Community Resistance Against Data Center Development in 'Data Center Alley'

Background and Purpose: Artificial intelligence (AI) and cloud computing technology require physical infrastructures to house servers and network equipment for data storage, known as data centers. Narratives surrounding AI often abstract it from its physical operation, yet these centers are resource-intensive and require significant amounts of land, water, energy, and fossil fuels to operate. Dominant media often justify data center development as "worth the environmental tradeoff," portraying them as drivers of economic growth and contributing to social good. Yet globally, many communities have begun to resist the rapid expansion of data centers, advocating against their development. It is critical for social workers to understand community-led organizing efforts around data centers' environmental, economic, social, and political effects, particularly as vulnerable communities bear the brunt of these consequences. As media coverage, municipal communications, and social media shape public understanding of data center expansion and growing resistance efforts, this study examines: What messaging about the environmental, economic, social, and political impacts of data centers are conveyed by whom, and how has the messaging changed over time?

Methods: A reflexive content analysis of 120 public reports, news articles, blogs, and community publications discussing data center development in Loudoun County, Virginia, was conducted. Loudoun County, known as "data center alley" and the "data center capital of the world," has seen rapid data center growth due to its proximity to Washington, DC, low land costs, and early server infrastructure investments. The study covers discourse from 2010 to 2025, grouping data into five-year increments and by source type to track shifts in public perception, corporate messaging, and resistance efforts. The analysis utilized coding techniques to identify dominant narratives, community organizing shifts, and corporate, municipal, and community-level strategy changes.

Results: Loudoun County's data center expansion initially faced little opposition, with public discourse centered on economic incentives and job creation. However, as the environmental impacts, such as increased water and energy consumption, noise pollution, and ecosystem disruption, became more apparent, community concerns intensified. Advocacy groups have identified several inequities exacerbated by data center growth, including resource extraction, environmental degradation, and asymmetries of power. Early resistance was fragmented, but over time, more than 30 community organizations have now formed a coalition, shifting from protests and education campaigns to political engagement and legal action. In response, corporations are shifting their climate commitments. Despite ongoing resistance, local leaders continue to view data center development as inevitable, with promised economic benefits seen as long-term advantages for the community.

Conclusions and Implications: As AI-driven data center expansion intensifies, it will increasingly affect more densely populated areas. While social work has begun addressing AI ethics, the impact of data center development on communities remains underexplored. This study highlights the need for social work advocacy in environmental justice, policy reform, and community organizing. It calls for a re-evaluation of data center development, emphasizing sustainability and including participatory action initiatives to mitigate the exacerbation of existing inequities.