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

Data centers, particularly those optimized for artificial intelligence workloads, are frequently characterized in public discourse as a disruptive threat to grid stability and ratepayer affordability. But behind-the-narrative as we are, the AI‑driven data center growth is simply illuminating pre‑existing systemic weaknesses in electric infrastructure that have accumulated over more than a decade of underinvestment in transmission, substations, and interconnection capacity. Over the same period, many utilities operated under planning assumptions shaped by slow demand growth and regulatory frameworks that incentivized incremental upgrades rather than large, anticipatory capital programs. As a result, the emergence of gigawatt‑scale computing campuses appears to be a sudden shock to a system that, in reality, was already misaligned with long‑term decarbonization, electrification, and digitalization objectives. Utilities have been asked to do more with aging grids, slow permitting, and chronically constrained capital, and now AI and cloud are finally putting real urgency — and real investment — behind modernizing that backbone. In that sense, large‑scale compute is not the problem; it is the catalyst that makes it impossible to ignore the problem any longer. We are at a moment when data centers, and especially AI data centers, are being blamed for exposing weaknesses that were already there, when in reality they are giving society a chance to fix a power system that has been underbuilt for more than a decade. Utilities have been asked to do more with aging grids, slow permitting, and limited investment, and now AI and cloud are finally putting real urgency — and real capital — behind modernizing that backbone. In that sense, data centers aren’t the problem; they are the catalyst that makes it impossible to ignore the problem any longer. AI Demand Provided a Long‑Overdue Stress Test The nature of AI workloads intensified this dynamic. High‑performance computing clusters concentrate substantial power

The AI data center market is no longer defined by speed alone. For much of the past three years, capital moved aggressively into digital infrastructure, chasing land, power, and platform scale as generative AI workloads began to reshape demand curves. But as Melissa Kalka, M&A and private equity partner, and Kimberly McGrath, real estate partner at Kirkland & Ellis, explain on the latest episode of the Data Center Frontier Show, the industry is now entering a more complex and more consequential phase. The land grab is over. Execution has begun. Capital remains abundant, but it is no longer forgiving. From Capital Rush to Capital Discipline As noted by Kalka and McGrath, the period from roughly 2022 through 2025 marked a rapid acceleration in AI infrastructure investment. Take-private deals involving CyrusOne, QTS, and Switch signaled a structural shift, while hyperscale demand scaled from tens of megawatts to hundreds, and now toward gigawatt-class campuses. But the current phase is not defined by a pullback in capital. Instead, it reflects an expansion of investment pathways and a corresponding increase in scrutiny. “There’s actually more deal flow now,” Kalka notes, pointing to the growing range of entry points across the capital stack, including development vehicles, yield-oriented structures, and private credit. With more capital chasing larger and more complex opportunities, investors are evaluating not just platforms, but the full lifecycle of assets from early-stage development through stabilization and long-term hold. That shift has pulled capital earlier into the process, where risk is higher and less defined. Power availability, permitting, and execution timelines are now central to underwriting decisions. What Defines a “Bankable” Platform In this environment, the definition of a bankable data center platform has tightened. Execution history remains foundational. Investors are looking for consistent delivery, operational reliability, and clean contractual performance. But those factors alone