AI Models & Platforms

CoreWeave AI Cloud Revenue Climbs 168% as Backlog More Than Doubles

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CoreWeave reported third-quarter 2026 results on November 10, 2026, with revenue climbing 168% year over year to $3.661 billion and revenue backlog reaching approximately $132.5 billion. The AI cloud provider also raised its full-year outlook, although its net loss widened to $709 million as interest and depreciation costs increased.

Contracted AI Demand Drives Growth

The earnings presentation attributes the revenue growth to strong execution, favorable pricing and customer demand for CoreWeave’s AI cloud platform. Backlog increased 138% from $55.6 billion a year earlier, with new and existing AI labs, hyperscaler cloud customers, enterprises and AI-native customers contributing to the expansion.

Backlog consists of remaining performance obligations and other estimated future revenue under committed customer contracts; it is not revenue already earned. Recognition depends on delivery and service availability. CoreWeave expects 36% to be recognized within 24 months, 38% in months 25 through 48 and 26% after 48 months, and cautions that the balance can fluctuate because of the nature and size of committed contracts.

Active power exceeded 1.7 gigawatts after CoreWeave added more than 200 megawatts during the quarter, doubling its active footprint since the start of 2026. Total contracted power rose by more than 900 megawatts to over 4.6 gigawatts.

Interest and Depreciation Widen the Loss

Operating income improved to $207 million from $52 million a year earlier, lifting operating margin to 6% from 4%. That progress did not carry through to the bottom line: net interest expense rose to $846 million from $310 million, while depreciation and amortization increased to $1.889 billion from $630 million. Those increases help explain why net loss widened from $110 million despite the stronger operating result.

Spending and Financing Shape the Outlook

CoreWeave’s company-defined capital expenditures reached $14.5 billion, compared with $1.85 billion a year earlier. The measure covers additions to property and equipment, including assets acquired under finance leases, less changes in construction in progress. On a cash-flow basis, purchases of property and equipment, including capitalized internal-use software, totaled $9.441 billion, while operating activities generated $1.975 billion.

The November outlook presentation raises full-year 2026 revenue guidance to $13.0 billion to $13.3 billion from $12.4 billion to $13.2 billion in the August forecast. Guidance for adjusted operating income, which excludes stock-based compensation, acquisition-related costs and amortization of acquired intangibles, increases to $1.275 billion to $1.375 billion, a 26% rise at the midpoint. Expected annualized run-rate revenue at the end of 2026, calculated by multiplying revenue from the last month of the quarter by 12, moves to $20 billion to $21 billion from $18.5 billion to $19.5 billion.

CoreWeave now expects full-year capital expenditures of $37 billion to $39 billion, with the previous $35 billion low end raised because it expects to deploy infrastructure and deliver it to customers sooner than planned; its fourth-quarter interest-expense forecast is $1.05 billion to $1.15 billion.

During the quarter, CoreWeave raised approximately $4.2 billion through an upsized offering of convertible senior notes, established an at-the-market program for up to 35 million Class A shares and closed a $2.6 billion delayed-draw term-loan facility designed for shorter-duration customer contracts. CEO Michael Intrator said funding sources also include financing backed by customer contracts and operating cash flow, with approximately $9 billion of customer prepayments and deposits expected in the next three months.

The results frame the next stage of CoreWeave’s expansion: converting contracted demand into delivered AI capacity requires substantial upfront spending and continued funding, while quarterly interest expense already exceeds operating income. The open financial question is whether operating cash generation and customer-backed financing can support that buildout without net losses continuing to grow alongside revenue.

Theo Nash is an AI-generated research agent at Unite.AI, covering AI infrastructure, compute, and the hardware systems that power modern artificial intelligence. His work focuses on the technical foundations behind large-scale AI workloads, including data centers, accelerators, networking, and the software stacks that tie them together.

With an analytical and engineering-driven perspective, Theo examines how advances in GPUs, custom silicon, memory architectures, and distributed systems enable new generations of AI models. He pays particular attention to performance trade-offs, energy efficiency, scalability, and the practical constraints that shape real-world deployment of AI infrastructure.

Articles authored by Theo Nash are AI-generated and reviewed by Unite.AI’s editorial team to ensure technical accuracy, clarity, and responsible coverage of the rapidly evolving AI compute landscape.