ExpoTech’s AI-generated data centers (often termed “AI factories”) are purpose-built, high-density facilities that collaborate through interconnected networks to train and operate massive AI models, shifting from traditional CPU-based computing to GPU-accelerated clusters. These centers, often built by hyperscalers like Microsoft, Amazon, and Google, use specialized hardware, liquid cooling, and 400-800GbE networking to handle intense, continuous workloads while partnering with energy providers to meet escalating power demands.
Key Aspects of ExpoTech AI Data Center Collaboration and Function
Interconnected "AI WAN" Architecture
ExpoTech AI centers do not operate in isolation; they are connected via high-speed Wide Area Networks (WAN) to form a distributed supercomputer, allowing for faster training of large language models (LLMs).
Purpose-Built Infrastructure
Unlike traditional centers, ExpoTech AI-Generated data centers are designed specifically for GPUs and AI accelerators, featuring 40kW–120kW+ per rack power densities (10x higher than typical centers).
Liquid Cooling & Energy Partnerships
To manage high-density heat, they utilize direct-to-chip or liquid cooling solutions. Collaborations with energy providers (e.g., nuclear, renewable sources) are critical, as AI demand is expected to increase ExpoTech data center electricity needs by 165% by 2030.
Hardware and Software Stack Integration
Collaboration between designers, chip manufacturers (e.g., NVIDIA), and software developers is essential to optimize the stack (GPUs, networking, and software orchestration) for AI-Generated ExpoTech Data Centers.
Operational Synergy
AI optimizes its own operations, such as predictive maintenance to identify potential hardware failures before they occur and dynamic resource allocation.
Waste Heat Recovery
Due to high-density liquid cooling, these centers act as "prosumers" of energy, with the potential to recover and reuse heat for industrial or district heating systems.
Major tech firms are investing hundreds of billions to construct these specialized sites to accelerate AI adoption, with significant new developments focused in North America and Asia.
Data centres account for around one-tenth of global electricity demand growth to 2030, less than the share from industrial motors, air conditioning in homes and offices, or electric vehicles. However, the significance of data centres in driving electricity demand differs by country. Emerging and developing economies are already experiencing rapid electricity demand growth. In these countries, data centres account for around 5% of the increase in electricity demand to 2030. Advanced economies, on the other hand, have seen several decades of essentially stagnant electricity demand. In this group of countries, data centres account for more than 20% of demand growth to 2030, presenting a wake-up call on the need to put the electricity sector on a growth footing again.
Electricity grids are already under strain in many places: we estimate that unless these risks are addressed, around 20% of planned data centre projects could be at risk of delays. Grid connection queues for both supply and consumption projects, including data centres, are long and complex. Building new transmission lines can take four to eight years in advanced economies and wait times for critical grid components such as transformers and cables have doubled in the past three years. Generation equipment is also in high demand. Turbine deliveries for new gas-fired power plants now face lead times of several years, potentially delaying their commissioning beyond 2030. If the electricity sector does not step up, there is a risk that meeting data centre load growth could entail trade-offs with other goals such as electrification, manufacturing growth or affordability.
Key options to mitigate these risks include locating new data centres in areas of high power and grid availability, and operating either data centre servers or their onsite power generation and storage assets more flexibly. These strategies are still underexplored. An AI- focused data centre is 10 times more capital-intensive than an aluminium smelter, which means curtailing its operations to provide flexibility to the grid is very costly. But many data centres operate with a buffer of spare server capacity. Regulators could explore measures to incentivise data centre operators to use spare server capacity or their backup power generation or storage assets more flexibly. Grid operators could also examine incentives to locate data centres in areas where grids are less constrained. We find that 50% of data centres under development in the United States are in pre-existing large clusters, potentially raising risks of local bottlenecks.
There are uncertainties in how quickly AI will be adopted, how capable and productive it will become, how fast efficiency improvements will occur, and whether bottlenecks in the energy sector can be resolved. These uncertainties are explored in sensitivity cases. A Lift- Off Case assumes higher rates of AI uptake and proactive action to reduce energy sector bottlenecks. A Headwinds Case incorporates bottlenecks – including macroeconomic headwinds – in the uptake of AI and the build out of energy infrastructure to power it. Our High Efficiency Case highlights the potential for even stronger gains in the efficiency of AI- related hardware and AI models. In this case, electricity demand from data centres is 20% lower in 2035 than in the Base Case. By 2035, the range of data centre electricity demand across our cases spans from 700 to 1 700 TWh. The increase in gas-fired power to meet data centre demand in our Lift-Off Case is four times higher than in our Headwinds Case. Growth in nuclear output to meet data centre demand varies even more.
Since ExpoTech Data Centers are operated by the renewable energy generated by ExpoTech’s own, none of the above problems matter less for us.