AI's Energy Challenge: Balancing Innovation and Sustainability in the Race to Net Zero

Electrification could be the key to net zero - and AI can boost the transition

Judicious use of AI means not forgetting the human element needed to successfully deploy it. The energy landscape is undergoing a profound transformation. The urgent need to decarbonize and the relentless pursuit of energy efficiency have ignited a worldwide movement towards a more sustainable future. At Schneider Electric, driven by our purpose to empower people to use energy and resources responsibly, and our mission to be a trusted partner for sustainability and efficiency, we believe that the key to unlocking this future lies in the power of electrification, digitalization, and automation. But it is human ingenuity that will truly drive this transformation and shape a brighter future for all. What if we could electrify the world in a way that drastically cuts greenhouse gas (GHG) emissions? What if artificial intelligence (AI) could optimize energy use, so it could become our ally in the fight against climate change? What if the data centres powering AI, and our increasingly digital lives, could operate and scale up without leaving a massive carbon footprint? These aren't hypotheticals. They are questions begging for answers. We can't assume there's time to spare gambling with our planet's future. Electricity, the most versatile and efficient form of energy, is at the heart of our vision. By electrifying across sectors, industries and communities, we can dramatically reduce GHG emissions. The International Energy Agency (IEA) reports that by 2050, electrification could account for up to 60% of the total reductions needed to achieve net-zero emissions. Digital technologies, like the internet of things and AI, are revolutionizing the energy industry, just as they are transforming the way we live and work. They offer tools to optimize consumption, transform power grids, and seamlessly integrate renewable energy sources. AI can sift through mountains of data to uncover patterns we couldn't detect on our own, enabling smarter decisions and fostering sustainable business practices. Energy companies can harness the power of data by collecting and analyzing information from across their value chains. The AI revolution presents a tremendous opportunity for growth and innovation, projecting a 14% increase in the global economy by 2030, driven by enhanced labour productivity and product enhancements through AI, with a projected market cap of $1.3 trillion by 2032. But there's a paradox at the heart of the AI revolution: The very technology we rely on for data-driven solutions is driving unprecedented demand for energy-intensive data centres. Our challenge is to scale these operations sustainably. Traditional data centres weren't designed to handle the high-density AI workloads of today. AI workloads can increase power demands significantly, putting immense strain on cooling systems. Advanced cooling technologies, like direct-to-chip and immersion cooling, are emerging to address this challenge. However, cooling technologies are just pieces of a larger puzzle. Data centres currently account for about 1.5% to 2% of global electricity consumption, a figure that could more than double by 2030 without energy-efficient practices. To curb the substantial environmental impact of accelerated data centre build, we must decouple the growth from energy consumption right from the start. This can be achieved through a multifaceted approach: We must optimize energy usage, leverage renewable energy sources, and design energy-efficient facilities at the forefront. Additionally, retrofitting existing data centres and strategically locating them in regions with abundant renewable energy can make a big difference. Looking ahead, with emerging technologies like quantum computing on the horizon, designing scalable and modular data centre infrastructure will be essential. By prioritizing energy efficiency, advanced cooling and strategic planning, we can ensure that data centres power innovation while minimizing their environmental footprint. But let's step back for a moment. While technology is a powerful tool, it is human ingenuity that wields them effectively, driving innovation and progress. The true value of AI comes to life when technology meets human expertise. Successful AI requires more than data science; it must be business-oriented and responsible. Domain expertise is essential for successful AI implementation. It plays a key role in selecting features, models, training and perfecting AI models, and ensuring the creation of ethical, unbiased algorithms. It is also key to ensuring explainability and safe implementation of AI solutions. As AI and automation advance, we face ethical questions that can't be ignored. We must ensure that AI is used responsibly, with a focus on human well-being. By following the highest ethical standards when implementing AI and automation, we can harness the power of technology and contribute to a more sustainable future. Remember, the future of AI depends on how we, as humans, leverage it to solve real-world problems and improve lives. So, what's the best way to safeguard our planet and ensure a sustainable future? Embracing a circular economy - a system where resources are reused and regenerated - is imperative. Circularity offers dual benefits for the planet and the economy. With 45% of global emissions associated with the production and consumption of products, the current rate of resource consumption is unsustainable. Circular transformation is highly effective because it keeps materials in use, and energy embodied, for as long as possible. A circular economy requires access to reliable energy for all. Schneider Electric is committed to Access to Energy initiatives, having already improved the lives of over 46 million people. We aim to reach 100 million people by 2030 through sustainable energy solutions. Our innovative solutions, powered by the latest technologies, enable businesses to optimize energy consumption, reduce carbon emissions, and improve operational efficiency. By combining technology with human expertise, we can unlock the full potential of digital transformation and accelerate the transition to a sustainable future. To achieve a net-zero future, we must focus on both the supply and demand sides of energy. By electrifying our economies and optimizing energy consumption, we can significantly reduce global emissions. This means transitioning to renewable energy sources, electrifying transportation and industries, and implementing energy-efficient practices. By embracing innovation, electrification, and circularity, as well as investing in clean energy solutions, we can collectively mitigate climate change and ensure a better future for generations to come.

Powering the future: The energy shift for sustainable AI

Private sector actors are starting to create ways to make data centres more efficient -- but work must also be done in the energy sector to ensure data centre expansions do not undermine the energy transition. A world where our energy needs are met without harming the planet, and where artificial intelligence (AI) not only boosts productivity but also bridges social gaps, isn't a distant dream -- it is a necessity, and it is within our grasp. Climate change is reshaping our world in ways that cannot be ignored. To protect our planet and future generations, we must accelerate the energy transition at scale -- shifting from fossil fuels to sustainable, renewable and low-carbon energy sources. The stakes couldn't be higher, and the time to act is now. At the same time, revolutionary AI technologies like generative artificial intelligence, agentic AI and artificial general intelligence have tremendous potential to drive the next wave of productivity improvements and social equity, ultimately improving the quality of life for people all over the world. AI is promising great change, driving a positive impact on labour productivity, access to healthcare and education, especially in disadvantaged communities and much more. AI is expected to add as much as $4.4 trillion annually to the global economy -- the equivalent of the GDP of Japan today. This potential comes at a cost, however. AI is a power-hungry giant poised to drive 160% increase in data centre power consumption. Supporting the immense computational requirements of both training and deployment of its models requires massive energy. Inference -- the process of deriving output for multiple users from AI models -- adds to the load, fueling data centre demand. Left unchecked, AI will add to the energy crisis our world is already facing. How can we harness AI's promise of impact while maintaining or even accelerating the energy transition? We must keep net zero deployment at heart. While it's not a low-cost transition, it is a feasible one. Ensuring sustainable AI along the journey will be as essential to the planet as it is proving to be for the people who call it home. To make AI a significant part of our future, we must look at how we reinvent the future responsibly. To make AI initiatives more sustainable, we must address both sides of the equation. On the supply side, this means prioritizing the use of clean energy sources and building power efficient infrastructure. On the demand side, it involves optimizing AI systems to be more energy efficient. First, we can prioritize using or training models run by efficient, net zero data centres that are powered by renewable and low-carbon energy sources, like nuclear. But why stop there? Leaders need to promote programmes that enable their organizations to capture carbon and store their emissions. This also requires a completely new approach to our existing data centre networks. As-is, data centres require large footprints, have enormous energy consumption requirements and are concentrated in specific regions due to their networking efficiency needs. By shifting to an all-photonics network (APN), we can connect data centres via photonics replacing electronics, which allow for direct data connections to improve efficiency. It's like taking a direct flight for data instead of multiple stopovers, which is inefficient and time-consuming. The APN approach allows data centres to be located closer to renewable energy sources, reducing their impact on the electrical grid and high-density data centre locations. The private sector is moving to enable and implement the move from electronics to photonics in data centres. For example, NTT's Innovative Optical and Wireless Network technology, which consists of an APN to enable data processing 125 times greater than today's networks, enables near-instant transmission with latency reduced by 200 times and ultra-low power consumption to achieve 100 times more efficiency as compared to today. As AI's demand on our energy systems rises, especially with the next wave of inference-driven applications, infrastructure and resources will come under increased strain, reinforcing the need for sustainable approaches. The demand side of AI brings its own set of challenges and opportunities. Efficient AI compute architecture and infrastructure, including the ones powered by photonics, will be key. And this can be supported by small and medium language industry- and domain-specific models that deliver high-quality outcomes but consume less energy. We also can and should be focused on innovative cooling technologies as well as leveraging the benefits of energy efficiency that can be gained particularly in countries with colder climates. But we can't manage what we don't measure. This is why we need a standardized measurement strategy to measure our carbon footprint, as well as the impact of moving from fossil fuels to renewable energy sources to gauge improvements. It also means applying energy-efficient standards throughout the cycle - from AI's infrastructure, development, deployment and usage. AI comes with tremendous potential for progress. It has the power to level the playing field, opening doors to opportunities that were once out of reach for many. At the same time, the ecological crisis we're facing is more dire than ever before. We must harness the power of AI with vision and bold action -- and responsibility must be at the core of everything we do. From becoming a catalyst for the energy transition, to ethical energy usage, to AI governance, we're just scratching the surface of what sustainable and responsible AI can and will mean for the world.