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AI Infrastructure Scales Out and Up: Edge Expansion Meets the Gigawatt Campus Era

The AI infrastructure boom is often framed around massive hyperscale campuses racing to secure gigawatts of power. But an equally important shift is happening in parallel: AI infrastructure is also becoming more distributed, modular, and sovereign, extending compute far beyond traditional data center hubs. A wave of recent announcements across developers, infrastructure investors, and regional […]

The AI infrastructure boom is often framed around massive hyperscale campuses racing to secure gigawatts of power. But an equally important shift is happening in parallel: AI infrastructure is also becoming more distributed, modular, and sovereign, extending compute far beyond traditional data center hubs.

A wave of recent announcements across developers, infrastructure investors, and regional operators shows the market pursuing a dual strategy. On one end, developers are accelerating delivery of hyperscale campuses measured in hundreds of megawatts, and increasingly gigawatts, often located where power availability and energy economics offer structural advantage, and in some cases pairing compute directly with dedicated generation. On the other, providers are building increasingly capable regional and edge facilities designed to bring AI compute closer to users, industrial operations, and national jurisdictions.

Taken together, these moves point toward a future in which AI infrastructure is no longer purely centralized, but built around interconnected hub-and-spoke architectures combining energy-advantaged hyperscale cores with rapidly deployable edge capacity.

Recent developments across hyperscale developers, edge specialists, infrastructure investors, and regional operators illustrate how quickly this model is taking shape.

Sovereign AI Moves Beyond the Core

On Feb. 5, 2026, San Francisco-based Armada and European AI infrastructure builder Nscale signed a letter of intent to jointly deploy both large-scale and edge AI infrastructure worldwide.

The collaboration targets enterprise and public sector customers seeking sovereign, secure, geographically distributed AI environments. Nscale is building large AI supercomputer clusters globally, offering vertically integrated capabilities spanning power, data centers, compute, and software. Armada specializes in modular deployments through its Galleon data centers and Armada Edge Platform, delivering compute and storage into remote or infrastructure-poor environments.

The combined offering addresses a growing challenge: many governments and enterprises want AI capability deployed within their own jurisdictions, even where traditional hyperscale infrastructure does not yet exist.

“There is increasing demand from enterprises and governments for operational AI, and meeting that need requires infrastructure that is scalable, distributed, and ultimately sovereign,” said Josh Payne, Founder and CEO of Nscale. “By working with Armada, we will be able to offer customers a flexible foundation for deploying advanced AI workloads wherever they need to operate, without compromising performance, security, or control.”

The model envisions hyperscale campuses providing economic efficiency while Armada’s modular deployments, such as its megawatt-scale Leviathan Galleon units, extend sovereign compute into edge geographies.

“As AI adoption accelerates, organizations need infrastructure that can reach beyond centralized clusters, on Earth and even beyond,” said Dan Wright, Co-Founder and CEO of Armada. “One of Armada’s key differentiators is that we enable sovereign AI, with speed and scale. Partnering with Nscale allows us to extend our modular AI infrastructure into new global markets.”

Speed matters. Modular deployments can arrive far faster than greenfield builds, enabling sovereign AI capacity where full-scale campuses would take years.

Regional Markets Become Strategic Edge Nodes

Distributed AI infrastructure is also reshaping secondary and regional U.S. markets.

On Jan. 30, 2026, US Signal announced acquisition of a data center in Aurora, Illinois, reinforcing its strategy to build a distributed, edge-focused national platform.

The facility strengthens US Signal’s Chicago-area footprint while enabling immediate investment to expand capacity to roughly 4 MW critical IT load (or 6 MW commercial capacity). The company will also deploy its OpenCloud private cloud and virtualization platform locally.

“This facility aligns perfectly with our long-term growth strategy,” said Daniel Watts, CEO of US Signal. “We’re investing immediately to expand capacity and will deliver OpenCloud at scale, giving customers in this market a local, enterprise-grade private cloud option backed by our national network and proven operational model.”

The Aurora facility supports:

• Private and hybrid cloud deployments
• AI inference and latency-sensitive workloads
• Enterprise and carrier colocation

The site connects directly into US Signal-owned fiber routes and benefits from continued investment in ultra-dense fiber expansion and in-line amplification sites, allowing low-latency connectivity from edge to core.

Infrastructure investor Igneo Infrastructure Partners has committed more than $200 million to accelerate US Signal’s expansion across data centers, fiber, and cloud infrastructure.

“This acquisition and the immediate investment that follows reinforces our confidence in the platform and its ability to deliver long-term value,” said Michael Ryder, Partner and Co-Head of North America at Igneo Infrastructure Partners.

One takeaway here is that regional markets are increasingly becoming operational AI nodes rather than secondary infrastructure markets.

Hyperscale Campuses Still Form the Backbone

While distributed deployments expand outward, centralized campuses continue to form the backbone of AI compute, particularly for model training and large-scale inference workloads that demand massive contiguous power delivery.

On Jan. 29, 2026, Manulife Investment Management-backed developer Serverfarm announced it secured a $3.0 billion credit facility, closed in December 2025, to accelerate hyperscale campus development across strategic North American markets.

Supported by a syndicate of 23 institutional lenders, the facility provides development and construction capital enabling Serverfarm to rapidly deliver AI-ready infrastructure for cloud providers and AI innovators requiring deployments at unprecedented scale and density.

The financing supports multiple hyperscale projects already advancing, including the company’s:

• Houston hyperscale campus offering more than 500 MW of development potential across 250 acres in Houston’s energy corridor, supported by dual on-site substations designed to support phased expansion at scale.
• Atlanta expansion in Covington, Georgia, delivering a 498,960-square-foot facility with 60 MW of critical IT capacity for a single hyperscale tenant deployment.
• Toronto expansion adding 4 MW of capacity to support continued hyperscale demand growth in the Canadian market.

Serverfarm executives emphasized that speed of delivery is now as critical as scale itself, particularly as hyperscalers race to bring GPU-intensive AI workloads online.

“This $3.0 billion facility provides the capital foundation to accelerate our hyperscale campus development pipeline at a time when speed to market is a competitive differentiator,” said CEO Avner Papouchado. “Our proven basis-of-design enables accelerated delivery timelines, allowing cloud providers and AI innovators to deploy GPU-intensive workloads when timing matters most.”

Manulife Investment Management, which backs the Serverfarm platform, underscored continued institutional appetite to deploy capital into data center infrastructure as AI demand reshapes long-term infrastructure investment strategies.

“We see attractive opportunities to deploy capital to develop data center infrastructure globally and are excited to continue to support the Serverfarm platform in executing its growth plans,” said Recep Kendircioglu, Global Head of Infrastructure at Manulife Investment Management.

Serverfarm, which operates across key global markets including Houston, Northern Virginia, Chicago, Atlanta, Los Angeles, London, Amsterdam, Tel Aviv, Moses Lake, and Toronto, has increasingly positioned its campuses around AI-ready designs, including deployment of closed-loop water-cooling systems across locations to support high-density AI workloads while minimizing water waste.

The result is a portfolio built to support both hyperscale cloud expansion and the rapid emergence of AI training and inference clusters requiring sustained high-density operation.

Texas—and Now Appalachia—Show How Energy Is Becoming the Primary AI Constraint for Infrastructure Deployment

If distributed infrastructure is scaling outward, Texas shows how far hyperscale development itself is scaling upward.

On Jan. 16, 2026, New Era Energy & Digital announced a partnership with Primary Digital Infrastructure to co-develop Texas Critical Data Centers (TCDC), an approximately one-gigawatt-plus hyperscale campus in Ector County near Odessa, Texas.

The campus is engineered specifically for next-generation hyperscale and AI compute demand, combining grid-supplied electricity with behind-the-meter generation solutions and offering substantial expansion potential in the Permian Basin energy corridor, one of North America’s most energy-rich regions.

E. Will Gray II, CEO of New Era Energy & Digital, described the partnership as a validation of the company’s infrastructure-first strategy.

“The formation of our partnership with Primary Digital is a watershed moment… bringing the critical expertise required to execute a development of this scale,” Gray said. “We remain on track to sign a hyperscale anchor tenant… and we believe this development will deliver significant and durable value.”

As lead capital partner and co-sponsor, Primary Digital Infrastructure brings both hyperscale tenant relationships and capital markets expertise needed to structure and finance a development of this magnitude while de-risking execution.

Leveraging deep relationships with global cloud and AI companies, the firm is positioned to help secure an anchor hyperscale tenant for the project’s initial phase while simultaneously arranging the multi-billion-dollar financing required for delivery.

“The next wave of hyperscale and AI infrastructure is being built where power is abundant, flexible, and economically advantaged,” said Bill Stein, Executive Managing Director and Chief Investment Officer at Primary Digital Infrastructure. “With an experienced, well-capitalized partner like New Era, together we will deliver a strategically located, hyperscale-ready campus designed to meet the demands of investment-grade tenants seeking reliable solutions for advanced computing needs.”

Primary Digital’s Texas momentum also includes participation in Stargate-related development, including a previously announced $15 billion joint venture with Crusoe Energy Systems and Blue Owl Capital to develop a 1.2-gigawatt AI campus in Abilene supported by more than $11.6 billion in debt and equity financing.

Momentum behind TCDC accelerated further as New Era announced a binding agreement to acquire Sharon AI’s 50% ownership interest in the project for $70 million, consolidating full ownership under New Era as development shifts from planning toward execution.

The transaction structure, reported as combining cash, deferred equity, and a senior secured promissory note, was designed to minimize shareholder dilution while enabling faster project execution.

In parallel, New Era closed on the acquisition of an additional 203 contiguous acres, expanding the campus footprint to 438 acres and strengthening its ability to support a multi-phase, gigawatt-scale AI and HPC development.

“Full ownership allows us to align capital with development, accelerating the project’s execution… As the campus now moves from planning into execution, we believe a simplified ownership structure is the right next step,” Gray said.

Together, these moves reinforce Texas’ position as one of the primary proving grounds for energy-anchored hyperscale AI infrastructure.

Beyond Texas: Energy-Centric AI Campuses Scale Further

Texas is not alone in redefining how compute and energy infrastructure are being co-developed.

In late January, Fidelis New Energy and 8090 Industries launched American Intelligence & Power Corporation (AIP Corp), a platform purpose-built to develop and operate fully integrated AI compute campuses paired with dedicated power microgrids.

The platform is anchored by the Monarch Compute Campus in Mason County, West Virginia, structured around as much as 8 gigawatts of planned generation capacity, combining natural gas generation and battery storage to supply AI workloads through a fully behind-the-meter microgrid.

Authorized under West Virginia’s HB2014 framework as an islanded microgrid utility, the project is designed to deliver high-reliability power for AI workloads without increasing costs for existing utility customers while enabling accelerated infrastructure deployment.

“AIP Corp was deliberately designed as an infrastructure-first platform focused on execution, reliability, and scale,” said Daniel Shapiro, CEO of AIP Corp.

Rayyan Islam, Co-Founder of 8090 Industries, framed the shift succinctly: “AI leadership is not a software problem. It is an energy and infrastructure problem.”

Backed by LuminArx Capital Management and other investors, AIP aims to replicate this integrated power-and-compute model nationally.

If Texas demonstrates how hyperscale AI development is accelerating today, projects like Monarch suggest how deeply power and compute may be integrated in the next phase of infrastructure expansion.

The New AI Infrastructure Model: Hub and Spoke

Viewed together, these developments reveal how AI infrastructure deployment is evolving beyond pure hyperscale expansion.

Hyperscale campuses provide training-scale compute and economic efficiency. Regional and edge deployments deliver latency, compliance, and sovereign control. Fiber and cloud platforms stitch them together.

This is not hyperscale versus edge. It is the emergence of a layered infrastructure stack in which centralized power and distributed intelligence increasingly operate together.

Where the Industry Is Heading Next

The AI infrastructure race is no longer simply about who can build the biggest campus or secure the largest power contracts. Increasingly, competitive advantage belongs to operators who can align power, capital, and deployment timelines; placing capacity where energy, regulation, and customer demand intersect.

The pattern is becoming clear across markets. Modular edge deployments extend AI capabilities closer to users and industrial operations, while energy-advantaged hyperscale campuses continue to scale the core infrastructure required to train and operate increasingly complex models.

In that sense, AI infrastructure is beginning to look less like a technology land grab and more like traditional infrastructure development again: capital-intensive, power-constrained, and execution-driven.

And as 2026 unfolds, the companies best positioned to succeed may simply be those able to deliver capacity where it is actually possible to build.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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