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AI is changing data-center design from a roughly 1 kW-per-cabinet problem into one that can require 40–50 kW today and more than 500 kW in future systems. In DCD Studio, DCD host Seb Moss interviews Hans Obermillacher, Panduit’s senior business development manager for Data Centers EMEA, about the infrastructure, ownership models and edge locations needed to support that shift.
The 17-minute, 22-second interview was published by DCD on 14 October 2026. Obermillacher, who has more than 20 years of data-center experience and a Dipl.-Ing. degree, describes how AI workloads, edge computing and service-based infrastructure are forcing operators to rethink electricity distribution, heat removal, connectivity and deployment speed.
AI has multiplied cabinet power requirements
Obermillacher contrasts current AI infrastructure with the systems he saw earlier in his career. “Looking back 20 years, we saw power consumption of 1kW per cabinet,” he says. The lowest power level he now associates with an AI cabinet is approximately 40–50 kW, while future cabinets could reach around 500 kW or more.
That is not simply a larger utility bill. A cabinet at those levels changes the physical design of the room: busways, power-distribution units, backup systems, cabling, rack layouts and floor loading all have to be selected for substantially higher continuous loads. Obermillacher calls the change “a tremendous shift from a power and cooling perspective.”
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| Cabinet generation | Power figure cited | Source and qualification |
|---|---|---|
| Historical data-center cabinet | About 1 kW | Hans Obermillacher, discussing conditions from roughly 20 years ago |
| Lower end of current AI cabinets | 40–50 kW | Obermillacher’s range in the 6 October 2026 interview |
| Possible future AI cabinet | 500 kW+ | Obermillacher’s forward-looking estimate |
Why air cooling alone is no longer enough
As processors and accelerators concentrate more watts in the same rack, removing heat through room air becomes increasingly difficult. Panduit states that traditional air cooling alone often cannot handle AI and high-performance-computing thermal loads.
Rear-door heat exchangers
A liquid-based rear-door heat exchanger captures heat as air exits the rack. It can be added alongside an air-cooled room design, allowing operators to deal with high-density racks without converting every part of the facility to liquid cooling.
Direct-to-chip and on-chip cooling
Direct-to-chip systems bring coolant to cold plates attached to high-power processors. Obermillacher describes the broader move toward on-chip cooling, used with liquid-based heat exchangers and conventional air cooling where appropriate. The practical choice depends on rack density, equipment compatibility, facility water or coolant distribution and the operator’s ability to service the system.
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Many facilities will use more than one method: air for ordinary racks, rear-door exchangers for denser deployments and direct-to-chip cooling for the highest-power servers. Designing those zones together is important because future AI cabinets may require upgrades to pumps, heat-rejection equipment and distribution piping, not just replacement servers.
Ownership is moving toward multi-tenant and XaaS models
The interview also distinguishes an enterprise-owned facility from a multi-tenant data center (MTDC) operated as a service platform.
Enterprise-owned data center
An enterprise typically owns and operates the building infrastructure and the IT equipment. It controls the cabinet layout, power systems, cooling plant and hardware lifecycle, but it also carries the capital cost and responsibility for expansion.
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MTDC and XaaS model
In the model Obermillacher describes, a service provider owns the compute equipment, cabinets, uninterruptible power supplies and storage. Customers consume capacity as a service rather than purchasing and operating every physical component themselves. This can make high-density AI capacity available without each customer building a specialized facility, while the provider takes on equipment refreshes, electrical capacity and thermal planning.
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Why edge computing is expanding in operational industries
Edge computing places processing capacity near the activity that generates data. Obermillacher names banks, supermarkets and transportation companies as examples.
- Banks: Local processing can support time-sensitive transactions and branch or site operations where sending every data stream to a distant region is impractical.
- Supermarkets: Stores can analyze cameras, inventory systems and checkout activity close to the site, reducing dependence on a constant round trip to a central cloud.
- Transportation: Vehicles and transport infrastructure generate data where bandwidth and latency matter. Obermillacher cites autonomous-driving scenarios, in which delayed responses can make a remote-only architecture unsuitable.
Edge sites are usually smaller and more distributed than hyperscale campuses. That makes standardization, remote monitoring, compact power protection and reliable cable routing especially important.
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Infrastructure needed for AI-ready builds
Panduit presents its data-center portfolio as an integrated stack for hybrid IT, hyperscale, cloud, on-premises, colocation and edge facilities. The categories include power, fiber, cooling, cabinets, pathways, cable cleats, grounding and bonding.
Power distribution and cabinets
Higher rack loads require appropriately rated power-distribution units and cabinet systems. Panduit’s named product families include ES2P PDUs and smart cabinets. Ratings, connector types, redundancy and local electrical requirements must be checked against the actual server configuration.
Fiber and high-density connectivity
The portfolio lists pre-terminated fiber, PanMPO connectors and FiberRunner routing. Pre-terminated assemblies can reduce field termination work, while high-density connectors and dedicated pathways help keep AI clusters serviceable as link counts rise.
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Routing, restraint and safety hardware
Panduit also names overhead wire-basket routing, cable cleats, Pan-Steel stainless-steel ties, StrongHold metal locking ties, BlackFin crimping tools and VeriSafe 2.0 voltage testers. These are installation and maintenance components rather than compute equipment. Buyers should verify rack dimensions, cable diameter and type, load ratings, environmental conditions and applicable electrical standards before specifying them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prefabrication can shorten colocation deployment
Panduit says its pre-configured, converged infrastructure can cut colocation-tenant production time by up to 80 percent. That is a vendor-published claim, not an independent benchmark, and the result will depend on the site, design maturity, approvals and installation scope. The underlying approach is to assemble repeatable power, connectivity and cabinet elements before they reach the customer floor, reducing field work and configuration errors.
Market context and what operators should plan for
Panduit’s solution page quotes BCC Research forecasting a global data-center market of $418.2 billion in 2025 rising to $691.6 billion by 2030. Those figures are reproduced by Panduit and should be treated as BCC Research’s estimate as quoted by the vendor.
For operators planning an AI or edge deployment, the interview points to six linked decisions:
- Set a realistic present and future power density per cabinet, rather than designing only for today’s servers.
- Select air, rear-door, direct-to-chip or hybrid cooling based on the thermal design of each rack zone.
- Decide whether the organization will own equipment or consume capacity through an MTDC or XaaS provider.
- Choose fiber density, connectorization and routing that leave room for expansion and maintenance.
- Use repeatable or prefabricated assemblies where faster deployment has operational value.
- For edge sites, validate latency, bandwidth, physical security, backup power and remote service procedures at every location.
What the DCD interview establishes
Obermillacher’s central message is that AI infrastructure is a physical-infrastructure challenge as much as a computing one. Moving from 1 kW cabinets to tens or hundreds of kilowatts changes power delivery and cooling; service-based ownership changes who funds and operates the equipment; and edge deployments change where capacity must be located. Any AI-ready build therefore needs coordinated electrical, thermal, connectivity and operational planning instead of a server-only upgrade.
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