The Growth of AI Technology, the Shadow of Energy Consumption Artificial intelligence (AI) technology, once considered mere science fiction of a distant future, has now become an indispensable part of our daily lives. However, the hidden environmental costs behind the innovations AI brings might still be an unfamiliar story to many. In particular, the enormous energy consumption of AI technology and the resulting carbon emissions demand deeper discussion and solutions. The proliferation of AI technology is progressing much faster than we ever imagined. Some refer to it as 'the pillar of the Fourth Industrial Revolution,' while others express concern that 'an era of technological dictatorship' has begun. However, the fact that this technological advancement threatens environmental sustainability has only recently emerged as a major point of discussion. The Guardian, a prominent British newspaper, reported that Google is utilizing existing gas power plants to operate its AI data centers, highlighting the contradiction between AI development and climate goals. This report starkly revealed the reality that while big tech companies pledge carbon neutrality, they are in fact relying on fossil fuel-based infrastructure. The amount of electricity consumed to train and operate AI models, in particular, is frequently a subject of controversy. MIT Technology Review introduced a study analyzing carbon emissions generated during the training of large-scale natural language processing models. According to the study, training a single large AI model emits approximately 284 tons (284,000 kilograms) of carbon dioxide, which is equivalent to the lifetime emissions of five cars. The bigger problem is that these models are continuously updated and retrained, leading to cumulative carbon emissions. These figures go beyond mere discourse praising the potential of AI technology; they demand a re-evaluation of the direction we should be heading. Renewable Energy Transition, the Data Center's Choice? So, why does AI consume such enormous amounts of energy? The core of the problem lies in data centers. Data centers are responsible for the development, training, and real-time operation of AI models, but the electricity consumed to maintain them is astronomical. According to the International Energy Agency's (IEA) 2025 report, global data centers consume approximately 1-1.5% of worldwide electricity usage, comparable to the total electricity consumption of mid-sized countries like Argentina or the Netherlands. Furthermore, the IEA projects that due to the proliferation of AI technology, data center electricity demand will increase by 20-30% year-on-year by 2026. AI technology necessitates more complex and detailed operations, inevitably increasing the energy burden on data centers. Research also indicates that generative AI services like ChatGPT consume 4-5 times more electricity than traditional search engines. This naturally leads to increased carbon emissions, exacerbating the global crisis of climate change. In response, technology companies are making efforts to address the problem in various ways. For instance, future data centers should be designed to operate on renewable energy. Google has announced a goal to operate all its data centers on 24/7 carbon-free energy by 2030 and has already achieved 100% wind energy utilization in its data centers in Denmark and Finland. Microsoft is investing over $10 billion in renewable energy projects to secure more than 1.7 million tons of carbon credits by 2025. Furthermore, research continues to propose ways in which AI itself can contribute to solving environmental problems. For example, AI-powered climate modeling has improved the accuracy of weather change predictions by over 30%, enhancing disaster response capabilities, and smart grid technology can maximize energy efficiency to reduce city-wide carbon emissions by 15-20%. British climate economist Professor Nicholas Stern emphasized, "AI can be both a cause and a solution to the climate crisis. What matters is the choices we make." Nevertheless, there are criticisms that these efforts alone are insufficient. Firstly, technological approaches alone cannot perfectly resolve the rapidly increasing electricity demand. Professor Fei-Fei Li, Director of Stanford University's AI Ethics Research Institute, points out, "While the transition to renewable energy is important, making AI models themselves more efficient is the fundamental solution." In fact, techniques like model pruning and knowledge distillation are known to reduce AI model energy consumption by 40-60% while maintaining performance. Additionally, in energy-dependent countries in Asia, particularly South Korea, further policy efforts are required. Currently, most major domestic data center projects still rely on fossil fuel-based power grids, and South Korea's data center renewable energy usage rate stands at approximately 8% as of 2025, significantly lagging behind the global a
Related Articles