# Do We Really Need Mega Next-Gen Data Center Campuses? > Source: > Published: 2026-08-06 09:00:00+00:00 # Do We Really Need Mega Next-Gen Data Center Campuses? AI and cloud demand, siting flexibility, and power economics are pushing developers toward multi-building campuses measured in gigawatts. Over the past three decades, data centers have expanded in size and scope like heated gas – rapid and relentless. What began as modest server rooms, or a large air-conditioned hall with a mainframe supporting enterprise applications, has evolved into sprawling hyperscale campuses with hundreds of thousands of servers spread across multiple buildings. Facilities once measured in square feet are now measured in acres. The largest under development in the US includes [Meta’s 3,650-acre Hyperion campus](/data-center-construction/meta-s-5-gw-hyperion-campus-signals-ai-s-shift-into-utility-territory) in Richland Parish, Louisiana. And an even bigger concept has been floated: investor and “Shark Tank” co-host Kevin O'Leary has discussed a [40,000-acre site](/build-design/stratos-and-the-rise-of-the-ai-power-stack) in Utah, later pared back to 20,000 acres after [significant public blowback](https://www.nbcnews.com/tech/tech-news/kevin-oleary-utah-data-center-project-stratos-ai-shrink-hayley-rcna348430). Mind you, 20,000 acres is roughly the size of Manhattan. Not all of that land is built up. Within each facility, a portion of the constructed area is IT “white space,” the data halls that house compute and storage, while “gray space” supports mechanical and electrical systems such as power distribution, UPS, switchgear, and cooling. At the campus scale, additional acreage is dedicated to substations, utility corridors, roads, yards, and setbacks. The result – a built environment so extensive that you need a car to get around it – prompts the question: Do operators truly need campuses so large? Peter Skae, managing director of technical services at data center construction firm JLL, said the driver behind these giant facilities is straightforward: workloads have simply outgrown the capacity of a single building. “Hyperscale data centers have grown massive because of cloud computing, AI, and digital services that all require enormous computational power and storage capacity that's most efficiently delivered at scale,” Skae said. “Consolidating into larger facilities achieves significant cost advantages through economies of scale while enabling the advanced power, cooling, and redundancy infrastructure needed for mission-critical operations.” ## Modular Design at Campus Scale Rather than betting everything on a single monolithic structure, developers are building campuses composed of multiple, reasonably sized buildings. Skae noted that this model brings capacity to market faster, is easier to manage, [improves redundancy](/uptime/data-center-redundancy-a-guide-to-n-levels-and-tier-classifications), spreads risk across multiple assets, and supports multi-tenant demand. There is, however, a practical limit. “Of course, you can only build so many buildings at a time, and trade labor isn’t exactly plentiful,” he said. Analysts agree that these massive campuses start small and are positioned to grow. They rarely open at full size, and there is no guarantee they will ever reach maximum capacity. Alan Howard, senior analyst for facilities at Omdia, pointed out that these sites are really collections of buildings – typically 150,000 to 400,000 square feet each – developed over time. “Some campus builds can take 10 years or more,” he said, adding that the surge in AI demand – and the capital chasing it – has accelerated timelines on select projects as service providers move quickly to capture market opportunity. This shift from a single building to a multi-building campus somewhat mirrors what is happening inside server CPUs. Instead of a big monolithic design, chipmakers are embracing [chiplets](/data-center-chips/arm-steps-deeper-into-silicon-implications-for-the-semiconductor-value-chain) – separate pieces tied together with high-speed interconnects. Likewise, these new data center campuses function as interconnected modules. Alex Cordovil, research director at Dell'Oro Group, echoed the module theme, noting that many AI deployments are being built as cluster building blocks of 10-50 MW. Plenty of operators are content to run only a few of those. “The headline-grabbing giants are one end of a much wider spectrum, not the whole story,” he said. ## When Scale Pays for Itself Why are some developers chasing 1 GW-plus total campus capacity? Cordovil said the constraint on [site selection](/data-center-site-selection/urban-vs-rural-why-data-centers-are-built-where-they-are) has changed. For many advanced AI workloads, the time a frontier model takes to compute an answer dwarfs network latency. Users already wait seconds for a model to respond, so whether results arrive in five milliseconds or a hundred is rarely noticeable. “That breaks the old rule that compute had to sit close to the user, and it frees developers to build in remote, isolated locations where the usual siting constraints all but disappear,” Cordovil said. “Once you remove those constraints, scale becomes the lever you pull.” At that scale, the economics compound. If a developer is effectively building its own power – through [on-site generation](/energy-power-supply/why-data-centers-produce-their-own-power), dedicated substations, high-voltage interconnects, or long-term offtake arrangements – the undertaking is enormous and complex, Cordovil explained. A larger footprint helps amortize those investments. The same logic applies to permitting: securing approval for one very large campus and then building it out in phases over several years can de-risk the project more than pursuing many smaller, separate approvals. ## Operating the Megacampus Operating at this scale is less about heroics and more about discipline. According to Skae, it’s achievable through systematic processes, proven technology, and specialized expertise. JLL relies on integrated technology platforms that provide real-time visibility across massive footprints; designs for longevity and future-proofing over 50-year asset lifecycles; collaboration across the delivery team; and a zoned approach that breaks large facilities into manageable areas with dedicated teams responsible for each zone. Howard emphasized resource planning from day one. “Power and water planning for multi-GW scale campuses is critical at the very earliest stages,” he said. “Each campus is different, but on-site generation and backup are likely even with a grid connection. For cooler climates like Utah and Wyoming, the use of water is likely to be minimized by using air-cooled chillers. To the extent direct-to-chip cooling is used, the closed-loop system will have little water demand,” he said. In short, the industry’s pull toward ever-larger campuses isn’t mere spectacle. It’s a response to outsized computational demand, shifting constraints, and the economics of building and operating mission-critical infrastructure at scale – even if it means a data center now requires a map and, occasionally, a set of car keys.