Controlling the Energy-Consuming Nature of AI
In a groundbreaking analysis, global consulting firm Accenture has revealed that the rapid growth of artificial intelligence (AI) is consuming unprecedented amounts of electricity and water, with AI's share of global power consumption predicted to rise from 0.2% in 2024 to 1.9% by 2030 at an extraordinary pace of 48% Compound Annual Growth Rate (CAGR). This increase, if unchecked, could lead to significant carbon emissions.
The report, which emphasises a balanced, thoughtful approach to AI usage, suggests that addressing AI's energy-guzzling nature comes down to using artificial intelligence thoughtfully. One strategy is to deploy AI at the edge, which involves running AI applications on local devices rather than centralized cloud servers. This approach, suitable for industries like manufacturing, healthcare, retail, and financial services that depend on real-time data processing, can cut down on cloud energy use and improve performance by reducing latency.
Another key strategy is to adopt dynamic scaling and smart load balancing. This approach, which optimises power usage in relation to AI workloads, can be achieved by designing AI infrastructure with energy proportionality and dynamic efficiency scaling. Adaptive scheduling, which shifts AI processing to times when energy is cheapest and cleanest, can also help reduce peak demands.
The report also recommends choosing right-size AI models. Instead of relying on large-scale models like large language models (LLMs) for everything, deploying task-specific models that are smaller and more efficient can help reduce inference costs. Techniques such as Retrieval-Augmented Generation (RAG) further aid in this by accessing only necessary data.
In terms of power efficiency in data centres, the report suggests using low-carbon energy sources, implementing advanced cooling innovations (including AI-driven precision cooling), and improving overall data centre energy management to reduce emissions from AI infrastructure.
To drive sustainability, profitability, and competitiveness, the report encourages businesses to use AI selectively with a focus on sustainability. Better governance over AI sustainability initiatives is also essential. Accenture has developed a Sustainability-Adjusted Intelligence Quotient (SAIQ), a measure of how efficiently AI systems convert money, electricity, water, and carbon into actual performance.
The report also highlights AI-driven automation's potential to enforce sustainability policies and manage environmental risks in real time. AI-driven automation can also make it easier to select the most sustainable infrastructure for each model deployment. Karmada, a Kubernetes-based Multi-Cloud, Multi-Cluster Orchestrator, can help optimise AI workloads across regions based on carbon intensity.
In conclusion, the report underscores the need for a balanced, thoughtful approach to AI usage. By employing technology selectively and efficiently while investing in energy-efficient infrastructure, we can avert potentially massive increases in AI-related carbon emissions. The future of AI lies in its ability to drive sustainability, profitability, and competitiveness while minimising its environmental impact.
- The environmental-science community could benefit significantly from the deployment of AI at the edge, a strategy employed to cut down on cloud energy use, thus reducing carbon emissions as predicted by the Accenture report.
- In the pursuit of balancing AI's growth with environmental concerns, the report proposes the use of Artificial Intelligence (AI) in driving AI-driven automation for enforcing sustainability policies and managing environmental risks in real-time, thereby fostering a symbiosis of technology and the environment.
