Basics

Since the release of generative artificial intelligence (AI) models like ChatGPT, Gemini, and Perplexity, American businesses, consumers, and the federal government have rapidly integrated these tools into their daily operations. This widespread adoption has increased attention to the substantial computing power required to train and run AI systems. Unlike traditional computing tasks, training advanced AI algorithms requires immense processing power and vast data, making it significantly more energy-intensive. As AI models grow more complex, the data centers that support them consume more electricity, placing mounting pressure on the strained U.S. electricity grid. Expanding the grid’s capacity to meet this new demand will take years—if not decades—prompting industry leaders and policymakers to seek alternative solutions.  Without proactive measures to address this growing demand, regions with high concentrations of data centers—such as Northern Virginia, California, and Texas—could face rising utility costs and, potentially, rolling blackouts. On-site mini-grids and alternative energy sources can alleviate data centers’ burden on the grid, promoting their energy security and reliability.