- Neoclouds are seeing massive hyperscaler demand as companies race to scale AI infrastructure, resulting in rapid revenue and backlog growth.
- Leaders like CoreWeave and Nebius enable this through access to the latest Nvidia GPU’s while also optimizing compute utilization.
- However, the bearish argument behind hyperscaler demand lies in their desire to offload their capex spending and shift costs to the operating expense line.
- CoreWeave’s and Nebius’ growth is far from profitable, as they seek to capture AI demand with limited cash flow and soaring debt loads in an increasingly tough macro backdrop.
- Circular financing, demonstrated by Nvidia’s investments and financial backstopping, is another key item to monitor closely
Neoclouds are one of the more hotly debated AI business models, with CoreWeave and Nebius being the two most widely recognized names. These companies have seen their sales, backlog, and share prices soar, differentiating themselves through quick access to the latest GPU compute and GPU utilization advantages that allow hyperscalers to rapidly add efficient compute capacity.
Notably, CoreWeave and Nebius have each secured 3.5 GWs of contracted power capacity; while these power footprints are key considering power is a hindrance to data center expansion, the vast majority of their contracted power capacity has yet to come online. CoreWeave is targeting 1.7 GW of active power by the end of 2026, while Nebius is targeting 800 MW to 1 GW of connected power.
In turn, they are quickly working to convert their contracted power to active power, and thus convert large backlogs into revenue. Yet doing so is extremely expensive, and neoclouds do not have the same cash nor operating cash flow profiles of Big Tech. This is leading neoclouds to employ unique and circular financing structures, raising some red flags.
In this analysis, I dive into the two public neoclouds that are riding Nvidia equity, hyperscaler contracts, and GPU-backed debt to fund the buildout, and what it means for the durability of the surge.
Microsoft and Meta’s $120B+ Bet on Neoclouds
The size of hyperscaler-neocloud partnerships compared to their current revenue is astounding. Microsoft has struck the most neocloud deals, with approximately $60 billion worth of commitments between CoreWeave, Nebius, and other private players such as Nscale. Meanwhile, Meta has committed $35.2 billion to CoreWeave in total after its recent $21 billion expansion, and an up to $27 billion deal with Nebius for a total commitment of up to $62.2 billion. Along with Meta, OpenAI is one of CoreWeave’s two largest customers, while CoreWeave also has a multi-year compute agreement with Anthropic.
Alone, Microsoft and Meta’s total commitments extend up to $122.2 billion – for perspective, that is ~90% of the TTM revenue of AWS being allocated towards neoclouds over long-term capacity deals. When factoring in hyperscaler-backed deals from OpenAI and Anthropic (although exact deal value is unknown), total potential commitments surpass $145 billion.
Keep in mind, CoreWeave’s FY2026 estimated revenue is $12.6B and Nebius FY26 revenue is expected to be $3.4B – therefore, these partnerships are leading to commitments that are an order of magnitude higher than current sales.
The reason hyperscalers are willing to allocate this capital to a relatively new business model in the neoclouds is three-fold – quick access to leading GPU generations, optimized compute utilization, and the added benefit of not having to recognize capex on the balance sheet – we look at each of these drivers below.
Neocloud Advantage is Offering Quick Access to GPUs
At its root, neocloud demand is a product of hyperscalers’ insatiable demand for compute capacity. However, neoclouds can often add compute capacity much faster than hyperscalers can through internal builds, offering a key value proposition for Big Tech. As hyperscalers spend hundreds of billions a year on AI compute, minimizing the lag between data center expenses and revenue generation is critical to maximizing their return on investment.
Supporting the argument around neocloud’s advantage lying within time to deployment, commercial real estate giant JLL notes, “Neoclouds can deploy high-density GPU infrastructure within months compared to multi-year builds for hyperscale data centers, providing crucial time-to-market advantages for businesses needing rapid AI development.”
In CoreWeave’s S-1 Registration filing, it lists “Faster access to the latest AI infrastructure advancements” as one of its key benefits to customers. Specifically, CoreWeave says “we were among the first to deliver NVIDIA H100, H200, and GH200 clusters into production at AI scale, and the first cloud provider to make NVIDIA GB200 NVL72-based instances generally available. We are able to deploy the newest chips in our infrastructure and provide the compute capacity to customers in as little as two weeks from receipt.”
Nebius makes a similar statement in its Annual Report, noting its “consistent track record of being one of the first to deploy the latest generation of NVIDIA GPU chips.”
CoreWeave and Nebius’ relationship with Nvidia is key to acquiring the latest GPUs ahead of others. Nvidia recently invested $2 billion in both CoreWeave and Nebius. Under these partnerships, CoreWeave and Nebius will each look to deploy more than 5 GW of data center capacity by 2030.
CoreWeave recently demonstrated its ability to offer quick access to the latest chips and newest architectures to hit the market once again, being the first to have a Vera Rubin system up and running at the start of June. This provides evidence that partnering with CoreWeave and Nebius can help hyperscalers access as much of the latest GPU compute as possible in short order.
Beyond Hardware: Neocloud Platforms Offering Higher GPU Utilization
Aside from raw compute access, CoreWeave and other neoclouds layer on software and additional capabilities that improve GPU utilization – a key value add for hyperscalers.
For example, CoreWeave Kubernetes Service (CKS) helps coordinate the allocation of workloads across thousands of GPUs, while its SUNK service helps optimize GPU utilization by allowing training and inference workloads to run on the same cluster. CoreWeave Tensorizer enables high-speed model loading, reducing GPU idle time.
Combining these software and optimization capabilities with rapid fault detection and remediation services, CoreWeave believes it can offer higher GPU utilization rates than hyperscalers, based on the model FLOPs utilization (MFU) metric. The “MFU gap” is a metric that describes the gap between compute capacity and usage, which today often ranges between 30% to 40%.
The MFU gap can become quite costly as it represents a more realistic way to measure the performance of GPUs — rather than only taking into account if a GPU is sitting idle or not. According to Trainy AI: “GPU Utilization is only measuring whether a kernel is executing at a given time. It has no indication of whether your kernel is using all cores available, or parallelizing the workload to the GPU’s maximum capability.”
When going public, CoreWeave published its MFU rate at 35% to 45%, stating it is 20% higher than competitors, which means other AI data centers had MFU rates more in the 30% range. However, in a March 2025 blog post, CoreWeave noted that it was achieving an MFU of >50% on Hopper GPUs. This ability to stand up next-generation GPU hardware in short fashion combined with improved utilization rates is where the neoclouds’ advantage lies.
Behind the Balance Sheet: Why Hyperscalers Are Leasing Neocloud Capacity
By leasing compute capacity from neoclouds, hyperscalers shift their cost timeline from being a large upfront capex outflow to an operational expense outflow spread over long-term contracts. The need to spread costs is becoming increasingly evident due to the massive spending hyperscalers are engaged in.
Although this is the “bear” case on why hyperscalers work with neoclouds—contrasting this with the rationale behind GPU access and utilization is key because one could argue that hyperscalers are quite capable of software optimizations and GPU utilization on their own (in fact, they are the longstanding incumbent here with deep expertise in cloud operations and workload optimizations).
Take Meta for example. Analysts are currently expecting the company to generate $136 billion in cash from operations in 2026. With its stated capex guidance of $125 billion to $145 billion, the company could easily be free cash flow negative during the year. However, as noted, Meta also has up to $62.2 billion in neocloud agreements. If Meta built the equivalent value of capacity itself, the firm would recognize that spending as balance sheet capex, weighing further on its already pressured free cash flow.
On the other hand, neocloud agreements add nothing to Meta’s capex, as the costs are recognized as operating expenses over the life of the contracts. Notably, Meta’s contracts with CoreWeave and Nebius extend through 2031-2032, meaning that opex payments could average less than $10 billion annually.
Looking at Microsoft, we can see a similar situation. In calendar year 2026, the company is guiding for capex of $190 billion, while analyst forecast $200 billion in cash from operations over the same period. If these figures materialize, the company would consume 95% of its OCF on capex. The $60 billion in neocloud agreements, recognized as operating expenses over many years, expands its capacity while keeping that spend off its cash flow statement.
As hyperscalers offload their capex, neoclouds are the ones taking that capex on—resulting in their massive funding needs.
Circular Financing: Nvidia’s Role as an Investor, Supplier, and Demand Backstop
Both Nebius and CoreWeave lend some of their advantage to Nvidia, as it is this partnership with the GPU leader that offers them that ability to be among the first providers to stand up and deploy next-gen platforms such as Blackwell Ultra and now Rubin.
Having Nvidia as a partner also could play a role in helping CoreWeave and Nebius secure funding at much better terms, extending presence and support beyond the hyperscalers to another investment-grade firm with a strong balance sheet and cash flows. Nvidia’s LTM free cash flow was $119 billion, the second highest of any company in the world, only behind Apple. The downside, however, is that Nvidia’s relationship with the two is one of the most identifiable instances of circular financing.
This stems from the multi-billion-dollar investments that Nvidia has made in CoreWeave and Nebius. Notably, Nvidia’s latest $2 billion investments in each company were not its first. Nvidia’s Q1 2025 13F filing revealed a CoreWeave stake worth $896.7 million at the time, while its Q4 2025 13F revealed a $33 million stake in Nebius. Thus, the investment relationship between Nvidia and these firms extends well beyond one year.
Furthermore, in the case of CoreWeave, Nvidia has also provided a significant financial backstop against unsold GPU capacity. Under the agreement with an initial value of $6.3 billion, “in instances where [CoreWeave’s] datacenter capacity is not fully utilized by its own customers, NVIDIA is obligated to purchase the residual unsold capacity through April 13, 2032.” In other words, Nvidia is committed to purchasing unsold GPU capacity if CoreWeave is unable to find another buyer. With an initial value of $6.3 billion, there is the potential that the arrangement could become larger over time.
As Nvidia makes these investments, CoreWeave and Nebius are going right back to Nvidia to purchase large volumes of GPUs – a clear representation of circular financing. By providing a relatively small amount of equity funding, Nvidia secures relationships with these neoclouds that intend to purchase tens of billions’ worth of GPUs.
