The Data Center Frontier Executive Roundtable features insights from industry executives with lengthy experience in the data center industry.

Here’s a look at the Q2 2026 insights from Steve Altizer, President and CEO of Compu Dynamics.

Steve Altizer is President and CEO of Compu Dynamics, a premier data center infrastructure solutions provider established in 2002. The company specializes in comprehensive construction, white space integration, and operational support services, leveraging deep expertise across mechanical, electrical, network, and liquid cooling infrastructure to transform empty shells into fully operational, future-ready data centers.

With nearly four decades of experience in highly engineered construction environments, Steve has built his career around delivering complex facilities where reliability, security, performance, and customer responsiveness matter. Since founding the Andrew Browning Group, now Compu Dynamics, Steve has focused extensively on the data center industry, leading work across projects ranging from 20kW environments to campuses exceeding 100 MW.

Steve also founded Compu Dynamics Modular (CDM), expanding the company’s role in the next generation of AI infrastructure. At the end of 2025, CDM acquired a modular manufacturing company, strengthening its ability to deliver end-to-end turnkey modular data center solutions for high-density AI, HPC, and edge deployments.

Data Center Frontier:  As rack densities and cooling demands accelerate simultaneously, where do you see the greatest disconnect today between AI infrastructure ambition and deployment reality?

Steve Altizer, Compu Dynamics: Today, the traditional data center development industry is trying to support a new class of AI workloads inside environments that were largely designed and optimized for a different operating profile.

Demand for large scale and multi-tenant data centers still exists and will continue to grow. Those facilities play a critical role in the market. But many were built around more cloud-level rack densities, longer refresh cycles, and a relatively static handshake between the supporting infrastructure and the enterprise IT environment inside the data hall.

AI workloads function better in a different hosting environment - one that offers more flexibility than today’s data centers. We are seeing GPU deployments move well into the hundreds of kilowatts per rack. This introduces highly disruptive thermal profiles, heavier equipment, more dense power distribution, massive interconnect requirements, and hardware roadmaps that can shift every six to twelve months.

Bridging that gap between the facility and the workload is exactly what we do. For decades, Compu Dynamics has been a market leader in designing and building high-efficiency, mission critical environments. AI is changing virtually every element of the traditional environment. Data centers now have to be planned around the application, the cooling demands, especially the shift toward liquid cooling, and the supporting infrastructure from the beginning.

We are having these conversations frequently with data center owners, hyperscalers, and neocloud companies. In essence, AI infrastructure is not simply a denser version of cloud infrastructure. It operates best on a completely different kind of power and cooling foundation, with a different deployment model, and at a pace that is unprecedented.

Data Center Frontier:  How tightly integrated must power, cooling, and facility operations become to support the next generation of AI deployments?

Steve Altizer, Compu Dynamics: Integration has to be foundational. It has to start at the first planning conversation, not after the equipment is selected or once the building is already designed.

In previous generations of data center development, mechanical, electrical, IT, and operations teams could often work in parallel and bring the pieces together later. That worked when the load profile was more predictable and the facility had more room to absorb change. Before the introduction of ChatGPT, there was very little change to absorb.

AI removes that tolerance. A change in rack density can affect electrical distribution, structural requirements, thermal strategy, commissioning, service access, and the way the site is operated. These are no longer independent decisions. They are all part of one performance system. As AI systems move toward POD-scale platforms, the boundary between IT and facility infrastructure becomes much harder to separate.

The challenge is that AI workloads are too varied for a one-size-fits-all approach. Training clusters, inference nodes, enterprise AI environments, and edge sites can all have different requirements for density, cooling architecture, network connectivity, security, site conditions, and serviceability.

That is why many companies are adopting a modular approach, while others are embracing hybrid models where turnkey modular AI capacity is integrated into larger campus environments.

At the campus level, that means standardizing the backbone infrastructure that serves the site (utility power feeds, central cooling capacity, and network pathways), while allowing the IT environment and the integrated critical infrastructure components to evolve as workload requirements change. The goal is not modularity for its own sake. The goal is to support the next generation of AI deployments without forcing every hardware change to become a major redesign.

AI infrastructure cannot be planned as a collection of disparate systems. It has to be designed as one coordinated environment, from the utility backbone all the way to the IT rack.

Data Center Frontier:  The industry is moving from prototype AI environments toward industrialized deployment at scale. What becomes materially harder once AI infrastructure moves into live production?

Steve Altizer, Compu Dynamics: The defining challenge is keeping pace with the rate of change in the IT environment. It takes time to design, permit, build, and commission a data center. AI hardware operates on a completely different timeline. New GPU families are being introduced every 12 to 18 months, and from one generation to the next, rack power densities can double or even triple.

At prototype scale, you can design around a single cluster or a specific density profile. At production scale, that approach becomes a real liability. The facility has to support today’s deployment while remaining adaptable for the next compute profile. We are not just talking about adding more power. We are preparing for major architectural shifts, including the move toward DC power delivery or cooling systems that may rely on two-phase liquid to remove heat at scale.

That is what becomes materially harder. You are no longer solving for a single, static deployment. You are solving for a moving target inside a live operating environment.

This is where strategic modularity proves its value. It helps decouple the lifecycle of the building from the lifecycle of the IT hardware. Instead of treating the data center as one monolithic design, modularity creates a more agile framework that can absorb new power and cooling architectures without requiring a full facility retrofit every time the IT roadmap shifts.

At Compu Dynamics Modular, we are seeing this play out in real time. The value of a turnkey modular approach is not simply speed. It is the agility owners need to keep pace with ever-evolving rack densities, power delivery requirements, and cooling architectures.

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