecedented power density to train and run large AI models. That expansion has pushed demand for generation capacity, grid interconnection, electrical gear, and industrial cooling well beyond historical data-center norms — turning previously unglamorous equipment categories into strategically contested supply.
A storm damaged the cooling system at a data center in Phoenix. A data center is a warehouse-sized building full of computers that must be kept cold to keep working. Without enough cooling, the web company Namecheap went dark for about 15 hours.
Websites, email and even Namecheap’s own customer help line went offline. Fixing the cooling took hours. Getting everything else running again took much longer.
This matters more as computers built for artificial intelligence, which run far hotter, fill new buildings. Backup cooling inside one building may not be enough.
Namecheap, a leading domain registrar and web host, suffered an outage of roughly 15 hours on August 14 after what it called “a failure of cooling systems” at its data center in Phoenix. TechRadar reported that the company’s hosting plans, its EasyWP managed WordPress service, DNS (the internet’s address book, which points domain names at servers) and email were all offline, along with customer accounts and Namecheap’s own support desk. The facility’s operator, PhoenixNAP, said heavy overnight storms had caused damage that led to “elevated white space temperatures” in the data halls.
Two of the data center’s four chillers were back online around 12pm EDT. Core databases returned at 5:30pm EDT, Namecheap.com and live chat about 40 minutes later, and full service at 3:50am EDT. Chief executive Hillan Klein apologised, saying “we failed you today,” and committed to publishing a post-mortem.
Executive Summary
A storm damaged the cooling plant of a Phoenix data center, and one of the best-known names in domain registration and small-business hosting went down for most of a day. Customers lost websites, email and name resolution. They also lost the usual way to get help, because Namecheap’s help desk and live chat went down with everything else.
The incident matters beyond Namecheap’s customers because it isolates a failure mode the industry usually discusses in terms of power: the thermal plant. The building had four chillers, the industrial refrigeration units that produce the cold water used to pull heat out of server rooms. That redundancy did not prevent an outage when a single external event, a storm, affected the cooling system. Even after partial cooling returned, restoring the service stack took many more hours.
That lesson gets sharper as operators pack far hotter AI hardware into new halls. When heat density rises, the time between losing cooling and losing servers shrinks. Resilience that stops at the building’s own chiller plant leaves less margin than it once did.
Four Chillers, One Storm
Data center cooling is normally designed with spare capacity. The common “N+1” approach means one more chiller than the load requires, so a single unit can fail or be serviced without consequence. Namecheap’s Phoenix facility had four chillers. Bringing two of them back online was the milestone that began recovery, which indicates the plant had been running well short of what the halls needed. Neither company had said, as of August 15, exactly how many units failed or which part of the cooling system the storm damaged.
This is the core distinction between component redundancy and site resilience. Spare chillers protect against one machine breaking. They protect far less well against a common-mode event, meaning a single cause that hits several pieces of equipment at once. Storm damage to shared infrastructure is exactly that kind of event. PhoenixNAP’s own explanation traced the problem to weather rather than to an isolated equipment fault, so the redundancy on paper did not match the resilience in practice.
Cooling Came Back Hours Before the Service Did
The published timeline is the most instructive part of this incident. Partial chiller capacity returned around noon. Core databases did not come back until 5:30pm, and the public website and live chat returned about 40 minutes after that. Full service was not restored until 3:50am. Most of the outage therefore happened after the thermal problem had begun to ease.
That gap reflects how hosting platforms behave after a thermal event. When a room overheats, servers throttle, shut themselves down, or are powered off by operators to protect the hardware. Bringing them back is not a single switch. Storage has to be verified, databases checked for consistency, and dependent services restarted in the right order. For anyone writing recovery objectives, the lesson is that restoring the cooling plant starts the recovery clock. It does not stop it.
Why AI Halls Can’t Rely on One Chiller Plant
Namecheap’s outage involved conventional web hosting, not AI hardware. The mechanism still applies directly to AI buildout. A room’s temperature rises after cooling fails at a rate set by how much heat the equipment keeps producing. Conventional hosting racks give operators some buffer. AI training and inference racks, built around dense GPU servers, concentrate far more heat in the same floor space, so the window for an orderly shutdown or failover gets shorter.
Liquid cooling, now standard in many AI designs, moves heat off chips more efficiently than air. It still hands that heat to the facility’s central plant: chillers, dry coolers or cooling towers. A storm that damages that plant affects a liquid-cooled AI hall as surely as it affected Namecheap’s hosting floor, and the higher density leaves less time to react. The practical conclusion for operators and tenants is that resilience needs to extend beyond one plant. That can mean heat-rejection equipment that is physically separated and weather-hardened, or the ability to move workloads to another site. Buyers of AI capacity have reason to ask colocation providers how their cooling redundancy performs against shared causes, not only against a single failed unit.
When the Help Desk Shares Fate With the Outage
The outage also took down Namecheap’s support channels, leaving staff unable to answer live chat or email. The company fell back on its status page and on Klein’s posts on X. Namecheap did keep customers informed with detailed status updates throughout. The episode still shows why tools for talking to customers during a crisis are best hosted somewhere other than the infrastructure they report on.
Klein’s commitment to examine “where our existing safeguards and protocols failed” is the right question. The test will be whether the post-mortem addresses architecture as well as the storm, specifically whether services like DNS and email had anywhere else to run when one building lost its cooling.
Background
Namecheap is one of the most widely used domain registrars, the companies that sell and manage website addresses. It also sells web hosting, managed WordPress through its EasyWP service, email and DNS, mostly to individuals and small businesses. Because many customers buy their domain, website and email from Namecheap together, one infrastructure failure can take down a business’s whole online presence at once.
Data centers spend a large share of their engineering effort on cooling, because nearly all the electricity servers use becomes heat. Chilled-water plants are the workhorse of larger facilities and are normally built with spare units. Heat density per rack is rising sharply as AI hardware spreads, so the reliability of this thermal plant is becoming as central to uptime as power supply and backup generators.
A company planning California’s largest data center, a huge building full of computers like the ones that run AI tools, was refused the water it needs to keep those machines cool. It is now suing the public agency that controls the local water.
The water comes from the Colorado River. About 40 million people in seven states depend on that river, and drought has strained it. The company wants to take over water that farms use today.
The court case could help decide whether water, not only electricity, limits where the AI boom can be built.
BGR reported on August 12, 2026 that Imperial Valley Computer Manufacturing has sued the Imperial Irrigation District (IID), the public agency that delivers Colorado River water to California’s Imperial Valley. IID had denied the developer’s request to use the district’s water supply and wastewater to cool a planned 330-megawatt data center, which would be the largest in California. The facility is expected to require 287 million gallons of water.
The developer plans to source that water by reallocating it from farmland. Its lawsuit argues there is “no rational basis” for IID to bar an industrial company from combining several fields into a single unit for water reallocation while allowing agricultural users to do the same.
Executive Summary
Most debate about where AI data centers can be built focuses on electricity: whether the grid can deliver hundreds of megawatts, and how fast. The Imperial Valley dispute puts a different constraint in front of a court. The developer’s cooling plan depends on moving existing farm water allocations to an industrial user, and the agency that controls those allocations has said no.
The legal question is narrow, whether an industrial user may aggregate and transfer water the way farmers already can. The practical stakes are broad. If water rules can block a 330-megawatt project in one of the most water-contested basins in the country, developers eyeing arid regions will have to treat water rights as a gating item on par with a power connection, not as a permit to settle later.
Sebastian Rucci, the figure leading the project, argues the data center would use about as much water as a 160-acre farm and would benefit the local economy. Critics counter that reallocating farm water puts rural communities at risk, and local residents broadly favor a moratorium on new data center construction.
Why Water Rights, Not Just Megawatts, Gate This Project
A data center’s electrical rating, here 330 megawatts, describes how much power its computers and support systems can draw. Nearly all of that electricity ends up as heat, which has to be removed continuously. Many large facilities do this with evaporative cooling, in which water is evaporated in cooling towers to carry heat away. That design is efficient on electricity but consumes water in proportion to the heat load, which is why a 330-megawatt campus arrives with a 287-million-gallon water requirement attached.
In the Imperial Valley, that water has to come from somewhere already spoken for. IID delivers Colorado River water to the region, and the developer’s plan is to reallocate water currently tied to farmland. IID’s denial covered both its water supply and its wastewater, closing off reclaimed water as well as fresh. As things stand, the project’s cooling design cannot proceed on the terms the developer proposed, whatever its power situation. That is the mechanism the title points to: a local water agency’s allocation rules, not the grid, are the decision point currently holding up this capacity.
The people affected extend beyond one developer. Anyone siting large facilities in the arid Southwest, where land and solar power are attractive, now has a live example of water access acting as a hard stop. Water agencies are affected too, since a ruling will signal how much discretion they have to treat industrial and agricultural transfers differently.
The Legal Question: Can Industry Aggregate Water Like Farms Do?
The lawsuit’s core argument is about equal treatment. According to the complaint as described, IID lets agricultural users combine multiple fields into a single unit for water reallocation but refused to let an industrial company do the same. The developer says there is “no rational basis” for the distinction.
That phrasing echoes a well-established legal standard under which a government body’s line-drawing generally survives if any reasonable justification exists. It is typically a demanding standard for challengers. IID may point to its mandate to serve agriculture, drought conditions on the Colorado River, or the difference between water that stays in farming and water permanently moved to a new industrial use. The developer will argue that water is water and that the district cannot favor one class of user without a principled reason.
Either outcome sets a marker. A win for the developer would suggest farm water allocations in the district can be assembled and redirected to data centers, which would change land and water economics in the valley. A win for IID would confirm that water agencies can effectively decide whether water-intensive data centers get built in their service areas.
Testing the 160-Acre Farm Comparison
Rucci’s claim that the facility would have as much impact as a 160-acre farm can be checked against the stated figure. Dividing 287 million gallons across 160 acres works out to roughly 1.8 million gallons per acre, or about 5.5 acre-feet, the unit water managers use, where one acre-foot covers an acre of land a foot deep. Whether that matches a typical Imperial Valley farm depends on the crop and on whether 287 million gallons is an annual figure, which has not been specified.
The comparison also answers a different question from the one critics are asking. Their objection is less about the total volume than about where it comes from: water moved out of farming, in a region where agriculture sustains rural livelihoods, from a river that serves 40 million people across seven states. It also leaves open how much of the data center’s water would be consumed through evaporation versus returned to the system, a distinction that matters for downstream users.
Local Opposition Is Part of the Siting Math
Residents in the area generally favor a strict moratorium on new data center construction, according to the reporting, reflecting concerns about utility prices and water strain that have followed data center projects in other regions. Rucci has argued the project would bring economic benefits, but the specifics of jobs, tax revenue or community agreements have not been laid out.
For the industry, the lesson is that community sentiment and water governance are converging into a single siting risk. A project that must win both a court case over water allocation and a skeptical local public faces a longer and less predictable path than one that secures water and community support before it announces its scale.
Background
The Imperial Valley sits in California’s southeastern desert and depends on Colorado River water delivered by the Imperial Irrigation District, much of it for farming. The Colorado River supplies roughly 40 million people across seven U.S. states and has faced years of drought, making any new large draw on it contentious.
Data centers have become one of the fastest-growing sources of industrial demand for both electricity and water as companies build capacity for artificial intelligence. Their water use comes mainly from cooling, and communities in several regions have pushed back on new facilities over utility costs and water strain, making water sourcing an increasingly visible part of the siting process.
In July, hackers broke into the control systems of a drinking-water provider in Oregon. Officials have not said which one. It was part of a wave that reached at least 100 American water systems in seven states.
The attackers reached small computers that run pumps and valves over the internet and locked operators out. Some towns saw water pressure drop, flooding, or a precautionary boil-water notice.
This matters beyond your tap. Many of the huge computer buildings behind artificial intelligence use local tap water to stay cool, so a hacked water utility is their problem too.
Oregon Public Broadcasting reported on August 7, 2026, that Oregon Gov. Tina Kotek’s office confirmed hackers gained access to an Oregon water district’s core operating technology in July. Oregon Enterprise Information Services, which provides cybersecurity support to state agencies, confirmed a July incident involving unauthorized access to operational technology used by an Oregon drinking-water provider. State officials would not name the district, would not say whether the incident is linked to a wider campaign, and said the impact is still being assessed.
The Oregon disclosure follows a July 30 New York Times report that at least 100 US water systems had detected malicious attacks, which the paper said were initiated by hackers in Iran. An FBI release the same day acknowledged attacks in seven states and said some of the activity degraded water operations. At least 30 systems were targeted in Minnesota, where one treatment plant temporarily went offline; in Georgia, one system lost water pressure and briefly issued a boil-water precaution.
Executive Summary
An unnamed Oregon drinking-water provider is the latest confirmed victim of a July campaign against municipal water and sewage systems. According to the FBI, attackers connected over the internet to programmable logic controllers, the small industrial computers that run pumps and valves, then changed their IP addresses and passwords, cutting operators off from monitoring and control. Reported effects included loss of pressure and flooding.
For the data center industry, the episode matters because water is an operating input, not only a sustainability metric. Facilities that reject heat through evaporative cooling draw continuously on local utilities. The campaign shows that at least 100 of those utilities could be reached directly from the internet within days. A data center’s resilience therefore depends partly on the security of infrastructure it neither owns nor audits.
The same class of controller also runs chilled-water plants, pumps and valves inside data centers. The FBI’s guidance to water utilities, to limit remote access and keep manual fallback procedures, reads as a checklist for cooling plant operators as well.
What the FBI Says Happened
The FBI’s description of the attack is short and specific. Between July 27 and July 30, attackers connected to programmable logic controllers (PLCs), rugged single-purpose computers that open valves, start pumps and read sensors, directly over the internet. They then changed the devices’ IP addresses and passwords, which the agency said resulted in a loss of monitoring and control. Operators could no longer see or steer parts of their own systems.
The consequences were physical. The FBI said operational effects included loss of pressure and flooding, and warned that pressure loss could potentially allow untreated groundwater to seep into pipes. In Minnesota, where at least 30 systems were targeted, one treatment plant temporarily went offline. In Georgia, a pressure drop prompted a precautionary boil-water notice.
Attribution remains partly open. The New York Times reported the campaign was initiated by hackers in Iran. Oregon officials declined to say whether their incident belongs to that campaign and referred questions to federal investigators. That caution is reasonable while the investigation continues, and the operational lessons do not depend on who was behind the attacks.
Why AI Cooling Risk Sits Outside the Fence
Many data centers remove heat with evaporative cooling towers, which lose water continuously to evaporation and replace it, as so-called makeup water, from the local municipal supply. AI clusters pack far more heat into each rack than conventional servers. Even when the chips are cooled directly by liquid, that heat still has to leave the building, and in many designs it leaves through those same towers. For a significant share of facilities, water pressure at the meter is as much a dependency as power at the substation.
The failure modes the FBI listed match that dependency closely. A pressure loss or an offline treatment plant is exactly the event a cooling plant’s onsite water storage exists to ride through. What the July campaign changes is the likelihood and breadth of that event. At least 100 systems in at least seven states were affected in a matter of days. The technique did not require first breaching a corporate network, because the controllers were reachable from the internet. A risk that site selection teams once modelled as drought or main breaks now has a cyber path, and it can hit many utilities simultaneously.
The people exposed are the ones who build, lease and finance capacity: operators sizing onsite storage, tenants whose service agreements assume cooling continuity, and lenders underwriting sites in water-dependent markets. Oregon hosts major data center clusters around Hillsboro, The Dalles and Prineville. Nothing disclosed so far indicates the affected district serves any of them. The point is structural, not local.
The Same Controllers Run the Chiller Plant
PLCs are not unique to water utilities. The same kind of device, often from the same vendors, sequences chillers, modulates pumps and controls valves in data center mechanical plants. Remote access is common there for the same reason Tualatin Valley Water District spokesperson Justin Dyke gave for utilities: it lets operators respond quickly when demand shifts. The convenience cuts both ways when a controller’s management interface is reachable from the open internet.
The FBI’s recommendations to water systems translate almost directly. Limit remote access to controllers. Enforce authentication and other security protocols. Maintain manual procedures for when automation is lost. Dyke described that last practice at his district: if online systems go down, operators make changes by hand. Data center operators can apply the same test to their cooling plants and ask whether staff can run the chilled-water system safely with the controls network unavailable.
A Defense Spread Across 2,500 Districts
Oregon has more than 2,500 water districts, and all of its drinking-water systems are locally managed. Oregon Enterprise Information Services consults with districts on cybersecurity and threat assessments but does not run them. That structure is typical of US water and puts security decisions in the hands of many small operators with limited budgets and staff.
It also limits disclosure. The Portland Water Bureau said it routinely examines vulnerabilities with the city’s security team, the Cybersecurity and Infrastructure Security Agency and the FBI, but declined to say whether it was targeted in July. Tualatin Valley reported an increase in attempts, none successful. Klamath Falls and Corvallis reported no increase, and Redmond and Bend reported no breach. For a large water customer, that patchwork means a utility’s security posture is mostly invisible unless the customer asks directly.
Background
Water utilities have spent years connecting their treatment plants, pumping stations and reservoirs to remote monitoring. This lets small operating teams adjust flow and reservoir levels quickly as demand changes. The same connectivity, when controllers are reachable from the internet, exposes physical equipment to remote tampering. Federal agencies, including the FBI and the Cybersecurity and Infrastructure Security Agency, work with utilities on these risks, but in states like Oregon, day-to-day operation and security remain local responsibilities.
Oregon is also one of the country’s established data center markets, with large campuses around Hillsboro, The Dalles and Prineville. Water supply has featured in local debate about their growth. As AI workloads raise heat density per rack, cooling design, and for many facilities the municipal water behind it, has become a primary constraint on where and how capacity gets built.
A company wants to build a data center, a large building full of computers, in Salem, Oregon. On the hottest days, its design lets it use up to 3 million gallons of water, about four and a half Olympic swimming pools, mostly to keep those computers cool.
Over a full year it expects to use far less, about 39 million gallons. The catch is timing: its thirstiest days are the same hot days when everyone else in town is also using the most water.
Salem’s city council is now discussing whether to pause projects like this.
A data center proposed for Salem, Oregon, is designed to use up to 3 million gallons of water a day on the city’s hottest days and roughly 39 million gallons over a full year, the Salem Reporter reported on August 5, 2026. The figures describe the first phase of a proposed multi-phase project from developer Verrus.
The numbers arrive as local opposition grows. Residents holding signs against the project attended a Salem City Council meeting this summer, where the council heard public comment and discussed the process for a possible moratorium on the project.
Executive Summary
The two water figures attached to Salem’s proposed data center describe very different things. Roughly 39 million gallons a year works out to an average of about 107,000 gallons a day. The design peak of 3 million gallons on the hottest days is roughly 28 times that average. The annual number describes volume; the peak number describes how much capacity the city’s water system must be able to deliver on a single day.
That distinction matters because water systems are sized, and water is rationed, around the highest-demand days of the year. A facility whose use spikes during heat waves asks for the most water precisely when residential and irrigation demand is also at its maximum and western Oregon is in its dry season.
For the wider data center industry, Salem is a preview of the question operators will increasingly face: not how much water a campus uses in a year, but how much it needs on the worst day, and who gets priority when the two collide.
The Number That Matters Is the Peak Day
An annual figure of 39 million gallons sounds modest next to a city’s total water use, and that is the figure most likely to appear in a sustainability report. But the arithmetic behind the two disclosed numbers tells a different story. Spread evenly, 39 million gallons is about 107,000 gallons a day. The 3-million-gallon design peak is roughly 28 times that. Put another way, just 13 days at full peak would consume the entire annual total. The facility’s water demand is not a steady trickle; it is a low baseline with sharp spikes tied to temperature.
Water utilities plan around those spikes. Treatment plants, pumps, pipes and storage reservoirs are built to meet the maximum-day demand, not the yearly average, because running short on one hot afternoon is the failure that matters. A large user whose demand concentrates in heat waves therefore consumes a disproportionate share of the system’s capacity, even if its annual share looks small. That is why the annual figure understates the strain, and why the peak figure is the one city engineers and councils need to evaluate.
The people most affected are the ones already competing for water on those days: households watering lawns and gardens, farms irrigating crops, and the rivers that carry less water in late summer. A data center’s peak does not create that summer squeeze, but it lands directly on top of it.
Why Cooling Demand Tracks the Thermometer
Data centers turn nearly all the electricity they use into heat, and that heat has to go somewhere. One of the most energy-efficient ways to move it out is evaporation: warm water passes through cooling towers, some of it evaporates, and the evaporation carries heat away, much as sweat cools a person. The hotter and drier the outside air, the more water that approach consumes. A design that uses little water most of the year but spikes on hot days is the typical signature of cooling that leans on evaporation when outside air alone is not enough.
The alternative, often described as closed-loop or dry cooling, recirculates the same water and rejects heat through fans and chillers, much like a car radiator. It uses far less water but more electricity, and its electricity demand also rises on hot days, when the regional power grid is under its own peak load. Neither choice is free. The design decision determines whether a facility leans on the water system or the power system during a heat wave, and on the hottest days both are stretched.
A First Phase, and a Council Weighing a Pause
The disclosed figures cover only the first phase of a multi-phase project. Whether later phases would add proportionally to peak water demand, use a different cooling design, or rely on another water source has a direct bearing on how the city should read the first-phase numbers. A peak figure that is manageable for one building could look different across a full campus.
The council’s discussion of a possible moratorium reflects how quickly the conversation shifts once peak-day figures are public. Communities elsewhere have used tools short of an outright pause: caps on peak-day withdrawals, contracts requiring a facility to curtail water use during declared shortages, requirements to use recycled rather than drinking water, and on-site storage that fills during cool periods. Which of these, if any, are on the table in Salem is not yet clear, but they show that the peak problem has engineering and contractual answers, not only yes-or-no ones.
Background
Salem is Oregon’s capital, and Oregon has long been a data center destination, with large campuses in Hillsboro west of Portland and along the Columbia River in The Dalles and in Central Oregon at Prineville. Cool climates, available land and access to power have drawn operators, and water has become a recurring point of friction: in The Dalles, the scale of Google’s water use became public only after a public-records dispute.
Across the industry, water disclosure has lagged behind energy disclosure. Operators often report annual totals or efficiency ratios in sustainability reports, but peak-day demand, the figure that determines how much capacity a municipal system must provide, is rarely published. As data center demand grows, local governments are increasingly asking for that number before approving new campuses.
A coordinated cyberattack hit more than 30 local public water systems in Minnesota this week. Federal investigators are now involved.
The damage was small. One city’s well and treatment plant briefly shut down, and there are no signs the tap water became unsafe. Officials say early clues point to hackers linked to Iran, but that is not confirmed.
Why it matters beyond Minnesota: the giant computer buildings behind apps and artificial intelligence often cool their machines with water from these same local utilities. A hack at the water plant is a risk to them too.
A coordinated cyberattack affected more than 30 community water systems in Minnesota in the week of July 27, according to Minnesota IT Services, the state’s technology agency, Al Jazeera and Reuters reported on July 30. Local officials said the well and treatment plant in at least one city temporarily went offline, but the attack appears to have had minimal impact, and there are no indications the water supply became unsafe to drink. The FBI said on Wednesday that it was “actively engaged with victims.”
State officials said the incidents resemble activity attributed to hackers aligned with Iran’s government, and cautioned that the assessment is preliminary. The New York Times, citing unnamed state and federal officials, reported that Iranian hackers were the likely culprits, while noting that attribution is not definitive and that the attackers may have posed as Tehran-aligned actors. The attack comes amid renewed US–Iran hostilities after a June memorandum of understanding that paused the fighting broke down this month.
Executive Summary
More than 30 local water systems in a single US state were hit in what state officials called a “coordinated cyberattack.” The physical consequences were limited: one city’s well and treatment plant briefly went offline, and drinking water was not reported unsafe. But the breadth of the event and the FBI’s involvement put the security of utilities’ operational technology (OT) — the industrial control systems that run pumps, wells and treatment processes — back at the top of the critical-infrastructure agenda.
For the data center industry, the relevance is indirect but concrete. Many facilities that use evaporative cooling draw their water from municipal systems like these. The Minnesota incident is a reminder that a data center’s cooling supply depends not only on its own cybersecurity but on the cybersecurity of utilities it neither owns nor controls, and whose security resources are set by local public budgets rather than by the data center.
No data center has been reported as affected, and attribution of the attack remains preliminary. The episode’s value for operators is as a live test of a common assumption: that water outages are local, physical events rather than coordinated ones spanning many utilities at once.
The Cooling Risk Sits Outside the Fence
Data centers that use cooling towers or other evaporative systems reject heat by evaporating water, which has to be continuously replaced — industry shorthand calls this “makeup water.” Where a campus draws that water from a municipal utility, the utility’s wells, pumps and treatment plant become part of the data center’s cooling chain. Those assets are run by OT: programmable controllers and supervisory software that start pumps, open valves and dose chemicals. An intrusion that reaches them is, for every large water customer downstream, a cooling-supply risk.
Minnesota shows what that looks like in practice. More than 30 community water systems were affected in a single coordinated event, and in at least one city the well and treatment plant went temporarily offline — precisely the kind of interruption that stops water flowing to a large commercial customer. Water resilience planning has traditionally centered on familiar, local failures: a main break, a drought restriction, a fault at one plant. A coordinated intrusion is different in kind, because it can reach many independent systems at once, which may also affect neighboring utilities or backup supplies an operator expected to lean on.
The exposure is not uniform. AI training and inference hardware concentrates far more heat per rack than conventional servers, pushing operators toward liquid cooling and larger heat-rejection systems. Closed-loop liquid cooling and air-cooled chillers consume little water; evaporative designs trade lower electricity use for continuous water dependence. The operators most exposed to an event like Minnesota’s are water-reliant sites on a single municipal feed with limited on-site storage. Nothing reported so far indicates that any data center served by the affected systems saw an interruption, and the outage that did occur was brief.
Minimal Impact Is the Good News, Not the Whole Story
The most important outcome is that drinking water was not reported unsafe and service disruption appears to have been limited. That is a credit to the utilities and responders involved, and it should temper any alarm.
But officials’ own description points to a campaign rather than a one-off. Minnesota IT Services spokesperson Emily Zimmer said “the timing, methods of access, and targeted infrastructure share characteristics with other coordinated cyber incidents our federal partners have observed involving critical infrastructure.” Community water systems often run lean, with limited dedicated security staff, which makes a campaign that sweeps many of them at once hard to defend against one utility at a time.
The number that matters for infrastructure planners is therefore the 30-plus, not the single outage. Breadth indicates that access was obtainable across many separate operators simultaneously. A contained result this time says more about where the attackers stopped than about how far they could have gone.
Attribution Is Unsettled; the Threat Picture Is Not
State officials said the incidents resemble those attributed to Iran-aligned hackers but called that assessment preliminary. Officials cited by The New York Times said Iranian hackers were the likely culprits, while acknowledging the conclusion could change and that the attackers may have impersonated Tehran-aligned groups to raise tensions. Former intelligence officials told the paper a false-flag operation was less likely. The fair reading is that the available indications point one way but are not conclusive, and no technical evidence has yet been published that would let outside analysts test either view.
For defenders, final attribution matters less than the context. Water infrastructure has become a target across the current conflict: reported US strikes have hit Iranian water plants, and Iran has targeted desalination plants in neighboring Gulf states. Whoever is ultimately responsible for Minnesota, that backdrop raises the baseline risk to water-sector OT, and to every large customer that depends on it.
What Operators Can Ask Their Water Utility
Data center operators cannot secure a municipal utility’s control systems, but they can reduce how much a compromise of them would cost. Practical steps include confirming how and how quickly the utility will notify large customers of a cyber incident, knowing how many hours of cooling the site’s on-site water storage actually covers, qualifying alternative supplies such as reclaimed water or trucked delivery, and weighing low-water cooling designs for new builds.
The relationship can run both ways. Large water customers have a direct stake in their utility’s security posture, and utilities facing coordinated campaigns have an interest in knowing which customers carry the highest consequences from an outage. Minnesota makes that conversation easier to start.
Background
Water utilities are among the more exposed parts of US critical infrastructure: many are small, locally run and operate industrial control systems designed for reliability rather than security. US agencies have previously linked attacks on water-sector equipment to Iran: in late 2023, intrusions into industrial controllers at several US water utilities, including the Municipal Water Authority of Aliquippa in Pennsylvania, were attributed to a group affiliated with Iran’s Islamic Revolutionary Guard Corps.
Data centers become significant water users when they rely on evaporative cooling, which trades lower electricity use for continuous water consumption. As AI workloads raise heat density, cooling design, and the water supply behind it, has become a central siting and permitting question. The Minnesota incident arrives amid renewed US–Iran hostilities: the US joined Israel in a war against Iran on February 28, and a June memorandum of understanding that paused the fighting broke down in July.
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.
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.
