AI Energy Footprint
The Challenge
The rapid advancement of artificial intelligence, particularly large language models and other deep learning systems, has led to exponential increases in computational requirements and energy consumption. This growing energy footprint raises serious environmental concerns and challenges sustainability goals. Key issues include:
- Escalating Compute Demands: Each new generation of AI models typically requires significantly more computational resources than its predecessors, with training energy requirements doubling approximately every 3-4 months for frontier models.
- Carbon Emissions: Depending on energy sources, AI training and inference can generate substantial carbon emissions, with a single large model training run potentially producing hundreds of tons of CO₂ equivalent.
- Water Consumption: Data centers require significant water for cooling systems, with AI operations increasingly straining local water resources in already water-stressed regions.
- Infrastructure Pressure: The rapid growth in AI deployment is creating unprecedented demand for new data centers and energy infrastructure, potentially competing with other sectors for renewable energy capacity.
Our Approach
The Global Tech Governance Institute takes a multifaceted approach to addressing AI's energy footprint:
- Measurement Standards: Developing standardized methodologies for measuring and reporting the energy consumption and environmental impact of AI systems throughout their lifecycle.
- Efficiency Research: Promoting research into more energy-efficient algorithms, hardware, and system designs that can deliver comparable performance with significantly reduced resource requirements.
- Policy Frameworks: Creating governance frameworks that incentivize energy efficiency in AI development while ensuring continued innovation and equitable access to AI capabilities.
- Industry Collaboration: Working with technology companies to establish best practices, voluntary commitments, and transparency standards around the environmental impact of AI systems.
Current Initiatives
Our work in this area currently includes:
AI Carbon Tracker
A monitoring initiative that tracks and publishes the estimated carbon footprint of major AI models and systems, creating transparency and accountability.
Part of the Sustainable Computing Initiative
Sustainable AI Standards Consortium
A multi-stakeholder initiative developing industry standards for measuring, reporting, and reducing the environmental impact of AI systems.
Part of the Technology Assessment Program
Green AI Policy Lab
A research initiative exploring regulatory approaches and incentive structures to promote energy-efficient AI development and deployment.
Part of the Sustainable Computing Initiative
Efficiency Benchmarking Project
A technical program developing standardized benchmarks that evaluate AI systems not just on performance but on performance-per-watt and other efficiency metrics.
Part of the Technology Assessment Program
Matrix Integration
Related Programs
Scientific Foundations
- Energy-Efficient Computing
Research on reducing energy consumption in AI training and inference
- Carbon Accounting for Digital Systems
Study of methodologies for measuring the carbon impact of digital technologies
Key Publications
Get Involved
There are several ways to engage with our work on AI energy footprint:
- Participate in our AI Carbon Tracker initiative
- Contribute to the Sustainable AI Standards Consortium
- Attend our workshops and events on green AI development
- Support our research and advocacy work