How much energy does AI use? Ask ChatGPT and this is what it says:

“AI systems vary widely in energy consumption depending on their complexity and usage, but they generally require significant amounts of electricity to process and analyse data efficiently.”

That response required around ten times the electricity of a Google search, by some estimates. And with 100 million users of ChatGPT every week, the extra energy demand starts to add up. And that’s just users on one platform.

The rapid advancement of artificial intelligence (AI) is transforming industries, enhancing efficiencies, and driving innovation across various sectors. However, this technological revolution also brings significant environmental challenges, particularly concerning the sustainability of data centres that power AI applications.

AI technologies, including machine learning and deep learning, rely heavily on vast amounts of data and computational power. This demand translates into substantial energy consumption by data centres, which are the backbone of AI operations. According to Sustainable Views, these data centres are energy-intensive, consuming significant amounts of electricity to manage and process data. As the volume of data and complexity of AI models increase, so too does the energy required, leading to heightened concerns about the environmental impact of this technological growth.

The primary challenge lies in the carbon footprint associated with powering these data centres. Many data centres rely on non-renewable energy sources, contributing to increased greenhouse gas emissions. This environmental impact is at odds with the broader goals of sustainability and climate action. As AI continues to expand, it is imperative that the industry addresses these concerns by adopting more sustainable practices and transitioning towards greener energy sources.

AI is also helping to transform the energy efficiency of other carbon-intensive industries, from modelling buildings to predict energy use and optimize the performance of heating and air conditioning to improving the efficiency of manufacturing through predictive maintenance. In agriculture, sensors and satellite imagery are helping to predict crop yields and manage resources. In that sense, we are constantly considering applications across the value chain of our businesses.

However, to effectively balance AI’s energy use and emissions with its societal benefit takes in many complex, interlinked challenges, and requires a multistakeholder approach. We apply a cross-industry and industry-specific lens to understand how AI can be leveraged to transform sectors and drive impact on innovation, sustainability and growth.