By Liz Porter, Global ESG Consult Lead at Kyndryl, and Avintha Moodaly, Director, Environmental Management at Kyndryl
Generative artificial intelligence (AI) tools and solutions bring the promise of creating societal and business value at scale. Surveys indicate that enterprise adoption of this technology has increased substantially over the last year, with most companies testing generative AI in one or more parts of their operations.
This exponential growth comes as 2024 is on track to become the hottest year on record. Running generative AI requires massive computational power, which is increasing the cooling requirements for datacenters and upending existing power usage management plans.
How much energy does generative AI use?
According to an IEEE Spectrum article, current AI technology could consume as much electricity annually as the country of Ireland (29.3 terawatt-hours per year). Meanwhile, a single Large Language Model (LLM) interaction may consume as much power as a low-brightness LED lightbulb on for one hour. This may not sound like much until one realizes that some of these workloads might call for millions of LLM interactions per day.
Despite its power demands, generative AI also can play a major role in conserving energy by making business operations more efficient. While there is no consensus on the magnitude of its power requirements, organizations must become aware of the impact that it is having on their carbon footprints and adjust their strategies to make generative AI more sustainable. To achieve their sustainability goals, companies should begin integrating sustainability data into business decisions and invest in technologies that provide greater visibility into sustainability metrics.
Creating a generative AI sustainability strategy
Kyndryl enables our customers to operate at the intersection of innovation, social impact and environmental stewardship by focusing on four key protocols: measuring an enterprise’s energy consumption baseline, optimizing energy usage, designing energy-efficient generative AI and seeking clean energy sources.
An important first step involves developing a clear picture of an organization’s current GHG emissions and energy spend. Continuous monitoring is required to facilitate more efficient energy optimization. But even before that happens, organizations need to determine whether generative AI is the best choice for their particular business needs.
Once an organization determines its energy-usage baseline, it will be better prepared to optimize its generative AI systems. As part of our transition to growth, Kyndryl took a hard look at our asset utilization — including datacenters and real estate. We reduced our real estate footprint, and shifted operations to more efficient, state-of-the-art datacenters. We also consolidated many of our servers by employing virtualization and reduced energy consumption. A critical part of our transformation involved forging partnerships with the world’s major hyperscalers to help increase operational efficiency.
4 steps to making generative AI more sustainable
Harnessing generative AI for corporate sustainability
Today’s existing efficiency improvements won’t be enough to mitigate the increasing energy demands of generative AI. That’s why we need to harness this technology to help manage its own energy usage. Whether through virtualization, optimized coding practices, cloud migration or more efficient Application Programming Interfaces (API), having visibility into energy usage will equip organizations with the insights they need to monitor and leverage generative AI for sustainability.
Additionally, how organizations collaborate to leverage cleaner and more renewable energy sources largely will determine the future of generative AI. Without this ability to identify ways to conserve more energy than it uses, much of its promise will escape our grasp. Designing new types of batteries for backup, running generators on biodiesel and shifting computational workloads to cleaner energy sources will be part of what must be an “all of the above” approach to developing sustainable generative AI.
AI has the potential to help us solve some of the world’s biggest problems. But because it poses significant challenges to bringing about a net-zero emissions world, we must capitalize on generative AI’s tremendous power to help us preserve and protect our planet and build a truly sustainable future.
Read more on “From Vision to Impact: The Global Sustainability Barometer.”
