Neoclouds and the Three Business Models
Neoclouds sit at the center of the AI infrastructure durability debate because they are increasingly the external channel through which hyperscalers secure additional power and compute to convert demand into revenue. CoreWeave ended 2025 with $66.8 billion in contracted backlog. Nebius signed a deal with Meta worth up to $27 billion. IREN landed a $9.7 billion contract with Microsoft. Core Scientific is locked into $10.2 billion over 12 years from CoreWeave. Cipher Digital secured $9.3 billion across two flagship leases. Together, those deals show hyperscalers are still reaching beyond their own footprints (while still rapidly trying to secure first-party contracts) to secure scarce capacity wherever it can be found. That suggests the buyers with the best visibility continue to view AI demand as durable enough to justify locking up external power and compute today. From there, the more useful question is how that demand maps onto the very different business models now being grouped together under the Neocloud label.
Our view has been that, for some time, the category has been analyzed too broadly. These companies are often discussed as though they are variations of the same model responding to the same opportunity. We do not think that is right. What the market is calling Neocloud increasingly breaks into three distinct businesses with different economics, different risk exposure, and different claims on long-term durability. That distinction is more important now because the constraint that created this category is changing. For the past two years, the defining bottleneck has been access to GPUs, and that remains the case. But we have to equally appreciate the constraint that is deliverable power.
That shift is why simple revenue comparisons can be misleading if they are not tied back to business model. CoreWeave generates roughly $10 to $12 million in annual revenue for every energized megawatt of capacity it operates. Core Scientific generates about $1.4 million per megawatt. At first glance, that spread seems to settle the comparison. In reality, it captures two very different positions in the value chain. CoreWeave owns the GPUs, builds the software stack, manages the orchestration layer, and retains the technology-refresh risk on hardware that some would argue is likely depreciating economically faster than its accounting life suggests. Core Scientific owns the land, power, and cooling, while letting its customer bring the hardware, absorb the obsolescence risk, and pay for long-duration access. The revenue per megawatt is lower, but so is the exposure to the most volatile part of the stack.
We think that distinction is where the category begins to separate. Full-stack AI platforms such as CoreWeave and Nebius capture the most value per megawatt, but they also carry the greatest exposure to hardware refresh, financing, and utilization risk. Bare-metal GPU cloud providers such as IREN monetize owned power and owned hardware with less software differentiation and a different return profile. Then there is a third group that we think should be viewed on its own terms: former bitcoin miners such as Core Scientific, Cipher Digital, and TeraWulf that are increasingly functioning as long-duration infrastructure hosts. These businesses may ultimately prove to have the most durable economics in the group precisely because they sit lower in the stack and avoid much of the technology-refresh cycle that compresses returns higher up. A related question, and an increasingly important one, is which of these models is best positioned to deliver better customer economics as the market shifts from securing capacity to optimizing token production.
The reason this takes on more weight now is that power is becoming the strategic asset that is hardest to replicate on a useful timeline. Switchgear can take a year to procure. Transformers can take more than two. High-voltage substations often run three to five years. ERCOT has reported 137 new large-load submissions totaling roughly 140,000 megawatts of potential demand by 2036. That number is important not simply because it is large, but because it shows how quickly grid demand is compounding against infrastructure that remains slow to permit, slow to procure, and slow to energize. The companies that secured power early, whether through owned sites, brownfield conversions, or interconnection rights, have a strategic advantage that cannot be recreated quickly with capital alone.
At the same time, stakeholders also need to keep sight of who is writing the largest contracts. The same hyperscalers outsourcing capacity today are also the entities most capable of internalizing that demand over time. Microsoft represented 67% of CoreWeave’s 2025 revenue, up from 62% the year before. That is a sign of strong current demand, but it is also a reminder that concentration and renewal dynamics will matter much more once hyperscalers bring more of their own capacity online. That is the real debate from here. Demand is clearly real. The harder question is which business model can convert that demand into durable returns once power, not GPUs, becomes the primary constraint and once the largest customers have more internal options.
The full report explores each of these models in depth. It is available to paid subscribers below.
What the full report covers:
The three-business-model framework: why full-stack AI platforms, bare-metal GPU clouds, and Power Landlords should not be valued on the same basis
Revenue per megawatt by company: triangulated estimates for CoreWeave, Nebius, IREN, Core Scientific, Cipher, and TeraWulf, with margin and capital-intensity context
A near-term deliverability ranking: which companies can actually energize capacity versus which are still in permitting queues
The software-yield test: what separates a defensible platform from scarce-capacity rental, and who passes
Hardware refresh and the duration mismatch: why six-year depreciation schedules on three-to-four-year economic lives create a hidden fault line
The Power Landlord case: contract structures, NOI margins, tenant credit quality, and why lower in the stack may mean cleaner on duration
The hyperscaler paradox: how the same customers funding the buildout could compress the opportunity, and what contract structures protect against it
Valuation framework: current market snapshot plus street-derived analytical frameworks, with the three traps investors should avoid
What changes the view: the specific variables to monitor and how they affect each business model differently