Nebius Group (NBIS) signed a five-year $17B deal with Microsoft and operates data centers 20% more power-efficiently than standard cloud facilities.
IREN secured a $10B deal with Microsoft to provide GPU cloud computing and targets 60,000 GPUs by 2026 with potential $2.5B in cloud revenue.
Cipher Mining (CIFR) signed a 15-year $5.5B lease with Amazon and a decade-long multi-billion dollar agreement with Alphabet for powered data center space.
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Artificial intelligence (AI) is advancing rapidly, but it faces significant constraints in power availability and physical space for data centers. While there are abundant graphics processing units (GPUs) available from Nvidia (NASDAQ:NVDA), the real bottleneck lies in securing affordable, reliable energy to power them and the infrastructure to house them efficiently.
This creates opportunities for companies that specialize in converting low-cost power into high-utilization compute resources for AI workloads. Three stocks stand out for their ability to address these issues: Nebius Group (NASDAQ:NBIS), IREN (NASDAQ:IREN), and Cipher Mining (NASDAQ:CIFR).
By focusing on efficient data centers, vertical integration, and leased infrastructure, these firms position themselves as key enablers in the AI ecosystem, potentially driving growth as demand for compute surges.
Nebius Group leads as a comprehensive AI utility provider, emphasizing pre-sold capacity that generates revenue before construction even starts. This approach minimizes risks and ensures steady cash flow, as its recent five-year, $17 billion deal with Microsoft (NASDAQ:MSFT) underscores, as it locks in multi-year contracts.
The company’s data centers operate about 20% more power-efficiently than standard cloud facilities, reducing operational costs where energy can account for over 40% of expenses. This efficiency allows Nebius to undercut competitors like Amazon‘s (NASDAQ:AMZN) AWS or even Microsoft’s Azure while preserving higher margins.
What sets Nebius apart is its software integration, offering tools like ClickHouse for data management and MLOps pipelines alongside compute resources. This creates a sticky platform where developers face high switching costs once deployed. The result is a synergistic model: affordable energy, optimized hardware, and embedded software tools reinforce one another, blending energy-like stability with software growth potential.