The rapid rise of artificial intelligence (AI) is fundamentally transforming the datacenter industry. As AI workloads and applications grow, datacenter operators are compelled to rethink the design, power management, and location of their facilities. This shift is driven by the unprecedented computational power and energy demands of AI technologies, including generative AI, which require significantly more power than traditional datacenter tasks.
Increased Demand on Datacenters
Strain on Existing Infrastructure
The surge in AI applications has led to a significant increase in demand for datacenter infrastructure. Existing facilities, often not equipped to handle the complex and power-intensive needs of AI, are under immense pressure. Hyperscale providers like Amazon Web Services (AWS) and Microsoft consume a large share of datacenter capacity in Europe, seeking even more space to support their expanding digital services. This demand pressures datacenter developers to enhance supply. The strain is becoming increasingly evident, as current infrastructures grapple with the rising tides of data and computation required by AI.
Moreover, this rapid increase in demand isn’t showing signs of slowing down. Datacenter operators are continuously finding themselves at the brink of capacity, pushing for rapid expansions and upgrades. Existing datacenters were originally designed for traditional IT functions, which starkly contrast with the high demands of AI workloads. Retrofitting these facilities with the necessary hardware and cooling solutions often proves difficult and sometimes inefficient. As the computational needs continue to grow, it becomes apparent that newer, more specialized facilities will be necessary to accommodate advanced AI requirements.
Projected Energy Consumption
The International Energy Agency (IEA) projects that AI-powered web searches will amplify electricity demand tenfold, forecasting an increase from approximately 460 TWh in 2022 to over 1,000 TWh by 2026. This surge in energy consumption underscores the need for datacenters to evolve and adapt to the growing power requirements of AI technologies. The exponential rise in energy demand directly correlates with the complexities and depth of AI computations, which include extensive machine learning models and inference processes.
Powering and cooling these operations require substantial electrical infrastructure, making energy efficiency a critical concern for datacenter operators. The challenge lies not only in providing enough power but also in managing the heat generated by AI hardware. Traditional cooling methods fall short, necessitating more innovative approaches to maintain temperature and efficiency. As AI continues to expand its footprint, datacenters must prioritize advancements in energy management and cooling solutions to sustain the industry’s growth.
Datacenter Design Evolution
Need for Advanced Cooling Solutions
To accommodate AI workloads, datacenters must evolve, driving the need for significantly more processing power, which in turn generates more heat. Traditional air-cooling methods are inadequate for these high-power densities. As a result, datacenter operators are shifting to liquid cooling systems, including direct-to-chip and immersion cooling, to manage the increased heat. Liquid cooling provides a more effective solution to the intense thermal output of AI systems, maintaining optimal operational temperatures while ensuring performance.
These advanced cooling techniques involve circulating a liquid coolant directly to the hardware components that generate the most heat, drastically improving cooling efficiency compared to air-based systems. Immersion cooling, where hardware is submerged in a thermally conductive but electrically insulating liquid, further enhances heat dissipation. Such methods not only improve cooling efficiency but also reduce the physical space required for cooling infrastructure, making room for more computational hardware within the same datacenter footprint. This innovation in cooling technology is crucial for supporting the continued rise of AI applications.
Construction of New Facilities
These advanced cooling systems require more physical space, often necessitating the construction of new facilities designed specifically for AI workloads from scratch. This evolution in design is crucial to support the power and cooling needs of AI applications, ensuring efficient and reliable operation. Creating new datacenters from the ground up allows operators to incorporate state-of-the-art technologies and design principles optimized for the unique demands of AI, rather than retrofitting existing infrastructure.
New constructions can address the specific spatial, power, and cooling requirements necessary for AI, creating an environment tailored to high-performance computing. They can also incorporate sustainable practices and renewable energy sources to mitigate the environmental impact of these energy-intensive operations. Building new facilities provides an opportunity to push the boundaries of datacenter design, incorporating flexibility and scalability to cope with future technological advances and the ever-growing demands of AI applications.
Challenges in Metro Markets
Limited Grid Capacity and Land Availability
Europe’s metro markets face challenges with limited grid capacity and land availability. Growing sustainability and political pressures further complicate the situation as datacenter operators compete for resources with residential developers and other commercial users. The scarcity of land that offers both high-speed network connectivity and proximity to existing datacenters means that suitable locations are rare and expensive. These constraints necessitate innovative solutions to address the burgeoning requirements of the AI sector.
As urban centers grapple with these shortages, securing sufficient power from the grid becomes increasingly difficult. New AI-centric datacenters often necessitate substantial upgrades to the electrical infrastructure, which can be both time-consuming and costly. Additionally, balancing the energy needs of datacenters with community and regulatory expectations for sustainable practices adds another layer of complexity. Operators must navigate these multifaceted challenges while striving to ensure uninterrupted and efficient performance of their facilities.
Dwindling Datacenter Space
As a result, available datacenter space is dwindling in major European cities. This scarcity challenges operators to meet the demand for hyperscale and cloud providers alongside the burgeoning AI sector. The need for innovative solutions to address these constraints is more critical than ever. Datacenters must strategically manage their resources, utilizing cutting-edge technologies to optimize existing space while exploring novel avenues for expansion.
Enhancing the capacity of current facilities through technological upgrades alone isn’t sufficient to meet the escalating demands. Additionally, geopolitical considerations and varying local regulations across Europe can hinder uniform solutions. Each datacenter must adapt to its unique environment, finding tailored strategies that align with specific regulatory and physical constraints.
Shifting Development Strategies
Pivot to Secondary Markets
To address AI’s demands, the datacenter industry must pivot from traditional metro markets to more secondary markets where power and land might be more readily available. This shift encourages development in cities such as Marseille and Lyon in France, as well as areas outside London in the UK, where there is potential for AI-ready datacenters. By exploring these secondary markets, datacenter operators can find locations with fewer constraints and more supportive infrastructures.
Secondary markets often offer greater availability of land at more affordable prices, reducing the initial investment needed for constructing new facilities. These areas also present opportunities for building from the ground up with advanced design principles tailored for AI. Moving to these less congested regions supports the growing requirements of AI workloads while alleviating some of the pressures faced by crowded urban centers.
Examples of New Developments
In the UK, a growing trend involves datacenter development outside London. For instance, Virtus has acquired land in Saunderton, northwest of London, with plans to provide 75 MW of capacity for AI applications. These new developments highlight the strategic shift towards secondary markets to meet the increasing demands of AI. By establishing these new facilities, datacenter operators can leverage more favorable conditions in terms of space and power availability.
These moves also reflect a long-term vision where operators look beyond immediate needs and consider future growth potential. Building in secondary markets allows datacenters to scale more efficiently and sustainably, preparing for the anticipated expansion of AI technologies. This strategic relocation helps balance the geographical distribution of datacenters, offering resilience against growing regional pressures and ensuring robust service coverage.
Future Outlook and Infrastructure Needs
Alternative Energy Sources
The ongoing AI boom profoundly impacts the datacenter market, creating a dire need for new capacity that sophisticated AI-ready infrastructure entails. Given the power required for AI technologies and Europe’s struggling power grids, alternative energy sources like Small Modular Reactors (SMRs) and renewables (wind and solar) are being explored. However, these alternatives are not yet viable at large scales. Despite these constraints, progress is ongoing, and the potential for integrating these energy sources in the future remains promising.
For AI to reach its full potential, datacenters must pursue a balanced mix of energy resources. Incorporating renewable energy can help alleviate dependence on traditional power grids, enhancing sustainability and resilience. Operators are increasingly investing in research and development to make these alternative energy solutions more viable and scalable. Finding a harmonious integration of renewable energy sources with traditional grids will be a critical step in supporting the long-term growth of AI-centric datacenters.
Ensuring Robust Networks
Datacenter sites must not only ensure access to ample power but also robust high-speed networks. Identifying appropriate new locations that meet these criteria is challenging. To fill the gaps left by traditional markets, operators are increasingly looking beyond the conventional hotspots to develop new capacities and meet the AI-driven demand. Strong network infrastructure is crucial for effective data processing and transmission, which are at the core of AI operations.
Operators must evaluate potential sites with a keen focus on their connectivity capabilities, ensuring that they can provide the high-speed, low-latency connections needed for AI workloads. This involves a thorough assessment of existing network infrastructures, as well as plans for expansions and upgrades. The essence of success lies in creating an interconnected ecosystem of datacenters that can efficiently handle the rigorous demands of AI workloads while sustaining growth and operational excellence.
Implications and Strategic Shifts
Retrofitting Existing Facilities
The rapid adoption and integration of AI fundamentally alter the dynamics and strategies of the datacenter industry. Existing colocation facilities may accommodate AI workloads temporarily through retrofitting with specialized hardware and advanced cooling systems. However, the scale and growth of AI usage indicate significant new capacity construction will be necessary. Retrofitting can provide a short-term solution, but it often falls short in addressing the full scope of AI demands.
These adjustments include integrating specialized hardware capable of handling AI computations and upgrading cooling systems to manage the additional heat generated. While retrofitting can extend the life and use of existing infrastructure, it often represents a stop-gap measure rather than a long-term strategy. Ultimately, the immense and growing computational power required by AI will necessitate purpose-built facilities designed to meet these unique needs from the ground up, ensuring sustainable growth and performance.
Innovation in Design and Power Sourcing
Datacenter operators must innovate in design, power sourcing, and site selection to sustain AI’s growth trajectory. AI readiness is becoming a key driver in datacenter development strategies, necessitating innovative approaches to cooling, power management, and location scouting. The industry must reconcile the demand for enhanced computational power with environmental sustainability and logistical constraints. This balancing act requires forward-thinking and holistic planning to integrate AI wisely into the datacenter ecosystem.
Innovation involves adopting cutting-edge technologies and methodologies that can optimize operations while reducing environmental impact. This shift includes exploring new cooling systems, energy-efficient hardware, and sustainable power sourcing options. Finding the right locations for new builds, particularly in less congested areas with stable power supply and excellent connectivity, will be crucial. Successfully merging these technological advancements with responsible planning will support AI expansion while maintaining the industry’s commitment to sustainability.
Emerging Opportunities in Secondary Markets
Growth Potential in New Locations
The rapid rise of artificial intelligence (AI) is fundamentally altering the datacenter industry. As AI workloads and applications expand, datacenter operators must rethink the design, power management, and location of their facilities. This shift is driven by the enormous computational power and energy demands of AI technologies, like generative AI, which consume far more power than traditional datacenter tasks.
Operators are exploring innovative designs to accommodate the unique hardware needs of AI, such as custom cooling solutions and specialized processing units. Energy efficiency has become more critical, prompting the adoption of renewable energy sources and advanced power management systems. Additionally, the strategic placement of datacenters is now essential to minimize latency and ensure robust connectivity, particularly for real-time AI applications like autonomous driving and advanced analytics. This transformation underscores the need for constant innovation to keep pace with AI’s evolving demands, reshaping the entire landscape of the datacenter industry.
