TL;DR

• The global edge data center market is projected to surge from USD 14.7 billion in 2025 to USD 71.9 billion by 2035 at a rapid 17.5% CAGR.
• Demand is fueled by the need for low-latency processing, real-time computing, near-source AI inference, and strict data residency compliance.
• Leading global providers are prioritizing modular deployments, energy efficiency, automation, and strategic telecom partnerships to handle rising workloads.

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The global edge data center market was valued at USD 14.7 billion in 2025 and is estimated to grow at a CAGR of 17.5% to reach USD 71.9 billion by 2035, according to a recent report by Global Market Insights Inc.

Market expansion reflects the accelerating demand for low-latency digital infrastructure as real-time computing requirements continue to rise across connected ecosystems. Organizations increasingly require computing resources to be deployed closer to end users and data generation points to support instantaneous data processing, faster response times, and uninterrupted service delivery. The rapid rollout of next-generation connectivity and distributed computing frameworks is reshaping infrastructure strategies, encouraging decentralized architectures that reduce reliance on centralized cloud facilities.

As data volumes surge, localized processing enables enterprises to manage workloads more efficiently while reducing network congestion and improving operational continuity. Artificial intelligence adoption is further reinforcing the need for geographically distributed computing, as near-source inference enhances speed, accuracy, and reliability. Additionally, compliance requirements related to data residency and governance are pushing enterprises and public institutions to store and process information within defined geographic boundaries, reinforcing the strategic importance of edge data centers in modern digital ecosystems.

The edge data center market from the solution-as-a-service segment accounted for 74% share in 2025 and is forecast to reach USD 50.3 billion by 2035. This dominance is driven by demand for integrated platforms that enable seamless coordination across distributed infrastructure while ensuring consistent performance, scalability, and operational resilience.

The hyperscale and enterprise data center segment held a 45% share in 2025 and is expected to reach USD 32.1 billion by 2035. Large operators continue to deploy smaller, distributed facilities closer to population and data hubs to improve latency performance, workload resilience, and computing efficiency.

China Edge Data Center Market is projected to reach USD 9.7 billion by 2035. Policy support, digital infrastructure expansion, and national technology initiatives are accelerating localized data processing investments.

Key companies active in the edge data center market include Schneider Electric, Dell Technologies, Vertiv, Huawei Technologies, Hewlett Packard Enterprise Company, Eaton, IBM, Fujitsu, EdgeConnex, Rittal, Flexential, Vapor, and EdgePresence. Companies operating in the global edge data center industry are strengthening their market position through strategic infrastructure expansion, modular deployment models, and technology integration. Providers are investing in scalable and standardized solutions to accelerate deployment timelines and reduce operational complexity. Partnerships with telecom operators, cloud platforms, and enterprise clients support ecosystem integration and long-term contracts. Firms are also prioritizing energy efficiency, automation, and remote management capabilities to improve reliability and cost performance.

The post Edge Data Center Market to Surpass USD 71.9 Billion by 2035 appeared first on Data Center POST.

TL;DR

• Implementing automation cuts manual errors by 60% and slashes server provisioning times by 80%, reducing setup tasks that once took several hours down to under 30 minutes.
• Nearly 70% of IT infrastructure failures are caused by human mistakes, equipment issues, or delayed troubleshooting responses. Automated monitoring detects issues instantly to trigger pre-established recovery processes, reducing response times from hours to minutes.
• Cooling represents up to 40% of total data center energy consumption. Smart automation dynamically manages cooling, airflow, and resources to handle the extreme power densities and thermal loads created by massive AI clusters and edge computing deployments.

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The quick growth of cloud computing, artificial intelligence (AI), edge computing, and digital services is changing how today’s data centers work. Canters that used to depend on human control have to now oversee thousands of servers, sophisticated networking equipment, and escalating workloads with the least downtimes possible. With systems getting bigger and more dispersed, it is more difficult to stick to old management approaches than before.

As reported by Data Intelo, the world’s data center automation market was worth $12.8bn in 2025 and is expected to grow to $42.6bn by 2034, showing considerable CAGR (14.3%) within the 2026-2034 period. The high market dynamics indicate growing demand for smart management of infrastructure in line with IT advances.

Experts estimate that global data center capacity increases by 15% or more each year, while enterprise data increases by over 25% every year. Such rapid growth means that automated approaches are becoming a must instead of an additional facility. Companies use automation to get the advantages of stabilized operations, optimized resource use, and increased resilience of infrastructure without increasing complexities in operations.

Increased Complexity of Infrastructure Calls for Smart Operations

Data centers of today do not consist only of several server racks used for running internal business processes. Current facilities have thousands of servers to monitor, along with virtual machines, containers, networks defined by software and environments based on hybrid clouds.

Hyperscale data centers have smartphones and tablets and therefore are capable of hosting the work of over 100,000 servers simultaneously. Even medium-sized companies have about 5,000 devices, and finding an effective way to monitor all of them becomes incredibly challenging.

Automation platforms are responsible for monitoring the performance of infrastructure, actions are taken without human intervention. Automation capabilities allow to minimize the number of repetitive processes, thus allowing engineers to focus on planning, development and protection.

Boosting Operational Efficiency Using Automation

Automation has been crucial in minimizing the amount of maintenance tasks performed while also increasing the reliability of infrastructure. Many repetitive tasks like server provisioning, software deployment, backup scheduling, and resource allocation can now be done automatically in minutes instead of hours.

According to industry estimates the use of automation technologies can cut provisioning times by as much as 80% while automation software can decrease the number of manual errors by 60% and speed up deployment which in turn speeds up application delivery.

The following picture illustrates the role of automation in different operational areas.

These improvements help keep high levels of service provided by infrastructure teams despite the growth of digital loads.

The Importance of Automation in Ensuring System Reliability and Availability

Unless they haven’t experienced an unexpected outage of their operation, data center owners understand that it has disastrous consequences and can affect business applications, cloud services, banking systems, and even customers’ experiences.

Research shows that nearly 70% of IT infrastructure failures are caused by human mistakes, equipment failures, or untimely measures undertaken in response to occurring glitches in the operation of data centers. With the introduction of automated monitoring systems, such regularity is eliminated, as the systems detect performance deviation at once and start executing pre-established recovery processes when problems arise.

Some of the regular functions of automated monitoring that ensure reliability of IT operations are:

• Constant health monitoring of servers, storages, and networks
• Automatic distribution of workloads in case of excess demand
• Predictive maintenance based on the hardware operation history
• Automatic switching to a backup equipment in case of failure
• Immediate notification in cases of vital events

The use of automation reduces response time from hours to minutes and increases reliability as well.

Energy Efficiency is Becoming a Priority Area

Electricity costs have become one of the biggest running costs for today’s data centers. These facilities can demand between 20 to 100 megawatts of electricity during operation, with cooling systems often representing up to 40% of total energy consumption.

Automation enables the intelligent management of electricity through constant manipulation of cooling systems, airflow, workload placement, and server use. The trend towards sustainability now forces automation to help energy efficiency versus the growing needs of computing.

Assistance AI, Cloud as well as Edge Computing Growth

Artificial intelligence as well as edge computing create some new operational challenges for traditional management of infrastructure since it is hard to cope with such challenges. Ah, that would mean that if AI training clusters need thousands of GPU working at the same time, that would create much greater power density as well as thermal loads than that of traditional computing venues.

World spending on AI infrastructure is expected to exceed hundreds of billions in times of the next decade while various deployments of edge computing are continuing on the way expanding through many spheres of our existence including manufacturing, healthcare, telecommunications and smart cities.

The process of automation allows infrastructures to adapt to their environment dynamically. That is to say that they are able to manage computing resources, manage the load on the system, and cool the system without any interference from people.

Anticipating the Future

The new data center of the future does not only require computing power. It also requires smart systems capable of managing increasing operational complexity. As cloud services, AI operations, and edge deployments continue to grow, automation evolves into an essential element of contemporary data center strategy.

Automation is not meant to replace human specialists. It is meant to free specialists from routine work and allow them to use their talent and skills for more important engineering decisions. As infrastructure is likely to get more distributed and become more data-driven in the next few years, automated operations are likely to remain at the core of building reliable, sustainable, and scalable data centers.

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About the Author

Ashish Kolte is a Marketing Manager at DataIntelo with expertise in marketing, market intelligence, and business strategy. He combines marketing insights with industry research to analyze market trends, identify growth opportunities, and provide data-driven perspectives on emerging industries and global business developments.

The post Why Data Center Automation Is Essential for Next-Generation Infrastructure appeared first on Data Center POST.

TL;DR

• Community-driven collaboration can deliver more sustainable infrastructure solutions.
• Local knowledge is essential when addressing complex infrastructure challenges.
• Scalable models can improve public health and quality of life in underserved communities.
• Successful infrastructure projects balance engineering expertise with community engagement.

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Written by iMiller Public Relations on behalf of Nomad Futurist.

Community collaboration helps create lasting solutions.

Infrastructure challenges are often viewed as technical problems, but lasting solutions require community involvement. In the latest Nomad Futurist article, Student Advocate Prathyaj Mantha highlights how Alabama’s Black Belt Unincorporated Wastewater Program (BBUWP) is transforming rural sanitation through collaboration among residents, engineers, environmental health professionals, and government partners.

Rather than applying one-size-fits-all solutions, the program addresses local challenges with community-driven planning and tailored infrastructure. The result is a scalable model that improves public health while demonstrating how collaboration can create sustainable, long-term impact for underserved communities.

This content originally appeared on the Nomad Futurist website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post It Takes a Community to Solve a Problem appeared first on Data Center POST.

TL;DR

• Replacing sequential on-site construction with parallel factory production allows modular units to deliver in just 8-16 weeks, compared to the 18-36 months required for traditional builds.
• Modular infrastructure is roughly 15-25% cheaper to construct, with total project costs (including financing and risk-adjusted contingency) favoring modular by 15-25% for deployments below 10 MW. This rapid deployment significantly reduces exposure to specification obsolescence driven by fast-evolving AI workloads.
• Rather than assembling separate components on-site, modular units integrate power, cooling, racks, and security. These systems undergo full integration testing in a controlled factory environment before delivery, eliminating the most common sources of on-site commissioning delays.
• While traditional builds remain the most cost-effective choice for massive hyperscale projects above 50 MW, modular architectures are ideal for timeline-constrained projects, phased capital deployments, and sites with physical, geographical, or logistical limitations.

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What Is a Modular Data Center, and How Does It Differ from a Traditional Build?

A modular data center refers to a pre-engineered, factory-built infrastructure unit that delivers IT capacity, power distribution, precision cooling, racks, monitoring software, and physical security as an integrated system rather than as separately specified components assembled on-site.

A traditional brick-and-mortar build refers to constructing a data center facility in place, with civil works, electrical infrastructure, mechanical cooling plant, fire suppression, and physical security all designed, procured, and installed sequentially at the deployment site.

5 Key Differences Between Modular and Traditional Data Center Builds

The term “modular” is applied inconsistently across the industry. The precise definition is one where the core build work happens in a controlled factory environment, with on-site activity limited to foundation preparation, utility connection, and final commissioning.

Modular data center solutions significantly differ from traditional brick-and-mortar builds in cost and speed, with 60-75% faster deployment timelines compared to 18-36 months in traditional brick and mortar builds and around 15-25% cheaper infrastructure costs. This makes it an ideal choice for many companies starting out or seeking cost-effective solutions.

Why Is the Industry Moving Toward Modular Deployment Now?

The data center industry is operating under a deployment clock it has not had to watch this closely before. According to the International Energy Agency (IEA, 2024), global data center capacity is projected to more than double between 2024 and 2030, with AI workloads driving the majority of that growth. The window in which infrastructure decisions are tolerated as “in progress” has collapsed.

A facility that takes two years to come online today delivers its value into a market that has already moved past the assumptions it was designed around. This is the structural reason modular has moved from a niche option to a mainstream choice for hyperscalers, enterprises, and edge operators.

Did You Know?

AI rack densities have increased from an industry average of 8-15 kW per rack in 2020 to deployments now routinely exceeding 70-132 kW per rack, meaning infrastructure specifications written even 18 months ago may no longer match the workloads they were intended to support by the time a traditional build is completed.

How Do Modular Data Centers Cut Deployment Timelines by 30-50%?

The timeline compression is not a single saving; it is the cumulative effect of removing several sequential delays inherent in traditional construction. According to Uptime Institute’s deployment analysis (2024), the timeline difference between factory-built and stick-built data center projects of equivalent capacity ranges from 30% to 50% in favor of modular projects.

The compression comes from four specific mechanisms:

1. Parallel rather than sequential workflows. In a traditional build, design, procurement, civil works, electrical installation, and cooling commissioning largely occur in sequence. In a modular deployment, factory production runs in parallel with on-site preparation. The two streams converge at delivery rather than alternating across an extended timeline.
2. Factory testing replaces on-site commissioning iterations. Integration problems between independently installed power, cooling, and monitoring systems are typically discovered during on-site commissioning in traditional builds. Modular units complete this integration testing in the factory before delivery, eliminating the most common source of commissioning delay.
3. Reduced civil and structural lead times. Permitting cycles in urban environments routinely exceed six months for traditional builds. Modular deployments require significantly less civil work because the infrastructure unit provides its own structural enclosure, environmental control, and physical security.
4. Standardization removes specification cycles. Modular units use pre-validated reference designs where infrastructure calculations have already been performed. The design timeline collapses from months to weeks because the design choices have largely been made and tested in prior deployments.

What Does the Cost Comparison Actually Look Like?

The cost discussion around modular versus traditional builds is more nuanced than the timeline discussion. On a per-megawatt basis, large traditional builds at scale can achieve lower unit capital costs than modular equivalents, particularly at 50 MW and above, where extensive customization is justified by deployment size.

The cost advantage of modular shows up under different conditions:

• Deployments at the 1-10 MW scale where traditional construction overheads do not amortize efficiently
• Deployments where time-to-revenue matters more than absolute unit cost
• Deployments where capital must be released in phases rather than committed upfront
• Deployments where physical site constraints make traditional construction impractical

According to a 2024 451 Research analysis of mid-scale data center deployments, total project cost, including financing, delayed revenue, and risk-adjusted contingency, favors modular by 15-25% for deployments below 10 MW even when raw unit costs are comparable.

Did You Know?

A six-month deployment timeline difference is not just a six-month delay in revenue; it is also six months of avoided exposure to specification obsolescence. AI workload projections from twelve months ago routinely look conservative today, meaning faster deployments carry less risk of being commissioned into a market that has already moved past the original design assumptions.

What Does a Modular Data Center Architecture Actually Include?

Several vendors operate in the modular data center space, and their architectural choices vary meaningfully. The Schneider Electric prefabricated modular data center range provides a useful reference point for what a full modular architecture typically covers.

The EcoStruxure Row Data Center configurations integrate the following components into pre-engineered, factory-tested units:

• Racks: NetShelter SX enclosures with cable management and aisle containment
• Power distribution: Metered rack PDUs (Power Distribution Units) with outlet-level monitoring
• UPS (Uninterruptible Power Supply): Rack-mount UPS in N+N redundancy with 10-minute backup at full load
• Cooling: InRow DX precision cooling in N+1 redundancy
• Management: EcoStruxure IT monitoring software for remote visibility across all components
• Physical security: Biometric access control, CCTV, and environmental sensors
• Fire suppression: Integrated detection and suppression systems

For deployments at scale, prefabricated power, cooling, and IT modules can be combined to deliver megawatt-class capacity with the same factory-built advantages. Reference designs co-developed with NVIDIA support AI rack densities up to 132 kW per rack, demonstrating that modular architectures are not limited to lower-density traditional workloads.

When Is Modular the Right Choice, and When Is Traditional Still Better?

The modular-versus-traditional question is rarely about which approach is universally better. It is about which approach matches the specific constraints of the deployment in question.

Four scenarios consistently favor modular deployment:

1. Timeline-constrained deployments where lease expiries, support contract endings, regulatory deadlines, or AI rollout commitments make a traditional construction timeline structurally incompatible with the business requirement.
2. Phased capacity scenarios where the deployment will scale incrementally over multiple years and the operator wants to align capital deployment with actual demand growth.
3. Site-constrained deployments with limited land, leased premises, multi-tenant buildings, or remote locations where traditional construction logistics are difficult.
4. Operator capability constraints, where the organization lacks in-house engineering capacity to manage a traditional build and prefers to consume infrastructure as a delivered solution.

Traditional builds remain the right choice for hyperscale deployments at 50 MW and above, where unit economics at scale outweigh timeline pressures; for highly customized facilities with unique architectural or operational requirements; and for organizations with established construction expertise and long-horizon capital deployment models.

Which Vendors Lead the Modular Data Center Market in 2026?

The modular data center vendor landscape has consolidated around several established providers, with meaningful differences in product range, scale capability, and integration depth.

The following comparison reflects vendor positioning based on publicly available product information, deployment case studies, and industry analyst coverage as of 2026:

Schneider Electric

• Capacity Range: 10 kW – multi-MW
• Differentiator: Full-stack integration: NetShelter racks, Galaxy UPS, InRow cooling, EcoStruxure IT monitoring, validated AI reference designs with NVIDIA up to 132 kW per rack
• Best Fit For: Enterprises and operators seeking end-to-end pre-integrated solutions with global service infrastructure

Seimens

• Capacity Range: 10 kW – multi-MW
• Differentiator: Industrial automation and digital twin capability via Siemens Xcelerator, strong building management system integration, and SIVACOM modular electrification platforms
• Best Fit For: Hyperscale and industrial deployments where automation, digital twin orchestration, and integrated building infrastructure are primary design drivers

ABB

• Capacity Range: Up to 1 MW per module
• Differentiator: Strong electrification heritage with HiPerGuard and DPA UPS portfolios, integrated medium-voltage switchgear, and a focus on grid-interactive modular power infrastructure
• Best Fit For: Operators prioritising electrical infrastructure depth, grid integration, and high-availability power architecture in mission-critical environments

Huawei

• Capacity Range: 10 kW – multi-MW
• Differentiator: FusionModule range with strong presence in Asia-Pacific markets; integrated cooling and IT modules
• Best Fit For: Operators in Asia-Pacific regions where Huawei’s deployment footprint is strongest

Stulz

• Capacity Range: 10 kW – mid-scale
• Differentiator: Cooling-led architecture with precision cooling expertise; CyberRow and CyberHandler product lines
• Best Fit For: Deployments where cooling specification is the primary design constraint

Evaluation should always be supplemented by direct reference checks and site visits to meet specific deployment requirements.

What Should Decision-Makers Take from This?

The 50% timeline compression is not the whole story of why modular has moved from niche to mainstream. Still, it is consistently the headline reason that justifies the deeper conversation about which approach is right for a specific deployment.

The infrastructure decisions that match the pace of AI-driven capacity demand are the ones that will look right in retrospect. For most deployments below 10 MW, edge and enterprise capacity expansions, and any scenario where time-to-deploy is materially constrained, modular data centers are the architecture that aligns with the speed at which the industry is now required to operate.

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About the Author

Mayank Srivastav is Segment Director for Cloud & Service Providers (C&SP) at Schneider Electric, where he drives segment strategy and strategic client relationships across India’s cloud, colocation, and managed service provider market. With extensive experience across service delivery, sales leadership, business planning, and operations management, Mayank advises India’s leading cloud and AI infrastructure organisations on scalable modular power architecture and infrastructure planning for AI workload growth. He is a graduate of Jamnalal Bajaj Institute of Management, Mumbai University.

The post Speed-to-Deploy: How Modular Data Centers Cut Deployment Timelines by Up to 50% appeared first on Data Center POST.

TL;DR

• Operators must navigate high-density workloads, supply chain limits, and labor shortages that make traditional scaling timelines increasingly difficult to maintain.
• Power, cooling, and controls can no longer be treated as isolated workstreams; they must be integrated early to avoid late-stage bottlenecks.
• Factory-built modular methods allow teams to standardize core elements, reduce on-site complexity, and scale predictably phase-by-phase.

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Written by iMiller Public Relations on behalf of Compu Dynamics.

Data center operators in the AI era face the critical challenge of rapidly adding digital infrastructure capacity without overbuilding or introducing operational risk. Traditional construction timelines are increasingly strained by supply chain constraints, limited labor availability, and complex site readiness issues. These pressures are particularly acute in artificial intelligence and high-performance computing environments, which require infrastructure capable of supporting extreme power densities, complex liquid cooling, and strict performance metrics.

To overcome these modern speed barriers, operators must shift away from slow, sequential on-site construction phases. Speed in today’s market is no longer about saving a few weeks, but rather shaving entire months off deployment schedules. Achieving this level of acceleration requires early, integrated coordination of highly interdependent systems to prevent costly, late-stage bottlenecks.

A hybrid approach combining traditional integration with modular infrastructure offers a highly flexible path forward. While traditional builds remain essential for certain environments, factory-built modular methods allow operators to standardize critical elements while customizing density, cooling, and layouts. This approach minimizes on-site complexity, enables predictable, phased expansion, and provides the agility needed to respond to urgent demand without sacrificing planning discipline.

This content originally appeared on the Compu Dynamics website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post Navigating Speed and Scale Challenges in the AI Era appeared first on Data Center POST.

TL;DR

• Network expansion: Harbor Link is extending its underground fiber network into CoreSite’s Washington, D.C. campus.
• Greater resiliency: The deployment adds new carrier diversity across multiple data center facilities.
• Regional connectivity: The expansion strengthens connectivity options for enterprises, carriers, cloud providers, and network operators.

Reliable connectivity depends on more than bandwidth alone. Diverse network paths improve resiliency, reduce risk, and provide organizations with greater flexibility as infrastructure requirements continue to evolve.

Harbor Link is expanding its underground fiber network into CoreSite’s DC2 data center, extending connectivity across the company’s Washington, D.C. campus and Northern Virginia facilities. The deployment will provide additional carrier diversity across DC2, DC1, VA1, VA2, and VA3, enhancing one of the country’s most interconnected data center ecosystems.

Purpose-built underground infrastructure has become increasingly valuable in dense metropolitan markets where adding new network routes can be challenging. By creating additional physical pathways beneath rivers, highways, and other major crossings, providers can improve network resiliency while giving customers more options for connecting critical workloads.

“This expansion brings additional fiber diversity directly into CoreSite DC2, which is a key interconnection point in the region,” said Felix Dialoiso, Founding Partner at Harbor Link. “It strengthens the options available to customers that rely on scalable, low-latency connectivity across the Washington, D.C. metro ecosystem.”

The Washington, D.C. and Northern Virginia corridor continues to be one of the nation’s most significant interconnection markets, supporting a dense concentration of enterprises, cloud providers, carriers, and government organizations. As demand for connectivity grows, additional route diversity helps organizations strengthen redundancy while maintaining access to highly interconnected environments.

Harbor Link’s latest deployment reflects the continued investment in underground fiber infrastructure needed to support future connectivity requirements across the Mid-Atlantic. By expanding access into CoreSite’s regional campus, the company is adding another pathway for organizations seeking resilient, high-capacity connectivity throughout the Washington metropolitan area.

Additional details are available in the full announcement here.

The post Harbor Link Expands Underground Fiber Network into CoreSite’s Washington, D.C. Campus appeared first on Data Center POST.

TL;DR

• Unprecedented electricity demands from massive AI workloads, where individual server racks can exceed 100 kW, are overwhelming traditional electrical architectures, making robust power design a key differentiator for hyperscale data centers.
• Operators are shifting to “super-grid” transformers that connect directly to high-voltage transmission networks (at 380 kV or above). This direct link bypasses local distribution networks, dramatically reducing transmission energy losses and boosting overall reliability.
• Connecting directly to the transmission grid allows the Wustermark site to operate on 100% certified renewable energy, utilize 2N electrical redundancy, and support a “zero-generator” backup strategy.

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As artificial intelligence (AI) reshapes the global digital economy, the conversation around data centers often focuses on graphics processing units (GPUs), liquid cooling, and high-density compute. Yet behind every AI workload sits a less visible but critical piece of technology – the transformer.

These systems rarely attract headlines, but they have become foundational to the future of hyperscale infrastructure. Transformers regulate voltage, stabilize power delivery, and enable electricity to move efficiently from generation sources to compute environments. Without them, large-scale AI deployment simply cannot happen.

Today’s hyperscale campuses consume unprecedented amounts of electricity. AI training clusters operate continuously at massive scale, while rack densities continue to climb well beyond traditional enterprise thresholds. In this environment, power infrastructure is no longer a utility consideration buried in the background of facility design. Instead, it has become a strategic differentiator.

This shift is particularly visible in Europe, where data center operators are increasingly redesigning electrical architecture around transmission-level connectivity, renewable integration, and long-term sustainability goals.

Why traditional electrical architectures are under pressure

Transformer technology is based on the electromagnetic induction principle first demonstrated by Michael Faraday in the nineteenth century. Alternating current flowing through a primary winding creates a magnetic field that induces voltage in a secondary winding. By adjusting winding ratios, transformers either increase or decrease voltage depending on operational requirements.

That capability is central to modern power transmission. Electricity transported at higher voltages experiences lower current flow, dramatically reducing load related energy losses during transmission. Since power loss rises in proportion to the square of the current, minimizing current becomes essential for efficiency at scale.

Conventional data center infrastructure has historically relied on multiple stages of voltage transformation between the utility grid and the server floor. However, AI-scale environments are exposing the limitations of this approach.

Modern AI workloads require enormous, uninterrupted power capacity. Individual racks can now exceed 100 kW, while large training environments consume tens of megawatts continuously. Every inefficiency across the electrical chain increases operational costs, cooling demands, and carbon emissions.

As a result, operators are moving toward transmission-level infrastructure strategies that reduce transformation stages and improve overall efficiency.

The rise of super-grid transformers

A new generation of “super-grid” transformers is emerging as the preferred solution for hyperscale campuses. Unlike conventional transformers connected through regional distribution networks, these systems connect directly to high-voltage transmission infrastructure, often at 380 kV or above.

This approach fundamentally changes how large data centers interact with the power grid.

By connecting directly to transmission networks, operators can reduce electrical losses, improve reliability, and access significantly greater capacity. The architecture also enables tighter voltage regulation and enhanced operational resilience.

Super-grid transformers themselves are enormous engineering assets. Typically oil-filled for cooling and insulation, they are designed for decades of continuous operation under fluctuating load conditions. Their size reflects the scale of the challenge they are intended to solve – that is to support AI-driven infrastructure growth without compromising efficiency or stability.

The importance of this shift cannot be overstated. Global data center electricity demand is projected to rise dramatically over the next decade as cloud computing and AI adoption accelerate. Legacy electrical models were simply not designed for this level of sustained consumption.

For hyperscalers, securing scalable and reliable power access has become as important as securing land or fiber connectivity.

Wustermark: A new blueprint for AI infrastructure

One of the clearest examples of this evolution is the new hyperscale campus being developed west of Berlin by VIRTUS Data Centres.

The Wustermark campus is designed around direct connection to the 50Hertz 380 kV transmission network, enabling an initial deployment of 300 MW with future expansion capability scaling to 500 MW.

At the center of the project are two 185 MVA super-grid transformers, among the largest deployed within a European data center environment.

The significance of the project lies not only in its scale, but also in its architecture. By operating at the transmission level, the campus minimizes dependency on local distribution networks, avoiding strain on residential and commercial energy users while improving overall reliability.

The site is also engineered around full 2N electrical redundancy, providing dual independent paths for critical infrastructure systems. Combined with the highest transmission-level connectivity, this creates exceptionally high levels of resilience and availability.

Equally important is the operational flexibility the design enables. While diesel backup generation capability remains available, customers can also pursue a zero-generator strategy supported by the stability of the transmission network and renewable energy integration.

The campus will operate using 100% certified renewable energy, aligning high-performance compute infrastructure with Europe’s long-term decarbonization objectives.

The infrastructure behind the AI economy

The future of AI will depend not only on advances in semiconductors and software, but also on the ability to deliver enormous quantities of reliable, sustainable electricity.

Transformers may not command the same attention as GPUs or AI models, but they are becoming one of the most strategically important components in digital infrastructure.

Projects such as Wustermark demonstrate that it is possible to combine hyperscale growth, operational resilience, and environmental responsibility within a single infrastructure model.

As AI adoption accelerates globally, the operators that master power architecture, transmission-level connectivity, and renewable integration will define the next generation of digital infrastructure leadership.

The transformer, once considered basic utility equipment, is rapidly becoming the backbone of the AI economy.

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About the Author

Mike Golding, SVP Design & Build at VIRTUS Data Centres, leads Design and Construction activities in EMEA. He is a member of the VIRTUS executive leadership team, bringing over 30 years’ experience in the wider construction industry and 20 years of specific data center design and delivery expertise delivering over 750MW of capacity for lease providers and self-build programs of work during this time.  Mike is a strong advocate of construction safety, scheduling predictability, sustainability & quality and brings deep experience of end-to-end data center delivery in established and new geo expansions in EMEA.

The post The Super-Grid Transformer Revolution Powering AI Data Centers appeared first on Data Center POST.

TL:DR

• While early discussions focused mostly on land, power, and water, companies are increasingly forced to integrate logistics directly into their broader deployment, maintenance, and expansion plans.
• Because AI infrastructure projects must move much faster than traditional development cycles, logistics providers must offer accelerated, secure, and highly reliable deployment models to bring capacity online quickly.
• Companies are moving away from purely centralized distribution models in favor of regional logistics hubs near major infrastructure corridors to drastically reduce transit times and keep critical inventory close to data centers.

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The rapid growth of artificial intelligence is driving unprecedented investment in digital infrastructure. Across the United States, hyperscalers and AI infrastructure providers are expanding their footprints to support growing demand for computing power, creating new opportunities for regions that can support large-scale data center development.

While discussions around AI infrastructure often focus on availability of land and technology or resources like power and water, logistics is becoming an increasingly important part of the equation. As data center development accelerates, companies are rethinking how supply chains, inventory, transportation, and deployment services support infrastructure growth. This shift is changing logistics strategy in several important ways.

Speed-to-deployment is becoming a competitive advantage

AI infrastructure projects are moving at a different pace than traditional development cycles. Organizations are under pressure to bring new capacity online quickly, creating demand for logistics models that can support faster deployment timelines.

As a result, logistics providers are being asked to do more than move products from one location to another. They must help ensure critical equipment is available when and where it is needed, while maintaining security and reliability throughout the process. The ability to support accelerated deployment timelines is becoming a key differentiator as infrastructure investment continues to grow.

As data center development expands across the country, logistics networks are evolving alongside it. Rather than relying solely on centralized distribution models, companies are increasingly evaluating how regional logistics hubs can support emerging data center markets. Facilities located near major infrastructure corridors can help reduce transit times, improve responsiveness, and position critical inventory closer to deployment sites. This approach reflects a broader shift toward logistics networks designed around proximity, flexibility, and speed.

Hyperscalers have new expectations for logistics partners

The needs of hyperscalers and AI infrastructure companies differ significantly from those of traditional logistics customers. Data center environments often require specialized handling, secure storage, coordinated deliveries, and services that support equipment throughout its lifecycle. As a result, logistics providers are increasingly expected to manage complex infrastructure deployments while meeting stringent operational and security requirements. With continued growth in AI infrastructure investment, these expectations are becoming a more prominent part of the logistics landscape.

Logistics is becoming part of infrastructure strategy

As AI investments accelerate, logistics is increasingly embedded in broader infrastructure planning. Organizations are evaluating not only where to build, but also how to support deployment, maintenance, and future expansion. The ability to position inventory strategically, manage complex delivery requirements, and enable faster deployment timelines is becoming an important consideration in how infrastructure projects are designed and executed.

The next phase of AI growth will require more than computing power and physical facilities. It will also depend on logistics networks that bring that infrastructure online efficiently and at scale.

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About the Author

Andreas Podwojewski serves as Managing Director for Arvato North America & Brazil, leading the organization and overseeing strategic and operational performance across the company’s Americas footprint. Based in Pleasant Prairie, Wisconsin, he is responsible for driving growth, accelerating operational excellence, and ensuring cross‑regional alignment with Arvato’s global strategy.

Andreas provides executive leadership on enterprise‑critical initiatives, including footprint expansion, automation, and digital transformation, designed to embed continuous improvement and long‑term strategic discipline across the organization. With deep experience leading high‑impact teams and complex operations, Andreas brings a global perspective and forward‑looking leadership philosophy.

The post How AI Investment Is Changing Logistics and Infrastructure Strategy appeared first on Data Center POST.

TL;DR

• Responsibly planned data centers can provide lasting economic and community benefits.
• Local investments often extend beyond the facility to infrastructure and workforce development.
• Community engagement is essential to balancing growth with sustainability.
• Digital infrastructure can help strengthen local economies while supporting the AI-driven future.

# # #

Written by iMiller Public Relations on behalf of Nomad Futurist.

As communities evaluate new data center developments, public discussion often centers on power consumption, water usage, and environmental impact. While these are important considerations, Nomad Futurist Student Advocate Prathyaj Mantha encourages communities to also recognize the long-term economic and infrastructure benefits responsibly planned data centers can deliver.

Beyond supporting AI and the digital economy, modern data centers can generate local tax revenue, create skilled jobs, strengthen utility infrastructure, and expand workforce development opportunities through partnerships with educational institutions. The article emphasizes that thoughtful planning and community engagement can help ensure these facilities become valued long-term partners while supporting sustainable growth.

This content originally appeared on the Nomad Futurist website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post The Case for Community-Centered Data Centers appeared first on Data Center POST.

• Permits secured: VFE Networks has cleared the key licenses and permit needed to move Phase 1 forward.
• Local partners: Express-tek and S&N Infrastructure are helping support the build and deployment.
• Phase 1 scope: A 126-mile diverse fiber route from Northern Virginia to Richmond-Sandston, designed for redundancy and AI-ready capacity.

# # #

Virginia Fiber Express Networks LLC (VFE Networks), a wholly owned subsidiary of Infraforward Strategies LLC, has secured the licenses and permit needed to advance Phase 1 of its planned fiber network. In partnership with Express-tek and S&N Infrastructure, the company is moving forward with a project built to support rising demand for digital infrastructure across Virginia and beyond.

Phase 1 will deliver a 126-mile diverse fiber route from Northern Virginia to Richmond-Sandston, creating a redundant path outside the I-95 corridor. The network is being designed to support conduit, dark fiber IRUs, dark fiber, waves, and spectrum capacity, with an AI-ready focus built for edge inference.

“These licenses and permit unlock our ability to build and operate infrastructure designed for the demands of the data center industry pushing south out of an increasingly constrained Northern Virginia market,” said Joel M. Allen, CEO of VFE Networks. “Our diverse, non-I-95/ Route 1 route and interconnected network phases reflect a commitment to infrastructure that is resilient, future-proof and purpose-built to support autonomous network deployment while being aligned with Virginia’s long-term support of broadband expansion.”

Express-tek brings deep engineering and permitting experience to the project, while S&N Infrastructure adds decades of communications construction expertise across the Mid-Atlantic and Southeast.

“Our local knowledge of Virginia’s right-of-way landscape positions VFE Networks to move efficiently from licensure to construction,” said Stephen Brown, President of Express-tek. “Two generations of engineering and permitting expertise go into every route we design, and we are proud to help build infrastructure that will define the region’s digital future.”

“S&N has built and maintained communications networks across the Mid-Atlantic and Southeast for over 40 years, and we know how to execute in Virginia,” said Sean Davis, President and CEO of S&N Infrastructure. “This project is a meaningful step forward for the Commonwealth’s digital infrastructure, and we are committed to bringing the same reliability we are known for to Phase 1.”

With operator licenses and the initial permit now in place, VFE Networks is advancing construction and targeting a Ready for Service date in 2027.

To learn more about VFE Networks, visit vfenet.net.

The post Virginia Fiber Express Networks Advances Phase 1 of Statewide Fiber Build appeared first on Data Center POST.

TL;DR

• The data center industry is no longer evaluated solely on speed-to-market, power capacity, or capital deployment; long-term success is now heavily defined by community trust and relationship building.
• Responsible developers communicate with local officials and residents early before plans are finalized to address community concerns (such as noise, traffic, and water use) directly, preventing misinformation from filling the vacuum.
• Industry leaders align their projects with existing zoning frameworks and actively prioritize brownfield revitalization over rezoning farmland or green spaces.
• Operators build credibility by sharing real sustainability metrics and investing in advanced, water-efficient cooling systems that challenge outdated public narratives regarding resource consumption.
• The strongest players move past transactional mindsets to focus on multi-decade planning, working collaboratively with utilities to fortify the local grid and educating communities on the broad economic ecosystem they support.

# # #

As I take a moment to reflect on the past couple of months, and since an incredible conversation that was had at ITW 2026 during the panel titled: Debunking the data center misinformation dilemma. One thing became clear. the data center industry is no longer being judged solely on speed-to-market, power availability or capital deployment. It is being judged on trust.

And while headlines often focus on conflict, opposition and controversy, the reality is more nuanced. Many developers, operators and investors are already demonstrating what responsible digital infrastructure development looks like,  and those organizations are increasingly separating themselves from companies still relying on outdated development models.

The difference is becoming impossible to ignore. The companies succeeding today are not simply building faster. They are building smarter, more transparently and with greater respect for the communities they enter.

Based on our lively conversation, here are 10 ways industry leaders are earning trust, and how the industry can continue distinguishing responsible development from the practices that typically fuel opposition and distrust.

1. Responsible Developers Engage Early

One of the clearest distinctions discussed during the discussion at ITW 2026 was timing. The best operators engage communities before projects escalate into controversy.

They:

• Hold listening sessions
• Meet with local officials early
• Educate stakeholders
• Explain impacts transparently
• Answer difficult questions directly

An approach quickly becoming a red flag to communities and those opposed to data center developments is showing up with finalized site plans and expecting communities to simply approve them. It was discussed that many communities are overwhelmed by the pace and complexity of AI and digital infrastructure growth. So, when developers fail to communicate early, misinformation fills the vacuum. Industry leaders today need to understand that community engagement is not a permitting exercise. It is part of development itself.

2. Strong Projects Respect Existing Zoning

Some of the industry’s best-performing projects are succeeding because they align with:

• Industrial zoning
• Existing infrastructure corridors
• Long-term municipal planning
• Compatible land use

Our discussion at ITW 2026 repeatedly reinforced the importance of proper siting and respecting community planning frameworks. In contrast, some projects continue attempting to force incompatible development into areas never intended for large-scale infrastructure.

As a result, we are seeing this approach create:

• Political backlash
• Community distrust
• Unnecessary litigation
• Long-term reputational damage for the broader industry

The panelists convened informally after the session and shared that they are seeing developers pursuing parcels with little chance of community or zoning alignment, which is contributing to the problems and highlights that this creates a cascading effect for future and other developers that follow.

The strongest developers understand that just because land is available does not mean it is appropriate.

3. The Best Developers Revitalize Brownfields

One of the most compelling themes from the panel was the growing focus on brownfield redevelopment.

Leading organizations are transforming:

• Former factories
• Industrial sites
• Contaminated properties
• Aging manufacturing campuses

Examples discussed included projects that:

• Remediated environmental contamination
• Restored tax revenue
• Reused existing infrastructure
• Reduced prior industrial water consumption

These projects demonstrate what responsible digital infrastructure development can look like.

In contrast, projects that seek to rezone farmland or green space without broader community value creation, tend to become flashpoints for opposition. The lesson is not that communities oppose development.  It is that communities increasingly expect thoughtful development that fits the community and is mutually beneficial.

4. Transparent Operators Share Data

Another major differentiator emerging in the industry is transparency. The strongest operators increasingly provide:

• Sustainability reporting
• Energy metrics
• Water usage data
• Third-party certifications
• Measurable environmental benchmarks

The discussion among the panelists highlighted growing efforts by the Energy Information Administration to collect energy and water usage data from data centers as part of broader policy planning discussions. The industry leaders are already adapting. Communities today expect evidence, not talking points. And increasingly, regulators do too.

5. Responsible Companies Acknowledge Impacts

One of the panel’s most important undercurrents was the recognition that not all community concerns are irrational. As a matter of fact, the top concerns include: Noise, traffic, visual impact, power planning and water consumption.

The best companies address those concerns directly and explain things like:

• Mitigation strategies
• Operational realities
• Tradeoffs
• Infrastructure investments
• Long-term planning considerations

The weaker approach is dismissiveness. Communities lose trust quickly when developers imply residents are simply “anti-growth” or uninformed. Responsible operators understand that acknowledging concerns builds credibility.

6. The Industry’s Best Players Explain the Full Economic Ecosystem

Another major distinction discussed during the session was how economic impact is communicated. The industry often undersells itself by focusing only on direct facility employment. The reality is far broader.

Data centers support:

• Engineering
• Utilities
• Construction
• Hospitality
• Remote workforce enablement
• Local service businesses
• Environmental consulting
• Technology ecosystems
• Municipal tax bases
• And more

The  positive economic ripple effect of data center developments extends far beyond the facility footprint itself. Think of all of the newspapers sold; environmental firms raising membership; service providers; printers and other ancillary business or opposition support. It is an economic driver from all directions.

The strongest companies help communities understand the full ecosystem impact beyond what the local facility can employ.

7. Modern Developers and Operators Invest in Efficiency

During the panel conversation, water usage became one of the most heavily discussed topics. Importantly, the conversation highlighted how dramatically cooling technologies have evolved.

For example:

• Campuses using less water than prior industrial uses (golf courses, papermills, are two examples that notoriously use a lot of water)
• Facilities reducing agricultural-era consumption
• modern cooling systems lowering overall water demand substantially

The industry’s best operators are investing heavily in:

• Liquid cooling
• Efficiency optimization
• Advanced thermal management
• Closed-loop systems
• Smarter infrastructure design

Meanwhile, outdated public narratives often still assume every modern data center operates like facilities built decades ago. And, it’s this assumption that is causing consternation in the market and driving opposition narratives that are false and misleading.

The industry must continue educating communities on how rapidly technology is improving.

8. Responsible Operators Build with and for fortification of the Grid

Another myth directly addressed during the panel was the idea that data centers operate continuously on diesel generators or receive priority over homes during outages.

The panel discussion made it clear::

• Generators are backup systems
• Utilities prioritize homes and hospitals first
• Many operators invest in their own infrastructure capacity
• Grid coordination is becoming increasingly sophisticated

The strongest operators work collaboratively with utilities and municipalities on long-term planning. The weakest approach infrastructure as purely transactional, focusing only on securing capacity instead of helping strengthen regional resilience. That distinction matters more every year.

9. Industry Leaders and Proven Developers Think Long-Term

One of the more subtle themes throughout the session was the difference between long-term infrastructure planning and speculative development behavior that we are seeing from a lot of land developers seeking to get into the data center arena.

The best organizations are:

• Planning multi-decade infrastructure investments
• Engaging stakeholders consistently
• Adapting designs thoughtfully
• Integrating sustainability goals
• Aligning with regional planning frameworks

Others continue chasing opportunistic land acquisitions and rapid entitlement strategies without broader alignment. And, this is where communities are becoming increasingly sophisticated at identifying the difference.

Unfortunately, once trust is broken in a market, it becomes significantly harder for responsible developers to operate there afterward.

10. The Future Belongs to Companies That Understand Trust Is Infrastructure

Ultimately, the panel reinforced a powerful reality: Digital infrastructure development is no longer only an engineering exercise nor is it for the land-use attorneys to navigate. It is a relationship exercise.

The companies leading the industry forward understand that successful projects require:

• Technical excellence
• Community trust
• Political awareness
• Transparency
• Long-term accountability
• Respect for land use planning

And importantly, many companies are already demonstrating what that future looks like. The goal now is not to reinvent the industry. It is to scale the best practices already emerging across it, and continue separating responsible infrastructure leadership from the behaviors that continue to create resistance.

Because the strongest projects are not simply the ones that get approved fastest. They are the ones communities are ultimately proud to have.

To learn more about how Ilissa Miller and the iMiller Public Relations team can help well intended developers to approach the market with compassion, empathy and respect, visit  www.imillerpr.com.

The post 10 Ways the Data Center Industry Is Earning Community Trust — And Where the Industry Still Gets It Wrong appeared first on Data Center POST.

TL;DR

• Lightpath is extending its AI-grade fiber network to support two new hyperscale data center campuses under construction in Saline, Michigan and Port Washington, Wisconsin.
• Each campus is planned to exceed one gigawatt of capacity, requiring route-diverse, multi-terabit fiber connectivity.
• The projects continue Lightpath’s expansion into the markets where hyperscale AI infrastructure is being built.
• The deployments highlight the growing role of fiber infrastructure in supporting AI-driven compute demand and next-generation digital infrastructure.

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Artificial intelligence is reshaping where digital infrastructure is built and how networks are designed to support it. As hyperscale data center development accelerates, fiber infrastructure has become a critical component of ensuring these facilities can move the massive volumes of data AI workloads require.

To support this next phase of AI infrastructure growth, Lightpath has announced new fiber builds serving two hyperscale data center campuses currently under construction in Saline, Michigan and Port Washington, Wisconsin. Each campus is planned to exceed one gigawatt of capacity and will be connected through route-diverse, multi-terabit fiber infrastructure designed to support high-capacity, low-latency connectivity.

The Saline deployment is expected to be completed by the end of 2026, with the Port Washington build scheduled for the second quarter of 2027. Both projects are being delivered in partnership with an anchor hyperscale customer, extending Lightpath’s AI-grade network into markets experiencing significant investment in AI and cloud infrastructure.

These deployments build on Lightpath’s broader network expansion strategy, following recent growth across Phoenix, Eastern Pennsylvania, Columbus, and the company’s first long-haul corridor connecting Columbus and Chicago. Together, these investments reflect a continued focus on expanding owned fiber infrastructure into the regions where hyperscale computing capacity is growing most rapidly.

“Lightpath is playing an increasingly central role in partnering with hyperscalers to build new fiber infrastructure to address AI-driven demand across the U.S.,” said Chris Morley, CEO of Lightpath. “Fiber infrastructure remains a critical component in the evolving and accelerating AI ecosystem.”

As AI campuses continue increasing in both scale and power requirements, connectivity demands extend beyond simply bringing fiber to a site. Large-scale deployments require engineered, route-diverse networks capable of delivering resilient, multi-terabit capacity while integrating new construction with existing network assets and strategic partner infrastructure.

These latest projects reinforce how AI infrastructure development is changing the geography of network investment. As hyperscalers expand into new markets selected for available land and power, the supporting fiber infrastructure must evolve alongside them to deliver the connectivity these facilities require.

Read more in the press release here.

The post Lightpath Expands AI-Grade Fiber to Meet Growing Hyperscale Demand appeared first on Data Center POST.

TL;DR

• A Stronger Platform for Growth: Sabey Data Center Properties (SDCP) has welcomed a minority equity investment from Ares Secondaries funds, expanding its ownership base and strengthening its long-term growth strategy.
• Meeting Rising Infrastructure Demand: The investment comes as demand for data center capacity continues to grow, creating additional opportunities to expand SDCP’s existing campuses.
• A Vote of Confidence in SDCP: The partnership reflects confidence in SDCP’s operating platform and positions the company to continue delivering mission-critical infrastructure for customers across the United States.

# # #

As demand for digital infrastructure ramps up, the companies building tomorrow’s data centers are increasingly looking beyond traditional financing to support long-term expansion. Strategic investment has become an important part of that equation, providing both capital and confidence in a platform’s ability to meet growing customer demand.

Sabey Data Center Properties (SDCP) has taken another step in that direction with a minority equity investment from Ares Secondaries funds. The investment expands SDCP’s ownership base while strengthening the long-standing partnership between Sabey Corporation and National Real Estate Advisors. It also provides additional institutional capital to support the company’s disciplined approach to future growth.

Driven by the rapid growth of cloud computing and artificial intelligence, demand for data center infrastructure continues to rise. SDCP currently operates six energized campuses totaling approximately 251 megawatts of operating capacity, with existing land holdings that provide the opportunity to nearly triple that capacity by 2036. The additional investment helps position the company to accelerate development across its current campuses while pursuing future opportunities in key markets.

“Welcoming Ares as an investor is a strong endorsement of SDCP’s platform, our team and the long-term demand we’re seeing for scaled data center infrastructure,” said Tim Mirick, President of SDCP. “With Sabey, National and now Ares aligned behind the business, we are well positioned to execute our disciplined growth plan and deliver the mission-critical capacity our customers need.”

The investment also reinforces the strength of the partnership that has guided SDCP’s growth. National Real Estate Advisors described the addition of Ares as a natural extension of its long-standing relationship with Sabey, while Ares noted that SDCP’s high-quality operating portfolio and future development opportunities made the platform an attractive investment. Together, the three organizations are aligned around supporting the next phase of SDCP’s expansion.

As enterprises continue investing in AI and other digital technologies, the need for reliable data center infrastructure is expected to grow alongside them. With additional institutional support and a clear path for expansion, SDCP is well positioned to continue developing the capacity needed to support customers while reinforcing its role as one of the nation’s leading privately held data center platforms.

The post Strategic Partnership Accelerates Growth at Sabey Data Center Properties appeared first on Data Center POST.

TL;DR

• The global data center chillers market is projected to grow from USD 2.8 billion in 2026 to USD 6.4 billion by 2035 at a 9.6% CAGR, propelled by escalating rack densities from generative AI and high-performance computing workloads.
• Water-cooled systems remain the dominant technology, holding a 61.8% market share (USD 1.6 billion) in 2025 with a projected 10% CAGR. Meanwhile, air-cooled systems (32.9% share) are scaling rapidly in water-scarce regions.
• Technological innovation is shifting toward oil-free and magnetic-bearing platforms, which can cut compressor power consumption by up to 49% at part-load cycles to help operators hit strict Power Usage Effectiveness (PUE) targets.

# # #

The global data center chillers market is poised for significant expansion, projected to grow from USD 2.8 billion in 2026 to USD 6.4 billion by 2035. According to a comprehensive industry report published by Global Market Insights Inc., the market will advance at a steady compound annual growth rate (CAGR) of 9.6% during the forecast period. This follows a strong baseline year in 2025, where the market was valued at USD 2.6 billion.

The primary catalyst for this aggressive growth is the global transition into a hyper-digital era. The exponential rise of generative artificial intelligence (AI), machine learning, and high-performance computing (HPC) workloads has radically increased rack power densities. Because cooling demands scale directly with IT loads, modern multi-megawatt hyperscale and colocation facilities require massive, industrial-grade chiller systems to maintain operational stability and prevent thermal stress.

Water-Cooled Systems Dominate, Air-Cooled Solutions Scale in Arid Regions

• Water-Cooled Chillers: This segment dominated the landscape in 2025, capturing a 61.8% market share and generating USD 1.6 billion in revenue. Prized for their superior heat transfer efficiency and scalability, water-cooled systems are projected to grow at a 10% CAGR through 2035, especially across large-scale hyperscale campuses.
• Air-Cooled Chillers: Holding a 32.9% market share in 2025, air-cooled units are expanding rapidly due to speed-to-capacity advantages, simpler installation, and strict water-conservation mandates in drought-prone regions like the Western U.S. and Northern China.

The Rise of Oil-Free and Magnetic-Bearing Innovations

In response to tightening environmental policies and stringent Power Usage Effectiveness (PUE) targets, the industry is shifting rapidly toward technological innovations. Magnetic-bearing centrifugal chillers and oil-free platforms are emerging as key growth drivers. Validated data indicates these advanced systems can cut compressor power draw by up to 49% at part-load cycles, making them highly attractive to procurement teams retrofitting older halls or building out dedicated AI zones.

Regional Outlook: North America Leads, Asia-Pacific Accelerates

North America remains the largest market, valued at USD 1.3 billion in 2025, heavily propelled by massive domestic investments in AI infrastructure. Meanwhile, the Asia-Pacific region is slated to log the fastest growth rate with a 10.8% CAGR through 2035. Rapid infrastructure updates in China and aggressive government-backed digital initiatives in India are positioning APAC as a primary hub for future data center deployments.

Consolidated Competitive Landscape

The data center chillers market is highly consolidated, with the top five players, Johnson Controls, Carrier Global, Daikin, Trane Technologies, and Vertiv, collectively commanding 74.2% of the market in 2025. Johnson Controls emerged as the individual market leader, holding a 20.9% market share. Prominent players are actively introducing modular, water-free, and high-capacity units (such as recent 2026 launches from Panasonic and Johnson Controls) to capture emerging demand from generative AI infrastructures.

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TL;DR

• Every Industrial Revolution has fundamentally changed workforce requirements and influenced the evolution of education.
• Digital technologies and AI are transforming how knowledge is delivered, accessed, and personalized for future learners.
• Universal access to education remains essential to developing the skilled workforce required for continued innovation.
• Digital infrastructure organizations can play an important role by supporting workforce development and expanding educational opportunities.

# # #

Written by iMiller Public Relations on behalf of Nomad Futurist.

How Industrial Revolutions Have Influenced Learning

Throughout history, every Industrial Revolution has transformed not only the way people work, but also how they learn. From mechanized manufacturing and electrification to digital technologies and artificial intelligence, each wave of innovation has reshaped workforce demands and driven changes in educational systems designed to prepare future generations.

In the latest Nomad Futurist thought leadership article, Melissa Reali-Elliott explores how advances in technology have influenced education over time, rom apprenticeships and public schools to standardized curricula, digital learning, and today’s AI-powered educational tools. As technology has evolved, so too has the need for broader access to knowledge and new approaches to workforce development.

Preparing for the Next Era of Opportunity

The article suggests that the next Industrial Revolution will be driven by artificial intelligence and personalized learning experiences that make education more accessible and adaptable than ever before. As digital infrastructure continues to expand, organizations have an opportunity to contribute by supporting educational initiatives, sharing industry knowledge, and helping prepare the workforce that will build tomorrow’s technologies.

Understanding how previous industrial revolutions shaped education provides valuable perspective for today’s digital infrastructure leaders as they invest in both technology and the people who will power the industry’s future.

This content originally appeared on the Nomad Futurist website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post The Ripple Effect of Industrial Revolutions: How Innovation Continues to Shape Education appeared first on Data Center POST.

TL;DR

• New agreement: Duos has signed a Master Services Agreement with Nistar for up to 2 MW of critical IT-load capacity and related utility infrastructure.
• Columbus expansion: The deployment will be located at Duos’ Columbus, Georgia campus, bringing the current contracted total there to 10 MW.
• Built for AI: The deployment is designed for high-density AI and HPC workloads with Tier III-equivalent infrastructure and N+1 redundant power and cooling.

# # #

Duos Technologies Group Inc. (Nasdaq: DUOT), through subsidiary Duos Edge AI, has executed a Master Services Agreement with Nistar to deploy up to 2 MW of critical IT-load capacity together with supporting utility infrastructure. The agreement marks another step in the continued expansion of Duos’ AI infrastructure platform, as it’s designed to support efficient server installation, commissioning, and operations while allowing final footprint and technical requirements to align with Duos’ deployment schedule.

“This important step with Nistar establishes a framework for material expansion of our existing compute footprint, and we look forward to advancing toward full deployment in the coming months,” said Duos CEO Doug Recker. “The growing demand for AI infrastructure continues to create significant opportunities for scalable, edge-focused data center deployments, and we believe our platform is well positioned to support these emerging requirements.”

The deployment will be located at Duos’ Columbus, Georgia campus and delivered as a fully Ready for Service environment for high-density AI and high-performance compute workloads. The agreement brings the current contract total at the site to 10 MW, with billing expected by the end of August 2026.

“Duos’ high-density data center infrastructure gives Nistar a practical path to serve an institutionally backed AI-compute client requiring approximately 2 MW of critical IT-load capacity to support a planned deployment of 1,024 NVIDIA B200 GPUs,” said Jay Sivam, Chief Executive Officer of Nistar. “…It represents an important step in Nistar’s broader strategy of delivering scalable, financeable AI and high-performance-computing infrastructure where speed to deployment, power availability, and high-density design are critical.”

The dedicated deployment will be supported by Tier III-equivalent infrastructure with N+1 redundant power and cooling systems to support high availability and reliability. Duos also plans to install an additional 10 MW between October and November 2026 as it continues scaling the Columbus campus.

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

The post Duos Edge AI Signs Agreement for an Additional 2 MW Deployment appeared first on Data Center POST.

TL;DR

• AI continues to influence facility design, infrastructure planning and long-term operational strategies.
• DICE National reinforced the importance of integrating power, capital, construction and workforce planning into every phase of development.
• Regional DICE events demonstrated how local market conditions continue to shape investment, site selection and deployment strategies.
• Construction innovation, operational readiness and industry collaboration remain essential to delivering the next generation of digital infrastructure.

# # #

The second quarter of 2026 continued the momentum of BISNOW’s Data Center Investment Conference & Expo (DICE) series, bringing together leaders from across the digital infrastructure ecosystem to examine the issues influencing data center development around the world. From regional markets in the United States to one of Europe’s fastest-growing investment hubs, each conference provided a platform for discussions on AI infrastructure, power availability, construction, capital investment and operational strategy.

The quarter featured Data Center Investment Conference & Expo (DICE) Southwest, DICE National, DICE Pacific Northwest, DICE Ohio, Data Center Construction, Design & Development Conference West, and the UK Investment & Development Conference. While each event addressed the priorities of its respective market, the conversations revealed many of the same opportunities and challenges facing developers, operators, investors and infrastructure providers.

AI Continues to Influence Infrastructure Planning

Artificial intelligence remained at the center of nearly every t discussions focusing on how AI workloads are changing infrastructure requirements across every stage of the project lifecycle. Developers are planning campuses capable of supporting higher rack densities, larger power demands and next-generation g cooling technologies while maintaining the flexibility to accommodate future advances in compute infrastructure.

Those conversations extended well beyond facility design. Sessions throughout DICE National, DICE Pacific Northwest and Construction, Design & Development West examined how AI is influencing network architecture, construction sequencing, operational planning and long-term investment decisions. Rather than designing for today’s requirements alone, organizations are building infrastructure that can adapt as AI technologies continue to mature.

Regional Markets Are Defining New Growth Opportunities

The regional DICE conferences highlighted how local market conditions continue to shape development strategies while contributing to broader industry growth. Each location presented its own set of opportunities, demonstrating that successful projects require solutions tailored to regional infrastructure, regulatory environments and available resources.

In the Southwest, conversations centered on site selection, water availability, sustainability and operational planning as Arizona continues to attract AI-driven investment. The Pacific Northwest focused on modular design, construction technologies and operating high-density facilities while balancing reliability, efficiency and deployment speed. Meanwhile, DICE Ohio examined one of the country’s fastest-growing emerging markets, with discussions addressing power systems, resilient infrastructure, construction delivery and the practical realities of supporting AI-ready facilities in a rapidly expanding region.

Across the Atlantic, the UK Investment & Development Conference examined many of the same issues from a European perspective. Investment activity, planning approvals, land availability and energy procurement remained central topics as organizations evaluated strategies for expanding capacity while meeting sustainability objectives and navigating an increasingly competitive marketplace.

DICE National Focused on the Industry’s Biggest Priorities

As BISNOW’s flagship conference , DICE National, brought together industry leaders from across the digital infrastructure ecosystem for three days of discussions spanning the entire project lifecycle. The topics ranged from capital deployment and site selection  to AI-ready network architecture, power economics, cooling technologies, workforce development and responsible AI.

Power availability remained one of the event’s defining  themes. Sessions explored how organizations are evaluating utility partnerships, powered land, battery storage, on-site generation and diversified energy strategies while balancing cost, sustainability and long-term resiliency. Additional discussions examined community engagement, permitting, construction delivery and workforce development, reflecting the increasingly interconnected nature of modern data center projects.

Construction Innovation and Operational Readiness Continue to Advance

Construction remained another major focus throughout the quarter as the industry looks for ways to accelerate deployment without compromising quality or reliability. Modular construction, prefabrication, standardized designs and digital project management technologies were recurring topics across multiple conferences, reflecting the industry’s effort to improve predictability while reducing project schedules.

Operational excellence also received significant attention as higher-density computing environments continue to introduce new challenges for facility management. Sessions explored advanced monitoring, automation, liquid cooling, thermal management and operational controls designed to improve efficiency while supporting increasingly demanding AI workloads. These discussions reflected the growing recognition that successful projects require equal attention to both construction execution and long-term operations.

Collaboration Remains Central to Industry Progress

One theme consistently e emerged across every DICE event: delivering digital infrastructure has become a collaborative effort that extends well beyond individual organizations. Developers, utilities, contractors, investors, engineering firms, technology providers and public agencies all contribute to successful project outcomes, particularly as developments become larger and more complex.

Early coordination, transparent communication and integrated planning were recurring themes throughout the quarter. Whether discussions focused on power procurement, permitting, construction sequencing or community engagement, participants agreed that collaboration is helping organizations reduce project risk while improving delivery timelines and long-term operational success.

Building Momentum Across the Digital Infrastructure Industry

The second quarter of BISNOW’s DICE event series reflected an industry focused on preparing for sustained growth rather than simply adding capacity. Organizations are approaching development with greater attention to energy strategy, construction planning, operational resilience and long-term flexibility as AI continues to influence infrastructure requirements around the world.

Although each conference addressed different regional priorities, the conversations consistently pointed toward a common objective: building digital infrastructure that can support future demand while responding to evolving technology, energy and market conditions.

To learn more about upcoming DICE events and register for future conferences, visit BISNOW’s events page at www.bisnow.com/events.

The post BISNOW DICE Event Series Spotlight the Evolution of Data Center Development appeared first on Data Center POST.

TL;DR

• Fuel degradation happens quietly over long storage periods, leading to clogged filters and catastrophic generator failures when emergency power is needed most.
• Hydrotreated Vegetable Oil (HVO/R99) offers excellent storage stability, direct engine compatibility, and cleaner emissions, helping facilities meet sustainability goals while securing reliable power.
• Under the 2026 Renewable Fuel Standard, end users (such as data centers) face higher RIN obligations, making supplier transparency and strict compliance documentation vital to managing regulatory and financial risks.

# # #

Mission-critical facilities do not get a second chance when fuel goes bad, storage conditions drift, or supply chains tighten. For data centers, hospitals, utilities, and emergency systems, the difference between resilience and disruption often comes down to fuel quality, testing, and planning.

Why fuel failures happen

Fuel failures usually start quietly. Water intrusion, microbial growth, oxidation, contamination, and long storage periods can all degrade diesel before anyone notices. In backup power systems, that can lead to clogged filters, poor starts, engine wear, or generator failure exactly when the system is needed most.

The risk is growing because many facilities are balancing longer runtime expectations, more frequent testing, and tighter environmental goals. At the same time, supply chain conditions and regulatory changes are making fuel selection and handling more complex.

What resilient facilities monitor

Strong fuel programs focus on a few basics. Regular sampling and testing help catch contamination early, while tank inspections and housekeeping reduce the chance of water and sediment buildup. Documentation matters too, because quality issues are easier to prevent when the facility can trace fuel origin, storage history, and compliance records.

For renewable diesel users, the same discipline applies. ESI Total Fuel Management notes that hydrotreated vegetable oil (HVO/R99) offers high storage stability, direct compatibility with existing diesel engines, and cleaner combustion than conventional diesel or biodiesel blends. ESI also highlights the importance of Certificates of Compliance, supplier transparency, and testing for ESG compliance.

Renewable diesel and compliance

Renewable diesel can improve resiliency, but only if the fuel is sourced and managed correctly. ESI’s materials explain that HVO is a hydrocarbon fuel made from waste oils and animal fats, with strong cold-weather performance and lower emissions than fossil diesel. The company also notes that biomass-based diesel is changing under the 2026 Renewable Fuel Standard, with mandates shifting to RIN-equivalent gallons and higher RIN obligations for end users such as data centers using renewable diesel for stationary generators.

That means cost, compliance, and reliability now sit closer together. Facilities need to know whether RINs are properly retired, whether supply is documented, and whether the fuel they buy is truly fit for backup power use.

Practical steps to reduce risk

A resilient fuel strategy usually includes a few non-negotiables:

• Test fuel on a schedule, not just after a problem appears.
• Keep tanks dry, clean, and monitored for water and sediment.
• Verify supplier documentation, including compliance and quality records.
• Use storage and replenishment plans that account for supply disruptions.
• Review whether renewable diesel or HVO fits both operational and regulatory needs.

For large facilities, the goal is not just to buy fuel. It is to create a system that keeps fuel usable over time, especially when weather, market conditions, or compliance changes put pressure on operations.

Closing perspective

Operational resiliency depends on fuel that performs as expected, on the day it is needed most. That is why ESI’s approach emphasizes stable supply, fuel quality, and clear compliance practices alongside the technical benefits of renewable diesel. Contact ESI Total Fuel Management today to ensure your operations are running at optimal resiliency.

The post Ensuring Operational Resiliency: Preventing Fuel Failures in Mission-Critical Facilities appeared first on Data Center POST.

TL;DR

• In the rapid deployment of AI and digital infrastructure, speed to market, rather than capital. has become the primary driver of success.
• A global developer managing 15 projects relied on quarterly reviews with three-month-old data, leaving leadership entirely unaware that a critical project was actually 245 days behind schedule.
• Drowning in Reporting Overhead: A Portfolio Management Office of over 30 employees spent 40% of their time reacting to ad-hoc executive information requests rather than supporting project delivery.

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Written by iMiller Public Relations on behalf of Foresight.

Demand for artificial intelligence and digital infrastructure is at an all-time high, making time the critical constraint in delivery. However, many organizations manage billion-dollar programs with fragmented and delayed reporting, effectively driving projects blind. Research by Dr. Atif Ansar of Foresight and the University of Oxford details how a global data center developer managing 15 projects struggled with quarterly reviews based on three-month-old data, while a stretched Portfolio Management Office spent 40% of its time answering executive requests instead of supporting delivery. This lack of visibility created a false sense of security, leaving leadership unaware that a project they believed was ahead of schedule was actually 245 days behind.

To resolve these issues, Foresight consolidated schedule data from Primavera P6 and Microsoft Project into a single portfolio view within three weeks. This allowed the developer to transition from lagging quarterly reviews to monthly executive reviews supported by weekly project discussions, successfully shifting the focus from validating reports to actively solving delivery problems. As a result, annual reporting costs dropped by 90%, falling from $2 million to $200,000. More importantly, internal estimates showed a 20% reduction in project delays, preventing more than $100 million in lost revenue across the portfolio by enabling leadership to intervene before delays became irreversible.

In the accelerating race to build digital infrastructure, achieving clear, timely visibility is a critical competitive advantage. Better project outcomes do not stem from producing a larger volume of reports, but from providing leadership with a trusted, unified view of performance while there is still time to influence the results.

This content originally appeared on the Foresight website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post Visibility Is Becoming the Competitive Advantage in Data Center Delivery appeared first on Data Center POST.

TL;DR

• True data sovereignty goes beyond physical storage location (residency) to demand enforceable organizational control over encryption keys, backup systems, administrator access, and legal jurisdictions.
• Managing massive, proprietary, or regulated datasets for artificial intelligence creates high costs and compliance hurdles, pushing enterprises to run AI workloads closer to governed data via private or hybrid infrastructure.
• While private infrastructure has its own physical costs, running predictable, data-heavy workloads on-premises or in colocation facilities provides more stable economics and easier auditability compared to public cloud sprawl.

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For years, the public cloud was treated as the natural destination for enterprise infrastructure. The logic was simple: move workloads to hyperscale platforms, gain speed and flexibility, and reduce the burden of managing physical hardware. That model still works for many applications, especially those with unpredictable demand, distributed users, and strong cloud-native design.

But a new pressure is reshaping enterprise cloud strategy: data sovereignty.

Enterprises are no longer asking only where their applications run. They are asking where their data resides, who can access it, which jurisdiction governs it, how it is encrypted, how it is backed up, and whether they can prove all of that during an audit or incident. For many organizations, especially in regulated industries, those questions are pushing certain workloads back toward private cloud, colocation, and on-premises data center environments.

Data Sovereignty Is More Than Data Residency

Data residency is about location. It answers the question: “Where is the data stored?”

Data sovereignty goes further. It asks whether the organization has enforceable control over the data, the infrastructure, the administrators, the encryption keys, the backup copies, the logs, and the legal environment around all of it. A workload can technically be hosted in the right country and still create sovereignty concerns if management access, support operations, replication, or metadata flows cross borders in ways the enterprise cannot fully govern.

That distinction matters because enterprise IT environments are rarely simple. Data may be created in one region, processed in another, backed up in a third, and accessed by support teams from multiple jurisdictions. In a single-cloud or multi-cloud environment, this can happen quickly and invisibly unless governance controls are built into the architecture from the beginning.

Why Cloud-First Is Becoming Workload-First

The movement back from the cloud is not a rejection of cloud computing. It is a sign that enterprise infrastructure strategy is maturing.

A decade ago, “cloud-first” became the default strategy for many companies. Today, more IT leaders are adopting a workload-first model. Instead of asking, “How do we move this to the cloud?” they are asking, “Where should this workload live based on risk, cost, performance, compliance, and control?”

That change is especially visible in workloads involving sensitive customer records, financial data, healthcare information, intellectual property, AI training datasets, backup repositories, and operational systems that cannot tolerate uncertain jurisdictional exposure. For these workloads, the economics and governance of public cloud can become more complicated than expected.

The result is selective repatriation. Enterprises may keep collaboration tools, web applications, and elastic development environments in public cloud while moving steady-state databases, high-volume storage, backup data, and latency-sensitive systems into infrastructure they control more directly.

Regulation Is Raising the Bar

Regulatory pressure is one of the strongest drivers behind this shift. Laws and industry frameworks around privacy, operational resilience, cross-border data transfer, and third-party technology risk are becoming more detailed. Boards and regulators increasingly expect organizations to demonstrate control, not simply outsource trust to a cloud provider.

For financial services, healthcare, government, defense, and critical infrastructure organizations, the question is no longer whether a provider has strong security. Most major providers do. The question is whether the enterprise can produce evidence that the right controls are in place, that data is handled according to policy, and that recovery can happen under pressure.

This is where private infrastructure, sovereign cloud models, and colocation environments are becoming more attractive. They allow organizations to define stricter boundaries around data location, administrator access, encryption key custody, network paths, and backup retention. In many cases, they also simplify audits because the environment is easier to map and explain.

AI Is Making the Problem Bigger

Artificial intelligence is adding another layer to the sovereignty discussion. AI systems depend on large volumes of data, and many enterprises are now trying to determine where that data should be stored, processed, indexed, and used for model training or inference.

Moving massive datasets into and out of cloud platforms can create cost, latency, and governance challenges. More importantly, organizations must understand whether sensitive data is being exposed to external services, retained in logs, used in model workflows, or replicated across regions.

For some AI use cases, public cloud will remain essential because of GPU availability and managed AI services. But for workloads involving proprietary datasets, regulated information, or long-term data retention, enterprises are increasingly exploring private AI infrastructure, hybrid architectures, and data-center-based processing models. The goal is not to avoid AI; it is to bring compute closer to governed data.

Cost and Control Are Connected

While sovereignty is often discussed as a compliance issue, it is also a cost issue. Large cloud environments can become expensive when workloads are predictable, data-heavy, or constantly moving between services and regions. Egress fees, storage growth, backup retention, replication, and underused resources can turn cloud flexibility into cloud sprawl.

Repatriating a workload does not automatically make it cheaper. Private infrastructure comes with its own costs: hardware, power, cooling, staffing, lifecycle management, and physical security. But for steady workloads with known utilization patterns, enterprises may gain more predictable economics by running them in private environments or colocation facilities.

That predictability matters. When infrastructure supports critical data, cost control and governance are linked. Organizations need to know not only what they are spending, but also what risk they are accepting in exchange for that spend.

What Enterprises Should Evaluate Before Repatriating

Pulling workloads back from the cloud should not be a reactionary move. It should be a structured decision based on technical and business requirements.

Enterprises should begin by classifying their workloads and data. Which systems contain regulated information? Which datasets are subject to regional restrictions? Which applications generate high data movement costs? Which workloads have stable demand? Which systems require low latency or strict recovery guarantees?

Next, they should review access and control. Who can administer the environment? Where are encryption keys stored? How are backups protected? Can the organization prove where data is located and how it is replicated? What happens if a provider outage, contract dispute, or geopolitical event affects access?

Finally, enterprises should design for portability. The strongest infrastructure strategies do not lock every workload into one environment. They use cloud where cloud adds value, private infrastructure where control is required, and colocation where resilient, connected, professionally managed facilities can bridge the two.

The Future Is Hybrid, but More Intentional

The future of enterprise infrastructure is not purely public cloud or purely on-premises. It is a more intentional hybrid model.

Public cloud will continue to support innovation, scalability, global reach, and managed services. But data centers, colocation facilities, private cloud platforms, and sovereign infrastructure will play a larger role in workloads where jurisdiction, auditability, cost predictability, and operational control are critical.

For many enterprises, the answer is not to abandon cloud completely, but to regain control over the parts of the environment that carry the most regulatory or operational risk. Private infrastructure, colocation, and hybrid storage models are becoming more important because they help organizations define clearer boundaries around data location, encryption, access control, backup retention, and recovery. In this model, private cloud storage can support a more controlled approach to storing and protecting sensitive workloads without fully giving up cloud-like scalability.

The data sovereignty problem is forcing enterprises to rethink an assumption that shaped the last decade: that moving to the cloud always means gaining control. In reality, control depends on architecture. For some workloads, the best answer is still public cloud. For others, the answer is bringing data and compute closer to infrastructure the enterprise can govern directly.

The companies that succeed will not be the ones that simply move everything back or push everything forward. They will be the ones that understand their data, classify their risk, and place each workload where it belongs.

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About the Author

George Williams has over 13 years of experience in the data storage, backup and disaster recovery, and archiving markets. A true geek with love for ease and simplicity in data storage, George has been working for StoneFly Inc. for over a decade. Ever since StoneFly started shipping products in 2006, George has been working to ensure that technical information is relayed in a simple and effective way to customers and targeted audiences. George helps curate content and works with numerous publishers and technology blogs to spread awareness and knowledge of data storage technology.

The post The Data Sovereignty Problem: Why Enterprises Are Pulling Workloads Back from the Cloud appeared first on Data Center POST.

TL;DR

• Traditional auto-scaling fails for AI workloads because LLM inference is constrained by GPU memory and KV cache rather than CPU. Relying on legacy CPU metrics leads to quiet, expensive budget bleeds while request queues bottleneck behind pinned GPUs.
• Inference latency is solved via weight compression and smart routing rather than traditional CDN caching. Shrinking model footprints via quantization and routing queries dynamically based on complexity prevents expensive frontier models from being wasted on simple requests.
• AI FinOps must automate the scale-down process to curb runaway idle GPU costs. Teams should adopt a “scale-to-floor” policy to maintain a minimal warm SLA pool, use cheap preemptible spot capacity for asynchronous batch work, and treat GPU utilization gaps as operational defects.

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The first time a real-time inference cluster runs away with the budget, it rarely shows up as a spike. It shows up as a slow, expensive bleed that no alert ever fired on because every metric you trusted for the last decade is measuring the wrong thing. Your horizontal auto-scaler watched CPU hover at 30% and concluded all was well, while a queue of token-generation requests stacked up behind a GPU that was already pinned at 100% memory utilization. The pod count never moved. The latency graph climbed. The invoice climbed faster.

This is the quiet structural problem facing anyone running AI-powered applications in 2026: the assumptions baked into cloud-native architecture were written for stateless, CPU-bound web services. Large language model inference breaks almost all of them. If you are still sizing clusters, scaling policies, and storage tiers the way you did for a CRUD app, you are subsidizing your provider.

Let’s get specific about where the old playbook fails and what replaces it.

Compute: From CPU Sizing to GPU Orchestration

Classic horizontal scaling assumes work is fungible. Add a node, spread the load, done. Inference doesn’t behave that way. The binding constraint is GPU memory, not CPU, and a single accelerator can only hold so much. An H100 carries 80GB of HBM; an H200 stretches to 141GB; Blackwell-class B200 parts push past 190GB. Once a model’s weights plus its KV cache fill that memory, the next request doesn’t get “a little slower,” it waits in line or gets evicted.

That changes the unit of scaling. You are no longer scaling pods against CPU; you are scheduling sequences against finite GPU memory and managing the KV cache as a first-class resource. Modern serving stacks vLLM, TensorRT-LLM, and their orchestration layers exist precisely to solve this, using continuous batching to interleave requests at the token level and paged attention to stop the KV cache from fragmenting the card. Cluster health, in turn, stops meaning “are the nodes up?” and starts meaning “what is my real GPU utilization, my batch fill rate, and my queue depth per replica?” A fleet of H100s sitting at 40% utilization is not a healthy cluster. It is a six-figure monthly rounding error.

Two failure modes deserve naming because they stay invisible on a traditional dashboard. The first is cold start: spinning up a fresh GPU pod means pulling a multi-gigabyte model into VRAM, and that can take minutes or an eternity when a request is already waiting. The second is coarse allocation, where a small model monopolizes a large card it barely touches. Both are solvable in the scheduling layer through techniques like Multi-Instance GPU partitioning and model-aware bin-packing, but only if you architect for them up front. Bolted on after launch, they become a rewrite.

The teams building serious conversational products feel this first. A company offering production-grade chatbot development services quickly discovers that the model is the easy part; the hard part is keeping a GPU pool warm enough to hit sub-second first-token latency without paying for idle silicon around the clock. That tension responsiveness versus utilization is now the central design problem of the AI cloud, and it has no setting in your old auto-scaling group.

The Data Layer: Why SQL and NoSQL Quietly Fall Apart

Your relational and document stores were built to answer questions about exact values. Where user_id = 4471. Where status = ‘active’. They are exceptional at it. They are also structurally incapable of answering the question every AI application actually asks: what is most similar in meaning to this?

Semantic retrieval operates on high-dimensional embedding vectors with hundreds or thousands of dimensions, and “closeness” is a distance calculation across that space, not a B-tree lookup. Bolt a similarity search onto Postgres without the right index, and it degrades into a full scan that gets slower with every row you add. This is why the data layer is being re-stratified rather than replaced wholesale. Native vector capability now lives in three broad tiers: extensions like pgvector for teams that want similarity search alongside their existing transactional data; purpose-built distributed engines like Milvus when recall and throughput at billions of vectors matter; and managed services like Pinecone for organizations that would rather buy the operational burden than run it.

The deeper architectural shift is that retrieval has become stateful infrastructure, not an application-layer afterthought. Even the choice of index is now a real engineering decision rather than a default: graph-based indexes like HNSW deliver excellent recall at low latency but cost memory, while partition-based approaches like IVF trade some accuracy for a smaller footprint, and that tradeoff shifts again the moment your corpus updates in real time rather than sitting static. A retrieval-augmented generation (RAG) workflow is a pipeline embed the query, runs an approximate-nearest-neighbor search against the index, assembles the retrieved context, then calls the model, and every stage has its own latency, scaling, and consistency profile. Treating that pipeline as “just a database call” is how you end up with a chatbot that answers correctly in the demo and times out under load. The conversational front end and the vector store are one system, and they have to be containerized, versioned, and scaled as one.

Network and Edge: The Token Latency Problem

Web performance used to be a payload problem: ship fewer bytes, cache aggressively, push assets to a CDN. Inference performance is a generation problem. The model produces output one token at a time, and the user perceives quality through two numbers: time to first token, and tokens per second after that. Neither improves by moving a static file closer to the user.

What moves the needle is making the model itself cheaper to run, and that is where quantization has become non-negotiable rather than optional. Compressing weights from FP16 down to FP8 or INT8 using approaches like GPTQ or AWQ shrinks the memory footprint and raises throughput, often with negligible quality loss for well-chosen workloads. Smaller, quantized models are also what make edge and regional inference viable: instead of routing every request back to a central GPU farm, you can serve a distilled model from infrastructure physically nearer the user, collapsing round-trip latency for the requests that don’t need the largest frontier model. The architectural decision is no longer “which model?” but “which model, quantized how, served where, for which class of request?” Routing logic that grades requests by complexity and dispatches them accordingly is becoming a standard layer in any mature AI chatbot app development services stack, because serving every query with your most expensive model is the fastest way to make the unit economics collapse.

FinOps: Engineering the Scale-Down

Here is the part most teams under-build, because scaling up is exciting and scaling down is just discipline. Specialized compute is expensive enough that idle capacity is the dominant line item on most AI infrastructure bills, and traffic to conversational and inference workloads is rarely flat it follows business hours, regional patterns, and campaign spikes. If your GPU pool is provisioned for peak and never contracts, you are paying peak rates twenty-four hours a day.

Real FinOps for AI is structural, not a quarterly spreadsheet review. A few techniques that actually hold up in production:

• Tiered serving by request class route, cheap, common queries to small quantized models, and reserve frontier-model GPUs for the requests that genuinely need them.
• Scale-to-floor, not scale-to-zero, keep a minimal warm pool sized to your latency SLA, then expand on queue depth and token throughput rather than CPU, so you absorb spikes without paying for a full peak fleet overnight.
• Spot and preemptible capacity for batch and asynchronous work, embedding generation, re-indexing, and offline evaluation don’t need on-demand reliability and shouldn’t pay for it.
• Utilization as a first-class SLO track GPU-hours billed against GPU-hours actually doing useful inference, and treat a persistent gap as a defect, not a cost of doing business.

The organizations that win the next eighteen months won’t be the ones with the largest GPU allocations. They’ll be the ones whose architecture lets specialized compute breathe expanding under load and contracting the moment demand dips without a human in the loop.

The Reframe

None of this is a reason to slow down on AI. It’s a reason to stop treating AI workloads as ordinary applications that happen to call a model. The compute, the data layer, the network path, and the cost model have all changed shape at once, and the teams that re-architect deliberately instead of patching last decade’s patterns and hoping the bill stays reasonable are the ones that will ship reliable, affordable AI at scale.

If your inference costs are climbing faster than your usage, the problem usually isn’t the model. It’s the infrastructure underneath it, and that’s a solvable, architectural problem.

EncodeDots helps engineering teams design and operate the GPU orchestration, vector data layers, and FinOps controls that make AI-powered applications both fast and economical. If your AI infrastructure has outgrown your cloud playbook, let’s pressure-test the architecture together.

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About the Author

Piyush Chauhan, CEO and Founder of EncodeDots, is a visionary leader transforming the Digital landscape with innovative web and mobile app solutions for Startups and enterprises. With a focus on strategic planning, operational excellence, and seamless project execution, he delivers cutting-edge solutions that empower thrive in a competitive market while fostering long-term growth and success.

The post Your Auto-Scaler Is Lying to You: What AI Workloads Actually Do to Cloud Infrastructure appeared first on Data Center POST.

TL;DR

• Belonging is a business imperative that directly influences collaboration, innovation, and organizational resilience.
• Diversity initiatives are most effective when supported by workplace systems that encourage inclusion and participation.
• Organizations that recognize diverse perspectives are better positioned to retain talent and improve performance.
• Building resilient digital infrastructure requires investing in people as intentionally as organizations invest in technology.

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Written by iMiller Public Relations on behalf of Nomad Futurist.

Why Belonging Matters in Digital Infrastructure

As digital infrastructure becomes increasingly critical to supporting AI, cloud, and connectivity, organizations are investing heavily in technology and talent. However, according to Nomad Futurist, building a resilient workforce requires more than hiring a diverse team, it requires creating an environment where employees feel they genuinely belong.

The article argues that belonging should be viewed as an organizational asset rather than simply a cultural initiative. When employees feel comfortable sharing ideas, raising concerns, and contributing their expertise without unnecessary barriers, organizations become more innovative, collaborative, and resilient. Conversely, when individuals feel excluded, valuable knowledge is often lost, communication suffers, and business performance can be impacted.

Designing Organizations Where Talent Thrives

While many organizations have prioritized diversity initiatives, the article emphasizes that representation alone does not create inclusion. Sustainable workforce development requires intentional systems that recognize different perspectives, encourage collaboration, and enable employees to contribute without feeling pressure to conform to traditional norms.

As the digital infrastructure industry addresses workforce shortages and prepares for the next generation of innovation, fostering a culture of belonging can help organizations strengthen employee engagement, improve retention, and unlock the full potential of their teams.

This content originally appeared on the Nomad Futurist website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post Belonging, Not Diversity, Is Key to Building Stronger Digital Infrastructure Teams appeared first on Data Center POST.

TL;DR

• The acceleration of AI has thrust data centers from the invisible background of digital life into the active foreground of public debate.
• Infrastructure literacy is distinct from community engagement; it builds a shared baseline of understanding regarding how physical components connect to essential digital services.
• While improving public literacy does not eliminate community concerns, it makes public inquiry informed and serves as the essential foundation that makes better engagement possible.

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As artificial intelligence, cloud computing and digital public services become more deeply embedded in everyday life, data centers are moving from the background of public awareness into the foreground of public debate.

For years, data centers were largely invisible to the average person. They were understood by engineers, operators, investors, planners and the organizations that depended on them, but rarely by the wider public. That is changing.

AI has made digital infrastructure more visible. Questions around energy, water, land use, planning, workforce development, community benefit and resilience are now part of broader public conversations. At the same time, many people still do not have a clear understanding of what a data center is, what it does, why it matters, or how it connects to the digital services they use every day.

That creates a challenge for the sector.

Community engagement asks people to respond to infrastructure. Infrastructure literacy helps people understand it.

That distinction matters.

Public concern about data centers is not always rooted in opposition to technology itself. Often, it reflects a deeper uncertainty about systems people rely on but cannot see. People use cloud storage, streaming platforms, healthcare systems, online banking, school platforms, AI tools and public services every day. Yet the physical infrastructure behind those services remains poorly understood.

When infrastructure is invisible, the public conversation can become fragmented. Data centers are discussed through the lens of energy, AI, jobs, planning, water, security, big tech or local impact, often without a shared baseline of understanding. These are all important issues, but they are not the same issue. Without infrastructure literacy, they can easily collapse into a single narrative: data centers are either good or bad.

The reality is more complex.

Data centers are part of the physical layer of digital life. They depend on buildings, power, cooling, fiber, servers, land, skilled people and local communities. They support services that many people now consider essential. At the same time, they raise legitimate questions about resource use, community benefit, ownership, resilience and the pace of AI-driven growth.

The sector needs a clearer public language for this complexity.

Many operators are already thinking seriously about community benefit, sustainability and future skills; infrastructure literacy can help connect those efforts into a clearer public conversation.

Infrastructure literacy is not about persuading communities to accept every development. It is about giving people the language, context and confidence to ask better questions. What is being built? Why is it needed? Who benefits? What are the trade-offs? How will energy and water be managed? What opportunities will be created for local people? How does this infrastructure connect to education, healthcare, public services and future skills?

These are not only communications questions. They are civic questions.

For data center operators, developers and industry bodies, this presents an opportunity. Public understanding should not begin only at the point of planning, permitting or local concern. It can be built earlier, through schools, libraries, community organizations, family learning, workforce pathways and accessible public education.

This is where education becomes strategic.

If children and communities understand that the internet is not just “in the cloud,” but depends on real places, people and systems, the conversation changes. Data centers stop being mysterious buildings at the edge of public imagination and become part of a wider story about modern infrastructure.

That does not remove public concern. It makes the concern more informed.

As founder of ThreadPoint, I have been developing an infrastructure literacy approach through children’s publishing, school workshops and community programs. My children’s book, Where the Internet Goes to Sleep, introduces young readers to data centers and the physical infrastructure behind the internet. Through our workshops, children and families explore cloud computing, servers, fiber, energy and digital systems through story-led learning and hands-on activities.

I have also been awarded a Churchill Fellowship to research public understanding of the physical infrastructure behind AI, cloud computing and digital life across the USA, UAE, the Netherlands, Sweden and Denmark. Through this research, I will be exploring how different countries approach infrastructure literacy, education, digital inclusion, community engagement and workforce pathways – bringing insights and recommendations back to support UK communities, schools, policymakers and industry.

For the data center sector, the timing matters.

AI growth is accelerating demand for compute. Public debate is becoming more active, and public trust is becoming more important. Communities are asking more questions. Governments and local authorities are considering how digital infrastructure fits into wider priorities around growth, resilience, sustainability and skills.

The sector can treat public understanding as a communications risk to manage, or as a long-term public value layer to build.

The second approach is the more sustainable one.

If data centers are now part of critical infrastructure, then public understanding should be part of responsible infrastructure delivery. That means moving beyond one-way messaging and creating accessible ways for people to understand the systems shaping their lives.

The next phase of digital infrastructure will not only be judged by what the sector builds. It will also be judged by how well the sector helps people understand what is being built, why it matters and how communities can be part of the conversation.

Infrastructure literacy is not a replacement for community engagement.

It is what makes better engagement possible.

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About the Author

Willow Williams CF, MSc is the founder of ThreadPoint and author of Where the Internet Goes to Sleep, a children’s book introducing young readers to data centers and the physical infrastructure behind digital life. She has been awarded a Churchill Fellowship to research infrastructure literacy and public understanding of the physical systems behind AI, cloud computing and digital life.

The post Why Data Centers Need Infrastructure Literacy in the AI Era appeared first on Data Center POST.

TL;DR

• Reaching students at younger, earlier levels of education builds familiarity and comfort with technology, which helps both the students and their families understand its value.
• Industry leaders must treat education as a core component of workforce development to establish clear pathways into the field.
• Bridging the talent gap requires using accessible language to address local concerns and clearly demonstrate how digital infrastructure connects to everyday life.

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This is reality, not just a mere notion: The future of digital infrastructure depends on more than servers and software; it depends on people, awareness, and the next generation of talent. In a recent Nomad Futurist conversation with co-hosts Nabeel Mahmood and Phillip Koblence, Brittani Clarke Clayman and Jason Clayman made that case with a rare mix of industry insight and classroom perspective.

Brittani, Director of Marketing and Sustainability at ESI Total Fuel Management, has spent her career helping make complex ideas more understandable and relevant to the communities they affect. Jason, an educator and curriculum leader at Dobyns-Bennett High School, sees the same challenge from the classroom: Students cannot pursue careers they have never been exposed to.

Together, they form a powerful voice for early education and real community engagement. Their message is simple: The data center industry cannot rely on technical growth alone. It also has to explain its value, listen to local concerns, and create clear pathways into the field.

This means speaking in plain language, reaching students earlier, and showing families how digital infrastructure connects to everyday life. It also means treating education as part of workforce development, not an afterthought. As Brittani and Jason discuss, awareness starts with conversation, but it only becomes meaningful when it leads to opportunity.

“I could speak at you, but if you’re not understanding what I’m saying, then we’re not communicating,” Brittani said. “I’m just throwing information at you.”

Reaching students early is key, said Jason: “If we do start at the younger and earlier levels of education, just getting them comfortable with it and kind of understanding what it is, not only does it help them, but it helps their families.”

Their story is a reminder that the industry’s next chapter will be shaped not just by innovation, but by trust. For data center leaders, that is both a challenge and an opening.

Read the full conversation here.

The post Why Education and Community Matter to the Future of Digital Infrastructure appeared first on Data Center POST.

Written by iMiller Public Relations on behalf of Nomad Futurist.

TL;DR

• The Nomad Futurist Foundation partnered with Bisnow to provide students with direct access to DICE National and the digital infrastructure industry.
• Students gained valuable mentorship, networking opportunities, and firsthand exposure to careers supporting AI, cloud, and data center infrastructure.
• Workforce development remains a critical priority as demand for digital infrastructure talent continues to accelerate.
• Programs that connect education with industry help build the next generation of professionals driving the digital economy.

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Preparing the Next Generation of Industry Talent

As AI, cloud, and digital infrastructure continue to expand, the industry faces a growing challenge: developing the workforce needed to support that growth. To help address this need, the Nomad Futurist Foundation partnered with Bisnow during DICE National to provide nine master’s students with direct exposure to one of the digital infrastructure industry’s premier events.

The initiative gave students the opportunity to engage with industry leaders, explore career opportunities, and gain firsthand insight into the technologies and businesses driving the digital economy. Students represented disciplines including engineering, AI strategy, data science, finance, operations, and marketing, highlighting the diverse skill sets supporting today’s digital infrastructure ecosystem.

Creating Connections Beyond the Classroom

Each participant was paired with a Nomad Futurist mentor who provided guidance throughout the conference while facilitating introductions to executives and industry professionals. The experience helped students better understand career pathways within digital infrastructure while expanding their professional networks and industry knowledge.

The program reflects the Nomad Futurist Foundation’s ongoing commitment to workforce development through mentorship, education, and industry engagement. By connecting students with experienced professionals early in their careers, the Foundation is helping strengthen the talent pipeline needed to support the continued growth of AI and digital infrastructure.

This content originally appeared on the Nomad Futurist website and has been adapted for syndication on Data Center POST. Read the complete blog here.

The post From Classroom to Career: Nomad Futurist Bridges the Gap Between Students and Digital Infrastructure appeared first on Data Center POST.

TL;DR

• The digital divide has shifted beyond connectivity. Today’s challenge is helping communities understand, evaluate, and participate in decisions surrounding digital infrastructure and AI.
• Community engagement is essential for project success. Transparent communication and early stakeholder involvement build trust and lead to better long-term outcomes for digital infrastructure initiatives.
• Digital literacy is becoming a competitive advantage. As AI adoption accelerates, the ability to critically evaluate information and make informed decisions is just as important as access to technology.
• Collaboration drives sustainable growth. Industry, policymakers, educators, and local communities must work together to develop planning frameworks that support responsible digital infrastructure development and economic opportunity.

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Written by Authority Magazine Editorial Staff featuring Ilissa Miller, Founder and CEO of iMiller Public Relations

The Digital Divide Has Evolved

The digital divide is often framed as an issue of internet access, but according to Ilissa Miller, Founder and CEO of iMiller Public Relations, today’s challenge is increasingly centered on understanding, trust, and informed decision-making.

As digital infrastructure and artificial intelligence continue reshaping economies and communities, the gap is widening between those who understand these technologies and those left trying to navigate complex decisions without sufficient context. This shift has implications far beyond connectivity. It influences economic development, workforce readiness, and community planning.

Miller explains that communities are frequently asked to evaluate projects such as data centers, AI infrastructure, and connectivity initiatives without the technical knowledge or planning frameworks needed to make confident, long-term decisions. The result is often uncertainty, misinformation, and resistance rather than productive dialogue.

Community Engagement Is Critical to Digital Infrastructure

Drawing from her experience working with large-scale digital infrastructure developments, Miller emphasizes that successful projects require more than engineering and investment, they require meaningful community engagement.

Rather than viewing digital infrastructure solely through the lens of technology deployment, she advocates for creating opportunities for education, transparency, and collaboration. Helping residents understand how data centers, fiber networks, and AI infrastructure contribute to economic growth, workforce development, and future innovation allows communities to participate more effectively in decisions that shape their future.

She also notes that closing the digital divide requires coordinated planning among industry leaders, policymakers, and local communities. As AI becomes more integrated into everyday life, digital literacy and critical thinking will become just as important as physical access to technology.

The conversation ultimately reframes the digital divide as a challenge of participation rather than connectivity, one that requires greater trust, education, and collaboration across the entire digital infrastructure ecosystem.

This content originally appeared in Authority Magazine on Medium and has been adapted for syndication on Data Center POST. Read the complete interview here.

The post Ilissa Miller on the Digital Divide: Why Understanding Matters More Than Access appeared first on Data Center POST.

TLDR;

•  Foresight has been named a Platinum winner in the 2026 Pinnacle Technology Awards for its Predictive Project Delivery platform, recognizing its role in helping infrastructure teams identify schedule risks earlier and improve delivery certainty.
• The award follows a year of strong momentum for the company, including winning Best AI Innovation at the 2026 Datacloud Global Congress and closing a $25 million Series A funding round.
• As AI and digital infrastructure investment continues to grow, Foresight is helping organizations reduce project delays, protect schedule value, and bring critical infrastructure online faster.

# # #

As the pace of digital infrastructure development continues to accelerate, organizations are looking beyond traditional project management tools to improve delivery certainty. That shift is reflected in the 2026 Pinnacle Technology Awards, where Foresight has been named a Platinum winner for its Predictive Project Delivery platform.

The Pinnacle Technology Awards recognize organizations that are driving innovation through technology while delivering measurable business outcomes and demonstrating market leadership. Foresight was honored for helping owners and operators of major infrastructure programs identify delivery risks earlier, improve schedule certainty, and accelerate time-to-revenue across data centers, energy infrastructure, advanced manufacturing, transportation, and other large-scale capital projects.

The recognition comes at a pivotal time for the industry. Global investment in AI infrastructure continues to surge, but delivering projects on schedule remains one of the sector’s greatest challenges. Nearly nine out of ten data center projects experience schedule overruns, making early visibility into delivery risk increasingly valuable for organizations racing to bring new capacity online.

Rather than relying on historical reporting alone, Foresight’s Predictive Project Delivery platform continuously analyzes project data to identify emerging schedule risks before they escalate. The platform provides leadership teams with actionable insights that help improve decision-making and reduce the impact of project delays. Across a global portfolio of 15 concurrent data center projects, Foresight helped reduce reporting overhead by 90%, while preventing more than $100 million in projected lost revenue through earlier identification of schedule risks.

“This recognition reflects a growing industry realization that project delivery is fundamentally a data and decision-making challenge,” said Dr. Atif Ansar, Co-Founder and Executive Chairman of Foresight. “Organizations have long invested heavily in systems that help manage cost and capital, yet time remains one of the least governed and most valuable assets in infrastructure delivery.”

The award adds to a series of recent milestones for Foresight, including being named Best AI Innovation at the 2026 Datacloud Global Congress and closing a $25 million Series A funding round led by Macquarie Capital Venture Capital.

To continue reading, read the full press release here.

The post Foresight Recognized with 2026 Pinnacle Technology Award for Advancing Infrastructure Project Delivery appeared first on Data Center POST.

TL;DR

• A severe voter backlash against Kevin O’Leary’s proposed 9-gigawatt Project Stratos AI data center led to a major political upset in Utah’s primary election, ousting Senate President Stuart Adams and two county commissioners.
• The project’s failure stemmed from bypassing local control using an unelected state authority (MIDA) and dismissing community anxieties over massive water and double-state-capacity power demands as foreign-funded misinformation.
• Utah has raised the regulatory bar for digital infrastructure through Governor Spencer Cox’s “Operation Gigawatt” initiative, enforcing a strict new framework of total transparency, rigorous environmental reviews, and utility ratepayer protections.
• Quietly succeeding developments like the Delta Gigasite, Joule Energy Campus, Pole Canyon, and Antelope Data Campus prove that engaging directly with local officials, securing local permits, and respecting community resource constraints is the only viable path to getting built.

# # #

When Utah Senate President Stuart Adams conceded his primary election defeat recently, it sent a shockwave through the state’s political establishment. For a politician so deeply entrenched, an upset by a political newcomer was considered virtually impossible just a few months ago. Along with Adams, two Box Elder County commissioners were also ousted by voters.

To outside observers, this sudden reshaping of Utah’s leadership seemed unforeseen. But to anyone closely tracking the digital infrastructure space, the cause was clearly a severe, localized voter backlash against Kevin O’Leary’s massive, 9-gigawatt Project Stratos.

The Shark Tank investor’s multi-billion-dollar AI data center campus has quickly become a textbook example of how not to build digital infrastructure. The trouble with Stratos was never a lack of capital or vision, or even the project’s merits; it was a fundamental failure of process, transparency, and respect for the community.

The Mistake of Bypassing Local Control

Project Stratos was aggressively pushed through using the Military Installation Development Authority (MIDA), an unelected state entity. Critics suggest this was a state-level workaround that effectively stripped residents of permanent control over land use, water rights, and taxation.

When residents voiced anxieties about how a 40,000-acre project that would demand double the state’s current electrical capacity could impact their water and agricultural way of life, they weren’t met with collaboration. Instead, they were met with an outsized executive ego in O’Leary that publicly dismissed grassroots opposition as misinformation funded by foreign interests.

This heavy-handed, top-down approach backfired. Sustained protests, lawsuits challenging MIDA’s authority, and hundreds of formal objections forced the developer to withdraw multiple water permit applications. Even after Senator Adams pressured O’Leary to slash the project’s physical footprint by 75% before the primary, the political damage was done. The post-facto work on the ground failed because the human element had been ignored.

Operation Gigawatt: Utah Raises the Regulatory Bar

This high-profile collapse stands in sharp contrast to Utah’s actual macroeconomic goals. Governor Spencer Cox has actively championed “Operation Gigawatt,” a sweeping state initiative designed to rapidly scale Utah’s energy infrastructure to support the AI boom. The state explicitly wants data center investment.

However, as the Stratos controversy peaked, leadership recognized the extent of public concern over unbridled tech growth, the lack of understanding of data centers, and the need to respect local voices and bring the community along. Governor Cox issued a decisive executive order to raise the bar for data center development. The mandate established a strict new framework requiring total transparency, rigorous environmental reviews, and strong protections for local utility ratepayers. Operation Gigawatt proved that Utah wants growth, but only if developers align with local values.

The Quiet Winners Getting Things Built

While Project Stratos dominated the news cycle and tanked political careers, a completely different story was playing out across the rest of the state. Other major hyperscale and AI developments have proved that when you respect local autonomy and invest time in a community, you can actually get things built. Several large-scale projects have quietly navigated the right path, securing their permits and putting shovels in the dirt without the political drama.

In Millard County, both the 9,700-megawatt Delta Gigasite and the 4,000-megawatt Joule Energy Campus are moving forward. Developers like Creekstone Energy and Joule Capital Partners skipped the state-level workarounds. They engaged directly with county officials, secured proper Heavy Industrial zoning, and obtained the necessary Conditional Use Permits for their on-site natural gas generation. Today, both multi-gigawatt sites are fully permitted and actively under construction.

The same holds true for Tract’s 1,700-megawatt Pole Canyon development in Eagle Mountain. Tract progressed through transparent planning with city officials to establish long-term infrastructure guidelines rather than using backdoor maneuvers. Down in Iron County, Pronghorn Development recently secured its local permits for the 1,500-megawatt Antelope Data Campus. They did so by strictly complying with local regulations, investing time in local community outreach, treating resource limits as engineering parameters rather than obstacles to bypass.

The Real Lesson: A Social License to Operate

The message out of Utah’s primary election is a stark warning to hyperscalers and developers nationwide. The AI expansion demands unprecedented amounts of power and land, but it still fundamentally requires a social license to operate.

You cannot buy, legislate, or steamroll past your way past a community’s real anxieties regarding resource scarcity—whether rooted in technical misunderstanding or deep-seated regional concerns. Utah remains one of the most promising frontiers for digital infrastructure in the world. But as the dust settles on the ballot boxes, the industry must absorb the real lesson of the Beehive State: a loud, top-down approach driven by overconfidence and insularity may trigger a fatal political reaction and mire a project in controversy.

The developers who treat local communities as partners, rather than obstacles to be bypassed, will be the ones building the future.

# # #

About the Author

Scott Cuthbertson is the Founder and CEO of Alpen Systems, a boutique advisory firm helping developers, communities, and contractors navigate large-scale infrastructure deployment in the energy and data center sectors globally. He previously served as the President and CEO of the Economic Development Corporation of Utah (EDCUtah), where he directed corporate recruitment and regional infrastructure strategy for one of the nation’s fastest-growing economies. Prior to his leadership in public-private economic development, Scott spent 15 years as a capital projects and infrastructure management consultant at PwC and Booz Allen Hamilton, advising global enterprises and investors on large-scale asset development.

The post The Limits of Top-Down Expansion: What Utah’s Primary Election Teaches the Data Center Industry appeared first on Data Center POST.

TL;DR

• AI Workloads are Driving Unprecedented Power Densities: The massive compute demand from artificial intelligence is pushing rack densities to extreme new highs, forcing an industry-wide transition from traditional air cooling to more efficient liquid and immersion cooling technologies.
• AI and Smart Tech are Powering Green Data Centers: Operators are increasingly relying on AI-driven energy management and continuous infrastructure monitoring, which can reduce cooling energy consumption by 20% to 40% and help achieve highly efficient Power Usage Effectiveness (PUE) ratings.
• Localized Manufacturing Enhances Speed and Sustainability: To quickly deploy complex, liquid-ready environments, providers like DPI are utilizing onshore, localized manufacturing (such as in the UAE) to eliminate global shipping bottlenecks, cut embodied carbon emissions, and reduce deployment timelines from months to weeks.

# # #

As digitization advances into our society so too do data centers, and they have become one of the largest users of electricity worldwide. According to the International Energy Agency (IEA) global electricity consumption by data centers is about 1%-1.5%. Given the extraordinary growth in AI, cloud, and edge computing, the demand for this will continue to grow. Therefore, the expansion of this industry has resulted in using sustainable principles in constructing data center infrastructure and providing a basis for furthering the development of green data centers.

Currently, there are no defined characteristics for identifying a data center as being a green facility. Instead, green data centers rely on energy efficient hardware, sophisticated software, usage of renewable energy, and future friendly cooling methods to increase performance while minimizing their impact on the environment.

Using AI Based Energy Management to Improve Operational Efficiency

AI technology is one of the main driving forces behind sustainable data center operation improvements. AI-driven monitoring platforms analyze thousands of operational parameters continually, including the use of the server, cooling demand, airflow and electricity.

Intelligent optimization systems can help lower cooling energy usage by 20% to 40% based on facility size and workload characteristics, according to research. In addition, machine learning algorithms predict potential equipment failure before it happens, thereby reducing the chances for unnecessary maintenance and hardware waste.

As the use of AI workloads continues to rise, operators are continuing to utilize AI not just in servers but also to optimize the supporting infrastructure.

Temperature Control in Data Centers is on the Rise

Cooling typically accounts for 30-40% of a conventional data centre’s energy consumption, which provides an opportunity to make substantial sustainability improvements.

There are several next-generation cooling techniques that are spurring change.

• Liquid Cooling – A liquid cooling technique that uses specialized coolants to transfer heat directly from processors, thereby enabling improved heat dissipation.
• Free Air Cooling – Adjusting cooling systems to take advantage of outdoor air’s temperature, which can significantly reduce mechanical cooling requirements.
• Immersion Cooling – Submerging servers in dielectric fluids, which reduces energy used for cooling and allows for the operation of very dense AI computing workloads.
• Intelligent Airflow Management – Implementing temperature sensors and automated control of airflow to minimize temperature variation across server racks.

The majority of the new hyperscale data center facilities that are currently under development incorporate the use of multiple cooling techniques to optimize energy efficiency and reduce total energy consumption.

Renewable Energy is becoming a Core Strategy for Infrastructure.

Renewables have shifted from simply being an option to becoming a fundamental part of how green data centers are built. There is now an increase in how much renewable energy companies are utilizing through either direct investments or long-term power contract agreements; many companies’ sources of electricity now occur from the sun, wind, hydro, or geothermal.

Previous industry data assessment suggests many large/massive data center companies are using 90%+ of their yearly power through renewables in locations around the world.

Another report conducted by Data Intelo suggests the Size of the green data centre Industry will grow from $92.4 billion in 2025 to $298.7 billion by 2034; thus, forecasting a CAGR rate of 13.9% between 2026-2034 should reflect increasing amounts being invested by companies towards energy-efficient/low carbon sources; as well as sustainable operations worldwide.

Energy Efficient Hardware Supports Sustainable Computing

Today’s CPUs, memory, storage and networking devices consume far less electricity than those of previous generations but provide greater computing power.

An energy-efficient power supply can achieve up to 96% efficiency which means lower losses during the conversion of alternating current (AC) into direct current (DC) and vice versa. Virtualizing your servers allows you to co-locate multiple applications on one physical machine which results in higher average utilization (historically 15%-20% of capacity) now exceeding 60% in most places.

SSD’s have also replaced many traditional HDDs reducing electrical demand while simultaneously improving performance across a variety of applications.

Continuous Optimization Through Smart Infrastructure Monitoring

Operators benefit from having immediate access to information about the performance of their facilities thanks to IoT sensors, digital twins, and the implementation of automated systems for managing buildings.

By utilizing a range of sensor technologies, such as monitoring temperature, humidity, airflow, electrical load and the health of equipment at least once every second, facilities can receive an ongoing supply of operational data to use in making automated adjustments that improve energy performance without compromising on reliability.

In addition to the use of the above technologies, many facilities monitor their PUE (power usage effectiveness) and are now seeing values as low as 1.2 for newer generation Green Data Centers, while older generation data centers typically operate at values between 1.7 and 2.0.

Sustainable Practices Are Evolving into Technology-Based Actions

The evolution of sustainable development and the transition of green data centres indicates that sustainability is more heavily reliant on technology (intelligent technology) versus traditional stand-alone initiatives focused on environmental improvements.

Many technologies, including AI-based optimisation of data centres; cooling systems; renewable energy integration; high-efficiency hardware; and continuous monitoring of the physical infrastructure, have come together to define how digital infrastructure is being created and operated.

With the ongoing growth of demand for cloud computing and artificial intelligence, technologies will increasingly provide a means to achieve an appropriate balance between sustainable long-term environmental responsibility, and the ever-increasing demand for computation.

# # #

About the Author

Ashish Kolte is a Marketing Manager at DataIntelo with expertise in marketing, market intelligence, and business strategy. He combines marketing insights with industry research to analyze market trends, identify growth opportunities, and provide data-driven perspectives on emerging industries

The post What Technologies Are Powering the Evolution of Green Data Centers? appeared first on Data Center POST.

TL;DR

• Generative AI is changing data center planning by introducing higher power density, greater cooling requirements and more complex infrastructure.
• Traditional forecasting models were designed around relatively predictable enterprise and cloud workloads, not large-scale AI training and inference clusters.
• AI workloads create significant challenges in power provisioning, rack density, cooling design, network architecture and capacity planning.
• Data center operators are responding with new planning techniques, liquid cooling deployments, higher-capacity power systems and more flexible infrastructure designs.

# # #

The rise of generative AI is forcing a significant shift in data center planning. What began as a technological breakthrough has quickly evolved into an infrastructure challenge, as organizations race to deploy increasingly powerful AI models that demand vast computing resources.

Why Traditional Data Center Planning Is Struggling

Historically, data center planning focused on balancing anticipated business growth against available infrastructure capacity. Planners examined historical trends, projected future demand and designed facilities around gradual increases in utilization.

This approach worked because most workloads exhibited relatively consistent behavior. Storage growth, virtualization adoption and cloud migration initiatives typically followed multiyear trajectories that could be modeled with reasonable accuracy.

However, generative AI workloads behave differently. Training large language models and other advanced AI systems requires massive clusters of GPUs and accelerators operating simultaneously. These deployments can dramatically increase power demand within a short period, creating infrastructure requirements that exceed what many facilities were originally designed to support.

The Power Density Problem

Perhaps the most visible impact of generative AI is the large increase in rack power density. For years, many facilities were designed around rack densities ranging from 5 kW to 15 kW. Even in high-performance environments, conventional cooling and power distribution systems often remained within manageable thresholds.

However, modern AI deployments frequently operate at densities far beyond those levels. GPU-intensive racks can exceed 50 kW or even 100 kW per rack, depending on configuration.

These increases create significant challenges throughout the facility. Energy distribution systems must deliver more electricity to individual racks, and backup power infrastructure must accommodate larger loads. Utility connections will require expansion, and capacity planning models that once projected gradual increases now must account for sudden spikes driven by AI initiatives.

The U.S. Department of Energy reported that domestic data center electricity consumption is projected to double or triple by 2028, due to AI adoption and expanding digital infrastructure needs.

Cooling Systems Are Reaching Their Limits

Power and cooling have always been interconnected, but generative AI is intensifying that relationship. As computing density increases, heat generation increases accordingly. As such, traditional air-cooling systems that worked effectively for lower-density environments can struggle to remove heat from modern AI clusters.

Many data centers now face thermal constraints to the point that they may have sufficient electrical capacity available but lack the cooling capability needed to support additional AI infrastructure. Operators are recognizing that traditional cooling approaches alone may not be sufficient for future AI growth.

This reality is forcing planners to rethink facility design. Airflow management, containment strategies, cooling distribution and equipment placement are becoming increasingly critical considerations.

Network and Capacity Assumptions Are Changing

Generative AI is also challenging conventional assumptions about network architecture and capacity utilization. Large AI clusters require substantial east-west traffic as GPUs communicate during training and inference. Additionally, network bottlenecks that might have been acceptable in traditional environments can negatively impact AI performance.

Data center operators must consider higher-bandwidth networking infrastructure, low-latency interconnects and scalable architectures capable of supporting data-intensive workloads. Infrastructure decisions that once centered on server deployment now require a more holistic evaluation of power, cooling, networking and storage resources.

Similarly, utilization models are becoming more difficult to predict. AI projects can scale rapidly, creating infrastructure demands that exceed previous forecasts. Therefore, organizations often move from experimentation to production deployment much faster than the traditional application life cycle. This creates greater uncertainty and a need for more adaptive planning frameworks.

How Data Center Planning Is Evolving

To address these challenges, operators are fundamentally rethinking how data center planning is performed. They are redesigning facilities, reevaluating planning assumptions and making significant investments to support a new generation of high-density workloads.

Rather than relying primarily on historical growth trends, planners are incorporating scenario-based modeling. This approach evaluates multiple potential growth paths, including aggressive AI adoption scenarios that may exceed historical demand patterns. As such, flexibility is now becoming a key design principle.

New facilities are often designed with higher baseline power capacities, modular expansion capabilities and infrastructure to accommodate future increases in rack density. This shift to adaptable and modular design allows for capacity to be added incrementally as AI demand evolves.

Cooling infrastructure is also undergoing rapid transformation. While air cooling remains effective for many workloads, operators supporting large-scale AI deployments are now adopting liquid technologies. Direct-to-chip liquid cooling and other advanced thermal management solutions allow facilities to support rack densities that would be difficult or impossible to manage through conventional methods alone.

Power planning is also becoming more integrated with business strategy. Operators are securing utility capacity years in advance and exploring alternative energy strategies to ensure long-term scalability.

Real-World Adaptation in the AI Era

Some of the industry’s largest operators are already making substantial adjustments. For example, many colocation providers are upgrading existing facilities to support higher-density deployments and redesigning power distribution systems to accommodate AI customers.

As AI loads grow, many colocation data centers are moving toward single-tenant facilities. This allows operators to dedicate power, cooling and network resources to large-scale AI deployments without the constraints that can arise in multitenant environments.

Additionally, hyperscale providers have invested heavily in AI-optimized infrastructure, including advanced cooling technologies, expanded power capacity and purpose-built data center designs. For example, Microsoft will build a renewable, scalable next-generation hyperscale AI data center valued at $6.2 billion in Narvik, Norway.

The Future of Data Center Planning

Generative AI reveals the limitations of traditional data center planning models and drives innovation in the industry. The assumptions that guided facility design for decades, including predictable growth rates, moderate rack densities and stable power requirements, are being challenged by AI-driven computing demands.

Infrastructure needs will evolve rapidly as organizations integrate generative AI into products, services and operations. Data center planners need to use flexible forecasting, adopt higher-density architectures, and prepare for higher power and cooling demands.

# # #

About the Author

Lou Farrell is the senior editor of AI content at Revolutionized Magazine. He has over five years of experience analyzing AI advancements across business, manufacturing, and engineering fields, providing insightful commentary on the latest breakthroughs, applications, and ethical responsibilities.

The post How Generative AI Workloads Are Breaking Traditional Data Center Planning Models appeared first on Data Center POST.

Originally posted on 1547realty.

TL;DR

• Hybrid and multi-cloud strategies depend on connectivity, not just colocation space.
• Carrier-rich, interconnection-focused facilities improve performance, flexibility, and scalability.
• 1547’s connected data centers help organizations build low-latency, future-ready infrastructure.

# # #

Modern colocation facilities have evolved far beyond providing basic power, cooling, and space, transforming into the critical connective tissue for hybrid and multi-cloud architectures. Driven by concerns over latency, egress costs, and vendor lock-in, the vast majority of organizations now blend private, on-premises systems with multiple public cloud platforms. This architectural complexity is increasingly managed within carrier-dense interconnection hubs, which provide the resilient and flexible foundation needed to connect private deployments directly with hyperscaler environments.

1547 Critical Systems Realty addresses this shift by offering facilities that function as rich, interconnected ecosystems rather than simple hardware storage. These hubs grant enterprises access to diverse carriers, direct cloud on-ramps like AWS Direct Connect and Azure ExpressRoute, and private peering opportunities. By anchoring infrastructure in strategically located, connectivity-rich markets, organizations can avoid unnecessary network hops and maintain the low-latency performance essential for modern and AI-driven workloads.

As U.S. data center demand is projected to nearly triple by 2030, evaluating infrastructure through the lens of “data gravity” has become paramount. Facilities equipped with a dense mix of existing network and cloud providers create a compounding ecosystem advantage, allowing organizations to provision new cross-connects in hours rather than weeks. Because 1547 specifically acquired its facilities for their established carrier density, these environments are inherently designed to provide the seamless, scalable connectivity that hybrid and multi-cloud IT strategies demand.

To continue reading, please click here.

The post Beyond Basic Colocation: Building Hybrid and Multi-Cloud Hubs in Interconnection-Rich Data Centers appeared first on Data Center POST.

TL;DR

• The Shift to Inference: As AI transitions from model training to revenue-generating applications, inference systems must be managed as complex chains where a single weak link can bottleneck the entire system.
• Listening to System Diagnostics: Instead of treating inference as a black box, teams must learn to translate continuous performance signals, such as “Time to First Token” or latency spikes, to accurately diagnose issues like memory constraints or scheduler pressure.
• Workload-Specific Testing: Generic benchmarks fail to predict real-world performance because different AI applications (e.g., fraud detection vs. legal analysis) stress systems in unique ways; testing must model actual prompt shapes, multi-turn sessions, and traffic bursts.

# # #

Over the last several years of AI expansion, it’s felt like everyone’s been obsessed with scale. Train bigger models, build bigger clusters, and run bigger experiments. But scale only gets you so far. The next phase of AI will revolve around revenue, turning big models into big business.

For technology leaders, that shift revolves around inference. Training is where a model learns; inference is where it provides answers and where it delivers value. It’s how AI models handle customer complaints, flag fraudulent transactions, draft legal briefs, explain lab results, or answer a developer’s question at 2 a.m. However, it’s also where costs accumulate, latency becomes a UX problem, and operational blind spots get increasingly expensive.

In response, people tend to treat inference like any other GPU problem: buy more accelerators, tune the model, and let the tokens flow. But inference systems demand a different equation. Inference pipelines run across load balancers, security layers, retrieval systems, KV-caches, memory bandwidth, storage, network fabrics, orchestration software, and GPU compute. That means a bottleneck in any one place slows everything else downstream. Without proper tuning, costly infrastructure can sit idle while users wait for models to respond.

The problem stems from looking at stacks like they’re a single machine. Instead, we should consider inference as a chain; and chains can fail at their weakest link.

What the numbers say

It’s tempting to think of inference like a black box, an impenetrable fortress that leaves you at the mercy of lagging indicators like latency complaints 6 months after deployment. But that couldn’t be further from the truth. Inference systems are constantly generating performance signals. The problem is that most teams don’t speak their language enough to understand them in context.

Take Time to First Token, for example. If it’s climbing, that often means prefill compute is struggling with long prompts or retrieval-augmented context. If decode cadence is wobbling, memory bandwidth may be the constraint. Perhaps KV-cache is swelling? That means long sessions or agentic workflows are expanding the memory footprint in ways the architecture wasn’t designed for. Maybe P99 latency is spiking, even though median latency looks clean? That likely means that burst traffic is creating queue buildups, or the scheduler is under pressure.

These signals aren’t isolated issues. They’re diagnostics. But they show up scattered across different teams, different monitoring systems, and in different contexts. Connecting them demands a layer of telemetry that most organizations have yet to build.

Why workload type matters

One of the key challenges with inference systems is reliably predicting performance. Pre-deployment benchmarks and load tests pass, and the architecture diagram makes sense. Then the system goes live and users reveal something that synthetic tests failed to anticipate.

The problem is the inference workloads themselves. They behave in different ways based on their model’s industry application and use case. Simple “one-size-fits-all” tests cannot account for this kind of variability and oftentimes mask the specific problems that determine real-world performance.

For example, legal AI tools that read long contracts will stress context windows and memory. Fraud detection systems care about microseconds, not minutes. Healthcare workflows need sustained throughput and careful data handling. Ecommerce chatbots need to absorb traffic spikes that arrive without warning. Developer copilots accumulate state across long conversations, growing their memory footprint with every turn.

Each of these applications present different problems with different bottlenecks. If you were to benchmark any of these models against generic HTTP traffic or peak GPU throughput, you’ll get a clean number that tells you next to nothing about how the system will ultimately behave when real users show up with messy prompts, uneven sessions, and edge cases your test harness never covered.

Three things that need to change

1. Test against the actual workload. 

“How fast are our GPUs?” is the wrong question. The right one is more along the lines of: “How does our system perform when users, applications, security requirements, and traffic patterns hit it at the same time?” Answering that means modeling real prompt shapes, real response sizes, real bursts, multi-turn sessions, retrieval calls, and the adversarial inputs that show up in production.

2. Observe the system as a whole.

A clean metric in one layer doesn’t mean the system is healthy. Inference performance is determined by the weakest link across compute, memory, storage, networking, and orchestration. That means getting time-aligned telemetry that connects what went into the stack with how each layer responded. That way, it’s easier to distinguish between compute limits, memory constraints, network delays, retrieval bottlenecks, and guardrails that aren’t scaling under load.

3. Optimize for Cost Per Token, not just speed. 

Throwing more hardware at a bottleneck is often the most expensive way to fix it, if it’s not the wrong fix entirely. Instead, a more precise method of intervention is identifying weak links, such as subsystems and workload shapes. Sometimes the answer is more memory, other times it’s better batching, a redesigned storage layer, or a change in how orchestration handles concurrency. However, the goal is always the same: ensuring predictable latency, lower cost per token, and right-sized infrastructure deployments that aren’t overprovisioned as insurance against uncertainty.

The larger shift

Teams that understand inference well are moving from infrastructure-level confidence to system-level assurance. It’s no longer sufficient to know that each layer works. For CIOs and CTOs, this changes the questions worth asking. Try looking deeper under the hood by determining “Which workloads are driving our economics?” or “Can we show that a new architecture lowers cost per token without degrading the user experience?”

Inquiries like these turn inference from a black box into something measurable, auditable, and improvable over time. They also create a common tongue between technical and executive teams. In this language, performance depends on evidence, cost is something traceable, and risk is something testable rather than assumed.

Figuring this out means you can make better architecture decisions, avoid expensive over-investments, and know what to fix before users ever notice it needs fixing. After all, the infrastructure already has the answers. The challenge is learning how to translate.

# # #

About the Author

Mike Hodge is AI Solutions Lead at Keysight, where he drives global strategy and go-to-market execution across the company’s AI, network test, and security portfolios. He specializes in connecting innovation with real-world applications, helping organizations harness AI for smarter, more secure systems.

The post Inference, Decoded: Unlocking AI’s Potential by Learning the Language of Inference Systems appeared first on Data Center POST.

TL;DR

• Context Over Quantity: Finding more vulnerabilities no longer guarantees better security; relying solely on standard severity scores often wastes resources on low-impact alerts while delaying critical fixes.
• Prioritizing Business Risk: To properly manage threats, security teams must incorporate real-world business context to prioritize the most dangerous vulnerabilities.
• Automation is Essential: To keep pace with AI-driven discovery, organizations must move away from fragmented, manual tools and adopt unified security orchestration to automatically trigger, assign, and track remediation workflows.

# # #

For years, cybersecurity teams have operated under a shared assumption that finding more vulnerabilities will help reduce risk. That assumption no longer holds.

With the emergence and rapid advancement of cyber-capable frontier LLMs such as Claude Mythos and GPT 5.5 Cyber, discovery is not the limiting factor. Models are now capable of identifying vulnerabilities at a scale and speed that far exceeds what human teams can process. This includes not only known issues, but a potential slough of zero-day vulnerabilities surfacing in software and infrastructure previously believed secure.

As these capabilities become more widely available, organizations are entering an inflection point in security operations. Their challenge will be to sort through the noise and make sense of found issues and their potential business impact before attackers can exploit newly uncovered weaknesses.

The Vulnerability Tsunami is a Context Problem

The industry is being approached by what can truly be described as a vulnerability tsunami. As Mythos-class AI dramatically accelerate vulnerability discovery, the number of identified issues will mount to previously unseen levels. But more findings quickly become the enemy of an overburdened security team; and without equally accelerated triage & remediation, more findings ≠ better security.

In practice, most organizations are struggling to translate this influx of data into meaningful action. Vulnerabilities are flagged, categorized, and scored, but often without the context needed to determine their real impact on the business.

A critical vulnerability in isolation does not tell the full story. Is the affected asset exposed to the internet? Is it tied to sensitive customer data? Is there a compensating control already in place? Without answers to these questions, security teams must make judgment calls based on incomplete information.

This leads to inefficiencies across the board. Analysts spend large amounts of time triaging alerts that don’t matter. Development and operations teams receive remediation requests that may not align with actual risk. Important issues become delayed, while less impactful ones consume valuable resources.

There is a growing disconnect between what organizations know about their risk and what they can actually do about it.

Prioritizing by Business Risk

To move forward, enterprises need to rethink how they prioritize vulnerabilities. Traditional methods that rely primarily on severity scores are no longer sufficient in a high-volume environment.

What is needed is a more complete view of risk. We need to incorporate business context alongside technical findings. This includes understanding how vulnerabilities map to critical assets, how those assets support business operations and key data, and how likely a given issue is to be exploited in the real world.

When context is applied consistently, prioritization becomes more focused and defensible. Security teams can direct their attention to the vulnerabilities that pose the greatest risk to the organization, rather than trying to address everything at once.

This change also improves collaboration. When remediation requests are backed by clear business impact, they are easier for engineering and operations teams to understand and act on. Conversations move from abstract severity levels to concrete risks, which helps reduce friction and accelerate response.

Automation is Now a Requirement

Even with better prioritization, the scale of modern enterprise environments makes manual remediation unsustainable. As discovery continues to accelerate, the gap between identification and resolution will widen unless organizations adopt more automated approaches.

Automation plays a critical role in turning insight into action. When vulnerabilities meet defined risk criteria, response workflows should be triggered automatically. This can include creating and assigning tickets, enriching them with needed context, and tracking progress through to completion.

The goal is to ensure that routine actions happen consistently and without delay. Standardizing how vulnerabilities are remediated and managed helps organizations reduce bottlenecks and improve overall efficiency.

More importantly, automation helps establish accountability. Every identified risk should have a clear owner, a defined path to remediation, and visibility into its status. This level of coordination is difficult to achieve through manual processes alone, though human oversight is still very much needed in the process.

Orchestrated Security vs. Managing Fragmented Tools

Leading organizations are already adapting to this new reality. They are moving away from fragmented toolsets that generate isolated findings and toward taking more unified approaches that bring data together in one place.

Security teams are moving towards improved security orchestration. They need the ability to correlate findings across different systems, apply consistent context, and drive coordinated responses.

When done effectively, this approach changes how security is managed and measured. Instead of focusing on the number of vulnerabilities discovered, organizations can track how quickly and effectively risk is reduced. Reporting becomes more aligned with business priorities, giving leadership a clearer view of where the organization stands.

The pace of vulnerability discovery will only increase from here. As models like Mythos and GPT 5.5 Cyber become widely adopted for vulnerability discovery, organizations will have access to more rapid insights about their security gaps than ever before–more than they can triage and resolve without help. And as attackers are already harnessing these same models to develop next-gen threats, the time to elevate vulnerability management programs to AI-scale is now. Investing in context-driven prioritization and automated orchestration will make the difference between floating above the tsunami, or drowning under it.

# # #

About the Author

Mark Lambert is the Chief Product Officer for ArmorCode, a leading application security posture management (ASPM) provider. Mark has built products for more than 20 years, and helped organizations streamline the delivery of secure, reliable and compliant software applications across the enterprise, embedded and IoT markets.

Prior to ArmorCode, he held product leadership positions with Parasoft, Advanced Visual Systems (AVS) and more. Mark holds a bachelor’s and master’s degree in computer science from Manchester University, UK.

The post When AI Finds Everything, Context Will be the Future of Vulnerability Management appeared first on Data Center POST.

TL;DR

• Transparency Builds Trust: While hyperscale AI data centers often face local pushback over resource usage, developers can earn community trust through responsible, transparent planning rather than just focusing on facility size.
• Water Conservation: Utilizing closed-loop cooling systems drastically reduces continuous water consumption, alleviating concerns about straining local water supplies.
• Economic Benefits: Beyond infrastructure, data centers drive local economic growth by creating jobs in both construction and long-term operations, while boosting nearby businesses.
• Mutual Success: Protecting a community and developing AI infrastructure are not mutually exclusive; responsible developers can provide necessary technological advancements while safeguarding local resources.

# # #

Around the country, AI data centers on a grand scale are increasingly a source of local controversy. In some cases, citizens have expressed concerns in “Save Our City” campaigns, worrying about excessive water consumption, grid strain, traffic and more from these massive facilities.

It is understandable that people would worry. For one thing, data centers are no longer seen as mere silent entities at the edge of the digital world. With the advent of artificial intelligence, data centers have become bigger, more energy-intensive, and more noticeable.

That doesn’t mean a valid concern equals actual harm.

The real question is whether the project has been conceived responsibly. The design for an advanced AI data center can take care of all of the key concerns – water conservation, grid safety, environmental sustainability, and economics.

Water Use Must Be Understood, Not Assumed

Water is typically the first question when discussing data centers. And that’s particularly true in areas where there is already an issue of water availability. In a closed-loop cooling approach, the cooling medium circulates through a contained system instead of being released through evaporation. In cases of high-density AI loads, this could take the form of a liquid-cooled system, a heat exchanger, a coolant distribution unit, a dry cooler, or air-cooled chiller. The critical community issue is not just whether water is used in the facility at all but, more importantly, how much water would be added after the initial filling, whether that water is potable, and what the facility’s water usage effectiveness (WUE) is.

Not all data center cooling systems are equal. Conventional evaporative cooling can mean continuous water consumption. A closed-loop system, by contrast, circulates cooling fluid within a sealed circuit, so it does not draw water continuously.

It makes a difference.

If the facility uses a closed loop system, then the discussion must revolve around real facts about water usage: how much water is needed; whether it is a one-off filling or regular; what the source of water is; how maintenance is done in the system, etc. Sometimes, water may be trucked in or sourced in a way that will not put strain on the community water supply or the city.

People have the right to information. But they also have a right to correct information.

Power Planning Is Part of Responsible Development

The second issue concerns electricity. Citizens are worried about whether the new facility will drive up prices, decrease reliability, or compete with residences and businesses for access to electricity. In terms of AI data centers, power planning goes beyond megawatt capacity; it starts with electrical design. Substations, grid interconnections, utility redundancy, UPS, battery storage, and on-site power generation are all parts of the solution that allows you to isolate the critical loads from local distribution. All of these elements allow you to achieve uptime, manage reliability risk, and define accountability for the necessary infrastructure. This is a legitimate concern. But here too, the answer lies in the infrastructure of the facility in question.

A proper data center is not a facility that suddenly appears and connects to the grid. A data center can have dedicated substations, improved interconnections, power reliability facilities, natural gas turbines, battery backup or other means of redundant power. This is all meant to increase reliability, while at the same time minimizing any strain on the current local infrastructure.

The public is entitled to raise questions such as who pays for the electrical facilities and how the facility will get its power, what are the facilities for backup power, how often the facility will generate power itself and what emissions controls are in place.

But these are very different questions than those that suggest that every data center imposes the same problem.

Economic Impact Should Be Part of the Conversation

Protecting the community matters, and so does recognizing what the community stands to gain.

Large-scale construction of a data center generates real economic activity in the area. The build itself puts electricians, mechanical contractors, civil contractors, truck drivers, safety experts, and engineers to work, and it drives business for nearby hotels, restaurants, suppliers, and service firms.

Once constructed, the data center might offer employment in operations, security, maintenance, facilities, landscaping, and other technical support. It can even assist with attracting workforce training programs and business investment.

This type of development can even become a part of an economic development plan for many communities, if done properly.

Responsible Development and Community Protection Can Work Together

This discussion is often reduced to a dichotomy of either protecting the community or opening it up for development. This is too narrow.

A community can both safeguard its future and provide the necessary infrastructure at the same time. A data center can consume substantial energy and build its own electrical infrastructure. A data center can require cooling and employ closed-loop technology to limit the amount of water consumed. It can be large and still provide local employment and business opportunities.

The competition in AI infrastructure is not only about who can build the biggest, most advanced facilities. The winner will be the responsible developer that earns public trust.

Data centers have become an essential part of America’s digital and economic infrastructure. The responsibility of the industry is to show the community not only what it plans to build but also how it plans to do that and whose interests will be served through this development.

Responsible data center development should not be seen as the opposite of protecting the community. Done right, it becomes one of the ways a community builds its future.

# # #

About the Author

Harshada Jadhao is a Senior Project Engineer at DPR Construction with experience in mission-critical and hyperscale data center construction. She holds a master’s degree in Construction Management & Technology from Arizona State University, along with graduate and undergraduate degrees in engineering from Nagpur University. Her work focuses on schedule and procurement coordination, cross-trade integration, quality control, turnover readiness, and constructability in complex project environments. She has contributed to major data center projects, including the Meta Hyperscale Data Center and the Abilene Data Center, and brings a practical field-based perspective to the evolving role of project management in AI-driven infrastructure.

The post When “Save Our City” Meets the AI Data Center: How Responsible Developers Earn Community Trust appeared first on Data Center POST.

Originally posted on Datalec Ltd.

TL;DR

• AI-Driven Density Surge: AI workloads are pushing data centre rack densities to extreme levels (projected to reach 600kW by late 2027), accelerating a critical industry transition from traditional air cooling to advanced liquid-cooling technologies.
• Localized Infrastructure Manufacturing: To support these complex thermal and structural demands, DPI has expanded its bespoke manufacturing in the UAE to produce liquid-ready whitespace solutions, including heavy-duty Hyperion ceiling systems, modular pods, and dedicated stainless-steel technology cooling loops.

# # #

Artificial intelligence is fundamentally reshaping data centre design by driving unprecedented increases in power density, cooling requirements, and infrastructure complexity. Traditional data centre racks that once operated between 5kW and 15kW are now being replaced by high-performance deployments routinely reaching 227kW, with projections suggesting demands could hit an extraordinary 600kW by late 2027. Because air can no longer efficiently remove heat at these extreme densities, the industry is rapidly transitioning to advanced liquid-cooling technologies, like direct-to-chip and immersion systems, which significantly alters how whitespace environments must be designed and built.

To meet the demands of liquid-ready infrastructure, Datalec Precision Installations (DPI) has significantly expanded its localized engineering and manufacturing capabilities in the UAE. This includes the local bespoke production of freestanding structural Hot Aisle Containment (HAC) units, standardized modular HPC pods, and the Datalec Hyperion Ceiling System, which is uniquely designed to support heavy mechanical and electrical loads without relying on HAC supports. Furthermore, to support the growing need for specialized process water infrastructure, DPI has established a dedicated stainless-steel fabrication workshop in the region to manufacture precision technology cooling loops under rigorous global quality controls.

Beyond structural fabrication, DPI provides complete end-to-end integration and global lifecycle support, working alongside leading Coolant Distribution Unit (CDU) and glycol manufacturers to offer everything from system flushing and commissioning to 24/7 rapid response maintenance. Shifting to an onshore, import-independent supply chain and championing the “Made in the Emirates” mark provides distinct commercial and environmental benefits for these deployments. This localised approach drastically reduces embodied carbon by eliminating international freight, compresses project delivery timelines from months to weeks, and mitigates supply chain risks while actively supporting regional economic growth.

To continue reading, please click here.

The post Engineering the Future: How DPI is Evolving for AI Infrastructure Data Centre Deployments appeared first on Data Center POST.

TL;DR

• AI capacity is set to surge: Structure Research projects AI-focused data center capacity will grow from 2.3 GW today to 150 GW by 2030, reflecting unprecedented infrastructure expansion.
• New research brings clarity: The report provides a bottom-up view of who is funding, building, and consuming AI infrastructure, offering a more complete picture of the rapidly evolving ecosystem.
• Power and strategy will shape the future: Findings highlight power availability as a key constraint while examining how hyperscalers, AI labs, neocloud providers, and sovereign AI initiatives are driving the next phase of AI infrastructure growth.

# # #

Artificial intelligence is driving one of the fastest infrastructure expansions the data center industry has ever experienced, but understanding where capacity is being built and who ultimately consumes it has become increasingly complex.

To provide greater visibility into the evolving market, Structure Research has released its new AI Infrastructure Report, offering a bottom-up analysis of the organizations funding, building, and utilizing AI infrastructure worldwide. According to the report, AI-focused data center capacity is projected to grow from approximately 2.3 gigawatts (GW) today to 150 GW by 2030, representing a 66-fold increase over the next several years.

Built using Structure Research’s proprietary datasets, the report combines company-level infrastructure tracking with analysis of neocloud providers and sovereign AI initiatives to create a unified view of the AI infrastructure ecosystem. The research reconciles both infrastructure ownership and compute consumption, helping organizations better understand where capital is flowing, where capacity is being deployed, and how market dynamics are changing.

Among its key findings, the report identifies power availability as one of the largest constraints to future AI infrastructure growth and highlights significant differences in how organizations convert infrastructure investment into available compute capacity. It also examines how hyperscalers, AI labs, neocloud providers, and sovereign AI programs are pursuing increasingly distinct infrastructure strategies that will shape the market through 2030.

The AI Infrastructure Report is designed for hyperscalers, cloud providers, data center operators, investors, policymakers, and enterprise technology leaders seeking a deeper understanding of the forces reshaping AI infrastructure deployment and the future of global compute capacity. Executive summaries are now available here from Structure Research.

The post Structure Research Forecasts AI Data Center Capacity Will Reach 150 GW by 203 appeared first on Data Center POST.

Data Center Post had the opportunity to connect with Andy Fenton, an accomplished telecom and data centre executive with over 20 years of experience in the industry. Currently serving as the VP of Sales and Marketing for Telehouse Canada, Andy oversees the company’s sales strategy to drive long-term growth and manages relationships with major telecom and big tech clients. Beginning his career at PSINet, he managed sales, business development, and the national sales team, where he gained valuable expertise in the colocation business. He then transitioned to Bell Canada as the General Manager for Bell’s Systems Integrator Group, managing relationships with key partners, including Accenture, Wipro and IBM Canada.

Prior to joining Telehouse, Andy spent 14 years at Cogeco as head of the Carrier and Strategic Accounts Team playing a crucial role in the company’s growth from a single location to over 15 data centres across Canada, the U.S. and the U.K. Over his career Andy developed and managed relationships with major telecom and technology companies including IBM, Accenture, Microsoft, Rogers, Telus, Hydro One and Tencent.

What does your company do?

As a subsidiary of KDDI Corporation, a global leader in telecommunications, Telehouse brings 35 years of global expertise in providing reliable, secure, and flexible colocation services. Our family of data centres are all strategically placed in more than 40 locations around the world. From Paris to Shanghai, we maintain optimum SLA uptime standards of 99.999%.

In Canada, Telehouse operates three interconnected data centres in downtown Toronto, forming one of the country’s most established connectivity hubs. Telehouse Canada’s facilities provide direct access to a dense ecosystem of carriers, cloud providers, ASPs, and ISPs, enabling fast, low‑latency connectivity to key North American markets. Telehouse Canada supports organizations across diverse industries with secure, resilient, and highly interconnected infrastructure that drives growth and enables global reach.

We offer both shared and dedicated colocation space in all our data centres. Each data centre is connected through our dark fibre network, providing rapid access to a range of 200 + connectivity providers, including leading cloud service providers.

Our data centres act as the nerve centre of Canadian telecommunications, serving as a meeting point for internet service providers (ISP), application service providers (ASP) and Canada’s largest carrier networks to connect and exchange data.

Telehouse also supports high-density AI workloads while enabling edge deployment closer to end users through robust connectivity and interconnection.

What problems does your company solve in the market?

Telehouse Canada helps organizations address critical infrastructure challenges, including the need for diverse, reliable low- latency connectivity, scalable capacity, and secure, high‑performance environments to support digital growth and AI workloads.

As part of a global platform of data centres, Telehouse enables direct access to leading cloud platforms, helping businesses to seamlessly connect to the services they need to optimize their infrastructure, support hybrid and multi-cloud strategies, and expand internationally.

Our carrier‑neutral colocation and interconnection solutions provide the flexibility to scale infrastructure on demand, optimize network performance, and improve business continuity. By tailoring solutions to each customer’s technical and business requirements, Telehouse Canada enables organizations to accelerate digital transformation, improve resilience, and compete effectively in the global digital economy.

This is particularly important as organizations deploy increasingly data-intensive applications, including AI workloads, which require greater infrastructure density, scalable capacity, and reliable connectivity.

What are your company’s core products or services?

Telehouse Canada provides a comprehensive portfolio of colocation, interconnection, and connectivity services from Canada’s most connected carrier-neutral data centre campus in downtown Toronto. Our colocation services range from single cabinets in shared environments to customized cages and dedicated suites, supporting everything from enterprise IT to high-density AI workloads.

We deliver seamless interconnection services through seven on-site Meet-Me Rooms, enabling customers to connect directly to a wide ecosystem. Our fully managed, network-neutral environment simplifies network expansion and accelerates deployment, while diverse pathways and secure infrastructure ensure resilience, redundancy, and route diversity.

Connectivity services provide low-latency, flexible solutions designed to support cloud on-ramps, hybrid and multi-cloud architectures, and high-performance data exchange. These services together enable businesses to support latency-sensitive applications and scale efficiently within a highly interconnected digital ecosystem.

What markets do you serve?

We serve a variety of markets, including enterprises and small to medium businesses, cloud services providers, tech startups and scale-ups, financial institutions, healthcare organizations, AI developers, e-commerce platforms, content providers, government agencies, and educational institutions.

What challenges does the global digital infrastructure industry face today?

As AI adoption accelerates, the demands placed on digital infrastructure are rapidly evolving. Organizations are seeking data centre environments capable of supporting performance-intensive workloads at scale, driving the need for higher-density architectures and more advanced, efficient cooling solutions.

At the same time, the focus is shifting toward infrastructure that can balance performance with resilience and sustainability. Businesses increasingly require low-latency, energy-efficient environments that can handle growing volumes of data exchange while supporting broader environmental and operational goals.

These challenges are further compounded by constraints around power availability and energy capacity, which are reshaping how infrastructure is designed, deployed, and scaled. In parallel, the importance of sovereign infrastructure continues to grow, as organizations look to ensure data residency, meet regulatory requirements, and maintain greater control over critical digital assets.

How is your company adapting to these challenges?

Telehouse Canada is actively advancing its infrastructure to meet the growing demands of AI, high‑density deployments, and increased connectivity requirements. We are investing in infrastructure designed to support performance-intensive AI workloads, incorporating advanced technologies such as direct‑to‑chip liquid cooling to efficiently support performance‑intensive workloads. Liquid cooling is more thermally conductive than air, allowing Telehouse Canada to remove up to 80 per cent of heat directly from high-power server components. As a result, reliance on power-intensive computer room air conditioners and server fans is reduced, lowering overall energy consumption while delivering a more sustainable and efficient cooling model. Combined with our dense interconnection ecosystem and carrier-neutral environment, these upgrades help customers scale AI workloads while maintaining fast, reliable access to networks, cloud providers and end users.

To address rising power and efficiency challenges, Telehouse Canada focuses on optimizing energy use through innovative cooling approaches and a high‑efficiency facility design. Our Toronto data centres leverage renewable energy sources and integrate sustainable cooling solutions, including Enwave’s Deep Lake Water Cooling system, reducing environmental impact while supporting continued capacity growth.

Additionally, in response to growing demand for sovereign infrastructure, Telehouse Canada offers secure, Canadian‑based data centre solutions that support local data residency, regulatory compliance, and greater control over critical digital assets. This is particularly important for organizations in regulated industries and those prioritizing national data sovereignty.

Through a combination of advanced cooling technologies, energy‑efficient design, rich interconnection, and locally operated infrastructure, Telehouse Canada enables organizations to scale AI workloads, improve performance, and build resilient, future‑ready digital platforms.

What are your company’s key differentiators?

More than half (50 per cent) of all Canada’s carriers, service providers (xSPs) and content providers have a presence at Telehouse Canada’s data centres, making this interconnected hub the main gateway for Canada’s internet traffic flow, helping Canadians connect digitally with each other and the rest of the world. By connecting different networks and providers together, our data centres play an important role in facilitating Canadians’ experience of the digital world as vast, seamless, fast, and interconnected.

For Canadian consumers, data centres act as conduits for the flow of all our important data. Whether it’s the content we stream on our devices or the emails we send to colleagues, most of that content likely passes through our data centres as it moves between networks.

For businesses and public sector organizations, 151 Front Street West and its sister data centres are the primary gateway through which all commerce, content and communications data is shared both domestically and globally.

As part of a global platform of data centres, Telehouse enables customers to seamlessly extend their infrastructure internationally, supporting global expansion with consistent interconnection and colocation services.

What can we expect to see/hear from your company in the future?

Canada is known around the world for its technology leadership, including advanced research, top-tier talent and the growth of innovative companies. We’re focused on expanding the capacity of our facilities and advancing their capabilities to meet the growing demand for connectivity services.

We also remain focused on enhancing customer experience and delivering greater value— staying ahead by fostering partnerships, innovating our own operations, and aligning with global best practices.

What upcoming industry events will you be attending?

We will be attending RAISE Paris 2026 (July 8-9), ALL IN 2026 (Sept. 16-17), and AI Infra Summit (Sept. 15-17).

Do you have any recent news you would like us to highlight?

Telehouse Canada has completed a major infrastructure upgrade featuring a first-of-its-kind deployment of direct liquid cooling within an interconnection hub in Canada, enabling high-density AI workloads and cabinet densities of up to 120 kW per rack within its downtown Toronto data centre campus.

The investment strengthens Telehouse Canada’s ability to support AI-driven operations by combining advanced cooling technology with resilient, low-latency, interconnection-rich infrastructure designed for performance-intensive workloads at scale.

The upgrade also advances sustainability efforts by reducing overall energy consumption and repurposing captured heat through Enwave’s district energy system, while supporting Canada’s broader digital infrastructure and AI innovation goals.

Readers can learn more about the upgrade here.

Is there anything else you would like our readers to know about your company and capabilities?

Given our established connectivity hubs and our major infrastructure upgrade, Telehouse Canada continues to prioritize a strong commitment to supporting Canadian businesses and the economy.

As content distribution and edge computing continue to evolve, neocloud providers and AI companies are increasingly looking for power and capacity closer to carrier hotels and major connectivity hubs. While early AI factories were often built in remote locations focused on training workloads, there is now a growing trend toward inference workloads being deployed closer to end users and closer to the edge.

At the same time, rack density requirements are increasing significantly, with demand moving from roughly 40kW to 120kW per rack. While many new AI-focused data centres are being built, they still require fibre connectivity and interconnection back to carrier hotels like Telehouse Canada. The industry expected this shift toward highly interconnected AI infrastructure to happen eventually, but it appears to be occurring sooner than anticipated. As a result, facilities located adjacent to carrier hotels and internet exchanges are becoming increasingly important, and Telehouse is well positioned to support this evolution by providing both high-density infrastructure and the connectivity required to bring these environments together.

Where can our readers learn more about your company?

Readers can visit telehouse.ca to learn more about our company and follow us on our social platforms, like LinkedIn, X, Facebook, and YouTube, to stay up to date on announcements and events we’re attending.

How can our readers contact your company?

Readers can contact us via our website online, linked here.

The post Corporate Profile: A Conversation with Andy Fenton, VP of Sales and Marketing at Telehouse Canada appeared first on Data Center POST.

TL;DR

• Shift to Edge Automation: Data centers are moving away from inefficient, manually monitored Security Operations Centers (SOCs) toward automated, decentralized security located directly at the “edge” (the entrance of the data hall).
• Operational Efficiency: Implementing automated entry and screening lowers operational expenses (OpEx) by using AI to handle routine access, freeing up human staff for strategic oversight, and logging all events centrally for audit compliance.
• The Orion DataGuard™ Solution: To support this new “Lean SOC” model, Orion DataGuard™ offers a single modular unit that integrates AI/LiDAR tailgating prevention, biometric authentication, and digital-media detection to provide highly secure and frictionless access.

# # #

The traditional Security Operations Center (SOC) is under siege; not by threats, but by its own inefficiency. The 2026 SIA Security Megatrends explicitly notes that SOCs and monitoring will be disrupted and automated. For data center operators, this disruption is the key to unlocking massive ROI.

For years, physical security at data centers relied on a “manual triage” model: cameras and sensors fed data to an SOC, where security monitoring staff spent most of their time on repetitive tasks. In 2026, that model is turning obsolete. The goal now is the reduction or elimination of the SOC as we know it, moving security intelligence directly to the “edge”, the actual entrance of the data hall. This reflects a shift toward more decentralized, real-time security – positioned closer to the physical infrastructure.

Turning Security into a Value Chain

Data centers are moving away from security staff and implementing fewer SOC’s, towards remotely monitored/controlled automated security systems; especially for security entrance control, visitor management, and surveillance. By implementing self-service, automated security screening, data centers can finally bridge the gap between high security and low OpEx. This is where the ROI becomes undeniable.

Moving authentication and screening to an automated portal can:

• Eliminate Tier 1 Bottlenecks: AI handles the “noise” of daily access.
• Optimize Staffing: Shift human capital from “watching doors” to high-level strategic oversight.
• Ensure Compliance: Every event is centrally logged via AI based event management systems, such as Orion Infinity™ for audit-ready reporting.

Introducing Orion DataGuard™: The Modular Solution for 2026

At Orion Entrance Control, Inc., we’ve listened to the pressure data center developers face; balancing many conflicting priorities like real estate and energy costs with the absolute need for data center server security. Orion DataGuard™ is our answer.

DataGuard™ integrates AI/LiDAR-based tailgating prevention through Orion DoorGuard™, identity and biometric authentication, digital-media detection and secure asset management into a single modular unit. It is a highly secure, ADA-accessible screening environment that satisfies stringent AHJ fire codes while providing a frictionless experience. Whether for new builds or retrofits, DataGuard™ enables a “Lean SOC” model by automating all the processes involved in providing secure entry. By partnering with best-of-breed technology providers, Orion ensures that DataGuard™ fits into your existing tech stack while preparing you for a future where security is automated, autonomous, and incredibly efficient.

# # #

About the Author

Mike McGovern, VP Data Center Security at Orion Entrance Control, Inc.

For over 26 years Mike has helped develop, design and integrate pedestrian entrance control systems (security revolving doors, turnstiles, speed gates, security portals) working with end users, architects, integrators and security engineer consultants. His portfolio includes architectural optical turnstile, revolving door, and portals systems at many of the USA’s most iconic and recognizable buildings and campuses. He is a frequent speaker and panel moderator on integrated security and data center security topics at leading data center trade shows and seminars. McGovern is an active member of the ASIS SAEC (Security Architecture and Engineering Community) Steering Committee and the SIA (Security Industry Association) Data Center Advisory Panel. As VP Business Development at Orion Entrance Control, Inc., he leads their data center security entrance control business and product development strategy.

About Orion Entrance Control, Inc.

Orion Entrance Control, Inc. is an American technology company that specializes in speed lanes, optical turnstiles, full height turnstiles, security tailgating solutions, and occupancy sensor solutions. Serving high-rise buildings, government and corporate offices, and other facilities, Orion delivers secure, visually appealing, and easily integrated entry solutions. With decades of experience working with security integrators and architectural planners, Orion provides the expertise and tools to streamline lobby security projects while delivering actionable data and metrics that enhance operational efficiency and safety. For more information, please visit www.orioneci.com or follow us on LinkedIn.

The post Data Center Security Enhanced with DataGuard™: The Disruption and Automation of Security Operations Centers (SOCs) appeared first on Data Center POST.

TL;DR

• Massive Market Growth: The global data center refrigerant market is projected to reach $1.62 billion by 2035 (up from $633 million in 2025), driven by the rapid expansion of artificial intelligence, cloud platforms, and high-density computing.
• Shift to Sustainable Solutions: Regulatory pressures and sustainability goals are forcing a transition to low-emission alternatives, with Hydrofluoroolefins (HFOs) capturing a dominant 67.7% market share in 2025 due to their low global warming potential and high cooling efficiency.
• U.S. Market Leadership: The United States accounted for a massive 79% of the market in 2025, heavily supported by continuous investments in hyperscale expansions and strict energy efficiency mandates.

# # #

The global data center refrigerant market was valued at USD 633 million in 2025 and is estimated to grow at a CAGR of 9.6% to reach USD 1.62 billion by 2035, according to recent report by Global Market Insights Inc.

The market is undergoing a major transformation driven by the rapid expansion of digital infrastructure, including hyperscale facilities, cloud platforms, and accelerating global data consumption. Increasing computational workloads from artificial intelligence, big data analytics, and high-density computing environments are significantly raising thermal management requirements across modern data centers. As a result, demand for advanced refrigerants that ensure efficient heat control, operational stability, and optimized energy performance is increasing steadily.

The industry is also experiencing a structural shift away from traditional high-global-warming-potential refrigerants toward low-emission and energy-efficient alternatives. Regulatory pressure from global climate agreements and regional environmental policies is accelerating the adoption of next-generation refrigerants. Operators are increasingly focusing on reducing emissions, improving leakage control, and enhancing lifecycle management practices to comply with evolving sustainability requirements. This transition is further reinforced by rising emphasis on lowering operating costs and improving energy efficiency across large-scale digital infrastructure.

The data center refrigerant market from HFOs segment held a 67.7% share in 2025 and is projected to grow at a CAGR of 10.2% through 2035. HFO refrigerants continue to dominate due to tightening environmental regulations and the global push toward decarbonization. Their significantly lower global warming potential, combined with strong cooling efficiency, makes them a preferred solution for next-generation data center cooling systems. The ongoing phase-out of high-GWP refrigerants is further accelerating their adoption across the industry.

The hyperscale segment accounted for 40% share in 2025 and is expected to grow at a CAGR of 10.4% from 2026 to 2035. This segment leads the market due to extremely high computing densities and large-scale infrastructure operated by major cloud and technology providers. These facilities generate substantial heat loads from artificial intelligence workloads, cloud computing, and advanced digital services, requiring highly efficient refrigerant-based cooling systems to maintain continuous operations and system stability.

U.S. data center refrigerant market held a 79% share in 2025, generating USD 176.3 million. Growth in the country is strongly supported by rapid hyperscale data center expansion driven by artificial intelligence, cloud computing, and data-intensive applications. Continuous investments in new facilities by leading technology firms are increasing demand for advanced cooling systems. Rising adoption of high-density server infrastructure is further intensifying thermal loads, strengthening the need for high-performance refrigerants. In addition, strict energy efficiency regulations and sustainability mandates are encouraging the shift toward low-GWP refrigerant solutions and modern thermal management technologies.

Key companies operating in the global data center refrigerant industry include Daikin Industries, Linde plc, Honeywell, Chemours, AGC, Dongyue, Zhejiang Juhua, Arkea and Sinochem. Companies in the market are actively focusing on developing low-global warming potential refrigerants that align with tightening environmental regulations and sustainability targets.

A major strategy involves increasing investment in research and development to enhance refrigerant efficiency, thermal stability, and compatibility with high-density computing environments. Market players are also strengthening partnerships with data center operators and cooling system manufacturers to integrate advanced refrigerant solutions into next-generation infrastructure. Expansion of production capabilities in high-demand regions is being prioritized to ensure supply chain stability and faster delivery. Additionally, firms are emphasizing compliance-driven innovation, improving leakage detection systems, and enhancing lifecycle management solutions.

The post Data Center Refrigerant Market to Surpass USD 1.62 Billion by 2035 appeared first on Data Center POST.

TL;DR

• The Hyperscale Mismatch: Large wholesale data centers in Miami cater to massive enterprise volumes, forcing small and medium-sized businesses (SMBs) into expensive minimum commitments that far exceed their actual requirements.
• The Cost of Unused Capacity: Under these enterprise models, SMBs end up paying for stranded power and empty rack space, making professional colocation financially unviable and leaving them vulnerable to risks like housing critical systems in office server closets during hurricane season.
• Fractional Colocation Solutions: Instead of accepting preset sales minimums, businesses can utilize right-sized options like quarter-rack (10U) or half-rack (20U) setups, which provide the same enterprise-grade facility standards but scale power and space commitments to exact hardware loads.
• Accessible Resilience: Local, right-sized colocation allows South Florida businesses to affordably access vital infrastructure, such as robust hurricane resilience, physical security, and low-latency connectivity, while preserving the flexibility to scale up seamlessly as demand grows.

# # #

The South Florida business landscape is booming, cementing its status as a critical gateway for national and international commerce.

As a growing business or enterprise IT leader in this region, migrating your core infrastructure to a Miami data center can solve on-premises space limitations, remove cooling headaches, and provide real physical security against extreme weather events. But when evaluating local options, smaller businesses quickly hit a commercial wall with the industry’s massive wholesale giants.

Search for Miami colocation today, and the biggest names dominate the results: global hyperscale and wholesale providers with massive campuses, enterprise sales teams, and pricing models built for customers leasing entire cages and data halls. What those results don’t show is the fine print that matters most to a small or medium-sized business: the minimum commitment.

For a South Florida business that needs to house a handful of servers, a storage appliance, and a firewall, that floor can make professional colocation an impossible infrastructure decision because of the budget problem. The business ends up paying for empty cabinet space and stranded power capacity, or it walks away from colocation entirely and accepts the risk of an office server closet during hurricane season.

This guide explains why hyperscale minimums fail smaller deployments, what a right-sized data center engagement looks like with quarter-rack, half-rack, and full-rack options, and why power should be provisioned to your actual requirements rather than the maximum a cabinet can support.

The Enterprise-Focused Structure of the Miami Colocation Market

Miami is one of the most strategically important data center markets in the Western Hemisphere. It is the primary connectivity gateway between North America, Latin America, and the Caribbean, home to dense subsea cable landings and carrier ecosystems that route an enormous share of intercontinental traffic. That strategic value has attracted the largest global colocation operators, and their presence shapes how Miami colocation is sold.

Hyperscale and wholesale providers optimize for volume. Their ideal customer leases hundreds of kilowatts across cages and suites, signs multi-year enterprise agreements, and consumes interconnection services at scale. Everything about their operation, from sales process to contract structure, reflects that buyer. The smallest unit they are willing to sell is set accordingly: one full cabinet, a 5 kW committed power floor, and terms designed for enterprise procurement.

None of that is wrong as a business strategy, it just means that the most visible Miami data center options are built around enterprise buyers, without consideration for smaller businesses that make up a significant part of the South Florida market.

What Large Provider Minimums Mean for Small Businesses

At the largest Miami colocation providers, retail colocation often starts at one full rack paired with a 5 kW minimum power commitment. For a multinational enterprise, that may be a small line item. For an SMB, it can define the entire economics of the deal.

Consider a typical small business deployment: 8 to 12 rack units of equipment drawing 1 to 2 kW under normal load. Under a hyperscale minimum, that business may pay for:

• A 42U or 45U cabinet where most of the space sits empty
• 5 kW of committed power when actual draw peaks closer to 2 kW
• Cross-connect fees, remote hands rates, and setup charges scaled to enterprise budgets
• Multi-year terms with escalators built around enterprise renewal cycles

Power is usually where the mismatch hurts most: if a business draws 1.8 kW but commits to 5 kW, the unused 3.2 kW still shows up on the bill every month, and over a multi-year term, that stranded capacity can become a serious cost. It can seriously raise the bill, and push smaller businesses into awkward infrastructure choices, from delaying hardware refreshes to keeping critical systems in office server rooms or moving steady workloads into cloud environments that may cost more over time.

What Right-Sized Data Center Colocation Looks Like

Right-sized Miami data center colocation starts with the actual deployment, not a preset sales minimum. The amount of space and power should reflect what the business is really putting in the rack.

Smaller environments may only need a quarter rack or half rack, while larger or denser deployments may require a full rack with a higher committed power level. The footprint follows the equipment, not the other way around.

Power should follow the same logic as space, with the commitment based on the actual hardware load rather than the maximum theoretical capacity of the rack. A deployment drawing 1.5 kW should be provisioned differently from a high-density setup that needs 8 kW. The hardware profile sets the number, with enough headroom to grow without overbuying from day one.

Half-Rack and Quarter-Rack Miami Colocation Options Exist for Small Businesses

Many SMBs don’t realize these options exist because many Miami colocation options are marketed around full racks and enterprise deployments. In reality, though, smaller footprints like quarter racks and half racks can be a much better fit for businesses that only need a modest amount of equipment.

A quarter rack provides roughly 10U of secure, individually lockable space, which is often enough for a basic production environment with a few servers, storage, a firewall, and a switch. The smaller footprint doesn’t mean a lower facility standard. Quarter rack deployments can still use core data center infrastructure like redundant power paths, N+1 cooling, physical security, fire suppression, and 24/7 monitoring.

A half rack, around 20U, gives growing businesses more room for virtualization clusters, shared storage, network equipment, and backup infrastructure. It is also useful for companies consolidating servers from multiple offices or closets into one professional environment. In both cases, power should scale to the deployment itself: a half rack drawing 2.5 kW commits to 2.5 kW, not an arbitrary floor.

Scaling From Quarter Rack to Full Rack Without Overcommitting

Businesses that genuinely need a full cabinet still benefit from the right-sizing principle. The goal is not to stay small, but to match the deployment to current requirements.

Because the model is fractional by design, scaling is built in. A company can start in a quarter rack, move into a half rack, and later expand to a full rack within the same facility. That kind of path helps avoid buying more space and power than the business is ready to use.

Why Location Is Still Part of the Equation

Right-sizing addresses the economics. Location addresses operations. For businesses across Miami-Dade, Broward, and Palm Beach counties, local colocation can offer advantages that are difficult to replicate from another market.

For South Florida businesses, hurricane resilience is often the biggest driver behind moving infrastructure out of an office server room and into a professional facility. Proximity makes everyday operations easier, too. When hardware needs to be installed, replaced, or physically inspected, engineers can drive to the facility instead of coordinating logistics across the country. Miami’s position as a connectivity hub also benefits organizations serving Latin America and the Caribbean, serving a core need: lower latency.

The Bottom Line: Miami Colocation Should Fit Your Business

Miami colocation doesn’t have to start with a full rack, an excessive power commitment, or more infrastructure than the business currently needs. The better approach is to look at the actual hardware first, then choose the footprint and power profile that fits it. That sounds obvious, but it’s often missing from the way colocation is sold. In many cases, the better fit already exists; businesses simply have to know where to look.

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About the Author

Michael Zrihen is the Senior Director of Marketing & Internal Operations Manager at Volico Data Centers

The post Why Hyperscale Data Center Pricing Fails Small Businesses Seeking Miami Colocation appeared first on Data Center POST.

TL;DR

• AI Workloads Overwhelming Traditional Cooling: AI’s demand for concentrated compute power is driving rack densities from 5-10 kW to beyond 50-100 kW, creating extreme heat that traditional, water-intensive air cooling systems can no longer handle.
• Infrastructure Must Evolve: Thermal management, power delivery, and resource efficiency are now co-dependent, requiring developers to treat data center infrastructure as an integrated system rather than sacrificing sustainability for performance.
• The Immersion Cooling Advantage: Fully submerging IT components in thermally conductive fluid provides superior, uniform heat removal, enabling higher rack densities while significantly lowering both water and energy consumption compared to traditional methods.
• Fluid Selection is the Linchpin: The ultimate success and sustainability of immersion cooling rely on selecting the right engineered fluids, which dictate heat transfer efficiency, hardware lifespan, and overall environmental impact.

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The Current State of Data Center Cooling

In 2026, data centers are caught between surging demand and tightening constraints. AI workloads are driving the need for higher compute density, while developers face limits related to power availability, water usage, and permitting timelines. Policymakers and communities are scrutinizing new projects, slowing approvals due to various concerns ranging from grid strain to land and water use. Meanwhile, hyperscalers and enterprise operators must meet aggressive sustainability targets even as their infrastructure footprints expand.

These pressures are exposing fundamental structural challenges in how data centers are built. Traditional air cooling with water-intensive systems cannot scale to meet AI’s mounting demands. Developers must now tackle multiple variables at once: more compute per square foot, lower energy and water intensity, and alignment with evolving regulatory frameworks. Meeting these challenges requires rethinking infrastructure as an integrated system where performance, efficiency, and sustainability are engineered together, not sacrificed for each other.

AI as the Catalyst for Convergence

Artificial intelligence has fundamentally changed infrastructure development strategies. Unlike traditional workloads, AI training and inference require highly concentrated compute power, delivered through GPUs and accelerators operating at significantly higher thermal design power (TDP). Rack densities that once averaged 5–10 kW are now pushing beyond 50–100 kW, with next-generation deployments expected to drive densities even higher.

The implications for power infrastructure are already appearing. According to Dean Nelson, founder and chairman of Infrastructure Masons, the industry is building as much data center capacity in the next three years as was built in the past thirty. The infrastructure required to support that pace of growth must align with these operational realities. After all, the cascading effects are significant. Higher compute density drives more heat, which demands more cooling. Cooling systems consume more power and, in many cases, more water, putting compounding pressure on resources already under strain. Power availability is becoming a gating factor, with utilities struggling to keep pace with the speed and scale of data center growth.

As a result, AI performance is determined by the infrastructure that supports it, not just by model architecture or silicon. Thermal management, power delivery, and resource efficiency are now co-dependent variables. As such, decisions from the chip to the facility cooling plant must be evaluated through a single, system-wide lens that takes these factors into account.

Immersion Cooling as an Infrastructure Strategy

Cooling is now a critical lever for enhancing performance and efficiency. As TDPs rise across the technology stack, liquid cooling is becoming a requirement at leading AI facilities. Immersion cooling, in particular, is uniquely positioned to address the three constraints underpinning modern data center development: heat management, water use, and power consumption.

Compared to traditional air cooling and direct-to-chip liquid cooling, immersion cooling delivers significant improvements in thermal management efficiency. By fully submerging IT components in a thermally conductive fluid, heat is removed directly and uniformly at the source, eliminating the limitations of air cooling and the complexity of localized cold plates and plumbing. Direct-to-chip cooling targets high-heat components like GPUs and CPUs but still relies on air or secondary systems to cool the rest of the hardware. Immersion cooling, by contrast, manages the entire system within a single thermal environment.

The Operational Benefits of Immersion Cooling

The superior heat capacity and thermal conductivity of immersion fluids enable consistent, high-efficiency heat transfer across all components, even at extreme power densities. This helps data centers support significantly higher rack densities without complex airflow management, extensive piping networks, or oversized mechanical cooling systems. Benefits such as better performance, longer hardware lifespan, and greater operational reliability are proving critical for high-density AI workloads.

Immersion cooling also reduces reliance on air handling and evaporative systems, significantly lowering water consumption. It mitigates overall energy demand for cooling as well, easing pressure on constrained power infrastructure. The net effect is not only thermal efficiency, but a more balanced and sustainable resource profile that regulators, investors, and communities increasingly prioritize.

Fluid Selection: The Linchpin of Data Center Infrastructure Sustainability

However, realizing these benefits depends on fluid selection. Fluid qualities like thermal stability, heat transfer efficiency, material compatibility, and environmental profile all determine system performance and lifecycle outcomes. The right fluid enhances heat transfer, extends system longevity, and reduces maintenance requirements. It also shapes sustainability metrics, from lifecycle emissions to end-of-life handling.

As the demands of AI workloads intensify, these decisions carry increasing weight. Now, when selecting a cooling technology, data center developers are making long-term infrastructure commitments that will determine performance, scalability, and environmental impact for years to come. Immersion cooling, when paired with thoughtfully engineered fluids and integrated system architectures, can help developers align all three qualities to improve operational efficiency while addressing common challenges.

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About Robert Schuetzle

Robert Schuetzle is President and CEO of Infinium, a technology company transforming how the world powers, moves, and computes. Backed by strategic investors including Amazon, MHI, NextEra Energy, and AP Ventures, Infinium is best known for its leadership in ultra-low carbon electrofuels and is now expanding that expertise into digital infrastructure through Infinium Edge™, its platform focused on energy efficiency and thermal management for power- and heat-intensive systems such as data centres.

The post Fueling Performance at the Edge: Why Cooling, Power, and Sustainability Are Intertwined in the AI Data Center Era appeared first on Data Center POST.

TL;DR

• A Labor Agreement Built Around People: Quantica Infrastructure and the Southeastern Montana Building and Construction Trades Council have signed a Memorandum of Understanding (MOU) to guide union construction for the Big Sky Campus, a large-scale data center and energy project.
• Workforce Development as a Core Commitment: The agreement establishes a framework focused on apprenticeship opportunities, training pathways, workforce development, and qualified local labor.
• Long-Term Benefits for Montana: The partnership is designed to support job creation, career development, and economic activity while helping deliver critical infrastructure for the state’s future.

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As discussions around data centers often focus on power, technology, and infrastructure, one critical component can sometimes get overlooked: the people who build them.

For the Big Sky Campus, Quantica Infrastructure, LLC (“Quantica”) and the Southeastern Montana Building and Construction Trades Council (SEMTBCTC) are working to make workforce development part of the project from the beginning.

The two organizations recently announced a labor Memorandum of Understanding (MOU) that will help guide construction of the Big Sky Campus, a large-scale data center and energy project being developed by Big Sky Digital Infrastructure (BSDI), a Quantica company.

The agreement establishes a cooperative framework focused on workforce development, apprenticeship and training pathways, project stability, and qualified local labor. It also creates opportunities to expand participation in the skilled trades while supporting construction of the infrastructure needed to power the next generation of digital growth.

For Montana’s construction workforce, that means more than jobs on a single project. It means opportunities for training, career development, and long-term participation in an industry that continues to grow across the country.

“The Big Sky Campus represents a significant opportunity for Montana’s skilled trades workforce,” said Clint A. McCulloch, President of SEMTBCTC and Business Manager of UA Plumbers and Pipefitters Local Union 30. “We are pleased to work alongside Quantica to help create family-supporting careers, expand apprenticeship opportunities, and ensure Montana workers have a role in building the infrastructure that will support the state’s future economy.”

The partnership also reflects a broader goal shared by both organizations: ensuring that major infrastructure investment creates lasting local benefits.

Through apprenticeship programs, training opportunities, and a focus on qualified local labor, the agreement is intended to help build a highly skilled workforce while creating pathways into the construction trades for future generations of Montana workers.

“Major infrastructure projects like the Big Sky Campus have the potential to deliver meaningful economic benefits when they prioritize skilled labor and long-term workforce development,” said Riley McCauley, Vice President of SEMTBCTC and Representative of Western States Regional Council of Carpenters. “We see a real opportunity to support a strong, skilled workforce in Montana by partnering with Quantica on apprenticeship programs, training pipelines, and local hiring efforts, helping create durable career pathways while supporting responsible infrastructure development in the region.”

For Quantica, the agreement aligns with the company’s vision for how large-scale infrastructure projects should be developed.

“Our vision for the Big Sky Campus extends beyond digital infrastructure,” said John Chesser, Chief Executive Officer of Quantica. “We are committed to creating high-quality jobs, supporting workforce development, and building lasting opportunities for Montanans. This agreement reflects our belief that strong partnerships with Montana workers can help deliver world-class infrastructure safely, efficiently, and with meaningful benefits for our communities.”

The Big Sky Campus is expected to bring substantial investment, long-term employment, and economic activity to the region. BSDI has also emphasized its commitment to funding the new generation and infrastructure required for the campus without increasing electricity costs for Montana ratepayers.

As development moves forward, the agreement provides a framework for ensuring that the benefits of the project extend beyond the infrastructure itself and into the workforce, businesses, and communities that will help build it.

The post Union Partnership Powers the Big Sky Campus: Quantica and Montana Trades Council Forge Labor Framework appeared first on Data Center POST.

Originally posted on Mod42.

TL;DR

• Modular is a manufacturing methodology, not a shipping container.
• Mod42 manufactures complete data center buildings off-site using volumetric modular construction.
• Factory-built modules enable parallel construction, reducing deployment timelines by up to 50%.
• Purpose-built modular facilities deliver the same performance, quality, and serviceability as traditional data centers.
• The modular approach provides greater flexibility to adapt to evolving AI workloads, cooling technologies, and power requirements.
• Mod42 helps organizations deploy AI infrastructure faster while maintaining long-term scalability and future readiness.

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A common misconception in the digital infrastructure industry is equating modular data centers with shipping containers filled with servers. In reality, modular construction is a manufacturing methodology that shifts work into a controlled factory environment to reduce risk and accelerate schedules. Moving beyond simple containerized solutions, Mod42 utilizes volumetric modular construction to manufacture entire building sections off-site. These purpose-built modules incorporate the exact same structural, electrical, mechanical, and cooling systems found in traditional facilities, resulting in a fully operational, enterprise-grade data center rather than a temporary deployment.

The rapid deployment speed of this methodology comes from parallel manufacturing rather than the physical shape of a container. While site preparations, foundations, and utilities are being completed on location, the actual data center is built simultaneously in the factory. By overlapping these critical phases instead of executing them sequentially, organizations can bypass traditional construction bottlenecks and bring complete, factory-built AI capacity online in under seven months.

Beyond speed, this volumetric approach provides the critical adaptability required by rapidly evolving artificial intelligence workloads. Because these facilities are engineered as actual buildings rather than restricted transportation packages, they offer the necessary flexibility to accommodate soaring rack densities, changing cooling strategies, and future hardware upgrades. This ensures that operators can efficiently scale their operations, easily add modules as demand grows, and future-proof their long-term infrastructure investments.

To continue reading, please click here.

The post A Modular Data Center Is Not a Container: Why Mod42 Manufactures Complete AI Data Centers appeared first on Data Center POST.

TL;DR

• Tournament proceeds will fund workforce development initiatives focused on digital infrastructure education and career access.
• Supported programs include scholarships, mentorship, internships, educational tours, conference participation, and industry engagement.
• The event aims to unite executives, investors, and technology leaders around a shared commitment to developing future industry talent.
• Sponsorship opportunities enable organizations to support educational initiatives while participating in a high-level networking experience.

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As the digital infrastructure industry continues to expand to support AI, cloud, and connectivity demands, workforce development remains a critical priority. To help address this need, the Nomad Futurist Foundation has announced the inaugural Nomad Ho’ohui Invitational, an invitation-only charity golf tournament that will take place January 15-16, 2027, on Oahu, Hawaii, immediately preceding PTC’27.

Hosted at the Kapolei Golf Club, the two-day Ryder Cup-style competition is designed to bring together executives, investors, technology leaders, and innovators from across the digital infrastructure ecosystem. Beyond the competition itself, the event will feature networking receptions, a gala dinner, and opportunities for industry participants to build relationships while supporting a larger philanthropic mission.

Unlike many industry gatherings that focus primarily on business development, the Nomad Ho’ohui Invitational is centered on investing in the future workforce. Proceeds from the event will directly benefit the Nomad Futurist Foundation’s initiatives to increase awareness of careers in digital infrastructure and provide students with meaningful educational and professional opportunities. Funding will support scholarships, educational resources, data center tours, mentorship programs, internships, conference participation, and direct engagement with industry professionals.

The name “Hoʻohui,” a Hawaiian term meaning to join, unite, or connect, reflects the event’s broader purpose of bringing together the industry to create lasting impact. By aligning with PTC’27, one of the sector’s premier annual gatherings, the Invitational provides participants with an opportunity to strengthen professional relationships while contributing to programs that help prepare the next generation of talent.

Industry leaders have emphasized that the future of digital infrastructure depends not only on continued investment in technology, but also on sustained investment in people. As AI infrastructure, cloud computing, telecommunications, and data center development accelerate worldwide, organizations increasingly recognize that cultivating skilled professionals will be essential to supporting long-term growth and innovation.

The Nomad Futurist Foundation is inviting organizations to participate as founding partners through sponsorship opportunities that include player participation, event branding, guest access, and advisory committee involvement. Every contribution supports programs intended to expand career pathways and increase access for students and underserved communities interested in pursuing opportunities within digital infrastructure.

As the industry prepares to gather in Hawaii for PTC’27, the Nomad Ho’ohui Invitational offers a unique opportunity to combine networking, collaboration, and philanthropy in support of workforce development. By investing in education, mentorship, and career access today, participants can help strengthen the talent pipeline that will power tomorrow’s digital economy. To learn more about the Nomad Ho’ohui Invitational, please click here.

The post Nomad Ho’ohui Invitational Combines Industry Collaboration and Workforce Development Ahead of PTC’27 appeared first on Data Center POST.

TL;DR

• From Policy to Infrastructure: AI governance is no longer just a high-level policy issue; because AI is increasingly embedded directly into operational platforms and automation pathways, it must be managed as a core infrastructure strategy.
• Visibility Precedes Governance: AI capabilities are quietly accumulating within everyday SaaS apps and monitoring tools, meaning organizations must first establish a clear inventory of where AI exists, what data it processes, and what decisions it influences to prevent systems from gaining unchecked operational authority.
• Transparency as a Competitive Edge: With customers increasingly demanding to know how AI interacts with their environments and data, service providers that proactively communicate their governance frameworks, vendor controls, and safeguards will differentiate themselves as trusted operational partners.

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For many organizations, AI governance still sounds like a policy discussion. In reality, it is rapidly becoming an infrastructure discussion.

AI systems are increasingly embedded into the platforms organizations use to manage infrastructure, monitor environments, automate workflows, support users, and analyze operational data. In some cases, AI systems directly influence operational changes across environments. As adoption accelerates, organizations are facing a new challenge: governing the AI models themselves, and also the infrastructure access, automation pathways, and operational influence those systems inherit inside the environment.

For MSPs and SMBs, this shift carries significant implications. AI capabilities are quietly appearing inside monitoring systems, endpoint operations, automation engines, service management platforms, and collaboration tools. In many environments, organizations may already be relying on AI-assisted capabilities without formally recognizing them as AI systems.

Visibility Must Come Before Governance

One of the biggest AI governance mistakes organizations can make is assuming AI systems are easy to identify. AI is increasingly embedded across SaaS applications and operational tooling in ways that may bypass centralized oversight. A workflow platform introduces automatic AI-assisted script creation, a monitoring tool adds AI-generated recommendations, or a collaboration platform that begins summarizing operational data automatically. In each case, the AI recommendation itself acts as an anchor: the first number, option or plan presented becomes the reference point around which human judgement is adjusted. Over time, organizations accumulate a growing number of AI-enabled capabilities without a centralized understanding of what systems exist, what they can access, or what operational decisions they can influence.

The first step toward effective governance is visibility. Organizations need a clear operational inventory of where AI exists across the environment, what infrastructure it connects to, what data it processes, what decisions it influences, and what level of operational authority it possesses. Without centralized tracking and accountability, businesses risk creating environments where AI systems quietly gain operational influence without sufficient oversight.

AI Is Changing the Meaning of Change Management

Traditional change management processes were designed around human-driven activity. AI introduces a more dynamic operating model, where systems can generate recommendations, accelerate workflows, and potentially influence operational decisions at machine speed. Even when humans remain involved, the pace and scale of operational activity can change dramatically.

Organizations should begin treating AI-enabled systems with the same operational discipline they apply to automation platforms and critical infrastructure tooling. That includes understanding whether AI systems can modify infrastructure directly, trigger automation workflows, or influence operational decisions without clear approval boundaries. It also means ensuring rollback mechanisms exist, maintaining strong audit logging, and validating whether human oversight remains in place before operational changes occur.

The operational failures associated with AI may not resemble traditional outages. In many cases, organizations are more likely to encounter subtle configuration drift, unintended automation behavior, or operational decisions made from incomplete context. These issues can spread quietly across environments before teams fully understand what changed or why.

Transparency Is Becoming a Competitive Requirement

Organizations are also facing growing pressure from customers who increasingly want transparency into how AI systems interact with their environments and data. Customers want to understand whether AI systems process operational information, whether external vendors are involved, whether customer data contributes to model training, and what safeguards exist if systems behave unexpectedly.

For MSPs and service providers, this creates both risk and opportunity. Organizations that proactively communicate governance practices, including oversight models, logging standards, vendor controls, and data handling policies, will increasingly differentiate themselves as trusted operational partners rather than reactive adopters of emerging technology.

Operational Maturity Will Define AI Governance Success

The organizations making the most progress today are not necessarily the ones building the most complex governance frameworks. They are the ones building practical governance models grounded in operational reality. The goal is to establish enough visibility, accountability, and operational control to scale AI safely while maintaining resilience and customer trust.

Organizations that move early on operational AI governance will be better positioned to scale automation safely, respond to customer concerns confidently, and adapt as regulatory expectations continue evolving.

Ultimately, AI governance is also a conversation about infrastructure access, operational influence, automation boundaries, and accountability. As AI systems become more deeply integrated into operational environments, governance will increasingly determine whether organizations can scale AI safely and confidently over the long term. That makes AI governance more than a compliance issue – it should be a core infrastructure strategy.

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About the Author

Nicole Reineke is a technology executive and AI strategist currently serving as a Distinguished Product Manager and Director of AI Strategy at N-able. She previously served as Senior Vice President of Innovation at Iron Mountain and Senior Distinguished Engineer at Dell Technologies, and brings more than 25 years of experience leading high-tech ventures and driving enterprise innovation. In addition to holding dozens of granted patents, she teaches AI and innovation at Georgetown University and co-authors publications focused on breakthrough success and applied AI strategy.

The post Why AI Governance Must Become an Infrastructure Strategy appeared first on Data Center POST.

TL;DR

• The Rise of Bare Metal: Bare metal is now the dominant model for renting GPU clusters, sitting directly between traditional colocation and public cloud by allowing customers to rent entire, provider-owned servers without an intervening virtualization layer.
• A Distinct New Asset Class: GPUs have separated from data center real estate to become a standalone asset class because they possess all four necessary traits: identifiable assets, observable pricing, active resale markets, and lenders willing to accept them as collateral.
• The Portability Advantage: Unlike traditional real estate, GPU servers are portable and serial-numbered, meaning lenders can physically repossess and re-market the hardware in the event of a loan default.

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Bare metal has become the dominant way GPU clusters are rented. The customer’s software runs directly on the hardware, with no hypervisor, no virtualization layer, and no managed services in between.

Since 2023, it has pulled the server into a separate asset class of its own from Data Centers. GPU clusters are financed in the capital markets, where it is now priced, traded, and borrowed against.

Where bare metal sits between colocation and cloud

Bare metal sits between the two models the industry already knows. Colocation has the tenant owning the servers and renting space, power, and cooling. Public cloud has the provider owning everything and selling virtual slices with managed services on top.

Bare metal is in the middle. The provider owns the servers, and the customer rents them whole, by the hour or on multi-year contracts. Neoclouds such as CoreWeave, Lambda, and Crusoe, cloud providers built specifically around GPU capacity, run their entire business on this model.

And now, traditional colocation operators are looking to do it too. They’re adding bare metal and GPU-as-a-service lines to capture the same demand, taking on hardware ownership their business model never required before.

The hardware became an asset class

An asset class needs four things: identifiable assets, observable pricing, a resale market, and lenders willing to take the asset as collateral. GPU servers now have all four.

The GPU can leave the building. When a data center loan defaults, the lender ends up owning real estate in one location. But when a GPU loan defaults, the lender holds serial-numbered servers that can be repossessed and re-marketed. That portability is what allows the hardware to be financed separately from the building it sits in.

In March 2026, CoreWeave closed an $8.5 billion facility rated A3 by Moody’s, the first investment-grade rating ever assigned to GPU-backed debt, supported in part by Meta contracts worth at least $19 billion.

The pricing of that debt tells the story faster than the headlines. CoreWeave’s earliest GPU-backed borrowing reportedly priced in the low teens. The floating-rate portion of the March 2026 facility priced at 2.25 percentage points over SOFR, the overnight benchmark rate. Lenders re-rated the asset from exotic to ordinary in under three years.

The data center industry spent three decades learning to finance buildings. Now, racks inside carry capital markets of their own.

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About the Author

Bernie Margulies is the founder and CEO of American Compute, which helps GPU buyers get access to better GPU financing, and structures residual value insurance for lenders/lessors.

The post The New GPU Asset Class appeared first on Data Center POST.

TL;DR

• Grain Management announced plans to combine Ritter Communications and Great Plains Communications under a new organization and brand, Rightfiber.
• The combined company will serve more than 400 communities across 20 states with a 28,000-mile fiber network connecting 300,000 homes and businesses.
• Rightfiber aims to pair expanded scale and investment capacity with the local service, community focus, and customer relationships that have defined both organizations.

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The broadband industry is often described in terms of speed, scale, and coverage. But the reason this deal matters now is simpler: Communities, businesses, and service providers all need reliable fiber networks that can grow with demand, not just keep up with it.

That is the context behind Grain Management’s announced combination of Ritter Communications and Great Plains Communications into a new organization called Rightfiber. The move brings together two established regional providers with long operating histories and a shared focus on fiber infrastructure, customer service, and community investment.

Two Companies, One Direction

Ritter Communications and Great Plains Communications each built their reputations in different parts of the country, but their business philosophies are closely aligned. Both companies have spent decades expanding fiber access, serving local markets, and investing in the kind of infrastructure that supports everyday life as well as business growth.

According to the announcement, the new company will be led by Heath Simpson as CEO, with Todd Foje serving as Executive Chairman. The leadership structure brings together executives from both organizations as the combined company moves forward under the Rightfiber brand.

What Rightfiber Brings

The new organization will serve more than 400 communities across 20 states and connect 300,000 homes and businesses through a 28,000-mile regional fiber network. Those numbers matter because they show how much the company’s footprint expands with this combination.

But the story is not just about size. It is also about what scale can make possible. Rightfiber is expected to have more resources to support network expansion, continued investment, and growth through both organic buildout and acquisitions. For customers, the promise is that local service and personalized support remain part of the model even as the company gets bigger.

Why Local Still Matters

In telecom, bigger can sometimes feel distant. This announcement makes a point of saying the opposite: The combined company will continue to operate with a strong local focus, preserve customer relationships, and keep investing in the communities it serves.

That matters because fiber is not just a technical upgrade. It is the backbone for remote work, education, health care, business operations, and the digital tools people now expect to use every day. A stronger network can change what is possible for a town, a business district, or a region that needs better connectivity to compete.

The Takeaway

Rightfiber brings together two established fiber providers under a single organization designed to expand scale, resources, and investment while maintaining a local operating approach.

The transaction still needs customary approvals and regulatory review, but the larger message is already clear: The future of broadband will be shaped by companies that can combine reach, investment, and local trust. That is what makes this combination worth watching.

For full details, read the press release here.

The post One Fiber Story, Bigger Reach: Strategic Combination Forms Rightfiber appeared first on Data Center POST.

TL;DR

• AI demand is accelerating infrastructure investment and driving major changes in facility design, deployment strategies, and capacity planning.
• Power availability has emerged as one of the most significant constraints on future data center growth.
• Investors continue to support digital infrastructure expansion, but financing structures are becoming more sophisticated and risk-conscious.
• Workforce development remains essential as operators seek the talent needed to support long-term industry growth.

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The rapid expansion of artificial intelligence is creating unprecedented demand for digital infrastructure, forcing the industry to rethink everything from power procurement and cooling strategies to financing models and workforce development. Those themes dominated conversations at Datacloud Global Congress 2026, held June 1-4 in Cannes, France, where industry leaders examined how to scale capacity while navigating increasingly complex operational, regulatory, and economic challenges.

Across four days of sessions, networking events, and the  Datacloud Awards, one theme remained constant: AI is accelerating infrastructure demand at a pace that is forcing the industry to rethink how future capacity will be planned and deployed. As AI workloads continue to grow, the industry is facing a reality where traditional development approaches are no longer sufficient. Operators are being asked to deploy larger facilities faster, secure power in constrained markets, attract investment, and build infrastructure that can remain relevant despite rapidly evolving technology requirements.

AI Demand Is Reshaping Infrastructure Strategy

Artificial intelligence remained the dominant topic throughout the event as operators, technology providers, and investors assessed how the market is evolving to support next-generation compute requirements. Conversations focused on how the growing share of AI workloads is changing infrastructure priorities, from facility design and cooling architectures to deployment models and regional investment strategies.

During the keynote panel examining how the data centre ecosystem is keeping pace with AI demand, Olivier Micheli, CEO & President of DATA4, joined fellow industry leaders to explore how rapidly changing workload requirements are influencing infrastructure strategies. Panelists discussed the projected growth of AI workloads, the increasing importance of inference-driven deployments, and how neocloud providers are emerging as a disruptive force with infrastructure requirements that differ from traditional hyperscale and enterprise environments. The conversation also examined how enterprise AI adoption is reshaping demand patterns and what Europe must do to remain competitive as global investment in AI infrastructure accelerates.

Many sessions also explored Europe’s efforts to strengthen its position in the global AI market. Discussions surrounding AI gigafactories, sovereign cloud initiatives, and large-scale infrastructure investments reflected growing momentum across the region as governments and private-sector organizations work to expand digital capacity.

Power Has Become the Industry’s Most Valuable Resource

While AI is driving demand, power availability is increasingly determining where and how projects move forward. Multiple sessions focused on the challenge of securing reliable energy in markets facing grid constraints, permitting delays, and growing competition for available capacity.

One of the recurring themes was the need to diversify power strategies. Industry leaders explored a range of solutions including battery energy storage systems, behind-the-meter generation, renewable energy partnerships, microgrids, and emerging nuclear technologies. Discussions also addressed the growing importance of flexibility, as operators seek to build facilities capable of adapting to changing energy markets over the coming decades.

The industry’s focus is shifting from simply obtaining power to managing energy risk. As data centers become larger and more energy-intensive, operators are evaluating how to improve resiliency while balancing sustainability goals, operating costs, and long-term growth plans.

New Financing Models Are Emerging to Support AI Expansion

The scale of AI-driven infrastructure investment is creating demand for more sophisticated funding approaches. Traditional financing structures are being supplemented by joint ventures, alternative debt vehicles, portfolio financing strategies, and new forms of risk sharing between investors, operators, and customers.

During the keynote panel, “How are DC Projects Being Funded?”, Alex Hernandez, CEO of PowerBridge, joined industry leaders to examine how capital is being deployed across traditional and AI-focused data center developments. The discussion explored the quality of customer offtake agreements, the long-term profitability of AI investments, how neocloud providers are securing financing, and the increasing use of joint venture structures to fund large-scale infrastructure projects.

Several investment-focused sessions also explored consolidation across the sector, off-balance-sheet financing models, and evolving approaches to underwriting AI infrastructure projects. While capital remains available, investors are placing greater emphasis on execution capability, power availability, and the long-term viability of infrastructure assets.

Risk Allocation Continues to Evolve

As projects grow larger and more complex, operators and customers are reassessing how risk is allocated throughout the development lifecycle. Contracting structures that worked for traditional data center projects are being reexamined as AI facilities require significantly larger investments and longer planning horizons.

Atif Ansar, Executive Chairman and Co-Founder of Foresight, participated in a panel examining how contracting models are evolving as AI gigafactories increase the scale and complexity of infrastructure projects. Discussions focused on balancing risk between tenants and operators, including how power failures, supply chain disruptions, early termination rights, and warranty and indemnity insurance are being addressed in a new generation of AI-focused agreements.

The conversation reflected a broader trend across the industry. As infrastructure projects become larger and more expensive, contractual frameworks are evolving to provide greater certainty for both developers and customers while supporting continued investment in new capacity.

Developing the Workforce Needed for Future Growth

The industry’s expansion is creating significant demand for skilled workers across engineering, construction, operations, sustainability, and energy management disciplines. While AI is driving much of the growth, workforce development remains a critical factor in determining how quickly new capacity can be delivered.

During a panel focused on creating a resilient workforce pipeline, Susanna Kass, Senior Advisor to the Board and Operating Partner at Digital Gravity Infrastructure Partners, joined industry leaders to discuss how workforce development programs can help address ongoing labor shortages across the sector. The conversation explored the evolving skill sets needed to support data center growth and strategies for expanding the talent pool across engineering, construction, operations, and energy disciplines.

Workforce challenges continue to affect projects across multiple regions, particularly as operators pursue larger campuses and increasingly complex infrastructure deployments. Industry leaders emphasized that long-term growth will depend on building sustainable talent pipelines capable of supporting the next generation of digital infrastructure.

Preparing for the Next Phase of Growth

Throughout the conference, discussions repeatedly returned to a common challenge: how to scale infrastructure quickly without sacrificing resiliency, efficiency, or long-term value. AI may be the catalyst driving today’s investment cycle, but the decisions being made around power, financing, workforce development, and facility design will influence the industry for years to come.

The path forward will require collaboration across the entire digital infrastructure ecosystem. As operators pursue larger campuses, higher-density deployments, and more ambitious growth targets, success will increasingly depend on the industry’s ability to align capital, energy, technology, and talent at unprecedented scale.

To learn more about Datacloud Global Congress and future Datacloud events, visit the official Datacloud website.

The post AI Infrastructure Growth Tests the Limits of Power, Capital and Scale  appeared first on Data Center POST.

TL;DR

• The Midwest AI Boom: A massive 100-Terabit connectivity corridor is being built in the Midwest, which is rapidly becoming a strategic hub for AI infrastructure due to its availability of land, power, and buildable space.
• Symphonic Collaboration: Building such large-scale network infrastructure requires immense trust and deep coordination across service providers, equipment vendors, and operational teams to achieve shared goals.
• Supply Chain and Efficiency: High global demand is creating supply chain bottlenecks for critical networking gear, pushing the industry to focus on smarter transport models that prioritize power and spectral efficiency over pure speed.
• Future Constraints: Moving forward, massive fiber and multi-gigawatt power requirements will be the industry’s biggest hurdles, alongside community permitting resistance. Furthermore, the extreme cost of downtime makes network diversity and resilience an absolute business imperative.

# # #

In Episode 66 of the NEDAS Live! Podcast, host Ilissa Miller sits down with Priscilla Favors of Aureon, Chris Crowe of T3 Broadband, Kurt Raaflaub of Nokia, and Jeff Sanders of Midco for a timely conversation about one of the industry’s most coordinated transport builds to date. Centered on the theme “Delivering the AI Era: Building a 100TB Midwest Connectivity Corridor,” the discussion explores why the Midwest is becoming increasingly important to AI infrastructure, and what it takes to bring large-scale network projects like this to life.

As Miller notes, the conversation goes beyond data centers and compute to spotlight the transport layer that makes AI possible. The panel makes clear that none of the AI buildout works without the underlying network infrastructure, or the “nervous system,” as one speaker describes it, that moves massive amounts of data between facilities, regions, and end users.

Why the Midwest, Why Now

One of the most compelling themes in the episode is the Midwest’s rising role in the digital infrastructure landscape. Priscilla Favors explains that the region is benefiting from the growing need for land, power, and buildable space, especially as hyperscale and AI deployments expand beyond traditional coastal markets. What once may have been viewed as secondary territory is now proving to be strategically positioned for the next wave of infrastructure growth.

Chris Crowe adds that the industry is in a period of unprecedented adoption, with demand rising faster than many expected. As AI accelerates, so does the need for connectivity that can support new workloads, new architectures, and new expectations around speed and resilience.

Collaboration at Scale

A major takeaway from the conversation is that projects of this magnitude cannot be executed by one company alone. Jeff Sanders describes the deployment as a symphony, with each participant contributing specialized expertise while staying tightly aligned on the shared goal. From design and fulfillment to construction and interconnection, the build required deep coordination across service providers, equipment vendors, and operational teams.

Favors reinforces that point by emphasizing how critical trust was throughout the process. The work moved quickly, required executive approval at unusual hours, and demanded collaboration across technical, financial, and leadership levels. In her view, the project succeeded because the partners were willing to move together, adapt quickly, and rely on one another’s expertise.

Supply Chain Pressure

The episode also highlights a growing operational challenge across the industry: supply chain constraints. Crowe explains that major and minor components alike can become bottlenecks when global demand spikes. From cabling and shelves to amplifiers and other critical gear, large projects are increasingly impacted by component availability and timing.

Kurt Raaflaub expands on the larger market backdrop, pointing to dramatic increases in data center capital expenditures and the pressure that AI and cloud providers are placing on the broader ecosystem. He notes that regional service providers and integrators are essential to keeping projects moving, especially as the market decentralizes and more connectivity is needed between secondary markets.

Networks Need to Evolve

If the first phase of digital infrastructure was about more bandwidth, the next phase is about efficiency, flexibility, and smarter transport. Raaflaub explains that optical networking is moving beyond pure speed and toward spectral efficiency, lower power consumption, and better cost per bit. In his view, the infrastructure that worked a decade ago is no longer enough for today’s AI-driven requirements.

Sanders echoes that shift, pointing to solutions such as 800-gig wavelength services, managed optical fiber network services, and more adaptable transport models. The era of simply “throwing bandwidth at it” is giving way to a more nuanced approach built around customer needs, geography, diversity, and timing.

Looking Ahead

When asked what the industry may underestimate over the next five years, the panel repeatedly returns to two words: fiber and power. Sanders predicts that intra-building fiber requirements inside AI campuses may become even more massive than many expect, while power demands could escalate toward multi-gigawatt clusters.

Favors adds that land and power availability will remain significant constraints, while also warning that downtime is becoming far too expensive to ignore. She shares that even a single day of outage can cost millions, reinforcing why diversity and resilience are now business imperatives, not just technical preferences.

A Race Defined by Partnerships

The episode closes on a bigger industry truth: AI infrastructure is not just a technical race, but a partnership race. The panel agrees that success will depend on collaboration across suppliers, integrators, service providers, and customers, as well as the ability to educate stakeholders and adapt to changing political, social, and operational realities. Sanders, in particular, warns that community resistance and permitting hurdles may become defining constraints on where AI data centers are built.

As Miller concludes, the industry is at the tip of a much larger transformation, and this Midwest deployment may be just the beginning. With AI demand accelerating and infrastructure expectations changing rapidly, the message from Episode 66 is clear: the next era will be built by those who can move fast, think collaboratively, and deliver at scale.

To continue the conversation, listen to the full episode here.

The post Delivering the AI Era: Building a 100-Terabit Midwest Connectivity Corridor appeared first on Data Center POST.

TL;DR

• The True Data Center Bottleneck: While power and space often dominate the conversation, the real strategic obstacle facing operators is a severe lack of trusted, near-real-time visibility into their own physical, logical, and virtual infrastructure.
• The Demise of Static Records: Managing dense, distributed infrastructure via fragmented spreadsheets and outdated documentation is now a major liability; operators must replace passive record-keeping with a continuous, audit-ready layer of operational intelligence.
• The HDIM Solution: To eliminate these blind spots, organizations are adopting Hybrid Digital Infrastructure Management (HDIM) platforms to create a unified operational digital twin, which translates disconnected data into actionable facts for capacity, compliance, and resilience planning.

# # #

By Herman Chan, President and CEO, Sunbird Software

Data Center Infrastructure Management (DCIM) software has matured considerably over the past decade. Deployments are faster, interfaces are easier to use, integrations are deeper, and organizations across industries are seeing real, measurable results.

According to Gartner, DCIM software has reached a critical inflection point in the Hype Cycle: the “Plateau of Productivity.” This is a recognition that DCIM software has gone mainstream, adoption is accelerating, and proven solutions are delivering measurable value.

That’s why, if DCIM software is not yet in your plans, now is the time to take a serious look.

What Is the Gartner Hype Cycle and Where Does DCIM Software Stand?

The Gartner Hype Cycle tracks how technologies mature from early innovation through inflated expectations, disillusionment, and eventually to sustained productivity. It is used by organizations to assess and reduce the risk of adopting new technologies.

DCIM software’s trajectory through the hype cycle began about 15 years ago. First-generation tools struggled with long deployment cycles, poor usability, and limited integration capabilities. Early adopters found a significant gap between vendor hype and the reality of slow, difficult tools. DCIM software fell into the Trough of Disillusionment, and many vendors that couldn’t close that gap didn’t survive.

Over the past decade, modern DCIM platforms have addressed those shortcomings. Deployments became faster. Interfaces became more intuitive. Out-of-the-box connectors and open APIs enabled easier integration with adjacent tools. As successful customers evangelized their results, DCIM software climbed the Slope of Enlightenment.

Today, Gartner’s placement of DCIM software in the Plateau of Productivity confirms that DCIM has earned its place as a proven, mainstream tool for data center operations.

Uptime Institute offers a complementary view. Rather than seeing DCIM software as a standalone product, Uptime Institute frames it within a broader concept called Data Center Management and Control (DCM-C): a framework in which multiple specialized tools work together across complex environments. In that model, DCIM software serves as the central hub connecting facility systems, IT operations tools, and business platforms.

What’s Driving DCIM Software Deployments Today?

At a certain point, the complexity of managing distributed infrastructure outgrows the point tools most teams are still relying on. Spreadsheet updates take too much time, manual processes break down, and inaccurate data leads to increased risk.

That’s the problem DCIM software was built to solve, and that’s why adoption has accelerated as environments have gotten harder to manage and the C-suite is recognizing the importance of data centers supporting their mission critical corporate operations.

Today, more and more organizations are deploying DCIM software to:

• Maintain an accurate asset inventory across all sites, including remote edge sites and IDF closets, plus manage the lifecycle of those assets.
• Visualize sites with 3D digital twin modeling to support remote management
• Make informed capacity planning decisions to reduce overprovisioning or stranded capacity of space, power, and cooling
• Automate repetitive tasks such as updating rack elevations, generating reports, and processing move, add, and change requests
• Monitor power and environmental conditions with threshold alerts for early issue detection
• Map relationships and dependencies for faster troubleshooting and impact analysis
• Consolidate key information from multi-vendor tools into a single pane of glass

What ROI Are DCIM Software Customers Achieving?

The business case for DCIM software is now well-documented. Real-world DCIM ROI stories include:

• 40% more usage out of facilities and power resources
• 33% fewer cabinets to do the same work
• 66% consolidation of colo cages, saving $40,000 per month
• $160,000 saved by identifying decommissioned assets still under active maintenance contracts
• 75% less manual effort to perform asset audits
• 75% fewer physical visits to the data center
• $100,000 saved through tool consolidation

What drives these results is visibility. DCIM software surfaces what manual tracking misses: stranded capacity, inaccurate asset records, and operational inefficiencies.

How Are Organizations Leveraging DCIM Software to Achieve a Single Pane of Glass?

The most mature DCIM software deployments go beyond a standalone implementation. Organizations are integrating DCIM software with their multi-vendor toolset to create a single pane of glass: one intuitive GUI with one enterprise-class relational database for all users.

Common integration points with DCIM software include CMDB and ticketing systems, server and network management tools, public and private cloud platforms, colocation monitoring tools, observability platforms, and DevOps tools. When DCIM software serves as the single pane of glass and single source of truth, assets automatically stay synchronized across systems, workflows cross team boundaries without manual handoffs, and leadership has access to real-time dashboards rather than static reports.

Published case studies of real-world deployments illustrate what this looks like in practice.

The World Bank integrated DCIM software with ServiceNow to automate provisioning throughout the asset lifecycle from purchasing through deployment. ServiceNow, in turn, is integrated with SAP, creating an end-to-end automated workflow. Assets flow from purchase orders in SAP into ServiceNow, and from there into DCIM software with cabinet locations, installation status, and lifecycle events synchronized to the other systems without manual effort.

eBay used DCIM software APIs to integrate DCIM software with ServiceNow to synchronize 600 daily activities across systems. Moves, adds, changes, and decommissions that are documented in ServiceNow automatically flow to their DCIM tool, eliminating the double manual data entry that had previously been required to keep both platforms up to date.

Brussels University Hospital integrated DCIM software with Dell OpenManage Enterprise to automatically pull key asset and configuration data into their DCIM. With DCIM software as their single pane of glass, they eliminated manual lookups and gained the ability to launch a server console with a single click from its asset record in their DCIM tool.

These integrations were built using out-of-the-box connectors and documented APIs available in modern DCIM software and reflect how far DCIM’s integration capabilities have come.

What Should You Look for When Selecting a DCIM Vendor?

The time is now to deploy DCIM software, but selecting the right vendor for your organization is critical. When evaluating vendors, focus on:

• Ease of deployment and use. Prioritize platforms that are fast to deploy, easy to use, and fully browser-based and mobile-ready.
• Completeness of functionality. For a comprehensive solution, look for coverage across asset, capacity, change, energy, environment, power, visualization, security BI & analytics, and connectivity.
• Openness and integration. Fully documented RESTful APIs and out-of-the-box connectors are essential. The platform should fit into your existing tool ecosystem, not require you to rebuild around it.
• Vendor focus. A pure-play DCIM vendor will invest more consistently in the platform than one managing it as a secondary product line.
• Customer results and references. Ask for documented case studies and peer references in environments similar to yours.
• Quality of support. Look for strong customer satisfaction data, responsive technical teams, and SLA commitments.
• User engagement and community. The best vendors treat customers as partners and are focused on their success. Look for active user groups, customer workshops, and opportunities to provide input on the product roadmap.

The Time to Act Is Now

According to Gartner, DCIM software has reached a turning point by achieving its place on the Plateau of Productivity.

The organizations that have switched to modern DCIM software are already operating more efficiently, making better capacity decisions, and achieving real cost savings.

The question is no longer whether DCIM software can deliver, but how much longer it makes sense to manage your data centers without it.

# # #

About the Author

Herman Chan is the CEO and President of Sunbird Software, a global leader in DCIM software. Since Sunbird’s 2015 spin-off from Raritan, Chan has led Sunbird through a period of significant growth, scaling the organization into a global company that serves Global 2000 customers. He is recognized for his strategic leadership, focus on product innovation, and commitment to delighting customers by simplifying complex data center operations through second-generation DCIM software.

The post The State of DCIM and the Gartner Hype Cycle appeared first on Data Center POST.

TL;DR

• The True Data Center Bottleneck: While power and space often dominate the conversation, the real strategic obstacle facing operators is a severe lack of trusted, near-real-time visibility into their own physical, logical, and virtual infrastructure.
• The Demise of Static Records: Managing dense, distributed infrastructure via fragmented spreadsheets and outdated documentation is now a major liability; operators must replace passive record-keeping with a continuous, audit-ready layer of operational intelligence.
• The HDIM Solution: To eliminate these blind spots, organizations are adopting Hybrid Digital Infrastructure Management (HDIM) platforms to create a unified operational digital twin, which translates disconnected data into actionable facts for capacity, compliance, and resilience planning.

# # #

By Oliver Lindner, Director of Product Management at FNT Software 

Data centers have long been considered an organization’s operational backbone. While that is still true today, they are also, unfortunately, becoming a strategic bottleneck. Several forces are driving this shift. AI demand is accelerating capacity requirements, power and water availability are shaping site decisions, and new regulations are turning infrastructure documentation into compliance evidence. At the same time, operators are expected to scale faster, report more transparently, and run increasingly distributed environments with limited specialist talent.

Looking at this situation from a PESTLE view, which organizations use to understand how external factors can affect their business, sheds light on the situation and makes one thing clear: the pressure is coming from every direction. The good news, however, is that the underlying requirement is the same across all six dimensions: data center operators and enterprise infrastructure leaders need a trusted, near-real-time view of their physical, logical, and virtual infrastructure.

What surprises me, after 25+ years working with data center operators, is how often the conversation still defaults to “we need more capacity” or “we need to lease more power” when the real constraint is data quality. The PESTLE forces described below all converge on the same blind spot, and it isn’t power, water, or workforce talent. It’s that most operators cannot trust what their own documentation tells them.

Component 1: Political

Sovereignty Becomes an Architecture Decision

Data localization rules, cloud sovereignty strategies, and trade tensions are reshaping where workloads, data, and infrastructure documentation are allowed to reside. At the same time, public funding and capacity-allocation programs across the EU, North America, and Asia-Pacific — from EU sovereign-cloud initiatives, to U.S. federal infrastructure investment, to Singapore’s Green Data Centre Roadmap and Japan’s METI cloud and AI-infrastructure subsidies — are creating opportunities for expansion, while attaching new sustainability and resilience conditions to that growth.

Sovereignty is no longer just a contractual topic. It has become an architecture decision.

For data center leaders, sovereignty means knowing not just where workloads are hosted, but where the supporting infrastructure and documentation are managed — and being able to prove it. And here the blind spot shows itself first: most organizations cannot answer “where physically does this workload’s underlying infrastructure reside, and who controls it?” from a single trusted source. Without that, every sovereignty argument is rhetorical.

Component 2: Economic

From Build-Out to Better Utilization

The economics of data center operations are changing quickly. AI workloads are driving new demand for space, power, and cooling. Energy availability is becoming a limiting factor in some regions, while rising electricity costs are putting additional pressure on operating models. At the same time, the workforce talent gap remains a major challenge. Many operators are being asked to run larger, denser, and more distributed estates with teams that are already stretched.

As a result, the focus is shifting from pure build-out to better use of existing infrastructure. CFOs and infrastructure leaders increasingly need answers to practical questions such as: Where do we still have usable capacity? Which sites are constrained? Which assumptions are outdated? Where can we optimize before investing in new capacity? These questions cannot be answered reliably with fragmented spreadsheets or static documentation. In nearly every conversation I have on this, the team suspects they have unused capacity somewhere — but no one trusts the documentation enough to commit to it without a physical walk-through first.

Component 3: Social

Transparency Becomes Part of the License to Operate

Data centers are becoming more visible to communities, regulators, investors, and employees. Local concerns around land use, noise, water consumption, energy demand, and grid impact are now part of the public conversation. Internally, the same visibility challenge affects talent. The industry must attract new professionals while competing with more visible areas of the technology sector. Younger professionals often expect modern tools, transparent processes, and meaningful sustainability commitments – not manual documentation work and disconnected systems.

For operators, this creates a dual challenge. They must communicate more clearly to external stakeholders while also making infrastructure work more manageable and attractive internally. Visibility is no longer only an operational advantage; it is becoming part of the industry’s social license to operate. That social license is only as strong as the data behind it. The first time an outside stakeholder traces a public claim back to its source and finds the documentation can’t support it, the credibility loss is hard to recover from.

Component 4: Technological

Complexity Outpaces Static Documentation

Data center infrastructure is becoming denser, more distributed, and more technically complex. AI clusters are pushing power and cooling requirements beyond traditional assumptions. Liquid cooling, on-site generation, hybrid cloud, edge architectures, and OT convergence are expanding the technical surface area that operators need to understand and control.

The challenge is not only that infrastructure is growing; it’s that physical, logical, and virtual dependencies are becoming harder to trace. The operators I speak with are no longer asking how fast they can build. They are asking which assumptions in their existing estate are still true. Most discover that several of those assumptions quietly stopped being true months ago, and nothing in the documentation flagged the change.

A change in one domain can affect capacity, service availability, compliance, energy consumption, or resilience in another. Without a connected view of infrastructure, teams are forced to make critical decisions based on incomplete or outdated information. In this environment, documentation can no longer be a passive record of what was installed. It must become an operational intelligence layer.

Component 5: Legal

Compliance Becomes Continuous

Regulatory pressure is increasing across the data center and digital infrastructure landscape globally. In the EU, the Data Act, NIS2, DORA, and the Energy Efficiency Directive raise expectations around transparency, resilience, data control, and operational evidence. In the U.S., SEC cybersecurity disclosure rules, the forthcoming CIRCIA incident-reporting regime, and state climate-disclosure laws such as California SB-253 push the same agenda. In Asia-Pacific, Australia’s Security of Critical Infrastructure (SOCI) Act now mandates Critical Infrastructure Risk Management Programs covering data storage systems, Singapore’s Cybersecurity Act extends to critical information infrastructure, and Japan’s Economic Security Promotion Act designates cloud programs as critical products requiring resilient supply. The common direction is unmistakable: operators must shift from periodic documentation to continuous proof.

In practice, that means demonstrating not only that policies exist, but that infrastructure dependencies are understood, risks are visible, and reporting data is reliable. For this reason, energy, water, waste heat, ICT capacity, service dependencies, and asset relationships are all increasingly relevant to compliance and audit processes. Manual evidence collection is too slow and fragile for this new environment where compliance readiness needs to be built on current operational data. Auditors under NIS2 and DORA are increasingly skipping the policy binder and asking a single question first: “show me the live dependency map.” Most teams can’t, and the conversation gets harder from there.

Component 6: Environmental

Sustainability Moves from Reporting to Operational Proof

Energy efficiency, water usage, carbon impact, and waste heat reuse are becoming strategic data center topics. Sustainability expectations are no longer limited to ESG reporting. They increasingly influence permitting, customer requirements, investor confidence, and regulatory obligations.

This creates a practical documentation challenge. Operators need to understand how infrastructure assets consume energy, how capacity is used, where inefficiencies exist, and how sustainability KPIs connect to real infrastructure. High-level metrics are no longer enough. Leaders need infrastructure-level transparency across sites, rooms, racks, assets, services, and dependencies. Sustainability reporting is far more credible when it is based on connected infrastructure data rather than manual consolidation pieced together from disconnected tools.

The inverse is worth keeping in mind: once an auditor or investor traces a published KPI back to its source and finds the underlying data unreliable, the credibility damage takes far longer to repair than the documentation work would have taken in the first place.

The Common Thread: You Cannot Manage What You Cannot See

All these external challenges expose the same underlying weakness: many organizations lack a trusted, connected view of their infrastructure. The good news is that visibility is achievable with a unified foundation of trustworthy, near-real-time infrastructure information. Such a holistic view surfaces hidden realities within the infrastructure to inform decision making and combat PESTLE forces.

• Political pressure requires sovereignty and control. A single trusted source for where infrastructure resides and who controls it turns sovereignty from a contract clause into an architectural fact.
• Economic pressure requires better use of existing capacity. Current data on space, power, cooling, connectivity, and utilization replaces walk-throughs and assumptions with decisions teams can actually commit to.
• Social pressure requires transparency, both inward and outward. Modern, connected tools make infrastructure work attractive to new talent, and make external claims defensible when stakeholders look behind them.
• Technological pressure requires dependency visibility across physical, logical, and virtual layers. Without it, a change in one domain creates surprises in others; with it, “what if?” becomes a planning question rather than a recovery one.
• Legal pressure requires audit-ready evidence. Mapping dependencies from business services down to the infrastructure they rely on lets teams answer “show me the live dependency map” without maintaining parallel documentation.
• Environmental pressure requires reliable sustainability data. Consolidated asset, capacity, energy, and water data make recurring reports a byproduct of operations rather than a quarterly scramble.

Many organizations face a visibility gap because they rely on spreadsheets, isolated DCIM tools, BMS-centric views, and siloed CMDBs. While each of these may reveal part of the picture, they often fail to provide the connected infrastructure intelligence needed to plan, operate, report, and prove resilience across complex hybrid environments.

How Infrastructure Management Software Can Help

This is the gap that Hybrid Digital Infrastructure Management (HDIM) platforms are built to address. Such software consolidates physical, logical, and virtual infrastructure data across sites, domains, and vendors, turning fragmented documentation into the operational digital twin behind capacity, compliance, and sustainability decisions. In practice, operators can use this type of software to:

• Plan capacity decision on actual utilization
• Manage multi-site and edge environments through a single source of truth, regardless of vendor mix
• Overcome skills shortages by replacing manual documentation work with automated reconciliation
• Generate sustainability KPIs from live infrastructure data instead of quarterly consolidation
• Prove resilience by tracing every business service to the physical, logical, and virtual assets it depends on
• Demonstrate sovereignty by design with a defensible record of where infrastructure resides and who controls it

Infrastructure Visibility Is Becoming Strategic

External forces are reshaping the data center landscape. AI demand, regulatory pressure, sustainability expectations, energy constraints, and skills shortages are not temporary challenges. They are structural forces that will define infrastructure strategy over the coming years.

The data centers that navigate the next 24 months successfully will not simply be those with the most capacity. They will be those with infrastructure data reliable enough to guide capacity planning, dependency management, compliance evidence, sustainability reporting, and operational decision-making. In the next phase of data center strategy, space, power, and cooling will only get you as far as the data behind them lets you go.

# # #

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 Six Pressures, One Blind Spot: A PESTLE View of the Data Center appeared first on Data Center POST.

TL;DR

• Foresight won the 2026 Datacloud Global Award for Best AI Innovation for its Predictive Project Delivery platform.
• The platform uses predictive AI to identify project risks early, improve schedule certainty, and accelerate time-to-revenue for large-scale infrastructure projects.
• The recognition highlights the growing importance of AI-driven project intelligence as investment in data centers, power infrastructure, and other critical assets continues to rise.
• The award follows strong company momentum, including a $25 million Series A funding round and increasing adoption among leading infrastructure organizations.

# # #

Foresight, the digital Chief Time Officer for major infrastructure programs, has been named the winner of the Best AI Innovation category at the 2026 Datacloud Global Awards, one of the digital infrastructure industry’s most prestigious awards programs. The award was presented during Datacloud Global Congress in Cannes and recognizes organizations delivering measurable impact through the application of artificial intelligence across the digital infrastructure ecosystem.

Foresight received the award for its Predictive Project Delivery platform, which helps owners and operators of large-scale infrastructure programs identify delivery risks earlier, improve schedule predictability, and accelerate time-to-revenue across hyperscale data centers, power infrastructure, advanced manufacturing facilities, transportation programs, and other capital-intensive projects.

The recognition highlights the growing importance of artificial intelligence in addressing one of the infrastructure sector’s most persistent challenges: project delays. As investment in AI infrastructure continues to accelerate globally, organizations are increasingly seeking new ways to improve delivery certainty and bring critical capacity online faster.

Foresight’s platform applies predictive AI to help project teams identify emerging risks before delays become unavoidable. Leveraging one of the world’s largest datasets of planned versus actual project outcomes, the platform provides leaders with greater visibility into schedule performance and the factors most likely to impact successful project delivery.

According to Dr. Atif Ansar, Co-Founder and Executive Chairman of Foresight, the award validates the company’s mission to help organizations manage time with the same rigor traditionally applied to capital.

“Receiving the Datacloud Global Award for Best AI Innovation is a tremendous honour and a validation of a simple idea: time is money,” said Ansar. “Every major organisation has rigorous governance around capital allocation and financial performance. Yet project time, despite being one of the largest drivers of value creation and value destruction, often receives far less visibility and control.”

The award follows a period of significant momentum for Foresight, including the recent completion of its $25 million Series A funding round led by Macquarie Capital Venture Capital and continued adoption of its platform among leading infrastructure owners, operators, and delivery organizations.

To continue reading, read the full press release here.

The post Foresight Earns Best AI Innovation Recognition at Datacloud Global Congress appeared first on Data Center POST.

TL;DR

• The Angelwish WIFFLE Ball Classic demonstrated how industry communities can come together in support of a meaningful cause.
• Approximately 21 teams participated, reflecting strong engagement from organizations across the digital infrastructure and telecommunications sectors.
• More than $25,000 was raised to help Angelwish create memorable experiences for children living with chronic illnesses.
  The event highlighted the impact that collective action, community involvement, and charitable giving can have beyond the workplace.

# # #

On June 4, members of the iMiller Public Relations (iMPR) team joined professionals from across the digital infrastructure, telecommunications, and technology communities for the 2026 Hoboken Angelwish WIFFLE Ball Classic. While the competition on the field was spirited, the focus of the day extended far beyond wins and losses.

Hosted by Angelwish, the annual tournament brings together companies, colleagues, friends, and families to raise funds for children living with chronic illnesses. Founded in 1999, Angelwish helps fulfill wishes for children around the world while also promoting philanthropy and community engagement. The organization has spent more than two decades creating meaningful experiences for children and their families, helping bring moments of joy during some of life’s most difficult circumstances.

At Frank Sinatra Park, set against the backdrop of Hoboken’s waterfront, the Angelwish WIFFLE Ball Classic has become a longstanding tradition that combines fundraising, networking, and a healthy dose of childhood nostalgia. What makes the event unique is its simplicity: a  game that many participants grew up playing becomes a vehicle for something much larger for supporting children who are facing challenges most adults would struggle to navigate.

Over the years, the tournament has attracted participation from organizations across the communications and digital infrastructure sectors, creating an atmosphere where friendly competition  quickly gives way to collaborate and to come together in support of a meaningful cause.

This year’s tournament featured approximately 21 teams representing organizations from across the tech industry, telecommunications, technology, and professional services sectors. Participants included companies such as NJFX, CoreWeave, Ciena, Megaport, 1547, and many others that came together in support of the cause. Throughout the day, participants stepped away from conference rooms and video calls to spend time together outdoors, reconnect with industry peers, and contribute to a mission that impacts children and families around the world.

For the iMPR team, the tournament served as a reminder that some of the most meaningful industry gatherings are not centered on business development or market discussions, they are centered on people. Events like the Angelwish WIFFLE Ball Classic demonstrate how a community can come together in support of something bigger than itself, creating tangible impact while building connections that extend beyond the workplace.

Another successful tournament came to a close, raising over $25,000. The lasting takeaway is not who advanced the furthest in the bracket or who hit the longest home run, but  the collective effort of dozens of companies who dedicated their time, energy, and resources to support children living with chronic illnesses. That shared commitment is what continues to make the Angelwish WIFFLE Ball Classic such a special event year after year.

iMPR is proud to have participated in this meaningful tradition and support the important work being done by Angelwish, and we look forward to returning next year to continue contributing to a cause that makes a meaningful difference in the lives of children and families.

The post More Than a Game: Supporting Angelwish at the 2026 Hoboken Annual WIFFLE Ball Classic appeared first on Data Center POST.

TL;DR

• Power availability continues to influence where and how new data center capacity is developed.
• AI workloads are driving changes in facility design, construction strategies, and infrastructure planning.
• Regional markets are attracting increased investment as operators expand beyond traditional data center hubs.
• Workforce development and risk management remain critical to supporting long-term industry growth.

# # #

AI Demand Reshapes Power, Construction, and Expansion Strategies Across BISNOW’s DICE Event Series

The data center industry entered 2026 facing unprecedented demand for digital infrastructure. To address the opportunities and challenges ahead, industry leaders gathered at three major BISNOW events: National DICE Construction, Design & Development – East, Data Center Investment Conference & Expo (DICE) Southeast, and National Data Center Investment Conference & Expo (DICE) Power Capacity, Energy & Sustainability. Together, the events examined how operators, developers, investors, utilities, and technology providers are adapting to evolving infrastructure requirements driven by artificial intelligence, cloud computing, and continued digital transformation.

Although each event focused on a different segment of the industry, several themes emerged consistently across the discussions. Power availability, site selection, construction innovation, workforce development, and long-term resiliency remain among the most important considerations for organizations seeking to support the next generation of data center growth.

AI Is Accelerating Infrastructure Planning and Development

Artificial intelligence continues to reshape the way data centers are planned, designed, and deployed. As organizations invest in AI infrastructure, operators are being challenged to support higher-density workloads, larger campuses, and increasingly sophisticated technology environments. These changing requirements are forcing the industry to rethink traditional approaches to development and operations.

Discussions throughout the DICE event series highlighted how organizations are adapting to these demands through advanced cooling strategies, modular construction techniques, integrated project delivery models, and more flexible infrastructure planning. Attendees also explored how AI is influencing everything from facility design and procurement decisions to construction schedules and long-term capacity planning. The consensus was that AI is no longer simply creating incremental demand; it is fundamentally changing how infrastructure is developed and operated.

Emerging Markets Continue to Attract Investment

As demand for capacity continues to increase, operators are expanding beyond traditional data center markets in search of new growth opportunities. Regional markets throughout the Southeast and other parts of the United States are attracting attention due to available land, economic development incentives, connectivity assets, and opportunities to support future expansion.

At the same time, site selection has become increasingly complex. Organizations must evaluate a wide range of factors that extend well beyond land acquisition, including power availability, utility partnerships, permitting timelines, transportation infrastructure, workforce availability, and community engagement. These considerations are playing a growing role in determining where projects move forward and how quickly new facilities can be delivered.

Conversations at DICE Southeast reflected the industry’s continued focus on balancing growth with long-term sustainability. As more markets compete for data center investment, stakeholders are increasingly seeking strategies that support both economic development and responsible infrastructure expansion.

Power Availability Remains a Critical Industry Focus

Few topics generated more discussion across the DICE event series than power. As AI workloads continue to increase energy requirements, operators are facing mounting pressure to secure reliable capacity while navigating grid constraints, lengthy utility interconnection timelines, and evolving regulatory requirements.

The National Data Center Investment Conference & Expo (DICE) Power Capacity, Energy & Sustainability focused extensively on how organizations are responding to these challenges. Discussions examined a range of solutions, including utility partnerships, powered land strategies, on-site generation, battery energy storage systems, microgrids, renewable energy integration, and alternative energy technologies.

A recurring theme was the need for greater flexibility in energy planning. Rather than relying solely on traditional utility models, many organizations are evaluating diversified approaches that can help accelerate deployment schedules while improving resiliency. As infrastructure demand continues to grow, power strategy is becoming a key component of long-term business planning rather than simply an operational consideration.

Construction, Workforce Development and Risk Management Are Evolving

The rapid pace of industry growth is also creating new challenges for project delivery. Larger campuses, higher-density environments, and compressed timelines are requiring organizations to adopt more integrated approaches to construction, engineering, procurement, and operations.

Throughout the events, attendees explored how new construction technologies, design methodologies, and delivery models are helping improve efficiency and reduce project risk. Organizations are increasingly emphasizing collaboration across stakeholders to improve planning, streamline execution, and maintain quality throughout the development lifecycle.

Workforce development was another important topic discussed across multiple sessions. The industry continues to face growing demand for skilled professionals across engineering, construction, operations, and technical disciplines. As projects become more complex, building sustainable talent pipelines is becoming essential to supporting future growth and maintaining operational excellence.

Risk management discussions also highlighted the importance of preparing for supply chain disruptions, cybersecurity threats, regulatory changes, and climate-related challenges. Industry leaders emphasized that resiliency must be considered throughout every stage of development, from site selection and design to construction and ongoing operations.

Positioning for the Next Phase of Growth

The conversations held across BISNOW’s DICE event series in the first quarter of 2026 demonstrated that the data center industry is entering a new phase of maturity. Demand remains strong, but success increasingly depends on an organization’s ability to align infrastructure planning, energy strategy, construction execution, and operational readiness.

Organizations that can effectively navigate power constraints, support AI-driven workloads, attract skilled talent, and identify strategic growth markets will be well positioned for long-term success. As the industry continues to evolve, collaboration across the digital infrastructure ecosystem will remain essential to delivering the capacity required to support future innovation. To learn more about upcoming DICE events and register for future conferences, visit BISNOW’s events page at www.bisnow.com/events.

The post Digital Infrastructure Growth Creates New Challenges and Opportunities appeared first on Data Center POST.

TL;DR

• Panhandle expansion continues: Duos Edge AI will host an open house June 18 to showcase its patented modular EDC deployed with Hereford ISD.
• A century of community, now with edge computing: Hereford ISD spans 900 square miles and serves 4,000+ students with infrastructure built for AI, digital access, and workforce readiness.
• Sustainable by design: The Hereford EDC runs on on-grid power and air cooling, reducing environmental impact while supporting digital growth.

# # #

Duos Technologies Group Inc. (Nasdaq: DUOT), through subsidiary Duos Edge AI, Inc., has announced an open house in Hereford, Texas on Thursday, June 18, 2026, from 9:00 AM to 11:00 AM CDT to showcase its patented modular Edge Data Center deployed in partnership with Hereford Independent School District.

Beyond supporting K-12 education, the Hereford deployment creates new opportunities for local businesses to adopt advanced computing and data-driven technologies. The facility operates with a compact footprint, meets SOC compliance standards, and is designed to scale alongside the community’s evolving needs, fostering stronger digital ecosystems and long-term economic development across the Panhandle.

“Bridging the digital divide is core to our edge expansion, particularly in the Texas Panhandle and across the state,” said Doug Recker, CEO of Duos Edge AI and Duos Technologies Group,Inc. “We look forward to connecting with the community at the Hereford open house to share how this facility is already bringing tangible benefits to schools, businesses, and healthcare organizations in the area.”

“We look forward to connecting with the community at the Hereford open house to share how this facility is already bringing tangible benefits to schools, businesses, and healthcare organizations in the area.”

To learn more about Duos Edge AI and register for the open house, visit https://freeevite.com/event.php?e=3wk0cpbdauds1_E5Uyor-A.

The post Duos Edge AI to Host Hereford Edge Data Center Open House appeared first on Data Center POST.

TL;DR

• The Huge Potential of Waste Heat: Liquid-cooled AI data centers generate massive amounts of heat that, instead of being uselessly vented into the atmosphere, can be efficiently captured using established heat exchanger technology and redirected to local district heating networks.
• Massive Efficiency Gains: Properly implemented heat recovery systems could drastically reduce global energy waste, tapping into an estimated 3,100 thermal terawatt-hours of wasted heat annually to potentially save up to €140 billion worldwide.
• Geographic and Contractual Barriers: While the technology is readily available, the main hurdles to widespread adoption are the physical distance between data centers and municipal heating networks, alongside the complex contracts needed to manage fluctuating, seasonal community heat demand.
• Regulatory and Community Incentives: Forward-thinking operators can leverage this opportunity to improve sustainability and benefit from new government incentives, such as European tax exemptions for reusing heat, while establishing themselves as valuable, environmentally responsible community neighbors.

# # #

Data centers, especially those powering AI, generate vast amounts of heat. But that heat doesn’t have to be wasted: using well established technology, it can be recovered and reused in heating and hot water systems. Ulrik Vadstrup, HVACR Segment Manager for ABB asks why, in a sector built for speed, are so many data centers slow to take up this opportunity?

Picture a cruise ship gliding through Arctic waters – on-deck swimming pools steaming in the frigid air. On the surface, this looks incredibly wasteful: how much fuel must the ship need to burn to keep multiple pools hot in such freezing conditions?But in fact, this is an incredibly efficient use of energy. A cruise ship’s engines generate huge amounts of heat as a matter of course. With nowhere to go, that heat would make the engine room and surrounding gangways unbearable for the crew, and could even damage the ship. Swimming pools give the ship somewhere useful to put waste heat, cooling the engine and pleasing the passengers at the same time. It’s a win-win.

The same thinking applies to commercial refrigeration, brewing, food and beverage plant, plastic extrusion, and now especially to data centers: facilities like these also generate waste heat. Without somewhere to go, it ends up heating the roof of the building, then the air outside, which benefits no one.

Both a need and an opportunity for data centers

The latest generations of data centers use liquid cooling. Cold liquid, often water, is piped through the hot servers, absorbing heat before being piped away again.

Liquid cooling is common in other industries as well. And once upon a time, businesses would dispense the hot used coolant into the sewer, and pipe in fresh coolant to keep their processes cooled. But no longer. Today, regulations require businesses to recirculate coolant rather than continuously drawing and venting it. That means they need a way to quickly cool down used coolant, ready for recirculation. This requirement is the norm for new liquid-cooled data centers.

The value in heat recovery is clear. A facility generates unwanted heat in the course of its business; elsewhere, there is demand for heating and hot water. But despite the potential, McKinsey estimates that at least 3,100 thermal terawatt-hours of recoverable heat a year is still going to waste worldwide.

Technology is not the barrier

Waste heat recovery is not a new technology, or an especially complex one. All it takes is a heat exchanger, and often a heat pump. Using the same thermodynamic principle as liquid cooling, the heat exchanger transfers heat from where it’s not wanted into water or another agent, which is then piped to somewhere needing heating.

That could be somewhere in the same facility. Danish cooling compressor specialist Advansor offers combined heating and cooling systems for supermarkets, for example, which recover waste heat from the refrigeration and air conditioning systems and use it for comfort heating and hot water in the same building.

Or it could be in the local community, via a district heating network. This is where heat pumps come in. Waste heat tends to be at around 30-60°C. Older district heating systems operate at 90°C in the winter, and around 60°C in the summer. For those, heat recovery systems use a heat pump – a technology well known and widely used in heating, ventilation, and air conditioning – to lift the water temperature to a suitable level.

Naturally the higher the waste heat source temperature – which equates to how much energy is available – then the better the process efficiency, as measured by the heat pump’s coefficient of performance (COP).

Advancing technology may soon remove the need for even the relatively minor complexity of adding a heat pump. This is because the temperature of the coolant coming from the data centers is increasing due to liquid direct-to-chip cooling, while the latest generation of district heating systems work perfectly well at lower temperatures, so waste heat at 30-60°C can be directly used in those.

What about efficiency?

Any amount of recovered heat is a bonus when the alternative is expending energy to generate heat that just goes to waste.

But it still makes sense for heat recovery systems to be efficient. The point of the system is to reduce wasted energy; if the system uses inefficient motors or drives, it cannot fully realise its waste heat recovery potential.

The more efficient the variable speed drive (VSD) controlling a heat pump motor and the more efficient the motor itself, the more thermal energy the pump can provide for a given amount of electrical energy. A heat pump also needs a drive that doesn’t generate electrical harmonics which can impair power network performance and cause electrical problems.

Distance can be a challenge

In energy efficiency terms, heat recovery is clearly a positive for data centers. But there is still an upfront cost to install the system, which could take a while to pay for itself in saved heating costs.

And what if those savings are being passed on to a district heating network instead?

Often in these cases, the network offers to pay for the heat recovery system. Yet even when the system costs the data center nothing and solves a cooling problem, district heating networks still sometimes fail to convince data centers to donate their waste heat.

Sometimes it comes down to geography. Large operators generally aim to build their data centers in the lowest cost location possible. This often means building them far from the nearest district heating network connection. More distance between the data center and the heating network means longer pipes and more heat lost along the way. Above a certain distance, the connection is no longer worthwhile.

Data centers run 24/7: demand for heating is not constant

Any time a data center’s servers aren’t running, they’re losing the owner’s money. So data centers are always generating heat they need to get rid of.

On the other hand, district heating networks don’t always need heat. On hot summer days, people don’t want to heat their homes, even when they can do it for free.

Data centers need certainty, usually written into a contract, about how much heat their district heating network partner will take from their facility, and what will happen if at any point the network can’t or doesn’t want to take that much. Agreeing these contracts is the biggest barrier to increase waste heat recovery in this fast growing sector.

And even if it doesn’t directly benefit the data center, recovering waste heat is still a good idea. Data centers use a lot of energy. If even a percentage of that massive energy expenditure is going to waste, when the technology exists to recover and reuse it, data centers have a responsibility to cut that waste. European politicians have started to make that responsibility an obligation – but there is still so much more potential in this field.

Good neighbours

In the European Union, the amount of unrecovered waste heat across all industries rivals the total demand for heat and hot water in all residential and service sector buildings. Worldwide, if we recovered and reused all the heat that is currently going to waste, it could save as much as €140 billion.

And the demand, and the potential savings, are still on the rise. When electricity and gas prices can increase unexpectedly, free heat becomes more attractive, even when it calls for some up front investment. Heat pumps also become more economical as heating becomes more expensive.

For once, governments are moving faster than some businesses to recognize the opportunity. Several European governments have recently changed the law so businesses are no longer taxed for reusing waste heat. And when it’s used in district heating, waste heat is officially recognized in Europe as a clean heat source, a climate mitigation measure, and a green investment.

Today, district heating and cooling supplies 13% of the European Union’s demand. If data centers and other businesses take their governments’ hints and recover more of their waste heat, this could increase to 50% by 2050.

For some data center operators, this will mean stepping out of their comfort zone. But those who show leadership have everything to gain: more efficient operations, a more sustainable business, and a valuable role in the lives of their nearest neighbours.

# # #

About the Author

Ulrik Vadstrup is the HVACR Segment Manager for Europe at ABB, bringing over 30 years of experience in industrial automation to the role. With a background spanning system design, software programming, and OEM and direct sales, Ulrik has developed deep expertise across the full project lifecycle, working with clients and partners worldwide.

The post Don’t Waste The Waste Heat Opportunity appeared first on Data Center POST.

TL;DR

• The Nordic Integration Model: Leading data centers in the Nordic region demonstrate that true resilience requires treating the physical environment, energy strategy, and security as a single interconnected system, rather than treating them as isolated, vulnerable functions.
• Connecting Operational Signals: True protection comes from connecting data rather than just collecting it; operators must integrate video analytics, access events, and environmental sensors into a unified platform to detect anomalies before they compound into costly outages.
• Security as a Performance Driver: As facilities scale to handle complex AI workloads, treating security merely as a reactive cost center is a severe liability; if a security system does not actively contribute to facility uptime and operational efficiency, it is already obsolete.

# # #

Sienna Cacan, Global Enterprise Segment Marketing Manager, Axis Communications, explains why the Nordic data center market offers a useful model for treating security as a core part of the operating environment.

The data center has a security problem – and it’s not what most operators think. Data centers excel at what almost amounts to theatrical security: the fences, cameras, access control systems and guards which surround the perimeter. These measures are necessary, but they create a false sense of completion, and the illusion of control. Modern facilities require a shift toward operational intelligence, where security systems actively contribute to uptime, efficiency, and risk mitigation.

Data centers are operationally complex models of critical infrastructure. They carry workloads that entire economies depend upon. AI compute, cloud expansion, and edge deployments are pushing facilities to operate at unparalleled scale and complexity. In this environment, the margin for error is nil. Security cannot, therefore, simply manifest as a layer built around a center’s operations: it must be a core part of those operations, extending from fence to rack. The world is dependent on data centers getting it right.

The Nordic operating model

Across Sweden, Denmark, Norway, and Finland, a different model is emerging. The Nordic market has established itself as a global data center hub. It is an ideal habitat for data centers in general, offering renewable energy, cooler climates and political stability to an industry which needs all three. The key is that Nordic data centers operate with their environment, and not against it.

In Helsinki, for example, North’s FIN02 facility has been designed to feed excess heat into the local district heating network, turning what would once have been treated as a cooling problem into a community energy resource. In Norway, Green Mountain has explored a different version of the same idea, working with seafood producers to use waste heat in land-based aquaculture.

Data centers are becoming active parts of local infrastructure, with responsibilities that extend beyond their own walls. Remote facilities are being used to support sovereign data strategies, with Lefdal Mine Data Center in Norway acting as an extreme but useful example. Built inside a former olivine mine, it uses rock, depth and geography as part of its resilience proposition, alongside cold fjord water for cooling.

Not every facility can be built into a mountain, but the Nordic market is unusually comfortable thinking about the physical environment, energy strategy and security as parts of the same design problem. Every major function must make that system more resilient and form part of a combined whole, because if one part is treated as separate it becomes the weak point. If security is left as a mere visible perimeter rather than treated as an operational function, it will not keep pace with the facility it is supposed to protect.

From the fence line to the rack

The increased dependence on data centers – both internationally and, as centers become more integrated into their environments, locally – means the role of the fence line has been reduced at best. The industry needs to confront the uncomfortable truth that most legacy security systems contribute little to operational performance. They record incidents but don’t prevent them, generate data but don’t produce actionable insights. A camera with analytics which can pick out abnormal movement, spot equipment stress or monitor for environmental changes essentially helps run the site.

There is a place for visible security. It shows that a facility has defences and creates the appearance of control. But the interdependent Nordic approach teaches us that we must build security for the data center environment specifically and acknowledge that an effective security approach is one which considers the intersection between physical incidents, IT, and operational technology (OT). Video analytics and connected sensor platforms enable real-time, remote decision-making across distributed sites, an increasingly important capability as operators scale geographically.

A forced door in a data center can quickly become a cyber incident. A compromised connected device can be a route through the virtual perimeter and into the network. A cooling issue can become an availability event – or a threat to physical safety. Very different categories of incident, but they share a common outcome: disruption.

Security through connected signals

Operators need systems that connect signals rather than simply collect them. Video, access control, audio, environmental sensors and analytics all have value on their own, but their real strength comes when they contribute to a shared view of a facility. A single alert may mean very little. A door event, a record of unusual movement, a temperature change and an equipment alarm in combination may tell a different story.

This is where the region’s interest in digital twins and real-time facility models becomes relevant. In a large or remote Nordic site, the useful view may not be a guard watching a flat wall of feeds, but an operational model based on a trusted sensor platform that brings video, sensors, access events and environmental data into the same frame. A failing door seal, abnormal rack vibration or unexpected movement pattern may each be minor in isolation. Together, they may point to an issue worth acting on before it becomes an outage.

This is what real resilience looks like: not stronger barriers, but visual verification, audit trails, SLA evidence and increased customer trust built through smarter integration and a willingness to rethink what security is actually about. As hyperscale and colocation facilities continue to expand, the industry needs to be honest with itself that security has often not evolved at the same pace as capacity, and scaling conventional security into larger, denser and more automated data centers does not automatically improve protection.

Building true resilience with integrated thinking

In truth, the next wave of data center disruptions is unlikely to result from inadequate perimeter defences. Instead, it will stem from blind spots in operational visibility, delayed responses to emerging risks, and an over-reliance on manual processes in increasingly automated environments. Essentially, it will come from treating security as a cost center rather than a strategic capability.

The lesson is not necessarily to copy the Nordics exactly, of course. Geography, energy markets, and local regulations differ. But copying the key lesson, that resilience comes from integration, is viable everywhere. As data centers become ever more critical to the global economy, the systems that protect them must also enable performance. If your security system doesn’t improve uptime, it is already obsolete.

A data center that looks secure may still be fragile. A data center that understands itself, monitors its operations and connects security intelligence with operational decision-making is far harder to disrupt. Security should not sit around the data center; it should run through it. Anything less is theatre, and that is a poor foundation for critical infrastructure.

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About the Author

Sienna Cacan, Global Enterprise Segment Marketing Manager, Axis Communications

Leads strategic marketing initiatives focused on the Technology & IT and Commercial Real Estate verticals. With more than a decade of experience across both B2C and B2B marketing, she has spent the past six years driving global demand creation and market development at Axis Communications. She works closely with end customers, ecosystem partners, and industry stakeholders to support growth and innovation across key enterprise markets.

About Axis Communications

Axis enables a smarter and safer world by improving security, safety, operational efficiency, and business intelligence. As a network technology company and industry leader, Axis offers video surveillance, access control, intercoms, and audio solutions. These are enhanced by intelligent analytics applications and supported by high-quality training.

Axis has around 5,000 dedicated employees in over 50 countries and collaborates with technology and system integration partners worldwide to deliver customer solutions. Axis was founded in 1984, and the headquarters are in Lund, Sweden.

The post From Theatre To Resilience: Lessons From The Nordic Data Center Rulebook appeared first on Data Center POST.

TL;DR

• DāSTOR has partnered with Myota to enhance its Backup as a Service (BaaS) offering.
• The solution combines DāSTOR’s managed backup expertise with Myota’s Shard and Spread™ technology.
• Customers benefit from improved cyber resiliency, instant recoverability, and reduced storage costs.
• The enhanced offering is available immediately for enterprise customers.

# # #

As organizations face growing threats from ransomware, infrastructure outages, and increasing data volumes, many are reevaluating how they approach backup and recovery. Traditional backup environments can be costly, complex, and difficult to manage, creating new challenges for IT teams focused on resiliency and business continuity.

To help address these challenges, DāSTOR has announced a strategic partnership with Myota to enhance its Backup as a Service (BaaS) offering with advanced data protection and storage optimization capabilities.

The enhanced solution combines DāSTOR’s infrastructure and managed backup expertise with Myota’s patented Shard and Spread™ technology. By encrypting, sharding, and distributing data across multiple storage locations, the solution helps improve cyber resiliency while reducing storage infrastructure requirements.

As ransomware attacks continue to target organizations of all sizes, backup and recovery strategies have become a critical component of cybersecurity planning. Many traditional backup environments rely on multiple copies of data stored across different systems, which can increase storage requirements, management complexity, and overall costs. Organizations are increasingly looking for solutions that can strengthen protection while improving operational efficiency.

Myota’s approach helps address these challenges by securing data at the point of creation and distributing encrypted data fragments across multiple storage locations. Even if attackers gain access to part of the environment, the individual data fragments are computationally impossible to reassemble without authorization. At the same time, authorized users can quickly access and recover data when needed, helping reduce downtime and improve business continuity.

“Organizations need backup solutions that improve security and resiliency without increasing costs,” said Kevin Mulqueen, CEO of DāSTOR. “Our partnership with Myota strengthens our Backup as a Service offering with a more secure and efficient approach to data protection that helps reduce storage costs for customers.”

Beyond strengthening security, the partnership is designed to help organizations better manage growing storage demands. By reducing reliance on traditional redundancy-based approaches, customers can benefit from a more efficient backup environment that lowers storage overhead while maintaining fast recovery capabilities.

The partnership supports DāSTOR’s broader strategy of delivering scalable backup and disaster recovery solutions for modern IT environments, including hybrid cloud, colocation, and high-density infrastructure deployments.

The announcement reflects a broader shift across the industry as organizations seek backup solutions that can support increasing data growth without introducing additional complexity or cost. Security, recoverability, storage efficiency, and operational simplicity are becoming equally important considerations when evaluating modern data protection strategies.

The enhanced Backup as a Service offering is available immediately to enterprise customers across healthcare, legal, financial services, manufacturing, government, and other data-intensive industries.

Read more in the full press release here.

The post DāSTOR Strengthens Backup as a Service with Advanced Cyber Resiliency Through Myota Partnership appeared first on Data Center POST.

• ZincFive was named to TIME’s World’s Top GreenTech Companies 2026 list for the second consecutive year, recognizing its innovation and sustainability leadership.
• The award highlights the growing need for safer, more sustainable power solutions as AI-driven infrastructure increases demands on data center power systems.
• ZincFive’s nickel-zinc battery technology delivers high power density, inherent safety, and sustainable performance for mission-critical applications.

# # #

In an industry where every second of uptime matters, ZincFive^® is once again earning recognition for helping shape the future of mission-critical power. The company has been named to TIME’s World’s Top GreenTech Companies 2026 list, marking its second consecutive year on the global ranking and underscoring the growing relevance of nickel-zinc technology in today’s power landscape.

The recognition arrives at a moment when data centers, AI infrastructure, and other high-demand environments are under pressure to deliver more power with fewer trade-offs. That is exactly where ZincFive has made its mark. Built around patented nickel-zinc (NiZn) battery technology, the company’s solutions are designed for immediate power applications that demand high performance, compact design, and inherent safety.

TIME and Statista evaluated more than 8,300 companies before selecting just 250 for inclusion on the 2026 list, making the recognition especially meaningful. Companies were judged on environmental impact, financial strength, and innovation drive – criteria that reflect not only what a company makes, but how it contributes to advancing the broader GreenTech sector. For ZincFive, the honor highlights the broader shift underway in power infrastructure: Sustainability is no longer a side note, but a core requirement.

As AI workloads continue to drive greater power density inside data centers, operators face increasing pressure to deploy solutions that deliver reliable performance without introducing additional safety risks or environmental concerns. Traditional battery technologies often require trade-offs between footprint, safety, sustainability, and performance.

Tod Higinbotham, CEO of ZincFive, said being named among the World’s Top GreenTech Companies “reinforces our belief that the future of power infrastructure requires solutions that are both high performing and sustainable.” He added that as AI-era infrastructure places unprecedented demands on power systems, the recognition reflects a broader industry shift toward safer, more sustainable energy storage and validates the role nickel-zinc technology is playing in delivering performance without compromise.

What makes ZincFive stand out is not just the chemistry, but the philosophy behind it. The company’s systems are engineered to combine high power density, reliable operation, and a more sustainable footprint in one compact solution. For customers navigating the future of data center power, that combination offers a compelling path forward. Designed for today’s most demanding environments, ZincFive’s nickel-zinc technology delivers reliable operation without the safety and environmental tradeoffs associated with traditional battery chemistries.

As the GreenTech landscape continues to evolve, ZincFive’s place on TIME’s 2026 list reflects more than a single achievement. While the award highlights ZincFive’s continued growth and innovation, it also points to broader changes taking place across the power infrastructure industry.  It signals the company’s continued role in shaping safer, smarter, and more sustainable power solutions for the world’s most demanding environments.

For full details, please read the press release here.

The post TIME Recognizes ZincFive for Advancing Safer, More Sustainable Power appeared first on Data Center POST.

TL;DR

• PowerBridge appointed Melissa Podesto as Vice President of Hyperscale Leasing and Data Center Sales to lead customer engagement and sales strategy.
• Podesto brings more than 20 years of hyperscale and digital infrastructure sales experience, most recently at Aligned Data Centers and previously at Digital Realty.
• The company is advancing its Alpha Digital Campus in Pecos, West Texas, a planned two-gigawatt project anticipated as part of ERCOT’s Batch 0.
• PowerBridge says the appointment supports its integrated strategy of converging power, connectivity and digital infrastructure to serve hyperscale and AI customers.

# # #

As hyperscale and AI infrastructure demand continues accelerating, customers are increasingly prioritizing speed to power, long-term scalability and integrated infrastructure execution when evaluating where to deploy capacity.

That market shift is one reason PowerBridge has appointed Melissa Podesto as Vice President of Hyperscale Leasing and Data Center Sales.

Podesto joins PowerBridge at a moment when hyperscale, cloud and AI customers are prioritizing long-term infrastructure readiness and speed to power. In her new role, she will lead customer engagement and sales strategy as the company advances its growing portfolio of powered digital infrastructure campuses across North America.

Her appointment brings more than 20 years of relationship-driven sales leadership to the team. Podesto most recently served as Senior Director of Sales at Aligned Data Centers and previously spent nearly a decade at Digital Realty as Global Sales Director, where she led hyperscale and global account sales for one of the world’s largest data center platforms. Across both companies, she became known for translating complex hyperscaler and digital native infrastructure requirements into long-term partnerships at scale.

The appointment reflects broader changes happening across the digital infrastructure industry, where hyperscale and AI customers are increasingly seeking partners that can deliver power, connectivity and campus-scale infrastructure as part of a unified strategy rather than through fragmented development models.

For PowerBridge Founder and CEO Alex Hernandez, that combination of commercial experience and customer insight aligns directly with where the company is focusing its growth.

“Melissa is a highly respected commercial leader with customer insight, a long-standing track record and market experience that will complement our energy convergence strategy,” said Hernandez, Founder and Chief Executive Officer of PowerBridge. “As demand for powered digital infrastructure continues to accelerate, Melissa’s long-standing relationships with hyperscale, cloud and AI customers will accelerate our mission to deliver large-scale powered campuses built for speed, certainty and growth.”

That strategy is anchored in an integrated approach to digital infrastructure and connectivity. PowerBridge was formed by Hernandez and the leadership team behind Cumulus Data, in partnership with FivePoint Infrastructure, which announced a $1 billion equity commitment to the company in May 2025. Today, PowerBridge is advancing several multi-gigawatt developments across West Texas, where abundant energy resources, available power infrastructure and fiber conduit connectivity are positioned to support the next phase of large-scale digital infrastructure growth.

Among those developments is the company’s Alpha Digital Campus in Pecos, West Texas, a two-gigawatt project scheduled to deliver first power in the second half of 2027 and into early 2028 as an anticipated ERCOT Batch Zero power generation and data center project.

For hyperscale and AI customers, projects like Alpha Digital Campus represent a growing shift toward infrastructure environments designed around long-term power availability, execution certainty and scalable campus development.

Podesto said the company’s differentiated platform was central to her decision to join.

“I am thrilled to join PowerBridge at such an important moment for the industry,” Podesto said. “Alex and the team were years ahead of our industry in understanding the rapid convergence between power and digital infrastructure. PowerBridge’s assets in West Texas offer hyperscale and AI customers a differentiated and compelling platform built around fundamentals, execution and scale. I look forward to working with the team to expand customer relationships and support the company’s continued growth.”

As hyperscale and AI deployments continue to accelerate, the industry is increasingly moving toward infrastructure models that prioritize integrated execution, scalability and long-term power availability. PowerBridge’s latest leadership appointment reflects how developers are positioning themselves to meet that next phase of growth.

Read the full release here.

The post PowerBridge Adds Hyperscale Sales Leadership as AI Infrastructure Demand Accelerates appeared first on Data Center POST.

Originally posted on CD-Modular.

TL;DR

• The AI Construction Bottleneck: Traditional, labor-intensive data center construction models are sometimes too slow to support the rapid scale and precision required by modern, purpose-built AI deployments.
• Full-Scale Prefabrication: To accelerate timelines, the industry is moving beyond just modularizing power and cooling by prefabricating entire IT white spaces and operational areas in controlled factory settings.
• Engineered for Extreme Density: These customized, factory-built units are specifically designed to handle AI’s heavy structural demands, sophisticated liquid cooling, and extreme power requirements that exceed 4,000 watts per square foot.
• Bypassing Labor Shortages: Moving complex infrastructure assembly off-site allows operators to avoid acute industry labor shortages, transforming months of on-site construction into a competitive advantage where units are fabricated in just weeks.

# # #

The data center industry is facing a critical inflection point as traditional, labor-intensive construction models struggle to keep pace with the demands of purpose-built AI deployments. While pouring concrete and relying on large on-site workforces remains viable for standard colocation facilities, this approach creates severe bottlenecks for modern AI environments that prioritize rapid scalability, speed, and precision. To overcome these hurdles, the industry is moving beyond simply modularizing power and cooling components to prefabricating the entire IT white space and integrated operational areas.

Modern AI workloads demand heavy structural infrastructure, sophisticated liquid cooling, and dense power delivery capable of exceeding 4,000 watts per square foot. By shifting the assembly of these complex IT environments to controlled factory settings, organizations can drastically reduce their reliance on on-site labor while ensuring consistent quality and significantly faster deployment cycles. This turnkey approach allows fully customized modular units to be fabricated in a matter of weeks, offering a competitive advantage over traditional, prolonged construction timelines and actively mitigating the impacts of acute industry-wide labor shortages.

To continue reading, please click here.

The post How Prefabrication is Solving the AI Data Center Bottleneck appeared first on Data Center POST.

TL;DR

• The Dominance of Air Cooling: Despite the growing adoption of liquid cooling, approximately 80% of data centers worldwide still rely on air as their primary cooling method, making precise environmental control essential for managing increasingly complex workloads.
• The Massive Cost of Inaccuracy: A minor temperature deviation of just 0.5°C in a typical 10 MW data center can waste more than $800,000 in cooling energy over ten years, representing a potential $805 million annual loss across the global industry.
• Compounding Operational Risks: When environmental sensors provide inaccurate data, cooling systems either overcompensate, leading to significant energy waste and inflated operating costs, or risk overheating, which threatens equipment reliability and causes costly downtime.

# # #

Air cooling still underpins most data center operations. Even as liquid and hybrid solutions gain traction, roughly 80% of the world’s data centers still rely on air as their main cooling method, underpinning the importance of precise environmental control. With data center workloads growing in complexity and density, maintaining reliable environmental conditions has never been more critical.

As operators strive for higher efficiency and sustainability, small environmental errors can quietly drive significant energy waste and cost. Even minor inaccuracies can magnify in large, high-power environments.

A temperature deviation of just 0.5°C in a typical 10 MW air-cooled data center can result in more than $800,000 in wasted cooling energy over ten years. Across the global installed base of roughly 12,000 data centers, correcting this half-degree error could prevent approximately $805 million in unnecessary energy consumption each year. These figures demonstrate that what seems like a minor measurement difference can translate into substantial financial and energy impacts.

Why Measurement Accuracy Matters

Cooling systems respond to the environmental readings they receive. Precise measurements allow operators to maintain optimal setpoints while ensuring safe conditions for critical IT equipment. When sensors provide inaccurate data, systems may overcompensate, leading to overcooling, increased energy use and higher operating costs. On the opposite end of the spectrum, consistent and accurate readings are essential in preventing overheating, supporting long-term reliability and minimizing the risk of costly downtime.

High-accuracy monitoring is the foundation for operational efficiency. The ideal solution combines precise temperature and humidity sensing with the ability to track multiple parameters, such as dew point and carbon dioxide, on the same platform.

For example, Vaisala Origo, a next-generation modular measurement platform, illustrates how a modular, high-accuracy measurement system can address these challenges. The platform provides ±0.1°C temperature accuracy and ±1 % relative humidity accuracy, with support for multiple environmental parameters through interchangeable probes. Systems like this demonstrate the type of precision and flexibility needed to manage complex environments effectively.

Operational Implications

The consequences of even small errors extend beyond energy costs. Data centers currently account for roughly 1.5 percent of global energy consumption, and demand is expected to reach just under 3% by 2030, with cloud services, AI workloads and expanding digital infrastructure. Small inaccuracies can quietly drive energy waste, reduce efficiency and increase operating expenses. Addressing the half-degree problem is a practical step toward improving operational efficiency and sustainability.

In mission-critical operations, precision matters. Even a half-degree difference in environmental measurement can have cumulative effects on energy use, costs and equipment reliability. Facilities that prioritize accurate monitoring, using modular, high-accuracy systems that track multiple environmental parameters, can realize meaningful efficiency gains while ensuring operational stability. As a result, small improvements in measurement accuracy can translate into large energy savings and more reliable operations.

# # #

About the Author

Kelly King is a Business Development Manager at Vaisala and has 10+ years of experience across technology, security and data center environments. She helps customers apply Vaisala’s industry-leading environmental measurement solutions to modern data centers and combines strong technical understanding with commercial acumen to build strategic customer relationships and deliver practical, scalable solutions.

The post Why A Half-Degree Error is Quietly Costing Data Centers Millions appeared first on Data Center POST.

TL;DR

• The Blind Spot of Standard Monitoring: Traditional infrastructure management relies on temperature and flow rates to confirm circulation, which completely misses the internal chemical health of the fluid until physical damage to cold plates or heat exchangers is already underway.
• The Limits of Periodic Testing: Relying on quarterly or biannual lab testing is insufficient because intense AI workloads can degrade coolant within weeks, and isolated lab snapshots fail to capture rapid degradation trends or brief, damaging contamination events.
• The Shift to Predictive Analytics: By continuously tracking real-time indicators like pH, conductivity, and turbidity, predictive systems can detect the early “fingerprints” of failure, such as active metal dissolution, allowing operators to transition from reactive emergencies to condition-based planning.
• Protecting Hardware and Warranties: Implementing predictive coolant health monitoring prevents costly GPU downtime, extends hardware lifespan, and serves as a vital warranty shield by providing OEMs with continuous proof that fluid chemistry remained within required specifications.

# # #

Why liquid-cooled AI infrastructure needs predictive analysis of fluid condition, not just temperature and flow monitoring.

As direct-to-chip liquid cooling becomes standard for GPU clusters that are pushing rack densities far beyond traditional air-cooling limits, one critical reliability layer remains underdeveloped: the health of the coolant itself. Operators need to move from periodic coolant checks to predictive fluid health monitoring that detects early degradation before it becomes corrosion, clogging, thermal throttling, or unplanned downtime.

Coolant Failure Is Usually Gradual, Not Sudden

In liquid-cooled environments, catastrophic failures rarely begin as thermal events. They begin as chemistry problems that eventually become thermal problems. Performance is lost gradually through small, compounding inefficiencies. Whether the fluid is a water-glycol mix with corrosion inhibitors or a treated water loop, degradation follows a predictable pattern. Early chemical shifts, such as pH drift, rising conductivity, or inhibitor depletion, appear long before physical symptoms like scale formation, galvanic corrosion, or biofouling become visible.

In high-density AI clusters, where cold plate channels can be extremely narrow, even minor fluid chemistry changes invite microscopic scaling, biofilm, and particulate shedding. These forces accelerate flow restrictions and create hot spots. The difficulty is that these early indicators are invisible to standard data center infrastructure management tools. Most monitoring stops at supply and return temperatures, flow rate, and pressure differential. Those parameters confirm that coolant is circulating, but they say nothing about the fluid’s internal condition. An operator can have perfect thermal readings while the coolant inside the loop slowly becomes corrosive. By the time temperature anomalies appear, physical damage to cold plates or heat exchangers may already be underway.

Why Periodic Lab Testing Falls Short

Many facilities operate their cooling loops like driving a high-performance vehicle with no dashboard gauges, relying only on a red warning light that illuminates after the engine has seized. By the time Building Management System (BMS) alarms fire for supply temperature or pump failure, the damage to the GPUs is already done.

Today, many operators rely on pulling coolant samples and sending them to a lab once or twice a year. This is better than no testing at all, but it carries significant blind spots. AI training workloads can stress coolant thermally and chemically within weeks. A six-month gap between lab reports can easily miss the entire degradation curve of a fluid that turns problematic in under three months.

Lab analysis also provides only a single snapshot. It cannot track the rate of change, detect brief contamination events, or correlate chemical shifts with specific GPU workloads. A short pH excursion caused by a mismatched top-up fluid might self-correct, but the momentary corrosive window can still etch cold plate surfaces. Without trended data, operators cannot link cause to effect, so root causes remain hidden and recurrence is likely.

The Shift to Continuous, Predictive Monitoring

To combat this gradual performance drift, the next reliability layer is continuous coolant health monitoring paired with predictive maintenance for liquid cooling. Instead of waiting for a quarterly lab report, operators can track key parameters, such as pH, conductivity, and inhibitor levels, in near real time. More advanced monitoring adds particle counting, turbidity sensing, and early corrosion indicators to catch problems at their very start.

When this sensor data feeds into predictive models, the system learns to recognize the fingerprints of failure from the data stream. For example, a slow, steady rise in conductivity coupled with a slow drop in pH over 72 hours is not random noise – it is the active signature of metal dissolving into the fluid. A spike in turbidity without a corresponding pressure change may point to biological activity, particulate buildup, or another fluid condition that requires investigation. By recognizing these deterministic signals, the system extrapolates the trajectory and calculates the moment of impact, shifting the maintenance strategy from reactive fire drills to condition-based planning.

What Predictive Coolant Health Means for Reliability

For AI data centers, where a single GPU node failure can idle many others and delay

large-scale training runs, the cost of coolant-related failure is severe. Corroded cold plates must be replaced. Clogged micro-channels require aggressive flushing or component swap-out. Unplanned downtime cascades through service level agreements. Predictive coolant health directly targets these risks at the source.

It also extends hardware lifespan and acts as a critical warranty shield. OEMs increasingly require proof that coolant chemistry stayed within specification throughout the hardware’s life; continuous monitoring provides a stronger operating record. Keeping the fluid clean keeps the cooling system efficient and focuses the power bill on compute.

Predictive Coolant Health: An Emerging Reliability Layer

Predictive coolant health is not just another maintenance task. It is an emerging reliability layer for liquid-cooled AI infrastructure. As GPU clusters become denser and cooling loops become more complex, operators will need systems that can analyze fluid condition over time, identify early degradation patterns, and forecast when coolant health is moving outside safe operating limits. This shift from reactive sampling to continuous, predictive analysis of liquid health represents the next maturity curve for data center cooling reliability.

The Future Is Predictive Liquid Health

As liquid cooling matures from a niche high-performance computing solution to standard AI infrastructure, the supporting practices must grow more sophisticated. Thermal management came first. Leak prevention came second. Now, predictive coolant health must become the third pillar of liquid cooling reliability.

This does not require replacing existing coolant distribution units or loops. It requires adding a layer of chemical and electrochemical awareness, connected to analytics, that can catch gradual degradation before it becomes catastrophic. For operators planning the next wave of AI deployment, establishing essential maintenance practices for direct-to-chip cooling systems and building coolant health monitoring into liquid-cooled infrastructure from day one is a practical step toward more predictable operations and longer asset life. Coolant is not just a consumable. It is an operating condition that directly affects AI infrastructure reliability.

# # #

About the Author

Rupesh Mainali is a Senior Member of Technical Staff at Reliability Engine, where he focuses on predictive analysis of liquid health for liquid-cooled AI data centers. His work spans coolant health monitoring, loop behavior, and early degradation signals in direct-to-chip cooling infrastructure. To learn more, follow Reliability Engine on X and LinkedIn.

The post Predictive Coolant Health: The Missing Reliability Layer in AI Data Centers appeared first on Data Center POST.

TL;DR

• Subsea infrastructure resilience, cable security, and operational readiness remained key priorities as operators evaluate how to protect critical communications infrastructure and support growing global connectivity demands.
• Investment in new cable systems, network expansion, and long-term infrastructure planning continues to accelerate as AI workloads, cloud adoption, and international data traffic drive bandwidth growth.
• Governments, operators, and infrastructure providers are placing greater emphasis on policy coordination, resilience planning, and public-private collaboration as submarine networks become increasingly strategic assets.
• The subsea, cloud, fiber, and data center ecosystems are becoming more interconnected, creating new opportunities for collaboration across the global digital infrastructure landscape.

# # #

Submarine Networks EMEA 2026, held May 27–28 in London, brought together leaders from across the subsea, telecommunications, cloud, and digital infrastructure sectors. Throughout the event, operators, policymakers, infrastructure providers, and technology companies examined how rising bandwidth demand, artificial intelligence, and evolving geopolitical considerations are shaping the future of global connectivity.

Subsea infrastructure resilience emerged as a major theme throughout the conference. Tansy McCluskie, Deputy Director for Maritime Security at the UK Department for Transport, discussed the growing importance of protecting submarine cable systems as governments and operators respond to rising geopolitical concerns, physical infrastructure risks, and increasing dependence on global digital connectivity. Conversations throughout the session focused on strengthening resilience strategies, improving monitoring capabilities, and supporting the long-term security of critical communications infrastructure. Conversations examined how governments and industry stakeholders are strengthening resilience strategies, improving monitoring capabilities, and preparing for increasing geopolitical and operational risks.

As global bandwidth demand continues to increase, long-term infrastructure investment remains a priority across the subsea sector. Steve Holden, Chairman of ESCA and Network Operations Engineer Infrastructure (Subsea) at Meta, discussed the future of submarine cable systems and the planning required to support continued network growth. The session highlighted the importance of maintenance readiness, route diversity, and industry coordination as operators prepare for rising traffic demands driven by cloud adoption and AI workloads.

Policy and regulatory considerations also played an important role throughout the event. Grace Koh, Vice President, Government Relations, Legal at Ciena, examined how policymakers and industry leaders are working together to address regulatory challenges and strengthen collaboration around critical communications infrastructure. Discussions focused on the increasing strategic importance of submarine cable systems and the need for coordinated approaches to support future network expansion.

Regional connectivity growth and international infrastructure development remained important topics. Maja Summers, Director, Strategic Accounts and Network Solutions at WIOCC Group, explained how operators are expanding connectivity opportunities across emerging and established markets. Conversations explored infrastructure investment priorities, regional interconnection strategies, and the role submarine networks play in supporting broader digital transformation initiatives.

Beyond the conference program, Submarine Networks EMEA continued to serve as a meeting point for cable operators, investors, infrastructure providers, technology companies, and policymakers exploring partnerships and future growth opportunities across the subsea ecosystem. Assured Communications participated as a sponsor and exhibitor, reinforcing its continued engagement in subsea communications, infrastructure strategy, and mission-critical connectivity initiatives.

Submarine Networks EMEA is expected to remain an important forum for conversations around cable deployment, infrastructure resilience, network security, and the long-term future of global connectivity. As AI adoption accelerates and international bandwidth demand continues to increase, operators, policymakers, and infrastructure providers face growing pressure to strengthen communications networks while planning for future scalability. Submarine Networks EMEA will return to London, June 15–16, 2027. Additional information on the 2027 event can be found on the official Submarine Networks EMEA website.

The post Submarine Networks EMEA 2026 Examines the Next Phase of Global Connectivity and Subsea Infrastructure Growth appeared first on Data Center POST.

TL;DR

• Critical Cybersecurity Vulnerability: A new report reveals that 27% of the top 100 U.S. data centers fail to enforce basic email authentication (DMARC), leaving this vital infrastructure highly exposed to domain spoofing and phishing attacks.
• Systemic Infrastructure Risk: These widespread security gaps present a major systemic threat given the immense scale of the sector, which currently encompasses over 4,500 active U.S. facilities and more than 700 additional sites under construction.

# # #

As cyber threats increasingly target critical infrastructure, a new analysis from cybersecurity firm Red Sift exposes major email security weaknesses across the nation’s largest data center operators. Despite powering the U.S. digital economy, more than a quarter (27%) of the top 100 data centers fail to enforce basic email authentication, leaving the door wide open to domain spoofing and phishing.

The review assessed the top 100 U.S. data centers’ use of core protections like DMARC (Domain-based Message Authentication, Reporting, and Conformance), the frontline defense against email impersonation. The findings signal systemic risk:

Key findings:

• 27% lack enforcement: Policies set to “none” or left unconfigured, creating widespread spoofing exposure across critical infrastructure.
• 10% have no DMARC record at all: The highest-risk category, with zero protection against impersonation attacks.
• Just 6% use the key brand standard BIMI: Meaning 94% of data center brands lack visual inbox verification, making it far easier for attackers to mimic trusted senders.

The gaps are especially alarming given the sector’s scale and strategic importance. The U.S. now hosts more than 4,500 active data centers consuming roughly 176 TWh annually, roughly 4.4% of total U.S. electricity with more than 700 additional facilities under construction nationwide. Virginia alone accounts for over 665 sites, underscoring how concentrated and exposed this critical infrastructure backbone has become.

# # #

About the Author

Brian Westnedge leads alliances for Red Sift, an integrated cloud email and brand protection platform that automates BIMI and DMARC processes, making it easy to identify and stop business email compromise, and securing domains from impersonation to prevent attacks. He has worked in the DMARC space since its inception, has 20 years of experience in email deliverability, security and authentication.

The post New Report: Over One Quarter of Top U.S. Data Centers Lack Email Security Protections to Block Spoofing Attacks appeared first on Data Center POST.

TL;DR

• Explosive Market Growth: Driven by the rapid expansion of AI, cloud platforms, and high-density computing, the global data center refrigerant market is projected to reach $1.62 billion by 2035.
• The Push for Sustainability: Stringent environmental policies and global climate agreements are forcing operators to abandon traditional high-global-warming-potential (GWP) refrigerants in favor of low-emission, energy-efficient alternatives.
• Hyperscale and U.S. Dominance: Accounting for 40% of the market, hyperscale data centers are fueling the most rapid growth, particularly in the U.S., as major tech providers invest heavily in advanced thermal management to handle unprecedented AI workloads.

# # #

The global data center refrigerant market, valued at USD 633 million in 2025, is projected to experience robust growth, reaching an estimated USD 1.62 billion by 2035. According to newly released industry data, the market is set to expand at a compound annual growth rate (CAGR) of 9.6% over the forecast period of 2]026 to 2035.

This significant market transformation is being driven by the rapid expansion of global digital infrastructure, including cloud platforms and hyperscale facilities. As artificial intelligence (AI), big data analytics, and high-density computing continue to scale, the computational workloads are generating unprecedented thermal management requirements. Data center operators are increasingly turning to advanced refrigerants to ensure optimal energy performance, operational stability, and efficient heat control.

Sustainability and Regulatory Shifts Fuel Innovation

The industry is undergoing a major structural shift, moving away from traditional high-global-warming-potential (GWP) refrigerants toward low-emission, energy-efficient alternatives. Spurred by global climate agreements and stringent regional environmental policies, data center operators are prioritizing emission reductions, enhanced leak detection, and advanced lifecycle management to meet rigorous sustainability mandates while simultaneously lowering operating costs.

Market Segmentation Highlights: HFOs and Hyperscale Lead the Way

• HFO Refrigerants: Hydrofluoroolefins (HFOs) dominated the market in 2025 with a 67.7% share and are expected to grow at a CAGR of 10.2% through 2035. Their significantly lower global warming potential and high cooling efficiency make them the preferred choice for next-generation facilities amid the global phase-out of high-GWP options.
• Hyperscale Data Centers: Accounting for a 40% market share in 2025, the hyperscale segment is projected to grow at a rapid 10.4% CAGR from 2026 to 2035. Operated by major cloud and technology providers, these massive facilities require highly efficient cooling systems to manage the substantial heat loads generated by advanced AI and digital services.

U.S. Market Remains a Powerhouse

In 2025, the U.S. Data Center Refrigerant Market generated USD 176.3 million, holding a commanding 79% share of its regional landscape. U.S. growth is heavily supported by continuous investments in hyperscale expansions by leading tech firms, a surge in AI applications, and strict energy efficiency regulations driving the transition toward modern, low-GWP thermal management technologies.

Competitive Landscape and Strategic Initiatives

Key global players driving innovation in the market include Daikin Industries, Linde plc, Honeywell, Chemours, AGC, Dongyue, Zhejiang Juhua, Arkea, and Sinochem. These industry leaders are significantly increasing investments in research and development to enhance refrigerant efficiency, thermal stability, and high-density computing compatibility. Furthermore, market players are actively expanding production capabilities in high-demand regions and forming strategic partnerships with cooling system manufacturers to integrate next-generation, compliance-driven refrigerant solutions.

# # #

About the Author

Kanthamsetty Sri Lakshmi is a journalism graduate who turned her passion for writing into a thriving profession by curating insightful and well-researched articles across multiple sectors. She has established herself as a versatile content creator, focusing on the latest technologies, industry trends, and emerging business landscapes. Sri Lakshmi produces highly engaging and informative pieces. She continues to explore thought leadership topics and looks forward to publishing articles that provide readers with deep insights, actionable knowledge, and a forward-looking understanding of evolving markets and innovation.

The post Data Center Refrigerant Market to Reach $1.62 Billion by 2035 appeared first on Data Center POST.

TL;DR

• Open house announced: Duos Edge AI will host a Dumas open house on June 16 to showcase its newly operational Edge Data Center.
• Infrastructure in action: The facility delivers critical IT load capacity supporting high-performance compute, AI workloads, network infrastructure, and disaster recovery solutions.
• Regional connectivity boost: The project strengthens digital infrastructure in the Texas Panhandle and expands Duos Edge AI’s modular edge footprint.

# # #

Duos Technologies Group Inc. (Nasdaq: DUOT), through subsidiary Duos Edge AI, Inc., has announced the deployment of a newly operational Edge Data Center in Dumas, Texas, bringing advanced data center capabilities to an underserved Panhandle market with a compact footprint and zero operational water consumption.

The deployment serves as a regional hub for real-time data processing, enabling advanced educational tools, improved connectivity, and dependable infrastructure for Dumas ISD’s 4,300 students, as well as healthcare providers, carriers, enterprises, and the broader regional economy. Built to scale alongside community needs, the EDC supports long-term digital growth and economic development across the Panhandle.

“The Panhandle region has been crucial to Duos Edge AI’s journey as we deploy facilities across Texas with a specific focus on rural areas,” said Doug Recker, CEO of Duos Edge AI and Duos Technologies Group,Inc. “Our Dumas facility is already playing a key role in expanding access to advanced computing resources and driving growth throughout the region.”

Community leaders, education stakeholders, and industry partners are invited to tour the site at the open house on Tuesday, June 16, 2026, from 9:00 AM to 11:00 AM CDT at 421 W. 4th St, Dumas, TX 79029.

To learn more about Duos Edge AI and register for the open house, visit freeevite.com/event.php?e=QNAiq7mZalRfd87nV8tMOw.

The post Duos Edge AI to Host Dumas Edge Data Center Open House appeared first on Data Center POST.

Integrating IMS, DCMA Diagnostics, OFCI Visibility, TIA Governance, and Executive Risk Analytics

TL;DR

• The Shift to Predictive Governance: Traditional, reactive scheduling is inadequate for the complexity of multi-billion-dollar hyperscale AI data centers; operators must adopt a system of predictive, portfolio-level schedule governance.

• Managing Critical Constraints: Major project bottlenecks, specifically long-lead Owner Furnished Contractor Installed (OFCI) equipment and commissioning capacity, must be integrated into the schedule early to prevent late-stage conflicts and supply chain misalignments.

• Proactive Diagnostics and Delay Tracking: Instead of merely reporting past issues, operators should utilize DCMA 14-point diagnostics to continuously measure schedule health and enforce standardized Time Impact Analysis (TIA) for the consistent evaluation of delays and claims.

# # #

The rapid expansion of artificial intelligence infrastructure has transformed data center construction from a traditional capital project environment into a mission-critical delivery ecosystem. Hyperscale AI data centers are no longer isolated construction programs; they are capacity-enabling infrastructure platforms supporting cloud computing, machine learning workloads, enterprise applications, and national digital competitiveness.

As project portfolios grow in size and complexity, traditional schedule management practices are no longer sufficient. Data center delivery teams must manage overlapping construction sequences, long-lead OFCI equipment, commissioning readiness, power availability, contractor performance, claims risk, and executive reporting requirements across multiple campuses and regions.

In this environment, schedule governance must evolve from reactive reporting into predictive portfolio-level decision support.

The Challenge: Traditional Scheduling Is Too Reactive

Many large infrastructure programs still rely heavily on periodic schedule updates, milestone reports, and contractor narratives. While these tools remain necessary, they often identify risk after the schedule has already deteriorated.

Common issues include:

• Open-ended activities that weaken logic integrity
• Excessive hard constraints that distort the critical path
• Long-duration construction activities that reduce progress visibility
• Inconsistent General Contractor schedule integration into the Integrated Master Schedule
• Misalignment between OFCI supplier dates, need-by dates, and required-on-jobsite milestones
• Inconsistent Time Impact Analysis submissions
• Lack of standardized reason codes for delay tracking
• Limited portfolio-level visibility into schedule health trends

For a single project, these issues may appear manageable. Across a multi-billion-dollar hyperscale portfolio, they can create systemic delivery risk.

The solution is not simply “better scheduling.” The solution is integrated schedule governance.

1. Integrated Master Schedule Governance Must Be Standardized

The Integrated Master Schedule should serve as the single source of truth for executive milestone visibility, contractor coordination, supply chain alignment, commissioning readiness, and risk escalation.

However, IMS governance often breaks down when General Contractor schedules are not integrated consistently. Common problems include:

• Data dates that do not align between GC schedules and owner IMS schedules
• Activity IDs that do not follow Division 01 requirements
• Milestone logic that is linked incorrectly or not linked at all
• Inconsistent calendars and coding structures
• Missing relationships between procurement, construction, and commissioning activities

A strong IMS governance framework should include:

• Standard Activity ID requirements
• Required WBS structure
• Data date alignment rules
• Schedule coding standards
• Calendar governance
• Milestone integration requirements
• Baseline review checklists
• Contractor update expectations
• Portfolio-level reporting requirements

When the IMS is standardized, leadership can compare schedules across projects, identify recurring bottlenecks, and make better portfolio decisions.

2. DCMA 14-Point Diagnostics Should Be Used as a Governance Tool

DCMA 14-point analysis is often treated as a compliance exercise. In reality, it should be used as a scheduled health governance system.

Key metrics such as missing logic, open-ended activities, high lag, hard constraints, excessive duration, invalid dates, and logic density provide early signals of schedule reliability.

For example:

• A high percentage of open-ended activities may indicate weak schedule logic.
• Excessive hard constraints may artificially control milestone dates.
• High lag usage may hide true activity dependencies.
• Excessive durations may reduce the ability to measure progress accurately.
• Poor logic density may indicate insufficient schedule sequencing.

When these metrics are tracked consistently across a portfolio, leadership can identify which projects have reliable schedules and which schedules require corrective action before milestone risk escalates.

The goal is not to produce a perfect DCMA score. The goal is to improve confidence in the schedule as a decision-making tool.

3. OFCI Equipment Must Be Integrated Into the Schedule Earlier

Owner Furnished Contractor Installed equipment is one of the most critical schedule drivers in data center construction. Generators, switchgear, UPS systems, chillers, CRAH units, and other long-lead equipment can determine whether construction, commissioning, and turnover milestones remain achievable.

A common scheduling gap occurs when teams track either:

• supplier confirmed delivery dates, or
• required-on-jobsite dates,

but not both.

This creates limited visibility into whether equipment will arrive too early, too late, or at the wrong point in the construction sequence.

A stronger approach is to integrate three key milestones:

By comparing these dates, project teams can identify:

• early delivery storage risk,
• late delivery schedule risk,
• commissioning readiness exposure,
• installation sequence conflict,
• and procurement escalation needs.

This approach allows supply chain risk to become visible inside the schedule instead of being managed separately from project controls.

4. Time Impact Analysis Requires Standardization

Time Impact Analysis is one of the most important tools for evaluating delay events, yet it is often inconsistently applied.

Common issues include:

• contractors using different TIA formats,
• incomplete fragnet logic,
• unclear delay narratives,
• missing contemporaneous documentation,
• inconsistent reason codes,
• delayed submissions beyond contract timelines,
• and weak alignment between TIA findings and schedule updates.

A standardized TIA process should define:

• required submission timeline,
• fragnet requirements,
• narrative expectations,
• baseline schedule reference,
• impacted activity logic,
• reason code categories,
• contract review workflow,
• and approval or rejection criteria.

When standardized across projects, TIA governance improves claims evaluation, contractor accountability, and consistency in delay analysis.

5. KPI Dashboards Must Translate Schedule Data Into Executive Decisions

Project controls teams often produce large volumes of schedule data, but executive leaders need decision-ready information.

Effective dashboards should answer practical questions:

• Which milestones are slipping?
• Which projects have poor schedule health?
• Which contractors are repeatedly submitting weak schedules?
• Which OFCI equipment presents the highest risk?
• Which delays are weather-related, procurement-related, design-related, or contractor-caused?
• Which projects require recovery planning?
• Which risks require executive escalation?

Useful executive schedule dashboards may include:

• Schedule Performance Index
• Planned vs. Actual progress curves
• Milestone variance trends
• DCMA schedule health scores
• Risk log summaries
• TIA log with reason codes
• OFCI supplier date vs. ROJ variance
• commissioning readiness indicators
• recovery plan status

The value of a dashboard is not its appearance. Its value is whether leadership can make faster, better decisions from it.

6. Commissioning Must Be Treated as a Portfolio Constraint

In hyperscale data center delivery, commissioning is not simply a late-stage project activity. It is a portfolio-level capacity constraint.

Commissioning schedules depend on:

• construction completion,
• system turnover,
• equipment readiness,
• utility availability,
• testing resources,
• vendor support,
• integrated systems testing,
• and operational acceptance.

When commissioning is not integrated early into the IMS, project teams may discover conflicts too late.

Portfolio-level commissioning governance should include:

• commissioning milestone integration,
• system turnover sequencing,
• commissioning resource planning,
• cross-project team utilization analysis,
• readiness dashboards,
• and early warning indicators.

In large portfolios, optimizing commissioning resources across multiple projects can improve schedule performance and reduce idle time, bottlenecks, and turnover delays.

7. Division 01 Specifications Are the Foundation of Schedule Governance

Division 01 scheduling specifications define the rules of schedule management. If they are vague or inconsistent, each General Contractor may interpret requirements differently.

Strong Division 01 schedule language should clearly define:

• project naming conventions,
• Activity ID structure,
• WBS requirements,
• required calendars,
• data date rules,
• retained logic settings,
• allowable constraints,
• activity code requirements,
• weather day usage,
• TIA procedures,
• recovery schedule requirements,
• baseline review requirements,
• and weekly reporting expectations.

Standardized Division 01 requirements create consistency across campuses, improve contractor accountability, and reduce ambiguity in delay discussions.

8. Predictive Governance Creates Measurable Value

The greatest value of schedule governance is not reporting what happened. It is identifying what is likely to happen next.

A predictive schedule governance framework combines:

• IMS integration,
• DCMA diagnostics,
• OFCI variance tracking,
• TIA trend analysis,
• EVM KPIs,
• risk logs,
• commissioning readiness indicators,
• and executive escalation protocols.

When implemented effectively, this framework can:

• reduce milestone slippage,
• improve schedule health,
• reduce storage costs,
• improve contractor accountability,
• reduce claims exposure,
• improve commissioning readiness,
• and support faster executive decision-making.

For hyperscale AI data center portfolios, this is no longer optional. It is essential.

Conclusion

AI infrastructure delivery requires a new level of schedule governance maturity. Traditional project-level schedule tracking is not enough for multi-billion-dollar hyperscale portfolios where construction delays can affect capacity availability, operational readiness, and digital infrastructure growth.

The future of data center project controls will be defined by integrated, predictive, and portfolio-level scheduling systems.

The most effective organizations will be those that can connect construction schedules, supply chain data, commissioning readiness, contract requirements, and executive risk analytics into one coherent governance framework.

In hyperscale AI infrastructure delivery, the schedule is not just a project control tool.

It is a strategic operating system for mission-critical delivery.

# # #

About the Author

Pradeep Juturu is a PMI-certified Project Controls and Scheduling leader with 15+ years of experience delivering complex capital programs across hyperscale Data Centers, AI infrastructure, smart grid modernization, utility technology deployment, transportation infrastructure, and healthcare construction. He has a proven record leading Integrated Master Scheduling, Primavera P6 governance, DCMA 14-point diagnostics, Time Impact Analysis, Earned Value Management, OFCI supply chain integration, executive dashboards, risk logs, and portfolio-level schedule optimization. At Microsoft, He led regional schedule governance for an approximately $4B Data Center portfolio.

The post Predictive Schedule Governance for Hyperscale AI Data Center Delivery appeared first on Data Center POST.

By Andre Azevedo, Founder & CEO, Hype Telecom LLC

TL;DR

• The True AI Bottleneck: While capital and power receive the most attention, the defining constraint for the massive data center buildout is a projected shortfall of 340,000 qualified technicians by the end of 2026.
• The Limits of Traditional Hiring: The industry cannot simply recruit its way out of this gap; traditional apprenticeships take years, and the existing pool of fully qualified talent is vastly insufficient to staff multiple parallel megaprojects.
• A Scalable Training Solution: To rapidly qualify workers without sacrificing quality, operators must deploy centralized standard operating procedures, rely on local teams rather than labor arbitrage, and accelerate learning through structured knowledge transfer that pairs new hires with seasoned experts on live deployments.

# # #

Most coverage of the AI infrastructure buildout concentrates on the wrong constraints. Capital is not the binding factor: the hyperscaler capex cycle now driving this expansion is supporting a data center buildout valued at approximately $700 billion in 2026. Power has received considerable attention, and justifiably so; BloombergNEF projects U.S. data center demand could reach 106 gigawatts by 2035, a 36% upward revision in only seven months. Chips and permitting have likewise been thoroughly examined.

The constraint that will, in practice, determine which 2026 commissioning dates hold and which slip is none of those. It is the availability of qualified personnel, the technicians who pull cable, splice fiber, terminate copper, dress racks, commission equipment, and remain on site through the night when something fails. By the end of 2026, the U.S. data center industry is projected to be short approximately 340,000 of them, even as the broader buildout opens an estimated 650,000 positions across construction and operations.

The CEO of Randstad,  the world’s largest recruitment firm, stated the matter directly on CNBC earlier this year: the real constraint on global technology growth is not chips, energy, or capital, but the severe scarcity of the specialized talent required to build it. The most recent Uptime Institute survey reflects this directly. Fifty-three percent of operators report difficulty finding qualified candidates, up from 38% in 2018. A separate Uptime construction survey found 52% of firms reporting staffing-driven disruptions, against 43% the prior year. Forty-five percent of contractors experienced at least one project delay in the past year attributable to staffing constraints.

This is not a forecasting exercise; it is a structural gap, and it widens with every quarter. The question that matters is not where to find 340,000 people who already have the skills. It is how to qualify them faster than the traditional pipeline allows, and how to do so without sacrificing the consistency that hyperscale work demands.

Why hiring alone will not close the gap

The reflex response to a labor shortage is to recruit harder. In this sector, that reflex meets a hard ceiling. A journeyman electrician requires four to five years of training. A commissioning engineer with hyperscale experience requires longer. Approximately one in four tradespeople globally is at or near retirement age. The pool of fully qualified, immediately deployable technicians is not large enough, and it cannot be enlarged on the timeline the buildout requires.

The shift in scale makes this concrete. A decade ago, peak crews at major data center facilities were typically capped at around 750 workers. DataBank’s Red Oak campus is expected to reach a peak between 4,000 and 5,000 workers in early 2026. The arithmetic does not hold when the industry must staff dozens of such megaprojects in parallel, drawing from a finished-talent pool that simply does not contain those numbers.

If the qualified workforce cannot be hired into existence quickly enough, it has to be trained into existence quickly enough. That reframes the problem. The constraint is not a recruiting problem; it is a training-throughput problem. And training throughput is something an operator can actually engineer.

The hidden problem: inconsistency across geographies

There is a second constraint that receives even less attention than the first. As field-services work spreads across regions — Northern Virginia, Dallas-Fort Worth, Greater Phoenix, and increasingly across borders into Latin America and Europe — the standard to which the work is executed begins to drift. A splice performed in one metro is documented one way; the same task in another metro is documented differently. Acceptance criteria are interpreted locally. Photo evidence varies. Labeling conventions diverge.

For a hyperscale customer, that drift is unacceptable. A hyperscaler enforces the same build standard in every facility it operates, and it expects its partners to do the same. An operator that cannot guarantee identical execution in every geography it serves is not a scalable partner; it is a collection of local teams that happen to share a name.

So the real objective is twofold: qualify new technicians faster, and qualify them to a single global standard. Those two goals are usually treated as a trade-off: move fast or maintain consistency. They do not have to be.

Centralized standards, local teams, structured knowledge transfer

The model that resolves the trade-off has three components, and none of them involves moving labor across borders.

• Centralized standards. SOPs, acceptance criteria, labeling conventions, safety procedures, and photo-evidence requirements are defined once, centrally, and applied everywhere. The standard does not belong to a region; it belongs to the company. A technician in São Paulo and a technician in Phoenix execute against the identical specification.
• Local teams. The work in each geography is performed by technicians based in that geography: recruited, employed, and developed locally, under local labor and licensing frameworks. This is not labor arbitrage. It is local capacity, held to a global standard.
• Structured knowledge transfer. This is the component that actually accelerates qualification. New technicians are paired directly with experienced ones on live work, rather than being trained in isolation and released. That pairing is supported by a documented body of procedure, written SOPs and instructional video covering how each activity is performed the company’s way. The senior technician carries the company’s standard; the documentation makes that standard explicit and repeatable; the junior technician absorbs both at once, on real deployments.

The effect is that a new technician does not enter the field as an unknown quantity. They enter alongside someone who already embodies the standard, with reference material that removes ambiguity about how the work should be done. The ramp from hired to genuinely productive shortens, because the new technician is never learning the standard and the job separately — they learn them together. Operators that run this model also tend to see stronger retention, because technicians developed inside a clear system, with visible support, are less likely to leave it.

Why this is a competitive question, not an HR question

Treating workforce development as an HR line item is the most common and most expensive mistake in this segment. A multi-trillion-dollar global data center spending cycle through 2030 will not be delivered by organizations that regard training as overhead. It will be delivered by organizations that treat training throughput as core operational capacity and as deliberately engineered as their supply chain or their project controls.

For operators and developers selecting field-services partners for the 2026–2027 wave of activations, three questions are worth posing before the next site goes vertical:

1. Does the partner execute to a single documented standard across every geography, or does quality depend on which local team happens to be assigned?
2. How does the partner qualify new technicians? Is it through structured pairing with experienced staff and documented procedure, or by hiring finished talent that the market cannot actually supply?
3. Is workforce development treated as a board-level capability, or as an administrative function? The answer predicts whether the partner can scale with the buildout or will become a bottleneck within it.

A global industry needs a global standard

The AI infrastructure buildout is often described as a race for power, chips, and capital. Underneath those, it is a race to qualify a workforce: fast enough to meet demand, and consistently enough to meet the standard. Those two requirements are not in tension when training is engineered properly: centralized standards make consistency portable, local teams keep the work lawful and grounded in each market, and structured knowledge transfer compresses the time from hired to capable.

The operators who treat the qualification of their workforce as a core engineering discipline will be those whose commissioning dates hold. For the remainder, the consequences will be measured not in apologies but in delayed activations, contractual exposure, and the compounding cost of every missed milestone.

# # #

About the Author

Andre Azevedo is the founder and CEO of Hype Telecom LLC, a U.S.-headquartered telecommunications infrastructure and field-services company based in Winter Park, Florida. The company supports rack-and-stack, structured cabling, fiber installation, FLM/Smart Hands, and network deployment work across more than 20 U.S. states, with affiliated operations across Brazil and Latin America and operations expanding into Europe in 2026. With more than 15 years of entrepreneurial experience and an LL.M. in International Business Law from Queen Mary University of London, Andre leads Hype Telecom’s North American expansion and its workforce-development model. Learn more at www.hypetelecom.net.

The post The 340,000-Worker Question: Why the AI Buildout Will Be Won or Lost on Training appeared first on Data Center POST.

TL;DR

• AI infrastructure planning is shifting from future strategy to immediate execution, with discussions focused on compute demand, interoperability, edge deployment, and the network requirements needed to support increasingly data-intensive workloads.
• Energy access, power availability, and long-term infrastructure resiliency emerged as critical priorities as operators, policymakers, and investors evaluate how to support rapid digital infrastructure growth while maintaining operational stability.
• Public trust, permitting, and digital infrastructure policy are becoming increasingly important to project development, reinforcing the need for stronger communication, community engagement, and collaboration between public and private stakeholders.

# # #

ITW 2026, held May 18-21 at the Gaylord National Resort & Convention Center in National Harbor, Maryland, brought together  global leaders from across the telecom, cloud, digital infrastructure, subsea network, and investment sectors.  Across keynotes, panels and networking sessions, industry stakeholders explored how artificial intelligence, infrastructure investment, energy availability and policy considerations are reshaping the future of global connectivity. Throughout the week, operators, hyperscalers, policymakers, and investors discussed strategies for adapting communications networks and digital infrastructure to support rising AI demand, increasing capacity requirements, and the continued evolution of the global digital ecosystem.

Artificial intelligence and infrastructure readiness emerged as a major focus throughout the conference. During the keynote panel “Unleashing the 3 Pillars of AI,” Arno van Huyssteen, VP Global Telecom & AI Service Provider at Nscale, examined how AI is reshaping infrastructure strategies, accelerating demand for compute and connectivity, and influencing investment decisions across telecom and digital infrastructure ecosystems. Discussions explored how operators and infrastructure providers are preparing networks to support increasingly data-intensive workloads as enterprise expectations continue to evolve.

Policy and international coordination also shaped conversations across the Digital Infrastructure Policy & Investment Summit. Anjana Modi, Acting Principal Deputy Assistant Secretary, Bureau of Cyberspace and Digital Policy at the U.S. Department of State, discussed the strategic importance of AI innovation and the role digital infrastructure plays in supporting economic competitiveness, national priorities, and long-term resiliency. Broader discussions throughout the summit focused on how governments and industry stakeholders are navigating geopolitical shifts while planning for future infrastructure growth.

Energy supply and infrastructure scaling remained important themes as demand for AI capacity continues to grow. During the panel “Investing in Energy to Power Digital Growth,” Tate Cantrell, Chief Technology Officer at VERNE, highlighted the growing need for reliable energy strategies, grid resiliency, and infrastructure planning to support increasing digital demand. Conversations throughout the session explored how operators are balancing performance expectations with long-term infrastructure readiness as AI workloads continue to expand.

Public perception and permitting challenges surrounding infrastructure deployment also became a major topic of discussion. Ilissa Miller, Founder and Chief Executive Officer of iMiller Public Relations, participated in the panel “Debunking the Data Center Misinformation Dilemma,” where she addressed the role communication, transparency, and community engagement play in navigating permitting processes and addressing misconceptions around digital infrastructure development. The session explored how public trust, land use concerns, and local engagement increasingly shape infrastructure planning and project execution.

Artificial intelligence infrastructure deployment and operational readiness were also explored during the “ITW Meetup: AI & Data Infrastructure” session. Doug Recker, CEO of Duos Edge AI, led discussions around designing fit-for-purpose AI-ready data centers and the infrastructure requirements needed to support growing enterprise demand for low-latency AI workloads. Conversations focused on deployment flexibility, infrastructure readiness, and how operators are preparing environments capable of supporting increasingly distributed AI applications.

Sustainability, operational efficiency, and responsible infrastructure planning also shaped broader agenda discussions. During the panel “Building Responsible Data Centers: Delivering Sustainable Operations at Scale,” Sriram Natarajan, Chief Technology Officer at Hypertec, examined how operators are adapting infrastructure strategies and operational models to improve efficiency while supporting continued digital growth. Discussions explored long-term sustainability planning, infrastructure resilience, and the operational considerations tied to rising compute density requirements across data center environments.

ITW is set to return on May 10-13, 2027 at the Gaylord National Resort & Convention Center in National Harbor, Maryland. Information on future events is available through the official ITW website: www.internationaltelecomsweek.com

The post ITW 2026 Highlights AI Growth, Infrastructure Strategy, and the Evolving Connectivity Ecosystem appeared first on Data Center POST.

TL;DR

• The Modern Economy’s “Oil Fields”: Data centers are the foundational engines of 21st-century prosperity; regions that embrace them will secure vital talent and investment, while those that resist risk falling behind.
• Accelerating Medical Breakthroughs: In healthcare hubs like Greater St. Louis, immense computing power is essential for processing massive biological datasets, which speeds up drug discovery and precision oncology.
• Responsible Community Development: To build public trust, data center projects must prioritize transparency and offer direct local benefits, such as investments in grid modernization and innovative utility-sharing agreements.

# # #

When oil was first discovered in Texas in the early 1900s, many people saw it as dirty, disruptive, and uncertain.  Texans were rightfully concerned about land use, industrialization, and whether the economic promises promoting the black gold breakthrough would ever materialize. Yet history proved that oil would become one of the foundational drivers of America’s global leadership.

Today, data centers occupy a similar position in our economy.

The debate surrounding the proposed Festus data center in Jefferson County, MO has revealed understandable concerns from residents about transparency, environmental impact, and energy consumption. Those concerns deserve to be heard and addressed seriously. But we must also recognize the larger reality: data infrastructure is becoming as essential to the 21st-century economy as oil fields were to the 20th.

If America intends to remain a global leader in artificial intelligence, healthcare innovation, cybersecurity, and advanced research, we can’t afford to reject the infrastructure that powers those industries.

Data centers are no longer simply warehouses full of computers. They’re the engines behind nearly every major technological breakthrough happening today. They support artificial intelligence models, advanced manufacturing, logistics systems, financial networks, and medical research. Increasingly, they will determine which sections of the country prosper economically and which are left behind.

For the greater St. Louis metropolitan area, this conversation is especially important.

For over 130 years, Greater St. Louis has positioned itself as a center for healthcare, medicine, and academic research. We’re home to world-class hospitals, universities, bioscience companies, and research institutions. The next generation of medicine, from protein synthesis and precision oncology to AI-assisted diagnostics and designer pharmaceuticals, will be facilitated by immense computing power.

Modern drug discovery requires researchers to process massive biological datasets, model protein interactions, and train increasingly sophisticated artificial intelligence systems. These breakthroughs are computationally intensive. Faster access to computing infrastructure directly accelerates the pace of discovery.

In practical terms, that means data centers can help shorten the timeline for developing new therapies, improving patient outcomes, and strengthening America’s global medical competitiveness.

Regions of America that embrace this infrastructure will attract investment, talent, and exciting transformation. Regions that reject it risk watching opportunity move elsewhere.

That doesn’t mean citizens should simply accept any data center construction project without safeguards. Responsible development matters. In fact, if Missouri wants to lead in this space, we should demand that these projects become models for how modern infrastructure can coexist with community and environmental interests.

There are reasonable solutions available.

First, large-scale data centers should be required to incorporate renewable energy offsets or long-term investments in clean energy generation. If these facilities consume significant power, they should also help expand and modernize their local energy grids.

Second, the locales that host these projects should directly benefit from them. One innovative approach would be to require utility-sharing agreements that reduce energy costs for area residents and businesses. If a data center becomes a major economic engine for a community, the people living there should share in the upside.

Third, transparency must improve. Public trust is essential. Companies and municipal governments should communicate openly about environmental impact, water use, energy demand, and long-term economic benefits before projects are approved.

The United States is entering a global competition centered around artificial intelligence, biotechnology, and advanced computing. China, Europe, and other states across America are investing heavily in digital infrastructure. Missouri has an opportunity to become part of that future rather than watching it happen elsewhere.

The truth is simple: data centers are poised to be the oil fields of the modern economy.  The cities and states that understand this reality will help shape and contribute to the next century of American prosperity.

# # #

About the Author

Dr. David Lenihan, Ph.D., J.D., MBA is the Past President and CEO of Ponce Health Sciences University in St. Louis.  He’s also the CEO/Co-Founder of Tiber Health.  His POVs on innovation, management, and entrepreneurship have been featured in Fast Company, Startups Magazine, Management Today, Forbes, Ticker News, and many more.

The post To Boost Crucial Developments in Medical Research, Here’s How Missouri’s Data Center Infrastructure Can Lead, Not Lag appeared first on Data Center POST.

Data Center Post had the opportunity to connect with Nadya Melic, Vice President Product & Marketing at FLAG, where she focuses on how global connectivity infrastructure is designed, packaged and delivered to support the way customers actually build and run digital platforms today.

Melic spent over 20 years across the telecommunications and technology sector, working across product leadership, commercial strategy and customer-facing roles. Her career has taken her through Microsoft, BT, Telstra and Vodafone Business, where she worked closely with enterprise customers, carriers and hyperscalers as their architectures evolved from early international connectivity through to today’s distributed, cloud-led environments.

She led the transition from Global Cloud Xchange to FLAG when she joined the company in 2024, and since then, her focus has been on aligning our portfolio more closely with how customers consume global infrastructure today, integrating subsea, terrestrial and data centre assets into solutions that prioritise resilience, flexibility and scale.

How did you come to your current position with FLAG?

I entered the industry at a time when engineering and network roles were still very male-dominated, and that shaped how I think about visibility, representation and long-term career progression. Early on, it became clear that success in this sector isn’t just about technical expertise but instead about understanding customers, building credibility, and learning how complex systems behave in the real world.

Across my roles at BT, Telstra and Vodafone, I worked closely with customers as their needs shifted from relatively static, hub-based networks to far more distributed, latency-sensitive environments. That experience has heavily influenced how I approach product and market strategy today. I don’t see infrastructure as a set of assets. It is something that has to adapt continuously as customer architectures evolve.

At FLAG, that perspective has been central to how we think about portfolio development and positioning, ensuring what we build is genuinely usable, scalable and aligned with where demand is actually moving.

What is FLAG’s core mission?

At FLAG, we believe connectivity is a fundamental human right, and our mission is to build the infrastructure that makes that a reality.

As demand for AI, cloud and IoT accelerates, many organisations are still constrained with fragmented networks, limited route choice and architectures that were designed around a small number of global hubs. That creates risk, limits scalability and makes it harder for customers to adapt when conditions change.

Our role is to solve this by providing carrier-neutral infrastructure with built-in path diversity and resiliency, ensuring critical data continues to flow even when the unexpected happens.

We do this by integrating subsea cable systems with terrestrial networks, connecting to edge data centres and intelligent routing, creating a more flexible network layer that supports global scale.

Ultimately, our goal is to enable customers to scale, move traffic intelligently and maintain performance even when routes, regulations or demand patterns shift.

How would you describe the current state of the global digital infrastructure market in terms of challenges and opportunities for your business?

One of the biggest challenges is that traditional connectivity models, built around fixed routes and a small number of global hubs, no longer reflect how data is created, consumed and transferred

AI-driven workloads, hyperscale data centre expansion and regional cloud growth are dramatically increasing traffic volumes, placing strain on infrastructure that wasn’t designed for this level of scale or complexity.

At the same time, geopolitical and regulatory dynamics are playing a much bigger role in network planning. Route diversity, data sovereignty and permitting constraints now directly influence where customers place capacity and how they design resiliency

From our perspective, this is also where the opportunity sits. Customers are moving away from thinking about individual assets and towards infrastructure as a platform that delivers outcomes, whether that’s predictable latency, the ability to scale quickly, or the confidence that traffic can be rerouted when conditions change.

That shift is driving demand for carrier-neutral, route-rich networks and has directly informed investments such as our dedicated fibre pair on the ECHO subsea cable, connecting this with our investments from India to Singapore creating a high-capacity route between South Asia and the US in response to customer demand for scale and resilience across that corridor.

Who do you see as your primary customer base today, and how has that evolved over the past few years?

Our customer base is primarily made up of hyperscalers, telecom carriers, as well as large enterprises and financial services companies that rely on high-performance global connectivity to operate at scale.

These organisations depend on resilient, low-latency infrastructure to support everything from cloud services to real-time data exchange across regions, including Asia, the Middle East, Europe and the USA.

What’s changed in recent years is less about who those customers are and more about how they consume connectivity. Traditionally, demand was driven by relatively linear, point-to-point traffic between major hubs. Today, customers are operating in far more distributed environments, with workloads spread across multiple regions.

As a result, we’re seeing a shift towards integrated, carrier-neutral solutions that deliver outcomes, whether that’s scalability or flexibility, rather than simply access to capacity.

There’s also growing demand from emerging markets, where improved route diversity is enabling greater participation in the global economy beyond traditional centres.

What are the top priorities or concerns you hear from your customers, and how is FLAG addressing them?

The main priorities we’re hearing from customers today centre on scale and performance. As data demand continues to surge, organisations need infrastructure that can deliver high-capacity, low-latency connectivity across increasingly complex global environments.

Many are also looking to reduce reliance on single routes or legacy hubs. Outages, congestion and geopolitical disruption are no longer hypothetical risks – they’re things customers actively plan around.

In response, we’re investing heavily in expanding and diversifying our network across high-growth corridors such as the US, India, Southeast Asia and the Middle East – while strengthening links back into Europe. This is helping us build more globally integrated connectivity options, aligned with evolving data centre footprints and changing customer expectations.

Customers don’t want to stitch together subsea, terrestrial and data centre connectivity themselves. They expect those layers to work together as one and be provided by trusted providers.

Are there emerging customer segments or industries that you see as growth areas for your business?

Demand based on hyperscale AI connectivity remains a core growth area, particularly as they expand regional availability zones and invest in new data centre hubs outside of traditional markets.

We’re also seeing strong growth from data centre operators, carriers as well as enterprises running increasingly latency-sensitive applications. These organisations are looking for predictable performance and direct, resilient connectivity between regions to support workloads such as AI, real-time services and large-scale data replication.

From a geographic perspective, markets like India are becoming critical digital hubs, driven by both local demand and their role as aggregation points within global networks. This is creating opportunities to support a broader mix of regional players, as well as global organisations expanding into high-growth markets.

How are you aligning FLAG’s strategy to address sustainability and regulatory requirements in the coming years?

At FLAG, sustainability and regulatory alignment are key as we scale to meet growing global digital demand.

From an environmental perspective, we’re driving measurable reductions in emissions through high-efficiency optical platforms that reduce energy consumption per transmitted bit, alongside optimised network architecture that minimises failover inefficiencies.

This has already helped reduce emissions from 19,656 tCO₂e to 17,768 tCO₂e. We also lower embedded carbon by focusing on reductions on our fully owned and newly invested route-diverse subsea network, avoiding the duplication often seen in fragmented models.

Transparency and accountability are equally important. We provide full GHG Protocol-aligned reporting across Scope 1-3 emissions, enabling customers to better understand and manage their own connectivity footprint.

How is FLAG fostering partnerships to create value for customers and enhance your competitive position?

Partnerships are central to how we create value for customers and strengthen our position in an increasingly complex global landscape. We recognise that the traditional model of building own infrastructure end-to-end is neither realistic nor desirable.

Instead, we work within multi-partner ecosystems that combine our subsea assets with partners’ terrestrial networks and subsea investments to deliver seamless, end-to-end connectivity.

This allows us to extend reach into new markets, navigate regulatory complexity more effectively, and give customers greater reliability through diversified routing.

We also work closely with customers and industry partners as anchor tenants on new routes, helping align infrastructure investment with real demand and accelerate deployment in a way that benefits the wider ecosystem.

What long-term goals or initiatives are you most excited about, and how do you envision the industry evolving over the next decade?

What I’m most excited about is how our Vision 2030 strategy translates into very practical changes in how customers design and consume global connectivity.

At its core, Vision 2030 is about aligning our network and product mix with where demand is genuinely shifting, not where the industry has historically been anchored. Customers are moving away from centralised architectures and single-route dependencies, and they’re looking for infrastructure that can flex as workloads, regulations and traffic patterns evolve.

A key focus for us is making connectivity easier to deploy and scale. That means bringing subsea, terrestrial networks, landing stations and data centres together into coherent, end-to-end architectural environments, rather than forcing customers to manage those layers separately. When those pieces work as one, customers can expand capacity, add regions or reroute traffic without having to constantly redesign their network.

Over the next decade, I expect the industry to become far more distributed and demand-led. Growth in AI, cloud and digital services will continue to drive data volumes, but success will depend less on sheer scale and more on adaptability and optionality and how quickly networks can respond to new centres of activity, regulatory constraints and resilience requirements. Vision 2030 is really about positioning FLAG to support that shift in a way that’s usable, resilient and commercially relevant for customers.

What are FLAG’s core products or services?

Our core offering is end-to-end digital infrastructure and connectivity services designed to support global data flows at scale.

At the heart of this is our subsea and terrestrial network, through which we deliver high-capacity bandwidth and connectivity solutions, including leased capacity, dark fibre, and Layer 2 and 3 services for carriers, hyperscalers and enterprises.

Alongside this, we offer IP services such as IP transit, Ethernet and remote peering, enabling customers to connect securely and efficiently across global networks.

We also provide modular edge data centre solutions and cable landing station solutions, giving customers the space, power and infrastructure to deploy compute and storage closer to where data is created and consumed.

What ties these together is the way they’re integrated. Customers aren’t looking for standalone products. They want infrastructure that works across layers, reduces operational complexity and supports long-term scalability.

What markets do you offer FLAG’s solutions to?

Our network spans the US, Asia, the Middle East, and Europe – regions that form the backbone of today’s digital economy. These corridors support high-volume data flows and remain critical for hyperscalers, carriers and enterprises operating at an international scale.

We’re seeing particularly strong growth in Asia and the Middle East, where rising demand for cloud, AI and digital services is driving the need for high-capacity connectivity. At the same time, our presence in Europe and the US enables us to support customers with truly end-to-end, intercontinental infrastructure.

Our strength lies in solving some of the toughest connectivity challenges. By focusing on markets where demand for digital infrastructure is growing fastest, we’re able to expand access where it’s needed most

What upcoming industry events will you be attending?

We are speaking at Submarine Networks World in Singapore in September. The panel is called location, location, location, the next hot spot for submarine investments. We will also be attending Capacity Europe, ITW Asia, PTC’27, and Capacity Middle East.

Is there anything else you want to highlight?

• FLAG’s 2025 Rebrand
• FLAG’s Fiber Links Around the Globe
• 2025: A Year of Momentum at FLAG

# # #

About FLAG

FLAG is one of the largest privately owned subsea cable operators globally. With a network spanning 180+ countries, the company delivers high-speed digital connectivity via seven subsea and six terrestrial cables. Serving hyperscalers and enterprises, FLAG offers flexible capacity solutions across key routes in Asia, the Middle East, Europe, and the USA ensuring reliable, neutral connectivity for mission-critical data. Additionally, the company provides scalable modular data centres for high-performance computing and storage at edge locations and cable landing stations.

Learn more at flagtel.com or reach out directly to flag@theflywheelers.com.

The post Executive Profile: A Conversation with Nadya Melic, Vice President Product & Marketing at FLAG appeared first on Data Center POST.

By Matt Merbach, Vice President, National Solution Services (NS2) at Faith Technologies Incorporated (FTI)

TL;DR

• While public attention focuses heavily on constructing data centers, the real overlooked challenge is keeping these mission-critical facilities operational amid skilled labor shortages and complex environmental demands.
• To ensure constant uptime, operators must adopt a comprehensive, lifecycle-based “end-to-end” support system that integrates specialized professionals across engineering, mechanical systems, fiber networks, and cybersecurity.
• Technology alone is insufficient; success relies on aligning with dedicated service partners who prioritize continuous internal workforce training, offer 24/7 emergency support, and utilize modular manufacturing for safer, more consistent deployments.

# # #

What makes headlines when it comes to data centers? The large number currently under construction across the country. Their locations and the reaction of local communities. Their perceived power and water usage.

So much attention is paid to building data centers. What’s often overlooked ─ the story behind the headline ─ is the task of keeping data centers on once they’re up and running. What plans or service agreements can ensure mission-critical data center infrastructure operates efficiently and constantly?

No doubt, the data center world has service needs. And there’s a revolutionary solution. Yet, before exploring that, let’s quickly review three vital things that influenced the creation of this solution.

First, let’s consider all the demands within the data center space: skilled labor shortages, tight schedules, communication challenges, safety concerns, project complexity, public perception issues and more. As previously mentioned, other priorities overshadow data center service, and that’s a mental hurdle to overcome.

Second, not every data center has the same goal. A world-class service solutions strategy is flexible enough to adjust to solve different challenges. Having integration expertise not only supports modern state-of-the-art systems; it allows for adjustments tailored to the needs of the data center that ensure it achieves that company’s goals.

Third, compared to other commercial buildings or industrial construction jobsites, data centers require more. More communication. More planning. More oversight. More emphasis on safety. Contractors with operating experience within these strictly controlled environments often bring a mindset that prioritizes productivity, operational efficiency and superior service.

Let’s now dive into a revolutionary solution to meet data centers’ growing service needs. When the demands are many, goals are changing, the pressure is on and the mission is critical, it’s still possible to keep data centers on and achieve true peace of mind.

How is “End-to-End” the Answer?

Ideally, efforts to keep a data center up and running are part of an overall, comprehensive lifecycle-based support system. A system that may include an engineering team, integration experts, a real estate team, a design team, construction and safety professionals—all who know the equipment and the systems inside and out.

Why are there so many people with such a variety of skills? Because data center service scopes of work include electrical systems, mechanical systems, control systems, fiber optic networks, UPS and battery health/maintenance, equipment lifecycle analysis, energy and power quality audits, on-demand cybersecurity and more.

That is what’s required to provide service designed to keep modern, mission-critical data center infrastructure running efficiently throughout its lifecycle.

That is an end-to-end solution.

Teams Must Complement the Technology

The right data center service solutions strategy can help prevent downtime, predict performance by using monitoring and analytics, and address issues before they occur. The technology is comprehensive and impressive.

However, wise use of tech alone isn’t a strategy, it’s simply using a tool. Multi-disciplined technical experts embedded in the field are needed to monitor systems, provide proactive maintenance, resolve challenging issues, handle complex upgrades, coordinate warranties and fully support data centers.

The best field service representative teams aren’t contractors; they’re partners who understand and complement the strengths of the projects, they’re dedicated to onsite service, they reply promptly, and they offer around-the-clock emergency support.

When Core Values Align

Partnership becomes “culture fit” when field and support teams are aligned with the core values of data center projects. Some contractors are extremely selective, only working with end users that align with their philosophy. Culture fit is an underrated aspect of data center development and service that’s just as important as capabilities.

Want to further streamline a data center service solutions strategy? Look for two things: a partner that prioritizes education, and a partner that designs and manufactures modular products. Here’s why:

1. From apprenticeships to mentoring to continuing education courses, internal training programs create skilled workers and successful careers. For example, the field service representative teams I work with are trained through an internal program for licensed electricians (journeyman and master level), BICSI-certified technicians, and master technicians.
2. Moving labor off construction sites increases safety and consistency while allowing for rapid deployment to hyperscale data centers and other mission-critical facilities nationwide. Search for an Original Equipment Manufacturer that specializes in manufacturing electrical and modular solutions.

In my position at FTI’s National Solution Services, we care for assets at every step of the lifecycle. Plus, we service the switchboards and power distribution modules we manufacture at the highest possible level. Of course, maintenance is easier when the products are better.

Our newest offering, FTI ON, is a value-added service that provides comprehensive support specifically for data centers. We deliver what the name promises: we keep data centers on, tailoring support for each one along the way.

AI continues to boom, and more data center infrastructure is needed. The investment is huge. The stakes couldn’t be higher. And everything must be kept up and running at peak efficiency. A true end-to-end solution may sound complex and revolutionary, but it’s the smart way ─ perhaps even the only way ─ to achieve what the industry now demands.

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About the Author

Matt Merbach, Vice President, National Solution Services at Faith Technologies Incorporated (FTI), has extensive expertise in holistically managing service for data centers and other emerging technology. He creates comprehensive strategies and approaches tailored to support each data center resulting in optimized uptime and efficiency. Matt’s strategic leadership in asset management and preventative/reactive maintenance ensures seamless data center operations.

The post End-to-End: A Revolutionary Solution to Meet Data Center Service Demand appeared first on Data Center POST.

TL;DR

• Data Centre LIVE: The London Summit 2026 explored how artificial intelligence, sustainability and increasing compute demand are reshaping digital infrastructure strategy, operations and long-term investment priorities.
• Speakers from organizations including NTT Global Data Centers, Equinix, STACK Infrastructure, DXC Technology and GEICO discussed topics ranging from global expansion planning and hyperscale growth to AI readiness, cybersecurity, and emerging technologies.
• The event brought together data center operators, enterprise leaders, infrastructure providers, and technology companies to examine how the industry is adapting to rising demand for resilient, scalable, and energy-conscious digital infrastructure.

# # #

Data Centre LIVE: The London Summit 2026 brought together more than 1,000 senior executives across the digital infrastructure ecosystem in London on May 20–21 for discussions focused on AI, sustainability, resilience, and the future of data center growth. Through keynote sessions, fireside chats, and executive panels, the summit explored how operators, enterprises, and infrastructure providers are adapting to rising compute demand, evolving operational requirements, and increasingly complex digital infrastructure strategies.

The intersection of artificial intelligence and digital infrastructure strategy emerged as a major focus throughout the summit. Alex Bennett, Global Strategy Realisation & Transformation Director at NTT Global Data Centers, discussed how operators are approaching long-term growth planning, balancing resilience and scalability while adapting infrastructure strategies to support changing enterprise and hyperscale requirements. Discussions explored how global portfolios are evolving to meet increasing digital demand without sacrificing operational efficiency.

The growing role of emerging technologies in shaping infrastructure requirements was also explored across multiple sessions. Petrina Steele, Global Lead – Emerging Technologies at Equinix, examined how technologies such as AI and quantum computing are influencing infrastructure planning, connectivity requirements, and long-term digital ecosystem development. Speakers explored how operators are preparing for more data-intensive workloads while supporting increasingly distributed environments.

AI’s growing impact on infrastructure design, performance, and operations also shaped broader agenda discussions. Amy Daniell, Senior Vice President, Strategy & Development at STACK Infrastructure, contributed perspectives around how operators are preparing for rapid growth in compute demand, addressing infrastructure scalability challenges and evaluating the long-term implications of AI on data center strategy, capacity planning and operational models.

Security and operational resilience remained important themes as digital infrastructure becomes increasingly interconnected. Richard Wilkinson, Chief Technologist – Europe GIS at DXC Technology, examined cybersecurity preparedness, evolving infrastructure risks, and the technologies organizations are deploying to secure increasingly complex environments supporting AI-driven operations.

To learn more about Data Centre LIVE and to stay updated on future events, visit the events page here.

The post Data Centre LIVE: The London Summit 2026 Explores AI, Scale and the Future of Digital Infrastructure appeared first on Data Center POST.

Originally published on Medium.

Ilissa Miller, the Founder and CEO of iMiller Public Relations, brings a unique perspective to strategic communications that spans from her early training as an opera singer to her tenure as a local elected official. These diverse experiences taught her that messaging alone is insufficient for driving meaningful change; true progress requires understanding complex systems, taking proactive ownership, and aligning differing perspectives across business, policy, and community lines.

When addressing innovation within large organizations, Miller observes that businesses rarely suffer from a lack of ideas. Instead, innovation is frequently stifled by internal friction, unclear priorities, and bureaucratic misalignment. To overcome these hurdles, she emphasizes the importance of balancing structure with creative freedom, requiring teams to thoroughly understand existing processes before attempting to improve them. Furthermore, she distinguishes between the role of management in maintaining stable systems and the role of leadership in creating the alignment and permission needed to move new concepts forward.

To actively foster a culture of innovation, Miller relies on five core strategies: establishing clear goals, identifying decision-makers, defining responsibilities, creating a safe space for challenges, and aligning new initiatives with overall business priorities. Looking toward the broader industry, she advocates for a movement that treats digital infrastructure as modern civic infrastructure. By engaging communities early, demystifying technology, and highlighting new economic pathways, she believes the industry can replace public fear and misinformation with shared participation and trust.

To continue reading, please click here.

The post Ilissa Miller Of iMiller Public Relations On Strategies for Fostering Innovation Inside Big Business appeared first on Data Center POST.

TL;DR

• Capital discipline is reshaping investment strategies. Investors are reassessing valuation models, underwriting standards, and financing structures as risk is repriced across digital infrastructure.
• Power has become a primary investment consideration. Energy availability, water access, and permitting timelines are increasingly determining where AI infrastructure can be developed and scaled.
• Execution certainty now drives value creation. Developers and investors are prioritizing projects with realistic delivery timelines, secured power resources, and clear paths to deployment.
• Regional differences are influencing capital allocation. Variations in energy availability, regulatory environments, and market maturity continue to shape investment decisions across EMEA, APAC, and the Americas.

# # #

The Tech Capital’s International Finance Forum (IFF) 2026, held May 11-12, 2026, in London, provided a platform for senior leaders from the global digital infrastructure, investment, energy, and technology sectors to examine how risk is being reassessed across the market. Held under the theme Risk, Repriced, the fifth annual forum explored the changing assumptions surrounding valuation, underwriting, capital deployment, and infrastructure execution as AI-driven demand continues to accelerate. With participants representing more than 40 countries and organizations spanning data centers, connectivity, finance, and energy, the event focused on the realities shaping the next phase of infrastructure investment.

Rather than concentrating on future possibilities, the agenda focused on the practical challenges affecting infrastructure deployment today. Topics ranging from capital formation and valuation discipline to energy availability and project delivery reflected a market increasingly focused on fundamentals, execution, and long-term sustainability.

Capital Markets Enter a More Disciplined Era

One of the strongest themes emerging from the forum was the shift toward greater investment discipline. As financing conditions evolve and infrastructure requirements become more complex, investors are reassessing how risk should be measured and rewarded.

Marc Ganzi, CEO of DigitalBridge Group, opened the event with a discussion centered on capital allocation and market confidence. His remarks explored how investors are balancing the tremendous growth opportunity created by AI with concerns surrounding energy access, geopolitical uncertainty, and project execution. The message resonated across multiple sessions, where speakers examined the growing role of private credit, structured finance, and alternative funding models as organizations seek greater flexibility while maintaining prudent risk management.

Several panels also explored whether current valuation assumptions remain appropriate in a market where timelines are extending, costs are increasing, and infrastructure projects face greater operational complexity than in previous development cycles.

Valuation Models Face a Reality Check

As AI infrastructure expands, many of the assumptions that have traditionally supported digital infrastructure valuations are being tested. Speakers examined how leasing expectations, discount rates, utilization forecasts, and long-term growth projections are changing as investors place greater emphasis on execution certainty and operational performance.

Andrew Schaap, CEO of Aligned Data Centers, and Krupal Raval, Chief Strategy Officer of CyrusOne, participated in discussions examining supply, demand, and valuation trends across the sector. Their perspectives reflected a growing recognition that successful infrastructure development depends on more than projected demand. Access to power, realistic construction timelines, and the ability to deliver capacity when customers need it have become equally important considerations when assessing project value.

The result is a market that is placing greater weight on demonstrated execution capabilities and less emphasis on assumptions that may have been acceptable during periods of lower capital costs and fewer infrastructure constraints.

Energy Availability Is Reshaping Infrastructure Strategy

Power emerged as one of the defining topics of the event, appearing throughout discussions on  finance, development, policy, and risk. As AI workloads continue to increase power requirements, energy availability is becoming a critical factor in determining where infrastructure can be developed and how quickly projects can move forward.

Ayotunde Coker, CEO of Open Access Data Centres (OADC), joined discussions examining the physical limitations facing AI infrastructure growth. Conversations explored how power generation, water availability, grid access, and permitting timelines are influencing both investment decisions and project economics.

Many participants noted that energy is no longer simply an operational consideration. In many markets, access to reliable power has become one of the primary determinants of infrastructure value, influencing site selection, underwriting decisions, and long-term growth strategies.

Development Economics Continue to Evolve

The relationship between land, power, pricing, and project timelines was another recurring topic. As development conditions become more challenging, investors and operators are reevaluating how and where capital should be deployed.

Doug Recker, CEO of Duos Technologies Group, participated in the session “Build, Buy or Wait: When Land, Power and Price Stop Aligning,” which examined the changing economics of infrastructure development. Alongside fellow panelists, Recker explored how rising land costs, constrained power availability, permitting requirements, and community considerations are affecting investment decisions.

The discussion highlighted a growing shift away from assumptions that development automatically delivers superior returns. In some cases, acquisition opportunities may offer greater certainty, while in others, delaying investment until market conditions improve may become a viable strategic option. These decisions are becoming increasingly nuanced as infrastructure projects face more variables than ever before.

Regional Markets Continue to Follow Different Paths

While AI demand is global, the conditions supporting infrastructure investment vary considerably by region. Sessions focused on regional capital dynamics and diverging risk profiles examined how investors are evaluating opportunities across Europe, North America, Latin America, and Asia-Pacific.

Energy availability, regulatory frameworks, political stability, and market maturity all emerged as factors influencing where capital is flowing. Rather than pursuing uniform global expansion strategies, many organizations are tailoring investment decisions to regional realities, recognizing that the risks and opportunities associated with infrastructure development differ significantly from market to market.

These regional distinctions are expected to play an increasingly important role as investors seek opportunities that offer both growth potential and greater certainty of execution.

The Next Phase of Infrastructure Investment

A central conclusion from IFF 2026 was that digital infrastructure remains one of the most attractive sectors for long-term investment, but the criteria for success are changing. Demand continues to grow, particularly as AI adoption expands, yet investors are paying closer attention to the factors that determine whether projects can actually be delivered.

Power availability, permitting timelines, workforce capacity, financing structures, and operational execution are now influencing investment decisions as much as market demand itself. The organizations best positioned for success will be those capable of navigating these constraints while maintaining the flexibility required to adapt to a rapidly evolving infrastructure landscape.

To learn more about The Tech Capital and upcoming events, visit events.thetechcapital.com.

The post International Finance Forum 2026 Examines How Risk Is Being Repriced Across Digital Infrastructure appeared first on Data Center POST.

By Mike Hodge, AI Solutions Lead, Keysight Technologies

TL;DR

• The Shift to Inference: The AI industry is rapidly transitioning from episodic model training to continuous, 24/7 inference operations, which are projected to soon account for two-thirds of all AI computational workloads.
• Compounding Financial Risks: Because inference runs constantly, even minor network inefficiencies or latency issues compound over billions of runs, directly eroding operating margins and inflating the cost per token.
• The Danger of Late Validation: Waiting to test inference infrastructure until after it is physically installed is a massive financial risk; discovering bottlenecks late leads to expensive remediation, extends deployment timelines, and delays revenue.

# # #

Over the past few years, AI has dominated boardroom strategy and discourse. Executives have debated LLM deployments, approved record-breaking infrastructure investments, and reorganized day-to-day operations around axioms like “AI transformation.” For much of that timeframe, discussions have centered around deploying infrastructure and using proprietary datasets to train AI models.

But there’s a big shift on the horizon and it’s poised to change everything.

This year, Deloitte projects that only a mere third of computational workloads for AI will be used for model training. The other two thirds will be utilized for answering user prompts and queries, a process known as inference.

The signal is clear. AI is moving from experimentation to large-scale production. And along the way, organizations are discovering that the real bottleneck isn’t building models: it’s running them efficiently at scale.

Inference is the beating heart of the AI engine. It shapes customer experiences, protects (or erodes) operating margins, and generates revenue. But it’s also the source of a new and expensive form of friction: waiting.

In some ways, waiting has become the new normal for AI. Organizations must wait for hardware to arrive in supply-constrained markets. They need to endure lengthy validation cycles to confirm that systems behave as expected. It takes time to discover whether infrastructure performs under real workload conditions; but waiting compounds cost, delays revenue realization, and causes economic drag.

The Economic Shift from Training to Inference

The early days of generative AI focused on scale. The majority of infrastructure spending focused on training models. After all, larger clusters meant faster experimentation and stronger competitive positioning.

But the world has changed. Inference dominates infrastructure spending, and that means network architects have a new set of considerations.

Training workloads are episodic; inference workloads run 24 hours a day, seven days a week. Prompts consume compute cycles, memory bandwidth, networking capacity, storage I/O, and power, and every generated token carries incremental cost. However, this means any network inefficiency will have a compound impact; as runs can repeat millions, or even billions, of times a day.

Even modest issues can snowball at scale. For example, a mere five percent drop in sustained token throughput in a large deployment can cost millions of dollars in annual operating expenses. Slight instabilities in latency distributions can force network operations to provision excess headroom to maintain service-level agreements, inflating total cost of ownership.

Performance issues here aren’t limited to the network. When it comes to inference, inefficiency erodes margins.

The Real Cost of Waiting

The most underestimated financial risk in AI inference is late validation. In many environments, full inference testing occurs only after physical infrastructure is installed and configured.

However, at this stage, capital has already been committed. If bottlenecks emerge in networking fabrics, memory hierarchies, storage systems, or inline security enforcement layers, remediation becomes expensive and time-consuming. Architectural redesigns might require additional procurement or reconfiguration. That means deployment timelines extend and revenue realization windows slip.

To meet this moment, a new competitive variable is emerging: Time-to-AI.

Simply put, Time-to-AI means that waiting isn’t measured by schedule, it’s measured by cost. Organizations that identify and resolve issues earlier can move from capital expenditure to revenue faster. By contrast, those that discover inefficiencies after deployment often compensate by provisioning excess capacity to protect performance guarantees. While that approach preserves service levels, it also inflates cost per token and reduces overall return.

Inference Is Harder Than Training from a Structural Standpoint

Many organizations still treat inference as a scaled-down version of training. That assumption isn’t just wrong. It’s also expensive.

Training workloads are relatively uniform and compute intensive. Inference workloads couldn’t be more different. They’re diverse, use case-specific, and highly sensitive to latency, memory behavior, and concurrency patterns.

For example, a legal AI system may push massive context windows that strain memory subsystems. Financial AI assistants may prioritize microsecond-level determinism. Inference applications in the healthcare industry might need to combine large imaging datasets with sustained throughput demands.

Inference performance is multi-dimensional and not solely defined by peak accelerator throughput. This reality explains the industry’s move toward workload-specific accelerators and domain-optimized designs.

But hardware specialization on its own does not guarantee peak performance, or a good rate of return. If infrastructure isn’t validated against realistic inference workloads before deployment, organizations risk misallocating capital. They may scale GPUs when memory bandwidth is the true constraint; or expand clusters to compensate for networking variability that could have been resolved via architecture. In each case, spending increases faster than sustained value.

Shifting Left: From Expansion to Efficiency

The first phase of AI adoption rewarded expansion. The next phase will reward efficiency and discipline.

Boards are shifting focus from how much infrastructure has been deployed to how effectively that infrastructure converts capital into sustained business output. The more relevant executive questions are no longer about peak tokens per second; they’re focused on sustained cost per token under realistic demands. These are not just engineering concerns, they’re capital allocation decisions as well.

In response, networking teams need to embrace a shift-left mentality. Embracing workload-specific benchmarking, especially in the early stages of procurement, enables organizations to evaluate inference architectures before hardware ever hits the rack. Emulating real-world inference prompts and architectures helps identify potential imbalances across compute, memory, networking, and storage layers before additional capital is deployed. Some platforms even make it possible to recreate industry-specific prompts and LLM architectures, which can go a step further towards reducing iteration cycles and reactive overprovisioning.

In power-constrained data center environments, where energy availability increasingly limits growth, even incremental improvements in sustained tokens per watt can materially affect long-term ROI. Scale still matters, but efficiency now determines competitive advantage.

Certainty Isn’t a Mere Strategy, It’s the Way Forward

If the training era of AI was defined by scale, the inference era will be defined by certainty. Certainty that infrastructure can sustain real workload diversity. Confidence that latency distributions align with enterprise commitments. Proof that deployment timelines are predictable, and capital investments translate into measurable, sustained output.

The winners of the inference era will be the organizations that treat inference validation as a strategic capability, instead of a late-stage technical exercise. They won’t just move from reactive scaling to deliberate optimization. They will deploy faster, allocate capital more precisely, and protect margins more effectively.

Those that do not will continue to wait. And in an inference economy defined by Time-to-AI, time is money.

# # #

About the Author

Mike Hodge is AI Solutions Lead at Keysight, where he drives global strategy and go-to-market execution across the company’s AI, network test, and security portfolios. He specializes in connecting innovation with real-world applications, helping organizations harness AI for smarter, more secure systems.

The post The Inference Reckoning: AI’s New Bottleneck Isn’t Strategy, It’s Time appeared first on Data Center POST.

By Laurent Segneri, R&D executive director at Apx Data Centre Solutions

TL;DR

• The AI “Thermal Wall”: As AI-driven demands push rack densities to 100kW, traditional air-cooling methods have reached their physical limits, rendering standard, transactional cooling procurement obsolete.
• From Vendor to Collaborative Partner: Because every data centre now faces unique physical constraints and staffing shortages, operators must shift from buying cooling equipment as a commodity to treating providers as integrated engineering partners.
• Precision Engineering for Sustainability: Collaborative cooling strategies prevent wasteful “over-cooling” by tailoring compressors and closed-loop systems to specific thermal loads, helping operators optimize critical green metrics like pPUE and WUE.

# # #

As we move through 2026, we aren’t just seeing a slight uptick in data demand; we’re in the middle of a full-scale industrial revolution, powered by AI. While this is good news for the sector, we’re now in the era of the 100kW rack, with power consumption pushed to its limit by the new generation of chips and their successors.

This isn’t just an anecdotal shift. A January 2024 International Energy Agency (IEA) report forecasted that the global data centre industry’s energy consumption would hit a record high 1,000 TWh annually by 2026 —roughly equivalent to the electricity consumption of Japan. With these updated racks, it’s a prediction set to become reality.

While this innovation drives the industry forward, it’s also created a massive hurdle for those of us on the ground. Traditional air-cooling methods, which have served us well for decades, have officially hit a physical ceiling. It is pushing us towards a thermal wall, where blowing more cold air at a server isn’t just inefficient, it’s physically impossible. Operators need reliable and efficient solutions that are tailored to real-world challenges. In this new era, success in data centre cooling goes beyond simply providing equipment.

The commodity trap

As we’ve reached a physical limit, we can no longer treat cooling as a commodity. For too long, procuring the equipment has been a transactional, box-ticking exercise. In the past, operators asked for a set amount of kilowatts for cooling their data centres and the supplier sent them over the correct amount. No questions asked.

In 2026 though, cooling is taking a front seat, and this approach might just be a recipe for operational disaster. At Apx, we’re seeing first-hand that ‘standard’ data centres no longer exist. Every site, whether a hyperscale, enterprise or colocation has its own challenges.

Some operators find that their sites have structural load limits that can’t handle the weight of new liquid-cooling infrastructure. Others are facing a staffing crisis. According to recent Uptime Institute data, 53% of operators are reporting difficulties with finding qualified staff to manage increasingly complex systems.

A true collaborative partner fills the technical void that a mere equipment provider cannot. Being able to trust the person that you are working with and knowing they can rely on you is really important. It goes beyond providing the product– it’s being focused on customer needs and delivering them as best you can, right until the end.

We can’t solve today’s problems with an outdated approach. It’s time to treat cooling providers as partners rather than vendors. It doesn’t matter if the equipment used is modern, it’ll underperform if it hasn’t been designed with the people who have to maintain and install it.

I’ve worked in the HVAC industry for years and understand its intricacies as well as the importance of mechanical engineering. In both sectors, I’ve seen first-hand why working together is critical for infrastructure like this. Focusing on an ‘engineering in partnership’ mindset and collaborating to share our experience creates far better systems; not just built for the now, but the future. And, in doing so, the future’s problems.

Engineering for the boots on the ground

Mechanical engineering backgrounds mean we understand the fundamentals of what it takes to design products that actually work for specific environments. This means we’re not just building hardware; we’re designing around real-world challenges. As part of the LFB Group, we have over 60 years of HVAC experience with state-of-the-art testing facilities that enable us to put the solutions through rigorous, real-life tests first.

Integrating engineering expertise at the start of the process means that logistical issues can be solved or ‘designed out’ before they become expensive problems further down the line.

The industry is littered with cautionary tales of high specification cooling units that arrived on site, but didn’t fit through the door. Focusing on the ‘boots on the ground’ approach prevents logistical problems like this and makes installing a cooling unit a simpler process, reducing the risk of projects from running over on time and budget.

We also have to think about longevity. Although these highly specialised units are projected to have a lifespan of 15 years, they won’t last unless we future-proof them. We need to swap parts for these systems without them shutting down, and they must be easily maintained and have critical components that can be reached by maintenance engineers.

Without collaboration with specialist engineers, operators risk deploying units that are technically brilliant but logistically catastrophic.

Metrics with meaning

Crucially, collaboration doesn’t just mean making life easier for engineers; it also means hitting important sustainability targets. Over 100 of Europe’s data centres are now part of the Climate Neutral Data Centre Pact, striving to be carbon neutral by 2030.

Cooling, and the industry as a whole, often gets a really bad rep, primarily because there’s still a lack of understanding around the world about what data centres do, and where they draw their energy from. However, data centres actually use no more water than a typical golf course.

For us, it’s about flipping the script and working as closely with operators and suppliers as we can to ensure that we are delivering solutions tailored to operators’ specific needs and priorities so that we are providing effective solutions. We are part of an effort to change the narrative through our precision engineering mindset.

The success of cooling units in data centres isn’t just measured by its design, it’s also validated by green metrics such as pPUE (partial Power Usage Effectiveness) and WUE (Water Usage Effectiveness).

Tailoring fans and compressors to the specific thermal load of an AI cluster prevents the ‘over-cooling’ waste that’s less cost-effective. Collaboration with cooling specialists will allow them to implement closed-loop systems that keep units at an optimum temperature without draining the local water supply.

Precision engineering is far more important than updating systems at an unsustainable speed. It’s about being right, not just being fast.

A change in mindset

Three to five years ago, cooling sat in the shadows. But now, thanks to the demand for AI, we’ve gone from the proverbial ‘Fred Flintstone’ era, where they’re powering their car with their legs, to a Formula One car. And because of that, cooling can no longer sit quietly in the background or be seen as a ‘box-ticking’ exercise. Instead, it’s a vital and collaborative engineering discipline which determines whether a data centre thrives or fails.

Future success relies on listening to customers, understanding their physical constraints and addressing operational realities—crucially, ahead of time. Viewing cooling providers as partners rather than vendors enables the construction of solutions that not only fit the space, but operate effectively in real-world scenarios, ensuring that the 100kW racks of today keep running efficiently in 2040.

# # #

About the Author

Laurent Segneri is an experienced leader with a strong focus in R&D Management and transformation, portfolio management and end-to-end project execution in cross-cultural matrix environment while keeping a lean and pragmatic approach to all things.

The post Data Centre Cooling: How Collaboration Drives Performance And Long-Term Reliability appeared first on Data Center POST.

Originally posted on Telecom Newsroom.

Mission-critical Local Area Networks (LANs) in facilities like airports, hospitals, and industrial plants now support far more than traditional IT, handling essential 24/7 traffic such as security video, clinical systems, and building automation. Because these environments are typically saturated with high electromagnetic noise from sources like heavy machinery, elevator systems, and medical imaging equipment, the underlying cabling infrastructure must be able to maintain consistent performance in electrically harsh conditions.

To meet these rigorous demands, Shielded Twisted Pair (STP) cabling provides a distinct advantage over Unshielded Twisted Pair (UTP) by ensuring more predictable performance as data rates and Power over Ethernet (PoE) requirements increase. As modern edge devices demand higher bandwidths and power, shielding preserves the vital signal-to-noise ratio and aligns with strict industrial design standards. In zones with severe electromagnetic interference, STP allows for routing closer to power pathways than UTP, reducing conduit congestion while maintaining network reliability.

Ultimately, for electrically noisy and high-availability environments, STP offers a critical reliability buffer. Provided that grounding and installation are executed correctly, organizations benefit from stronger electromagnetic interference (EMI) immunity, lower signal emissions, and superior headroom to accommodate future speed and PoE upgrades. 

To continue reading, please click here.

The post Shielded Twisted Pair (STP) vs. Unshielded Twisted Pair (UTP) in Mission-Critical LANs appeared first on Data Center POST.

TL;DR

• Data center uptime and infrastructure strategy are now board-level commercial imperatives, driven by the increasing demands and density of AI workloads.
• Relying on fragmented, multi-vendor delivery models splits accountability and significantly amplifies the risk of service continuity issues.
• Adopting a unified delivery model for Mechanical & Electrical (M&E) engineering, connectivity, and electronic security minimizes integration risks and prevents costly rework.
• Integrated systems ensure long-term lifecycle resilience, providing simplified maintenance, predictable performance, and a stronger foundation for scalable growth

Originally posted on Datalec Precision Installations

Data centers have evolved from backend technical environments into critical strategic assets directly tied to revenue, compliance, and customer experience. As artificial intelligence workloads increase density and place higher demands on power, cooling, and security, the cost and complexity of ensuring resilience have surged significantly. Consequently, infrastructure strategy and uptime are no longer solely technical concerns but commercial imperatives that require board-level oversight. Even minor technical failures can escalate into broad service continuity issues, a risk that is heavily amplified when accountability is split across multiple vendors in fragmented delivery models.

To address these challenges, operators are increasingly shifting toward integrated delivery models that coordinate M&E engineering, connectivity, and electronic security from the outset. Consolidating these disciplines under a single partner reduces integration risks, clarifies accountability, and prevents the delays and costly rework frequently seen at the interfaces of disconnected systems. When power, cooling, safety, and monitoring systems are designed as a unified architecture, organizations benefit from enhanced visibility, simplified maintenance, and a stronger foundation for the site’s long-term operation.

Ultimately, the true value of infrastructure investment is realized over its full lifecycle, extending far beyond initial project completion. Integrated environments allow for faster diagnostics, efficient upgrades, and consistent uptime under real-world conditions, minimizing the operational burden of managing overlapping service level agreements across multiple suppliers. By treating system integration as a strategic priority rather than a secondary consideration, resilient operators can establish a future-ready ecosystem that sustains business continuity today and supports scalable growth tomorrow.

To read the full article please click here.

The post Beyond Fit-Out: Why Integrated M&E, Connectivity, and Security Are Now Board-Level Issues appeared first on Data Center POST.

TL;DR

• Atlanta Technical College officially opened one of Georgia’s first datacenter technical training academies through Microsoft’s Datacenter Academy program.
• The facility features a hands-on simulation lab designed to prepare students for careers in datacenter operations, cybersecurity, networking and energy management.
• Backed by more than $800,000 in funding and support from Microsoft, TA Realty and TA Digital Group, the academy creates direct pathways into Georgia’s rapidly expanding digital infrastructure sector.
• The launch reflects growing efforts to align workforce development with Georgia’s emergence as a major data center hub.

As Georgia continues to attract significant digital infrastructure investment, the need for a skilled workforce to support that growth is becoming increasingly urgent.

Atlanta Technical College is addressing that demand with the official opening of its new Microsoft Datacenter Academy lab facility, one of Georgia’s first dedicated datacenter technical training academies.

The ribbon-cutting ceremony, held May 20 on Atlanta Technical College’s campus, brought together leaders from Microsoft, TA Realty, TA Digital Group and regional stakeholders to celebrate a milestone that connects education directly to Georgia’s growing digital economy.

Part of Microsoft’s global Datacenter Academy network, the new facility is designed to create pathways into technical careers through curriculum alignment, scholarships, mentorship, simulation labs and hands-on learning opportunities. Students will gain practical experience in datacenter operations, networking, cybersecurity and energy management, all skill sets with a growing demand as hyperscale and cloud infrastructure continue expanding nationwide.

For Atlanta Technical College President Dr. Victoria Seals, the academy represents a major investment in both students and Georgia’s technology future.

“The future of digital infrastructure does not begin in a server room. It begins in a classroom,” said Dr. Seals. “Behind every advancement in A.I. is the power of H.I., human intelligence. I believe this transformational moment will be shaped by the people who are trained, skilled, and wise enough to guide this innovation. We believe technology will drive the future, but it is important to prepare the people who shape it. This academy represents our commitment to ensuring students gain the skills, access and opportunities needed to lead in one of the fastest-growing industries in the world.”

The initiative is backed by more than $800,000 in funding through the Atlanta Technical College Foundation, with support from Microsoft and industry collaborators including TA Realty and TA Digital Group.

Microsoft says the academy reflects its broader commitment to building local technical talent pipelines in communities where digital infrastructure investment is accelerating.

“Microsoft is proud to support the Microsoft Datacenter Academy at Atlanta Technical College and invest in students pursuing careers in the datacenter and IT industry,” said Bowen Wallace, corporate vice president, Americas, Microsoft. “This new simulation lab gives students hands-on access to equipment and servers, reflecting our shared commitment to strengthening the local talent pipeline and preparing learners for high-demand careers in cloud infrastructure.”

For TA Realty and TA Digital Group, the academy represents a critical link between infrastructure development and long-term workforce readiness.

“What we celebrate here is the result of alignment. Alignment between education and industry, between vision and execution, between opportunity and access,” said Tim Shaheen, partner at TA Realty and chief development officer for TA Digital Group. “At TA Realty and TA Digital Group, we believe workforce development must be intentional, practical and aligned with where the world is today and where it is going tomorrow. That means creating environments where students are not simply talking about the future but actively building it together.”

As Georgia continues to emerge as a major data center market, initiatives like this reflect a broader industry shift: workforce development is becoming a foundational component of digital infrastructure growth.

The post Atlanta Technical College Launches One of Georgia’s First Datacenter Academy Lab Facilities to Strengthen Regional Workforce Pipeline appeared first on Data Center POST.

TL;DR

• Victor expansion continues: Empire Fiber Internet is growing its 100% fiber network in Victor, NY.
• 2,000+ locations served: More homes and businesses across Ontario County now have access to high-speed internet.
• Regional investment underway: The buildout reflects Empire’s broader commitment to the Finger Lakes region and local economic growth.

Empire Fiber Internet, a leading fiber optic internet service provider serving communities across New York and Pennsylvania, is continuing the expansion of its 100% fiber-optic network in Victor, NY, bringing high-speed internet access to more than 2,000 homes and businesses. Additional neighborhoods are also coming online across Ontario County as the company continues construction throughout 2026.

Founded in the Finger Lakes region, Empire Fiber Internet has been serving communities across New York and Pennsylvania since 1896 and continues to grow its local fiber footprint. That regional foundation gives the announcement added significance because it shows the company expanding in an area where it already has deep roots and long-term presence.

The expansion is part of Empire Fiber Internet’s broader investment in Ontario County and the Finger Lakes region. Residents and businesses that previously lacked access to fiber may now be eligible for service, giving more of the community access to the speed and reliability needed for modern life and work.

“Victor and Ontario County deserve internet that works as hard as the people who live and work here,” said Kevin Dickens, CEO of Empire Fiber Internet. “From families relying on connectivity for school and work to businesses that depend on our internet to serve customers, fiber delivers the speed and consistency that modern life requires. And just as important, our customers can count on responsive, local support when they need it.”

As communities across the country demand stronger broadband infrastructure, fiber providers are under increasing pressure to deliver not just faster speeds, but long-term reliability and local service. Empire Fiber Internet’s continued growth in Ontario County reflects that trend, while also highlighting how regional providers can play a major role in strengthening connectivity and supporting local economies.

“We’re thrilled to have Empire Fiber Internet as a valued chamber member,” said Miranda Odell, President of the Finger Lakes Area Chamber of Commerce. “Their commitment to delivering high-speed internet services aligns perfectly with our mission to support local businesses. We love working with them and are overjoyed to hear about their recent expansion in Ontario County. This growth represents a significant boost to our community, enabling more businesses and residents to thrive with reliable connectivity. Congratulations to Empire Fiber Internet on this exciting achievement!”

To learn more about Empire Fiber Internet, visit empirefiber.com.

The post Empire Fiber Internet Continues Local Expansion in Victor, NY appeared first on Data Center POST.

TL;DR

• PowerBridge appointed Scott Hanna as Chief Revenue Officer to lead revenue strategy and hyperscale customer engagement.
• Hanna previously held leadership roles at Cumulus Data and CyrusOne, bringing experience in hyperscale and powered campus development.
• The company is advancing several multi-gigawatt powered digital infrastructure campuses across West Texas, including its planned Alpha Digital Powered Campus near Pecos, Texas.
• PowerBridge says the appointment supports its strategy of combining power, connectivity and digital infrastructure to meet growing AI and hyperscale demand.

The growth of AI infrastructure is forcing the digital infrastructure industry to rethink how large-scale campuses are developed, powered and delivered. Speed to deployment still matters, but increasingly, so does the ability to align power, connectivity and infrastructure execution from the beginning.

That evolving market dynamic is part of the reason PowerBridge recently appointed Scott Hanna as Chief Revenue Officer.

Hanna joins PowerBridge at a time when hyperscale and AI customers are placing greater focus on long-term infrastructure readiness. In his new role, he will lead the company’s revenue strategy, customer development and hyperscale engagement efforts as PowerBridge advances several powered digital infrastructure campuses across West Texas.

The appointment also reunites a leadership team that previously worked together on the development of the Cumulus Data campus in Pennsylvania. Hanna formerly served as Chief Revenue Officer for Cumulus Data, where he helped lead the development and sale of the Susquehanna campus to Amazon Web Services.

Before that, Hanna spent more than a decade at CyrusOne, including serving as Vice President of Hyperscale Sales during a period of major enterprise and cloud growth for the company.

For PowerBridge Founder and CEO Alex Hernandez, that combination of hyperscale relationships and execution experience aligns closely with where the company is focusing its growth.

“Scott brings proven hyperscale relationships, deep commercial experience and a strong track record of execution that aligns directly with our strategy,” Hernandez said.

That strategy centers on developing integrated powered campuses designed to support large-scale hyperscale and AI deployments. Rather than focusing solely on traditional data center construction, PowerBridge is building campuses that combine power infrastructure, connectivity and digital infrastructure development into a unified platform.

The company’s current roadmap includes several multi-gigawatt developments across West Texas, including the planned 2 Gigawatt Alpha Digital Powered Campus near Pecos. PowerBridge believes the region’s energy resources, available land and growing connectivity infrastructure position it to support the next generation of large-scale digital infrastructure growth.

Hanna said the opportunity to help expand that platform was a major factor in joining the company.

“I’m excited to rejoin Alex and our management team in this new chapter focused on building and deploying the powered-campus infrastructure required to support the next era of hyperscale and AI growth,” Hanna said.

As AI demand continues accelerating, the broader industry is increasingly moving toward infrastructure models that prioritize execution, scalability and long-term power availability. PowerBridge’s latest leadership appointment reflects how companies are positioning themselves to meet that next phase of growth.

Read the full release here: https://www.imillerpr.com/news/powerbridge-appoints-scott-hanna-to-drive-revenue-strategy-for-gigawatt-scale-powered-ai-data-center-campuses/

The post PowerBridge Strengthens Leadership Team as AI Infrastructure Demand Accelerates appeared first on Data Center POST.

TL;DR

• Direct Engagement is Essential: Data center developers must proactively communicate with local communities rather than relying on local governments to manage public concerns about resource usage, pollution, and grid strain.
• The Cost of Silence: Failing to address community worries transparently creates a narrative vacuum that invites misinformation, resentment, and severe political backlash, as seen in recent local elections where pro-data center officials were ousted.
• Proactive Problem Solving: By openly discussing practical engineering solutions, tax benefits, and past operational lessons, tech companies can build necessary trust, identify potential issues early, and secure the vital infrastructure needed for ongoing AI expansion.

# # #

Originally posted on The Kansas City Star.

The rapid expansion of artificial intelligence requires significant data center infrastructure, which is crucial for maintaining long-term global and economic leadership. However, this growth frequently faces pushback from local communities that are rightly concerned about environmental and civic impacts, such as excessive water usage, noise pollution, unfair tax treatment, and the strain placed on local electrical grids. When these valid worries are ignored by developers or solely delegated to local governments, they can quickly escalate into widespread skepticism and organized opposition that is easily fueled by misinformation and propaganda.

Relying on underfunded local municipalities to manage public sentiment has proven to be a flawed and short-sighted strategy for the tech industry. Because local officials may prioritize the potential for new tax revenues over community concerns, especially in areas facing financial distress or population decline, citizens often feel their questions are being evaded. Recent elections in Missouri illustrate this growing backlash, with voters actively ousting politicians who supported massive data center tax breaks without providing sufficient transparency to the public.

To secure the infrastructure necessary for future technological advancements, developers must take a proactive approach to community outreach. By directly participating in civic dialogues, operators can clarify their engineering solutions for noise abatement and resource efficiency, explain the logic behind tax incentives, and address community challenges before they derail projects. Ultimately, direct and transparent engagement is essential for data center operators to build trust and establish themselves as responsible neighbors.

To continue reading, please click here.

The post Engaging the Public on the Realities of Digital Infrastructure appeared first on Data Center POST.

TL;DR

• As edge AI increasingly automates industrial infrastructure monitoring, a critical reliability problem has emerged: these AI systems are only as accurate as the observations feeding them.
• Facilities face a dangerous “ground truth gap” because isolated sensors and static deployments often fail to capture true environmental conditions, missing localized issues like thermal layering or airflow disruptions.
• Faster analytics cannot solve this blind spot; increased processing speed only accelerates responses to observed events and completely fails to compensate for anomalies the sensing layer misses.

# # #

Modern edge infrastructure monitoring environment showing real-time telemetry dashboards, connected industrial systems, and AI-driven operational analytics inside a data center or industrial operations facility.

The rapid growth of edge AI is changing how industrial infrastructure is monitored and managed.

Across data centers, logistics environments, connected facilities, and industrial operations, organizations are increasingly deploying AI-driven systems capable of analyzing telemetry in real time, detecting anomalies, optimizing cooling, automating alerts, and improving operational responsiveness without direct human intervention.

But underneath this accelerating layer of intelligence is a reliability problem many organizations still underestimate:

AI systems are only as reliable as the observations feeding them.

In many industrial and edge environments, that observational layer remains incomplete.

The Operational Visibility Assumption

Modern monitoring platforms are highly effective at processing telemetry streams. However, there is an important difference between processing data efficiently and accurately representing real-world conditions.

In practice, many operational systems still rely on isolated sensing points, periodic logging intervals, and static deployment strategies that cannot fully capture environmental variability across physical infrastructure.

This creates what many engineers increasingly describe as a “ground truth gap” — the difference between actual operating conditions and the subset of conditions sensors are capable of observing.

The issue becomes especially visible inside temperature-sensitive environments.

Within data centers and industrial infrastructure, airflow disruption, rack density, cooling inefficiencies, thermal layering, and equipment cycling can create highly localized environmental deviations. Yet monitoring dashboards may continue displaying stable conditions simply because the sensing layer never directly observed the anomaly.

The monitoring system itself may remain fully functional.

The environment may not be.

Why Faster Analytics Does Not Solve the Problem

 Thermal visualization showing localized hot spots and airflow inconsistencies inside a data center or industrial environment.

One of the most common assumptions surrounding edge AI is that reducing latency automatically improves operational awareness.

It does not.

Faster analytics only improve response speed to observed events. They cannot compensate for events the sensing layer failed to capture in the first place.

This distinction becomes increasingly important as AI systems move closer to autonomous infrastructure operations.

Today’s edge environments increasingly depend on AI-driven operational logic to manage cooling efficiency, environmental controls, predictive maintenance, workload optimization, and infrastructure resiliency. As organizations continue automating these decisions, incomplete telemetry becomes more operationally significant.

Three recurring factors continue to distort real-time operational visibility:

• Sensor Placement Bias – Sensors are frequently positioned in locations that are operationally convenient or compliance-oriented rather than areas with the highest environmental variability.
• Temporal Gaps – Many monitoring architectures still rely on fixed logging intervals. Short-duration environmental fluctuations occurring between measurements may never appear inside recorded telemetry.
• Spatial Variability  – Physical environments are rarely uniform. Airflow dynamics, infrastructure density, thermal layering, and operational movement continuously create localized variability across facilities.

Single-point sensing cannot fully represent these conditions.

The Risk of Confidently Incomplete Systems

One of the least discussed challenges in operational AI is amplification.

AI systems do not independently validate reality. They operationalize observations at scale.

When telemetry contains blind spots, automated systems inherit those same limitations while continuing to generate highly confident operational outputs.

This creates a growing disconnect between perceived operational visibility and actual environmental awareness.

In many edge environments, organizations believe they have achieved real-time visibility when they have only achieved real-time reporting from limited observation points.

Those are not the same thing.

As enterprises continue investing in edge intelligence, predictive operations, and AI-driven infrastructure automation, improving sensing fidelity may become just as important as improving analytics sophistication.

The long-term reliability of operational AI systems will increasingly depend on whether organizations can improve the representational accuracy of the telemetry entering those systems.

Because in edge AI environments, intelligence is only as reliable as the observations behind it.

 # # #

About the Author

Aity Ritesh Raj is an intern at Mindlabs Cloud focused on industrial IoT, edge monitoring systems, and operational intelligence across connected infrastructure environments. His work explores how telemetry integrity, sensing reliability, and environmental variability impact AI-driven operational decision-making in modern industrial systems.

The post The Hidden Reliability Problem Behind Edge AI and Industrial IoT appeared first on Data Center POST.

TL;DR

• Data Center Cooling Expansion: Driven by the rising thermal loads of high-performance computing and expanding digital infrastructure, the global data center chillers market is projected to reach $6.4 billion by 2035.
• Scalable and Sustainable Thermal Management: Hyperscale data centers and water-cooled systems are experiencing significant growth as operators prioritize energy-efficient, scalable, and automated cooling technologies to maintain system stability under environmental constraints.
• High-Density Fiber for AI Workloads: To support bandwidth-heavy applications like GPU clusters and migrations to 400G/800G fabrics, data centers are maximizing rack capacity using multifiber interfaces and very small form factor (VSFF) connectors

# # #

The global data center chillers market was valued at USD 2.6 billion in 2025 and is estimated to grow at a CAGR of 9.6% to reach USD 6.4 billion by 2035, according to a recent report by Global Market Insights Inc.

Increasing reliance on digital infrastructure is driving demand for advanced cooling systems capable of handling rising computing loads across modern data centers. As power consumption continues to increase, cooling requirements scale proportionally, creating strong demand for efficient thermal management solutions. The growing density of high-performance computing systems is reshaping chiller design, requiring more compact, energy-efficient, and high-capacity systems. Innovations in cooling technologies are addressing operational constraints while improving performance in space-constrained environments.

Geographic variations in infrastructure development and environmental conditions are also influencing deployment strategies. Resource availability is becoming an important consideration, particularly in regions facing environmental constraints, which is shaping the adoption of sustainable cooling technologies. Overall, the increasing need for reliable and efficient cooling across evolving digital ecosystems is expected to drive sustained growth in the market.

The data center chillers market is also benefiting from the rising complexity of IT infrastructure and the need for advanced thermal management systems. Increasing computing intensity is pushing operators to adopt more efficient cooling solutions that ensure system stability and performance. Continuous technological advancements are enabling improved energy efficiency and operational optimization, further strengthening market growth.

The water-cooled systems segment is projected to grow at a CAGR of 10% between 2026 and 2035. This growth is supported by their superior heat transfer capabilities, scalability, and ability to deliver high efficiency in large-scale operations. These systems are increasingly preferred in facilities requiring consistent and high-capacity cooling performance.

The hyperscale data centers segment is anticipated to grow at a CAGR of 10.8% during 2026 to 2035. Growth in this segment is driven by the increasing demand for large-scale computing capabilities, which require advanced cooling solutions to manage rising thermal loads. The expansion of high-capacity data infrastructure is creating a strong need for robust and efficient chiller systems.

U.S. data center chillers market reached USD 1.2 billion in 2025 and is expected to grow at a CAGR of 9.3% from 2026 to 2035. Market growth in the country is supported by the rapid expansion of advanced computing infrastructure and increasing energy requirements. Rising demand for efficient cooling solutions is encouraging the adoption of next-generation technologies designed to handle high-performance workloads while improving energy efficiency.

Key players operating in the data center chillers market include Carrier Global, Trane Technologies, Johnson Controls, Daikin, Mitsubishi Electric, LG Electronics, Vertiv, Schneider Electric, STULZ, and Rittal. Companies in the market are focusing on innovation, efficiency, and scalability to strengthen their competitive position. They are investing in advanced cooling technologies that enhance performance while reducing energy consumption.

Many players are integrating smart monitoring and automation capabilities to improve system management and reliability. Strategic partnerships and collaborations help companies expand their global footprint and address evolving customer requirements. Additionally, manufacturers are prioritizing sustainable solutions, including energy-efficient designs and environmentally friendly refrigerants, to align with regulatory standards. Continuous research and development, along with capacity expansion and product diversification, are enabling companies to meet the growing demand for high-performance cooling systems in modern data center environments.

The post Data Center Chillers Market to Surpass USD 6.4 billion by 2035 appeared first on Data Center POST.

TL;DR

• Bridging the Power Gap: As AI and high-performance computing accelerate hyperscale growth beyond the immediate capacity of permanent grids, operators are increasingly relying on flexible, deployable energy solutions like mobile compressed natural gas (CNG) to launch projects faster.
• Mobile Energy Expansion: Highlighting this shift, Certarus is expanding its portable CNG operations, including a new Utah supply hub, to provide turnkey, interim power for major developments, such as a recent 60 MW hyperscale data center.
• Scaling High-Density Fiber: Inside the data center, soaring bandwidth demands from 400G/800G networks and GPU clusters are driving the adoption of high-density fiber platforms that maximize rack space using multifiber interfaces and very small form factor (VSFF) connectors.

# # #

Hyperscale data center development, driven by artificial intelligence and high-performance computing, is accelerating across North America, increasing demand for reliable power that can be deployed ahead of permanent grid and pipeline infrastructure.

At the same time, industrial customers across the region are increasingly seeking flexible, reliable natural gas supply to support operations, manage peak demand, and ensure continuity in environments where infrastructure is constrained or evolving.

Together, these trends are driving demand for fast, flexible energy solutions that can be deployed quickly and scaled as projects grow.

This is creating demand for delivered energy solutions, such as over-the-road compressed natural gas (CNG). Certarus, the leader in mobile CNG solutions in North America, provides an integrated, scalable solution to help customers access reliable energy and move critical projects forward.

Certarus’ mobile CNG platform, anchored by the industry’s largest portable compression fleet, is purpose-built for these needs, providing scalable, turnkey natural gas delivery from early-stage project deployment through long-term operations. The recently announced Utah hub and gas supply award are the latest in a series of data center and industrial wins that reflect Certarus’ ability to accelerate access to energy across a range of applications.

In April, Certarus announced multiple contract awards, including a two-and-a-half-year gas supply agreement for a hyperscale data center project with more than $300 million of expected revenue over the life of the contract. Operations for that project are expected to begin in mid-2027.

Building on this momentum, the company is commissioning a new CNG supply hub southeast of Salt Lake City, Utah, and has been selected as the primary natural gas supplier for a 60 MW hyperscale data center project in the region.

Operations at the Utah facility – and gas supply to the data center – are expected to commence this month. The agreement will support the data center through commissioning and early operations until permanent infrastructure is in place, highlighting the role of mobile energy in bridging infrastructure gaps.

“Across both data center and industrial markets, customers are increasingly limited by infrastructure that cannot keep pace with energy demand,” said Dale Winger, President, Certarus. “Certarus provides an integrated, scalable solution to help customers access reliable energy to move valuable projects forward. The new Utah hub strengthens our growing network of strategically positioned supply points that allow us to serve customers quickly and efficiently across multiple end markets.”

Certarus brings a differentiated set of capabilities to both data center and industrial customers, including:

• The largest portable compression fleet in the industry, paired with the largest CNG transport trailer fleet in North America, enabling unmatched scale and deployment flexibility
• Proprietary high-flow pressure-reduction systems engineered for prime power and industrial applications
• An integrated gas supply and logistics platform that delivers turnkey energy solutions without reliance on fixed infrastructure
• An experienced field operations team with a proven track record of executing complex, large-scale projects safely

This combination positions Certarus as a trusted partner for customers who need to move quickly, scale efficiently, and operate reliably, whether as a primary energy source in early project stages or as a complementary solution alongside future pipeline infrastructure.

For more information, visit www.certarus.com.

The post Certarus Expands Energy Access for Data Centers and Industrial Customers with New Utah Hub and Major Data Center Award appeared first on Data Center POST.

TL;DR

• Student support: Aureon awards $12,000 in scholarships to Iowa students pursuing STEM and technology paths.

• Local impact: Focus on rural and statewide access to education opportunities.

• Industry relevance: Programs contribute to building the next generation of digital infrastructure talent.

# # #

As digital infrastructure expands, so does the need for skilled talent to support it across engineering, operations, and emerging technology fields.

That demand is becoming more visible across the industry, particularly as AI workloads, cloud adoption, and network expansion increase. Roles tied to cybersecurity, data analysis, and field operations are evolving quickly, requiring a workforce that can keep pace with more complex infrastructure environments.

In Iowa, Aureon is contributing to that effort through its annual STEM scholarship programs, awarding a total of $12,000 to nine high school students preparing to enter technology-focused fields. Rather than a single initiative, the scholarships are structured to reach students across different communities and educational paths. One program supports students from areas served by independent telecommunications providers, while another is designed for those pursuing broader technology education across colleges, universities, and trade schools within the state.

This reflects a growing recognition that workforce development needs to start early and extend beyond major metro areas. Rural communities are becoming increasingly important to the digital infrastructure ecosystem, both as locations for network expansion and as sources of future talent.

The students selected for this year’s scholarships plan to pursue disciplines that closely align with current industry needs, including computer engineering, cybersecurity, software development, and data-driven business fields. Many will remain in-state for their education, reinforcing a local pipeline between Iowa’s education system and its evolving technology landscape.

“Every year, we see Iowa students whose talent and drive will power the state’s technology future,” said George O’Neal, CEO of Aureon. “By investing in their STEM education today, we’re strengthening the communities and businesses we serve across Iowa.”

Investments in education like this are becoming an important way to support both workforce development and local communities. As networks grow and new technologies place greater demands on performance and security, companies are recognizing that long-term success depends as much on people as it does on platforms.

Aureon plans to continue the program, with the next round of applications expected to open later this year. Additional details are available in the full announcement: https://www.imillerpr.com/news/aureon-awards-12000-in-stem-scholarships-to-nine-iowa-high-school-students/

The post Aureon Invests in Future Tech Talent with STEM Scholarships Across Iowa appeared first on Data Center POST.

TL:DR

• Beyond Simple Job Counts: Communities often evaluate digital infrastructure projects based solely on the number of permanent jobs created within the facility, which causes them to overlook the massive chain reaction of economic opportunity these developments generate.
• Immediate Economic Activation: The financial impact of a facility begins long before construction starts; early planning and design phases immediately drive demand for local services, consultants, legal teams, and hospitality sectors.
• High Value, Low Community Strain: Data centers act as central nodes that stimulate massive supply chains and professional services across the region, while bringing the added benefit of placing little to no long-term stress on local resources like schools, housing, and traffic.
• A Holistic Economic Catalyst: To fully leverage the next generation of technology investment, communities must adopt greater economic literacy to see beyond narrow employment metrics and recognize digital infrastructure as a comprehensive catalyst for regional growth.

# # #

Understanding infrastructure architecture is critical, but so is recognizing how economic value actually materializes. Much of the public conversation still evaluates digital infrastructure through narrow employment metrics, overlooking the broader chain reaction of opportunity these developments activate. To plan effectively, communities need greater economic literacy around how infrastructure investment shapes regional growth over time. This is the final article in my eight-part series exploring the convergence of industry and governance to solve the digital infrastructure build-out dilemma.  You can read the previous posts in my series here, to learn more.

When communities evaluate digital infrastructure projects, the conversation often centers on a single question: How many permanent jobs will this facility create?

It is an understandable question. Local leaders are accountable for economic development outcomes, and employment metrics are familiar, tangible indicators of value. But when digital infrastructure is assessed primarily through job-count comparisons, an enormous portion of its economic impact can be overlooked.

Because the economic story begins long before a facility is built.

Economic activity begins the moment a digital infrastructure project is conceived. Local vendors are engaged, consultants are hired, impact studies are commissioned, and hospitality and service sectors immediately feel the benefit. A data center doesn’t just create jobs inside the facility; it activates an entire ecosystem of opportunity across industries.

From the earliest stages of planning, projects generate demand for a wide range of local services. Developers engage research firms to assess feasibility. Environmental consultants evaluate land and resource considerations. Architects and engineers begin design work. Legal teams structure transactions. Lobbyists and policy advisors navigate regulatory environments. Activists raise funds and the ecosystem designed to bring us technology becomes an interchange of dollars, even to those who don’t want them there.

Even basic logistical needs create ripple effects. Before a project even goes in front of a planning board, developers are already hiring local printers, using local hotels and restaurants, bringing in consultants, commissioning impact studies: economic activity starts the moment a project is conceived.

As development progresses, the ecosystem expands further. Skilled trades such as electricians, plumbers, construction specialists, become essential. Security providers, transportation firms, and equipment suppliers play critical roles. Connectivity providers, chip supply chains, and energy innovation partners contribute to the broader infrastructure landscape.

Yet many traditional impact assessments remain narrowly focused. Impact studies are often too narrow. They focus on permanent jobs, maybe 100 or 150 roles for a large data center, but during construction there can be thousands, and even that still misses the broader economic activity.

This gap between perceived and actual impact can shape public sentiment. When residents see a large facility but hear relatively modest employment figures, questions naturally arise about whether development aligns with community priorities.

And remember, the inverse is also happening. The stress on schools, housing, traffic for a long-haul large-scale development is low to no impact locally. When structured effectively, these large facilities provide incredible economic benefits to communities without the long-term strain on resources.

What is often less visible is the multiplier effect. People look only at the building, they don’t understand that digital infrastructure development builds entire local economies.

A data centers function as a node within a complex network of industries. A data center sits at the center of an entire ecosystem where computer chips, networks, professional services sit. Expanding off of that there are many more layers of economic opportunity. These economic opportunities span the country or world.

For example, consider that professional services firms expand. Hospitality businesses experience increased demand. New suppliers enter regional markets. Workforce development programs evolve. Adjacent technology investments may follow. Think of all of the consultants,  lawyers, lobbyists, and the associations that benefit from these businesses. The direct, indirect and tertiary benefits of these large scale deployments can reach well beyond a community’s immediate needs for local jobs.

Understanding this broader economic landscape is increasingly important as communities navigate infrastructure decisions in a rapidly digitizing world. Evaluating projects through holistic economic literacy, rather than isolated metrics , can help align development outcomes with long-term strategic goals.

This is one of the central objectives of iMPR’s proprietary Groundswell™ engagement  approach.

By helping communities identify where value is created across the full lifecycle of infrastructure development, stakeholders can move beyond transactional debates toward more informed planning conversations. The focus shifts from whether development creates jobs in a single facility to how it contributes to regional competitiveness, business attraction, and ecosystem growth.

Digital infrastructure is not simply a building. It is an economic catalyst.

Communities that understand this dynamic are better positioned to participate in the opportunities emerging from the next generation of technology investment.

Those that do not may underestimate the scale, and timing, of its impact.

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 The Hidden Economy of Digital Infrastructure appeared first on Data Center POST.

TL;DR

• Quantica Infrastructure filed interconnection applications with NorthWestern Energy tied to future power generation capacity for its Big Sky Digital Infrastructure Campus in Montana.
• The filing begins a formal review process that takes place years before construction and is intended to support potential long-term customer demand.
• The proposal includes renewable generation, firming generation, and battery storage.
• Quantica says it would fund the additional power infrastructure rather than passing costs to local ratepayers.

Quantica Infrastructure has taken another step in the development of its Big Sky Digital Infrastructure Campus in Montana by filing interconnection applications with NorthWestern Energy tied to future power generation capacity for the project.

The filing supports Quantica’s 1,100 MW Big Sky Campus in Yellowstone County and is intended to help meet potential long-term customer demand while supporting regional grid reliability. According to the company, additional capacity would be delivered in multiple phases over several years and would remain aligned with customer commitments and regulatory approvals.

The filing also marks the beginning of NorthWestern Energy’s formal interconnection review process, which occurs years in advance of construction for projects of this scale.

As proposed, the applications cover a mix of renewable generation, firming generation, and battery storage representing up to 7,235 MW of maximum additional capacity. Quantica noted that renewable generation output would be materially lower than maximum nameplate capacity and that generation proposals of this scale undergo extensive review processes intended to maintain system reliability.

A central component of the announcement is Quantica’s position that the company would be responsible for the cost and construction of the proposed additional generation infrastructure.

“Quantica will pay for the additional power capacity for the Big Sky Digital Infrastructure Campus in Montana. NorthWestern Energy’s ratepayers will not be responsible for the cost of the power. As a Billings resident, I appreciate this approach and it’s important to me that my neighbors don’t foot the bill for our project,” says Charlie Baker, Chief Accounting Officer of Quantica Infrastructure.

“Developed by Montanans and grounded in local priorities, this project is designed to advance statewide economic opportunity while responsibly safeguarding communities and minimizing negative impacts to Montana’s citizens.”

Quantica also stated that Big Sky Digital Infrastructure has engaged in preliminary discussions with the Montana Department of Environmental Quality regarding permitting requirements for the campus and associated generation infrastructure, including firming power options such as natural gas, geothermal, and other potential sources.

The company framed the filing as part of broader planning for long-term infrastructure needs.

“Data center companies are looking for places that can deliver power, connectivity, and building capacity at scale. By investing in additional capacity now, we plan to address what we’re seeing in terms of power demand from data center companies. This expansion significantly increases our total investment in Montana, and the additional power capacity would result in even more economic opportunity, construction jobs over several years, and strengthen the State’s power grid,” says John Chesser, CEO of Quantica Infrastructure.

The Big Sky Campus spans 5,100 acres approximately 30 miles north of Billings and is positioned adjacent to major infrastructure including a substation, highways, and rail access. Quantica says the site is designed to support multiple data center buildings and accommodate the proposed increase in power capacity without expanding the campus footprint.

The company expects the first building to become operational in 2029. Quantica also plans to connect the campus through hundreds of miles of new fiber-ready underground conduit intended to link the site with additional U.S. markets and support regional connectivity.

Read the full release here: https://www.imillerpr.com/news/quantica-files-interconnection-applications-to-expand-power-generation-for-its-big-sky-digital-infrastructure-campus/

The post Inside Quantica’s Plan to Prepare Big Sky Campus for Future Power Demand appeared first on Data Center POST.

TL;DR

• New high density inventory at 60 Hudson: Hudson InterXchange (Hudson IX) plans to bring a second 1 MW data hall online in July 2026, creating additional deployment opportunities inside one of New York City’s most interconnected carrier hotel environments.
• Supporting AI and next generation workloads: The new space is designed for high density cabinet deployments capable of supporting air cooled loads of 45kW and beyond, enabling organizations to accommodate AI, cloud, HPC, and compute intensive applications.
• Long term growth underway: Following the successful launch of its first 1 MW data hall, Hudson IX continues progressing toward a larger vision of delivering more than 10 MW of total capacity at 60 Hudson Street.

As artificial intelligence workloads, cloud ecosystems, and high performance computing environments continue reshaping digital infrastructure requirements, operators in major metropolitan markets are encountering a growing challenge: securing available power and deployable space in highly connected locations. In New York City where both remain at a premium, the ability to bring new inventory online has become increasingly valuable.

Hudson IX is continuing its growth at 60 Hudson Street with plans to bring a second 1 MW data hall online in July 2026. The expansion follows the successful launch of the company’s first 1 MW data hall, which is already supporting customer deployments and represents another step toward Hudson IX’s broader roadmap to exceed 10 MW of total capacity at the site. As AI, hyperscale platforms, and cloud environments continue driving demand, operators in mature urban markets are increasingly facing space and power constraints. 

Hudson IX remains among a limited number of providers able to deliver new contiguous space and surplus power within a highly connected New York City facility, an increasingly rare advantage in today’s constrained market. 

Connectivity remains a defining advantage at 60 Hudson Street which provides access to more than 300 carriers, cloud providers, exchanges, and service providers. As one of the world’s most network dense facilities, the ecosystem offers organizations meaningful benefits for latency sensitive and heavy interconnection environments. 

By making additional resources available inside this ecosystem, Hudson IX is offering more than physical deployment space. Customers gain entry into a deeply established connectivity environment that supports growth, performance, and operational efficiency at scale.

Atul Roy of Hudson IX emphasized the company’s ongoing commitment to serving customer requirements and strengthening its presence at 60 Hudson Street. He noted that Hudson IX continues investing aggressively to meet market demand while providing customers with rare access to scalable power resources, flexible deployment models, and dense interconnection opportunities.

Beyond this deployment, Hudson IX continues advancing a roadmap designed to expand its footprint beyond 10 MW at 60 Hudson Street, reinforcing a long term commitment to scalable growth in one of New York City’s most connected facilities. 

As digital transformation initiatives continue accelerating and demand for compute resources grows, developments like these underscore a larger reality: unlocking new opportunities within established infrastructure ecosystems may become one of the most important differentiators in constrained metro markets.

For organizations evaluating options in New York City, the continued buildout at 60 Hudson Street represents more than another facility enhancement. It creates access to one of the market’s most significant digital crossroads.

The post Hudson IX Continues 60 Hudson Growth with Second 1 MW Data Hall and Roadmap Beyond 10 MW appeared first on Data Center POST.

TL;DR

• Kasi Cloud commissioned West Africa’s first hyperscale-ready, AI-capable, carrier-neutral data center campus in Lagos, Nigeria.
• The Lekki campus is strategically located near major subsea cable landing stations and is expected to scale to approximately 100MW of IT capacity.
• LOS1 gives Nigerian enterprises, financial institutions, and government agencies a sovereign, in-country cloud and AI infrastructure alternative aligned with Nigeria’s National Cloud Policy 2025.
• Leaders across Lagos State, the Federal Government, and NSIA say the project positions Nigeria and Lagos as a major digital and AI gateway for Africa’s future growth.

# # #

Kasi Cloud Datacenters has officially commissioned West Africa’s first hyperscale-ready, AI-capable, carrier-neutral data centre campus in Lagos, marking an important step in Nigeria’s shift from digital consumer to digital owner. The recent flag-off ceremony for its Lekki campus signals the move of Kasi LOS1 from construction into operational readiness, opening up a sovereign, world-class cloud and AI infrastructure option for Nigerian enterprises, financial institutions, and government agencies.

Located across roughly four hectares in the Maiyegun area of Lekki, the campus sits next to six subsea cable landing stations, including Equiano and 2Africa. This strategic positioning strengthens Lagos’s role as a digital gateway for the continent. Once completed, the campus is expected to scale to around 100MW of critical IT capacity. LOS1 is built to support high-density AI and accelerated computing environments, alongside enterprise cloud and connectivity services, with sub-50ms latency for in-country workloads.

Keeping Nigeria’s data and value at home

Nigerian enterprises currently spend an estimated $850 million each year on foreign cloud infrastructure, sending significant capital offshore and placing data under foreign legal jurisdictions. With LOS1, Kasi is introducing the country’s first institutional-grade, AI-ready alternative built on Nigerian soil. This also aligns with Nigeria’s National Cloud Policy 2025 (NCP2025), which requires in-country hosting for sensitive government and financial data.

By providing a sovereign, enterprise-grade cloud and AI platform locally, Kasi ensures that data and the value it generates remain within Nigeria and under Nigerian jurisdiction. In practical terms, this allows enterprises, financial institutions, and government agencies to run critical workloads locally while meeting stricter compliance, data residency, and regulatory requirements.

Built for the AI era

The commissioning of LOS1 represents both a milestone in infrastructure and the delivery of a long-held mission.

“Kasi was founded on the belief that Africa deserves world-class sovereign digital infrastructure built for the AI era,” said Johnson Agogbua, Founder and CEO of Kasi Cloud Datacenters. “For too long, Africa’s data has powered someone else’s economy. Today, that changes. This flag-off marks the transition from development into commissioning and operational readiness — as we deliver world-class sovereign cloud and AI infrastructure, built in Lagos, for Africa’s digital future.”

Agogbua noted that the milestone reflects close collaboration with leaders at both state and federal levels. “We are honoured to celebrate this milestone with His Excellency Governor Babajide Sanwo-Olu of Lagos State, the Honourable Minister of Finance and Coordinating Minister of the Economy Taiwo Oyedele, and Dr. Segun Ogunsanya, Chairman of NSIA — partners and champions whose belief in Nigeria’s digital future made this moment possible” said Agogbua.

Lagos State’s long-term digital vision

The commissioning of LOS1 also reflects Lagos State’s ongoing commitment to building its economic future on strong digital infrastructure. Governor Babajide Sanwo-Olu, Executive Governor of Lagos State, returned to the Kasi Lekki Campus as Special Guest of Honour to officiate the flag-off ceremony for West Africa’s first hyperscale-ready, AI-capable data centre campus, having also presided over its groundbreaking in 2022.

His presence at both milestones reflects a continued partnership between Lagos State and Kasi Cloud, grounded in a shared belief that sovereign digital infrastructure is essential to the city’s future. He has consistently emphasized the importance of infrastructure in sustaining Lagos’s growth, stating: “If Lagos is to sustain its Centre of Excellence status in Nigeria, vital infrastructural development is critical to achieving human capital development. The economic impact that infrastructure improvement has on nation-building cannot be overemphasized.”

In December 2024, the state government publicly committed to hosting world-class data centres in Lagos. With LOS1 now commissioned, that commitment is beginning to take physical form and signals Lagos’s intention to lead Nigeria’s digital transformation.

Federal government’s Renewed Hope Agenda

At the federal level, the Honourable Minister of Finance and Coordinating Minister of the Economy, Taiwo Oyedele, also attended the flag-off ceremony, reinforcing the Federal Government’s recognition of digital infrastructure as a key pillar of Nigeria’s economic diversification strategy. The commissioning of Kasi LOS1 aligns with the Renewed Hope Agenda, which places technology and digital infrastructure at the center of growth, innovation, and job creation.

By enabling an AI-ready, sovereign platform in Lagos, Kasi supports national goals around economic resilience, innovation, and employment, helping ensure that investment in cloud and AI infrastructure translates into local value creation.

NSIA: backing sovereign digital infrastructure

The Nigerian Sovereign Investment Authority (NSIA) has played a foundational role in supporting Kasi’s vision. Also present at the flag-off was Mr. Aminu Umar-Sadiq, Managing Director and Chief Executive Officer of NSIA, one of Kasi Cloud’s foundational investors and a long-standing advocate for digital infrastructure as a driver of long-term economic transformation.

NSIA has described Kasi Cloud as a strategic asset, noting in its 2025 Annual Report that the platform is “advancing Nigeria’s digital infrastructure” as an indigenous hyperscale data centre. “We target high-impact projects that transform critical sectors of economic growth — including initiatives like Kasi Data Center. We congratulate Kasi on this momentous milestone. NSIA believes in the potential of digital infrastructure to serve as an enabler and accelerator for innovation,” Mr. Umar-Sadiq said. “We expect that the transformative impact of this infrastructure on the domestic tech space will reposition Nigeria. The Board and Management of the Authority is proud to be associated with this development.”

With LOS1 now AI-ready and open for business, that shift is already underway.

Africa’s next digital gateway

From an investor perspective, the Lekki campus is about much more than a single facility.

“Africa represents one of the most compelling long-term digital infrastructure growth markets globally,” said Mark Adams, Co-Founder of Kasi Cloud Datacenters. “As global cloud, AI, and content platforms continue expanding into emerging markets, Nigeria — and Lagos specifically — is uniquely positioned to become the strategic digital gateway for the continent. Kasi LOS1 is the infrastructure that makes that possible.”

The campus is designed to support that future, serving both global platforms expanding into Africa and regional enterprises seeking access to world-class infrastructure.

Read the full press release here.

The post Kasi Cloud Commissions West Africa’s First Hyperscale-Ready, AI-Capable Campus in Lagos appeared first on Data Center POST.

TL;DR

• DC BLOX expanded its green financing facility to $850 million, more than tripling its original October 2024 loan.
• The funding will accelerate hyperscale-ready data center and digital infrastructure growth across the Southeast.
• DC BLOX continues to strengthen regional connectivity through cable landing stations, dark fiber networks, and new hyperscale campuses.
• Company leadership says the investment supports long-term economic growth and rising cloud and AI demand throughout the region.

# # #

DC BLOX is taking another major step in its mission to build the digital backbone of the Southeast with a significant expansion of its green financing. The company has increased its Senior Secured Credit Facilities loan to $850 million, more than tripling the original $265 million facility secured in October 2024. This infusion of capital will accelerate the development of hyperscale-ready digital infrastructure across one of the fastest-growing data center regions in the United States.

The expanded green loan provides DC BLOX with greater flexibility to fund both current projects and a robust pipeline of new hyperscale data center developments. These include multiple investment-grade, preleased hyperscale facilities across the broader Southeast, where cloud and AI demand continue to rise. The financing reflects confidence in DC BLOX’s ability to consistently deliver complex, large-scale projects for hyperscale customers on time and to specification.

“Our best-of-breed financing terms and execution with our finance partners speaks volumes of our track record of execution and our strong customer composition,” said Melih Ileri, Chief Investment Officer of DC BLOX. “We were able to further develop our finance relationships with this loan and set ourselves up for accelerated growth. This financing also validates our development and operational capabilities, such as our ability to secure powered land, deliver projects on time, and meet the requirements of hyperscale customers.”

DC BLOX has focused on building the foundational digital infrastructure that enables sustained economic growth in the Southeast. The company’s cable landing stations in Myrtle Beach, South Carolina, and Palm Coast, Florida, bring global connectivity to the region, while its dark fiber network across South Carolina and Georgia provides a high-capacity backbone for regional hyperscale data centers. In parallel, DC BLOX continues to expand its footprint of edge and hyperscale campuses in markets including Alabama, Georgia, South Carolina, Tennessee, and Florida.

Leadership at DC BLOX views this financing as a catalyst not only for the company, but for the communities it serves. “DC BLOX is proud of our role in positioning the Southeast for future growth,” said Jeff Uphues, CEO of DC BLOX. “Data centers and the networks that connect them are vital for economic growth and we are thankful to our financing partners, local government leaders, customers, development partners, communities, and employees for sharing the vision and for the tremendous, sustained effort it takes to realize it.” The facility is backed by funds managed by Future Standard, a global alternative asset manager with $93 billion in assets under management and a long track record of investing in transformative infrastructure.

To learn more about DC BLOX’s expanding digital infrastructure platform across the Southeast, read the full press release here.

The post DC BLOX Secures $850 Million Green Financing to Accelerate Hyperscale Infrastructure Growth in the Southeast appeared first on Data Center POST.

TL;DR

• Driven by AI and High-Speed Fabrics: Data centers are increasingly adopting high-density fiber platforms to meet surging bandwidth demands from AI, distributed storage, and migrations to 400G/800G networks without expanding their physical footprint.
• Maximizing Rack Capacity: These platforms boost port density by utilizing very small form factor (VSFF) connectors, multifiber interfaces, and pre-terminated trunks, which simplify deployments and provide the massive connectivity required by modern GPU clusters.
• The Crucial Role of Cable Management: While high density saves space, it introduces operational risks like congested pathways and bend radius violations; successful deployments require meticulous horizontal and vertical cable management to ensure safe and efficient routine changes.
• Strategic Density Placement: The absolute highest density is not always the smartest choice. Operators should right-size their approach by deploying ultra-dense modules in stable cross-connect zones while preserving more working room in high-change areas to prevent accidental disconnects and simplify maintenance.

# # #

U.S. data centers are being pushed to deliver more bandwidth (cloud, streaming, enterprise SaaS, and support for AI) while holding the line on space, cost, and uptime. High-density fiber platforms (modular patch panels, optical distribution frames (ODFs), and pre-terminated trunk/harness ecosystems) help by packing more fiber terminations into less rack space. However: cable management and day-to-day serviceability determine whether density is an advantage or a liability, and the densest build is not always the best build.

Why port density matters now

Traffic patterns have shifted toward east–west flows inside the data center, driven by distributed storage, microservices, and GPU clusters. At the same time, fabrics are migrating from 100G/200G toward 400G and 800G, and guidance on these transitions often calls out higher port density and the rise of GPU-accelerated clusters as key design pressures. In the U.S. colocation market—where cabinets and cross-connects are billable units—packing more connectivity into fewer racks can materially impact cost and deployment speed.

High-density platforms typically increase capacity in three ways:

• More adapters per rack unit: ultra-dense 1U fields can land very large numbers of ports. High port density supports up to 120 ports per unit.
• More fibers per connector: multifiber interfaces (MPO/MTP and newer Base-16 variants) carry parallel lanes efficiently, simplifying 400G/800G breakouts and reducing the number of individual jumpers required.
• Smaller connector formats (VSFF): very small form factor connectors such as SN and CS shrink the interface itself. Use of VSFF connectors can increase fiber count to as much as 3456 in a 1U space.

The net effect is clear: significantly higher bandwidth per cabinet, and fewer racks dedicated purely to patching and distribution.

How high density supports the practical needs of deployments. 

Faster migration cycles are available at higher speeds. Modular panels and cassette-based systems make it easier to reconfigure a fiber field when switching generations (e.g., LC to MPO breakouts, Base-8/12/16 changes, single-mode expansion), without rebuilding entire rows. This matters as 400G/800G becomes common in new pods and expansions.

You get far more links per “hot” rack. AI racks can require many high-speed connections per rack position. There’s a clear link between rising connector density and GPU growth, with VSFF connectors playing a significant role in providing connectivity for increasing numbers of GPUs.

Easier labor and repeatability make execution fast and simple. Pre-terminated trunks and harnesses can reduce on-site termination work and improve consistency—valuable when schedules are tight and change windows are limited. Pre-assembled trunks and harnesses reduce installation time and labor costs while minimizing errors associated with manual termination and splicing.

Cable management: the hidden cost of maximum density

The tighter the fiber field, the easier it is to create congested pathways, exceed bend radius, or make routine MACs (moves, adds, changes) risky. Horizontal and vertical cable management is critical for maintaining bend radius and strain relief, and exceeding bend radius or placing strain can degrade performance or cause failures. A commonly cited bend-radius rule of thumb in fiber standards guidance is ~20× cable diameter while under pulling tension and ~10× when unloaded.

So “high density” must include the management ecosystem: adequate horizontal/vertical managers, front-access routing, slack storage that doesn’t crush fibers, clean labeling, and clear overhead/underfloor pathways. Dense panels often succeed or fail based on whether technicians can safely trace and dress cords without obstructing airflow or bending cords around sharp edges.

Why “not the absolute highest” can be the smartest choice

Chasing the maximum ports-per-U can increase operational risk if the environment changes frequently. A practical rule is to choose the highest density that still allows technicians to patch, trace, and dress cords without violating bend radius, blocking airflow, or turning every change into a high-risk task. Many operators right-size density based on how the area will be used:

• High-change zones where frequent repatching takes place often benefit from more working room—even if that means fewer ports per U—because it reduces accidental disconnects and speeds work orders.
• Stable zones (structured cross-connect fields) can justify ultra-dense modules because cabling changes less often and can be governed by strict procedures.
• If multiple teams touch the same cabinets, slightly lower density can improve MTTR by making circuits easier to identify and service.

Bottom line

High-density fiber platforms can be a major advantage: they compress the physical layer, support 400G/800G migrations, and keep high-bandwidth fabrics scalable as AI drives up per-rack connectivity. The payoff, however, depends on disciplined cable management— high port counts only yield optimal benefits when daily operations are aligned!

# # #

About the Author

Paul Campos is the President of R&M USA Inc.

The post High-Density Fiber: Enabling The Port Density Gains That Power Applications Of Today And Tomorrow appeared first on Data Center POST.

TL;DR

• Lubbock goes live: Duos Edge AI is opening its newest operational Edge Data Center in West Texas.
• Built for local demand: The site adds capacity for compute, AI, network, and recovery workloads across the region.
• Another step forward: The deployment expands Duos Edge AI’s edge footprint and supports long-term growth in Lubbock.

# # #

Duos Technologies Group Inc. (Nasdaq: DUOT), through Duos Edge AI, Inc., has announced a newly operational Edge Data Center in Lubbock, Texas. The facility supports the local market with scalable, low-latency infrastructure designed to serve carriers, enterprises, education, healthcare, and the broader regional economy closer to where data is created and consumed.

The Lubbock Edge Data Center serves as a central communications hub for carriers and network providers delivering services to mobile operators and other local users. The deployment marks another step in Duos Edge AI’s continued rollout of modular edge data centers built to bring high-performance computing, resilient infrastructure, and AI readiness to underserved and high-growth markets.

“Bringing this edge data center online in Lubbock delivers low-latency, high-performance computing where it’s needed most,” said Doug Recker, CEO of Duos Edge AI and Duos Technologies Group,Inc. “This deployment also reinforces our commitment to supporting local businesses and enabling long-term digital growth across the region.”

Duos Edge AI will host a Lubbock open house on Thursday, May 28, 2026, from 11:00 AM to 1:00 PM CT at 1634 18th Street, Lubbock, TX 79401. Community leaders, partners, and industry stakeholders will have an opportunity to tour the facility and learn more about the company’s edge infrastructure approach.

To learn more about Duos Edge AI and register for the open house, visit freeevite.com/event.php?e=R7tSexY4S22Yvnzuwn2S_Q.

The post Duos Edge AI to Host Lubbock Edge Data Center Open House appeared first on Data Center POST.

TL;DR

• AI infrastructure growth is significantly increasing global data center energy consumption and operational capacity requirements.
• Renewable and carbon-free energy adoption continues to accelerate across hyperscale data centers and colocation environments.
• AI-driven liquid cooling deployments in data centers are increasing industry focus on water consumption, closed-loop cooling systems, and water efficiency strategies.
• Data center operators are making measurable progress in operational efficiency and emissions reduction despite rising AI workloads.

# # #

The rapid expansion of AI infrastructure is reshaping the global data center industry and bringing renewed focus to sustainability, energy consumption, water usage, and operational efficiency. A newly released report from Structure Research takes a closer look at how hyperscalers and data center providers are responding to these growing environmental pressures while continuing to scale infrastructure to support AI-driven demand.

The 2026 State of Environmental Impact Report analyzes environmental data from 38 data center providers and nine hyperscale cloud platforms between 2020 and 2025. The report highlights how AI deployments and high-density compute workloads are accelerating infrastructure growth while also driving new investments in renewable energy, liquid cooling, and operational efficiency.

According to the report, data centers accounted for an estimated 1.23% of global energy consumption in 2025, up from 0.81% in 2020. Total data center energy consumption increased from 198.7 TWh in 2020 to 361.6 TWh in 2025 as hyperscalers and operators expanded capacity to support AI and cloud services.

The report also found that total operational IT capacity within the data center industry reached an estimated 80,242 MW in 2025, compared to 44,046 MW in 2020. Hyperscale self-build capacity grew at a five-year CAGR of 17.6%, reflecting the ongoing global expansion of AI infrastructure campuses and large-scale cloud deployments.

Despite rising resource demands, the report points to measurable progress in sustainability initiatives across the industry. Renewable energy usage among ESG Leaders grew at a five-year CAGR of 26.2%, significantly outpacing overall energy consumption growth. Hyperscalers sourced approximately 92% of their energy usage from carbon-free energy in 2025, while data center providers reached 69%.

As power constraints continue to impact major data center markets, the report notes that operators are increasingly exploring nuclear energy agreements, natural gas partnerships, and direct power procurement strategies to secure long-term energy availability.

Operational efficiency improvements also remain a key focus area. Average Power Usage Effectiveness (PUE) for data center providers improved from 1.44 in 2020 to 1.38 in 2025, while hyperscalers maintained industry-leading average PUEs near 1.21. Emissions intensity also declined during the same period, with average emissions per GWh of energy consumption decreasing from 328.3 mtCO2e/GWh in 2020 to 229.3 mtCO2e/GWh in 2025.

Water consumption emerged as another major theme throughout the report as AI-related liquid cooling deployments continue to expand. Total water consumption among ESG Leaders increased from 55.8 million cubic meters in 2020 to 114.9 million cubic meters in 2025. In response, operators are increasingly adopting closed-loop liquid cooling systems, hybrid cooling designs, and non-potable water strategies to improve water efficiency and reduce environmental impact.

The report also includes the updated Structure Research Sustainability Quadrant (SRSQ), a benchmarking framework designed to evaluate ESG Leaders based on transparency, renewable energy usage, and operational efficiency. The initiative aims to encourage greater consistency and transparency in ESG reporting across the digital infrastructure sector.

As AI infrastructure demand continues to accelerate, the report provides insight into how hyperscalers, colocation providers, enterprises, investors, and policymakers are navigating the environmental realities tied to next-generation digital infrastructure growth.

Read more in the press release and download the report here.

The post Structure Research Examines the Environmental Impact of AI Infrastructure Growth in New ESG Report appeared first on Data Center POST.

TL;DR

• Lightpath is expanding its NYC metro fiber infrastructure to support more than 2,400 macro cell tower locations across the Northeast.
• The deployment includes 265 new route miles of fiber construction across CT, MA, NY, and NJ.
• The expansion strengthens wireless backhaul capacity and network densification while supporting multi-tenant commercialization opportunities.
• Customers in expansion markets gain access to Lightpath’s full portfolio of AI-grade connectivity solutions, including optical transport, dark fiber, Ethernet, and dedicated internet access.

###

As demand for mobile data, AI-enabled applications, and high-capacity connectivity continues to accelerate, wireless service providers are placing increasing pressure on the fiber infrastructure supporting macro cell tower deployments. Scalable, resilient backhaul networks have become essential to maintaining performance, supporting densification, and enabling future wireless growth.

To help meet these evolving infrastructure demands, Lightpath has announced a major expansion of its NYC metropolitan fiber network to support deployments for leading national wireless service providers across more than 2,400 macro cell tower locations throughout Connecticut, Massachusetts, New York, and New Jersey.

The deployment includes 265 new route miles of fiber construction, extending and densifying Lightpath’s expansive 12,100 route mile network footprint. The infrastructure leverages Lightpath’s existing high-capacity backbone to deliver scalable 100 Gbps and 400 Gbps aggregation connectivity to multiple customer endpoints.

The expansion reflects Lightpath’s broader strategy of investing alongside strategic customers while building scalable infrastructure capable of supporting additional tenants and future growth opportunities. More than half of the deployed endpoints are served using existing fiber infrastructure, demonstrating the long-term value and commercialization potential of owned network assets through multi-customer adoption.

“This expansion demonstrates our ability to invest deeply in dense fiber infrastructure, in this case expanding within the NYC metropolitan area with follow-on commercialization via lease-up of those assets across multiple customer verticals and use cases,” said Chris Morley, CEO at Lightpath.

Customers within the expansion footprint will also gain access to Lightpath’s broader portfolio of AI-grade connectivity solutions, including optical transport up to 800 Gbps, Ethernet, dedicated internet access, dark fiber, and private network services.

This announcement follows Lightpath’s recent network expansions across Greater New York, Eastern Pennsylvania, Greater Miami, Phoenix, and Columbus as the company continues building scalable, low-latency infrastructure for carriers, hyperscalers, enterprises, and data-intensive applications nationwide.

Read more in the press release here.

The post Lightpath Expands Dense NYC Metropolitan Fiber Infrastructure to Support Major Wireless Providers appeared first on Data Center POST.

TL;DR

• Green Capital S.A. and Prime Capital AG are partnering to develop a 300 MW battery storage portfolio in Poland.
• The project will span multiple sites in Poland and is designed to support grid stability, renewable integration, and energy flexibility within the European market.
• The collaboration combines local development expertise and infrastructure investment experience to accelerate delivery and long-term growth.

Green Capital S.A. and Prime Capital AG are partnering to develop a 300 MW portfolio of Battery Energy Storage Systems (BESS) in Poland, marking a notable addition to the country’s growing energy storage market. The portfolio is designed to support grid stability, renewable integration, and the broader shift toward a more flexible power system.

Project Scope

The buildout is expected to span multiple sites across Poland, with one advanced-stage project to begin construction in Q2 2026. The partners will also jointly advance a second large-scale project, expected to reach Ready-to-Build status by the end of 2026, establishing a clear development path for both partners.

The collaboration combines Green Capital’s local development expertise with Prime Capital’s infrastructure investment experience. This partnership aims to move Green Capital from a developer to an Independent Power Producer, and help Prime Capital expand its battery energy storage footprint and further cement its presence in the ever-evolving European storage market.

Market Momentum

Large-scale storage is gaining traction across Europe as utilities, developers, and investors look for ways to strengthen grid resilience and support the energy transition. Poland is emerging as an increasingly important market in that shift, with projects like this helping lay the foundation for future growth.

Michał Polanowski, CEO, Green Capital, expressed, “We are delighted to welcome Prime Capital as our partner. By establishing a joint venture for the realization of a 300 MW / 1,200 MWh BESS portfolio, we are marking a transformative moment for Green Capital. Prime Capital brings proven expertise in hybrid infrastructure investment across Europe, including a strong BESS track record, and together we are the first to bring this combination of scale and institutional capital to the Polish storage market. This portfolio will play a crucial role in balancing the national grid, enabling a faster and safer energy transition.”

Company Background

Green Capital S.A. focuses on renewable infrastructure and utility-scale project development. Prime Capital AG brings experience across energy and infrastructure investment. Together, they are advancing a portfolio that is set to play a meaningful role in expanding Poland’s battery storage capacity and modernizing its energy landscape.

The post Green Capital and Prime Capital Partner on 300 MW Battery Storage Portfolio in Poland appeared first on Data Center POST.

TL;DR

• Great Plains Communications is acquiring Fastwyre Broadband’s Nebraska business, expanding its footprint across more than two dozen communities.
• Nebraska customers will gain access to GPC’s fiber network, local support, and future network upgrades.
• The deal strengthens GPC’s long-standing commitment to Nebraska and supports continued regional growth.

# # #

Great Plains Communications (GPC), the leading Midwestern digital infrastructure provider and a portfolio company of Grain Management, LLC, is expanding its Nebraska footprint through the planned acquisition of Fastwyre Broadband’s Nebraska business, a move that strengthens its position across the state and supports its long-term network growth strategy.

The transaction will bring more than two dozen Nebraska communities into GPC’s service footprint and give current Fastwyre customers access to expanded service options, future upgrades and the company’s MEF-certified 20,000+ mile fiber network. It also extends GPC’s locally rooted support model, with call centers, representatives and technicians who live and work in the communities they serve.

“Our vision has always been to expand our fiber network throughout the Midwest and beyond and acquiring Fastwyre’s Nebraska business is a strategic milestone in that journey,” said Todd Foje, CEO of Great Plains Communications. “This acquisition strengthens our position in key markets, opens new opportunities for growth and builds on our 115-year history as a trusted technology partner in Nebraska. What continues to set us apart is the strength of our high-performing network and the dedication of our high-performing people, enabling us to consistently create long-term value while delivering the superior experience customers expect and deserve.”

Following the closing of the transaction, which is subject to customary conditions including applicable regulatory approvals, GPC said it will work to ensure seamless continuity of service and a smooth transition for Nebraska Fastwyre customers. The company also plans to upgrade and integrate the newly acquired business as part of its reinvestment strategy. This investment is expected to enable both residential and business customers across these Nebraska communities to access advanced fiber-driven services, while positioning Nebraska and beyond for future growth as technology needs continue to accelerate.

Nick Wilkin, Chief Financial Officer of Great Plains Communications shared,“The financial strength of our company allows us to pursue strategic acquisitions like this while also funding the critical network upgrades that Nebraska homes and businesses need. We are well-positioned to integrate these communities seamlessly to bring GPC’s customer-first service to Nebraska Fastwyre customers. We are also excited to welcome over two dozen Nebraska-based Fastwyre employees to GPC.”

The acquisition reflects GPC’s continued focus on infrastructure investment, customer service and regional growth.

Read the full release here.

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TL;DR

• New long-haul route: Aureon and partners deliver a 100 Tb transport network across the Midwest.

• AI-driven demand: Built to support growing cloud and AI workloads, with expansion planned to 400 Tb.

• Strategic corridor: Strengthens the Midwest as a key on-ramp to major data center hubs.

As Artificial Intelligence continues to reshape the digital infrastructure landscape, the demand for high-capacity, low-latency connectivity is accelerating at an unprecedented pace. Across the United States, regional networks are playing a more critical role in supporting hyperscale growth, cloud expansion, and the infrastructure required to power next-generation workloads.

Against this backdrop, Aureon is expanding its long-haul transport capabilities with a new 100 Tb route designed to support AI and cloud-driven traffic growth.

Built alongside partners t3 Broadband, Nokia, and Midco, the route connects Ellendale, North Dakota to Chicago through a diverse fiber corridor spanning the Midwest. Designed for both performance and resilience, the network provides a direct, low-latency pathway into major data center hubs, reinforcing the region’s role as an increasingly important connectivity corridor.

The network is being brought online in phases, with initial capacity reaching 100 Tb and a clear path toward 400 Tb as demand increases. Aureon will oversee ongoing support and maintenance, ensuring consistent performance as traffic requirements scale.

“This deployment required a high level of coordination across networks, vendors, and timelines,” said George O’Neal, President and CEO at Aureon. “Thanks to the expertise and ambition of our team, and the strength of our partner relationships, we were able to deliver the solution our customer needed in the timeframe requested.”

The collaboration highlights the complexity involved in delivering high-capacity transport infrastructure at speed. The deployment also reflects how multi-vendor coordination is becoming standard for high-capacity builds, combining optical innovation, system integration, and existing fiber infrastructure to deliver performance at scale.

Together, these efforts underscore a broader industry shift. As AI workloads increase, network operators are being pushed to rethink how infrastructure is designed, deployed, and interconnected. High-density compute environments, GPU clusters, and data-intensive applications are placing new pressure on transport networks to deliver both scale and reliability without compromise.

In this environment, regional routes like Aureon’s Midwest corridor are becoming more than just connectivity pathways. They are strategic assets that enable access to compute, support data movement between markets, and help balance capacity across an increasingly distributed infrastructure landscape.

This announcement reflects a growing recognition that the Midwest is not simply a pass-through region, but a critical component of the national connectivity fabric. With access to power, land, and expanding data center ecosystems, the region is well-positioned to support the next phase of digital infrastructure growth.

As AI continues to drive bandwidth demand higher, investments in scalable, high-performance transport networks will remain essential. Deployments like this demonstrate how collaboration across operators, technology providers, and infrastructure partners can deliver the capacity needed to keep pace with that growth.

Learn more in the full release: https://www.imillerpr.com/news/aureon-and-partners-deliver-100-terabit-route-for-the-ai-era/

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