Summary: Bernstein's first-quarter read on the hyperscalers highlights Google Cloud as the current frontrunner in AI cloud platforms, driven by accelerating revenue growth and widening margins. Other major providers - Microsoft Azure, Amazon Web Services (AWS) and Oracle Cloud Infrastructure (OCI) - each reported notable AI-related growth and investment, even as capacity bottlenecks and heavy infrastructure spending shape the competitive landscape.
Google Cloud
Google Cloud recorded 63% year-over-year revenue growth in the quarter, outstripping Amazon Web Services, Microsoft Azure and Oracle Cloud. Bernstein noted that Google generated the largest increase in incremental cloud revenue among its peers, buoyed by demand for Gemini models, AI services and enterprise cloud workloads. The business also saw operating margins expand to about 33%, a sign that rising profitability accompanied the top-line acceleration despite continued heavy spending on AI infrastructure.
Microsoft Azure
Microsoft remained characterized as one of the strongest long-term beneficiaries of AI adoption. Azure grew about 40% year over year, and Microsoft’s annualized AI revenue surpassed $37 billion. Demand for Copilot, GitHub and Azure AI services continued to contribute to growth. Nonetheless, Bernstein highlighted that capacity constraints persist as a challenge for Microsoft.
Amazon Web Services
AWS maintained solid momentum with cloud revenue up 28% in the quarter. The provider’s backlog expanded to $364 billion. AWS’s growth was supported by reported triple-digit AI demand, adoption of Trainium chips and ongoing enterprise workloads, even while supply constraints continue to be a factor.
Oracle Cloud Infrastructure
Oracle was identified as the fastest-growing major cloud provider in percentage terms, with OCI revenue rising 93% year over year. Oracle’s remaining performance obligations increased to $638 billion as demand for AI infrastructure accelerated.
Sector-wide investment and capacity dynamics
Across the hyperscale sector, investment in AI infrastructure is rising sharply. Bernstein projects combined capital expenditures by the largest hyperscale providers will exceed $620 billion this year as companies expand data center capacity to meet surging AI demand. The report also described a shift in capacity constraints: where shortages had centered on GPUs, the bottleneck is increasingly powered data center capacity. As a result, deployment speed and overall infrastructure availability are becoming more important competitive advantages in the AI race.
Implications
The quarter’s results underline that rapid AI-related demand is reshaping revenue mixes and margins for the largest cloud providers, while simultaneously driving outsized capital allocation to data center expansion and infrastructure. The dynamics of supply constraints - moving from component availability toward powered capacity and deployment capability - are a central operational challenge across the sector.
Note: This article presents the findings and figures reported in the Bernstein analysis of hyperscaler performance in the first quarter, without additional interpretation or supplementary data.