Oracle’s AI-fueled cloud flywheel is spinning faster than ever, with management projecting Oracle Cloud to scale toward an ambitious $144 billion in annual revenue by fiscal 2030. This bold trajectory has ignited market excitement, sending Oracle’s stock soaring and drawing comparisons to Nvidia’s transformative “AI moment.” The surge is driven by an unprecedented AI order book and remaining performance obligations (RPO) ballooning into the hundreds of billions, signaling that demand, not short-term noise, is now the key driver of Oracle’s revenue and long-term valuation 📈.

Why $144B Matters 🌟

Oracle’s guidance outlines a clear path: cloud revenue is expected to grow from $18 billion in FY2026 to $32 billion, $73 billion, $114 billion, and ultimately $144 billion by FY2030. This positions Oracle Cloud as a capacity-and-contracts story, fueled by the scarcity of generative AI infrastructure. Analysts highlight that the surge in bookings and RPO growth mirrors Nvidia’s catalysts—where long-term demand visibility and GPU buildouts, not just quarterly metrics, are reshaping Oracle’s market narrative [cnn, ft].

The AI Backlog That Bent the Curve 📡

Oracle’s AI-related backlog and bookings have exploded, with RPO reportedly reaching $455 billion and recent bookings rivaling the prior four years combined. This dramatic shift has flipped Oracle’s perception from a cloud laggard to a leading AI platform. Multi-billion-dollar deals with top AI builders and multi-cloud partnerships embedding Oracle databases and OCI in hyperscalers like AWS, Azure, and Google Cloud have expanded its reach while capitalizing on GPU-intensive workloads 💻.

Why Investors Compare Oracle to Nvidia 🔍

Strategists see parallels between Oracle and Nvidia because the market is repricing Oracle for a multi-year capacity cycle. Gigawatt-scale data centers, access to Nvidia GPUs, and bare-metal performance optimized for AI training and inference translate into clear contractual demand. This visibility drove Oracle’s stock to its biggest one-day jump since the early 1990s, adding over $200 billion in market value as investors embraced the AI-driven revenue runway [bbc].

Real-World Impact: AI Model Training on OCI 🛠️

Take a large-model developer moving from a constrained GPU broker to OCI’s bare-metal GPU clusters for training next-gen multimodal models. By colocating data and compute on Oracle Cloud, the team slashed epoch times and costs through higher throughput and network efficiency. What once took months now supports continuous training cycles, enabling faster commercial launches. Predictable GPU availability under multi-year contracts improved unit economics and locked in cloud spend, showcasing why Oracle’s AI capacity is selling out 🏭.

Execution Risks to Watch ⚠️

Oracle must convert its massive backlog into realized revenue while scaling capital expenditures, supply chains, and data center buildouts without eroding margins or facing delays. The rapid pace of AI hardware upgrades adds pressure, and competition from AWS, Microsoft, and Google remains fierce. However, Oracle’s multi-cloud strategy and cost-performance edge provide near-term advantages. Investors will closely monitor deployment timelines, GPU utilization, and free cash flow conversion 📊.

FAQs ❓

Investor Takeaway 💡

This is an AI-capacity supercycle story. With multi-year visibility, OCI’s speed and cost advantages, and multi-cloud distribution, Oracle Cloud is poised to compound at scale as GPUs ship and contracts convert to revenue. The risk-reward hinges on execution and cash flow discipline, but missing this phase of Oracle’s AI buildout could mean overlooking one of the cycle’s most leveraged beneficiaries 🎯.