Artificial intelligence has rapidly become one of the defining technologies of the 21st century. Governments and businesses are investing billions into increasingly powerful AI systems, while new models are being released at an unprecedented rate.
Yet behind the excitement lies a less visible story. Training and operating modern AI systems requires vast computing infrastructure, growing amounts of electricity, and increasing pressure on energy systems.
These five charts explore the environmental cost of AI’s rapid expansion.
The number of notable AI models released annually has increased dramatically over the past two decades. While the United States has historically dominated AI development, China has rapidly increased its contribution in recent years, highlighting the intensifying global competition to lead the AI revolution.
Source: Stanford AI Index Report 2025 — Number of notable AI models by geographic region.
The rapid growth of artificial intelligence is not only reflected in the number of models being developed but also in the computing power required to train them. Over the past decade, the computational requirements of leading AI models have increased exponentially, creating growing demands for energy-intensive computing infrastructure.
Source: Epoch AI dataset (via Stanford AI Index 2025) — Training computation requirements of notable AI systems.
The rapid expansion of artificial intelligence has been supported by unprecedented levels of private investment. As governments and technology companies compete for leadership in AI, billions of dollars are being invested into research, infrastructure, and increasingly powerful AI systems.
Source: Stanford AI Index Report 2025 — Private AI investment by geographic region (billions of US dollars).
The rapid expansion of artificial intelligence is increasing demand for data centres, which already consume significant amounts of electricity. According to the International Energy Agency (IEA), global data centre electricity demand is projected to more than double by 2030, driven in part by AI workloads and the growing need for computing infrastructure.
Source: International Energy Agency (IEA), Energy and AI Report 2025 — Global data centre electricity demand projections.
The future environmental impact of artificial intelligence is not predetermined. Improvements in hardware efficiency, renewable energy adoption, and policy decisions could reduce electricity demand, while rapid AI adoption could increase pressure on energy systems. The future will depend on how AI is developed and powered in the years ahead.
Source: International Energy Agency (IEA), Energy and AI Report 2025 — Future scenarios for AI-related electricity demand.
Artificial intelligence has become one of the fastest-growing technologies in history. As AI models become more powerful, they require increasing amounts of computing infrastructure, investment, and energy. While AI offers significant economic and societal benefits, its environmental footprint is growing rapidly. The challenge for governments, technology companies, and researchers will be ensuring that future AI development remains sustainable. Balancing innovation with environmental responsibility may become one of the defining issues of the AI era.