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AI accelerators are changing data-center development from a conventional building exercise into a continuing effort to balance speed, density, cooling, power and environmental impact. In a DCD Studio interview published October 15, 2026, Seb Moss speaks with Matthew Baynes of Schneider Electric about the ways developers are responding to that shift. The discussion centers on modular construction, liquid cooling, future-proof facility design and grid innovation.
Why AI GPU growth changes the facility brief
Traditional data centers were generally planned around relatively stable equipment profiles and predictable growth. Large AI deployments are less static. GPU-based workloads can require far more power and produce substantially greater heat in a concentrated rack footprint than many earlier enterprise deployments. As a result, developers must consider electrical capacity, heat removal, floor layouts and expansion options much earlier in the project.
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The DCD Studio conversation presents AI-driven GPU growth as a development priority rather than a small equipment upgrade. A facility designed only for today’s servers may become difficult or expensive to adapt when the next generation of accelerators raises rack density or changes the balance between computing and networking equipment.
What modular construction contributes
Faster, repeatable deployment
Modular construction divides portions of a data center into standardized units that can be manufactured, assembled or commissioned in stages. That approach can shorten the amount of work that must be completed on site and make capacity additions more repeatable. It is particularly relevant when organizations need computing capacity sooner than a fully bespoke building program can deliver.
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Flexible expansion
Modularity also supports phased growth. Developers can establish the core site infrastructure and add capacity as demand, equipment availability and power access become clearer. This does not remove the need for careful planning: utility connections, structural loading, fire protection, controls and service routes still need to accommodate later modules.
For AI facilities, the value is flexibility. A modular strategy can make it easier to introduce different cooling or electrical configurations without redesigning an entire campus, although the modules must be engineered as an integrated system.
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Why liquid cooling is becoming central to dense AI racks
Air cooling moves heat through the room and out through air-handling equipment. As more computing power is concentrated in each rack, the amount of heat that air must carry away rises. At some point, adding more airflow, fans or room cooling becomes less practical than transferring heat directly from the electronics through a liquid loop.
The interview links liquid cooling with higher-density AI racks. Depending on the implementation, liquid can collect heat at cold plates attached to processors, through rear-door heat exchangers or with other rack-level systems. The important development principle is not a single hardware design but the need to plan cooling distribution, leak detection, maintenance access, water quality and heat rejection together with the server layout.
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Design implications for operators
- Cooling distribution must reach the racks that need it, rather than being treated only as a room-level service.
- Electrical, mechanical and control systems need coordinated commissioning so that pumps, heat exchangers and computing equipment operate safely.
- Maintenance procedures must account for liquid connections, isolation points and the consequences of a failed cooling component.
- Facilities may need hybrid arrangements because not every rack or future device will have identical cooling requirements.
Future-proofing when workloads keep changing
Future-proof design means preserving practical options, not predicting an exact server specification years in advance. Developers can reserve physical space, structural capacity, cable pathways, electrical distribution routes and cooling capacity for later changes. They can also select systems that can be reconfigured as rack layouts and heat loads evolve.
This matters because AI workloads, rack densities and power requirements are all moving targets. A design that performs well at initial occupancy can become a constraint if it leaves no room for additional transformers, switchgear, cooling equipment or network routes. Future-proofing therefore has a financial dimension: building optionality early may cost more than a minimum-capacity design, but retrofitting a live facility can be slower and more disruptive.
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Power availability and the grid are part of development
High-density computing increases the importance of the electrical supply beyond the data-center fence line. Developers must consider whether the local grid can provide the required connection, how long upgrades may take and how the facility will operate during periods of constraint. The DCD discussion identifies sustainable grid innovations as part of the challenge, alongside onsite engineering.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThat broader view changes the development sequence. Power availability can influence site selection, phasing and the amount of capacity brought online at each stage. Facilities may need controls that coordinate demand, backup generation, storage or other forms of flexibility with utility conditions. The specific solution depends on local regulation, utility arrangements and the site’s technical design; the interview does not provide a single universal configuration.
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Sustainability cannot be separated from capacity growth
AI expansion creates a tension between adding computation and limiting resource use. Efficiency therefore has to be considered across the electrical system, cooling plant, building design and connection to the grid. A facility that uses more efficient equipment but cannot obtain additional power, or that adds capacity without a workable heat-rejection plan, is not a complete development solution.
The themes highlighted by DCD place sustainability alongside modularity, liquid cooling and future readiness. That framing is significant: environmental performance is treated as an infrastructure and grid-design issue, not only as a choice of server hardware.
A practical planning checklist for AI-ready facilities
- Model several growth cases. Plan for changes in rack density, accelerator generations and phased occupancy rather than one fixed equipment list.
- Validate power early. Confirm utility capacity, connection timing, expansion rights and operating constraints before finalizing the site program.
- Choose a cooling architecture deliberately. Determine which racks may require liquid cooling and design distribution, monitoring and service access around that assumption.
- Use modularity where it creates real options. Standardized modules should align with the site’s electrical, mechanical, structural and controls strategy.
- Reserve expansion paths. Protect space and routes for future switchgear, cooling equipment, cabling and network capacity.
- Coordinate sustainability with operations. Evaluate how cooling, power controls and grid interaction will perform over the facility’s full operating life.
What the DCD Studio interview establishes
The October 15, 2026 interview is a strategic overview rather than a project case study. It identifies the main development responses to AI GPU growth—modular construction, liquid cooling, future-proofing and grid innovation—but it does not publish named project costs, regional power figures, product benchmarks or measured performance results. Those details must be assessed separately for each site and deployment.
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