Originally posted on Nomad Futurist.

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

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

Mana Hui: Aligning Leaders Around a Shared Mission 

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

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

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

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

Powering the Next Generation: From Conversation to Impact 

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

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

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

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

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

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

To continue reading, please click here.

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

As artificial intelligence infrastructure continues to scale, the physical networks connecting large GPU clusters are becoming increasingly complex. Training environments for large language models and advanced machine learning workloads require massive bandwidth between compute nodes, driving a rapid increase in fiber connectivity inside modern data centers.

Telescent’s latest system addresses these operational challenges with a new high-density robotic cross connect system designed for DR4 and DR8 parallel optics interconnects used in large-scale AI training clusters. The system extends the company’s G5 robotic platform to support the extremely high fiber counts now common in AI cluster architectures.

AI Infrastructure Is Driving Massive Fiber Growth

AI workloads are reshaping the internal design of data center networks. As GPU clusters grow larger and more interconnected, operators are increasingly deploying parallel optics technologies such as DR4 transceivers to support the bandwidth required between compute nodes. While these architectures enable faster data movement across GPU fabrics, they also significantly increase the number of fiber connections that must be installed and managed.

In some environments, a single AI training cluster can include an exceptionally high number of fiber links. Managing those connections manually can slow deployment timelines and increase the risk of configuration errors or service interruptions.

Automation at the Physical Layer

Telescent’s robotic cross connect system is designed to automate physical layer management in these high-density environments. By enabling automated fiber path configuration and reconfiguration, the system allows operators to turn up new cluster resources more quickly while minimizing the manual patching work that traditionally accompanies large-scale network changes.

“The bandwidth requirements of AI infrastructure are rewriting the rules of data center fiber management. A single AI cluster can require hundreds of thousands of fiber connections, and the move to parallel optics architectures like DR4 multiplies that count significantly,” said Anthony Kewitsch, CEO and Founder of Telescent. “Our new high density robotic cross connect system gives operators a powerful automated solution to manage this complexity to ensure maximum GPU utilization and operational efficiency while future proofing the physical layer for the next wave of AI innovation.”

Supporting the Next Phase of AI Infrastructure

As hyperscale operators and AI infrastructure providers deploy increasingly dense compute environments, the operational demands of managing fiber connectivity are growing alongside them. Automation platforms that bring intelligence and remote control to the physical network layer are becoming an important tool for maintaining reliability and flexibility.

Telescent’s robotic automation platform enables software-controlled fiber connectivity across large-scale deployments, helping operators reduce manual intervention while allowing network paths to be reconfigured quickly as infrastructure requirements evolve.

Demonstration at OFC 2026

Telescent will showcase a live demonstration of the new system at the Optical Fiber Communication Conference (OFC) 2026 in Los Angeles from March 17 to 19 at Booth #607. The demonstration will highlight how robotic automation can simplify the management of fiber-dense AI clusters and help operators address the growing connectivity demands of next-generation AI infrastructure.

To learn more about Telescent’s optical automation solutions, visit www.telescent.com.

The post Telescent Introduces High-Density Optical Circuit Switching for AI GPU Clusters appeared first on Data Center POST.

If the GenAI expansion runs out of gas, Taiwan Semiconductor Manufacturing Co, the world’s most important foundry for advanced chippery, will be the first to know. …

TSMC Has No Choice But To Trust The Sunny AI Forecasts Of Its Customers was written by Timothy Prickett Morgan at The Next Platform.

As the year came to an end, we tore apart IDC’s assessments for server spending, including the huge jump in accelerated supercomputers for running GenAI and more traditional machine learning workloads and as this year got started, we did forensic analysis and modeling based on the company’s reckoning of Ethernet switching and routing revenues. …

By Decade’s End, AI Will Drive More Than Half Of All Chip Sales was written by Timothy Prickett Morgan at The Next Platform.

Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have...

Modern AI workloads, ranging from large-scale training to real-time inference, demand dynamic access to powerful GPUs. However, Kubernetes environments have limited native support for GPU management, which leads to challenges such as inefficient GPU utilization, lack of workload prioritization and preemption, limited visibility into GPU consumption, and difficulty enforcing governance and quota…

Source