Insights

Artificial intelligence is hungry. Very hungry. Roughly three-quarters of the world's FLOPs (the raw computing horsepower that makes AI possible) are concentrated in the United States. That dominance is impressive, but it comes with an enormous price tag: electricity.

As AI adoption accelerates, infrastructure teams are under increasing pressure to deliver scalable, secure, and cost-effective AI capabilities—often within the tight constraints of on-premises or hybrid environments. The challenge is clear: support meaningful AI workloads without the hyperscaler overhead.

Google's unveiling of Willow, its latest state-of-the-art quantum chip, reminds us that just when we think we've mastered data center design, technology takes a quantum leap—quite literally.

AI is reshaping the future of data centers. But here's the catch: AI training and AI inference place very different demands on your infrastructure. If your data center strategy doesn't distinguish between the two, you might be missing out on efficiency, performance, and cost savings.