Artificial intelligence requires enormous amounts of energy, and much of this consumption is not visible to everyday users. Training large AI models like those used for natural language processing and image generation involves running thousands of powerful processors for extended periods. These processes demand significant electricity, often supplied by data centers that run around the clock and rely on both grid power and backup generation to maintain performance and uptime.
Beyond training, AI systems also consume energy while deployed. Every time an AI model responds to a prompt, analyzes data, or powers an application, it draws on computing resources that need electricity to run. The cumulative energy use of AI workloads can rival that of small cities, especially as demand grows and AI features are embedded into more products and services that people use every day.
Data centers — the physical facilities that house the servers powering AI — add further layers of energy demand because they must be continuously cooled to prevent overheating. Cooling infrastructure and power distribution systems themselves consume a large share of total energy use, meaning that the true carbon footprint of AI is often much higher than the electricity used by the processors alone.
Because of these energy implications, the article argues that AI’s environmental impact deserves more attention from tech companies, policymakers, and energy planners. Addressing hidden energy costs may involve broader adoption of energy-efficient hardware, renewable power sources, intelligent workload scheduling, and improved infrastructure planning. Without these measures, the rapid expansion of AI could strain power grids, increase greenhouse gas emissions, and heighten competition for limited energy resources.
