Panel 2

Green AI Summit 2025: Panel 2 - Towards a Low-Carbon AI and Data Center Ecosystem: Energy and Innovation

Session Date: April 25, 2025 Location: Harvard University Gutman Conference Center

Overview:

Following lunch on Day 1, the second panel of the Green AI Summit 2025 shifted focus towards actionable solutions for creating a low-carbon ecosystem for AI and data centers. Moderated by Andreas Klaube from the Electric Power Research Institute (EPRI), this session convened experts from utilities, policy research, public health, and academia to discuss the critical intersection of energy innovation, technology, and policy needed to build a greener digital future amidst rising AI-driven energy demand.

Key Discussion Points:

The panelists explored various strategies and challenges in reducing the carbon footprint of AI and data centers:

• Low-Hanging Fruit & Flexibility: Discussion highlighted immediate opportunities, including energy efficiency gains (which historically kept data center energy use flat despite increased demand) and demand-side flexibility. Leveraging load flexibility was emphasized as crucial for utilizing existing grid capacity, avoiding costly infrastructure build-out, and integrating renewables. Panelists noted that flexibility should be bidirectional, enabling data centers to both reduce and potentially increase consumption intelligently based on grid conditions.

• AI for Grid & Utility Challenges: Panelists discussed using AI to enhance utility operations, such as automating asset inspection with computer vision and improving knowledge management by structuring vast amounts of disparate data (documents, images, etc.). AI's role in designing better market mechanisms to incentivize data center participation in demand response was also suggested.

• Policy Frameworks & Environmental Justice: The conversation touched upon emerging regulatory efforts (like those in Texas and FERC proceedings) aimed at making data centers better "grid citizens" through incentives or potentially mandates. The need for tariff structures that appropriately allocate grid upgrade costs was highlighted. Panelists also stressed the importance of data disclosure policies and addressing the environmental justice implications of where energy generation facilities and data centers are often sited.  

• Energy Efficiency & System Optimization: While acknowledging AI's role in improving energy efficiency (e.g., optimizing power flow, using grid-enhancing technologies), a strong point was made about shifting focus from just asset-level efficiency to system-wide efficiency. This involves better utilizing existing grid infrastructure (which is often underutilized outside peak hours) rather than solely focusing on building new capacity.

• Public Awareness & Data Challenges: Panelists identified common misconceptions, such as underestimating AI's regional environmental impacts (water use, air pollution beyond immediate vicinity) and the lack of community engagement/transparency in facility development. The persistent challenge of data readiness and quality for training effective AI in the utility sector was also noted.

Panelists:

• Andreas Klaube (Moderator): Technical Leader III, Electric Power Research Institute (EPRI)

• Junhui Zhao: Manager, Transmission and Substation Data Innovation, Eversource Energy 

• Varun Sivaram: Senior Fellow, Energy and Climate, Council on Foreign Relations (CFR)

• Peng Gao: Assistant Professor, Environmental Health and Exposomics, Harvard T.H. Chan School of Public Health

• Shaolei Ren (Online): Associate Professor, Electrical and Computer Engineering, University of California, Riverside, Cooperating Faculty in Computer Science and Engineering

This panel provided a rich discussion on the practical steps, innovations, and policy shifts needed to integrate the growing AI sector sustainably within our energy systems, emphasizing flexibility, system-level thinking, and addressing environmental equity.