The data center construction boom has entered a new chapter

The data centre construction market has entered a new chapter, driven by strong demand for data and AI solutions. Experts from Bain & Company highlight six trends that have shaped the sector’s development over the past twelve months.

Strong growth
The data centre construction market has already experienced rapid growth in recent years, but with the acceleration of AI, the sector is now entering a next growth phase. Large enterprises are scaling up production-grade AI, prompting hyperscale technology companies to expand their compute capacity.

There are clear signs that AI momentum and growth will remain strong, which will continue to translate into robust demand for data centre construction.

The pace of new construction growth has begun to stabilize
The hyperscaler investment pullback many expected didn’t occur – their investments increased meaningfully in 2025 and are expected to grow in the coming years. That said, hyperscalers are focusing more on capital efficiency and getting more selective with new deployments, particularly for AI training.

Data centers are becoming larger but more flexible
Data center “mega-campuses” (those with power capacity of at least 1 gigawatt) will become standard for frontier model training, though it appears that a relatively limited set of these data centers in specific locations will suffice to serve global demand. Although average data center sizes are increasing, the more modest requirements of inference workloads are enabling smaller, distributed data center networks.

Data centers are being designed to accommodate flexibility between training and inference workloads, partly by implementing multiple cooling options. Operators are trying to avoid sunk assets amid increasing complexity. They’re also exploring distributed training, which may herald a change in training architecture.

Inference is now the center of gravity
AI workload patterns are shifting, with greater emphasis on inference at scale alongside continued frontier model training. This shift is partly due to clear traction in enterprise AI use cases. Test-time compute is reshaping infrastructure strategy, economics, and architecture, with meaningful implications for data center colocation vs. self-build, silicon diversity, and power provisioning.

Growth is concentrated but globalizing
North America has the largest data center capacity, fueled by capital expenditures of hyperscalers. Meanwhile, sovereign AI mandates and enterprise adoption are activating regional markets across the globe. Companies face decisions about which markets can serve different workloads; they’re seeking geographic flexibility as they align compute infrastructure with latency, data sovereignty, and energy sourcing considerations.

Power availability is the bottleneck
Even as GPU and construction constraints ease, power access is now the critical gatekeeper of growth. Behind-the-meter (BTM) power generation is shifting build decisions and timelines. So far, BTM projects are most common in the US and are relying mostly on independent gas power, though others are exploring technologies such as solid oxide fuel cells to serve this purpose.

Utilities, developers, and regulators face urgent coordination pressure, and there are already examples of utilities collaborating with data center operators to effectively plan for large load requests.