Sustainable AI: Balancing Innovation with Environmental Impact

As AI continues to transform industries and society, the urgency to balance its rapid growth with environmental responsibility has become a pivotal consideration. However, while AI undoubtedly leads to immense energy and power demands, it can also be used a tool for tackling wider sustainability challenges. 

Therefore, to harness AI’s potential and mitigate its potential drawbacks, the industry must adopt sustainable practices across development, deployment, and usage, while exploring ways to utilize it effectively to mitigate environmental impact across society.

AI’s Energy Appetite and Environmental Costs

The growth of AI is fueling a surge in energy demand, particularly in data centers that power its training and inference processes. According to Deloitte, global electricity consumption by data centers is projected to triple by 2030, with AI being a key contributor to this growth. 

Training large-scale models involves vast amounts of computational power. Meanwhile, inference – where these models are applied to new data – are estimated to be ten times more energy-intensive than traditional computational queries. 

This increased demand for energy not only raises costs, but also heightens greenhouse gas (GHG) emissions, which can exacerbate the effects of climate change. Without proactive measures, AI-driven energy consumption risks pushing the world further off track from climate targets, with projections showing an over 2°C rise in global temperatures.

However, this is not just applicable to carbon emissions. New modelling shows that generative AI could lead to a 1,000-fold increase in electronic waste by 2030 without effective waste reduction strategies. In fact, this study reveals that e-waste may rise by up to 2.5 million tons each year by 2030 if no reduction measures are introduced. 

Greening the AI Tech Stack

To minimize AI’s environmental footprint, the industry must embed sustainability into every layer of AI systems, from hardware to software, while supporting the broader energy transition.

Efficiency can be incorporated across all levels of the stack, from the foundational hardware to the software. These cover the following areas: 

• CPU and GPU designs that can be optimized for AI and power-efficiency; 
• Innovative silicon designs that utilize 3D chip architectures and memory hierarchies to minimize data movement and energy consumption; 
• Exploring innovative materials to replace those with a high environmental impact; 
• Innovations in edge computing enable high-performance, power-efficient AI solutions; and
• Focusing on efficient software designs where concepts, such as software carbon intensity, are utilized to ensure sustainability considerations are embedded from the start.

Beyond the stack, there are a number of initiatives that can support more sustainable AI. 

Firstly, moving the processing of AI inference workloads to edge devices to minimize the energy costs associated with data transmission to the cloud. Processing AI workloads closer to the data source, such as on local devices or servers, also reduces latency and energy transmission losses. This is particularly relevant for areas with limited network connectivity, as edge AI processing enables local AI applications with a lower environmental impact. In order to make edge AI processing easier, techniques like model compression and pruning can be applied which reduce computational loads without sacrificing performance.

Secondly, there are ways to optimize AI training workloads to limit their environmental impact. For example, AI training can be scheduled to take place during periods of low grid demand to stabilize energy usage or AI workloads can be shifted to regions and times with abundant renewable energy. 

Finally, there are various green coding practices that can be implemented, as writing optimized, resource-efficient code can reduce computational demands. 

AI as a Sustainability Ally

AI itself is a powerful enabler of sustainable practices, offering solutions for energy optimization, climate adaptation, and emissions reductions, with this being especially relevant as part of the wider Net Zero transition.

AI improves forecasting for renewable energy resources, like solar and wind, with this optimizing grid operations, and enhancing energy storage performance. It also supports grid efficiency by predicting peak demand and reducing energy losses.

For climate change, AI can improve climate modelling. AI-powered tools are already providing early-warning systems for natural disasters, such as UNICEF’s Arm-powered AI-powered flood modeling in Malawi. This demonstrates the ability of AI to mitigate climate risks, helping to save lives and recovery times. 

Finally, AI-driven innovations can reduce waste and emissions in critical sectors through optimizing supply chains and enhancing transportation systems.

Leadership, Policy, and Partnerships for Sustainable AI

A sustainable AI future requires coordinated efforts across government, industry, and academia. Examples include:

• Policy and regulation: The EU AI Act aims to guide responsible AI development, emphasizing transparency and accountability.
• Partnerships for change: Organizations like Arm, in collaboration with UN bodies and other stakeholders, are driving initiatives for climate-focused AI solutions.
• Sustainable semiconductor development: The U.S. NIST launched an initiative with $100 million in funding to use AI for creating sustainable semiconductor materials within five years, encouraging academia-industry collaboration.
• Standardization: Industry-wide efficiency metrics, such as those spearheaded by policymakers, can ensure AI aligns with sustainability goals. In fact, the world’s largest technology companies, including AWS, Microsoft, and Google, are advocating for Environmental Product Declarations to assess and reduce embodied emissions in data center infrastructure.

What the Industry Must Do Now

The AI industry has a unique opportunity and responsibility to lead by example. By prioritizing sustainability, it can demonstrate that technological innovation and environmental stewardship are not mutually exclusive. Key actions include:

• Investing in renewable energy to power AI operations;
• Innovating in power-efficient hardware and software; and
• Championing green AI practices that align with global climate goals.

This approach will ensure AI’s transformative potential is harnessed responsibly, enabling progress that benefits both humanity and the planet. Through bold commitments and collaborative action, the industry can define a future where AI accelerates solutions to the very challenges it contributes to today.

(This blog was excerpted from the Arm AI Readiness Index report)