The rapid rise of artificial intelligence is forcing a fundamental rethink of how data centres are designed, built and operated. What were once facilities optimised for predictable enterprise workloads are increasingly being reshaped to support AI factories, environments defined by extreme power density, rapid deployment timelines and infrastructure that must operate as a tightly integrated system rather than a collection of discrete components.
That shift is the focus of a new report from Vertiv, which argues that a combination of technological and macro forces linked to AI is now driving a new phase of data centre evolution. The Vertiv Frontiers report identifies extreme densification, gigawatt-scale buildouts and the growing need to treat the data centre itself as a unit of compute as the defining pressures shaping the next generation of facilities.
According to the report, these pressures are not incremental. They are structural changes driven by AI workloads, high performance computing and the diversification of silicon architectures. Together, they are pushing operators to reconsider everything from power distribution and cooling to where AI inference is delivered and how quickly new capacity can be brought online.
Power architectures under strain
One of the most immediate consequences of AI adoption is pressure on power systems. Most data centres today rely on hybrid alternating current and direct current power distribution, with multiple conversion stages between the grid and the IT rack. While this model has served conventional workloads well, Vertiv argues it is increasingly inefficient as rack densities rise.
The report points to higher voltage direct current architectures as a potential response. By reducing current, conductor size and the number of conversion stages, higher voltage DC can improve efficiency while centralising power conversion at the room level. Hybrid AC and DC systems remain common, but Vertiv suggests that as standards and equipment mature, higher voltage DC is likely to become more prevalent, particularly in AI driven environments.
This shift is closely linked to broader challenges around power availability. As data centres scale more quickly and to unprecedented sizes, operators are being forced to explore on-site generation and microgrids. These approaches, once primarily associated with resilience, are now being considered as part of longer-term energy strategies to support AI growth.
Where AI is delivered begins to matter
While much of the early investment in AI infrastructure has focused on large scale facilities supporting large language models, Vertiv believes the delivery of AI inference will become more distributed. For many organisations, particularly those in regulated sectors such as finance, defence and healthcare, data residency, security and latency requirements may limit reliance on centralised cloud services.
As a result, private or hybrid AI environments hosted in on-premise data centres are likely to remain important. Supporting these environments requires flexible, scalable power and liquid cooling systems capable of delivering high density capacity either through new builds or by retrofitting existing facilities. The implication is that AI infrastructure will not follow a single architectural pattern, but will instead reflect the operational constraints of different industries.
Energy autonomy and the return of on-site generation
The report also highlights a growing interest in extended energy autonomy. While backup generation has long been a feature of standalone data centres, widespread power availability challenges are prompting operators, particularly those building AI facilities, to consider more sustained on-site generation.
Investment in technologies such as natural gas turbines is being driven primarily by concerns over grid capacity rather than environmental considerations. Vertiv suggests strategies such as Bring Your Own Power and Bring Your Own Cooling are likely to form part of future energy autonomy plans, especially as AI workloads demand continuous, high-density power.
Digital twins and adaptive cooling
Speed is another defining requirement of AI infrastructure. As GPU density increases and deployment timelines compress, Vertiv argues that digital twin technology will play a growing role in both design and operations. By virtually mapping data centres and integrating IT and critical infrastructure through prefabricated modular designs, operators can deploy units of compute more quickly, reducing time-to-token by as much as 50 per cent.
Liquid cooling is also moving from a specialist solution to a mission-critical system for many operators. AI workloads have accelerated its adoption, but the report suggests AI itself could be used to optimise cooling performance. With enhanced monitoring and control, AI driven systems could predict failures, manage fluid dynamics and improve reliability and uptime for high value hardware.
Taken together, the trends outlined in the Vertiv Frontiers report point to a data centre industry in transition. As AI becomes central to digital strategy, infrastructure is being reshaped to support unprecedented density, scale and complexity. The data centre is no longer just a place where compute resides, but an integrated system designed around the operational realities of intelligent machines.
