As AI continues to transform industries and society, the urgency of balancing its rapid growth with environmental responsibility has become a central consideration. However, while AI undoubtedly drives immense energy and energy demands, it can also be used as a tool to address broader sustainability challenges.
Therefore, to harness the potential of AI and mitigate its potential downsides, the industry must adopt sustainable practices in its development, deployment and use, while exploring ways to use it effectively to mitigate environmental impact across society.
AI’s energy appetite and its environmental costs
The growth of AI is fueling an increase in energy demand, particularly in the data centers that power its systems. training And inference process. According to Deloitteglobal data center electricity consumption is expected to triple by 2030, with AI a key contributor to this growth.
Training large-scale models involves a lot of computing power. Meanwhile, inference – where these models are applied to new data – is estimated to be ten times more energy intensive than traditional computing queries.
This increased demand for energy not only increases costs, but also greenhouse gas (GHG) emissions, which can exacerbate the effects of climate change. Without proactive measures, AI-driven energy consumption risks pushing the world further away from climate goals, with projections of global temperatures rising by more than 2°C.
However, this does not only apply to carbon emissions. New modeling shows that generative AI could increase e-waste 1,000-fold by 2030 without effective waste reduction strategies. Actually, this study reveals that e-waste could increase by up to 2.5 million tonnes each year by 2030 if no reduction measures are introduced.
Making the AI tech stack greener
To minimize AI’s environmental footprint, the industry must integrate sustainability into every layer of AI systems, from hardware to software, while supporting the broader energy transition.
Efficiency can be built into all levels of the stack, from base hardware to software. These cover the following areas:
- CPU and GPU designs that can be optimized for AI and energy efficiency;
- Innovative silicon designs that use 3D chip architectures and memory hierarchies to minimize data movement and power consumption;
- Explore innovative materials to replace those with a high environmental impact;
- Innovations in edge computing enable high-performance, energy-efficient AI solutions; And
- Focus on effective software designs where concepts, such as software carbon intensity, are used to ensure sustainability considerations are built in from the start.
Beyond the stack, there are a number of initiatives that can support more sustainable AI.
First, move the processing of AI inference workloads to edge devices to minimize the energy costs associated with transmitting data to the cloud. Processing AI workloads closer to the data source, such as on local devices or servers, also reduces latency and power transmission losses. This is particularly relevant for areas with limited network connectivity, as AI processing at the edge enables local AI applications with lower environmental impact. To facilitate advanced AI processing, techniques such as model compression and pruning can be applied, reducing computational loads without sacrificing performance.
Second, 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 consumption or AI workloads can be shifted to regions and periods where renewable energy is abundant.
Finally, various green coding practices can be implemented, as writing optimized, resource-efficient code can reduce computational demands.
AI as an ally of sustainable development
AI itself is a powerful enabler of sustainable practices, offering solutions for energy optimization, climate adaptation and emissions reduction, which is particularly relevant in the broader Net Zero transition.
AI improves forecasts for renewable energy resources, such as solar and wind, thereby optimizing grid operation and improving energy storage performance. It also supports network efficiency by forecasting peak demand and reducing energy losses.
Regarding climate change, AI can improve climate modeling. AI-based tools already provide early warning systems for natural disasters, such as UNICEF’s AI-based flood modeling in Malawi. This demonstrates the ability of AI to mitigate climate risks, helping to save lives and reduce recovery times.
Finally, AI-driven innovations can reduce waste and emissions in critical sectors by optimizing supply chains and improving transportation systems.
Leadership, policies and partnerships for sustainable AI
A sustainable AI future requires coordinated efforts between government, industry and academia. Examples include:
- Policy and regulation: The EU AI law aims to guide the responsible development of AI, with a focus on transparency and accountability.
- Partnerships for change: Organizations like Arm, in collaboration with UN agencies and other stakeholders, are leading initiatives towards climate-focused AI solutions.
- Sustainable development of semiconductors: The US NIST has launched an initiative with $100 million in funding to use AI to create sustainable semiconductor materials within five years, encouraging collaboration between academia and industry.
- Standardization: Industry-wide efficiency measures, such as those put in place by policymakers, can ensure that AI aligns with sustainable development 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 needs to 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 management are not mutually exclusive. Key actions include:
- Invest in renewable energy to power AI operations;
- Innovate in energy-efficient hardware and software; And
- Promote green AI practices that align with global climate goals.
This approach will ensure that the transformative potential of AI is harnessed responsibly, enabling progress that benefits both humanity and the planet. Through bold commitments and collaborative action, the industry can define a future in which AI accelerates solutions to the challenges it contributes to today.
(This blog is taken from Arm AI Readiness Index Report)