As AI data centers evolve, densities rise, and workloads intensify, the cooling strategy must evolve

And why the right investment is necessary to keep up with AI workloads

Raising the importance of an often overlooked element: design

The AI race will be won by being quick; there's no time to fall behind

The amount of data being produced is exploding, and storage needs greater attention

AI is transforming industries, but it’s also reshaping the energy equation

Flashing lights, AI in action, and a surprising goodbye

Industry leadership will be measured less by the size of a single facility than by the ability to articulate a cohesive strategy for the age of omnipresent and holistic compute

With demand accelerating and complexity growing, the challenges facing data centers are not going away

The irony is striking: You're running a data center with efficiency levels that seemed impossible just a decade back, yet you're still defending against outdated complaints

No longer can data centers stand alone as isolated infrastructure, but are coming of age as critical supports for modern life and economies

AI inference is rapidly evolving to meet enterprise needs – becoming tiered, distributed and optimized for RAG, agentic, and generative AI. Storage is at the core of this transformation

The crypto world becomes ever more intermingled with the AI and cloud

Cooling design is no longer just an efficiency exercise; it’s an exercise in resilience

With Motivair liquid cooling solutions for AI factories managing pressure drop, ΔT, and flow rate, silicon like Nvidia or AMD isn’t just functional – it’s unleashed

Turn your physical infrastructure into a competitive edge

Power requirements and density are up, and the data center industry needs to be prepared

Universal intelligent infrastructure management and AI-driven tools are the only solution for complex data center estates

A proactive approach is essential, or risk falling behind in this AI race

Deploying liquid cooling doesn't just deliver a more efficient facility – done right, it can provide the agility required to support the next wave of AI demands

Flexible, scalable infrastructure designs are crucial for enabling AI innovations’ progress – ensuring that deployments and use don’t come at the expense of reliability or efficiency

Thousands of legacy data centers across North America sit dramatically underutilized. From a sustainability standpoint, reusing and retrofitting this infrastructure is the single most impactful step our industry can take

Can SLMs offer faster inference times and lower operational overhead?

How to implement it