As the global technology sector marks AI Appreciation Day, the focus tends to land on the extraordinary capabilities AI now delivers — from autonomous systems and language models to real-time decision-making. But true appreciation of AI means looking deeper at the infrastructure that makes this progress possible.

AI does not run on models alone. It relies on immense compute power delivered across increasingly distributed environments. As AI workloads continue to grow in both scale and complexity, the energy demands of data centers have reached a critical inflection point.

According to the International Energy Agency (IEA), electricity consumption from global data centers reached 460 terawatt-hours (TWh) in 2022, and is projected to double by 2026, driven largely by AI workloads. In some regions, data centers are already on track to surpass the energy demand of entire countries, with the IEA noting that Japan's total electricity consumption is roughly equivalent to the projected energy demand from data centers by 2030. Without a fundamental shift toward energy-efficient computing, the sustainability of AI is at risk.

To support AI at scale, infrastructure must evolve to be scalable, to accommodate exponential compute demand; high-performance, to enable the real-time processing required by advanced models; and sustainable, to reduce power consumption and carbon emissions at every layer of the stack. Modern processors from Ampere® are helping address this challenge by maximizing performance per watt for AI inference.

Purpose built for Cloud Native environments, Ampere processors take a fundamentally different approach from legacy architectures. Ampere CPUs are designed from the ground up for predictable high-throughput performance and linear scaling, all without the power overhead of legacy architectures. They also use a modern, single-threaded core design that eliminates noisy neighbors and improves deterministic execution, making them well-suited for cloud-based AI inference. They are deployed in hyperscale, enterprise and edge environments to support large AI workloads while significantly reducing total energy use.

This kind of innovation illustrates a broader shift underway in the industry: a move away from legacy, power-hungry architectures toward purpose-built designs optimized for modern AI demands. For organizations deploying AI at scale, architectural efficiency is no longer a nice-to-have but a strategic differentiator. Modern processors like those from Ampere enable more performance within the same power envelope, lowering operational costs and reducing environmental impact.

As we celebrate AI’s progress this week, it’s also time to broaden the conversation. As AI continues to evolve, its success will depend not only on smarter models, but on better infrastructure to power them. Now is the time to invest in compute infrastructure that can carry AI into the future responsibly and at scale.