Measure AI's Carbon Footprint with Purpose

A standardized specification extending the Software Carbon Intensity methodology to measure the carbon emissions of AI systems throughout their lifecycle.

AI systems have become increasingly resource-intensive, yet there's no consistent way to measure their environmental impact. SCI for AI changes that by providing the first consensus-based standard that makes AI's carbon footprint transparent, comparable, and actionable. Help refine this transformative specification with your feedback and experience as we revolutionize how we build and deploy sustainable AI.

Industry Impact

SCI for AI provides the first consensus-based, standardized approach to measuring AI's environmental impact. This standardization drives innovation in efficient AI architectures, influences procurement decisions, and helps organizations meet sustainability commitments. By revealing the true carbon cost of AI development and deployment, it enables meaningful comparisons between different systems and approaches, transforming how organizations think about AI investments.

Environmental Impact

SCI for AI directly addresses the growing carbon footprint of AI systems by providing metrics that incentivize real reductions rather than offsets. The specification reveals hidden impacts in data preparation and training that often dwarf inference costs, encouraging practices like model optimization, efficient architectures, and carbon-aware computing. This comprehensive approach could significantly reduce AI-related emissions by enabling informed choices about when, where, and how AI systems operate.