Published on May 25, 2025 by Ajit Gulliya
Artificial intelligence (AI) has the potential to be one of the most powerful things in the 21st century. From better diagnosing our health to predicting climate change, AI is changing our world and work for the better every day. Like all powerful technologies, AI carries the responsibility of its footprint, particularly in its environmental impact. As AI continues to scale across every industry, it becomes an electricity-consuming machine. Training a large model can require as much electricity as dozens of homes consume over weeks. Yet, this challenge is an opportunity. If we embrace it, AI isn't just smarter—it's cleaner, leaner, and more sustainable.
To create sustainable AI, the first step is measuring energy use and emissions within training, deployment, and inference. The carbon emitted from training a large AI model can be equivalent to several international flights. It’s critical to use tools like CodeCarbon and ML CO2 Impact to track real-time energy use and estimate CO₂ emissions. This data enables organizations to benchmark sustainability efforts and fulfill ESG objectives. Using efficient models—via distillation, pruning, quantization, and transfer learning—reduces computational cost while maintaining accuracy. Clean, labeled datasets also improve model performance and reduce training waste.
Sustainable AI infrastructure includes shifting workloads to energy-efficient data centers or cloud providers using renewable energy. Scheduling compute-heavy tasks during low-carbon electricity periods (carbon-aware computing) can drastically reduce emissions. Sustainability must be embedded across the AI lifecycle—from design and development to deployment and decommissioning. With such efforts in place, organizations not only reduce carbon footprints but also improve resilience, compliance, and long-term viability.
We’re at a critical moment where AI is growing rapidly—and so is our awareness of its environmental impact. The encouraging news is that sustainability and innovation don’t have to be at odds. In fact, they can go hand in hand.
By embracing responsible practices—measuring emissions, using efficient models, and optimizing workflows—companies and creators can build AI systems that are both powerful and planet-friendly.
This is not just about doing less harm, but about doing better. Cleaner AI helps organizations meet their ESG goals, appeal to eco-conscious consumers and investors, and pave the way for a sustainable digital future.
AI can power the future—but only if we power AI responsibly.
Written by Ajit Gulliya