As artificial intelligence accelerates across global data centers, conversations often focus on compute, power density, and next-generation infrastructure. But according to Nabeel Mahmood, Strategic Advisor at ZincFive and Brandon Smith, Vice President of Global Sales and Product at ZincFive, the most crucial element of AI scalability isn’t hardware. It’s people.

Moderated by Ilissa Miller, CEO of iMiller Public Relations, this webinar uncovered why the AI workforce, not compute, is the true limitation and what must change for sustainable growth.

People Are the Real Bottleneck in AI Scalability

Mahmood explained that scaling AI isn’t just a matter of adding more servers or GPUs. It requires practitioners who understand data pipelines, model governance, operational resiliency, and infrastructure design. Without skilled talent, organizations face operational risks despite abundant compute. Smith highlighted that AI and machine learning job postings have increased significantly, noting a recent figure showing a 450 percent rise, far outpacing available expertise.

Technical Silos Are Creating a New Skills Crisis

The discussion emphasized a growing gap across disciplines. Electrical, mechanical, IT, and data science teams frequently operate in isolation despite the interdependent nature of modern AI data centers. This fragmentation leads to delays, inefficiencies, and architectures unable to handle today’s dynamic workloads. Smith described the shift from traditional “white space versus black space” to today’s “blended gray space”, where cross-functional knowledge is essential. Mahmood added that the inability to transfer knowledge horizontally and vertically across teams is a major obstacle to scaling AI systems.

Energy Innovation Is Essential for AI Expansion

AI’s spiking, unpredictable workloads challenge a grid that was never designed for ultra-dense compute. Mahmood and Smith both pointed to advanced energy storage solutions, including ZincFive’s high-power nickel-zinc technology, as the key to unlocking performance. These innovations smooth electrical spikes, maximize usable capacity, and support emerging off-grid compute models that reduce dependence on constrained utilities.

Preparing the Future AI Workforce

Both speakers agreed that organizations must treat talent as core infrastructure. That means forecasting future skills, investing in upskilling programs, partnering with universities, and fostering environments where engineers can innovate across disciplines. As Smith noted, the strongest teams of tomorrow will be adaptive, coachable, and ready to evolve alongside rapidly changing AI infrastructure demands.

Watch the webinar below:

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