Airsys on Cooling for AI: Air, Liquid, and the Power Budget

Throughout the summer, TechArena has been sitting down with the companies behind the AI buildout to hear how requirements are shifting up and down the stack.

For this installment, we caught up with Paul Quigley, Chief Strategic Relations Officer at Airsys, a company with more than 30 years in mission-critical cooling, built on precision air systems and now expanding into rack-level liquid cooling as AI densities climb. With data center operators running air and liquid side by side, power is becoming the constraint behind nearly every infrastructure decision.

We talked about why air cooling keeps carrying part of the thermal load even in the densest AI environments; what operators run into when they retrofit liquid cooling into facilities that were never designed for this much heat; and how a new metric, Power Compute Effectiveness, measures the share of a facility's provisioned power that is structurally available for compute. Here's what we learned.

Q1: Airsys has spent more than 30 years in mission-critical cooling and its lineup now spans both precision air systems and rack-level liquid cooling with LiquidRack. What are data center operators asking you for today that they weren’t before the AI boom?

A: The conversation has changed considerably over the past few years. Before the AI boom, customers were primarily concerned with whether their cooling systems could reliably support existing infrastructure. Today, they must plan much further ahead. They are asking how cooling architecture can reduce the amount of provisioned electrical capacity assigned to supporting infrastructure and make more capacity structurally available for AI compute.

Power has become a primary constraint, so every infrastructure decision comes back to how much provisioned capacity can ultimately be allocated to compute. Customers are also looking for flexibility. They know server technology is evolving quickly, and they want cooling infrastructure that can adapt rather than become a bottleneck. Cooling is no longer simply supporting the infrastructure; it has become an integral part of the overall AI strategy.

Q2: Much of the liquid cooling conversation assumes a new build. When an operator raises density in a data center that already exists, what are the specific pain points they run into?

A: Most existing data centers were not designed for today’s AI workloads. As rack density increases, the facility must remove substantially more heat than it was originally designed to handle.

The challenge is that many liquid-cooling solutions assume the facility can be redesigned around them. That can require building-wide piping, large centralized cooling distribution units, and extensive infrastructure changes that add cost, complexity, and disruption. Many operators do not want to take that approach—and some facilities physically cannot accommodate it.

Operators need a practical way to introduce liquid cooling where it is needed, expand it over time, and avoid turning every retrofit into a major construction project. That is why we have focused so much attention on simplifying liquid-cooling retrofits.

Q3: As racks get denser, does air cooling stop making sense?

A: It is not that simple. Air cooling continues to play an important role, and many operators will manage hybrid air-and-liquid environments for years to come.

What AI has changed is the amount of heat each rack must handle. As thermal demands increase, physical limitations begin to determine the appropriate cooling approach. Liquid is more effective at transferring heat away from high-power components, which is why it is becoming the preferred option for the highest-density AI workloads.

Liquid cooling will carry an increasing share of the thermal load and become a centerpiece of many cooling strategies. Air cooling, however, will remain necessary for portions of the IT load and other heat-producing components. Both must therefore be considered as part of a comprehensive cooling strategy.

Q4: Airsys has put forward Power Compute Effectiveness as a new measure. What is it, and what led you to introduce it?

A: Power Compute Effectiveness (PCE) emerged from a fundamental question: within a data center’s provisioned electrical capacity, what proportion is structurally available for IT compute? As AI workloads drive higher power densities and available power becomes increasingly constrained, understanding this allocation is critical for capacity planning and infrastructure design.

PCE is the ratio of provisioned IT compute electrical capacity—after accounting for cooling and heat rejection, electrical conversion and distribution losses, and auxiliary facility loads—to total provisioned facility electrical capacity within a declared facility boundary and redundancy basis. PCE does not replace Power Usage Effectiveness (PUE): PUE measures operational energy efficiency using consumed energy, whereas PCE evaluates how provisioned electrical capacity is structurally allocated at the design and planning level. Together, the metrics provide complementary perspectives on operational efficiency and the capacity available to support compute.

Q5: As AI pushes rack density higher, what should operators understand about how cooling shapes the cost of the compute they deliver?

A: Every AI workload requires electrical power and produces heat that must be removed. The cooling architecture—and the provisioned electrical capacity required to support it—directly affects how much of the facility’s total power envelope remains structurally available for compute.