AI Growth Linked to $20 Billion in Annual Health Costs and 1,300 Premature Deaths by 2030

A study highlights the severe public health impact of air pollution from energy-intensive data centers powering artificial intelligence systems.

Researchers from UC Riverside and Caltech estimate that air pollution from AI-related energy demands will cause 1,300 premature deaths annually in the U.S. by 2030.
The total public health costs, including diseases like cancer and asthma, are projected to reach $20 billion per year due to emissions from power plants and diesel backup generators.
AI-driven data centers disproportionately affect low-income communities located near power plants and emit pollution that impacts regions far beyond the source.
Pollution from training AI models, such as Meta's Llama-3.1, is equivalent to thousands of cross-country car trips, illustrating the environmental toll of large-scale AI operations.
The study urges tech companies to report non-carbon air pollutants and compensate communities bearing the health burden of data center emissions.