By Jaspreet Bindra
Artificial intelligence—particularly generative AI— is one of the most transformative technologies of our era. From creating human-like content to accelerating drug discovery and enhancing business intelligence, GenAI is reshaping industries at an unprecedented pace. However, as this technology becomes more powerful, its environmental cost is also becoming apparent. The question we now face is both urgent and essential: Can we make GenAI more sustainable?
The hidden cost
Training large-scale AI models like OpenAI’s GPT-4, Google Gemini, or Meta’s LLaMA demands massive amounts of computational power. This process consumes energy on a scale comparable to powering hundreds of homes for extended periods. A 2019 study from the University of Massachusetts Amherst revealed that training just one deep learning model could emit as much carbon as five cars do over their entire lifetimes. Given the increasing adoption of AI in daily operations, this carbon footprint is projected to rise significantly if left unaddressed.
Fortunately, the AI community is not oblivious to these concerns. A range of strategies—technical, infrastructural, and regulatory—are being explored to reduce the environmental impact of generative AI systems.
Smarter AI models
Developers are now focusing on optimising model architecture. By implementing techniques like model pruning, quantisation, and knowledge distillation, it’s possible to reduce the size and complexity of AI models without compromising their performance. Additionally, transfer learning—the practice of building on pre-trained models instead of training from scratch—significantly cuts down the computational load and associated emissions.
Greener data centres
The backbone of AI computing—data centres—is being reimagined. Companies like Google, Microsoft, and Amazon are making substantial investments in carbon-neutral or renewable-energy-powered facilities. In addition, innovations like liquid cooling, smart power allocation, and dynamic load balancing are being deployed to enhance energy efficiency and reduce waste.
Energy efficiency
Specialised chips such as TPUs (Tensor Processing Units) and Nvidia’s green GPUs are designed to process AI workloads more efficiently, consuming less energy while maintaining high performance. Moreover, running models on edge devices (like smartphones or IoT sensors) instead of relying entirely on the cloud can drastically reduce energy usage and latency, making AI more accessible and sustainable.
Responsible governance
Just as important as technical improvements are policy and governance frameworks. Encouraging AI sustainability through regulations, carbon offset incentives, and environmental audit standards will be crucial in aligning AI development with global climate goals. The creation of AI ethics boards and sustainability rating mechanisms can also guide responsible innovation.
GenAI will continue to be a driving force in innovation— but its future must be sustainable by design. Businesses and developers need to prioritise energy efficiency and climate impact just as much as performance and scalability.
The writer is co-founder, AI&Beyond
