AI at Scale: Why Data Centers Will Define the Next Industrial Era

The next wave of AI innovation will not be limited by algorithms or talent, but by infrastructure. As the complexity and intensity of AI workloads increase rapidly, the world is facing a critical shortfall in the foundation required to support them. Demand is outpacing supply, and a global race is underway to redefine the data center, not as a traditional compute warehouse, but as an intelligent, sustainable, and strategically located digital backbone for the AI economy.

These are staggering numbers, and it should come as no surprise that driving this growth is the insatiable demand for AI-ready infrastructure, capable of supporting high-density, power-hungry workloads like generative AI and machine learning inference. In the United States alone, a 15 GW capacity shortfall is expected, even if all currently planned builds are completed.

Here’s part 1 of the two-part blog series, AI at Scale: Rethinking Data Center Strategy for a Digital Industrial Future, which outlines a set of strategic priorities for governments, utilities, cloud providers, and industrial leaders. Addressing the infrastructure demands of AI requires more than concrete and capital. It demands visibility. The ability to plan new sites, manage dynamic loads, optimize cooling, and align power generation with compute demand all depends on access to live, contextualized operational data.

The AI Infrastructure Gap: Data Centers Define the Next Industrial Era

The transformation is as architectural as it is economic. AI workloads are pushing rack densities from 8 kilowatts to as high as 120 kilowatts, driving the adoption of advanced cooling solutions such as direct-to-chip and liquid immersion systems. Electrical architectures are being redesigned, with 48-volt rack systems replacing traditional 12-volt designs to reduce energy loss and boost efficiency.

At the same time, the power and site strategy for new data centers is shifting. Developers are deliberately moving beyond traditional high-demand hubs like Northern Virginia and Silicon Valley toward regions such as Alberta, Indiana, and Iowa, where transmission capacity, permitting speed, and energy pricing offer structural advantages. Equally notable is the growing momentum behind nuclear power. Meta has signed a 20-year agreement to supply its AI and data center operations with carbon-free nuclear energy from Illinois’s Clinton Clean Energy Center, securing reliable power starting in 2027 and signaling a broader shift among hyperscalers toward nuclear-sourced infrastructure.

Traditional data center designs, optimized for latency-sensitive web and enterprise applications, are fundamentally unfit for the scale and intensity of AI. They were never built to handle rack-level power densities exceeding 100 kilowatts, nor to support the kind of real-time training and inference loads that define next-generation workloads.

What is needed now is not incremental retrofitting but a new class of infrastructure: AI-native data centers, engineered from the ground up to meet the energy, thermal, and architectural demands of artificial intelligence at an industrial scale. These facilities must integrate sustainable power sources, including nuclear and renewables, with high-density cooling, advanced electrical design, and a composable architecture that spans edge, core, and cloud environments. This is not a data center evolution—it is an infrastructure revolution, and the pace of AI innovation depends on how fast we build it.

Yet in most organizations, that data is fragmented across systems, delayed by outdated architectures, or locked behind brittle integrations. The next generation of data centers will not just consume power and run models. They will generate massive volumes of real-time telemetry that must be harnessed to make intelligent, responsive infrastructure decisions. From the grid to the rack, from the edge to the cloud, data is the connective tissue.

Demand Surge and How AI Workloads Change Everything

For over two decades, data centers were designed to support predictable, transactional workloads. These included email, e-commerce, video streaming, and cloud applications. High volume, but relatively stable and well-understood. Demand grew steadily and could be managed through linear improvements: more racks, more efficient cooling, and higher network throughput. Hyperscale architecture emerged to meet this model, favoring modularity, geographic proximity, and cost efficiency.

The same held true at the chip level. Processor architecture advanced through faster clock speeds, smaller transistors, and increased core count, but remained fundamentally general-purpose. CPUs were designed to perform multiple tasks reasonably well, rather than excel at a single specialized task.

That entire model fractured with the rise of AI. Large-scale model training and inference introduced a new class of compute demand, parallel, power-hungry, and operationally volatile. Traditional CPUs could not keep up. The breakthrough came with AI-optimized chips: NVIDIA’s A100, H100, and the new GB200 Grace Blackwell architecture. These processors are engineered specifically for the massive scale of AI workloads, featuring high-bandwidth memory, GPU-to-GPU interconnects, and tightly coupled CPU-GPU integration to maximize performance.

But chips alone are not enough. Their thermal and power requirements far exceed the capabilities of legacy data center infrastructure. Supporting them requires a fundamental rethinking of everything around the silicon, including power delivery, liquid cooling, high-speed networking, and real-time data intelligence.

These changes have ripple effects across every layer of infrastructure operations. AI workloads are not only more intense but also far less predictable. Training cycles can run continuously for days or weeks, consuming enormous amounts of sustained compute and energy. Inference, on the other hand, can spike unpredictably based on user traffic, real-world events, or application usage. This variability breaks traditional capacity planning models, which rely on consistent baselines and predictable growth curves. It also complicates billing and cost management.

Unlike cloud-native services that meter usage based on transactions or compute hours, AI infrastructure requires a new model—one that can account for fluctuating GPU utilization, dynamic power draw, and bursty resource allocation. Without real-time visibility into workloads, operators risk overprovisioning to hedge against spikes or undercharging for compute consumed during peak inference loads. The result is a growing disconnect between infrastructure cost and business value. To close that gap, data centers must become not only more powerful, but smarter—capable of sensing, adapting, and monetizing their capacity in real time.

The Hidden Challenge of Visibility and Instrumentation at Scale

As AI reshapes data center architecture, it also redefines what it means to operate a data center. New forms of computing demand new forms of visibility. Yet while the industry debates rack density, cooling technologies, and power draw, a more foundational issue is often overlooked. How is this being instrumented? Without precise, real-time data from every subsystem, even the most powerful infrastructure cannot perform to its potential.

“Wasting money on generative AI is easy,” said Mary Mesaglio, VP Analyst at Gartner. “Just as IT departments often miscalculate cloud costs, the same mistakes are being made with AI. Miscalculations of 500 to 1,000 percent are entirely possible.”

AI workloads are pushing rack densities from 8 kilowatts to 30, 50, even 120 kilowatts. This kind of escalation is unprecedented, and it’s driving a shift from traditional air-based cooling to advanced techniques, such as direct-to-chip liquid cooling and complete immersion systems. These aren’t just engineering upgrades. They represent operational inflection points that require real-time telemetry across thermal, electrical, and mechanical domains.

Liquid cooling systems, for example, introduce a new class of risks. Flow rates, coolant pressure, fluid temperature, and pump health all become mission-critical variables. A sensor failure or undetected anomaly can lead to thermal runaway and catastrophic hardware failure. Monitoring these systems using the same methods employed in air-cooled environments is insufficient. Precision is essential. So is real-time response.

The electrical side is evolving just as rapidly. To reduce energy loss and increase efficiency, many high-density racks are moving from 12-volt to 48-volt power architectures. But these systems have tighter tolerances and more complex load-balancing requirements. Transient spikes or uneven distribution can lead to equipment degradation or downtime if not actively managed.

Despite these changes, most data centers still rely on fragmented monitoring stacks. HVAC, power, compute, and network telemetry are often siloed, polled intermittently, or locked in proprietary systems. Operators get dashboards, not decisions. The result is infrastructure that’s technically advanced but operationally blind.

In an AI-native environment, that’s unacceptable. Workloads are not only more intense; they’re far more dynamic. Training jobs can run for weeks, while inference can spike in unpredictable bursts. Static monitoring tools and delayed feedback loops don’t just reduce efficiency. They increase risk. To meet the moment, data centers must become fully instrumented systems. This means treating telemetry as a high-priority workload in its own right—streamed, structured, and routed in real time. From coolant flow sensors to GPU temperatures, from power phase imbalance to network jitter, every signal is a potential insight. But only if it’s captured, contextualized, and delivered where it matters.

The future of AI infrastructure depends on more than chips and kilowatts. It depends on the intelligence embedded in the infrastructure itself. Visibility is no longer a nice-to-have. It’s the control plane for performance, resilience, and ROI.

Conclusion

The age of AI is rewriting the rules of data center design; yesterday’s strategies can’t meet today’s workloads. The first step is recognizing that infrastructure is no longer just hardware.

Stay tuned for Part 2 of this two-part series, AI at Scale: Rethinking Data Center Strategy for a Digital Industrial Future, where we’ll dive into treating infrastructure as a data problem, breaking free from brittle “spaghetti architectures,” and harnessing the power of a Unified Namespace (UNS) to create AI-native data centers. We’ll explore Alberta’s bold AI infrastructure strategy, show how real-time intelligence transforms operations from reactive to predictive, and outline how an event-driven, unified data fabric can eliminate silos, accelerate decision-making, and future-proof capacity planning.