- More than 27,000 organisations – including Airbnb, Dell and Scandinavian Airlines – are incorporating Microsoft Copilot, an AI platform, to increase the productivity of their software developers.
- Telecom providers are using AI to improve fleet dispatch for field technicians.
- Pharmaceutical firms such as Amgen are using AI to help with drug discovery and protein engineering.
- Financial services firms, like JPMorgan, are adding AI to their IT workloads. Jamie Dimon, chief executive at JP Morgan, noted the firm already has 300 AI use cases and has already spent $2bn on cloud migration, but is still early in its digital transformation.
As AI’s use cases proliferate, many workloads will begin in the cloud.
Annual cloud revenue contribution from AI and machine learning is expected to climb to $111bn (representing 36 per cent annualised growth) by 2030, according to estimates from Bank of America.
We believe there is a clear bull case for data centres as AI could drive incremental new demand, while existing data centres could benefit from non-AI applications.
AI workloads require significantly more computational power than non-AI applications (as much as five times by some estimates), which could absorb a significant amount of available power across major data centre markets.
As a result, end users with traditional workloads might gravitate toward existing facilities, as enterprises will likely not require the same level of power density that new AI workloads will likely require.
One risk for legacy data centres, however, is that older facilities could either face greater capital requirements to be upgraded or risk becoming obsolete.
Outsized cloud revenue growth and digital transformation have benefited data centre Reits so far, and we expect AI to be an additional secular tailwind.
AI’s impact beyond data centres
AI has the potential to disrupt numerous real estate sectors beyond just data centres, though we view much of the current investor discussion as speculative.
Ultimately, we believe that discerning winners and losers will require careful analysis, and that capitalising on opportunities will require active management.
We are currently considering both positive and negative scenarios across most real estate sectors.
AI and automation are likely to improve margins for operationally intensive businesses such as senior housing and hotels. AI is also likely to improve the success rate of new drug discovery, leading to increased research funding and demand for life science property.
In addition, further investment in wireless networks will be needed as mass adoption broadens to mobile applications, which should be a positive for cell towers.
Within office, job displacement as a result of AI could lead to lower demand for space, putting further pressure on occupancy.
Ji Zhang is a portfolio manager for global real estate portfolios at Cohen & Steers
