ManufacturingIn 2024, Google’s data centers consumed over 30.8 million megawatt-hours of electricity: more than double what they used in 2020. With massive GPU clusters humming 24/7, Google now uses as much electricity in a year as some countries. While the company has made strides in sourcing clean energy, the rate of demand growth is outpacing both infrastructure buildout and sustainability goals. And Google is hardly alone.
As AI workloads intensify across hyperscalers like Meta, Microsoft, and OpenAI, the energy requirements of data centers are on a collision course with the physical limits of the electric grid. The International Energy Agency projects data center electricity consumption could double globally by 2030, and the North American Electric Reliability Corporation has already warned of destabilization risks. In the United States, delays in interconnection can stretch as long as ten years. But a new class of companies, and a new software approach, may be offering an alternative path forward.
Emerald AI’s Grid-Aware Architecture
Launched with $24.5 million in seed funding this month, Emerald AI is a core enabler of what its founder, physicist and energy strategist Dr. Varun Sivaram, calls a paradigm shift: transforming data centers from inflexible power hogs into agile grid assets. The company’s Conductor platform uses AI to orchestrate data center energy consumption in real time, selectively throttling or redirecting compute tasks based on local grid conditions. That means deferring low-priority jobs or rerouting them to less-congested centers, while ensuring mission-critical inference tasks remain uninterrupted.
