Micron Technology has announced a new $10 billion research facility in Boise, Idaho, its longtime headquarters city, as reported by Boise State Public Radio. The announcement landed alongside pointed comments from Micron’s CEO, reported by Benzinga under the banner ‘No AI Without Memory,’ arguing that surging AI demand is breaking the chip industry’s historic boom-bust playbook.
Executive Summary
The announcement pairs a very large capital commitment — $10 billion for a single research facility — with a strategic thesis: that memory chips, long treated as a cyclical commodity, have become a structural constraint on artificial intelligence. Memory (the chips that store and feed data to processors) is one of the three pillars of AI computing alongside logic chips and the data centers that house them, and Micron is the only major memory maker headquartered in the United States.
Why it matters: R&D facilities, unlike fabrication plants, are where next-generation memory technologies are designed before they are manufactured at scale. Placing $10 billion of that work in Boise is a bet on sustained, multi-year AI demand — and a signal to customers, investors, and policymakers that Micron intends to anchor advanced memory development on U.S. soil. Whether the ‘boom-bust cycle is broken’ claim holds is the more contestable half of the story, and the one buyers and investors should test hardest.
Memory Moves From Commodity to Strategic Infrastructure
For most of its history, the memory business — DRAM, the fast working memory in servers, and NAND, the flash storage beneath it — has behaved like a commodity market: interchangeable products, brutal price swings, and profits that boom and collapse with supply. AI is changing the physics of that market. Large AI models are ‘memory-bound’: the processors doing the computation routinely sit idle waiting for data, which makes memory bandwidth and capacity a first-order constraint on AI performance, not an afterthought. High-bandwidth memory (HBM), the stacked memory packaged directly beside AI accelerators, has become one of the scarcest components in the AI supply chain.
Seen through that lens, a $10 billion research facility is less a factory announcement than an infrastructure claim: that memory R&D now belongs in the same strategic category as data center capacity, power, and advanced logic fabrication. The CEO’s ‘no AI without memory’ framing is self-interested — every supplier argues its layer is the critical one — but it is also directionally supported by how AI systems are actually built today.
Testing the ‘Boom-Bust Is Breaking’ Thesis
The bolder claim in these reports is that AI demand is breaking the memory industry’s boom-bust cycle. There is a plausible mechanism: HBM and other AI-grade memory are harder to manufacture, more differentiated between suppliers, and increasingly sold under longer-term agreements rather than spot pricing — all of which dampen the commodity dynamics that produced past crashes. A structurally less cyclical Micron would deserve a different valuation and a different risk profile from customers planning multi-year AI buildouts.
But the claim deserves the same scrutiny as any vendor narrative at a cyclical peak. Memory executives have declared the cycle tamed before, typically near the top of an upswing, and the industry has repeatedly answered strong demand with enough new supply to crash prices. The honest reading of the source material is that the thesis is asserted, not yet proven — it will be tested the first time AI infrastructure spending pauses. Committing $10 billion to R&D is itself evidence that Micron believes its own thesis; it is not evidence the thesis is correct.
What Boise Gets — and What the U.S. Gets
The location is not incidental. Micron was founded in Boise and is the only top-tier memory manufacturer headquartered in the United States, in an industry otherwise dominated by South Korean suppliers. Concentrating advanced memory research in Idaho deepens a domestic center of gravity for a technology that U.S. industrial policy has treated as strategically important, and R&D anchors tend to be stickier than factories: the engineering talent, university pipelines, and supplier ecosystems that grow around them are hard to relocate.
For the broader AI infrastructure market, the second-order effects matter most. Better memory roadmaps translate directly into more capable and more power-efficient AI data centers, since moving data between memory and processors is a major driver of both performance and electricity consumption. Anyone building or operating AI facilities has a stake in whether this R&D bet pays off — memory advances are one of the few levers that improve AI economics without simply adding more megawatts.
Background
Micron Technology was founded in Boise, Idaho, in 1978 and grew into one of the world’s three dominant memory manufacturers, alongside Samsung and SK Hynix — and the only one headquartered in the United States. The memory business has long been the semiconductor industry’s most cyclical segment, with prices and profits swinging sharply as supply and demand fall out of balance.
The rise of generative AI since 2023 recast memory’s role: AI accelerators depend on scarce high-bandwidth memory, and data center operators now treat memory supply as a planning constraint on par with power and processors. Micron has been expanding U.S. investment during this period, and the Boise research announcement extends that trajectory in its home city.
Broadcom is reportedly seeking a massive debt package — more than $60 billion according to a Bloomberg News report carried by Reuters, and as much as roughly $100 billion according to SiliconANGLE and Yahoo Finance coverage — to help finance an AI chip deal and related AI infrastructure expansion. Bloomberg’s framing calls it the company’s “latest AI debt deal,” indicating this is not the first time AI demand has sent Broadcom to the credit markets.
Broadcom has not publicly confirmed the financing, and the reports do not name the customer or specify terms. Shares of Broadcom (Nasdaq: AVGO) edged higher on the news, per Yahoo Finance.
Executive Summary
According to reports from Bloomberg News, relayed by Reuters, Yahoo Finance, and SiliconANGLE, Broadcom is in the market for one of the largest corporate debt raises ever contemplated — a package variously described as “more than $60 billion” and “up to $100 billion” — to fund an AI chip deal. Broadcom is one of the two dominant designers of custom AI accelerators, the purpose-built chips (often called ASICs or XPUs) that hyperscale cloud companies commission as alternatives to off-the-shelf GPUs.
Why it matters: until recently, AI buildouts were financed largely out of hyperscalers’ own cash flow. A chip designer borrowing at this scale to serve customer demand marks a structural shift — the AI supply chain itself is now leaning on debt markets to keep pace. If the reported figures are accurate, this single financing would rival the largest acquisition-related debt packages in corporate history, and it would tie Broadcom’s balance sheet directly to the durability of hyperscale AI spending.
The essential caveat: everything here is sourced to press reports of a deal in progress. The size, structure, purpose, and even existence of the final package remain unconfirmed by the company.
AI Demand Has Outgrown the Capex Budget
For the first two years of the generative-AI buildout, the money story was simple: hyperscale cloud providers funded chips, servers, and data centers from operating cash flow, and suppliers like Broadcom simply booked the revenue. A reported $60–100 billion debt raise by a chip supplier tells a different story. When order commitments get large enough, even a highly profitable designer may need external financing to bridge the gap between committing to wafer capacity, advanced packaging, and memory today and collecting customer payments over multi-year delivery schedules.
Bloomberg’s description of this as Broadcom’s “latest” AI debt deal is itself informative: it frames debt-funded AI expansion as a repeating pattern rather than a one-off. That pattern is visible across the ecosystem — data center developers, GPU cloud operators, and now silicon vendors are all layering credit on top of equity to finance AI capacity. The financing burden of the AI boom is being distributed across the supply chain, not concentrated at the hyperscalers.
Custom Silicon Is a Balance-Sheet Business Now
Broadcom’s AI franchise rests on custom accelerators — chips co-designed with a specific hyperscale customer for that customer’s workloads, in contrast to merchant GPUs sold broadly. Custom silicon deals are inherently lumpy: enormous multi-year commitments with a small number of counterparties. If the reported financing is tied to a single “AI chip deal,” as Reuters’ Bloomberg-sourced headline suggests, it implies a customer commitment large enough to justify tens of billions of dollars in upfront funding.
That concentration cuts both ways. It gives Broadcom visibility that most semiconductor companies would envy, but it also means the debt’s repayment logic depends on a handful of AI buyers sustaining their spending plans. Credit investors evaluating this package are, in effect, underwriting hyperscale AI demand itself — a notable transfer of AI-cycle risk from equity markets into fixed income.
What Bond Markets Absorbing AI Risk Means Downstream
For the broader infrastructure economy — data centers, power, connectivity — supplier-level debt financing at this scale is a demand signal with teeth. Companies do not typically pursue $60 billion-plus in borrowing against speculative interest; packages like this usually sit alongside firm commitments. If completed, the financing would suggest that the pipeline of custom accelerators, and therefore the facilities, megawatts, and network capacity needed to run them, extends well beyond current deployments.
The risk case deserves equal weight. Debt is unforgiving in a downturn in a way that deferred capex is not: if AI monetization lags the buildout, leveraged suppliers face fixed obligations against softening demand. The measured takeaway is that the AI cycle’s financial structure is maturing — larger, longer, more credit-dependent — which raises both the ceiling of what can be built and the stakes if demand disappoints. The market’s muted, modestly positive reaction in AVGO shares suggests investors currently read the reports as confirmation of demand rather than as a leverage warning.
Background
Broadcom is a semiconductor and infrastructure-software company whose chips sit throughout the modern data center: Ethernet switching silicon, optical interconnect components, and — most relevant here — custom AI accelerators designed in partnership with hyperscale cloud customers. As generative AI drove extraordinary demand for compute, Broadcom emerged alongside merchant GPU vendors as one of the principal beneficiaries, because several of the largest cloud companies chose to commission their own purpose-built chips rather than rely solely on off-the-shelf processors.
The financing backdrop matters as much as the company. The AI buildout was initially funded from hyperscalers’ operating cash flow, but as commitments have grown, debt markets have taken on a rising share of the load across data center developers, specialized cloud operators, and now chip suppliers. The reported Broadcom package — following what Bloomberg characterizes as earlier AI debt deals — is part of that broader migration of AI-cycle financing into corporate credit.
Pesach Lattin, who writes the advertising newsletter ADOTAT, recently made an argument that deserves a wider audience than the ad industry it was aimed at. Borrowing from the philosopher Harry Frankfurt’s essay On Bullshit, he draws a distinction that matters: a liar knows the truth and conceals it, while a bullshitter simply doesn’t care whether what he says is true. Lattin’s claim is that the advertising business is mostly doing the second thing about AI — making confident, unverifiable assertions with an apparent indifference to whether they hold up. He says he reviewed six months of conference talks and found four claims that were actually checkable.
I run an infrastructure company, not an ad agency. And reading it, I recognized the pattern immediately — because the same epistemics now govern how artificial intelligence gets sold one layer down, in the data centers, networks, and compute that everything else is built on.
The tell is verifiability, not sincerity
The useful part of Frankfurt’s framing is that it takes the argument away from intent. You do not have to decide whether a vendor is honest. You only have to ask a colder question: is this claim the kind of thing I could check? Most of the loudest statements in AI infrastructure marketing are not.
“AI-optimized” is not a specification. “Cloud-scale” is not a number. “Enterprise-grade reliability” is not an SLA. A GPU cloud that advertises a headline price per hour has told you almost nothing until you know the utilization you can actually achieve, the queue times at your scale, the egress charges, and whether the accelerators you were sold are the ones you get. A data center that markets a power-usage-effectiveness figure has told you something real only if it says whether that number is a design target or a measured annual average, at what load, in what climate. The gap between those two readings is where a year of operating budget hides.
The one uncontested number
Lattin points out that in his world, exactly one figure goes uncontested: the collapse in referral traffic as AI answer engines absorb the clicks that used to reach publishers — reductions he puts in the range of 20 to 90 percent. It is uncontested precisely because it is measurable. Everyone can see their own analytics.
Infrastructure has its own version of the uncontested number, and it is the electricity bill. You can argue about a model’s benchmark scores; you cannot argue with a utility invoice or a substation’s interconnection queue. This is why the most honest conversations in our industry right now are the ones about power and cooling. Megawatts do not bullshit. A grid operator’s capacity map is the least performative document in the AI economy, and it is quietly setting the ceiling on all of the confident projections layered above it.
A working buyer’s test
None of this is a case for cynicism. The technology is real, and the demand is real. The point is narrower and more practical: when someone sells you AI infrastructure, sort every claim into two piles before you sort it into true or false.
Testable now: Can it be written into a contract with a number and a penalty? Latency percentiles, delivered throughput, measured PUE over a defined period, uptime with real credits, a fixed price with the egress spelled out. Ask for the measurement method, not the headline.
Testable later: Can you run a bounded pilot that produces your own data — a parallel workload, a real month of your traffic — rather than the vendor’s reference benchmark? Insist on it before the multi-year commitment, not after.
Not testable: Adjectives, roadmaps, and transformation narratives. These are not lies. They are simply not evidence, and they should carry the weight of things that are not evidence.
The vendors worth working with will not flinch at this. In my experience, the willingness to be measured is the single most reliable signal of whether a claim was meant to be true or merely meant to be said. The ones who lead with the utility bill, the SLA, and the pilot are telling you something. So are the ones who change the subject to the future.
Lattin’s essay is about advertising, and it is worth reading on its own terms. But its real subject is a habit of mind that has spread well past his industry. The infrastructure layer is the last place that habit can safely live, because down here the claims eventually meet a power meter, a thermal limit, and a bill. Ask for the number. If there isn’t one, you have your answer.
The Associated Press reports that governors’ races across the United States are being increasingly buffeted by what it calls the toxic politics of data centers. The facilities that power the AI and cloud economy — and the electricity, water, and land they consume — have moved from zoning-board obscurity to the center stage of statewide campaigns.
Executive Summary
According to AP’s reporting, data centers have crossed a political threshold: they are no longer a local land-use question decided quietly by county boards, but a statewide campaign issue that candidates for governor are being forced to answer for. The word choice matters — ‘toxic’ signals that the issue now carries more downside than upside for politicians, regardless of party.
For the infrastructure industry, this is a material shift in the operating environment. Governors appoint utility commissioners, sign or veto tax-incentive legislation, and set the tone for state permitting agencies. When the people seeking that office campaign against — or hedge on — data center growth, the political risk premium on every new site goes up. Siting risk, long treated as a paperwork problem, is becoming an electoral one.
From Zoning Boards to the Ballot Box
For most of the industry’s history, data center approvals were decided in county planning meetings that almost nobody attended. The AI build-out changed the scale of the ask: modern campuses draw utility-grade electricity, meaningful volumes of water for cooling, and large tracts of land, often near residential areas. That scale made the facilities visible, and visibility made them political. AP’s framing — governors’ races ‘buffeted’ by the issue — captures the escalation: the debate has jumped two levels of government, from town hall to statehouse.
The mechanism is straightforward. Residents connect rising electricity bills, strained grids, and changed landscapes to the server farms appearing nearby, and they take that frustration to the most visible official on the ballot. Candidates then face a bad trade: embrace data centers and own the utility-bill anger, or oppose them and own the lost jobs and tax revenue. That no-win structure is what makes an issue ‘toxic’ in campaign terms.
Why Governors Matter More Than Mayors
A hostile county board can kill one project; a hostile governor can reshape an entire state’s pipeline. Governors influence public utility commissions that decide who pays for grid upgrades, sign the tax-abatement packages that make site economics work, and direct the environmental agencies that issue water and air permits. If campaigning against data centers proves to be a winning message, the policy consequences will outlast any single election cycle.
The economics compound the risk. Data centers are decade-scale capital commitments made against assumptions about power pricing, tax treatment, and permitting timelines. An election that flips a state from courting the industry to constraining it can strand those assumptions mid-project. Operators and their investors now have to underwrite political volatility the way they underwrite grid interconnection queues.
Winners, Losers, and the Flight to Friendly Ground
The likely near-term effect is sorting. Capital will tilt toward jurisdictions where the political climate is settled — states, and increasingly specific utility territories, where community benefit agreements, transparent power-cost allocation, and water-efficient designs have kept the backlash manageable. States where data centers become a campaign punching bag risk watching projects, and the associated construction jobs and tax base, route around them.
The industry’s own conduct will help decide which column each state lands in. Secretive land assemblies, non-disclosure agreements around utility deals, and cost-shifting onto residential ratepayers are the fuel of the backlash. Operators that show up early, disclose resource demands, pay their full share of grid costs, and design for minimal water draw are effectively buying political insurance. In an environment where a governor’s race can reprice a state’s entire pipeline, that insurance is no longer optional.
Background
Data centers are the physical backbone of the internet, cloud computing, and artificial intelligence — warehouse-scale buildings full of servers that require enormous amounts of electricity and, in many designs, water for cooling. For two decades states actively courted them with tax incentives, prizing their construction jobs and property-tax revenue while their modest visibility kept public attention low.
The generative-AI boom broke that equilibrium. Facilities grew from tens of megawatts to campus-scale power draws rivaling heavy industry, land acquisitions became front-page news in host communities, and questions about who pays for grid expansion landed on residential utility bills. The AP’s report marks the point at which that accumulated friction became statewide electoral politics.
Synergy Research Group reported on August 17, 2026 that “neoclouds” — the emerging tier of specialized GPU cloud providers built for AI workloads — are currently growing revenues at more than 200% per year. On that trajectory, Synergy forecasts the segment will reach $180 billion in annual revenues by 2030.
Executive Summary
Synergy Research Group, a market intelligence firm that has tracked cloud and data center markets for decades, put a striking pair of numbers on one of the fastest-moving corners of the infrastructure industry: neocloud providers are more than tripling their revenues each year, and the category is projected to become a $180 billion market by 2030.
The forecast matters because it treats neoclouds not as a temporary arbitrage on scarce GPUs, but as a durable market tier alongside the hyperscale clouds. If Synergy is right, a business model that barely existed three years ago will, within four years, rival the size of the entire global colocation industry — with all the capital, power, and data center demand that implies. It is worth noting the syndicated item we reviewed carries the headline figures but not Synergy’s full methodology, so the underlying assumptions deserve scrutiny alongside the projection itself.
What a Neocloud Is — and Why the Category Exists
“Neocloud” is the industry’s shorthand for cloud providers built specifically around GPU compute for artificial intelligence — renting out clusters of accelerators for model training and inference rather than offering the sprawling general-purpose service catalogs of AWS, Microsoft Azure, or Google Cloud. Commonly cited players in the category include CoreWeave, Lambda, Nebius, and Crusoe, though Synergy’s specific inclusion list is not visible in the syndicated item.
The category exists because AI demand outran what the traditional clouds could supply. Training frontier models requires dense, tightly networked GPU clusters, exotic power and cooling footprints, and pricing models closer to industrial capacity contracts than to on-demand virtual machines. Specialists that could secure GPUs, power, and data center space quickly found a seller’s market waiting for them.
The Economics Behind 200% Growth
Growth above 200% per year is extraordinary, but the arithmetic behind it is straightforward: the segment started from a small base, and demand for AI compute currently exceeds supply. When capacity sells out before it is built, revenue growth tracks how fast a provider can energize new data center capacity — which is why the neocloud story is inseparable from the power and data center construction booms.
The harder question is margin durability. Neocloud economics rest on expensive, fast-depreciating hardware, heavy debt financing in many cases, and — for several prominent players — revenue concentrated in a small number of very large AI customers. A $180 billion revenue projection says the market will be big; it does not by itself say the businesses in it will be uniformly profitable. Investors should distinguish between the size of the pie and the quality of any individual slice.
Winners, Losers, and the Hyperscaler Question
For data center operators, utilities, and connectivity providers, the forecast is almost unambiguously bullish: neoclouds are among the largest lessees of wholesale data center capacity and the most aggressive buyers of power. A tier growing toward $180 billion in revenue implies sustained demand for the physical layer beneath it — sites, substations, fiber, and cooling.
For the hyperscalers, the picture is more nuanced. Neoclouds are simultaneously competitors for AI workloads and, in some well-publicized arrangements across the industry, suppliers of capacity to the hyperscalers themselves. Whether the big clouds ultimately reabsorb this demand as their own GPU fleets scale, or the neocloud tier keeps a permanent structural advantage in speed and specialization, is the central competitive question the next few years will answer.
Can the Curve Hold to 2030?
Extending any 200% growth rate for years produces implausible numbers, and Synergy’s own forecast implies significant deceleration: a market compounding at 200% would blow far past $180 billion by 2030 from almost any plausible base. Read properly, the projection assumes today’s hypergrowth cools into merely strong growth — a reasonable but assumption-laden path.
The risks to the curve are the familiar ones for AI infrastructure: whether enterprise AI spending keeps converting into paid compute at current rates, whether power availability constrains buildouts, how quickly GPU generations depreciate, and whether customer concentration turns any single buyer’s pullback into a segment-wide shock. None of these invalidate the forecast; all of them are the difference between the projection and the outcome.
Background
The neocloud category rose to prominence after 2023, when generative AI demand created acute scarcity in GPU compute and a wave of specialists — several of them former cryptocurrency miners repurposing power-rich sites — pivoted to renting AI capacity. The segment has since attracted tens of billions of dollars in capital and become one of the largest sources of demand in the data center leasing market. Synergy Research Group, which has long published the benchmark market-share data for cloud infrastructure services, tracking the rise of AWS, Microsoft, and Google, now treats this GPU-specialist tier as a distinct market worth forecasting in its own right — itself a signal of how the AI buildout is restructuring cloud economics.
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 suburb outside Philadelphia voted down five giant buildings meant to hold the computers that run artificial intelligence. Together they would have covered roughly 80 football fields of floor space.
The builder promised $55.4 million a year in local taxes, mostly for schools. Neighbors worried about noise, dirty air and home values, and more than 17,000 people signed a petition against it.
The twist: to avoid raising local electric bills, the site would have made its own power with gas-burning generators. That fix became part of the fight. The builder says an appeal is coming.
WHYY reported that Upper Merion Township’s Board of Supervisors on Thursday, August 13, 2026, rejected all five applications from developer Brian O’Neill for a hyperscale data center campus totaling more than 4.6 million square feet near King of Prussia, Pennsylvania. The campus would have been built at Discovery Labs, a health care and life sciences office property run by O’Neill’s company, and was designed to generate its own power using natural gas turbines with diesel backup generators.
The vote followed a July planning commission recommendation against three of the five projects, a developer lawsuit seeking more review time that a Montgomery County judge cleared on the afternoon of the vote, and a resident petition with more than 17,000 signatures. O’Neill told 6ABC an appeal is planned.
Executive Summary
A suburban Philadelphia township has turned down one of the largest data center proposals in the region, despite the developer’s estimate of $55.4 million a year in local tax revenue, nearly $38 million of it for the local school district. The board’s stated grounds were procedural and specific: Chair Tina Garzillo said the township never received landscape, traffic impact, emergency management or sanitary sewer plans, and called the projects “at best speculative and conceptual in nature.”
The case matters beyond Upper Merion because of how the campus was to be powered. Generating electricity on site is increasingly pitched as the responsible way to build AI-scale facilities without burdening the grid or ratepayers. Here, that design put gas turbines and diesel generators in front of a township board and its residents, and air pollution became a central objection. Self-generation did not remove the local approval gate; it added a power plant to what that gate had to judge.
For developers, investors and utilities trying to forecast where AI capacity will actually get built, the vote is a reminder that a proposed campus is not capacity until it clears local land-use approval, and that the power strategy is now part of that approval.
Self-Generation Swapped a Grid Question for a Local One
The campus was pitched as a good grid neighbor: it would generate its own electricity so it would not strain the regional power system or push up household bills. That is an increasingly common design for AI-scale facilities, often called on-site or behind-the-meter power because the electricity is produced on the property rather than drawn from the utility. But in Upper Merion, the power plant came with the building. The proposal included natural gas turbines and diesel backup generators, and air pollution became one of residents’ leading objections, alongside noise and water use.
That is the mechanism this vote exposes. A data center that buys power from the grid leaves most generation questions to utilities and grid planners. A data center that makes its own power brings combustion equipment, fuel supply and emissions controls onto a site that a township board must approve, in front of neighbors who will live beside it. O’Neill said scrubbers would control emissions and that discussions had been held with Bloom Energy about using fuel cells on site, but no deal had been finalized, which left the board and residents to evaluate the turbine-and-diesel version of the plan.
Upper Merion is not the only place where on-site power has drawn local enforcement. In Vineland, New Jersey, where a data center under construction plans to use Bloom fuel cells, the city issued a stop-work order on parts of the 300-megawatt facility (a megawatt is a measure of electrical capacity), alleging that a liquefied natural gas tank and fuel cell units were being installed before city approval. Generating power on site may answer concerns about grid strain and electricity bills, but it adds a power plant to the list of things local officials get to review.
The Board Ruled on Missing Plans, Not Just Public Anger
The emotional scenes at the meeting can obscure what the board actually said. Garzillo cited the absence of landscape, traffic impact, emergency management and sanitary sewer plans. Those are ordinary engineering submissions for any large industrial project, and their absence gave the board a procedural basis for denial that stands apart from residents’ objections.
The process had been heading this way for weeks. In July, the township planning commission voted not to recommend three of the five projects. The developer then sued, seeking an extension and arguing there had not been adequate time to address township concerns; a Montgomery County judge vacated the injunction on the afternoon of the vote, clearing the board to reject the extension. Vice chair William Jenaway said the developer had earlier opportunities to respond to review comments and did not do so in a timely fashion. The two sides’ accounts of the timeline differ, and an appeal would likely test them.
For the industry, the lesson is about entitlement risk, meaning the risk that a site never receives the land-use approvals it needs. As AI demand pushes developers to repurpose existing properties, here a life sciences office campus, into data center sites, applications that arrive without complete plans risk being denied on the paperwork alone, whatever their scale or revenue promise.
A $55.4 Million Tax Pitch Met a 17,000-Signature Petition
The developer’s economic case was large on its face: an estimated $55.4 million a year in local tax revenue, with nearly $38 million going to the Upper Merion Area School District. For a suburban township, that is the kind of figure that usually carries weight. It did not carry the vote. Residents weighed it against quality-of-life costs that do not appear in a revenue projection, and one resident nearing retirement said a loss of home equity “would destroy our savings.”
Both sides’ claims deserve the same scrutiny. The tax figure is the developer’s own estimate, and its value depends on assumptions about how much gets built and when. Residents’ fears about property values and health are sincere, but their magnitude depends on specifics that were never settled, such as how many turbines would run, how often, and with what controls. The water debate is similar: O’Neill said a closed-loop system would reuse cooling water, while hyperscale facilities in general can consume up to 5 million gallons of drinking water a day. Closed-loop designs reduce consumption substantially, but how much make-up water this campus would need was not specified.
The backdrop is shifting against developers who lead with revenue. A University of Pennsylvania Annenberg Public Policy Center survey found a 12-point jump in widespread resistance to new data center construction over four months. In that climate, detailed answers on emissions, water and noise are likely to matter more to approval than headline tax estimates.
What Comes Next for AI Capacity Around Philadelphia
The denial is not necessarily final. The developer can appeal or submit new or revised plans, and has said an appeal is planned. Residents have said they will oppose any appeal or new application. O’Neill is also seeking approval for a data center in nearby Plymouth Township, which becomes the next test of whether a more complete application, and possibly a different power plan such as the fuel cells under discussion, changes the outcome.
For operators, investors and utilities counting future AI capacity in the region, the practical point is that local land-use approval is a gate that neither scale, tax revenue nor a plan to stay off the grid can bypass. Capacity forecasts that treat proposed campuses as inevitable will miss decisions like this one, which are made parcel by parcel at township meetings.
Background
Upper Merion Township sits in Montgomery County, Pennsylvania, and includes much of the King of Prussia area, a major suburban commercial and office hub northwest of Philadelphia. Data centers, the buildings that house the servers behind internet services, have existed since the mid-20th century, but the rise of power-hungry artificial intelligence has pushed demand toward much larger hyperscale campuses that need substantial electricity and cooling.
That demand has driven developers to look at existing commercial properties, such as office and life sciences campuses, as potential data center sites, and to consider generating their own power rather than waiting on the grid. It has also produced growing local resistance in the Philadelphia region and beyond, where township boards, not state or federal agencies, hold the zoning and land-use approvals that decide whether a campus can be built.
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.
West Virginia wants the giant computer warehouses that power artificial intelligence, and it has none of them today.
Its new 20-year plan sets the terms. Companies must supply their own electricity instead of drawing on the power households use, and they must pay for any grid upgrades themselves. Half the money the projects bring in is set aside to cut, and eventually end, the state income tax.
The catch is that none of this has been tested yet. No company has signed on, and no one knows how much money will actually arrive.
On August 11, 2026, West Virginia Governor Patrick Morrisey, Senate President Randy Smith, House Speaker Roger Hanshaw and Department of Commerce leadership announced the West Virginia Responsible Data Center Development Plan, the Office of the Governor said. The framework relies on House Bill 2014, the Power Generation and Consumption Act, to set state-level oversight and development criteria for hyperscale data centers, the very large facilities built to run cloud and artificial-intelligence workloads.
The state says it starts with zero active hyperscale facilities. The plan is a 20-year strategy managed by a Data Economy Office. It requires developers to supply their own energy, and it dedicates 50 percent of project revenue to reducing the state personal income tax.
Executive Summary
West Virginia has published the rules it will use to attract AI-scale data centers. Before any hyperscale campus exists in the state, it has set four conditions. Developers must prove financial capacity, committed capital and clear power load projections to be certified. They must build, bring or procure their own energy and pay for their own grid and microgrid infrastructure. They must operate under a demand-response model that curtails grid use during peak stress. And project revenue is split by formula, with half going to income tax relief and none to the state general fund.
The central design choice is the energy requirement. Many jurisdictions compete for data centers on land, tax incentives and fiber. West Virginia is asking developers to arrive with a power solution already in hand. That changes who can realistically build in the state and what a site proposal has to contain. It also shifts the risk of new generation away from existing utility customers, at least on paper.
The release is strong on principles and light on mechanics. The revenue base, the enforcement of the ratepayer pledge, water targets and the advisory council’s standards are all still to come. The state’s projections of billions in investment and thousands of jobs have no figures behind them yet.
Bring Your Own Power Is the Real Entry Fee
The plan’s most consequential clause is the Ratepayer Protection Pledge. Developers must build, bring or procure their own energy resources and fully fund their own infrastructure and microgrid costs. A microgrid is a local power system that can run alongside the main grid or apart from it. The state says utility customers will not bear construction, operational or grid upgrade expenses. Certification also requires clear power load projections, meaning a forecast of how much electricity a facility will draw. As a result, a developer cannot secure a site first and look for power later. The energy plan is part of the application.
This is what makes site selection in West Virginia a power project. An AI campus proposal there needs the same pieces as a generation development: a fuel or supply source, generation or procurement contracts, and the capital to build them. It also has to coordinate capacity with the local utility and comply with the reliability frameworks of PJM (the regional grid operator for much of the Mid-Atlantic and Midwest), FERC (the federal energy regulator) and NERC (the body that sets grid reliability standards). On top of that, facilities must operate under demand response. During peak stress periods they either switch to on-site backup generation or cut their consumption.
The requirement is likely to favor well-capitalized developers and those with established generation partners. It is likely to filter out speculative land plays, which the release says it intends to reject. It also changes the conversation for AI tenants. Uptime at a West Virginia campus will depend partly on assets the developer owns and runs, not only on the utility. Demand response adds a further consideration: a facility must be able to curtail or self-supply during grid emergencies. Buyers will want to know how that interacts with their own service-level expectations.
A Revenue Formula Designed for Voters, Not Just Developers
The state has specified exactly where project revenue goes. Fifty percent is dedicated to reducing and ultimately eliminating the state personal income tax. Thirty percent goes to host counties for schools and government operations. Ten percent is spread across all other counties, and ten percent funds local water, wastewater and electrical upgrades. None enters the general revenue fund, and host county budgets cannot be negatively affected.
This structure answers a common local objection to data centers, which is that the host community carries the disruption while the benefits flow elsewhere. Tying half of the revenue to a statewide income tax cut also builds a constituency for the program beyond host counties. Whether the income tax goal is realistic depends on figures the state has not published. The plan does not say what counts as project revenue under the High Impact Data Center designation. It also gives no estimate of how much revenue a typical facility would generate. With zero hyperscale facilities today, the tax promise is a long-horizon aspiration rather than a budget line.
A Clean Slate, and an Unfinished Rulebook
The release says that before HB 2014, more than 50 West Virginia counties had no mechanism to approve, condition or deny large data center projects. That governance gap exists in many places that have seen sudden data center interest. Closing it at the state level before the first hyperscale project arrives is the plan’s clearest structural advantage. Developers get one certification path. Communities get a screening step that did not exist before.
Much of the detail is still to be written. A Data Center Advisory Council will recommend standards for setbacks, noise, site security and proximity to sensitive areas. On water, the state keeps existing oversight and says it is pioneering closed-loop systems, immersion cooling and reclaimed water. It aims to lead on Water Usage Effectiveness, a measure of water consumed per unit of computing energy. It has not set a numeric target or a requirement. Two claims in the release are also unquantified: that development will help modernize the grid and drive down residential rates over time, and that it will bring billions in investment and thousands of jobs. Both are reasonable goals, and neither is substantiated yet.
Background
West Virginia has long been an energy-producing state, and it sits inside the PJM grid region. That region’s reliability and capacity rules shape how any large new electricity user connects. The state enters the AI data center race without a single hyperscale campus. Until House Bill 2014, more than 50 of its counties had no formal mechanism to approve, condition or deny such projects.
The August 2026 plan builds on that law. It combines a Data Economy Office, a certification process for developers, a fixed revenue-sharing formula and a requirement that developers supply their own energy. Governor Morrisey described the approach as leading without repeating the mistakes of other states, which he did not name.
Businesses are on track to nearly double what they spend renting powerful computers for artificial intelligence this year, to about $42 billion, according to research firm Gartner.
The bigger change is what that money pays for. For the first time, more will go to running artificial intelligence tools every day — answering customers, handling tasks at work — than to building new ones.
Building happens in bursts. Running never stops. So the giant computer warehouses behind these tools increasingly have to stay busy, and powered, around the clock.
Gartner said in a forecast published August 10, 2026 that worldwide spending on AI-optimized infrastructure as a service (IaaS) — rented cloud computing built for artificial intelligence workloads — will grow 96.4% in 2026 to $42.3 billion, up from $21.5 billion in 2025, and reach $66.1 billion in 2027, a further 56.5% increase.
The research firm also forecast that in 2026 spending on inference (running trained AI models in production) will reach $23.3 billion and surpass spending on training (building those models) at $19 billion. Inference is projected to account for 55% of AI-optimized IaaS spending in 2026 and 59% in 2027.
Executive Summary
The headline number is the near-doubling of rented AI cloud spending, but the more consequential figure is the crossover: in Gartner’s forecast, 2026 is the year inference overtakes training as the larger use of AI-optimized cloud capacity. Hardeep Singh, Sr Principal Research Analyst at Gartner, attributes the shift to fine-tuned and domain-specific models being built into customer-facing and operational systems that require “continuous, real-time execution rather than periodic training.”
That distinction matters for anyone who builds, powers or connects AI infrastructure. Training demand arrives as large, time-bound projects; inference demand scales with everyday usage and persists. A market increasingly weighted toward inference behaves less like a series of construction jobs and more like a utility load — steady, growing with adoption, and tied to where users and applications are.
Gartner’s numbers also put the AI boom in proportion: AI-optimized services are forecast at 14.7% of total IaaS spending in 2026, and conventional cloud infrastructure keeps growing at roughly 20% a year alongside it. Data center operators face rising demand on both fronts, not a wholesale swap of one for the other.
Inference Overtakes Training, and Demand Stops Arriving in Bursts
Training is the process of building an AI model by feeding it vast amounts of data; inference is the work of using that finished model to answer a question, draft a document or complete a task. Gartner forecasts inference spending of $23.3 billion in 2026 against $19 billion for training, with inference’s share of AI-optimized IaaS rising from 55% in 2026 to 59% in 2027. Singh ties the shift to models moving into production systems that require “continuous, real-time execution rather than periodic training.”
The mechanism is the shape of the demand. A training run is a project: a large block of accelerators — the specialized chips, usually GPUs, that do AI math — is reserved, runs for weeks, and is then released. Inference consumption instead scales with use: every customer query and every step an AI agent takes draws compute. Gartner specifically flags agentic AI, software that carries out multistep tasks on its own, as amplifying compute intensity. Demand that tracks usage behaves like a utility load — it persists, rises with adoption and follows the rhythm of the businesses it serves.
That changes the planning problem for several groups. Cloud providers get fleets that are more consistently busy, which improves the economics of expensive accelerators. Data center operators and utilities see more load that draws close to its contracted power much of the time, rather than in project-driven peaks. And because inference feeding customer-facing systems has to reach those customers quickly, well-connected sites near users and networks tend to gain value relative to remote campuses chosen purely for cheap power.
The Percentages Are Slowing. The Dollars Are Not.
On growth rates alone, AI-optimized IaaS looks like it is cooling: 180% in 2025, 96.4% in 2026, 56.5% in 2027. But the absolute increments rise each year — about $20.7 billion of new annual spending in 2026 and roughly $23.9 billion in 2027. For people who build capacity, the increment is what counts: under Gartner’s forecast, each year requires more new AI-ready capacity than the year before, not less.
The forecast also keeps AI in proportion. AI-optimized services are about 9.7% of total IaaS spending in 2025, 14.7% in 2026 and 18.4% in 2027. They account for roughly a third of the market’s growth in each forecast year. The remainder — general-purpose rented infrastructure — still grows by about 22% in 2026 and about 20% in 2027, on Gartner’s figures. AI is not crowding conventional cloud out of these numbers; builders face demand for both high-density AI halls and standard cloud capacity, which have very different power and cooling requirements.
Rented AI Capacity Is Not the Same as Built Capacity
Gartner’s figures measure what customers pay to rent AI-optimized infrastructure, not what cloud providers spend building it or what enterprises spend on hardware for their own facilities. The forecast therefore shows strong and rising demand for rented AI capacity, but on its own it cannot show AI investment shifting away from ownership toward the cloud, because it does not measure the ownership side.
What it does indicate is where enterprise AI is being consumed as it moves into production. Singh says the shift toward deployment is “accelerating the cloud consumption patterns.” Rental spending is the revenue that providers weigh their construction against, and a forecast in which that revenue nearly doubles and tilts toward always-on inference strengthens the case for facilities designed for sustained, high-density load — heavier power delivery and the advanced cooling dense AI racks often require. The caveat is that this is a forecast: whether chips, power and finished data center space arrive fast enough to meet it is a separate question.
Background
Gartner, Inc. (NYSE: IT) is a Stamford, Connecticut-based research and advisory firm whose market spending forecasts are widely used by technology buyers, vendors and investors. Infrastructure as a service is the part of cloud computing in which customers rent raw computing, storage and networking from providers such as Amazon Web Services, Microsoft Azure and Google Cloud, paying for what they use instead of running their own hardware.
The generative AI boom created a distinct segment within that market: cloud capacity built around accelerator chips and the high-speed networking needed to train and run large language models. Gartner’s figures show that segment grew 180% in 2025 to about $21.5 billion, and the firm now expects spending to shift from building models toward running them in everyday business applications.
