Originally posted on Datalec LTD.

Data centre leaders left ExCeL London earlier this month with one message ringing loud and clear: AI‑driven growth is accelerating, power is tight, and the choice of infrastructure partner is now business‑critical, not optional.

Against a backdrop of rapid hyperscale and colocation expansion, constrained power availability and rising energy scrutiny, the conversations at Data Centre World London 2026 underscored that operators need partners who can help them plan power‑first, deploy at speed, and operate reliably in high‑density environments.

For Datalec Precision Installations (DPI), DCW London was an opportunity to demonstrate exactly that kind of integrated, global capability, from modular data centre solutions through to facilities management, consultancy and lifecycle services. The questions operators brought to the stand were remarkably consistent, whether they were building in the UK, expanding in the Middle East, or planning their next phase of growth in APAC.

Below, we revisit three of the most important questions AI‑driven operators were asking in London and why they will matter even more as the industry converges on Singapore for DCW Asia later this year.

1. How quickly can you take me from secured power to live, AI‑ready capacity?

If there was one common theme at DCW London, it was that power availability has become the primary constraint on new data centre builds, not demand. Once operators have secured land and grid, the urgent requirement is simple: how fast can we safely turn that capacity into revenue‑generating, AI‑ready infrastructure?

This is where modular, pre‑engineered solutions dominated the conversation. Many visitors to the DPI stand wanted to understand how modular white space, plant and service corridors could compress design and construction timelines without sacrificing resilience or compliance. DPI’s next‑generation Modular Data Centre Solutions attracted strong interest because they are designed precisely for this challenge. They help clients move from planning to live halls at speed, whether that’s a new campus in a European hub, a hyperscale expansion in the Middle East, or an edge or colocation site in a fast‑growing APAC market.

To continue reading, please click here.

The post Three Questions Every AI‑Driven Operator Should Ask Their Infrastructure Partner appeared first on Data Center POST.

Originally posted on Nomad Futurist.

At DCD>Connect | New York, the Nomad Futurist Foundation didn’t just participate in the conversation about building the future workforce — we demonstrated what it looks like to actively create it.

Through two milestone moments, we brought together today’s leaders and tomorrow’s innovators, proving that meaningful change in the digital infrastructure industry happens when ideas are backed by action.

Mana Hui: Aligning Leaders Around a Shared Mission 

After Day 1 of the conference, we gathered some of the industry’s most forward-thinking voices at the rooftop of The Knickerbocker Hotel for our Mana Hui: Leaders Connect Networking Event.

More than a networking reception, Mana Hui created a dedicated space for leaders to come together around a shared purpose: how we can collectively inspire, educate, and open doors for the next generation of digital infrastructure talent.

Conversations focused on tangible solutions, from increasing visibility into career pathways, to strengthening mentorship opportunities, to ensuring students and early-career professionals understand the real-world impact of this industry. The room was filled with decision-makers, innovators, and advocates aligned around one idea: preparing the future workforce is not a side initiative; it is a responsibility.

Mana Hui set the tone by reinforcing the power of collaboration. When leaders unite with intention, momentum builds, and that momentum must translate into action.

Powering the Next Generation: From Conversation to Impact 

On Day 2, that momentum became measurable impact through our Powering the Next Generation Student Workshop.

Students and emerging professionals joined us for an experience designed not just to inform, but to connect. Industry leaders shared authentic stories about their career journeys, including challenges, pivots, and lessons learned, providing students with transparent insight into opportunities across the digital infrastructure landscape.

Rather than a traditional panel format, the workshop fostered dynamic dialogue. Students actively engaged, asked thoughtful questions, and contributed their own perspectives, creating an environment rooted in collaboration and curiosity.

A defining highlight came when a group of students from New York University presented a live demonstration of one of their own projects, offering a powerful reminder that the next generation is not waiting for opportunity. They are already building the future.

We were proud to welcome students representing an exceptional range of institutions, including Harvard Law School, Columbia University, Cornell University, Dartmouth College, University of Notre Dame, Stevens Institute of Technology, and more. Many of these students are preparing to enter the workforce within months and are eager to contribute meaningfully to the industry.

Following the workshop, members of the Nomad leadership team continued the experience with a visit to the iconic 60 Hudson Street building for a tour of the NYI and Hudson Interxchange facilities, led by Ambassador Arthur Valhuerdi. For even some of our own members, it was their first time inside a live data center environment, making it a meaningful extension of the day’s learning and a powerful reminder of the infrastructure behind the digital world.

To continue reading, please click here.

The post Turning Conversation into Action: Nomad Futurist Foundation at DCD>Connect | New York appeared first on Data Center POST.

In a strategic move underscoring the shift toward modular infrastructure, Compu Dynamics Modular (CDM), a Chantilly, Virginia-based specialist in prefabricated data center solutions, has acquired a majority stake in R&D Specialties, an Odessa, Texas, manufacturer of UL-certified control panels and modular electrical systems. Announced today, the deal expands CDM’s manufacturing footprint to 120,000 square feet, with room for growth on a 15-acre campus, positioning the company to meet skyrocketing demand for AI-ready, high-density deployments from hyperscalers, colocation providers, and enterprises.

This acquisition arrives at a pivotal moment. AI and high-performance computing (HPC) workloads demand unprecedented speed, density, and scalability – challenges traditional builds struggle to match. Modular solutions, once niche, are now the default for rapid, repeatable deployments.

“Modular infrastructure is where efficiency meets innovation,” said Ron Mann, vice president of CDM. “For decades, we’ve delivered solutions that solve real engineering challenges in high-stakes environments. Joining forces with R&D Specialties allows us to bring that expertise to the next generation of AI data centers at scale.”

Steve Altizer, president and CEO of Compu Dynamics, emphasized the market imperative: “This investment is about building the capabilities and capacity the market is demanding right now. AI infrastructure requires a different approach; one that delivers faster, scales smarter, and performs better. R&D Specialties brings the engineering depth and manufacturing precision that align perfectly with where this industry is headed.”

R&D Specialties, founded in 1983, excels in custom-engineered systems for mission-critical settings, complementing CDM’s vendor-neutral, end-to-end services – from design and liquid-cooled IT platforms to commissioning and maintenance. Brad Howell, president of R&D Specialties, noted the synergy: “Through joining forces with CDM, our growth opportunities for the combined teams have expanded even further. Being part of the AI infrastructure revolution and building what’s next is exciting.”

For data center operators, this signals broader ecosystem maturation. CDM’s turnkey modules accelerate time to market while integrating high-density power, low-latency networking, and sustainability features. With an extensive North American partner network, the combined entity can deploy campus-scale solutions anywhere, anytime – critical as AI power needs strain grids and supply chains.

This deal exemplifies how strategic M&A is fueling modular dominance, helping the industry navigate AI’s compute explosion with agility and reliability. Learn more at cd-modular.com.

The post CDM Acquires Majority Stake in R&D Specialties to Power AI Modular Data Center Surge appeared first on Data Center POST.

Artificial intelligence is a tool designed to power innovation, but it’s important to understand its primary fuel: data. Data is required not only for the outputs of AI algorithms but also for their training and operation. Because of this, in sectors where innovation has become driven by technologies like artificial intelligence, data has essentially become fuel for innovation, and it’s important to ensure the safety and quality of this data to stimulate it.

Understandably, many critics have expressed concern over the use of artificial intelligence in healthcare settings, considering the private, sensitive nature of the data used in the field. Patient personal information is not only highly sensitive but also protected by law, meaning there are strict regulations and guidelines dictating how entities in healthcare can use artificial intelligence with regard to patient data.

Why strong data governance is essential for AI in healthcare

However, that doesn’t mean artificial intelligence shouldn’t be used in healthcare whatsoever. Instead, it means there is a need for strong data governance, as this is an essential step in enabling safe and ethical AI use in any industry, particularly ones such as healthcare where the stakes are high. In addition to ensuring compliance with any applicable regulations, strong data governance helps create greater transparency and trust that inspires patient confidence.

It’s important to remember the reason why the healthcare sector wants to deploy artificial intelligence technology in the first place: AI can accelerate innovation and lead to improved patient outcomes. For example, innovators in the healthcare industry have used AI to accelerate drug discovery, conduct more accurate diagnostics, and streamline operations in a way that significantly improves efficiency. But to achieve these outcomes, systems must have access to accurate, well-managed data.

The key to this is creating compliance frameworks that reduce and mitigate the risks of artificial intelligence while still supporting scalable healthcare solutions. Of course, the core of any compliance framework in healthcare is data security and privacy, but these guidelines can also help control other risks, such as algorithmic bias and “black box” risks, ensuring that all decisions and recommendations made by an artificial intelligence are fair and explainable.

Enabling the responsible deployment of AI in healthcare

Ultimately, data governance isn’t about gatekeeping but about collaboration and enabling the responsible and ethical deployment of artificial intelligence. The mindset with which we approach AI shouldn’t be about limiting how we can use the technology, but instead how we can facilitate its use in a way that does not compromise data integrity or patient privacy.

Right now, the key goal of healthcare practitioners who hope to implement artificial intelligence should be to build trust and reliability in these systems. The steps required to achieve this include ensuring data quality and diversity, maintaining transparent communication, and continuous monitoring and validation.

The best way to look at AI systems in healthcare is as an analog to human employees. In healthcare, not even human employees have unfettered access to patient data. There are access controls based on the level of access an individual needs, with checks and balances and supervisory control.

The same philosophy should apply to autonomous systems. Just as approvals and access controls are required of human employees, so too should AI systems require approvals from human overseers.

Indeed, there is a world in which artificial intelligence can revolutionize the healthcare industry for the better, alleviating some of the burden on healthcare workers and contributing to improved patient outcomes. However, for this to happen, the adoption of AI must be done in a way that is responsible and ethical. With this mindset, prioritizing strong data governance, AI can become a reliable partner in patient care.

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About the Author

Chris Hutchins serves as the founder and CEO of Hutchins Data Strategy Consulting. The healthcare institutions benefit from his expertise in developing scalable moral data and artificial intelligence methods to maximize their data potential. His areas of expertise include enterprise data governance, responsible AI adoption, and self-service analytics. His expertise helps organizations achieve substantial results through technology implementation. Through team empowerment, Chris assists healthcare leaders in enhancing care delivery while reducing administrative work and transforming data into meaningful outcomes.

The post Data Governance and Clinical Innovation appeared first on Data Center POST.

ZincFive^®, a leader in nickel-zinc (NiZn) battery-based solutions for immediate power applications, has once again been recognized by TIME, earning a place on the America’s Top GreenTech Companies 2026 list for the third consecutive year. Developed in partnership with Statista, the ranking evaluates companies based on environmental impact, financial strength, and innovation, placing ZincFive among a select group shaping the future of sustainable technology.

This year, the company ranked #142 out of more than 3,500 evaluated organizations and is one of only two companies headquartered in Oregon to be included on the list.

The recognition reflects continued momentum for ZincFive’s nickel zinc battery technology, which has gained traction as an alternative to traditional energy storage options in mission critical environments. As data centers evolve to support artificial intelligence and increasingly dynamic workloads, the need for power solutions that can deliver both performance and safety has become more pronounced.

ZincFive’s approach centers on immediate power, delivering high power density in a compact footprint while avoiding the risks associated with other battery chemistries. Nickel zinc batteries are designed to provide reliable performance without thermal runaway concerns and rely on more abundant, recyclable materials, supporting both operational and environmental goals.

For ZincFive, continued recognition from TIME signals more than a milestone. It reflects a broader shift in how the industry is evaluating power infrastructure, with greater emphasis on safety, sustainability, and long term performance.

“Earning a place on TIME’s America’s Top GreenTech Companies list for the third consecutive year reflects the growing role of nickel-zinc technology in delivering safe, sustainable power,” said Tod Higinbotham, CEO of ZincFive. He emphasized the company’s “power of good chemistry” approach to balance performance, safety, and eco-friendliness.​

The company’s inclusion builds on a series of recent awards recognizing its innovation in energy storage, particularly in applications where reliability is critical. As demand for resilient and efficient power continues to grow, ZincFive’s technology is increasingly positioned to support the next generation of digital infrastructure.

For full details, read the press release here.

The post ZincFive Earns TIME GreenTech Recognition for Third Straight Year appeared first on Data Center POST.

Walk through the sales deck of almost any data center operator and you’ll find the same language: Tier III certified, N+1 redundancy, 99.999% uptime. The terminology is standardized because the underlying assumption is standardized that infrastructure is, at its core, a commodity.

That assumption is worth examining more carefully. Because what looks identical on paper can behave very differently under pressure. And the gap between a standard data center and a strategic one isn’t visible in a specification sheet. It shows up in an outage.

Engineering for Reality, Not for Ideal Conditions

MedOne, Israel’s largest data center operator with more than 25 years of experience building and managing critical infrastructure,serves some of the country’s most demanding clients , banks, healthcare providers, government agencies, defense-adjacent technology firms and large-scale enterprise platforms. These are mission-critical environments where downtime carries legal, financial and operational consequences that go well beyond a service credit. When a payment system goes dark or a hospital’s records platform becomes unavailable, the impact is measured in far more than lost revenue.

Building for that client base forced a different set of engineering questions from the start. Not “how do we achieve uptime under normal conditions?” but “how do we maintain continuity when normal conditions no longer exist?” That shift in the design brief changes almost every decision that follows.

MedOne’s facilities are built underground , not as a differentiating feature, but as a structural response to the requirement for physical isolation. Underground construction reduces exposure to environmental variables, provides stable ambient temperatures for cooling efficiency and removes a layer of external dependency that surface-level facilities carry by default. For mission-critical clients operating under strict regulatory and continuity requirements, physical hardening is not optional; it’s a baseline expectation.

Redundancy vs. Independence: A Difference That Matters

Most data centers are built around redundancy. Redundant power feeds, redundant cooling circuits, redundant network paths. Redundancy is valuable  but it operates on a specific assumption: that external systems are available, and that a backup path exists when the primary one fails.

Independence operates on a different assumption entirely: that external systems may not be available at all, and that the facility must be capable of sustaining itself regardless.

MedOne’s facilities are designed to operate independently for up to 72 hours without relying on external power or water infrastructure. This means on-site fuel reserves, independent power generation and self-sufficient cooling systems, the entire physical stack, sustained without input from the national grid or municipal utilities.

“Redundancy still assumes external systems are available somewhere in the chain,” says Eli Matara, chief commercial officer at MedOne. “Independence means we can continue operating even when they aren’t. For mission-critical clients -that’s not a philosophical difference. It’s the difference between staying operational and explaining an outage.”

The engineering logic becomes clearer when you think in layers. Modern infrastructure is a dependency chain: power feeds cooling, cooling enables compute, compute supports network, network delivers applications. Each layer inherits the risk of the layer beneath it. Redundant components within a single layer don’t eliminate risk if those components share an upstream dependency, a common substation, a shared conduit, or a single utility provider. Standard infrastructure is designed to recover when a layer fails. Strategic infrastructure is designed so that failure of an external input doesn’t cascade through the layers above it in the first place.

Connectivity Is Infrastructure, Not a Feature

For mission-critical clients, a facility that is running but unreachable is still down. That’s why MedOne treats connectivity not as a managed service sitting above the infrastructure layer, but as a core part of the architecture itself.

MedOne operates as one of Israel’s primary carrier-neutral interconnection hubs. Carrier neutrality means that multiple competing telecommunications providers, global carriers, regional operators and local fiber networks  all terminate directly inside MedOne’s facilities. Clients are not locked into a single provider and can choose, combine or change carriers without physical migration or dependency on a single network operator. In a region where geopolitical conditions can affect routing availability, that freedom is not a commercial convenience, it’s a risk management tool.

The connectivity architecture extends to direct cloud on-ramps, submarine cable landing stations and Israel’s core fiber backbone  all designed to avoid the hidden convergence points where redundant-looking network paths physically meet and paper diversity collapses into a single point of failure.

“A data center that’s operational but unreachable is still down from a customer’s perspective,” Matara says. “Path diversity and true interconnection aren’t add-ons. They’re part of the same design logic as power and cooling independence.”

Starting With Infrastructure, Not With Cloud

The prevailing assumption in enterprise infrastructure planning has been that cloud resilience is sufficient  that hyperscaler uptime guarantees translate into genuine continuity. MedOne’s model challenges that directly. With more than 15 years of experience supporting high-performance computing environments, the company brings a depth of technical understanding that extends well beyond standard enterprise workloads  and that shapes how it thinks about the relationship between physical infrastructure and the services built on top of it.

Cloud services are only as resilient as the physical infrastructure they run on. Starting with hardened, sovereign, physically isolated infrastructure — and building cloud and managed services on top of it  produces a fundamentally more resilient architecture than layering cloud on top of a standard facility and relying on the SLA to cover the gaps.

For mission-critical clients in regulated industries, this distinction carries additional weight. Data sovereignty, regulatory compliance and audit requirements often demand infrastructure that can be physically verified, locally governed and operationally isolated; a carrier-neutral, underground, autonomy-designed facility answers those requirements in a way that a hyperscaler availability zone cannot.

Meeting Sovereign and Regulatory Standards

For banks, insurers and payment providers in Israel, enforceable data sovereignty is now a hard regulatory expectation, not marketing language. MedOne’s underground, carrier-neutral facilities are designed to support Israeli privacy and data-security requirements, including strict controls over physical access, operations and data flows that enable financial institutions to demonstrate compliance and satisfy supervisory scrutiny.

The Real Test

Infrastructure decisions made under stable conditions tend to look similar. The divergence happens when conditions change.

Israel’s ongoing conflict with Iran brought missile alerts, physical security responses and disruptions to civilian utilities  creating operating conditions where continuity could not be assumed and where the difference between facilities designed for stability and those designed for disruption became impossible to ignore. MedOne’s facilities continued operating throughout. Not because the engineering was lucky, but because the architecture was designed from the ground up for exactly that scenario: external disruption as a baseline assumption, not an edge case.

That is the core argument for the strategic model. Resilience built into the architecture from the start performs differently than resilience added as a layer on top of a standard design. For organizations that cannot afford to find out which kind they have at the worst possible moment, the engineering choices made before a facility is ever switched on are the ones that matter most.

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About the Author

Eli Matara is Chief Commercial Officer at MedOne, Israel’s leading provider of underground, carrier-neutral data centers and a central connectivity hub linking Israel to global networks.

With more than 20 years in enterprise sales, Eli leads the company’s commercial strategy across colocation, cloud, and connectivity. He works closely with Israel’s largest enterprises, global S&P 500 companies, and mission-critical organizations, helping them secure long-term infrastructure partnerships built for resilience, scale, and AI-driven workloads.

The post Not All Data Centers Are Built the Same — Inside MedOne’s Infrastructure Strategy in Israel appeared first on Data Center POST.

In my last article, I wrote about the need for calm, evidence-based leadership in an increasingly polarized infrastructure environment. One of the realities that continues to surface in communities across the country is that what we often interpret as resistance to development is something more nuanced. In many cases, communities are not pushing back out of ideology, they are responding to complexity, uncertainty, and the absence of trusted frameworks to guide long-term decisions.

Across the United States, digital infrastructure projects, namely data center developments, are encountering growing community resistance.

Too often, this pushback is quickly labeled as anti-growth sentiment, environmental activism, or resistance to technology. But in many cases, that interpretation misses the deeper reality.

What is often labeled as opposition is actually overwhelm.

Communities are being asked to make decisions about infrastructure that will shape their economic future for decades; without the tools, context, or trusted guidance to evaluate those decisions confidently.

Digital infrastructure, particularly large-scale or hyperscale data centers and supporting connectivity systems represents a new class of development. These projects intersect simultaneously with power infrastructure, water resources, land use planning, tax policy, and even national competitiveness. That level of complexity is unprecedented for many local decision-makers.

As a former elected official in Westchester County, New York, and after serving two-terms I know for a fact that most elected officials did not run for office to evaluate hyperscale infrastructure proposals. They ran to address zoning disputes, improve roads, manage school budgets, and respond to everyday civic concerns. When faced with proposals involving megawatt-scale energy demand, unfamiliar technical terminology, global technology narratives, and uncertain long-term impacts, decision paralysis is a natural outcome.

In that environment, saying “no” can feel like the safest and most responsible choice. And for me, this is the crux of the matter. If elected officials don’t know what they are saying no to, it could have dire consequences on the future of their communities – and country.

Further fueling this sentiment are the political dynamics across our country. Local leaders operate within short election cycles and highly visible public scrutiny. Approving a controversial project can feel like a personal political gamble,  particularly when the information landscape is polarized and the benefits are difficult to quantify in near-term terms. And, let’s be honest, you have to live with your neighbors and their emotional reactions to things they too don’t understand.

Trust gaps also play a role. Communities observe large incentive packages (community benefit plans), opaque project branding (project names rather than company brands), and rapid land acquisitions that may span 100’s of acres or more. This can create perceptions of imbalance:  imbalance of information, imbalance of power, and imbalance of benefit. Even when development intentions are positive, the process can feel accelerated and asymmetric from the community’s perspective.

There is also a fear of irreversibility. Digital infrastructure is often perceived as permanent, transformative, and difficult to unwind once built. And fears from past industrial builds like aluminum smelters and energy production sites have not laid an easy path for large-scale developments in our country’s future. That perception alone can drive precautionary decisions, calls for moratoria, and emotional public hearings.

From the industry side, resistance is sometimes misread as anti-technology bias or organized opposition. But frequently the underlying issue is not ideology, it is cognitive and institutional readiness. Communities are not rejecting opportunity; they are struggling to evaluate it.

This is where structured engagement models become essential.

At my company, iMiller Public Relations, we approach these efforts through an effort I call The Groundswell™ approach. The Groundswell approach reframes community engagement from persuasion to empowerment. It begins with understanding local decision dynamics; who influences outcomes, what matters most to residents, and how technical issues translate into civic implications. It emphasizes early education before formal approvals, surfaces community benefit opportunities, and builds coalition narratives that reduce fear rather than inflame it.

Informed communities make more confident decisions. They are better positioned to align development with their long-term economic vision rather than reacting project by project.

When overwhelm occurs simultaneously across multiple regions, the implications extend beyond any single development. Infrastructure deployment becomes fragmented. Investor confidence can weaken. Regional competitiveness begins to diverge. National digital readiness ultimately suffers.

Community overwhelm, therefore, is not just a local planning challenge, it is a strategic issue.

Resistance is often the first signal that institutions need new tools, governance frameworks require modernization, and engagement models must evolve. Calm, structured dialogue is not simply good community relations. It is foundational to building the next generation of digital infrastructure in a way that is both sustainable and broadly supported.

The work I am leading at the OIX Association and the Digital Infrastructure Framework Committee (DIFC), is working to create practical guidance that helps communities evaluate digital infrastructure within their broader economic vision, not project by project, crisis by crisis.

Understanding this distinction may be one of the most important steps we can take right now.

Learn more about what we are doing at iMiller Public Relations to bridge the gap between industry and community for the digital infrastructure sector, go to www.imillerpr.com.

For information about the OIX DIFC, visit www.oix.org/standards-and-certifications/oix-dif-standard.

The post Community Resistance Is Often Overwhelm – Not Opposition appeared first on Data Center POST.

Originally published in the Idaho Statesman.

America’s most pressing ambitions — re-industrialization, artificial intelligence leadership, cleaner energy and thriving small businesses — are colliding with a hard reality: The nation lacks the power and energy grid infrastructure required to deliver them.

To compound the issue, local communities often oppose new data centers because, among other reasons, consumers fear that their own energy bills may rise. Nevertheless, by supporting new technologies, including a new generation of small modular reactors, or SMRs, policymakers can address America’s power needs in ways that benefit consumers.

During his State of the Union address, President Trump announced a “new Rate Payer Protection Pledge” to ensure that the tech companies, rather than consumers, bear the costs of new data centers. The pledge builds on an earlier bipartisan plan that encourages technology companies to build their own power plants. Google, Meta, Microsoft, xAI, Oracle, OpenAI, and Amazon signed the pledge in early March to “BYOP” — Bring Your Own Power — to the data center party.

As part of a comprehensive energy strategy, SMRs offer a practical path to expanding power capacity, pairing reliable power with comfortable safety margins. SMRs are compact, standardized nuclear plants built with factory-produced components that reduce construction time, lower costs and improve safety compared with traditional large- scale reactors. Unlike conventional nuclear plants that require massive, decade-long construction projects, SMRs can be prefabricated and deployed incrementally, making them ideally suited to today’s energy, AI and grid demands.

Idaho’s Role in SMR Development

SMRs’ potential provides another reason to watch the Idaho National Laboratory and its National Reactor Innovation Center. Last May, the White House issued four executive orders that significantly expanded the Department of Energy’s authority to regulate new advanced reactors and could encompass a prototype reactor for powering a data center.

One of these Orders directs DOE to approve at least three new reactors. DOE subsequently accepted 11 applicants into its reactor pilot program.

In fact, the need for data centers to provide their own power is a problem tailor-made for the NRIC, whose mission is to “bridge the gap between concept, demonstration, and commercialization of advanced nuclear technology.” NRIC recently announced its Nuclear Energy Launch Pad in response to this high private sector interest. The Launch Pad initiative is the new vehicle to test and operate these trailblazing technologies in partnership with private nuclear technology developers, with an eye toward eventual commercial deployment and proof of DOE’s plans to expand the private sector’s ability to obtain DOE Authorization.

In conjunction with the Launch Pad, the Department of Energy and the Nuclear Regulatory Commission should continue to pursue regulatory reforms that could significantly speed the growth of all nuclear power, including SMRs. One of the recent executive orders directed the Nuclear Regulatory Commission to modernize its regulations. Proposed revised regulations, which should prioritize safety, speed, and cost, are expected soon.

To continue reading, please click here.

The post These Nuclear Reactors Can Benefit Idaho, Power America’s Ambitions appeared first on Data Center POST.

Duos Technologies Group, Inc. (Nasdaq: DUOT), a provider of modular, colocation Edge and AI data centers and technology infrastructure solutions, has scheduled its fourth quarter and full year 2025 earnings call for Tuesday, March 31, 2026 at 4:30 p.m. Eastern Time.

Based in Jacksonville, Florida, Duos Technologies Group is focused on modular data center colocation facilities and infrastructure solutions through its Duos Edge AI and Duos Technology Solutions subsidiaries. The company continues to expand its digital infrastructure platform to support AI, enterprise computing, and edge deployments across Tier 3 and Tier 4 markets.

During the call, Duos management will discuss financial results for the quarter and full year ended December 31, 2025, followed by a question-and-answer session. The company said it will release its financial results prior to the call through the Investor Relations section of its website.

A live audio webcast will also be available online, with a replay posted after the event. Investors joining by phone can use the U.S. dial-in number +1 877 407 3088 and confirmation number 13759531, while international participants can access the call through the company’s dial-in matrix.

To learn more about Duos Technologies Group, Inc., visit www.duostechnologies.com.

The post Duos Technologies Group Schedules March 31 Call to Review Fourth Quarter and Full Year 2025 Results appeared first on Data Center POST.

Originally posted on Nomad Futurist.

Happy International Data Center Day! Today, we shine a spotlight on an industry that quietly powers our modern world. Behind every video call, online class, cloud application, and AI breakthrough is a network of infrastructure that most people never see — but rely on every single day: the data center industry.

This day is about more than celebrating technology; it’s about celebrating the people who make it all possible. From engineers and technicians to sustainability leaders, network specialists, and innovators, data centers are driven by talented professionals shaping the future of technology and connectivity.

Yet, one of the biggest challenges remains awareness. Many students and educators still don’t know that these careers exist, or the incredible opportunities they offer.

At the Nomad Futurist Foundation, we know that exposure changes everything. When students step inside a data center, meet the people behind the operations, and see the technology up close, curiosity transforms into possibility. Experiencing these environments firsthand opens doors to careers that are not only in high demand but essential to powering our digital future.

To continue reading, please click here.

The post Celebrating International Data Center Day: Inspiring the Next Generation of Tech Leaders appeared first on Data Center POST.

The AI revolution is pushing the data center industry toward gigawatt-scale campuses. But the real question today is not how large a facility can be built. The real question is how quickly power can be converted into revenue.

Consider a 1 gigawatt data center project. One gigawatt equals one thousand megawatts of capacity. In today’s market, typical infrastructure costs for large data centers range between 8 million and 12 million dollars per megawatt for standard facilities. That places the infrastructure cost of a 1 GW campus between 8 billion and 12 billion dollars.

In many U.S. markets, developers are seeing costs closer to 10 to 14 million dollars per megawatt, which would place a 1 GW campus between 10 and 14 billion dollars. AI optimized data centers can be even more expensive due to high density racks, liquid cooling systems, and larger electrical infrastructure. Those facilities can reach 15 to 20 million dollars per megawatt, pushing a 1 GW campus to 15 to 20 billion dollars in infrastructure alone.

Once servers, GPUs, networking equipment, and storage are installed, the total project value can easily exceed 30 billion dollars. But capital cost is no longer the biggest constraint, energy is.

According to the International Energy Agency, global data center electricity consumption reached roughly 415 terawatt hours in 2024, representing about 1.5 percent of global electricity demand. That number is projected to approach 800 terawatt hours by 2030 as AI adoption accelerates. At the same time, power infrastructure is struggling to keep up. The United States interconnection queue alone now exceeds 2 terawatts of generation capacity waiting for approval, and in many regions new grid connections can take three to six years. This creates a major financial challenge for traditional hyperscale development.

Large buildings are often constructed years before sufficient power becomes available. Hundreds of megawatts of capacity can sit idle while developers wait for substations, transmission lines, and utility upgrades. On a one gigawatt campus that could mean billions of dollars tied up in infrastructure waiting for power.

Now compare that with a modular campus strategy.

Instead of constructing massive buildings designed for the full gigawatt from day one, the campus can be deployed incrementally as power becomes available. A one gigawatt campus could begin with a 20 megawatt deployment. Using the same industry pricing ranges, that first deployment would require between 160 and 240 million dollars at eight to twelve million dollars per megawatt, or up to 300 to 400 million dollars if the facility is designed for high density AI workloads. What makes this model powerful is how quickly revenue can begin.

In many markets AI capacity is leasing between 150 thousand and 250 thousand dollars per megawatt per month depending on location and density. A 20 megawatt deployment can therefore generate roughly 3 to 5 million dollars per month, or approximately 36 to 60 million dollars per year, while the rest of the campus continues expanding. Instead of waiting years for a massive hyperscale facility to be completed, the project can begin generating revenue within 12 to 18 months.

As additional power becomes available the campus grows from twenty megawatts to one hundred megawatts, then several hundred megawatts, and eventually the full one gigawatt capacity. By the time the campus reaches full scale, the project may already be generating hundreds of millions of dollars annually.

There is also another strategic advantage that is becoming increasingly important: mobility of infrastructure.

If power availability changes, new energy sources come online, or grid constraints shift to another region, modular facilities can be redeployed where energy exists. Massive fixed hyperscale buildings cannot move.

This dramatically changes the risk profile.

Traditional hyperscale development concentrates 10 to 20 billion dollars into a single permanent structure. Modular campuses distribute capital across infrastructure that scales directly with available power.

In a world where energy has become the limiting factor for digital growth, the future of hyperscale development may not be one giant building. It may be gigawatt scale campuses built from modular infrastructure designed to grow with power.

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About the Author

Kliton Agolli Co-Founder, Board Member & Director of Global Growth Northstar Technologies Group | Naples, Florida.

Kliton Agolli is a senior security and international business development executive with more than 35 years of experience operating at the intersection of national security, executive protection, counterintelligence, and global commercial expansion. His career spans military service, law enforcement, VIP and diplomatic protection, healthcare and hospitality security, and cross-border business development in complex and high-risk environments.

At Northstar Technologies Group, Mr. Agolli leads global growth strategy, international partnerships, and strategic market expansion. He plays a key role in aligning advanced security and infrastructure technologies with government, defense, healthcare, and mission-critical commercial clients worldwide. His work focuses on risk-informed growth, regulatory compliance, and building long-term strategic alliances across Europe, the Middle East, and the United States.

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Originally posted on Compu Dynamics.

Discover how AI is transforming mission‑critical infrastructure: From modular data center design and liquid cooling to extreme power density to purpose‑built AI facilities, Steve Altizer, President and CEO of Compu Dynamics, covers these topics in this recent conversation.

At PTC 2026 in Hawaii, Isabel Paradis of HOT TELECOM held a discussion with Altizer to discuss how AI is reshaping the way modular data centers are designed now and in the future.

AI Is Rewriting the Rules of Data Center Design

AI is transforming data centers. While many are still trying to shoehorn AI workloads into traditional designs, that approach is only going to last a few more years. Hyperscalers are leading the way into an AI‑centric future, where liquid cooling – once a specialty – is now becoming standard across the industry.

Retrofitting conventional colo or cloud facilities for AI is not ideal. It’s not as cost effective as doing something that’s purpose built, yet building AI‑only facilities also carries risk, because repurposing that heavy investment later is difficult. The industry is therefore moving toward modular infrastructure, which allows for hybrid, purpose‑built AI facilities that remain flexible enough to serve a range of customers.

To continue reading, please click here.

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By Mike Hodge, AI Solutions Lead, Keysight Technologies

It’s the heart of the AI gold rush, and everyone wants to capitalize on the next big thing. Large language models, multimodal systems, and domain-specific AI workloads are moving from experimentation to production at scale. Across industries, enterprises are building their own proprietary models or integrating pre-trained ones to power applications spanning from video analytics to highly specialized inference services.

This shift has triggered a new wave of infrastructure investment. But while GPUs and accelerators dominate the conversation, scaling AI platforms has produced a less obvious constraint: front-end network performance. In increasingly distributed, multi-tenant AI environments, the ability to move data efficiently into (and across) platforms has become just as critical as raw compute density.

New AI platforms mean new expectations for infrastructure

AI infrastructure is no longer the exclusive domain of a handful of hyperscalers. A growing class of service providers has begun offering end-to-end AI platforms where compute, storage, networking, and orchestration are delivered as a service. Their value proposition is straightforward: customers bring data and models, while the platform handles the complexity of building, operating, and maintaining large-scale data center deployments.

Service models like these, however, place extraordinary demands on networking. Unlike traditional cloud workloads, AI jobs are defined by massive, sustained data movement and tight coupling between data pipelines and compute utilization. GPUs cannot perform at peak efficiency unless data arrives on time, in the right order, and at predictable speeds.

As a result, network performance is now one of the primary determinants of training, inference, and infrastructure efficiency.

The eye of the storm is moving from the fabric to the front end

AI infrastructure discussions often focus on back-end fabrics. Think about things like high-bandwidth, low-latency interconnects between GPUs, for example. However, while these fabrics are indeed essential, they are only part of the picture.

Before training or inference ever begins, data must first traverse the front-end network. This occurs in several ways, but some of the most common paths include:

• From remote object stores or on-premises repositories into the data center
• From ingress points into virtual machines or containers
• From storage into GPU-attached hosts

This is where north-south traffic (external to internal) intersects with east-west traffic (host-to-host and service-to-service). And in AI environments, these flows are not occasional spikes. They are sustained, high-throughput, latency-sensitive streams that run continuously throughout the lifecycle of a job.

When front-end networks underperform, the consequences are costly and immediate: idle accelerators, elongated training windows, unpredictable inference latency, and poor multi-tenant isolation.

Why traditional network validation falls short

Most cloud networks were designed around general-purpose workloads. Think about things like web services, databases, and transactional systems with relatively modest bandwidth demands and fluctuating traffic patterns punctuated by the occasional spike.

AI workloads, on the other hand, break these assumptions. On the front end, AI traffic is characterized by:

• Extremely large data transfers, often using jumbo frames
• Long-lived connections, sustained over hours or days
• Millions of concurrent sessions in multi-tenant environments
• Tight latency and jitter tolerances to avoid starving accelerators

Conventional network testing approaches — such as synthetic benchmarks, isolated link tests, or small-scale simulations — are unable to replicate this behavior. As a result, many issues only surface once customer workloads are already running, which also happens to be when the cost of remediation is highest.

The need for realistic workload emulation

Optimizing front-end AI networks requires the ability to reproduce real workload behavior at scale. That means emulating both north-south and east-west traffic patterns simultaneously, across distributed environments and under sustained load.

For north-south paths, this includes verifying that large datasets can be reliably pulled from diverse external sources into local storage. Moreover, the network must also be able to do so with consistent throughput, predictable latency, and no silent data loss. Transfers like these are essential, as any inefficiency propagates directly into longer training times and underutilized GPUs.

For east-west paths, the challenge shifts to connection density, latency, and scalability. Once workloads are running, virtual machines and services exchange data continuously. Sometimes within the same host, sometimes across racks, and sometimes across geographically separated data centers. Modern AI platforms increasingly rely on SmartNICs and offload technologies to make this feasible, so these components must also be validated under realistic connection rates and protocol behavior.

Without large-scale, workload-accurate testing, subtle bottlenecks — such as rule-processing limits, connection-tracking inefficiencies, or unexpected latency spikes — can remain hidden until production traffic exposes them.

Front-end optimization is a competitive differentiator

In response, the most advanced AI platform operators are shifting left: validating their front-end networks before customers ever deploy workloads. Along the way, their proactive approach is changing the economics of AI infrastructure.

Stress-testing networks under real-world conditions offers a range of benefits for network operators:

• Identifying performance cliffs at high line rates
• Understanding how different layers of the stack interact under load
• Resolving scaling limitations in NICs, virtual networking, or storage paths
• Delivering predictable performance across tenants and geographies

It’s not just about improving peak throughput. It’s about building confidence that platforms perform as expected under peak pressure. And in a market where AI workloads are expensive, time-sensitive, and strategically important, this confidence becomes a differentiator. Customers may never see the network directly, but they feel its impact in faster training cycles, lower inference latency, and fewer production surprises.

Looking ahead: front-end networks and the next generation of AI

AI workloads continue to evolve. Microservices-based architectures, distributed inference pipelines, and increasingly stateful services are placing even more emphasis on low-latency, high-availability front-end connectivity. At the same time, data is becoming more geographically distributed, pushing platforms to span multiple regions and network domains.

In this environment, front-end networks are no longer a supporting actor. They are a core component of AI system design. That means they must be engineered, validated, and optimized with the same rigor applied to compute and accelerators.

The lesson is clear: operators cannot optimize AI infrastructure by focusing on GPUs alone. The performance, efficiency, and reliability of tomorrow’s AI platforms will be defined just as much by how well they move data as by how fast they process it.

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Capacity LATAM 2026, held March 17-18 in São Paulo, Brazil, made it clear that Latin America’s digital infrastructure market is no longer defined by potential, but by execution. As demand for cloud, AI, and connectivity accelerates across the region, the conversation has shifted from future opportunity to immediate deployment where power, capital, and collaboration must align to keep pace with growth.

Across the event, the narrative moved well beyond subsea routes and international traffic flows. Instead, speakers focused on how Latin America is becoming a destination for data creation, processing, and storage. With the region’s data center market projected to nearly double by 2030, investment is accelerating across Brazil, Mexico, Chile, and Colombia, while emerging markets are beginning to play a more strategic role in regional infrastructure planning.

Collaboration emerged as a central theme, particularly as infrastructure deployments become more complex and capital-intensive. During the “From Fiber to Facility” keynote, Gabriel del Campo, Data Center Vice President at Cirion Technologies emphasized that scaling data centers and networks across Latin America requires tighter alignment between operators, fiber providers, and hyperscalers. That coordination is increasingly necessary to navigate supply chain challenges and accelerate time to market in a region where demand is rising quickly.

Investment momentum continues to build, with the “LATAM’s $100B Digital Surge” keynote framing the scale of capital entering the market. Rodolfo Macarrein, Partner at Altman Solon highlighted how shifting political and regulatory dynamics are influencing where and how capital is deployed while reinforcing that long-term demand fundamentals remain strong. Key markets such as São Paulo, Santiago, and Querétaro are emerging as focal points for AI-ready capacity, driven by hyperscale expansion and enterprise demand.

AI infrastructure is already beginning to shape the next phase of development. In the AI keynote, Ivo Ivanov, CEO at DE-CIX pointed to the rise of next-generation digital hubs designed for high-density compute, where power availability, connectivity, and scalability must be considered from day one. José Eduardo Quintella, CEO at Terranova reinforced this by highlighting how speed to deployment and execution are becoming critical differentiators, particularly as new facilities are being delivered on accelerated timelines to meet demand.

Connectivity remains the backbone of this transformation. The subsea keynote highlighted new systems such as Firmina and Humboldt that are expanding capacity and reducing latency between Latin America and global markets. Peter Wood, Senior Research Analyst at TeleGeography emphasized the strategic importance of these routes in supporting cloud expansion and future AI workloads, particularly as latency-sensitive applications become more prevalent across the region.

Energy is quickly becoming one of the most important variables in the region’s growth trajectory. As discussed throughout the energy and infrastructure sessions, access to reliable and sustainable power will ultimately determine how quickly Latin America can scale to meet demand. Renewable energy partnerships, evolving grid strategies, and new power procurement models are all playing a role in shaping where future capacity will be built.

What stood out most across Capacity LATAM 2026 was the level of alignment between stakeholders. Operators, investors, and policymakers are increasingly focused on the same challenge: how to scale infrastructure quickly while addressing constraints around power, supply chains, and regulatory complexity. The shift toward AI-ready infrastructure, combined with sustained cloud demand, is accelerating timelines and raising the stakes for execution.

As the event concluded, the broader message was clear. Latin America is no longer simply part of the global network, it is becoming a critical region where infrastructure must be built to support both local demand and international data flows. The next phase of growth will depend on how effectively the region can translate investment into deployable, scalable infrastructure.

Upcoming Capacity events will continue to spotlight the trends shaping digital infrastructure worldwide, from AI-driven demand to evolving connectivity models. Explore the full event calendar at www.capacityglobal.com/events to see where the industry is heading next.

Dates for Capacity LATAM 2027 are not yet available, for information please visit www.capacityglobal.com/events.

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AI workloads are pushing data centre infrastructure towards higher rack densities, new cooling strategies and greater power demand. Jamie Darragh, Data Centre Director, Europe, at global data centre engineering design consultancy Black & White Engineering, examines the design implications for the next generation of facilities.

AI and high-performance computing are placing new demands on data centre infrastructure. Rack densities are increasing; facilities are being delivered at larger scale and operators are under pressure to support workloads that consume far greater levels of power and generate far higher heat loads than conventional cloud environments.

Independent forecasts underline the pace of expansion. Gartner estimates global data centre electricity consumption will rise from around 448TWh in 2025 to roughly 980TWh by 2030, driven largely by AI-optimised computing infrastructure. Within that growth, AI servers alone are expected to account for close to 44% of data centre power consumption by the end of the decade.

For our engineering teams, these workloads are altering the practical limits of traditional infrastructure design. Rack densities exceeding 100–200kW are now appearing in project specifications, particularly where large AI training clusters are planned. These loads influence every part of the building environment, from electrical distribution and cooling capacity to structural loading and cable management.

Designing for extreme density

Under these conditions, air cooling alone becomes difficult to sustain across entire facilities. Liquid cooling is therefore increasingly included in the baseline design of new data centres rather than introduced later as a specialist solution. This cooling method is becoming increasingly favourable due to its higher specific thermal capacity compared with air, which enables more efficient heat transfer and removal. Direct-to-chip and rack-level systems are being designed alongside air cooling so facilities can accommodate different densities and equipment types across the same site.

The introduction of liquid systems requires careful coordination between disciplines. Facilities must manage environments where air and liquid cooling operate together, supported by monitoring platforms, safety controls and operational procedures capable of supporting both approaches.

Some IT chips require different liquid cooling temperatures than those used in air-cooling systems, creating technical hurdles for the overall heat rejection system and requiring precise control of the cooling circuit temperature. Another engineering challenge lies in integrating these systems with power distribution, control platforms and maintenance strategies rather than selecting one cooling method over another.

Higher density also narrows operational tolerance. Commissioning becomes more demanding and redundancy strategies require more detailed modelling. Infrastructure must be capable of supporting peak compute demand while maintaining efficiency when loads are lower, placing greater emphasis on flexible electrical and mechanical systems.

The scale of development is also increasing. Buildings that once delivered a few megawatts of capacity are now part of campus-scale developments where multiple data halls contribute to facilities delivering hundreds of megawatts. data centres are increasingly planned and delivered as long-term infrastructure assets rather than individual projects.

This environment encourages repeatable design and industrialised delivery methods. Developers and investors expect predictable construction schedules and consistent performance across multiple sites. As a result, engineering teams are placing greater emphasis on modular infrastructure systems and digital design methods that allow mechanical and electrical systems to be configured and deployed repeatedly.

Power, control and operational intelligence

Power availability is also becoming a determining factor in project planning. In many regions, grid connection capacity is now one of the main constraints on new development. Gartner has warned that by 2027 as many as 40% of AI data centres could face operational limits because of power availability.

Developers are therefore engaging more closely with utilities during early feasibility stages and exploring complementary infrastructure such as on-site generation and energy storage. In some cases, data centres are also being designed to contribute to wider grid stability through demand response and energy management capability.

Artificial intelligence is also beginning to influence how facilities themselves are operated. Machine-learning systems are already being used in some environments to optimise airflow patterns, cooling plant performance and power distribution using live operational data.

The next stage will see more widespread use of integrated control platforms and digital twins capable of modelling facility behaviour in real time. These systems allow operators to simulate infrastructure performance under different load conditions, test operational changes and identify maintenance requirements before faults occur.

Environmental performance remains another constraint as compute density increases. Higher workloads place additional pressure on energy supply while raising questions around water consumption, construction materials and waste heat recovery. Planning authorities and investors are increasingly looking for measurable improvements in efficiency and carbon reporting before approving new developments. Sustainability therefore sits alongside power and cooling as a central engineering consideration rather than a secondary design feature.

Taken together, these conditions create a more complex design environment for data centre infrastructure. Higher compute densities, power constraints and new operational technologies require mechanical, electrical and digital systems to be considered together from the earliest design stages.

Facilities intended to support AI workloads must accommodate far greater performance requirements than earlier generations of data centres while remaining adaptable as infrastructure technologies and operating practices continue to develop.

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About the Author

Jamie Darragh is Data Centre Director, Europe at Black & White Engineering. He leads the delivery of complex, mission-critical projects across the region, with a focus on technical quality, design coordination and strong client relationships. A Chartered Engineer and member of CIBSE and the IET, Jamie has worked across Europe, the Middle East and the UK since 2005. He brings a clear, practical approach to engineering challenges, combining technical expertise with commercial awareness. He is committed to developing teams that work collaboratively and perform at a high level. Jamie has received several industry awards, recognising both his technical capability and his impact on the built environment including ‘Engineer of the Year’ at leading Middle East industry awards.

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Over the coming weeks, I will be sharing a series of reflections on the realities shaping digital infrastructure development in the United States. These perspectives come from ongoing conversations with communities, policymakers, developers, investors, and industry leaders navigating one of the most consequential infrastructure build cycles in modern history. As artificial intelligence accelerates demand for computing capacity, the decisions being made today, often at the local level, will influence economic competitiveness, regional growth, and public trust for decades to come. This series is intended to create space for more calm, evidence-based dialogue about how we plan, communicate, and lead through this moment of rapid transformation.

We are living through one of the most consequential infrastructure build cycles in modern history, not dissimilar to the first industrial revolution, and yet many of the decisions shaping our digital future are being made in environments defined by urgency, fear, and ideological polarization.

Digital infrastructure, from AI-ready data centers (AI Factories) to edge computing nodes in your local stripmall, are now central to economic competitiveness, national security, innovation, and quality of life. And still, conversations about development often become binary: pro-growth or anti-growth, pro-environment or pro-industry, local control or national interest.

Reality is far more complex. We are living out a paradoxical dilemma in real-time.

What we are seeing across the United States is not simply opposition to projects. It is a collision of competing priorities: environmental stewardship versus economic opportunity, investor timelines versus civic process, national competitiveness versus local autonomy. These tensions are real. They deserve thoughtful navigation, not reactive decision-making. And when the decisions are polarizing, the complexities are at their greatest.

One of the structural challenges is governance itself. As a former elected official in Westchester County, New York, and after serving two-terms, it is clear as day that Federal policy direction does not automatically translate into local action. As I often say: “Federal mandates don’t mean much when governors and local jurisdictions can simply say no.”

This is not a criticism, it is a recognition of how our democratically designed system works. Infrastructure decisions are ultimately shaped at the state, county, and municipal levels. And many of the leaders tasked with evaluating these developments are doing so without the benefit of neutral frameworks, long-term planning guidance, or consistent industry education.

At the same time, the public narrative around digital infrastructure has become increasingly emotional. Headlines focus on water usage, energy demand, or tax incentives, often without equal discussion of the broader economic and societal value these projects create.

Because a data center is not just a building. It is a catalyst.

Data centers are not just buildings. They are an economic driver across a wide-variety of professional services, hospitality, supply chains, and innovation.

Economic activity begins long before construction starts and extends far beyond permanent on-site employment. Yet many impact assessments still rely on narrow metrics that fail to capture this ecosystem effect.

When you look at impact studies narrowly,  like counting permanent jobs, you miss the enormous economic ecosystem that infrastructure development activates.

This disconnect contributes to mistrust and polarization. Communities feel pressured. Investors feel blocked. Policymakers feel caught in the middle.

What is needed now is calm, evidence-based leadership.

Leadership that can hold multiple truths at once:

• Infrastructure development must be sustainable.
• Communities deserve transparency and engagement.
• Economic competitiveness cannot be taken for granted.

Long-term planning must transcend election cycles.

The work I am leading at the OIX Association and the Digital Infrastructure Framework Committee (DIFC), is working to create practical guidance that helps communities evaluate digital infrastructure within their broader economic vision, not project by project, crisis by crisis.

The goal is not to advocate for development at any cost.

The goal is to enable informed decision-making.

Because when stakeholders are equipped with context, data, and structured engagement models, conversations shift. Fear gives way to dialogue. Polarization gives way to planning. Urgency gives way to intentional action.

In a moment defined by technological acceleration, community leadership may simply need to be able to meet ability with reality. This will ensure that we, as a society, can move forward, together, with clarity.

Learn more about what we are doing at iMiller Public Relations to bridge the gap between industry and community for the digital infrastructure sector, go to www.imillerpr.com.

For information about the OIX DIFC, visit www.oix.org/standards-and-certifications/oix-dif-standard.

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Duos Technologies Group Inc. (Nasdaq: DUOT), through Duos Edge AI, Inc., has announced the deployment of its second Edge Data Center in the Amarillo, Texas market. The new carrier-neutral, SOC 2-compliant facility is located on Potter County land adjacent to the largest colocation facility in the Texas Panhandle, further strengthening digital infrastructure for carriers, healthcare organizations, enterprises, and public sector entities across the region.​

Building on the success of its initial Amarillo deployment, this latest installation expands Duos Edge AI’s footprint in the Panhandle and adds high-density, low-latency computing capabilities for real-time AI applications, enhanced bandwidth, and secure data processing.

“We are proud to deepen our commitment to the Amarillo market with this second deployment, building on the foundation established by our initial EDC, which brought high-performance computing directly to the heart of the Panhandle,” said Dave Irek, Chief Operations Officer of Duos Edge AI. “This expansion enhances capacity and capability in the region, and by partnering on Potter County land adjacent to a premier colocation hub, we are creating a robust, carrier-neutral ecosystem designed to support innovation, attract investment, and drive long-term economic growth.”​

The company said the deployment also helps reduce dependence on data centers located in tier one cities while supporting underserved and high-growth markets across Texas. Duos Edge AI’s broader Texas expansion includes recent installations in Lubbock, Waco, Victoria, Abilene, and Corpus Christi.​

Potter County Judge Nancy Tanner added, “This collaboration with Duos Edge AI represents a significant investment in our community’s future. Positioning this advanced, carrier-neutral data center on county land next to the Panhandle’s largest colocation facility will attract new businesses, improve connectivity for our residents and schools, and position Potter County as a leader in digital infrastructure.”​

The new EDC is expected to be fully operational in the coming months.

To learn more about Duos Edge AI, visit www.duosedge.ai.

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How Centralized Infrastructure Intelligence Turns Emergency Replacements into Controlled Operations

Most infrastructure professionals spend their careers building for the planned: capacity expansions, technology refreshes, migration cycles that unfold over quarters or years. And then a Monday morning email changes everything.

A government agency bans equipment from a trusted vendor. A threat intelligence report reveals that a state-sponsored actor has been inside your network switches for eighteen months. A manufacturer announces that the platform running your entire campus backbone loses support in nine months. In each case, the same question emerges: how quickly can you identify every affected device across every facility, and how fast can you replace them without breaking what still works?

For a surprising number of organizations, the honest answer is: they don’t know. That gap between confidence in steady-state operations and readiness for unplanned mass replacement is where real risk lives.

The Forces That Turn Infrastructure Upside Down

Emergency hardware replacement at scale is not hypothetical. Recent years have produced real-world triggers across four broad categories, each with distinct operational implications.

Regulatory and geopolitical mandates. The federal effort to remove Chinese-manufactured telecommunications equipment from American networks—driven by the FCC’s Covered List and Section 889 of the National Defense Authorization Act—has forced carriers and federal contractors into wholesale infrastructure replacement on compliance timelines that don’t flex for budget cycles. The FCC has estimated the total program cost at nearly five billion dollars. Any organization touching federal dollars must verify its infrastructure is clean; if it isn’t, replacement is a compliance obligation, not a planning exercise.

Security crises that outpace patching. The Salt Typhoon campaign revealed that Chinese state-sponsored hackers had penetrated multiple major US telecommunications providers, maintaining persistent access for up to two years—exploiting legacy equipment, unpatched router vulnerabilities, and weak credential management. Investigators found routers with patches available for seven years that had never been applied. For affected carriers, the response demanded physical replacement of compromised infrastructure that could no longer be trusted regardless of patch status. When an adversary achieves sufficient persistence, patching becomes insufficient. Replacement is the only reliable remediation.

End-of-life announcements. Vendor lifecycle decisions create quieter but equally urgent pressure. An organization running multiple hardware platforms faces different end-of-support timelines for each, and dependencies between them mean replacing one can cascade into forced changes elsewhere. Without a consolidated view of what is running, where, and when it loses support, these effects are invisible until they cause failures.

Architectural shifts. Zero trust adoption, SASE frameworks, and cloud-delivered security are rendering entire categories of on-premises equipment architecturally obsolete—not because they’ve failed, but because the security model has moved on. The question is not whether legacy VPN appliances and perimeter firewalls will be replaced, but how quickly, and whether the organization has the visibility to execute in a controlled manner.

Why Standard Processes Break Down

Every mature IT organization has IMAC processes: Install, Move, Add, Change. These handle the predictable rhythm of infrastructure life. Emergency replacement programs share almost none of their characteristics.

They are triggered externally. Their scope is massive—hundreds or thousands of devices across multiple sites. They arrive without allocated budgets or pre-positioned inventory, carrying compliance deadlines indifferent to resource constraints.

The organizations that handle these events well recognize them for what they are: standalone programs needing their own governance, funding, and dedicated teams—and their own information infrastructure. That last requirement is where centralized infrastructure management becomes not a convenience but a prerequisite.

What Centralized Infrastructure Intelligence Must Deliver

Four questions—answered immediately.

What is affected, and where is it? When a regulatory notice references a specific manufacturer, or a security advisory identifies a particular hardware model and firmware version, the operations team needs a definitive count within hours, not weeks. Organizations maintaining a continuously updated centralized inventory—capturing hardware models, firmware versions, physical locations, logical roles, and contractual associations—can answer by running a query. Organizations relying on spreadsheets and periodic audits cannot. The difference in response time is typically measured in weeks, and in a compliance-driven scenario, weeks are what you don’t have. Equally important is dependency mapping: understanding that replacing a core switch will affect upstream routers, downstream access switches, and out-of-band management paths. Without it, a replacement that looks straightforward on paper can produce cascading outages in execution.

What is the replacement path? A legacy switch may need to be replaced by different models depending on port density, power constraints, and compatibility with adjacent equipment. Workflow-driven execution ensures every replacement follows the same approval steps, documentation requirements, and validation procedures—preventing errors that compound in programs spanning hundreds of sites.

Where are we right now? Leadership needs a live view of progress—which sites are lagging, where tasks are stalled, which teams are hitting milestones. This enables resource reallocation, timely escalation of procurement bottlenecks, and an auditable record for regulators. It also surfaces patterns previously invisible: a region that consistently runs behind, or an approval step adding days of unnecessary latency.

What did we learn? Emergency replacements are no longer rare—any organization operating at scale should expect one every few years. Those that conduct structured post-project reviews build a compounding advantage: better scoping templates, more accurate resource models, and pre-validated replacement mappings that make the next response faster.

Building Readiness Before the Next Crisis

Emergency replacements cannot be made painless—they are disruptive, expensive, and stressful regardless of preparation. But the difference between an organization that navigates one in three months and one that takes twelve is almost entirely a function of work done before the trigger.

That preparation has three dimensions: information readiness (a continuously updated inventory with hardware identity, location, firmware status, and dependency relationships), process readiness (defined workflow-driven procedures that activate quickly rather than being reinvented under pressure), and organizational readiness (governance, budget authority, and executive sponsorship that allows an emergency program to stand up as a dedicated initiative).

The organizations best positioned for the next regulatory mandate, zero-day disclosure, or end-of-life cascade are investing in that readiness today—not because they know what the trigger will be, but because they’ve built a discipline prepared for all of them.

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About the Author

Oliver Lindner has over 30 years of experience in IT and the management of IT infrastructures with a focus on data centers. He has worked for many years at FNT Software, a leading provider of integrated software solutions for IT management. In his current position as Director of Product Management, he is responsible for the strategic direction and continuous improvement of the software products for data centers. The aim is to support customers in the efficient and transparent design of their IT infrastructure.

Oliver Lindner attaches great importance to customer focus, innovation and quality. His expertise also includes the development and provision of Software as a Service (SaaS) solutions that offer customers maximum flexibility and efficiency. To this end, he works closely with his own team, partners and customers to create sustainable and innovative software solutions.

The post When Your Data Center Becomes a Liability Overnight appeared first on Data Center POST.

The global data center HVAC market was valued at USD 13.7 billion in 2025 and is estimated to grow at a CAGR of 9.8% to reach USD 36 billion by 2035, according to recent report by Global Market Insights Inc.

Growth in the global data center HVAC industry is being fueled by rising computing intensity, expanding AI-driven workloads, and the continued development of hyperscale and enterprise facilities. As server densities increase and high-performance computing environments generate greater thermal loads, advanced cooling infrastructure has become essential to maintain operational stability and uptime. Research and development efforts across the HVAC industry are increasingly focused on liquid cooling technologies and next-generation thermal management systems capable of handling elevated power densities.

At the same time, stricter regulatory oversight related to energy consumption and environmental performance is encouraging operators to enhance system efficiency and reduce carbon output. ESG-focused initiatives and net-zero commitments are prompting facility upgrades aimed at optimizing Power Usage Effectiveness and lowering operating expenses. Improvements in airflow engineering, adoption of sustainable refrigerants, and integration of energy-efficient cooling architectures are reshaping infrastructure strategies. As regulatory expectations and energy costs continue to rise, demand for intelligent, high-efficiency HVAC solutions in data centers is expected to accelerate significantly.

Rising load capacities, sustainability targets, and regulatory compliance requirements are creating pressure for compact, scalable, and adaptable HVAC systems. Industry participants are responding by designing modular cooling platforms that can operate effectively across diverse geographies while maximizing space utilization and energy performance.

The data center HVAC market from solutions segment accounted for 76% share in 2025 and is forecast to grow at a CAGR of 8.9% from 2026 to 2035. Advanced monitoring tools equipped with artificial intelligence enable predictive maintenance, improve airflow management, and reduce unnecessary power consumption. Increased adoption of liquid-based cooling technologies is supporting high-density server environments while enhancing reliability and extending equipment lifespan through energy-conscious design.

The air-based cooling technologies segment held a 50% share in 2025 and is projected to grow at a CAGR of 8.8% during 2026-2035. Enhanced airflow optimization systems, variable-speed fan configurations, and intelligent environmental controls are improving thermal consistency and minimizing energy waste. Economizer-enabled designs are facilitating greater use of ambient air, while modular cooling units support scalability across both hyperscale and edge environments. Growing server power density is also accelerating interest in direct cooling and immersion-based methods supported by advanced coolant formulations that enhance heat transfer efficiency.

United States data center HVAC market reached USD 4.7 billion in 2025. Increasing cloud integration and AI-intensive applications are driving demand for more efficient cooling architectures. Investments are being supported by electrification incentives and decarbonization initiatives, encouraging broader adoption of intelligent HVAC controls and energy-optimized systems. Integration with smart building platforms and grid-responsive technologies is enabling facilities to manage peak loads, reduce demand charges, and incorporate renewable energy sources.

Key companies operating in the global data center HVAC market include Vertiv, Schneider Electric, Carrier Global, Daikin Industries, Trane Technologies, Johnson Controls, STULZ, Alfa Laval, Danfoss, and Modine Manufacturing. Companies in the global market are strengthening their competitive position through continuous innovation, strategic partnerships, and geographic expansion. Leading players are investing heavily in research and development to enhance liquid cooling efficiency, improve airflow intelligence, and integrate AI-driven monitoring systems. Collaborations with cloud service providers and data center developers are enabling customized cooling deployments for high-density environments. Firms are also expanding manufacturing capacity and regional service networks to support rapid infrastructure growth. Sustainability-focused product development, including low-global-warming-potential refrigerants and energy-efficient system architectures, is becoming a central competitive differentiator.

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As global investment in AI infrastructure, power, and advanced manufacturing accelerates, a critical constraint is coming into sharper focus—project execution.

A newly announced $25 million Series A funding round for Foresight underscores a broader industry shift: while capital continues to flow into large-scale infrastructure, delivering these projects on time and on budget remains a persistent challenge.

The current wave of infrastructure investment is unprecedented in both scale and complexity. Hyperscale data centers, energy systems, and advanced industrial facilities are being developed simultaneously across global markets, often with overlapping supply chains and tight delivery timelines.

However, execution has emerged as a systemic issue.

Research indicates that nearly 90% of large-scale infrastructure projects are completed late or exceed budget expectations. In the context of AI infrastructure, delays can have cascading effects—impacting capacity availability, increasing financing costs, and delaying revenue generation.

Industry observers note that as demand for compute continues to surge, particularly for AI workloads, the margin for error in delivery timelines is shrinking.

A Shift Toward Predictive Delivery Models

Foresight, which positions itself as a predictive project delivery platform, is part of a growing cohort of technology providers aiming to address these execution challenges through data and automation.

The company’s platform is designed to move beyond traditional project management approaches—often reliant on static schedules and retrospective reporting—by introducing continuous validation of project progress and early identification of risk factors.

According to the company, its system enables infrastructure owners to establish baseline schedules more quickly, integrate data across stakeholders, and forecast potential delays before they materialize. Early adopters report improvements in forecast accuracy and reductions in cost overruns.

While such claims reflect a broader trend toward digitization in construction and infrastructure delivery, they also point to a deeper industry need: greater predictability in increasingly complex builds.

Why Execution Matters More in the AI Era

For data center developers and operators, execution risk is becoming more consequential.

Unlike previous infrastructure cycles, AI-driven demand is both immediate and rapidly evolving. Delays in bringing capacity online can result in missed opportunities, strained customer relationships, and competitive disadvantages in key markets.

At the same time, projects are becoming more interdependent. Power availability, equipment procurement, and site development must align precisely—leaving little room for disruption.

This dynamic is prompting a reassessment of how infrastructure projects are planned and managed, with greater emphasis on real-time data, cross-functional visibility, and proactive intervention.

Expanding Beyond Data Centers

Although the initial focus is on sectors such as hyperscale data centers, the challenges associated with project execution are not unique to digital infrastructure.

Foresight plans to expand its platform into adjacent industries, including energy, defense, and advanced manufacturing—areas that share similar characteristics: large capital commitments, complex supply chains, and high sensitivity to delays.

The company’s recent funding, led by Macquarie Capital Venture Capital, reflects investor interest in solutions that address these systemic inefficiencies.

An Industry Inflection Point

The emergence of predictive project delivery tools signals a broader transformation in how infrastructure is built.

For years, innovation in the data center sector has centered on compute performance, cooling technologies, and energy efficiency. Increasingly, attention is shifting toward the process of delivery itself.

As infrastructure programs continue to scale, the ability to execute with precision may become a defining factor in project success.

In an environment where demand is high and timelines are compressed, the question facing the industry is evolving—from whether projects can be financed to whether they can be delivered as planned.

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