AI data centres will require renewable energy adoption, innovative cooling, and efficiency improvements to balance growth with sustainability, writes James Hart, CEO at BCS.
There is no doubt that AI data centres are indispensable for the continued advancement of AI – however, their impact on power and water usage poses significant environmental challenges. Addressing these issues requires a multifaceted approach that includes transitioning to renewable energy, adopting innovative cooling technologies, and leveraging AI for operational efficiency. As the demand for AI capabilities continues to grow, so too must our efforts to ensure that this growth is sustainable and responsible, balancing technological progress with environmental stewardship.
AI data centres are power-intensive by nature, primarily due to the high computational demands of AI workloads. Machine learning algorithms, especially deep learning models, require substantial processing power, often involving thousands of GPUs or TPUs running in parallel. These computational resources consume large amounts of electricity.
The International Energy Agency (IEA) highlighted that data centres that after globally consuming an estimated 460 terawatt-hours (TWh) in 2022, data centres’ total electricity consumption could reach more than 1,000 TWh in 2026. This demand is roughly equivalent to the electricity consumption of Japan. The energy consumption of AI data centres is driven not only by the need to power the servers but also by the cooling systems required to maintain optimal operating temperatures.
In our latest independent industry survey, over four-fifths of respondents reported experiencing an uplift in demand as a direct result of AI over the past year. However, 85% believe that the pace of widespread adoption of AI is currently being restricted by the lack of available power and facilities tailored for AI workloads.
The impact on the environment
The environmental impact of this power usage is significant and many data centres are still reliant on non-renewable energy sources. Efforts are being made to transition to renewable energy, but the pace of AI advancement often outstrips these sustainability initiatives. For instance, leading tech companies like Google and Microsoft have committed to carbon neutrality and investing in renewable energy, yet the rapid expansion of their AI capabilities continues to pose sustainability challenges.
In addition to power usage, AI data centres also have a substantial impact on water resources. Traditional water-cooled data centres can consume millions of gallons of water annually. According to The World Counts, an open-source community-driven project that aggregates consumption data from organisations around the world, more than 4.3 trillion cubic meters (approximately 1.1 quadrillion gallons) of water are consumed by data centres globally every year. This usage can strain local water supplies, especially in regions facing water scarcity. The environmental cost of water-intensive cooling solutions is exacerbated in drought-prone areas, where the diversion of water to data centres can compete with agricultural and residential needs.
The interplay between AI data centres and resource usage necessitates innovative approaches to mitigate environmental impacts. Advances in cooling technology, such as liquid immersion cooling and the use of recycled water, offer potential solutions. Furthermore, utilising recycled or non-potable water for cooling can alleviate the pressure on freshwater resources.
Moreover, AI itself can be leveraged to enhance the efficiency of data centres. AI algorithms can optimise energy use by predicting cooling needs, managing workloads more efficiently, and reducing idle times for servers. Predictive maintenance powered by AI can also prevent equipment failures, thereby reducing the need for excessive cooling.
Driving efficiencies
This is good news as the sector continues to use AI to benefit from greater efficiencies, cost savings, driving improvements in services with the expected impact of AI on the operational side for data centres expected to be very positive. Over 65% of our survey respondents reported that their organisations are regularly using generative AI, nearly double the percentage from their 2023 survey and around 90% of respondents expect their data centres to be more efficient as a direct result of AI applications.
In conclusion, the proliferation of AI is revolutionising industries, from healthcare to finance, by enabling advanced data processing and decision-making capabilities. Central to this technological leap are AI data centres, specialised facilities that house the hardware and software necessary for AI computations. While these centres are crucial for AI advancements, they also significantly impact power and water usage, presenting both challenges and opportunities for sustainability and resource management.
