Digest · Policy NewsZDNET’s key takeaways Data centers were once consolidating. Now they’re proliferating. AI is driving data center expansion within enterprises as well as externally Cisco exec sees growing opportunities in hardware, networking, and security.
Just a few years ago, there was talk of data center footprints actually shrinking due to virtualization, consolidation, and cloud services, meaning fewer roles for professionals managing and maintaining these sites. Now, with the need to power artificial intelligence workloads, data centers are swelling in size and volume, and with it, demand for data center professionals is growing too. Also: The best AI leaders are ‘a little bit off the wall,’ says Google Cloud exec Data center job postings have more than doubled over the past two years, even as overall US job postings declined approximately 12%, according to a July 2026 analysis from Indeed Hiring Lab : “Six in every 1,000 US job postings are now data center-related, up from 2 per 1,000 in May 2023.” More from ZDNET Why enterprises are going on-prem again It’s not just the hyperscalers or massive data centers that have many communities up in arms that are driving this demand — enterprises are also expanding on the centers within their walls, Ammar Maraqa, senior VP and chief strategy officer for Cisco, told ZDNET.
In his role, he is seeing firsthand the resurgence in demand for hardware and data center-centric roles, such as hardware engineers, network specialists, and security experts. Enterprises are increasingly looking inward — to manage their own data and infrastructure for security and cost reasons. “AI inferencing and training blew all of the assumptions about smaller data centers out of the water because you needed new infrastructure, you needed GPUs, not just CPUs,” he said.
In addition, there’s a shift toward enterprises owning their own proprietary data and keeping it in-house. “On-premises infrastructure also helps enterprises not only manage and control IT security and token costs, but also enables greater creativity,” Maraqa said. Importantly, sovereignty and data governance also play into the trend toward on-premises.
“That’s becoming the new [intellectual property].” The expertise in demand now That means more career opportunities in the infrastructure and systems management space, especially with the push to modernize infrastructure capable of handling massive AI workloads. Demand is especially rising for expertise in “deep-tech” fields such as hardware, networking, and cybersecurity, Maraqa said. “Racks and GPUs are becoming so large that specialized skills are needed to network them effectively, as well as network across data centers.” Sure, coding and building AI-driven applications may be getting easier and easier thanks to vibe coding .
“But these can’t just be built out of thin air,” he said. “There needs to be infrastructure, support, and security.” Data center professionals are needed to observe and manage the performance of these systems. “Are your GPUs running hot?
Is your storage failing?” Also: Could AI really destroy us all, or are humans still the bigger threat? Expertise at connectivity and networking is becoming very important, he said. “In the past, you had to understand the workloads that were running on your infrastructure.
Now, a lot of workloads are AI workloads. You need to understand the stack that’s being created, whether it’s the models, the interface of the models, or the prompt.” There is a shortage of people “who understand the deep-tech managing of data centers, and understanding how to optimize these really complicated large-scale systems,” said Maraqa. “We probably won’t have enough expertise relative to the demand.” Addressing these concerns requires “an entirely new set of security and observability,” he added.
“If you don’t solve these problems, it’s very difficult to get the full benefit of AI.” AI and associated models add a new set of priorities for those running data centers. “You need to monitor the behavior of agents that are in your environment,” he said. “It requires a very different type of security and observability where you’re testing the behavior against guardrails that you’ve set up.” The problem with outdated infrastructure Cisco’s own research shows that an AI agent requires four and a half times the load on the network as a human for an individual task.
“If you think about it, it makes sense; it’s constantly going; it keeps sort of pinging the network,” he said. An infrastructure absorbing agentic AI workloads, then, needs to be modernized. “There’s a lot of aging infrastructure, all up in a lot of enterprises and public sector customers.
Some of the infrastructure is so old that you can’t even patch it.” Also: Who’s responsible for catching rogue AI agents? You are Outdated infrastructure poses major security risks, he continued. “Our enterprise customers are worried about the security of all of these models — roles and agents and all of that,” Maraqa said.
“They’re also worried about how to make sure they have the right data access. How do they make sure they keep the data access within the enterprise? How do they expose them in the right way?” Fine-tuning your AI strategy Employing in-house models also avoids the overkill inherent in tapping into the large frontier models.
“There are tasks that only need cheaper small language models,” he explained. For example, Cisco has a resident deep-networking model to help its network administrators and customers troubleshoot networking. “You don’t need a frontier model for that.
You need a very specific local language model that is trained for networking expertise or security expertise. That needs to run on infrastructure that sits on-premises.” The advent of AI feels “very different” from previous technology incarnations, he said. “I’m old enough to have gone through the e-commerce transition, the cloud transition, and the mobile transition,” he recounted.
“I think all of those were monumental, but this one feels fundamentally different. Everything feels very demand driven and everything feels constrained all the same time. There are all kinds of use cases being discovered every day.
It feels like opportunities are pretty limitless in terms of how this technology can help and transform.”
Source: ZDNET
