In the pre-AI days of the internet, data centers were designed and built to store, process, and serve data. Racks of CPU-based servers and storage arrays filled cavernous rooms, storing data redundantly and distributing it to multiple service providers at once. The compute workloads dedicated to this process were manageable enough that they could be virtualized, allowing many virtual machines to share a single physical server. Thanks to this, most businesses didn’t need to manage their own data centers, instead renting capacity from cloud providers scaled to their own specific needs.
The AI era is changing all of that.
Rather than simple data centers, today’s hyperscalers are building AI factories, facilities purpose-built to generate intelligence. While they share some structural similarities, the massive power and compute needs of AI dwarf that of data centers, fundamentally changing how they’re designed, built, and operated.
The leap in energy consumption has created what’s been dubbed the 10x challenge: AI factories consume about 10 times as much energy, produce 10 times as much heat, and have 10 times the complexity of traditional data centers.
Meeting the 10x challenge requires careful engineering—and it’s still just part of what hyperscalers face in standing up AI factories. From grid efficiency and liquid cooling to virtual design and uptime demands, the entire playbook on building and operating digital infrastructure facilities is being rewritten in real time.
AI’s Massive Power and Cooling Demands
The 10x demands of AI factories over traditional data centers come from their distinct purposes. Rather than just storing and processing information, AI factories generate intelligence. They do so using dense clusters of high-performance GPUs that can require over 100 kilowatts per rack, more than an order of magnitude greater than the handful of kilowatts used by a traditional data center rack.
With that much energy in play, reliable power distribution is paramount. When an AI factory is built, either from the ground up or retrofitted from a traditional data center, the foundation is vital electrical equipment to power the facility and its processes. Medium- and low-voltage equipment dynamically balance energy loads with the aim of creating a robust and reliable foundation of power distribution. Meanwhile, a combination of switchgear, busway, and prefabricated modular solutions are used to meet the facility’s specific needs.
All of that energy generates lots of heat; the thermal loads in AI factories can be extreme. So extreme that the default air cooling of traditional data centers isn’t an option. Instead, liquid cooling is needed to help keep temperatures down. Siemens, Nvidia, and nVent created a joint reference liquid cooling architecture specifically for AI factories that integrates a facility’s industrial-grade electrical systems directly with liquid cooling tech, keeping the power flowing and the heat in check.
“We’re seeing a growing need to redesign data center infrastructure as operators shift from CPU‑based computing to GPU‑intensive AI workloads, which is driving unprecedented rack‑level power and cooling demands,” said Ruth Gratzke, President of Siemens Smart Infrastructure U.S. “At this pivotal moment in the AI industrial revolution, digital twin technology is essential to design and test complex scenarios. We can optimize compute performance and energy efficiency before infrastructure is deployed. Leveraging advanced industrial grade automation to manage the interplay of compute, power and cooling as well as the grid interface creates the scalable foundation for the AI factories of the future.”
Hyperscalers’ Responsibility to the Grid
When talking about the 10x challenge and the unprecedented power demands of AI factories, it’s impossible to ignore the hurdles of connecting to the existing grid. Grid interconnection queues can stretch for years as utilities tighten requirements, trying to meet previously unimaginable levels of demand.
It’s a systemic issue that’s confronting hyperscalers and utilities alike. With the rates of energy they consume, AI factory operators need to think of themselves as grid partners rather than just customers.
One way Siemens consultants help operators do this is by designing AI factories that support grid health. By load shifting during times of peak demand and dynamically coordinating with utility systems, an AI factory can send energy to the grid in addition to pulling from it. Liquid cooling plays a significant role as well, reducing energy waste by up to 90 percent compared to traditional air cooling.
The Importance of Designing for Efficiency
Another major difference between a traditional data center and an AI factory is the timeline for construction. While data centers can take years to build, hyperscalers want their AI factories operational within 12 to 15 months.
To achieve that tight timeline despite the 10x jump in complexity, GPU-powered, physics-based digital twin technology can be used to allow operators to model and stress test power configurations, cooling layouts, and grid integration virtually before a single shovel hits the dirt. This reduces trial and error and vastly speeds up design and construction. Once up and running, software-defined operations enable continuous real-time monitoring and optimization, improving efficiency across the full operational lifecycle of an AI factory.
Of course, with AI advancing at such a rapid clip, questions are swirling about just how long AI factory lifecycles will be. For hyperscalers, this makes adaptability all the more important. To ensure that today’s AI factories can also be tomorrow’s AI factories, they need to be designed for adaptability from day one.
Uptime Above All
All this complexity is in service of one thing: 99.99999 percent uptime under extreme constraints. Hyperscalers racing to build the infrastructure of the future know that cutting-edge AI demands it. There’s no tolerance for interruptions in model training.
Delivering perfect uptime at gigawatt scale is a tall order demanding innovation and a reimagining of how things are done. But when AI factory operators have meticulous, chip-to-grid-to-building integration across power, cooling, automation, and digital operations, they’ll be able to meet the 10x challenge head-on.
