Sustaining AI’s Growth: Addressing the Energy Challenge

The rapid advancement of Artificial Intelligence (AI) has ushered in transformative possibilities, from revolutionising healthcare diagnostics to driving autonomous vehicles. However, the significant rise of AI comes with an equally steep demand for energy. As AI advances and integrates more deeply into everyday life, recognising energy’s role for development and deployment is vital. This understanding is crucial not only to maintain operational efficiency but also to support sustainability initiatives and enhance security resilience across increasingly complex systems.

AI's foundation lies in data processing and complex computations, typically running on neural networks powered by GPUs and TPUs. These computational models require vast amounts of energy. Training GPT-3, can consume as much energy as a car driving hundreds of thousands of miles (1). GTP-4 is thought to use 50 times more electricity (2).  This energy-intensive process is necessary because AI requires the capacity to sift through enormous datasets to recognise patterns, learn, and optimise its outputs. Even after training, AI models require ongoing power for inference processes – essentially, the ‘thinking’ phase of AI that happens each time a user interacts with the model.

The energy demand doesn’t stop at computing. AI also relies on a vast ecosystem of data centres that house and support these models. This infrastructure, often spread across multiple geographical regions, with immense energy requirements for cooling, backup, and security. Data centres consume around 1% of global electricity, as AI adoption accelerates, the energy requirement for AI is expected double every 100 days.

Given the correlation between greenhouse emissions and energy, this is an area that has garnered attention. Stakeholders are actively trying to balance AI's benefits with environmental concerns and energy efficiency in AI applications. Cloud service providers have started to leverage renewable energy sources for data centres, providing organisations with a more sustainable option.

Researchers and developers are also working to optimise AI algorithms, reducing computational processing requirements without sacrificing performance. Adaptations, including model pruning, quantisation, and edge computing aim to reduce the energy requirements and thus cost effectiveness to run AI systems.

There are also developments in specialised hardware, including low-power TPUs and immersion cooling, designed to limit energy consumption and improve data centre efficiency. Efficiencies are also observed through a shift towards edge AI, where data processing occurs on local devices rather than in centralised data centres, further mitigates energy usage by reducing the need for data transmission.

Energy fuels AI’s potential but brings significant sustainability challenges. To responsibly harness AI’s transformative power, we must optimise infrastructure, use renewables, and adopt energy-efficient models, balancing progress with planetary health.