Data Centers Daily

Data centers undermine agriculture land

Mark White, June 26, 2026 (Sarah Low, Maria Kalaitzandonakes, Jonathan Coppess, and Brenna Ellison, Successful Farming, Rural Americans are Concerned About the Impact of Data Centers, https://www.agriculture.com/partners-rural-americans-are-concerned-about-the-impact-of-data-centers-12007246)

Artificial Intelligence (AI) has the potential to reshape our economy and workforce. Data centers serve as the physical infrastructure for AI, but they come with notable costs and unknowns. These unknowns have generated consumer concerns around issues such as land use — especially farmland — and their impact on infrastructure needs, energy consumption, and water use. Given the growing prevalence of data centers, the Gardner Food and Agricultural Policy Survey recently assessed U.S. consumers’ concerns about the impact of data centers and AI. This article discusses the results and presents potential implications for rural economic development and community leaders.

Data Centers Power the Digital and AI Ecosystem

Data centers physically house the servers, data, and infrastructure that support the internet, cloud computing, and artificial intelligence. These facilities require reliable access to large amounts of electricity as they use a lot of power, and significant amounts of water to support the liquid-cooling systems needed to prevent overheating (Lawson, Offutt, Ortiz, and Zhu, 2026). Relevant to rural areas, modern data centers built to support AI require 500-800 acres of land (Cvengros and Skae, 2024), and in some instances, these data centers will require more land. Given this growing need for developable land, many rural data centers are built on farmland.

Data center development can potentially contribute to local economic development. They are capital-intensive developments that, in some instances, can generate significant property tax revenue for some local jurisdictions. That said, specific tax incentives available to data‑center owners and operators will determine the overall magnitude of the tax benefits. Not all data centers are the same, and the employment benefits of data centers vary, but the jobs impacts are often overstated (Pipa and Aley, 2026). Outside of the construction phase, the local employment impacts related to data center operation, maintenance, and security are more modest. Data centers have also been shown to raise house prices. This can benefit property owners, but these increases can also increase costs for renters and prospective homeowners (Alvarez et al., 2026).

Many unknowns remain about the full impacts associated with data center growth and expansion. According to the UVA Weldon Cooper Center for Public Service, by 2030 the energy requirements for data centers are projected to more than double in Illinois and triple in Indiana, Michigan, Minnesota and Wisconsin (Ferreira, Strobe, Rephann, and Scheffel, 2026). In addition to the greater demands on energy generation and transmission, these developments will also impact water usage, wastewater discharge, and land use. Additionally, communities must also consider how these developments will affect air quality, noise pollution, and the economic trade-offs associated with data center development (Walker and Goldsmith, 2026).

Given these uncertainties, public views of data centers are mixed and may vary depending on where people live. In 2024, there were 115 data centers operating in Illinois — mostly located in the Greater Chicago area — and an additional 67 were expected to open by 2030 (Ferreira, Strobe, Rephann, and Scheffel, 2026). These data centers directly employed almost 10,000 people and generated $131M in state tax revenue and $127M for localities across the state (2024 dollars). However, the Pew Research Center reports that nationwide, 87% of existing data centers are in urban areas, but 67% of planned facilities are slated for rural communities. Moreover, 39% of planned data centers are in counties that currently have none (Seets and Radde, 2026). As these developments become more rural, data centers will increasingly affect farmland, as well as rural electricity and water systems.

AI requires hyperscaled data centers

Alejandra Martinez, June 26, 2026, Texas Tribune, Hundreds of data centers are coming to Texas. Here’s what you need to know., https://www.texastribune.org/2026/06/26/texas-data-center-guide-what-you-need-to-know/

A data center is a facility that houses computer servers, data storage drives and hardware. It serves as the physical backbone or brain of the internet, storing and processing everything from the digital world, including cloud files and streaming media to email, social media, banking transactions and artificial intelligence models. Older, smaller data centers run the internet or global network of computers and devices. Artificial intelligence is driving the demand of new data centers or so-called “hyperscalers.” These data centers not only store data, but run several calculations and analyze information to generate answers for users who are using AI assistants. These newer data centers, which are fueling the current data center construction boom, are meant to operate efficiently without any interruption. Keeping them running continuously requires immense amounts of power, which can also require lots of water for cooling.

Even with savings, net water use

Data center servers generate heat and most of the water used by data centers is used to to keep their systems cool, according to experts. The industry argues its technology is improving to require less water.

The amount of water used on the technology varies depending on the different cooling systems that are used, as well as the center’s location since hotter regions require more cooling. In some instances, a liquid (often water or another coolant) carries that heat away from the equipment. In a “closed-loop” system the liquid doesn’t get used up immediately — the system draws a large amount of water at the start but reuses it over some period of years by circulating inside the system.

Google plans to use closed-loop cooling systems in water-stressed regions of Texas. Ben Townsend, head of infrastructure and sustainability at Google, said that an initial fill for the system per building can range from 1.5 to 2 million gallons — equivalent to the average use of more than 6,000 U.S. households. Townsend added that the company is also looking at alternative water resources like brackish groundwater or salty groundwater.

Data centers also use water indirectly through the extensive energy they consume. And Texas’ energy production, especially from coal, nuclear and natural gas plants, requires massive amounts of water.

Water experts say data centers will put more pressure on Texas’ water supply. One estimate shows data centers could account for between 3% and 9% of Texas’ total water use by 2040 — up from less than 1% today, according to a recent white paper from The University of Texas at Austin. By comparison, manufacturing accounts for about 7% of the state’s water use, according to the current state water plan.

Saving water requires using more energy

Data center servers themselves require electricity, and some of the more water-efficient cooling systems use the most electricity to operate.

As AI computing chips get more powerful and hotter, the need for cooling increases and that means more energy, said Margaret Cook, a leading data center researcher with the Houston Advanced Research Center, noting that “great power becomes great responsibility.”

The energy demands are being felt in Texas.

1 million homes of power per data center

The Electric Reliability Council of Texas, the state’s main grid operator, has been flooded with requests for power. As of May, according to ERCOT, the estimated electricity that large development projects could need totaled 439 gigawatts of power capacity — which would equal roughly a third of all the power generation in America and is five times larger than the current all-time peak demand on the state’s grid. Of those projects, about 89% are data centers, most of which have aimed to start operating by 2030; but energy experts say it’s unlikely that all of them will be built.

In the rural Texas county of Hood, one developer has proposed three data center projects that could use enough electricity to power 3 million homes.

Data center expansion now, public opposition growing

MooMoo, 6-26, 26, AI data center expansion faces public opposition as over 300 local bans loom across the U.S., https://www.moomoo.com/news/post/72091791/ai-data-center-expansion-faces-public-opposition-as-over-300?level=1&data_ticket=1782465807237151,

In response to the expansion of AI data centers, over 300 local governments across the United States have enacted bans on new construction or suspended approvals, with more than 275 such measures added in 2026 alone. Public concerns over high electricity and water consumption, as well as community strain, are increasingly outweighing the perceived economic benefits. Although some projects offer favorable tax revenues, the growing wave of opposition is putting the AI industry’s ‘social license’ under severe pressure, transforming infrastructure expansion from a capital issue into a contest between public policy and public opinion.

The investment boom in AI infrastructure continues, but the data center expansion underpinning this wave is encountering public resistance in the United States.

According to The Information, since 2023, more than 300 state, city, and county governments across the U.S. have introduced new bans or moratoria on data center construction, with over 275 such measures added in 2026 alone. Local authorities are clearly tightening regulatory oversight of data center expansion. Although the vast majority of these measures are temporary restrictions, the growing wave of opposition has significantly increased uncertainty surrounding AI infrastructure development.

For an AI industry currently at the peak of its capital expenditure cycle, data centers are not only core infrastructure for training and inference but also pivotal to whether hundreds of billions of dollars in investment commitments can be fulfilled. However, from the public’s perspective, the high electricity and water consumption associated with AI, along with its strain on local communities and environmental resources, is increasingly overshadowing its economic benefits—putting the industry’s ‘social license’ to operate under scrutiny.

Restrictions multiply as local governments tighten approvals

It has been reported that over 300 local governments in the United States have enacted new restrictions on data centers, including permanent construction bans, temporary approval moratoria, revised zoning regulations, and heightened construction requirements.

Although most of these measures remain temporary moratoria that could eventually be lifted, the trend reflects local governments’ efforts to reassess the long-term impacts of data centers on power grids, water supply systems, and community resources.

Amid the rapid advancement of generative AI, major U.S. tech companies are building large-scale data centers at an unprecedented pace. Firms such as Microsoft, Meta, OpenAI, Google, and Amazon continue to ramp up investments in AI infrastructure, turning multiple U.S. states into the world’s largest hubs for AI computing capacity. However, the concentrated construction of data centers has begun to draw skepticism from local residents.

“Anti-data center” sentiment rises as AI faces a reputational crisis

Recently, John Carmack, legendary figure in the U.S. gaming industry and co-founder of Keen Technologies, stated on social media platform X that he has started seeing anti-data center signs in Texas neighborhoods and is considering funding advertisements himself to promote the message: “Data centers are great—Texas should stay ahead.”

He later added that current public sentiment toward AI reminds him of the historical trajectory of nuclear power in the United States. He noted that prolonged public opposition severely constrained the development of nuclear energy in the U.S., and expressed hope that the AI industry would avoid repeating the same mistakes.

The AI industry currently faces not only a perception problem but also a communication challenge. In response to growing skepticism, some Silicon Valley investors and entrepreneurs have shown a preference for addressing criticism on social media or even funding political groups that support AI development, rather than proactively explaining to the public why data centers deserve to be built. This has led to a continued deterioration of the AI industry’s image at the community level.

Economic benefits exist, but they are insufficient to offset community concerns.

In fact, large-scale AI data centers have also delivered tangible economic benefits to local communities.

For example, Richland Parish in Louisiana is expecting hundreds of local teachers to receive $50,000 bonuses this year, funded by increased tax revenues generated from Meta’s construction of an AI data center—a representative case of how AI infrastructure can improve local fiscal conditions.

However, such success stories remain insufficient to alleviate public concerns about the high energy consumption, substantial water usage, and environmental impact of data centers.

As AI models continue to scale up, global data center construction will likely remain robust over the next few years. Yet an increasing number of local governments are now calling for a reassessment of project approval processes, signaling that the AI industry will need to contend not only with traditional bottlenecks—such as chip supply and power capacity—but also with securing community and public support.

The opposition to data centers is really about AI

Mark Cuban, 6-25, 26, https://x.com/mcuban/status/2070211760196587534?s=20,

It’s time for everyone to realize that the fight against data centers has nothing to do with data centers.

They have become a proxy for the hate towards AI and the concentration and accumulation of wealth it’s creating.

Until those running the big LLMs understand this and start a community tour, not to explain the benefits of AI, it’s too late for that, but to help towns and cities that may be impacted by job losses (and I’m a believer their will be a net gains in a few years), this battle is only going to get more intense and let me tell you now , no matter how much money you pay to buy politicians and races, you will lose.

One thing I have learned is being hated is not good for business.

How can they help ? They will tell you. You will need to do what they ask. Billions of dollars is a lot of money across towns and city programs. Across the major LLMs, it’s a cost of doing business.

At the same time, I would go to LA and NYC and ask the arts and creative unions what kind of programs would help and protect their artists. DO NOT GO TO THE MUSIC OR FILM COMPANIES. that will make it worse.

Don’t try to pay famous people to endorse what you are doing. That’s dumb.

Talk to artists and ask them what you can do to provide financial and creative support. Every creative I know is TERRIFIED about what AI will do to their profession. You must meet them face to face and basically do what they say.

The big LLMs have lost the PR battle. Why ? Because they all suck at putting people first. They have an SV attitude that makes them all think they are John Galt saving the world

Given the number of data centers and power that is needed, today and going forward , If you don’t kiss the asses of the people that go to work every day, and are just trying to pay their bills, you will fall far far short of the capacity you need to make your business work.

Morrone 26 (Megan Morrone, 6-21-2026, “Data centers become the face of AI backlash”, Axios, https://www.axios.com/2026/06/22/ai-data-center-backlash-poll)

Only a small fraction of data center opponents actually live near one, according to new polling by a consulting firm that counsels leading AI labs and tech startups. Why it matters: The findings by Milltown Partners, shared first with Axios, highlight how data centers have become a stand-in for broader anger at an AI future many Americans don’t want but fear they’ll have to pay for. By the numbers: The public is still divided on data centers, with direct opposition not yet a majority view. But nearly half of respondents support a temporary construction ban, according to Milltown’s findings. 38% of respondents said they would support a data center being built near their home, while 34% would oppose it. Meanwhile, 49% say they support a moratorium on construction of new data centers, while only 16% oppose a moratorium. Another 27% neither support nor oppose a moratorium and 8% say they don’t know. Most opposition to data centers isn’t coming from neighbors. Only 8% of the respondents who oppose data centers say they know of one or more data centers near their home, the poll found. Between the lines: The split suggests many voters aren’t categorically anti-data center, but they are wary of the pace and terms of the buildout. A temporary moratorium could be a way to force companies and policymakers to answer questions about costs, water use and who benefits. Threat level: Both Steve Bannon on the right and Bernie Sanders on the left have attacked AI as a threat to working people. “This isn’t happening in a vacuum. The AI transformation is arriving at a time when Americans already feel angry, insecure and pessimistic,” Milltown Partners researcher Tom Brookes says. Context: Pew Research Center also found in an April poll that living near an existing or planned data center doesn’t have much effect on Americans’ views of the facilities. Two-thirds of planned data centers are in rural areas, even though 87% of existing data centers are in urban ones, Pew found. What they’re saying: Warnings from tech leaders that AI will bring mass job loss are handing critics more ammunition. If unemployment moves by two percentage points and people think this is caused by AI, we will see a “real populist backlash,” Andy Hall, professor at Stanford’s graduate school of business and senior fellow at the Hoover Institution, wrote on X last month. The intrigue: The backlash is hitting just as tech companies look for new ways to staff their data centers, at least temporarily. “People are building massive scale data centers everywhere and they’re facing a severe labor shortage. That’s the gap we want to fill,” Zhou Xian, co-founder and CEO of Genesis AI, tells Axios. But not always with humans. Genesis AI just launched a new general-purpose robot built to move in complex environments, like data centers. The fine print: Milltown Partners, a global public affairs and communications firm, surveyed 6,872 registered voters between May 10 and May 20 recruited from online panels. The margin of error is 3 percentage points. The polling oversampled voters in Texas, Georgia, Michigan, California, and North Carolina — states with current data center projects. The bottom line: The massive windowless warehouses packed with computing infrastructure have become a physical symbol of wider AI anxiety.

New data centers powered by gas turbines

Amy Alonzo, 6-26, 26, The Nevada Independent, Nevada data center rush drives request for a novel type of gas power planthttps://thenevadaindependent.com/article/nevada-data-center-rush-drives-request-for-a-novel-type-of-gas-power-plant,

Just weeks after a developer asked state energy regulators to let it build its own temporary natural gas plant to power Northern Nevada data centers, another developer is asking regulators to approve an even larger, permanent gas plant to power additional data centers. Nexus Fulcrum LLC is asking state energy regulators to allow it to construct a 510 megawatt (MW) natural gas plant at the former Sierra Biofuels Refinery at the edge of the Tahoe-Reno Industrial Center (TRIC), a swelling hub of industry and data centers. Unlike front-of-the-meter power that is generated or purchased by public utilities and transmitted on public transmission systems, behind-the-meter power does not rely on public utility infrastructure and therefore has less oversight by state energy regulators. “The proposed electrical generation facility is needed to support expanded development, including data centers, within the TRIC,” reads the application submitted June 17 to the state’s Public Utilities Commission. Minimal information exists online about Nexus Fulcrum LLC, a foreign limited-liability company out of Delaware that filed in April with Nevada’s Secretary of State Office. However, data center developer Nexus says on its website that it focuses on “purpose-built, large-scale campuses powered behind the meter.” And the applicant is seeking to build the plant on the former Sierra BioFuels Plant, shuttered by Fulcrum BioEnergy in spring of 2024 after less than two years of operations. The company filed for Chapter 11 bankruptcy later that year. Switch Ltd., a large-scale data center operator with a campus at TRIC, acquired the biorefinery. Neither Las Vegas-based attorney Linda Bullen, who represents Nexus Fulcrum LLC and chairs the State Bar of Nevada’s environmental and natural resources law section, nor Megan Peterson, senior environmental manager at the consulting firm Kimley-Horn that is involved in the application, returned a phone call from The Nevada Independent before publication. The application is the second in less than two months from companies seeking to build behind-the-meter power for Northern Nevada data centers. In April, Fleet Data Centers asked state energy regulators to approve more than 350 MW of natural gas and diesel behind-the-meter power for data centers at TRIC because data center growth at the industrial complex is outpacing the ability of NV Energy to provide enough power. NV Energy has estimated it needs 47 percent more energy statewide than it forecast just two years ago to meet the growing needs of data centers and other large-scale customers. Over the next 20 years, NV Energy expects to build out 1,200 MW of natural gas power based on current customer requests. The natural gas plants requested by Fleet Data Centers and Nexus Fulcrum LLC would, over just a couple of years, generate about three-quarters the amount of natural gas power the utility would develop over 20 years. In 2001, the state passed its so-called 704B law allowing businesses with large electric loads such as mines and casinos to leave NV Energy and purchase power from other providers. However, those businesses still rely on the utility’s infrastructure for transmission and are required to comply with the state’s renewable portfolio standard — that is, how much power utilities pull from renewable sources. Behind-the-meter customers do not have to hit state renewable targets. And, because the power doesn’t run through the state’s publicly traded utilities or use their transmission equipment, state energy regulators have a smaller role in its oversight. If approved, the Nexus Fulcrum project will include nine gas turbines, each capable of producing between 56 and 57 MW of power. Because the project is on private land, federal environmental review isn’t needed. But it still requires state permits and approvals, including an air quality operating permit for construction from the Nevada Division of Environmental Protection (NDEP). The project will produce more than 100 tons per year of both nitrogen oxide and carbon monoxide, according to the application, which is still being reviewed by NDEP. Construction would start in early 2027 and wrap up in late 2029, according to the application.

Data centers increasing data center and water demand

Let’s Data Science, 6-26, 26, AI Drives Data-Center Energy and Water Demand, https://letsdatascience.com/news/ai-drives-data-center-energy-and-water-demand-64d3692d

What happened

Forbes contributor Vaishali Nigam Sinha reports that the global expansion of AI infrastructure is driving rapid growth in data-center energy demand. Forbes reports data centers consumed about 1.5% of global electricity in 2024 and that demand rose 17% in 2025, outpacing overall electricity growth. Forbes cites projections that data-center cooling could consume 1.2 trillion liters of water annually by 2030. Forbes also reports that current GHG Protocol Scope 3 guidance technically covers AI as a purchased service but lacks explicit, consistent categorization and reliable activity data, creating a reporting “ghost room”.

Editorial analysis – technical context

Industry-pattern observations: measurement gaps matter for practitioners because energy and water intensity for AI workloads depend on multiple variables not routinely disclosed, including server utilization, workload mix, cooling technology, and regional grid carbon intensity. Companies and cloud providers that publish granular utilization metrics, PUE equivalents, and water-use metrics materially improve downstream Scope 3 accounting, while lack of standardization forces estimators to rely on approximations.

Context and significance

the article places the reporting gap amid a broader shift where AI is increasingly treated as foundational infrastructure rather than a discrete SaaS feature. That shift raises questions about lifecycle boundaries and shared responsibility across hardware manufacturers, hyperscalers, and enterprise users. Standard setters such as the GHG Protocol currently provide a framework but, according to Forbes, do not yet offer a clear, uniform taxonomy or data templates for AI-specific infrastructure impacts.

What to watch

For practitioners: observers should watch for three developments: emergence of sector-specific reporting templates from standard setters, more granular disclosures from major cloud providers on energy intensity and water use per workload, and third-party tools that translate utilization telemetry into Scope 3 inputs. These indicators will determine whether reporting evolves from proxy-based estimates to measurement-driven accounting.

Proposal for data center moratorium

Josh Seigel, 6-24, 26, Politico, Pallone calls for national data center moratorium, https://www.politico.com/live-updates/2026/06/24/congress/pallone-calls-for-national-data-center-moratorium-pro-00974063

House Energy and Commerce ranking member Frank Pallone of New Jersey on Wednesday called for Congress to impose a nationwide data center moratorium.

Pallone is now the most powerful congressional Democrat with jurisdiction over energy and environment issues to support such a policy in the face of public backlash.

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Pallone’s statement came at the beginning of an Energy Subcommittee session to vote on more modest legislation that seeks to make sure ratepayers don’t foot the bill for data center expansion.

“Americans across the county have expressed concern and opposition to the rampant construction of AI data centers, and Congress should take this political groundswell seriously with a data center moratorium,” Pallone said.

Pallone’s stance complicated the path for the Ratepayer Protection Act — or something like it — to become law anytime soon and positioned Pallone in alignment with progressives and environmental advocates seeking stricter oversight of data center development.

That bill — which passed by voice vote soon after Pallone spoke — would codify the principle behind President Donald Trump’s ratepayer protection pledge, where Big Tech companies committed to covering their own data center energy costs.

Pallone called that bill and others being considered by the subcommittee a “useful first step” but warned they are “not nearly enough.” “We need action, not toothless promises from Big Tech,” he added.

Senior Energy and Commerce Democrat Kathy Castor of Florida is co-sponsoring the Ratepayer Protection Act. Google and Microsoft came out in favor.

The Data Center Coalition has said the facilities have helped increase demand for electricity. The group’s leaders, however, say the reason for higher electricity prices is complicated, and data centers are not to blame.

Rep. Alexandria Ocasio-Cortez (D-N.Y.) and Sen. Bernie Sanders (I-Vt.) have been the most prominent lawmakers in calling for a moratorium.

Rep. Gabe Evans (R-Colo.), the main sponsor of the Ratepayer Protection Act, said, “Two things can be true: the AI phenomenon — that toothpaste is not going back in the tube. The only question is, do we lead on it or do we cede ground to the Chinese? The second thing is also true: These costs should not be borne by ratepayers, and people want to know how we’re going to pay for this.”

Data centers staff by Chinese humanoids

Derek Draplin | June 22, 2026, The MidWesterner, Dowagiac data center to deploy Chinese humanoid robots to support development of ‘autonomous workflows’, https://www.themidwesterner.news/2026/06/dowagiac-data-center-to-deploy-chinese-humanoid-robots-to-support-development-of-autonomous-workflows/

The technology company that owns the Dowagiac data center plans on staffing the facility with 30 humanoid robots from a Chinese manufacturer later this year.

Hyperscale Data and its subsidiary, Omnipresent Robotics, began production of the robots earlier this month as part of its broader plan to deploy 142 humanoid robots at the facility “to support the development of embodied artificial intelligence applications, autonomous workflows, and advanced robotics systems.”

The data center is facing complaints and a class action lawsuit from local residents who say the facility emits “constant noise.”

Hyperscale Data is partnering with AgiBot, a Chinese robotics manufacturer that it says is “one of the leading humanoid platforms in the world.” The company purchased 142 humanoid robots from AgiBot for $13.4 million, and Omnipresent Robotics will resell robots once trained, Data Center Dynamics reported.

“We are building in Michigan because Michigan still has industrial muscle memory,” Hyperscale Data Executive Chairman Milton “Todd” Ault, III, said in a note on the company’s website. “We are developing a campus where robots can be trained to perform real jobs, tested until the weak points show themselves, assembled here, and sent back into the field with software that actually fits the work. That will create engineers, technicians, operators, manufacturing jobs, and a place where people learn by doing instead of talking.”

The company is planning a 100,000-square-foot “Robotics Research, Testing and Innovation Center” at the Dowagiac campus, while the initial 30 robots “will work side-by-side with AI infrastructure personnel and data center employees.”

Lawmakers have warned about national security threats posed by robotics companies aligned with the Chinese Communist Party sending their technology to the U.S., particularly from the Chinese firm Unitree, one of AgiBot’s competitors.

Bipartisan members of the House Select Committee on China warned the Trump administration in a letter last year that Unitree has “participated in military-civil fusion programs, received Peoples Republic of China state funding, contributed to defense research, and produces robotic systems with clear military utility.”

Lawmakers also introduced the American Security Robotics Act earlier this year, which would ban government agencies from using humanoid robots and other autonomous technologies that come from “foreign entities of concern.”

“The Chinese Communist Party has shown that they are willing to lie and cheat to get ahead at the expense of the American people and our national security,” U.S. Sen. Chuck Schumer, D-N.Y., said in a statement. “They are running their standard playbook—this time in robotics—trying to flood the U.S. market with their technology, which presents real security risks and threats to Americans’ privacy and American research and industry. We must protect our country from these threats, starting with a ban on the federal government buying this CCP technology.”

Hyperscale Data’s 30-megawatt data center in Dowagiac has drawn noise complaints from residents, who say the noise can reach around 60 decibels.

The class action lawsuit claims that over 1,300 homes are being impacted by the noise.

“My husband and I have been unable to use our yard since this facility began operating,” one of the plaintiffs, Lindy Valenzuela, told WNDU. “It is impossible to stay outside for longer than 10 minutes at a time before succumbing to headaches. The noise also penetrates the inside of our house even with the windows closed,” she added.

Marjoriee Finn, who lives across the street from the data center, recently told MLive the noise is so bad “it’s like we’re living in a prison in our own yard, in our own house.”

It’s all about power (proving the two PF topics are interrelated)

Chris Gillett, 6-22, 26. Why American data centers can’t plug in, Chris Gillett is a market analyst in electric power. His views don’t represent his employer., Why American data centers can’t plug in, https://worksinprogress.co/issue/why-american-data-centers-cant-plug-in/

The AI buildout is bottlenecked by energy. But America has the electricity to power its data centers; the problem is getting it to them.

One of the most expensive projects in history is under construction in Abilene, Texas. This joint venture, Stargate, is the flagship of a bigger project by the same name led by OpenAI and Softbank, and is expected to cost well over $40 billion for a high-performance computing campus that will train new generations of AI models.

Stargate is just one major project in one of the biggest investment booms in history, driven by the belief that increasingly powerful AI models can deliver explosive economic growth. But it will require enormous amounts of electricity to work: Stargate is expected to draw 1.2 gigawatts, as much as 313,000 median American family homes, at peak load. A report by EpochAI and an energy research institute projected that total AI computing power would reach 100 gigawatts worldwide in 2030 if the 2025 growth rate stays steady. And data centers aren’t the only energy-hungry element of the AI revolution. The biggest battery manufacturing plants in the US draw energy at a rate of 115 megawatts, and the first phase of TSMC’s Arizona semiconductor plant will draw 200 megawatts.

The primary bottleneck to this growth is the availability of electricity. But this doesn’t mean there is an energy shortage. Instead, the constraint is connecting the flood of new data centers and the plants to power them to the electric grid. Before any new piece of infrastructure can be connected, grid operators must study how it will change power flows around the grid and determine whether upgrades to the system are required. That process is significantly backlogged. Though the median power plant in 2005 waited less than 20 months for interconnection, this had jumped to 55 months by 2023.

The interconnection process wasn’t created for today’s world. Grids use an inflexible first-come, first-served queue that leaves some of the most valuable projects stuck behind less important ones. They also evaluate according to rigid conditions that don’t reward plants for being willing to cover their own power needs for short periods. To prepare for the AI age, grid processes need to change.

A power-hungry future

Estimates vary for how much power will be needed by the data centers and chip manufacturers of the future, but the heads of every major AI company agree that they will need more than they are currently able to get. Jensen Huang, CEO of Nvidia, has said that ‘every data center in the future will be power-limited’. Mark Zuckerberg said Meta ‘would build … bigger [AI training] clusters … if we could get the energy to do it’. And OpenAI CEO Sam Altman told Congress that ‘the abundance of [AI] will be limited by the abundance of energy’.

Regardless of how the data center boom plays out, there is a long-term shift towards electrification across the economy. Electricity can be converted into work instantly, unlike fuels, and with little energy loss. Electricity creates motion directly, while fuels must first be combusted in an engine. This is why electric vehicles can cost half as much to fuel even though electricity is more expensive than gasoline. The simplicity of electric motors also means that electric vehicles have half the lifetime maintenance costs of gas-powered ones.

Electricity also transmits information. Transistors switch on or off depending on the voltage applied to their gates, which allows circuits to perform logical operations. Radios, screens, and computers cannot run on gasoline alone.

Between 1990 and 2024, the price of electric motors declined by 97.5 percent. Power electronics fell 99.5 percent in price, processors built into devices by nearly 99.9 percent, and batteries 98.8 percent. As prices have declined, performance has improved. For example, the amount of energy a battery can store per kilogram has increased five-fold over the same period.

These trends allowed the progression from the Walkman to the iPod and then the iPhone, as well as enabling battery-powered cars, delivery vans, and bicycles. At the same time, the increased computing power built into devices is giving autonomy to newly electrified cars and trucks, robotic vacuum cleaners and mowers, and drones. The commercialization of humanoid robots may also be on the horizon. The future will be largely defined by technologies that run on electricity.

The grid bottleneck

This growth in demand is already straining power grids. Operators are increasingly forced to use expensive local plants because of what grid operators call congestion: cheaper plants are on the other side of transmission bottlenecks and there aren’t enough cables to get the electricity through. In the United States, the additional costs incurred because of grid inefficiencies like congestion ran to $11.5 billion in 2023, an increase of 45 percent from the year before.

ERCOT, the grid that provides 90 percent of Texas’s electricity (including for the plant being built in Abilene), forecasts that it won’t have enough power to meet demand in summer 2028. PJM is the US’s largest grid, both by the amount of electricity it provides and the population in its coverage area, serving an area between Chicago, New Jersey and North Carolina. In 2025, PJM was not able to buy enough future generating capacity to meet projected demand. MISO, another large US grid operating between Louisiana and Minnesota, concluded in one study that ‘resource adequacy risks could grow… absent increased new capacity additions’. PJM’s CEO put it more plainly: ‘We need capacity – a lot of capacity.’

Discussions about expanding electricity supply to power the future often become debates about which source is most suitable: gas, nuclear, solar, or something else. But these are a distraction. Far more fundamental is ensuring power can be efficiently delivered where needed.

When different generation technologies coexist, they can average out to a grid that is cheaper, more reliable, and less polluting than any single type alone. The question isn’t which technology to use, but what balance. But this question isn’t best answered in the marketplace of ideas. Instead, it should be addressed in the marketplace for electricity.

The market for electricity

In the US and Europe, power grids are largely liberalized: provided they can get the necessary permits, independent developers build power plants, and the local utility is required to connect those plants to the grid. In the United States, 88 percent of large-scale power projects currently in development are privately organized and funded.

The power market is run by the grid operator’s economic dispatch software. Each power plant tells the operator its costs, and the operator commissions the cheapest plants, accounting for transmission constraints. As load increases during the day, the operator keeps commissioning the next cheapest plant. The price of power is set at the marginal cost of the next cheapest plant, and that’s the price every power plant is paid.

Market prices signal to power plant developers about levels of supply and demand. In the same way, prices balance different energy sources based on the strengths and weaknesses of each. For instance, as more solar panels are built, the value (and therefore price) of power during the middle of the day, when the sun is shining most, adjusts downward. From December 2020 to September 2025, maximum solar output in ERCOT increased from 4 to 29.8 gigawatts. And from 2020 to 2025, the value of power at 1pm relative to the highest-priced hour decreased from 92.9 percent to 38.7 percent. As one technology type becomes overbuilt, prices reflect that and developers react accordingly.

The evolving daily price shape in response to the abundance of solar energy was a signal that the grid needed storage capacity, and power plant developers responded. From 2020 to October 2025, ERCOT went from having almost no battery storage to a combined battery discharge of 8.6 gigawatts. The same process has played out in California and many European markets.

One might assume that the price of electricity for consumers is dictated by market forces, like those that regulate supply and demand across different power plants. But to a large extent, it is not. Grid infrastructure, like large power lines, is generally planned by the grid operator, and the cost is passed on to consumers at a price approved by state and federal regulators. In one typical utility territory within ERCOT, the portion of regulated costs on the average residential customer’s bill has grown from 28 percent in 2002 to 40 percent in 2025.

Regulators of all major grids have set caps on wholesale prices in response to public outrage at price volatility. As a result, the generators needed to keep the grid reliable are sometimes unprofitable. Under normal market circumstances, generators would stop running when it wasn’t profitable to do so. Instead, regulators put further policies in place to prevent this. Markets for capacity mean that generators are paid not for energy itself but for committing to be available if there is extra demand. Must-run agreements pay unprofitable generators to keep running if they keep the grid reliable. These are negotiated bilaterally between generators and the grid operator, outside of any wider competitive process.

Policy choices also shift the equilibrium. For example, America’s Inflation Reduction Act gives a $30 tax credit for every megawatt-hour produced by qualifying renewables. Power plants that opt in, typically wind, paradoxically often offer their power at negative prices, making money from the tax credit even when they literally pay consumers to use them. At times, a large enough share of the market offers electricity at a negative price that electricity overall (not just from one supplier) can have a negative price. A similar dynamic is playing out in some European countries.

Despite these interventions, which make them less efficient, markets still find an equilibrium. The interaction of supply and demand creates prices that power plant developers use as indicators for what the market does or doesn’t need more of. If prices are high, new power plants enter. That’s why arguing about the best power generation method is overrated. Well-designed energy markets answer this question automatically. The real bottleneck is connecting to the grid at all.

Power struggles

xAI’s Memphis data center operated partially off-grid for months. When it first came online in 2024, it could reportedly draw only eight megawatts from the grid (enough to power a few thousand electric toasters). Rather than wait for its grid connection to be upgraded, xAI installed 422 megawatts of on-site gas turbines. Once transmission upgrades were completed, the project would shift to consuming grid power and the on-site generators would be used only for emergency backup.

Such off-grid generation is a temporary solution. Grid power is more reliable and, on average, cheaper. But thanks to the long queue of projects waiting for connection, 62 percent of data centers are considering off-grid solutions, either to get up and running faster or to improve reliability. Google is even exploring the possibility of a data center in space powered by solar panels. Grids connect to new generators and energy-hungry infrastructure only after studying how to do so using an engineering model that simulates power flows during peak load scenarios. If the new infrastructure would cause overloads on any parts of the system, then those elements need to be upgraded. The utility then needs to estimate the cost of those upgrades and build them.

The grid operators who run these studies are typically public bodies charging a small fee on electricity sales to fund their operations without seeking to make a profit. State utility commissions and the Federal Energy Regulatory Commission have determined that grid operators should run the interconnection queue as a first-come, first-served process that is essentially free.

That process is moving very slowly, creating a huge backlog of mostly phantom projects. Every US grid has more power plant capacity waiting to be connected than there are gigawatts of peak demand. The backlog also exists for the separate but similar queue of infrastructure not yet connected to the grid. Across ERCOT, there were 143.5 gigawatts of data centers seeking to connect as of October 2025. This compares to ERCOT’s highest ever total demand of 85.9 gigawatts in August 2024.

This system was designed for a different era. The last time electricity use grew five percent annually or faster was between the 1950s and 1970s, during the adoption of air conditioning, refrigerators, dishwashers, and washing machines. From 2005 to 2023, electricity use was almost completely flat. But as that lull in growth comes to an end, the grid will have to adapt.

Crossed wires

The main flaw of the interconnection process is that it uses a first-come, first-served queue. This means that high-priority requests can spend years stuck at the back of the line behind other less important ones.

Some requests are speculative, submitted by developers before they have ready customers. Some are duplicative, meaning a developer fishing for a good spot has requested to interconnect the same project in multiple locations. These are among the reasons that 72 percent of requests to connect submitted since 2000 were ultimately withdrawn. Other requests are high quality but small and low value. This creates a feedback loop: the harder it is for developers to estimate the cost to interconnect, and the longer they have to wait for an answer, the more speculative requests are made, further clogging the system.

Every major grid has a roughly similar process. The system impact study comes first. The operator models the grid during peak demand conditions and assesses the cost of any necessary upgrades. After reviewing those costs, the interconnection customer (the developers of the power plant or power-hungry infrastructure) decides whether to proceed. Almost 40 percent of withdrawals between 2020 and 2023 were at this stage. If the customer proceeds, the grid operator then determines how to physically connect the project to the grid. Again, it delivers a quote for these costs to the customer, who then decides if they want to proceed. If so, they sign an interconnection agreement, and the utility begins work on the upgrades.

There are some opportunities to automate these workflows, but each request takes time. The median delay from initial request to the interconnection agreement was 34.2 months for agreements signed in 2023.

Another major issue is restudies. Once a system impact study identifies required network upgrades, those upgrades are added to the grid model that’s used to study subsequent projects in the queue. If those upgrades are not built because the earlier project withdraws, any subsequent studies may need to be redone. In one extreme case, a restudy increased a 242-megawatt wind project’s network upgrade costs from $33.5 million to $99 million after the project was already operating.

The slowness of the interconnection queue adds to households’ energy costs. The clearest example of this is found in PJM, which has the slowest queue of the US grids. In 2025, PJM’s electricity suppliers paid $14.7 billion at auction to make sure they’d have enough power to supply their customers, a huge jump from $2.2 billion the year before. Meanwhile, many gigawatts of renewable power projects have been sitting in the queue for five years or more. If just 30 percent of those projects had been interconnected, the auction’s cost would have been 63 percent lower.

Grids have adopted many sensible reforms to the interconnection queue, such as requiring higher deposits to enter the queue and forcing developers to prove they have land to build on and to disclose if they’re making duplicative requests. The most significant reform has been transitioning to cluster studies, which study multiple projects in the same simulation. This reduces restudies because removing any one project has less impact on the overall solution. Clusters also allocate costs more fairly. This prevents a single project triggering a major transmission upgrade that renders it uneconomical. In cluster studies, the cost of these backbone upgrades are shared among all the projects that would benefit from them. While these were necessary reforms, the backlog has only grown as they fail to address the fundamental irrationality of the interconnection process itself: the inflexible first-come, first-served queue.

Tragedy of the commons

In the early 2000s, regulators wanted to level the playing field between new independent producers and incumbents. They worried that utilities might preference their own projects. As there was plenty of spare capacity at the time, the first-come, first-served system seemed both simple and fair.

But today, transmission capacity is a limited resource and independent power producers are thriving. This means there is much less concern around the power of traditional utilities. In response, many grids are proposing mechanisms to allow more valuable projects to jump the queue. MISO, PJM, and SPP, three large grids in the US, have proposed mechanisms to prioritize projects that are most viable or most necessary for the system’s reliability. But these mechanisms are band-aid solutions.

The flood of requests is a typical ‘tragedy of the commons’. Everyone is incentivized to spam the queue with requests. Auctions can fix this.

In the fishing industry, when tradable fishing quotas were introduced worldwide in the 1970s, what had been a mad dash for fish became an orderly and efficient process. Before the quotas, the whole season’s supply was caught in a few days of dangerous, non-stop fishing. Market share went to whoever bagged the fish fastest. Once quotas were introduced, fishers could time their catches with market demand instead of catching everything at the start of the season and then freezing it. And since more efficient fishermen made more money, they were willing to pay more, and quotas went to them rather than the fastest fishers, lowering prices for consumers.

Auctioning new grid capacity could bring similar benefits. The scramble for interconnection would be replaced with an orderly process in which the highest quality projects would get priority. Developers’ bids would reflect both the likelihood that the project will come online and the projected value of the project to the grid if so. Less viable and less valuable projects would be weeded out.

The simplest way to implement an auction would be to create small ‘fast-track clusters’ that receive expedited studies throughout the year. Projects in the regular annual cluster could bid to enter the fast track, and the highest bidders up to some preset number would be admitted. The fast-track proposals recently adopted by some grid operators follow this structure, except that admittance to the fast-track cluster is based on an administrative scoring mechanism, not developer bids. But administrative scoring mechanisms could never capture the subtleties that a developer’s bid would. For example, developers have private knowledge about a project’s chances to get permitted, like whether the site has trees with endangered bird nests or unhappy neighbors generating pushback.

Only connect

Another inefficiency of the current transmission system is that it’s built for peak load conditions that occur only a few hours per year. The rest of the time, much of the grid’s generation and transmission capacity is sitting idle. In 2024, 42 percent of ERCOT’s capacity went unused for half the time.

Building the network upgrades that allow for this very high peak capacity can take years. But new power plants must wait for that construction to be completed before they can join the grid, even though the equipment being constructed will rarely be necessary. A survey of power developers found that transmission construction was the top cause of project delays.

The solution is to let the power plant come online before the upgrades are constructed on the condition that it agrees to turn off during peaks. The power plant doesn’t contribute to the reliability of the system since it won’t be available when the system is under stress. But it will produce electricity the rest of the time, which will lower costs. This is known as energy-only service.

ERCOT has made this energy-only service, also known as non-firm transmission because it implies no guaranteed ability to export or import power, their default. They call it connect and manage. That’s a large part of why projects move through ERCOT’s queue faster than any other grid operator.

But despite being an option in all organized US grids, 87 percent of interconnection requests by capacity opt for the slower firm transmission service. That’s because non-firm service disqualifies power plants from the payments they could otherwise receive in exchange for being available at times of high demand. Other grids could adopt connect-and-manage if they eliminated this payment system and, like ERCOT, raised price caps to allow energy prices themselves to reflect scarcity value. Short of that, they could make non-firm service more popular by allowing generators to apply for firm service after being interconnected on a non-firm basis.

Can’t we just use less energy?

These proposed reforms would allow fast interconnection of new power plants. But if AI is a race with national security implications, they won’t be fast enough.

The fastest way to increase capacity for new large loads would be to decrease demand from existing sources. Demand reduction is the option energy planners have always reached for in a pinch. The concept of energy conservation originated in the late 1960s as the US power grid faced resource constraints for the first time in its history. Energy conservation became a national imperative. For the first time, utilities began promoting things like turning off lights and using less air conditioning. Christmas lights were framed as being wasteful and unpatriotic, and in 1973 the National Chritmas Tree hd only a single light.

Yet while short-term crunches can be fought with conservation, they are difficult to sustain, and increased energy efficiency can simply lead people to using more energy for other purposes. What’s more, the vast majority of electricity users are completely indifferent to the real-time cost to produce electricity, because they’re on fixed rates for terms of months or years. The wholesale cost of a clothes dryer load, which could consume four kilowatt-hours, could range from negative $0.12 to $20, but the retail customer always pays $0.64 at the average US residential rate of $0.16 per kilowatt-hour. This creates irrational behavior, like someone starting a load of laundry while the grid teeters on the edge of a brownout. If customers faced prices more closely linked to real-time costs, they would adjust their energy use accordingly. Prices would be more stable and, on average, lower.

Already, we’ve seen that power costs change throughout the day because of the increasing availability of solar power. Some power suppliers offer time-of-use rates that charge a different rate at each hour of the day to reflect what it costs to provide. As these are adopted more widely, the highs and lows of demand will increasingly follow the same daily pattern as wind and solar availability. Few customers think about their bill enough for small price differences to influence their behavior, but their power-hungriest devices, like their thermostat or EV charger, could schedule themselves to automatically run when the cost is lower.

Daily reconfiguration could be helpful. More helpful would be reducing demand during the few hours per year with extremely high wholesale costs, when the grid is under greatest stress. Even without time-of-use rates, customers can give their utility provider control of their most power-hungry devices, and the utility can turn them down during grid emergencies in exchange for a reimbursement on their bills. The US had 30.5 gigawatts of this capacity, called demand response, in 2022. But the headroom this gives is unreliable because participants may override the utility’s control, turning their air conditioning back up if they are too hot, for instance.

That’s what makes batteries, installed at customers’ homes, the ultimate load-managing tool. Electronic devices continue to work as normal in periods of high demand, just switching from grid to battery power. And batteries offset the demand of the entire home, not just one or two devices. Combined with rooftop solar, they can significantly reduce a home’s reliance on the grid, making room for new large loads. Perhaps the greatest advantage of all is that these at-home power resources don’t have to go through the interconnection queue; the installation process takes weeks to months, not years.

But devices like this free up relatively little grid capacity compared to the huge amounts that will be needed to power the AI revolution. They’re also relatively expensive, as the cost of installation has to be repeated on every home individually. Matching the demand of a single two-gigawatt data center, which could be handled by a handful of utility-scale projects, would require installing solar and battery systems at nearly 175,000 households.

The data center trilemma

Most data centers have on-site generators as backup power for if the grid goes down because they need a stronger guarantee of reliability than the grid alone can provide. But this capacity could have another significant benefit.

Like generators, power-hungry infrastructure could come online on a connect-and-manage basis, interconnecting before all transmission upgrades are complete on the condition that it disconnects from the grid during peaks. Instead of turning off entirely, it would switch to their on-site backup power. And just like connect-and-manage lowers grid costs, flexible data centers would also lower costs for consumers by ensuring less capacity sits idle outside of peak usage.

Because grid peaks are brief and infrequent, data centers would have to disconnect for only a few hours per year. One study found that 76 gigawatts’ worth of new loads could be added (across an area covering most of the US) if these new loads were willing to disconnect during just 22 hours per year. And those 22 hours of disconnection aren’t consecutive. Each disconnection would last a few hours at most, meaning that data centers could rely on batteries rather than thermal generation. The National Renewal Energy Laboratory estimates that grid-scale, four-hour batteries can be built for $1,300 per kilowatt, as compared to around $2,500 for combined cycle gas turbines.

Disconnecting from the grid isn’t exactly ideal, but given the constraints, developers face a trilemma: data centers can be large, they can come online quickly, or they can receive firm grid service, but not all three. If large data centers want to come online quickly, they’ll have to be flexible.

The powerful shall inherit

The data center boom has exposed the weakness of Western grids. But these are not the only new source of demand for electricity that we can expect in the coming years. Industrial electrification, hydrogen production, water desalination, heat pumps and air conditioning to cope with climate change, electric vehicles, and perhaps even electric aircraft are all likely to increase demand.

The twenty-first century will belong to those who make the most of these developing technologies. Without sufficient power, this will be impossible. And the main constraint to supplying power-hungry infrastructure is not power generation – it is getting that power where it is needed.

This is a blessing: insufficient power would be a much harder problem to solve. Instead, the solutions are simple. The queue of infrastructure that needs to connect to the grid should become more flexible, allowing fast-track slots to be auctioned so that the most valuable and viable projects can start sooner. And payment systems should reward both producers and large-scale consumers that are willing to disconnect for a few hours per year when demand is highest. These simple fixes can give America a power grid fit for the future.