Data center real estate investment trusts took a hit Monday after major AI developers signaled a potential slowdown in model training. Digital Realty CEO Andrew Power told CNBC the sell-off is an overreaction - and that the physical infrastructure demands of artificial intelligence are not going anywhere.
"There's tremendous digital transformation happening that is not connected to AI," Power said in an exclusive interview. "There is tremendous cloud computing growth. Frankly, from my business lens, my seat, I think those demand trends, which are massive drivers of our business, have been stifled in these days of AI."
Stocks of Digital Realty and Equinix, two of the largest data center REITs, slumped following weekend warnings from Anthropic, OpenAI, and xAI about the pace of AI advancement. Power said pledges for a slowdown do not mean "pencils down" for AI or the real estate that supports it. He argued hyperscalers have been forced to choose between growing their commercial cloud businesses and allocating capacity to AI labs - creating pent-up demand that a slower training cycle could help release.
Inference, not training, drives the next wave of demand
Analysts point to a critical distinction: a slowdown affects training new models, but the bulk of data center growth comes from inference - the day-to-day use of AI tools by businesses and consumers. Andrew Batson, global head of data center research and strategy at JLL, said adoption still has a long runway.
"Only 1 in 4 Americans use AI daily, so even if models are slow to be released, there is significant runway for adoption to grow and data center demand to increase," Batson said.
JLL estimates the real estate portion of global data center investment could reach $3 trillion over the next five years. McKinsey projects AI will account for roughly 70% of global data center capacity demand by 2030, with total capital outlay nearing $7 trillion.
Location constraints protect key markets
Power emphasized that data center demand is not geographically flexible. Digital Realty's core markets - Northern Virginia, Dallas, Chicago, Singapore, Tokyo, Frankfurt, and Amsterdam - face supply constraints that a broad slowdown in AI training would not erase.
"Our markets' demand has been outpacing supply now for several years. There's pent-up need for infrastructure in those markets. There's locational sensitivity. Those workloads can't choose any one of the 50 states," Power said. "We have a global company portfolio, so we've got data sovereignty and support in other countries as well."
Institutional capital stays committed
Batson noted that large institutional investors have not pulled back. "Blackstone, BlackRock, and KKR have high conviction in this space," he said. "That, on paper, still looks quite strong, despite some of the headlines here."
Digital Realty has doubled its development pipeline to $20 billion under construction, up from $10 billion at the end of 2023. Power said the company restructured its funding model years ago to weather volatility. "We positioned the balance sheet in probably the most liquidity, the lowest leverage, the best place it could be in any potential storm," he said. "And I'm not suggesting today is an end-of-the-world storm or anything like that."
Why this matters for real estate and construction professionals
The physical build-out of data centers - shell construction, power infrastructure, cooling systems - represents a multi-trillion-dollar pipeline that does not evaporate when model training pauses. Inference workloads, cloud migration, and enterprise IT modernization all require floor space, power, and connectivity. For contractors, engineers, and developers in the AI for Real Estate & Construction space, the signal from operators like Digital Realty is that capital deployment continues. The demand drivers are structural, not speculative, and the supply-demand imbalance in tier-one markets means construction timelines will stay compressed for years. Professionals looking to understand how AI adoption shapes facility requirements can explore the AI Learning Path for Real Estate Brokers.
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