Blog

The data center construction boom has entered a new chapter. AI adoption is accelerating, and the floor of potential demand is rising, according to Bain’s latest global data center forecast through 2030.

The early scramble of generative AI–driven demand is giving way to a more disciplined, selective, power-constrained, and execution-focused phase of growth. Hyperscaler tech companies are absorbing compute capacity, large enterprises are scaling up production-grade AI, and data center construction patterns are starting to form around clearer plans for continued growth and demand. Although the capacity growth rate isn’t certain—due to variability in AI adoption and developers shopping around for the best sites to build on—there are clear signs that momentum and growth will remain strong. Going forward, winners will be defined not by scale alone but by their ability to navigate complexity with precision.

Our forecast combines bottom-up scenario modeling with insights from across our Energy & Natural Resources and Technology practices. The latest update reflects five sector trends that have defined the past 12 months.

1. 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.
2. 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.
3. Growth is concentrated but globalizing. North America has the largest data center capacity, fueled by hyperscalers’ capital expenditures. 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.
4. 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 also 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.

5. 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. Utilities, developers, and regulators face urgent coordination pressure, and we’re already seeing examples of utilities collaborating with data center operators to effectively plan for large load requests.

The authors wish to thank Paul Bockwoldt and Fernando Valdes for their contributions to this analysis.

Laid out in the chief executive’s 2025 policy address (118-page // 865KB PDF), the changes will reshape compliance obligations, contractual frameworks and operational risk management across multiple sectors of the economy, with particular impact on financial institutions, logistics providers, technology companies and in-house legal teams, according to technology law expert Jennifer Wu of Pinsent Masons.

The changes when shaped could affect how contracts are formed, how shipping documents are authenticated and what counts as admissible evidence in a dispute. Businesses moving goods across borders will require systems that can generate, sign and store documents electronically in ways that satisfy both Hong Kong law and the legal requirements of other jurisdictions involved in the transaction.

Wu said: “The 2025 Policy Address signals a regulatory pivot towards digital trade, smart mobility and AI accountability. For businesses, this is both a compliance challenge and a strategic opportunity.”

“Electronic trade documentation will streamline cross-border transactions but requires robust systems for authentication, security and evidentiary integrity,” she said.

“The shift raises practical questions about digital signatures, system security, audit trails, and records retention. Exporters, freight forwarders and trade finance providers should map their current processes against likely legislative requirements, particularly where paper documentation is still standard practice, or where multiple parties in different jurisdictions must access the same documents.”

Under the planned legislation, from 2026, business-to-business trade documents in electronic formats will be legally recognised. Hong Kong SAR’s trade platform will link with systems in operation in mainland China and other ASEAN countries, while upcoming trials are replacing paper bills of lading with digital equivalents.

Legislation for drones, air taxis and other low-altitude aircraft, part of an action plan to develop transport and logistics by air, was also announced. The rules will cover safety standards, flight routes, landing sites and traffic management, while a regulatory ‘sandbox’ will let companies test these services under government supervision.

Wu said: “For logistics companies, the regulatory requirements may differ significantly from ground transport.”

“For technology providers, the sandbox offers early-mover advantage but requires detailed submissions on safety, cybersecurity and operational protocols,” she said.

“Multi-party contracts between aircraft operators, technology suppliers, insurers and customers, will need clear risk allocation, particularly where regulatory standards are still developing. Businesses should monitor draft legislation for detail on certification processes and liability rules.”

The framework will need to address operator licensing, safety certification, insurance cover, liability for accidents, and data protection where aircraft capture images or location data.

Wu said: “Contracts must address certification, liability, and data protection in an evolving legal landscape.”

“Meanwhile, AI governance expectations are increasing, particularly for financial services, where regulators will demand transparency, bias mitigation, and secure data handling,” she said.

“Organisations that act now – by auditing processes, updating governance frameworks, and engaging with regulatory consultations – will not only reduce legal risk but also gain a competitive edge in adopting these technologies.”

Three AI-related measures were announced in the policy address: a mechanism for transferring data from mainland China to Hong Kong SAR for research under secure conditions; new infrastructure including an AI supercomputing centre and a data facility cluster; and governance requirements including mandatory testing for government AI systems.

Wu said: “These measures point to increased regulatory expectations on AI security, transparency and accountability in the adoption of AI, especially in financial services.”

“Organisations using AI should review their governance arrangements – data protection compliance, model testing, bias checks and vendor oversight – against the standards likely to be applied by supervisors,” she said.

“The data transfer mechanism may enable new AI applications using cross-boundary datasets, but only under defined security controls.”

Regulatory details of AI governance are expected to be released in the next 12 to 18 months. Organisations should assess gaps in their current AI risk management frameworks or be aware of the needs in their existing organisation as the requirements continue to develop, according to Wu.