 |
|
|
|||||
With community opposition growing, data center backers are going on a full-scale public relations blitz. Around Christmas in Virginia, which boasts the highest concentration of data centers in the country, one advertisement seemed to air nonstop. Virginias data centers are investing billions in clean energy, a voiceover intoned over sweeping shots of shiny solar panels. Creating good-paying jobs cue men in yellow safety vests and hard hats and building a better energy future. The ad was sponsored by Virginia Connects, an industry-affiliated group that spent at least $700,000 on digital marketing in the state in fiscal year 2024. The spot emphasized that data centers are paying their own energy costs framing this as a buffer that might help lower residential bills and portrayed the facilities as engines of local job creation. The reality is murkier. Although industry groups claim that each new data center creates dozens to hundreds of high-wage, high-skill jobs, some researchers say data centers generate far fewer jobs than other industries, such as manufacturing and warehousing. Greg LeRoy, the founder of the research and advocacy group Good Jobs First, said that in his first major study of data center jobs nine years ago, he found that developers pocketed well over a million dollars in state subsidies for every permanent job they created. With the rise of hyperscalers, LeRoy said, that number is still very much in the ballpark. Other experts reflect that finding. A 2025 brief from University of Michigan researchers put it bluntly: Data centers do not bring high-paying tech jobs to local communities. A recent analysis from Food & Water Watch, a nonprofit tracking corporate overreach, found that in Virginia, the investment required to create a permanent data center job was nearly 100 times higher than what was required to create comparable jobs in other industries. Data centers are the extreme of hyper-capital intensity in manufacturing, LeRoy said. Once theyre built, the number of people monitoring them is really small. Contractors may be called in if something breaks, and equipment is replaced every few years. But thats not permanent labor, he said. Jon Hukill, a spokesperson for the Data Center Coalition, the industry lobbying group that established Virginia Connects in 2024, said that the industry is committed to paying its full cost of service for the energy it uses and is trying to meet this moment in a way that supports both data center development and an affordable, reliable electricity grid for all customers. Nationally, Hukill said, the industry supported 4.7 million jobs and contributed $162 billion in federal, state, and local taxes in 2023. Dozens of community groups across the country have mobilized against data center buildout, citing fears that the facilities will drain water supplies, overwhelm electric grids, and pollute the air around them. According to Data Center Watch, a project run by AI security company 10a Labs, nearly 200 community groups are currently active and blocked or delayed 20 data center projects representing $98 billion of potential investment between April and June 2025 alone. The backlash has exposed a growing image problem for the AI industry. Too often, were portrayed as energy-hungry, water-intensive, and environmentally damaging, data center marketer Steve Lim recently wrote. That narrative, he argued, misrepresents our role in society and potentially hinders our ability to grow. In response, the industry is stepping up its messaging. Some developers, like Starwood Digital Ventures in Delaware, are turning to Facebook ads to appeal to residents. Its ads make the case that data center development might help keep property taxes low, bring jobs to Delaware, and protect the integrity of nearby wetlands. According to reporting from Spotlight Delaware, the company has also boasted that it will create three times as many jobs as it initially told local officials. Nationally, Meta has spent months running TV spots showcasing data center work as a viable replacement for lost industrial and farming jobs. One advertisement spotlights the small city of Altoona, Iowa. I grew up in Altoona, and I wanted my kids to be able to do the same, a voice narrates over softly-lit scenes of small-town Americana: a Route 66 diner, a farm, and a water tower. So, when work started to slow down, we looked for new opportunities and we welcomed Meta, which opened a data center in our town. Now, were bringing jobs here for us, and for our next generation. The advertisement ends with a promise superimposed over images of a football game: Meta is investing $600 billion in American infrastructure and jobs. In reality, Altoonas data center is a hulking, windowless, warehouse complex that broke ground in 2013, long before the current data center boom. Altoona is not quite the beleaguered farm town Metas advertisements portray, but a suburb of 19,000, roughly 16 minutes from downtown Des Moines, the most populous city in Iowa. Meta says it has supported 400+ operational jobs in Altoona. In comparison, the local casino employs nearly 1,000 residents, according to the local economic development agency. Ultimately, those details may not matter much to the ads intended audience. As Politico reported, the advertisement may have been targeted at policymakers on the coasts more than the residents of towns like Altoona. Meta has spent at least $5 million airing the spot in places like Sacramento and Wahington, D.C. The community backlash has also made data centers a political flashpoint. In Virginia, Abigail Spanberger won Novembers gubernatorial election in part on promises to regulate the industry and make developers pay their fair share of the electricity they use. State lawmakers also considered 30 bills attempting to regulate data centers. In response to concerns about rising electricity prices, Virginia regulators approved a new rate structure for AI data centers and other large electricity users. The changes, which will take effect in 2027, are designed to protect household customers from costs associated with data center expansion. These developments may only encourage companies to spend more on image-building. In Virginias Data Center Alley, the ads show no sign of stopping. Elena Schlossberg, an anti-data-center activist based in Prince William County, says her mailbox has been flooded with fliers from Virginia Connects for the past eight months. The promises of lower electric bills, good jobs, and climate responsibility, she said, remind her of cigarette ads she saw decades ago touting the health benefits of smoking. But Schlossberg isnt sure the marketing is going to work. One recent poll showed that 73 percent of Virginians blame data centers for their rising electricity costs. Theres no putting the toothpaste back in the tube, she said. People already know were still covering their costs. People know that. This article originally appeared in Grist. Grist is a nonprofit, independent media organization dedicated to telling stories of climate solutions and a just future. Learn more at Grist.org
Category:
E-Commerce
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners. Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs. Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services. As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered. Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution. A faster IEP, but how individualized? AI is already shaping special education planning, personnel preparation, and assessment. One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a childs strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals. But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a childs true individual needs. Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans. Training and diagnosing disabilities There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel. Some districts have begun using AI for assessments, which can involve a range of academic, cognitive, and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability. Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts. Privacy and trust concerns There are serious ethicaland practicalquestions about these AI-supported interventions, ranging from risks to students privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law. The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students data privacy and the rights of parents to access and hold their childrens data. What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a childs data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable. These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have strugged to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents. But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present. Embedded bias At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified. Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data. Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process. By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services. Filling the gap Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law. The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, de minimis progress, which weakens one of the primary rationales for pursuing AI that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo. But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides. Seth King is an associate professor of special education at the University of Iowa. This article is republished from The Conversation under a Creative Commons license. Read the original article.
Category:
E-Commerce
As Winter Storm Fern swept across the United States in late January 2026, bringing ice, snow, and freezing temperatures, it left more than a million people without power, mostly in the Southeast. Scrambling to meet higher than average demand, PJM, the nonprofit company that operates the grid serving much of the mid-Atlantic U.S., asked for federal permission to generate more power, even if it caused high levels of air pollution from burning relatively dirty fuels. Energy Secretary Chris Wright agreed and took another step, too. He authorized PJM and ERCOTthe company that manages the Texas power gridas well as Duke Energy, a major electricity supplier in the Southeast, to tell data centers and other large power-consuming businesses to turn on their backup generators. The goal was to make sure there was enough power available to serve customers as the storm hit. Generally, these facilities power themselves and do not send power back to the grid. But Wright explained that their industrial diesel generators could generate 35 gigawatts of power, or enough electricity to power many millions of homes. We are scholars of the electricity industry who live and work in the Southeast. In the wake of Winter Storm Fern, we see opportunities to power data centers with less pollution while helping communities prepare for, get through, and recover from winter storms. Data centers use enormous quantities of energy Before Wrights order, it was hard to say whether data centers would reduce the amount of electricity they take from the grid during storms or other emergencies. This is a pressing question, because data centers power demands to support generative artificial intelligence are already driving up electricity prices in congested grids like PJMs. And data centers are expected to need only more power. Estimates vary widely, but the Lawrence Berkeley National Lab anticipates that the share of electricity production in the U.S. used by data centers could spike from 4.4% in 2023 to between 6.7% and 12% by 2028. PJM expects a peak load growth of 32 gigawatts by 2030enough power to supply 30 million new homes, but nearly all going to new data centers. PJMs job is to coordinate that energyand figure out how much the public, or others, should pay to supply it. The race to build new data centers and find the electricity to power them has sparked enormous public backlash about how data centers will inflate household energy costs. Other concerns are that power-hungry data centers fed by natural gas generators can hurt air quality, consume water, and intensify climate damage. Many data centers are located, or proposed, in communities already burdened by high levels of pollution. Local ordinances, regulations created by state utility commissions, and proposed federal laws have tried to protect ratepayers from price hikes and require data centers to pay for the transmission and generation infrastructure they need. Always-on connections? In addition to placing an increasing burden on the grid, many data centers have asked utility companies for power connections that are active 99.999% of the time. But since the 1970s, utilities have encouraged demand response programs, in which large power users agree to reduce their demand during peak times like Winter Storm Fern. In return, utilities offer financial incentives such as bill credits for participation. Over the years, demand response programs have helped utility companies and power grid managers lower electricity demand at peak times in summer and winter. The proliferation of smart meters allows residential customers and smaller businesses to participate in these efforts as well. When aggregated with rooftop solar, batteries and electric vehicles, these distributed energy resources can be dispatched as virtual power plants. A different approach The terms of data center agreements with local governments and utilities often arent available to the public. That makes it hard to determine whether data centers could or would temporarily reduce their power use. In some cases, uninterrupted access to power is necessary to maintain critical data systems, such as medical records, bank accounts and airline reservation systems. Yet, data center demand has spiked with the AI boom, and developers have increasingly been willing to consider demand response. In August 2025, Google announced new agreements with Indiana Michigan Power and the Tennessee Valley Authority to provide data center demand response by targeting machine learning workloads, shifting non-urgent compute tasks away from times when the grid is strained. Several new companies have also been founded specifically to help AI data centers shift workloads and even use in-house battery storage to temporarily move data centers power use off the grid during power shortages. Flexibility for the future One study has found that if data centers would commit to using power flexibly, an additional 100 gigawatts of capacitythe amount that would power around 70 million householdscould be added to the grid without adding new generation and transmission. In another instance, researchers demonstrated how data centers could invest in offsite generation through virtual power plants to meet their generation needs. Installing solar panels with battery storage at businesses and homes can boost available electricity more quickly and cheaply than building a new full-size power plant. Virtual power plants also provide flexibility as grid operators can tap into batteries, shift thermostats or shut down appliances in periods of peak demand. These projects can also benefit the buildings where they are hosted. Distributed energy generation and storage, alongside winterizing power lines and using renewables, are key ways to help keep the lights on during and after winter storms. Those efforts can make a big difference in places like Nashville, Tennessee, where more than 230,000 customers were without power at the peak of outages during Fern, not because there wasnt enough electricity for their homes but because their power lines were down. The future of AI is uncertain. Analysts caution that the AI industry may prove to be a speculative bubble: If demand flatlines, they say, electricity customers may end up paying for grid improvements and new generation built to meet needs that would not actually exist. Onsite diesel generators are an emergency solution for large users such as data centers to reduce strain on the grid. Yet, this is not a long-term solution to winter storms. Instead, if data centers, utilities, regulators and grid operators are willing to also consider offsite distributed energy to meet electricity demand, then their investments could help keep energy prices down, reduce air pollution and harm to the climate, and help everyone stay powered up during summer heat and winter cold. Nikki Luke is an assistant professor of human geography at the University of Tennessee. Conor Harrison is an associate professor of economic geography at the University of South Carolina. This article is republished from The Conversation under a Creative Commons license. Read the original article.
Category:
E-Commerce
All news |
||||||||||||||||||
|
||||||||||||||||||