What powers artificial intelligence is no longer just code—but energy, labor, and massive physical infrastructure.

On June 6, 2026, the Sustainable Development Lab’s conference at KDIS explored the implications of artificial intelligence for energy systems, labor markets, and infrastructure development.

Conference participants, including students, professors, and speakers, gather for a group photograph.

Professors, researchers, and students convened to discuss these intersecting domains in three thematic sessions: Economics of AI and Energy, AI and Employment, and Machine Learning and Energy.

The opening session, “Economics of AI and Energy,” examined the relationship between AI deployment and sectoral energy consumption. Environmental economist Professor Tony Harding (Georgia Institute of Technology) presented preliminary findings indicating that the impact of AI adoption on electricity demand varies among different scales. Although data centers can place significant strain on local grids, aggregate national effects remain relatively modest due to productivity gains and efficiency offsets.

Professor Wilson sharing his research findings

Professor Charlie Wilson, Professor of Energy and Climate Change at the Environmental Change Institute, University of Oxford, discussed pathways to achieving net-zero emissions amid increasing AI-related energy demand. He pointed out the importance of aligning climate policy with renewable energy expansion and grid modernization.

Prof. Yeong Jae Kim, Prof. Changkeun Lee, and Dr. Chang Jae Lee sharing insights on artificial intelligence and labor trends.

The second session, “AI and Employment,” addressed the impact of artificial intelligence on labor markets and job structures. Professors Yeong Jae Kim and Changkeun Lee from KDI School presented research on productivity effects, task restructuring, and skill transformation in workplaces affected by AI. Associate Research Fellow Dr. Chang Jae Lee from the Korea Labor Institute, serving as discussant, emphasized that AI adoption is increasingly associated with task reconfiguration in place of direct labor substitution.

Dr. Min-kyeong Cha integrating key contributions and connecting key ideas across previous presentations.

The third session, “Machine Learning and Energy,” focused on applications of AI in energy systems. Javier Gonzalez Ruiz (Politecnico di Milano), Hyunjoo Lee (KDI School), Professor Seung Eun Kim (Department of Applied Artificial Intelligence, Seoul National University of Science and Technology), and Dr. Min-kyeong Cha (LSE-Newcleo Fellow, London School of Economics and Political Science) presented research on machine learning applications for energy efficiency, grid optimization, and data center energy management. This session illustrated the dual role of AI as both a driver of electricity demand and an instrument for improving system efficiency.

Following the presentations, students considered how the discussions related to their academic and professional interests.

Speaking about the first session, Richmond Kwadwo Fosu (MPP, Ghana) stated that it was “very educational and interesting”. As someone interested in energy economics, he found Professor Harding’s presentation and discussion on AI and data centersmemorable and particularly insightful.

Kanojia Pooja Ashok (MPP, 2025), a student participant working on green hydrogen development in India, noted the relevance of the event to her research, stating, “This event was highly relevant to my research interests, particularly the demand–supply gap for green hydrogen production in India as part of my master’s research.” She went on to say, “The presentations provided important insights concerning how increased AI adoption and renewable hydrogen energy can complement and strengthen renewable energy integration, accelerating the deployment of solar, wind, and other renewable sources.”

On larger implications, Pooja added, “My biggest takeaway is that AI has significant environmental implications. The growing demand for AI requires enormous computing power and energy consumption through data centers. This made me realize that so-called “green energy” is not always as clean and sustainable as it is often portrayed, especially when considering the environmental costs associated with supporting AI infrastructure.”

Alfada Maghfiri Firdani (MPP, Indonesia) reflected on labor market implications, stating, “One idea that changed my perspective was that data centers do not necessarily create many new jobs. Instead, I learned that data centers often lead more to job upgrading and skill transformation as opposed to large-scale job creation.”

According to Global Energy Policy student Muhamad Solahudin Al Ayubi (MDP, Indonesia), listening to the discussions provided him with important insights into his academic interests in energy policy and sustainable development. “Engaging with experts provides students with real-world insights that go beyond what is covered in the classroom,” he explained. When asked about key challenges in the energy transition, he emphasized that one issue deserving greater attention is how countries can meet the rapidly growing electricity demand from AI and data centers without undermining their decarbonization goals.

Solah, moreover, raised concerns about the global digital divide, noting that most large-scale data centers and AI infrastructure are concentrated in advanced economies, especially the United States and other developed countries. He pointed out that without greater international cooperation and investment in digital architecture, developing countries may struggle to benefit equally from the AI revolution, stressing that “securing that AI development supports both sustainability and inclusiveness and inclusiveness should therefore be a key priority.”

This broader perspective connects the conference discussion to the responsibilities of students, future policymakers, researchers, and technology users who will help shape the development, governance, and application of AI in society.

The questions raised by the conference go well beyond data centers themselves. The discussion underscored the need to understand AI not only as a technological innovation, but also as an energy, labor, infrastructure, and development challenge. Moving forward, building an inclusive and sustainable AI future will require well-informed decision-making, cross-sector collaboration, and a deeper awareness of how today’s infrastructure choices will shape tomorrow’s economies and societies.