{"id":12327,"date":"2026-04-16T13:05:20","date_gmt":"2026-04-16T17:05:20","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/?p=12327"},"modified":"2026-04-17T08:21:25","modified_gmt":"2026-04-17T12:21:25","slug":"the-rise-of-ai-factories-accelerating-the-design-and-operations-of-next-gen-data-centers","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/tecnomatix\/the-rise-of-ai-factories-accelerating-the-design-and-operations-of-next-gen-data-centers\/","title":{"rendered":"The rise of\u00a0AI Factories: accelerating the design and operations of next-gen data centers"},"content":{"rendered":"\n

AI infrastructure is entering a new era, one where data centers are no longer just storing information, but actively producing intelligence. These data centers, also known as \u201cAI factories,\u201d introduce a new level of complexity, driven by three forces: the need to build faster, operate more efficiently under tight power constraints, and engineer and validate hardware that doesn\u2019t yet exist. <\/p>\n\n\n\n

In this blog, we\u2019ll break down the core challenges shaping AI factory development, the emerging metrics that will define success, and why traditional approaches are no longer enough. We\u2019ll also explore how a new generation of tools, like Siemens\u2019 Digital Twin Composer<\/a>, helps teams design, build, and operate these facilities with greater speed, coordination, and confidence. <\/p>\n\n\n\n

Like any factory, AI infrastructure has a fundamental constraint: power. Today, the world cannot generate enough electricity to meet the growing demand for AI. That single fact is reshaping how these facilities are designed, built, and operated, and why a new class of software is urgently needed. <\/p>\n\n\n\n

Meet Siemens AI factory experts and authors<\/h2>\n\n\n\n
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Tim Schenk, Principal Key Expert, FT RPD Simulation & Digital Twin<\/em><\/figcaption><\/figure>\n<\/div>\n\n\n\n
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John DeBoer, Head of Data Center Vertical, Siemens North America<\/em><\/figcaption><\/figure><\/div><\/div>\n<\/div><\/div>
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A moment unlike any other in data center infrastructure<\/strong><\/h3>\n\n\n\n

Data centers are critical infrastructure that drives digitalization across sectors. As operations scale up, traditional setups are reaching their limits. A traditional data center was, at its core, a building full of servers. The new AI factory is simultaneously a small process-industry plant (because of its liquid cooling demands), a real power utility (because of its enormous electricity draw), and an IT environment, three worlds that have historically never had to deeply understand each other, now forced to co-exist and co-optimize in the same building, under the same roof, on the same compressed timeline. <\/p>\n\n\n\n

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We call them AI factories because these future-oriented data centers are actually manufacturing the building blocks of knowledge, the token.” – John DeBoer, Head of Data Center Vertical, Siemens North America.<\/p>\n<\/blockquote>\n\n\n\n

There are several players involved in AI factory development. The hyperscalers, Microsoft, Google, Amazon, Meta, are investing heavily to build out AI factories. Neo-cloud companies are racing to build and hand over the keys even faster. Co-location providers, Engineering, Procurement and Construction (EPC) firms, and a sprawling ecosystem of 150+ technology partners are all scrambling to play a role. The demand is extraordinary. And the physics are unlike anything traditional infrastructure teams have faced before. <\/p>\n\n\n\n

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And then there is a power problem. It is not a distant concern, it is the central constraint of the entire industry today. The US, and much of the world, simply cannot generate enough electricity to power all the AI factories that companies want to build at the scale they want to build them. That scarcity makes every design decision consequential. Wasted power in one part of the system is not just inefficiency, it is capacity that cannot be recovered. <\/p>\n\n\n\n

This is the context in which this article, and the solution it introduces, must be understood. The challenges are real, urgent, and compounding. But so is the progress. This piece explores what designing and operating an AI factory actually demands, who the key players are, and how Siemens\u2019 newly released Digital Twin Composer<\/a> is helping teams build these factories faster, smarter, and with far greater certainty than was possible before. <\/p>\n\n\n\n

The complexity of designing and operating AI factories<\/h2>\n\n\n\n

As AI infrastructure scales, building what some now call \u201cAI factories\u201d has become one of the most complex engineering challenges in the world. Across conversations with customers, builders, and technology partners, the same three challenges keep emerging. They\u2019re deeply interconnected, and solving one in isolation often makes the others worse. <\/p>\n\n\n\n

The first is speed, or \u201ctime to token,\u201d how quickly a team can go from concept to a fully operational facility. Every delay means lost output. While reference designs help, real-world constraints like land, building differences, and retrofits make each project unique. What\u2019s needed isn\u2019t just faster design, but an integrated process that moves efficiently from concept through construction to operation. <\/p>\n\n\n\n

The second challenge is efficiency, captured by the idea of \u201ctokens per watt.\u201d Power is no longer just a cost, it\u2019s the limiting factor. In many parts of the world, there simply isn\u2019t enough electricity to meet the growing demand for AI compute. That shifts the question from \u201chow do we reduce power costs?\u201d to \u201chow do we maximize output from the power we have?\u201d This requires thinking beyond individual components. Even if each piece of a system is efficient on its own, the overall system can still perform poorly if it isn\u2019t designed holistically. <\/p>\n\n\n\n

The third challenge is perhaps the most difficult: planning for hardware that doesn\u2019t exist yet. Unlike most industries, where improvements are incremental, advances in compute can be exponential. A new generation of chips might be 10x, or even 10,000x, more powerful, with completely different power and cooling requirements. At the same time, physical infrastructure takes years to build, while hardware evolves every 12\u201318 months. This creates a fundamental mismatch. The only viable way to manage it is through simulation, designing systems that can adapt to future, unknown technologies. <\/p>\n\n\n\n

As a result, new metrics are emerging. Traditional measures like PUE are no longer sufficient. Instead, the industry is converging on time to token and tokens per watt – benchmarks that directly reflect speed and output efficiency, and will likely define competitive advantage going forward. <\/p>\n\n\n\n

Introducing Siemens Digital Twin Composer for AI Factories<\/h2>\n\n\n\n

Digital Twin Composer<\/a> is a newly released solution from Siemens that connects design, simulation, real-time data, and AI into one unified environment, creating a continuous digital thread from the earliest design decisions all the way through to day-to-day operations.<\/p>\n\n\n\n

The core idea is deceptively basic but powerful: build twice. First in the virtual world, with advanced simulation, multi-discipline validation, and rapid iteration, then in reality, with the speed, certainty, and insight that only comes from having already stress-tested every major decision in a digital environment. Design problems are solved early, before concrete is poured or infrastructure is purchased. Sizing and layout are optimized against real physics models, not assumptions. And the same digital model used to design the AI factory doesn\u2019t get archived once construction begins, it carries forward into virtual commissioning, so teams can validate the facility\u2019s behavior before a single system goes live. <\/p>\n\n\n

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With Digital Twin Composer, explore how you can design and test complex scenarios (thermal, power, layouts, logistics) before build\u2011out to reduce deployment risk and time\u2011to\u2011compute. In operation, the executable twin enables ongoing optimization, predictive analytics, and scenario planning for resilient capacity. These solutions form the foundation of this next-generation experience and compose the industrial metaverse from Siemens digital twin technology.<\/p>\n\n\n\n

Today’s AI factories are predominantly using solutions like Simcenter StarCCM+, Simcenter Amesim, NX Line Designer and Simcenter HEEDs. But with Digital Twin Composer, it is possible to use all of the solutions listed below, extending your design-to-simulation environment even further and making your data foundation even more robust. <\/p>\n\n\n\n

The following solutions are the underlying technologies available in Digital Twin Composer from the Siemens Xcelerator Portfolio, including:<\/p>\n\n\n\n