Power: Redundant utility design in a power-constrained market The tour made equally clear that in Manhattan, power is still the central gating factor. The brochure describes SDC Manhattan as offering 18MW of aggregate power delivered to the building, backed by redundant electrical and mechanical systems, backup generators, and Tier III-type concurrent maintainability. The December 2025 press release updated that picture in a more market-facing way, noting that Sabey is one of the only colocation providers in Manhattan with available power, including nearly a megawatt of turnkey power and 7MW of utility power across two powered shell spaces. Bajrushi’s explanation of the electrical topology helped show how Sabey has made that possible. Standing on the third floor, he described a ring bus tying together four Con Edison feeds. Bajrushi said the feeds all originate from the same substation but take different paths into the building, creating redundancy outside the building as well as within it. He added that if one feed fails, the ring bus remains unaffected, and that only one feed is needed to power everything currently in operation. He also noted that Sabey has the ability to add two more feeds in the future if expansion calls for it. That matters in a city where available utility capacity is hard to come by and where many data center conversations end not with square footage but with a megawatt number. Bajrushi also noted that physical space is not the core constraint at 375 Pearl. He said the building still has plenty of room for future buildouts, including open areas that could become additional white space, chiller capacity, or other infrastructure. The bigger question, he suggested, is how and when power and supporting systems get installed. That observation aligns neatly with Sabey’s press release. The company is effectively arguing that SDC

Mills has pushed for an exemption protecting a proposed $550 million project at the former Androscoggin paper mill in Jay, arguing it would reuse existing infrastructure without straining the grid. Lawmakers rejected that exemption. Mills’ office did not immediately respond to a request for comment. A national wave, an unanswered federal question Maine is one of at least 12 states now weighing moratorium or restraint legislation, alongside more than 300 data center bills filed across 30-plus states in the current session, according to legislative tracking firm MultiState. The shared concern is energy cost. Data centers could consume up to 12% of total US electricity by 2028, according to the US Department of Energy. On March 25, Senator Bernie Sanders and Alexandria Ocasio-Cortez introduced the AI Data Center Moratorium Act in Congress, which would impose a nationwide freeze on all new data center construction until Congress passes AI safety legislation. The Trump administration has pursued a different path from the legislative approach being taken in states. On March 4, Amazon, Google, Meta, Microsoft, OpenAI, Oracle, and xAI signed the White House’s Ratepayer Protection Pledge, a voluntary commitment by hyperscalers to fund their own power generation rather than pass grid costs to ratepayers. The pledge, published in the Federal Register on March 9, carries no penalties for noncompliance or auditing requirements.

In a world of autonomous agents, identity and access become the de facto safety rails. Astrix is designed to inventory these non-human identities, map their permissions, detect toxic combinations, and remediate overprivileged access before it becomes an exploit or a data leak. That capability integrates directly with Cisco’s broader zero-trust and identity-centric security strategy, in which the network enforces policy based on who or what the entity is, not on which subnet it resides in. How this strengthens Cisco’s secure networking story Cisco has positioned itself as the vendor that can deliver “AI-ready, secure networks” spanning campus, data center, cloud, and edge. Galileo and Astrix extend that narrative from infrastructure into AI behavior and identity governance: The network becomes the high‑performance, policy‑enforcing substrate for AI traffic and data. Splunk plus Galileo becomes the observability plane for AI agents, linking AI incidents to network and application signals. Security plus Astrix becomes the identity and permission-control layer that constrains what AI agents can actually do within the environment. This is the core of Cisco’s emerging “Secure AI” posture: not just using AI to improve security but securing AI itself as it is embedded across every workflow, API, and device. For customers, that means AI initiatives can be brought under the same operational and compliance disciplines already used for networks and apps, rather than existing as unmanaged risk islands. Why this matters to Cisco customers Most large Cisco accounts are exactly the enterprises now experimenting with AI agents in contact centers, IT operations, and business workflows. They face three practical problems: They cannot see what agents are doing end‑to‑end, or measure quality beyond offline benchmarks. They lack a coherent model for managing the identities, secrets, and permissions those agents depend on. Their security and networking teams are often disconnected from AI projects happening in lines of business.

Not Falling Short—Just Not Optimized Altizer drew a clear distinction. Traditional data centers can run AI workloads, but they weren’t built for them. “We’re not falling short much, we’re just not optimizing.” The gap shows up most clearly in density. Legacy facilities were designed for roughly 300 to 400 watts per square foot. AI pushes that to 2,000 to 4,000 watts per square foot—changing not just rack design, but the logic of the entire facility. For Altizer, AI-ready infrastructure starts with fundamentals: access to water for heat rejection, significantly higher power density, and in some cases specific redundancy topologies favored by chip makers. It also requires liquid cooling loops extended to the rack and, critically, flexibility in the white space. That last point is the hardest to reconcile with traditional design. “The GPUs change… your power requirements change… your liquid cooling requirements change. The data center needs to change with it.” Buildings are static. AI is not. Rethinking Modular: From Containers to Systems “Modular” has been part of the data center vocabulary for years, but Altizer argues most of the industry is still thinking about it the wrong way. The old model centered on ISO containers. The emerging model focuses on modularizing the white space itself. “We’re not building buildings—we’re building assemblies of equipment.” Compu Dynamics is pushing toward factory-built IT modules that can be delivered and assembled on-site. A standard 5 MW block consists of 10 modules, stacked into a two-story configuration and designed for transport by trailer across the U.S. From there, scale becomes repeatable. Blocks can be placed adjacent or connected to create larger deployments, moving from 5 MW to 10 MW and beyond. The point is not just scalability; it’s repeatability and speed. Altizer ties this directly to a broader shift in how data centers are

The future is even less clear the further you go out. The vast majority of data centers planned for launch between 2028 and 2032 have yet to break ground and only a sliver are under construction. Those delays, it seems, appear to be twofold: first, the well-documented component shortage. Not just memory and storage, but batteries, electrical transformers, and circuit breakers. They all make up less than 10% of the cost to construct one data center, but as Andrew Likens, energy and infrastructure lead at AI data center provider Crusoe’s told Bloomberg, it’s impossible to build new data centers without them. “If one piece of your supply chain is delayed, then your whole project can’t deliver,” Likens said. “It is a pretty wild puzzle at the moment.” Second problem is the growing rebellion against data centers, both by citizens and governments alike. The latest pushback comes from the Seminole nation of Native Americans, who have banned data centers on their tribal lands. Of the data centers that are coming online in the next few months, the top states reflect what Synergy has been saying about data center migration to the interior of the country. Texas is leading the way, with 22.5 GW coming online, followed by New Mexico at 8.3 GW and Pennsylvania, which is making a major push for data centers to come to the state, at 7.1 GW.

The approval of the Joliet Technology Center signals that the Chicago region is being pulled into the Midwest’s next phase of AI infrastructure development, one that has so far been led by Ohio and defined by scale, power demand, and rising public scrutiny. It also underscores a growing reality: local governments are beginning to understand exactly what that shift entails. On March 19, 2026, the Joliet City Council voted 8–1 to approve the conditional annexation of roughly 795 acres for the proposed Joliet Technology Center, a $20 billion data center campus backed by Hillwood and PowerHouse Data Centers. The site, near Rowell and Bernhard Roads on Joliet’s east side, is planned as a 24-building, multi-phase development that would rank among the most consequential digital infrastructure projects ever approved in Illinois. Joliet is now a clear case study in how the Midwest’s data center market is evolving: massive land assemblies, utility-scale power requirements, front-loaded community concessions, increasingly organized local opposition, and regulators working to ensure that the costs of AI infrastructure are not shifted onto ratepayers. A Project Too Large to Call Routine The Joliet Technology Center is a campus-scale industrial platform built for the AI era. Plans call for 24 two-story buildings of roughly 144,500 square feet each, with total development estimated at approximately 6.9 million square feet and up to 1.8 GW of eventual capacity. That places the project firmly in the emerging “AI factory” category, e.g. far-removed from the incremental, metro-edge data center expansions that defined earlier growth cycles. The distinction is critical. AI-scale campuses operate on a different economic and technical model. Fiber access and metro proximity are no longer enough. These developments require large, contiguous power blocks, land to support phased substation and utility infrastructure, and a political framework capable of absorbing what is effectively heavy