• Nearly 100 GW of new data centers will be added between 2026 and 2030, doubling global capacity. The global data center sector will likely expand at a 14% CAGR through 2030, which will require energy innovations to alleviate grid constraints. Hyperscalers will remain a key driver of sector growth, executing a dual strategy of leasing and self-building.
• By 2030, AI could represent half of all workloads with inference becoming the primary driver. AI only represented about a quarter of all data center workloads in 2025, with training driving most of the demand. However, a significant shift is anticipated in 2027, when inference workloads could overtake training as the dominant AI requirement.
• The sector is experiencing an infrastructure investment supercycle requiring up to $3 trillion by 2030. Roughly 100 GW of new capacity is anticipated to come online between 2026 and 2030, equating to $1.2 trillion in real estate asset value creation. Tenants will likely spend an additional $1 to $2 trillion to fit out their space with IT equipment.

While AI has been quickly gaining daily active users, it only represented about a quarter of all data center workloads in 2025, with training driving most of the demand. However, a significant shift is anticipated in 2027, when inference workloads could overtake training as the dominant AI requirement.

While an AI model represents a one-time or periodic investment, once the model is created, inference generates ongoing revenue through actual application usage. Looking forward, every AI model deployment creates sustained inference demand that grows with user adoption. This growth, however, depends on the emergence and rapid adoption of inference applications that don't yet exist at scale.

Inference demand requires geographical distribution to reduce latency and serve users effectively. This will drive regional deployments and embedded systems at the edge.

The data center sector currently sits at the beginning of one of the largest infrastructure investment supercycles seen in the modern era. The interconnected nature of data centers means the AI-fueled expansion is reshaping a number of sectors including power, technology and real estate.

The transition from AI training to inference will redistribute workloads from centralized clusters to distributed regional hubs, fundamentally altering capacity planning and geographic deployment strategies.

Energy infrastructure has emerged as the critical bottleneck constraining expansion. Grid limitations now threaten to curtail growth trajectories, making behind-the-meter generation and integrated battery storage solutions essential pathways for sustainable scaling.

Investors and developers must balance speed to market with capital efficiency while navigating supply chain constraints and evolving demand patterns. Industry leaders must transform these converging forces into competitive advantages. The winners of this generational investment supercycle will be those who can anticipate demand inflection points while maintaining flexibility to adapt as AI models and use cases evolve.