The final opportunity worth discussing is how to optimize data center energy consumption. Rather like software-defined networking above and other optimisation problems, there are huge performance and efficiency gains to be had as datacentres get larger but also more complex. The integration of new cooling, networking, and computing technologies into existing facilities likely offers huge optimization oppotunities. This is somewhat of an application of the frontier AI we are looking to scale and deploy. The very same models we hope to scale with 100 MW data centers can be turned back around and used to optimize the data center it is being trained in. This is a riff on the recursive self-improvement motif, except that the AI isn’t improving itself but rather improving its house? We will talk about recursive self-improvement later in the context of AI improving the design of its own hardware. Among the most promising solutions here are reinforcement learning for HVAC control, AI workload scheduling and resource allocation, and AI-powered dynamic voltage and frequency scaling.

Opportunities

Reinforcement learning for HVAC control

AI workload scheduling and resource allocation

AI-powered dynamic voltage and frequency scaling

Others

• Computer vision for thermal mapping and hotspot detection uses infrared cameras and deep learning algorithms to create real-time thermal maps of data centers. This technology enables precise identification of heat buildup and inefficiencies in cooling systems, allowing for targeted interventions. By continuously monitoring temperature distributions, it can predict potential equipment failures and optimize cooling resource allocation, significantly improving energy efficiency and reducing the risk of thermal-related outages.
• Federated learning for multi-site energy optimization allows multiple data centers to collaboratively train machine learning models for energy management without sharing sensitive operational data. This approach enables organizations to leverage insights from diverse environments while maintaining data privacy and security. The resulting models can capture a wider range of operational scenarios, leading to more robust and adaptable energy optimization strategies across geographically distributed data center networks.