At Nvidia’s Paris GTC in the summer of 2025, CEO Jensen Huang highlighted what he believes will drive the next wave of AI innovation: Sovereign AI. This concept refers to the capacity of a nation or organization to independently develop, manage, and safeguard its own AI systems, infrastructure, and data centers. Achieving Sovereign AI means building and overseeing locally operated data centers, chip supply chains, and cloud platforms, ensuring that critical technologies and data remain entirely under local control.
Countries are increasingly prioritizing the development of sovereign AI for a variety of strategic reasons, including enhancing privacy, safeguarding national security, retaining high-value AI jobs at home to stimulate economic growth, maintaining control over sensitive data, preserving cultural norms and values, building critical infrastructure, and securing geopolitical influence. Recent months have seen several Middle Eastern nations announce their own sovereign AI initiatives. Given the exceptionally high ambient temperatures in the region, these efforts will confront distinctive cooling challenges in their AI data centers. Saudi Arabia, the UAE, Qatar, and Bahrain are among the countries poised to face these exacerbated thermal hurdles as they advance their AI ambitions.
Developing sovereign AI compels countries to build the entire ecosystem from the ground up, navigating every stage of the process independently. Among these steps, the creation and training of large language models (LLMs) is particularly demanding, requiring vast computational resources to process enormous datasets and refine model performance. This undertaking is both complex and energy-intensive, as advanced machine learning techniques push hardware to its limits around the clock. Where intense, continuous power usage prevails, the need for highly effective and precise cooling solutions becomes paramount. As LLMs often operate non-stop during their development, robust thermal management is not just important—it is a critical infrastructure challenge that nations must address to ensure the success of their sovereign AI ambitions.
As nations pursue sovereign AI, the demand for localized data infrastructure is intensifying. Unlike multinational tech companies that distribute workloads across global data centers optimized for climate, cost, and capacity, sovereign AI requires that compute infrastructure reside within national borders—often as a matter of law, security, or strategic control. This geographic constraint introduces a major technical hurdle: cooling. High-performance AI workloads, especially those involving large language models (LLMs), generate intense heat and require highly efficient, scalable thermal management systems.
The challenge becomes even more acute in regions where climate conditions are inhospitable to traditional cooling methods. For example, countries in the Middle East or Southeast Asia, pursuing sovereign AI as part of their national development agendas, must build data centers in hot, humid, or arid environments. These conditions drive up the energy cost of air and evaporative cooling and strain water resources and could prompt a shift toward liquid and solid state cooling technologies. This stands in stark contrast to Nordic countries, where naturally cool temperatures reduce the burden of thermal management and enable more sustainable AI operations with lower energy input.
This tension between political or geographic mandates and the technical realities of operating AI infrastructure underscores a critical, and often overlooked, aspect of AI sovereignty: environmental and engineering feasibility. Nations seeking to host AI domestically must now factor thermal efficiency, energy consumption, and water availability into their digital sovereignty strategies. Without addressing these cooling constraints head-on, the promise of sovereign AI may be limited not by ambition—but by thermodynamics.
Modern data centers powering sovereign AI workloads are moving away from legacy air cooling in favor of more efficient solutions. Liquid cooling now dominates in high-density environments, thanks to its ability to absorb and transfer heat far more effectively than air. However, in regions like the Middle East where water is scarce, traditional systems that consume vast amounts of water could face mounting scrutiny and operational limitations. Here, water-efficient systems are gaining traction, minimizing water use and environmental impact. Simultaneously, AI-driven airflow management is becoming essential for real-time optimization, leveraging machine learning to dynamically balance airflow, temperature, and humidity, maximizing performance and energy efficiency in complex compute environments.
Another transformative direction is the reuse of waste heat and the deployment of thermal energy storage. By capturing and repurposing the immense heat generated by AI hardware—whether to supply district heating or drive absorption chillers—operators can turn a liability into strategic value, improving sustainability and reducing total energy consumption. Among emerging technologies, Phononic’s solid state cooling stands out for its potential to revolutionize data center design. Unlike traditional compressor-based chillers, Phononic’s solid state systems rely on thermoelectric or caloric effects, featuring no moving parts, compact form factors, and scalable architectures. Solid state cooling systems like those offered by Phononic are an addition to incumbent systems and can precisely target high-density heat sources, such as chip hotspots, enabling localized thermal management directly at the silicon level. These systems are also flexible and responsive, providing cooling only where and when needed, minimizing energy use.
