On 2026-07-09, CNBC published a ranking of the ten U.S. states it judges best positioned to land new artificial-intelligence data center deals despite a rising tide of public opposition to large campuses. The list frames a national contest for hyperscale investment against the backdrop of grid strain, water concerns and local political pushback.

Executive Summary

The CNBC feature is essentially a state-by-state scorecard for AI data center attractiveness at a moment when siting has become the single hardest problem in the industry. Where a decade ago the debate was about tax abatements and fiber routes, it now turns on interconnection queues, gas turbine availability, water withdrawals and whether a county commission will approve a rezoning after a packed public hearing.

For infrastructure buyers, the ranking matters less as a definitive verdict than as a signal of where the pipeline is likely to concentrate. For host communities, it is a reminder that the states judged most ‘winnable’ by capital are precisely the ones facing the loudest local debates about who benefits from a multi-billion-dollar build.

What a ‘Best Positioned’ Ranking Actually Measures

Rankings of this kind typically blend a handful of durable inputs: available and dispatchable power, transmission headroom, permitting speed, tax treatment, land availability, workforce, fiber density and climate suitability for cooling. None of those variables is new, but their relative weight has shifted sharply. Power availability — measured in years to interconnect, not megawatts on paper — has overtaken tax policy as the binding constraint for gigawatt-scale AI campuses.

That reordering changes which states look attractive. Jurisdictions with vertically integrated utilities, permissive siting rules for gas peakers or nuclear uprates, and cooperative public utility commissions have a structural edge over states with congested interconnection queues, regardless of how generous their incentives look on a spreadsheet.

The Opposition Curve Is Bending

The CNBC framing — ‘despite rising public opposition’ — reflects a real inflection. Data center opposition, once confined to a few Northern Virginia counties, is now a recurring feature of local politics in Georgia, Texas, Arizona and the Midwest. Residents cite noise from cooling equipment, transmission line routing, water use, property tax abatements and the perception that grid costs are being socialized while benefits accrue to a handful of hyperscalers.

The important business question is not whether opposition exists, but whether it changes outcomes. So far the evidence is mixed: some projects have been delayed or downsized, others have proceeded largely on schedule after community benefit agreements. States that develop clearer siting rules and cost-allocation frameworks may quietly pull ahead of nominally cheaper jurisdictions where every hearing becomes a referendum.

Winners, Losers and the Second Tier

A top-ten list implicitly names losers — states that were competitive for cloud-era builds but are structurally disadvantaged for AI-scale campuses. The likely laggards are jurisdictions with tight grids, aggressive decarbonization timelines that constrain new gas generation, or moratoria under active consideration. That does not mean those markets go dark; they will still host inference, edge and enterprise workloads. But the trillion-dollar question of where training capacity lands is increasingly being answered elsewhere.

For the second tier — states that did not make the list — the strategic response is unglamorous: shorten interconnection timelines, publish transparent siting criteria, and negotiate cost-allocation rules that survive contact with a local newspaper. Incentive stacking alone no longer moves the needle.

What the Ranking Cannot Tell You

Any state-level scorecard obscures the fact that AI siting decisions are made at the substation, not the statehouse. Two counties within the same ‘winner’ state can face wildly different interconnection timelines, water availability and community sentiment. Investors reading the list should treat it as a starting filter, not a site selection tool. And host communities should recognize that being on such a list is a leading indicator of proposals to come, not a guarantee of net benefit.

Background

The U.S. data center industry has spent two decades clustering around a handful of markets — Northern Virginia, Dallas, Phoenix, Silicon Valley, Chicago and Atlanta — chosen for fiber, power and tax treatment. The AI training boom that accelerated after 2023 broke that pattern by demanding campuses an order of magnitude larger, with power needs measured in gigawatts and lead times measured in years.

As those requirements collided with congested grids and slow permitting in legacy markets, developers began scouting states with spare generation, cooperative utilities and available land. That shift, in turn, exported the siting debate to communities with little prior experience of large-scale digital infrastructure — and produced the public opposition the CNBC ranking now takes as its backdrop.

Source: These 10 states are best positioned to land AI data center deals despite rising public opposition — CNBC. CNBC ranks the U.S. states it judges most competitive for new AI data center investment as siting debates intensify.

Data Center Knowledge reported on May 30, 2026 that water and wastewater capacity have joined — and in some markets now rival — electrical power as the decisive factors in where AI data centers can be built. The report’s framing marks a shift in an industry that has spent the past several years describing its siting problem almost entirely in megawatts.

Executive Summary

The report argues that the availability of water for cooling, and just as importantly the capacity of municipal systems to accept the water a facility discharges, now determine whether an AI data center project is viable at a given site. That is a meaningful reframing: since the AI buildout accelerated, the industry conversation has centered on grid interconnection queues and power procurement, with water treated as a secondary sustainability metric rather than a gating constraint.

Why it matters: if water and wastewater capacity are genuine go/no-go criteria, the map of viable AI data center locations changes. Sites with abundant power but strained water or sewer systems lose ground, while regions with underused water and treatment infrastructure gain a new selling point. It also pulls a different set of actors — water utilities, sewer authorities, and municipal planners — into negotiations that were previously dominated by electric utilities.

From Megawatts to Gallons: A New Siting Calculus

For most of the AI infrastructure boom, the binding constraint has been electricity: how many megawatts a utility can deliver, and how fast. Water has been discussed mostly in sustainability reports. The shift Data Center Knowledge describes — water as a siting decision, not a disclosure line item — reflects how AI-scale facilities actually work. High-density computing throws off enormous heat, and many cooling designs, particularly evaporative systems, consume large volumes of water to reject that heat to the atmosphere. A campus that can secure power but not water is still an unbuildable campus.

Wastewater is the less obvious half of the equation, and arguably the more interesting one. Water that runs through cooling systems and is not evaporated must go somewhere, often into municipal sewer systems as industrial discharge. Treatment plants are sized for the communities they serve; a single large industrial user can consume capacity a municipality planned to allocate over decades of residential growth. Discharge from cooling systems can also be warmer and more mineral-concentrated than household wastewater, which treatment plants must be equipped to handle. A town can have a river next door and still lack the permits, pipes, and treatment headroom to host an AI campus.

Winners, Losers, and the New Bargaining Table

If this framing holds, the winners are jurisdictions that can offer both power and water headroom — including regions with cooler climates that reduce cooling demand, or with industrial water infrastructure left over from manufacturing that has since departed. Water utilities and engineering firms that design treatment and reuse systems gain leverage and business. The relative losers are water-stressed markets that have competed for data centers on power and tax incentives alone, and developers holding land banks in places where the sewer authority, not the electric utility, turns out to be the limiting party.

For operators, the economics push toward designs that trade water for electricity or capital: closed-loop liquid cooling, dry coolers, and water recycling all reduce consumption but raise power draw or upfront cost. That trade-off means water scarcity does not just move projects — it changes their engineering and their operating cost profile. Expect water-use effectiveness (WUE), the industry’s ratio of water consumed per unit of computing energy, to get the same contractual and public scrutiny that power-use effectiveness (PUE) received a decade ago.

What the Framing Does and Does Not Establish

A note of even-handedness: the source available to us is a report headline and premise, not a dataset. The claim that water now “decides” siting is directionally consistent with well-documented industry trends — public disputes over data center water use in drought-affected regions, and the growth of water-positive pledges from major cloud providers — but the strength of the claim varies by market. In cool, wet regions with modern treatment plants, water may barely register as a constraint; in arid, fast-growing metros it can be decisive. Readers should treat “water decides siting” as an increasingly common condition, not a universal law, and ask for market-specific evidence — permit denials, moratoria, or utility capacity studies — before generalizing.

Background

Since the generative AI boom began in late 2022, data center development has grown at a pace that strained electric grids, making interconnection queues and power procurement the industry’s defining bottleneck. Water surfaced periodically as a flashpoint — community disputes over data center water consumption in drought-affected regions drew attention, and major cloud providers responded with public water-stewardship and replenishment pledges — but it was generally treated as a reputational issue rather than a siting gate.

Data Center Knowledge, the trade publication behind the report, has covered the industry’s infrastructure constraints throughout the buildout. Its framing of water and wastewater as decisive siting factors reflects the arrival of AI-scale campuses whose cooling demands, and whose discharge volumes, exceed what many municipal systems were designed to accommodate.

Source: How Water and Wastewater Capacity Now Decide AI Data Center Sites — Data Center Knowledge’s May 30, 2026 report on water infrastructure becoming a primary constraint in AI data center site selection.

Utah’s governor has tightened the rules that apply to a giant AI data center project backed by investor Kevin O’Leary, according to a Business Insider report published May 30, 2026. The action places state-level conditions on one of the highest-profile celebrity-backed entries into the AI infrastructure race.

Details of the specific requirements were not spelled out in the available source material, but the reported move fits a broader pattern: states courting AI data center investment are simultaneously attaching guardrails around the resources those campuses consume — chiefly water and electric power.

Executive Summary

According to Business Insider, Utah’s governor moved to tighten the rules governing Kevin O’Leary’s planned large-scale AI data center in the state. O’Leary, the investor best known from Shark Tank, has spent the past two years positioning O’Leary Ventures as a developer of very large AI computing campuses, most prominently the multibillion-dollar ‘Wonder Valley’ concept announced in Alberta, Canada, in late 2024. A Utah project extends that ambition into one of the fastest-growing — and driest — states in the American West.

Why it matters: AI data centers are among the most resource-intensive facilities ever built at commercial scale. A single hyperscale campus can demand hundreds of megawatts of electricity — comparable to a small city — and, depending on cooling design, substantial water. Utah is an arid state where water politics are already charged, notably around the shrinking Great Salt Lake. When a governor personally intervenes to condition a marquee project, it tells the industry that resource guardrails are moving from county zoning boards up to the statehouse.

For developers, the message is that incentives and permits increasingly come bundled with obligations. For AI tenants and investors, it means project timelines and economics now carry a regulatory variable that did not meaningfully exist three years ago.

Guardrails Are Becoming the Price of Admission

Through 2023 and 2024, states competed for data centers almost purely with carrots: tax abatements, fast-track permitting, cheap land. The reported Utah action reflects the next phase. Legislatures and governors in Georgia, Virginia, Texas, and elsewhere have begun asking who pays for the grid upgrades a gigawatt-class campus requires, and whether existing ratepayers end up subsidizing a private tenant’s load. Utah itself passed legislation in 2024 creating a framework for ‘large load’ customers to be served under separate terms, precisely so that massive new consumers do not shift costs onto households. Tightening rules on a flagship AI project is consistent with that trajectory: welcome the investment, but ring-fence its externalities.

For laypeople, the key concept is that electricity and water are shared systems. A data center does not simply buy power the way a household does; at hundreds of megawatts it reshapes the utility’s entire planning horizon — what plants get built, what transmission lines get strung, and who bears the cost if the promised load never materializes.

Water Is the West’s Hard Constraint

Power can, eventually, be built. Water in the Great Basin largely cannot. Utah is one of the driest states in the country, and the decline of the Great Salt Lake has made every large new water commitment politically visible. Data centers vary enormously here: evaporative cooling designs can consume millions of gallons a day, while closed-loop and air-cooled designs use a small fraction of that — at the cost of higher electricity draw. Any state-imposed water condition effectively forces a design decision, pushing developers toward dry cooling and shifting the burden back onto the power system. That trade-off — water versus watts — is now a central engineering and political negotiation in every arid-state siting, and Utah’s reported action puts it on the record at the gubernatorial level.

The Celebrity-Capital Model Meets Institutional Reality

Kevin O’Leary’s data center ventures have been announced with characteristic showmanship — Wonder Valley in Alberta was unveiled with a headline figure of roughly $70 billion over its life. Announcements at that scale invite fair scrutiny: mega-campuses require anchor tenants, firm power agreements, water rights, transmission interconnection, and tens of billions in project finance, most of which is rarely secured at announcement time. A governor tightening the rules is, in one reading, simply the institutional system doing its job — converting a promotional vision into enforceable commitments. That is not necessarily adversarial. Projects that survive rigorous conditioning tend to be more bankable, because lenders and hyperscale tenants prefer sites where the regulatory ground has already been tested.

Winners, Losers, and the Signal to the Market

If the guardrails are well designed, the winners are Utah ratepayers, competing water users, and — perhaps counterintuitively — disciplined developers, who gain a clearer rulebook than rivals face in states still improvising. The risk side: conditions that are vague or shifting can chill investment, and Utah competes with Texas, Wyoming, and the Midwest for AI capital. AI tenants watching this will price in regulatory friction when choosing between states. The market signal is unmistakable either way: the era of announcing a gigawatt campus first and settling the resource questions later is closing.

Background

The AI boom that followed ChatGPT’s 2022 debut triggered a global race to build computing campuses of unprecedented scale, drawing in hyperscalers, private equity, sovereign funds — and celebrity investors. Kevin O’Leary entered the field through O’Leary Ventures, announcing the ‘Wonder Valley’ mega-campus in Alberta in December 2024 with a stated long-term vision of roughly $70 billion, and subsequently pursuing sites in the United States, including Utah.

Utah, meanwhile, has courted technology infrastructure — Meta and others operate large facilities there — while wrestling with the American West’s defining constraint: water. In 2024 the state established a legal framework for serving very large new electricity loads without shifting costs to ordinary ratepayers. The reported tightening of rules on the O’Leary project sits at the intersection of those two currents: aggressive AI-infrastructure recruitment and hardening resource guardrails.

Source: Utah’s governor just tightened the rules for Kevin O’Leary’s giant AI data center — Business Insider report, May 30, 2026, on new state-level conditions placed on the O’Leary-backed AI data center project in Utah.

The City of Cleveland has rejected a permit application for a hyperscale data center proposed in Slavic Village, a historically industrial neighborhood on the city’s southeast side, according to a report published by Ideastream Public Media on 14 May 2026.

The available report is a headline-level item. It does not identify the applicant, the size of the proposed facility in megawatts or square feet, the specific permit or approval that was sought, the body that issued the denial, or the stated grounds for the decision. Those details are treated as open questions throughout this article rather than assumed.

Executive Summary

A hyperscale data center is a very large computing facility — typically a windowless industrial building housing tens of thousands of servers, backup generators, and cooling equipment — built to serve cloud platforms or artificial-intelligence workloads. Cleveland’s denial of a permit for such a facility in Slavic Village is, on its face, a routine municipal land-use decision. Its significance lies in where it happened and what it interrupts.

For the past three years, the public conversation about data center siting has been dominated by electricity: interconnection queues, transformer lead times, generation shortfalls. That framing has quietly become incomplete. In dense, older cities, the first gate a project must clear is not the utility’s — it is the zoning counter. A grid constraint is a schedule problem that money and patience can often solve. A municipal denial is a binary outcome that money cannot buy through, and it arrives earlier in the development timeline.

The Slavic Village outcome matters most as a signal to site-selection teams who have been treating legacy industrial neighborhoods as underpriced opportunity: cheap land, inherited heavy-industrial zoning, and substation capacity left behind by departed manufacturing. That thesis is sound on the engineering merits and increasingly fragile on the political ones. What is not yet knowable from the available reporting is why Cleveland said no — and that distinction, between a denial grounded in specific code criteria and one grounded in general opposition, determines almost everything about what the decision means for the next applicant.

Zoning Has Quietly Overtaken the Grid as the Binding Constraint

Ask an infrastructure investor what stops a data center in 2026 and the answer is usually electrical: no available interconnection, no transformers, no firm capacity until the early 2030s. That answer is accurate for greenfield campuses in transmission-constrained regions. It is misleading for urban infill sites, where the sequence of approvals puts local government first. Before a utility study matters, a developer generally needs the right to build the use at all — through by-right zoning, a conditional-use permit, a variance, or a rezoning. Each of those runs through a planning commission, a board of zoning appeals, or a city council, and each is discretionary in ways an interconnection queue is not.

The asymmetry is worth stating plainly. Grid limits are negotiable: a developer can pay for network upgrades, accept curtailment terms, bring on-site generation, or wait. Those are cost and schedule variables. A municipal denial is not a variable — it is a stop, appealable only on narrow legal grounds and rarely reversible on the merits within a project’s option period. Capital markets have not fully repriced this. Entitlement risk on urban sites is still frequently modeled as a delay, when it should increasingly be modeled as a probability of total loss on pre-development spend.

Geography compounds it. Exurban and township sites sit in jurisdictions where a handful of trustees weigh a large new tax base against a small residential population. An urban site sits inside a ward whose council member answers to thousands of nearby households. The same building, with the same load and the same emissions profile, faces materially different political economics depending on which side of a municipal boundary it lands.

Why Legacy Industrial Neighborhoods Look Better on a Map Than at a Hearing

The appeal of a place like Slavic Village to a data center developer is genuine and not speculative. Neighborhoods built around heavy manufacturing carry three assets that are scarce elsewhere: parcels already zoned for industrial use, brownfield land available at a fraction of greenfield pricing, and — most valuable — electrical infrastructure sized for loads that no longer exist. When a mill or foundry closes, the substation and the transmission spurs that fed it often remain. Reusing that capacity is faster and cheaper than building it, and it is a legitimately good outcome for the grid as a whole.

The flaw in the thesis is that the zoning map records history, not the present. An “industrial” designation inherited from the 1950s describes what a parcel once was; it does not describe the residential blocks that grew around it, outlasted the factory, and now sit within earshot of it. The original bargain that justified heavy land uses in residential proximity was employment: thousands of jobs in exchange for noise, trucks, and air quality impacts. A hyperscale data center does not offer that trade. It is capital-intensive and labor-light, with permanent staffing typically counted in dozens rather than thousands relative to its land and power footprint.

That changes the local calculus in a way developers underweight. The residual impacts a data center does bring — periodic backup generator testing, continuous cooling equipment noise, construction traffic, water use where evaporative cooling is chosen, and a large share of a city’s electrical headroom consumed by a single customer — are real and locally felt, while the offsetting benefits are largely fiscal and diffuse. Where those fiscal benefits are further reduced by tax abatements, the arithmetic a neighborhood performs can end up looking different from the arithmetic in the development pro forma. Whether any of this drove Cleveland’s decision is not established by the available report; it is, however, the structural pattern into which such decisions have been falling.

Who Absorbs the Cost of a No

Permit denials are expensive in ways that do not appear in headlines. By the time an application reaches a hearing, a developer has typically spent on land options, geotechnical and environmental diligence, preliminary engineering, utility coordination, legal work, and sometimes a deposit toward electrical capacity. That spend is largely unrecoverable, and the option period consumed cannot be bought back in a market where schedule is the scarcest commodity. For a hyperscale tenant with committed capacity dates, a failed site does not merely cost money — it forces a re-planning cycle across an entire regional portfolio.

The beneficiaries are predictable. Sites with by-right entitlements — where the use is permitted outright and no discretionary vote is required — command a growing premium over sites that are merely well-located and well-powered. So do jurisdictions that have done the work in advance: pre-zoned data center overlay districts, published standards for noise limits, setbacks, generator testing hours, and water use. Those places convert a political question into an engineering checklist, which is exactly what a developer will pay for. Expect more capital to route toward them, and toward exurban parcels where the zoning conversation is simpler, even at the cost of building new electrical infrastructure that an urban site would have supplied for free.

Cities face a genuine trade-off here, and it is not obvious which way it cuts. A denial demonstrates that local standards are enforceable, which strengthens a municipality’s hand in negotiating community benefit agreements, noise covenants, water commitments, and payments in lieu of taxes with the next applicant. It also carries a cost to a city’s reputation for predictability, which is one of the few variables in site selection that a municipality fully controls. The durable answer for cities that want the investment on their own terms is not to approve or deny case by case, but to publish the terms in advance.

What a Thin Record Does and Does Not Support

The available source for this story is a single headline-level report. That imposes a discipline worth being explicit about: it establishes that a rejection occurred, and essentially nothing else. Readers should be skeptical of any account of this decision — from any direction — that supplies motive, vote counts, or project specifications without citing the underlying record.

The fair questions run in every direction. Of the applicant: what load, water use, generator testing schedule, noise modeling, and permanent employment figures were placed on the record, and were they disclosed early or late? Of any opposition: what evidence was presented, and was it technical analysis, procedural objection, or general concern — all legitimate inputs to a hearing, but different in weight and in legal consequence? Of the city: was the denial grounded in specific, articulable code criteria, or in a more general reading of neighborhood interest? That last distinction is not academic. In Ohio, as elsewhere, the reviewability of a zoning decision turns heavily on whether the record shows the decision-maker applied the standards in the code.

It is equally worth resisting the two lazy readings that tend to attach to stories like this one. The first treats organized neighborhood opposition as inherently manufactured; the second treats a municipal denial as evidence of hostility to investment. Neither is supported by anything in the available report, and neither should be asserted without the hearing record, the application file, and the written decision. Those documents exist. Until they are examined, the honest summary is that Cleveland said no in Slavic Village, and the reasons are not yet public.

Background

Slavic Village grew in the late nineteenth and early twentieth centuries around Cleveland’s steel and manufacturing corridor, and it retains the physical signature of that era: large industrial parcels, rail access, and electrical infrastructure originally sized for factory loads. Like much of Cleveland’s southeast side, the neighborhood experienced sustained industrial decline and was among the areas most severely affected by the 2000s foreclosure crisis, leaving significant vacant land alongside occupied residential blocks — precisely the mix that makes redevelopment both attractive and politically complicated.

Against that backdrop, northeast Ohio has drawn growing interest from data center developers during the current artificial-intelligence buildout, aided by state-level incentives for qualifying data center equipment, available water, and a moderate climate favorable to cooling. That interest has arrived alongside an unresolved public debate about how large computing loads should be charged for electricity and what obligations they should carry to the communities that host them. Cleveland’s May 2026 permit denial in Slavic Village sits at the intersection of those two trends: strong developer demand for legacy industrial land, met by municipal land-use authority that operates on entirely separate criteria from the grid or the tax code.

Source: Cleveland rejects permit for hyperscale data center in Slavic Village — Ideastream Public Media, 14 May 2026, reporting the city’s denial of a permit application for a proposed hyperscale data center on Cleveland’s southeast side.