By Mike Hodge, AI Solutions Lead, Keysight Technologies

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

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

New AI platforms mean new expectations for infrastructure

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

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

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

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

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

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

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

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

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

Why traditional network validation falls short

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

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

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

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

The need for realistic workload emulation

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

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

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

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

Front-end optimization is a competitive differentiator

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Metro Connect USA 2026 brought the digital infrastructure community together in Fort Lauderdale, Florida, Feb. 23 to 25, as executives, investors and network operators gathered to discuss the evolving connectivity landscape. Over three days, conversations across keynote sessions, panels and private meetings focused on how the industry is adapting to the rapid growth of artificial intelligence, cloud services and bandwidth demand.

The 2026 event drew more than 3,700 decision-makers representing over 1,200 companies, reflecting the scale of collaboration and investment shaping the next phase of digital infrastructure development in the United States.

Artificial intelligence was a central theme throughout the conference. Industry leaders discussed how AI workloads are driving new requirements for data center capacity, fiber connectivity and power infrastructure. As AI adoption expands beyond hyperscale environments into enterprise applications and edge deployments, operators are facing increasing pressure to scale networks capable of supporting high-volume data movement and compute-intensive workloads.

Fiber infrastructure also remained a key topic. Discussions throughout the event highlighted continued investment in metro fiber expansion, long-haul backbone routes and fiber-to-the-home networks. As cloud platforms, streaming services and AI applications generate greater data traffic, fiber continues to serve as the underlying foundation supporting the digital economy.

Several speakers addressed how infrastructure and investment strategies are evolving alongside these shifts. Marc Ganzi, Chief Executive Offer at DigitalBridge discussed the continued influx of capital into digital infrastructure and the importance of disciplined investment as the sector scales. Steve Smith, Chief Executive Officer at Zayo Group highlighted the role of fiber expansion in supporting enterprise connectivity and hyperscale demand. Alex Hernandez, CEO of PowerBridge, participated in discussions focused on the growing power demands associated with AI infrastructure, including how utilities, data center developers and investors are working to expand power capacity and modernize energy delivery to support large-scale computing environments.

From the investment perspective, Santhosh Rao, Managing Director, Head of Digital Infrastructure at MUFG explored the evolving capital structures supporting infrastructure development, including structured financing and private credit solutions. Anton Moldan, Senior Managing Director at Macquarie Group shared insights into how institutional investors continue to evaluate digital infrastructure assets as a long-term growth opportunity within global infrastructure portfolios.

Beyond the formal sessions, Metro Connect remains known for its highly productive networking environment. Thousands of meetings took place across the event’s exhibit floor, private meeting rooms and curated networking gatherings, reinforcing the conference’s reputation as a place where partnerships are formed and transactions begin.

Outside the formal sessions, attendees spent much of the week engaged in meetings and informal discussions across the venue’s networking areas. Many participants noted that the event continues to serve as a gathering point for companies exploring partnerships, investment opportunities and infrastructure projects.

Looking ahead, the industry will reconvene next year as Metro Connect USA 2027 moves to a new venue. The event will take place February 8–10, 2027 at the Diplomat Beach Resort in Hollywood, Florida.

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At Metro Connect USA 2026, held February 22-25 in Fort Lauderdale, Marc Ganzi, Chief Executive Officer of DigitalBridge, delivered a keynote outlining how artificial intelligence is reshaping the digital infrastructure industry. In his address, “Digital Infra 3.0: Building the AI Industrial Revolution,” Ganzi described how the sector is evolving from a connectivity-focused market into a broader ecosystem that includes data centers, fiber networks, edge computing, and energy infrastructure.

Ganzi emphasized that AI has moved beyond hype and is beginning to generate measurable outcomes across industries. While much of the public discussion focuses on applications and large language models, he noted that the true monetization of AI will occur through enterprise and industrial use cases. Manufacturing, agriculture, healthcare, and transportation are already integrating AI-driven automation, robotics, and predictive analytics to improve productivity and efficiency.

These developments rely on a layered infrastructure environment. Hyperscale facilities train AI models, while edge data centers support inferencing workloads closer to where data is used. Fiber networks provide the low-latency connectivity required to move massive volumes of data between locations, and wireless systems connect devices and sensors in the physical world. Beneath all of these components sits an increasingly critical factor: power.

Power availability was a central theme of Ganzi’s keynote. As AI workloads grow, electricity demand is rising faster than grid capacity can keep pace. The digital infrastructure industry is now leasing significantly more power than the grid can bring online each year, creating a widening gap between supply and demand. As a result, developers are increasingly operating as energy strategists, exploring diversified energy approaches that may include microgrids, battery storage, solar, wind, and natural gas generation.

The search for reliable power is also influencing where new infrastructure is built. While traditional hubs such as Northern Virginia remain central to the industry, developers are exploring additional markets where grid access and energy availability make large-scale AI deployments possible. In many cases, power availability has become the deciding factor in site selection.

Despite the focus on energy, Ganzi reminded the audience that connectivity remains essential to the AI economy. The ability to move enormous amounts of data across networks continues to depend on high-capacity fiber infrastructure and low-latency connectivity. Even as AI advances in software and hardware, the underlying network infrastructure remains fundamental.

Ganzi also described the evolution of AI infrastructure in phases. The industry has moved through the early stage of training large language models and is now entering a period where inferencing and edge deployments are expanding. The next stage will involve integrating AI directly into physical environments, where intelligent systems control machines, robotics, and automated processes across multiple industries.

As the sector expands, developers face growing challenges that include power constraints, permitting delays, supply chain pressures, water usage concerns, and increased scrutiny from investors. Ganzi stressed that success will depend on operational discipline, strong customer relationships, and the ability to deliver infrastructure projects reliably and on schedule.

Ultimately, he framed the current moment as the beginning of Digital Infra 3.0, a phase in which digital infrastructure converges with traditional infrastructure to support the AI economy. As AI adoption accelerates, the companies that successfully combine power, connectivity, and compute will play a defining role in building the foundation for the next era of global digital infrastructure.

The discussion around digital infrastructure, connectivity, and AI will continue at the next major Capacity event, International Telecoms Week (ITW) in Washington, D.C., May 18-21, 2026.

To learn more about upcoming events in the Capacity Media portfolio, visit www.capacitymedia.com/events.

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Data Center POST had the opportunity to connect with Clearfield’s Chief Commercial Officer, Anis Khemakhem, who is deeply passionate about technology, particularly in advancing fiber optics and telecommunications solutions. Throughout his career, he has consistently focused on leveraging cutting-edge technology to improve connectivity and enhance digital access across various sectors. His executive experience, including leadership positions at Clearfield, Amphenol and Carlisle Interconnect Technologies, demonstrates his executive engagement capabilities and capacity to handle complex, multi-stakeholder projects.

The information below is summarized to provide our readers a deeper dive into who Clearfield is, what they do and the problems they are solving in the industry.

What does Clearfield do?  

Clearfield designs and manufactures fiber connectivity solutions that simplify how operators build and scale modern networks. We focus on critical connection points across broadband, data center, edge, and wireless environments.

Since our inception, we’ve helped community broadband providers close the digital divide. Today, we also apply that modular, craft-friendly approach to wireless networks as well as data centers and distributed edge facilities that support AI-driven workloads. Our goal is to help operators deploy high-performance fiber faster, with less complexity and lower long-term operational costs.

What problems does Clearfield solve in the market?

Network operators are facing rising fiber density, limited space and labor constraints – not to mention pressure to scale quickly without disrupting live infrastructure. Clearfield addresses these challenges by simplifying fiber deployment and ongoing management.

Our solutions reduce installation time, streamline maintenance, and enable incremental growth. Whether supporting broadband expansion or high-density data center environments, we help customers reduce operational friction and future-proof their networks as data volumes and performance demands accelerate.

What are Clearfield’s core products or services?

Our core offerings include fiber management, protection, and delivery solutions, such as patch panels, cassettes, passive and edge cabinets, racks, enclosures, and fiber assemblies. A key recent introduction is our NOVA Platform, a modular, high-density fiber ecosystem designed for data centers, modern central offices, and edge environments.

The NOVA Platform features tool-less installation, front-of-rack access, and consistent documentation to simplify scaling. Across our portfolio, we focus on labor lite design and operational consistency to help customers deploy and manage fiber efficiently. NOVA is no exception.

What markets do you serve?

Clearfield serves community broadband providers, regional and national ISPs, incumbent telcos, utilities, municipalities, cooperatives, and enterprise networks. We also support hyperscale and colocation data centers, enterprise campuses, government and military networks, and distributed edge environments.

Increasingly, our solutions are used where fiber connects data centers to AI workloads and local compute resources at the edge. High-bandwidth, low-latency fiber is the only way society will be able to support data-intensive emerging technologies — from autonomous vehicles to precision agriculture. In rural broadband builds and high-density data halls alike, we serve operators that need scalable, reliable fiber infrastructure across diverse environments.

What challenges does the global digital infrastructure industry face today?

The industry is navigating explosive data growth driven by AI, cloud computing, and increasingly distributed architectures. Networks are extending beyond centralized data centers toward edge environments closer to users and applications. So, fiber counts, space, and power requirements are growing while skilled labor remains limited.

Operators must scale capacity quickly without sacrificing reliability or affordability. The challenge is not only bandwidth, but also density, manageability, and the ability to evolve without constant redesign.

How is Clearfield adapting to these challenges?

Clearfield is addressing these challenges by designing platforms that reduce complexity at every stage of deployment. The NOVA Platform exemplifies this approach, offering high-density, modular solutions with tool-less installation and all work performed at the front of the rack.

Across our portfolio, we emphasize consistent installation methods, clean documentation, and incremental scalability. This reduces training requirements, limits downtime, and allows operators to grow capacity without disrupting active networks — whether in a rural head end or a data center supporting AI workloads.

What are Clearfield’s key differentiators?

Our primary differentiator is how intentionally we design for the realities of the field. Clearfield solutions are modular, craft-friendly, and built to minimize labor and operational complexity.

Rather than isolated products, we deliver platform-based ecosystems that scale consistently across environments. This helps customers simplify inventory, standardize training, and deploy fiber with confidence. Our roots in community broadband give us a unique perspective that translates well to today’s data center and edge applications, where efficiency and scalability are critical.

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

You can expect Clearfield to continue expanding its footprint in data centers and edge computing while remaining committed to community broadband. We’ll introduce additional high-density, modular solutions that support AI-driven architectures and growing fiber demands. But our focus will remain on platforms that bridge environments.

We want to empower operators to apply a consistent, efficient approach as networks become more distributed. Ultimately, we aim to help customers scale faster, manage complexity more easily, and build infrastructure that supports both current and future workloads.

What upcoming industry events will you be attending? 

Clearfield launched the NOVA Platform at BICSI Winter 2026, where attendees were able to see live demonstrations of our high-density patch panels and cassettes and explore the broader ecosystem. That won’t be the last chance to see NOVA. We will participate in many major industry events this year, engaging with network operators, designers, and partners to share best practices and demonstrate how our solutions simplify fiber deployment.

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

Clearfield recently launched the NOVA Platform, a modular, high-density fiber ecosystem designed for data centers, enterprise networks, and edge environments. NOVA delivers tool-less installation, higher port density, and improved documentation. This innovative solution suite addresses the growing demands of AI-driven and 100G-plus networks. The platform includes patch panels, cassettes, cabinets, racks, and fiber assemblies that scale consistently across environments and are already generating strong interest across multiple markets.

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

Clearfield sits at the intersection of broadband and data center infrastructure at a time when AI is reshaping network design. Fiber is the common foundation, but operational simplicity is becoming just as important as speed. Our experience helping operators deploy efficient, scalable networks translates directly to today’s high-density and edge environments. Whether connecting communities or powering AI workloads closer to users, Clearfield delivers fiber infrastructure designed to scale cleanly and perform reliably.

Where can our readers learn more about Clearfield?  

Visit us online at www.seeclearfield.com and follow us on social media.

How can our readers contact Clearfield? 

The contact page on our website has multiple ways to get in touch with our team to learn more about the NOVA Platform and our other solutions.

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The post Company Profile: Clearfield on Simplifying Fiber for AI-Ready Networks appeared first on Data Center POST.

But virtue of its scale out capability, which is key for driving the size of absolutely enormous AI clusters, and to its universality, Ethernet switch sales are booming, and if the recent history is any guide, we can expect Ethernet revenues will climb exponentially higher in the coming quarters as well. …

Pushed By GenAI And Front End Upgrades, Ethernet Switching Hits New Highs was written by Timothy Prickett Morgan at The Next Platform.

Originally posted on Crosstown Fiber.

Digital equity is one of the most pressing challenges facing communities today. Many neighborhoods still struggle with the digital divide, the gap between those who have reliable, high-speed internet access and those who do not. This divide limits access to education, healthcare, and employment, making it harder for people and local economies to thrive.

Closing it requires more than cables and connections. It demands both strong, lasting infrastructure and human-centered investment, ensuring that individuals not only have access to technology but also the confidence and skills to use it effectively.

Understanding Digital Divide

Across the country, gaps in broadband access persist. In urban neighborhoods, affordability and access to reliable networks continue to create barriers. In rural areas, distance and limited infrastructure often restrict connectivity altogether. In both cases, the outcome is the same: limited opportunity to learn, work, and participate fully in an increasingly digital world.

The divide is not only about connectivity; it is about opportunity, equity, and inclusion. Building networks designed for the future is critical, but so is building the human infrastructure that turns access into empowerment.

To continue reading, please click here.

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