Digital infrastructure: Rising demand, rising complexity

In Europe, Q1 2026 saw record data centre leasing and pre-leasing demand after a quieter 2025. US Big Tech favoured primary markets, while the Nordics, which took 34% of demand, were led more by neoclouds and Chinese tech.[1] While strong growth may de-risk secondary markets, a primary-secondary divergence remains in tenant risk profile.

Strong US demand remains greater than that seen from Europe.1 H1 2026 global new leasing and pre-leasing already reached 96% of 2025’s entire total, positioning the year for record demand which outpaces live supply growth.[2] This is balanced, however, against an anticipated acceleration in supply towards 2030.

The continued expansion of GenAI remains the principal demand driver. Rises in Big Tech capex are outpacing cloud and AI revenues. This may not translate into infrastructure investment, however, given the growing share AI chips represent in capex. Furthermore, revenues increasingly represent demand for AI compute capacity, not just for AI applications. We are also seeing US Big Tech players moving more towards offering AI compute supply beyond developing their own AI models, increasing competition for neoclouds.

GenAI models are improving rapidly. Inference demand and adoption have grown and business AI spend has risen sharply through 2026.[3] However, enterprises limited usage after costs rose faster than expected.

Furthermore, open-weight Chinese models have narrowed the capability gap with the US, sometimes at significantly lower token costs[4]. The US government also temporarily limited foreign usage of powerful GenAI models. This has several critical implications:

• Greater enterprise cost discipline and a shift towards lower-cost models could moderate token-revenue forecasts. While this may bring a headwind for AI demand, lower token costs may better enable AI’s longer-term adoption, and AI chip rental pricing supports that short-term demand for AI compute remains strong.[5]
• Vertical integration of AI infrastructure – that is, converting energy into AI – becomes more critical, allowing AI platforms to offer lower-cost tokens while maintaining margins.
• As open-weight models approach the closed frontier[6], pricing power may weaken, with value shifting towards distribution, orchestration, efficient compute and trusted sovereign deployment.
• Model commoditisation may broaden inference demand but weaken some tenants’ unit economics. Data centre tenants who can generate stronger AI compute margins above leasing costs become better positioned, in our view.
• Stronger open-weight models could broaden AI infrastructure demand beyond hyperscalers to neoclouds, sovereign platforms, managed-service providers, and some large enterprises. For European investors, this could support demand for secure inference capacity that can host multiple models and adapt to evolving chip architectures, while increasing the importance of tenant credit quality and re-leasing potential.
• Governments’ ability to restrict foreign access to advanced AI models strengthens the strategic case for European sovereign AI and cloud capabilities. While private infrastructure capital can support data centre buildouts, sovereign AI requires models, chips, capital, power and customers. The commercial case will depend on whether Europe can build credible demand and technology ecosystems around it.