Abstract

Artificial intelligence is transforming how we work, build, and innovate, but it's also driving a rapid expansion of energy- and water-intensive data center infrastructure. Most users and even many technologists are unaware of the environmental cost behind every AI query. This talk presents a clear, data-driven look at the real energy, water, and carbon footprint of modern AI systems, from model training to daily inference at scale. Drawing on real-world metrics, infrastructure case examples, and proven technologies, the session explores practical solutions including efficient processor architectures, advanced cooling strategies, renewable energy integration, and smarter workload design. Rather than framing AI as "good" or "bad," this discussion focuses on responsibility: what engineers, developers, and technical leaders can do today to design, deploy, and scale AI systems more sustainably without sacrificing innovation.

What Will You Learn?

●      The real energy, water, and carbon footprint behind AI models and data centers, with specific numbers and metrics

●      How hardware choices, cooling technologies, and system design dramatically affect sustainability and ROI

●      Practical steps developers and engineers can take right now to reduce AI's environmental impact

●      A framework for evaluating whether an AI application is worth its environmental cost

Who Should Attend?

●      Software engineers, AI developers, and data scientists working with or building AI-powered applications

●      Cloud, infrastructure, and platform engineers responsible for deployment and operations

●      Sustainability, energy, and climate-focused technologists looking for concrete technical strategies

●      Technical leaders making architecture, infrastructure, or procurement decisions

Recording

This event will be recorded and the video will be posted to GDG Cloud Boston's youtube channel