A recent report from 404 Media highlights how a small research team is quietly mapping the rapid expansion of America\u2019s datacenter infrastructure, using publicly available information and satellite imagery to track facilities often missing from public debate.<\/p>\n
The work comes from Epoch AI, a non-profit research institute focused on understanding the scale and pace of artificial intelligence development.<\/p>\n
Its researchers use open-source intelligence to identify, analyze, and document datacenters rising across the United States.<\/p>\n
By reviewing satellite images, construction permits, and local regulatory filings, the team builds an interactive map that estimates cost, ownership, and power consumption.<\/p>\n
The project offers rare visibility into an industry expanding faster than public scrutiny.<\/p>\n
Mapping hidden infrastructure<\/p>\n
Datacenter construction has become a major flashpoint across the country. The facilities demand vast amounts of electricity and water. <\/p>\n
Many communities only learn about them after construction begins.<\/p>\n
Epoch AI\u2019s map places visual markers over known sites. Each marker links to satellite views and project details. One green circle appears over New Albany, Ohio.<\/p>\n
The marker identifies Meta\u2019s \u201cPrometheus\u201d datacenter complex. Epoch AI<\/a> estimates the project has cost $18 billion so far. It draws 691 megawatts of power.<\/p>\n \u201cA combination of weatherproof tents, colocation facilities and Meta\u2019s<\/a> traditional datacenter buildings, this datacenter shows Meta\u2019s pivot towards AI,\u201d Epoch said in its notes.<\/p>\n Users can scroll through a timeline and watch the complex grow. Satellite images show new buildings and cooling systems added over time.<\/p>\n How the estimates work<\/p>\n Much of Epoch AI\u2019s analysis focuses on cooling infrastructure. Modern AI systems generate extreme heat. Datacenters often place cooling units outside buildings or across rooftops.<\/p>\n \u201cModern AI data centers generate so much heat that the cooling equipment extends outside the buildings,\u201d Epoch AI explained on its website.<\/p>\n The team counts fans, measures their size, and analyzes their placement. <\/p>\n It feeds those details into a custom model to estimate energy use. That power estimate then helps infer compute capacity and construction cost.<\/p>\n \u201cWe focus on cooling because it\u2019s a very useful clue for figuring out the power consumption,\u201d Jean-Stanislas Denain, a senior researcher at Epoch AI, told 404 Media<\/a>.<\/p>\n The model carries uncertainty. Fan speed and configuration vary widely.<\/p>\n Epoch AI says real cooling capacity could be twice as high or half as low as estimates.<\/p>\n What remains unseen<\/p>\n The map remains incomplete. State and local disclosure laws vary. Some projects avoid publicity. Smaller facilities often escape detection.<\/p>\n Epoch AI estimates the current dataset represents about 15 percent of global AI compute delivered by chipmakers as of November 2025.<\/p>\n Zooming out reveals markers across the country. One near Memphis, Tennessee points to xAI\u2019s Colossus 2 project. <\/p>\n Epoch AI notes the company installed natural gas turbines across the Mississippi border, likely to secure faster approval.<\/p>\n