Hey everyone, I run a small independent research firm called Northwise.

We specialize in the neoclouds and just published a full model on Oracle. I thought this would be a great place to share and to get some feedback from the community.

I'll be around periodically today and this week to answer any questions you may have.

The full model is free and available, but full transparency we do gate our actual price targets, target zones, etc. This is what allows us to produce and release most of our research for free and to stay independent.

The biggest takeaway from the model that surprised us most is how little success of Oracle actually as to do with demand at this stage. Oracle has already signed the contracts, leased the sites, and committed the capital. The open question is how much of the value created by that build reaches the shareholders rather than financiers.

The company that existed before the cloud

Oracle sells the software large organizations use to run themselves, and it has done so for close to 4 decades. Customers pay in advance, renew at high rates, and face substantial cost and risk if they switch. That produces recurring revenue at high margins with very little capital required to sustain it. Of the $67.4 billion Oracle generated in fiscal 2026, roughly $49 billion came from those software and services lines.

Then look at what happened to the capital plan over 3 years.

Historical result	FY2024A	FY2025A	FY2026A
Revenue	$53.0B	$57.4B	$67.4B
OCI revenue	$6.8B	$10.2B	$18.1B
Operating cash flow	$18.7B	$20.8B	$32.0B
Capex	$6.9B	$21.2B	$55.7B
Free cash flow	$11.8B	$(0.4)B	$(23.7)B

Capital expenditure rose 8 times in 2 years as operating cash flow rose 71%. Oracle entered fiscal 2026 as a high-margin software company and exited it as a software company financing a hyperscaler-scale infrastructure build. The obligations it took on behave nothing like software costs, since they arrive years before the revenue they support, depreciate on schedules set by policy and physics, and resist reversal once construction begins.

One clarification governs the rest of this. The software franchise insulates Oracle from existential failure. It does not insulate existing shareholders from debt, dilution, or poor capital allocation, and the distance between those 2 forms of safety runs through the entire model.

What $638 billion establishes

Remaining performance obligations, reported as RPO, is contracted revenue signed and not yet delivered. Oracle's stands at $638 billion, or roughly 9.5 years of company-wide sales at the fiscal 2026 run rate. Large, sophisticated customers want the capacity and have committed to it years forward. That question is answered about as cleanly as a disclosure can answer it.

Several other questions go untouched by the figure. It does not establish when the capacity becomes available, what Oracle must spend to deliver it, what margins the contracts earn, how much cash arrived as prepayment, or how much of the resulting revenue survives to reach common shareholders. Backlog establishes demand. Return on capital has to be established elsewhere.

Oracle does disclose the recognition schedule, which is the most useful forward visibility the company provides. Roughly $76.6 billion recognizes within 12 months, $216.9 billion in months 13 through 36, another $216.9 billion in months 37 through 60, and $127.6 billion beyond that. Applied to our forecast window, the opening backlog supports approximately $402 billion of revenue recognition through fiscal 2030.

Confidence declines steadily as the schedule extends. The contracts recognizing in months 37 through 60 depend on capacity that does not yet exist, at sites still under construction, running hardware generations that have not shipped.

There is also a concentration problem that gets discussed poorly in both directions. Current revenue concentration is low. Customer concentration in the GPU business, in the backlog, and across the campus portfolio is high. Our reconstruction puts OpenAI near $300 billion of the backlog, other large-scale AI near $215 billion, and core OCI, SaaS, and software near $123 billion, which would make one customer close to half of Oracle's future growth without being anything close to half of current revenue. Oracle has not disclosed customer-level backlog, so treat that split as our estimate.

Why the unit is megawatts and why the word is slippery

Accelerators consume enormous power and reject enormous heat, so the binding constraint on how many can be installed in one place is electrical capacity and cooling. Land is widely available. Grid interconnection, transformers, substations, and cooling capacity are not. A megawatt therefore functions as a unit of production, and the business reduces to capacity multiplied by the rate attached to it.

The trouble is that the word does at least 6 different jobs in this sector. Campus power, on-site generation, critical IT power, customer-delivered capacity, billing-ready capacity, and utilized capacity all differ materially for the same facility. Doña Ana is described in generation terms. Port Washington is described in critical IT terms at 902 MW. Abilene mixes both across phases at roughly 1.2 GW.

Summing those markers produces a number with no consistent physical meaning, so we convert each site to a customer-delivered basis before it enters the model. Applying revenue calculated on one capacity basis to cost calculated on another can move project economics by billions of dollars.

Every megawatt then travels the same funnel from contract to energization to customer delivery to billing readiness to utilization to recognized revenue. We apply a 97% billing-readiness factor, which reserves a small share of the fleet for commissioning, maintenance, failed acceptance testing, and hardware in transition. Timing does more damage than leakage, since capacity delivered in the fourth quarter contributes almost nothing to that fiscal year, which is why revenue attaches to average billable capacity and never to the exit figure.

Here is the delivery schedule on the path that tests Oracle's own announced trajectory.

Fiscal year	New delivered capacity	Ending delivered	Average billable
FY2027	1.32 GW	2.82 GW	2.36 GW
FY2028	1.40 GW	4.22 GW	3.42 GW
FY2029	2.90 GW	7.12 GW	5.45 GW
FY2030	1.64 GW	8.75 GW	7.67 GW

Fiscal 2029 carries the largest single-year addition, with Port Washington, the expanded OpenAI estate, and later Stargate sites arriving inside the same 12 months. A 2-quarter slip there moves more revenue than a similar slip anywhere else in the schedule.

The decision that shapes the whole model

Even our weakest scenario reaches approximately 7.4 GW of exit delivered capacity, within 15% of our strongest. Contracts already exist, sites are already progressing, customers are funding part of the hardware, and much of the capital gets committed well before weaker economics become visible. Scenario dispersion has to come from somewhere other than construction, and in our model it comes from revenue density, margins, financing, and dilution.

Realized revenue per megawatt is a composite absorbing compute throughput, utilization, contract pricing, networking, storage, database services, security, support, and software attach. Two operators with identical megawatts and identical accelerators can produce very different densities, and the gap between them is commercial rather than physical.

Four drivers push density higher over the period. Compute per watt improves with each hardware generation, workload mix shifts toward higher-value inference and reasoning, attach rates grow as the platform matures, and the enterprise selling motion improves as reference customers accumulate. Vera Rubin and AMD Helios class systems raise productive compute per megawatt substantially against 2026 hardware.

Technical throughput does not convert one for one into revenue. Token-price compression, competitive cloud pricing, growing customer bargaining power, contracts priced before the improvement arrived, and the industry's construction wave landing all push toward passing the gain through. Hardware improvement is close to certain. Oracle's share of it is the variable, and the spread between our low and high density assumptions is largely a statement about bargaining power against some of the most sophisticated compute buyers in the world.

That is where the scenario spread lives.

Scenario	Average billable AI capacity	Revenue per MW	AI revenue
Stress	6.30 GW	$13.5M	$85.1B
Bear	6.90 GW	$15.5M	$107.0B
Base	7.45 GW	$18.0M	$134.1B
Bull	8.10 GW	$20.5M	$166.1B

Capacity varies by 29% across the distribution. AI revenue varies by 95%. A model that concentrated its uncertainty in construction schedules would produce a much narrower and considerably less honest range.

Oracle's structural edge on the density line is attach. Databases, enterprise data, security, storage, applications, networking, and multicloud access can ride the same megawatt a standalone provider sells bare. Inference running beside the data it needs avoids a migration that is expensive, slow, and risky, and that is a more defensible product than training capacity sold in isolation.

Who pays for the accelerators

Two contracts can produce identical revenue, identical reported margin, and very different returns to shareholders. We track 3 funding classes separately: Oracle-funded and Oracle-owned, customer-prepaid but Oracle-owned, and customer-supplied hardware.

Prepayment improves the cash position and reduces external capital needed for a deployment. The equipment still sits on Oracle's balance sheet, so none of the PP&E, depreciation, or replacement obligation goes away. Readers who treat prepaid capacity as capital-light will overstate the return on it.

Customer-supplied hardware changes the economics far more decisively. The customer owns the accelerators, Oracle provides power, integration, networking, orchestration, support, and software, and Oracle avoids most of the accelerator depreciation. Less revenue per megawatt, materially better capital intensity.

Oracle has disclosed roughly $75 billion combined for prepayments and customer-supplied hardware without disclosing the split, and that undisclosed split carries more weight than almost any other unknown in the forecast. Moving $10 billion from the prepaid column to the customer-supplied column reduces capex, reduces depreciation, improves free cash flow, lowers debt, and reduces dilution, all without changing revenue by a dollar. Gross margin cannot explain the shareholder outcome for exactly that reason.

The capital plan, and why free cash flow misleads

Capital expenditure peaks well before the revenue it funds, and the composition shift is the more informative half of the schedule.

Capital schedule	FY2027	FY2028	FY2029	FY2030
Gross capex	$92.9B	$86.8B	$77.1B	$57.8B
Customer and manufacturer offsets	$(22.5)B	$(16.0)B	$(13.0)B	$(10.0)B
Net cash capital outlay	$70.4B	$70.8B	$64.1B	$47.8B

Growth investment falls 65% across the period as refresh and corporate spending rises 50%. Oracle transitions from building a fleet to maintaining one, and the maintenance line becomes the dominant capital requirement before 2030 ends. Offsets total $61.5 billion, or roughly 20% of gross capex, and they decline as a share of spending, since refresh has no customer counterparty to share it.

Set the program against what it produces and the scale becomes legible. Oracle spends $314.6 billion of gross capital across 4 years against $630 billion of cumulative revenue, or $0.50 of capital per revenue dollar, falling to $0.40 net of offsets. Our CoreWeave forecast produced $1.18 on the same measure. The gap traces directly to the software business funding a meaningful share of Oracle's build.

Oracle depreciates server and network equipment primarily over 6 years, and we test shorter economic lives. Complete obsolescence is the wrong picture. Hardware cascades from frontier training to advanced inference, then enterprise training, general inference, fine-tuning and batch work, then retirement, earning less at each step. A fleet that cascades efficiently needs materially less replacement capital than one that does not.

That leads to the correction we think does the most work in this model, which is a normalized replacement reserve charging what the fleet costs to sustain instead of what management chose to spend. The ordering repays a moment of attention: Stress carries $46.0 billion, Bear $42.0 billion, Base $35.5 billion, and Bull $38.0 billion. Weaker monetization shortens the economic life of older hardware and forces earlier refresh, so the smaller fleets carry the heavier burden.

Apply it and both distortions appear. Stress reports $32.0 billion of free cash flow and produces $21.0 billion after the reserve. Base reports $30.5 billion and produces $53.0 billion. During the build, reported free cash flow understates future earning power. In Stress, it overstates business quality, since management has stopped spending on a fleet that still needs replacing.

The part that does not depend on Stargate

Strip every AI megawatt out of 2030 and Oracle still runs $58.5 billion of software revenue across Cloud Applications at $25.0 billion, support at $18.9 billion, services at $7.4 billion, license at $3.6 billion, and hardware at $3.5 billion. License declines from $4.7 billion as customers migrate to subscription, which is the pattern every large enterprise software transition has produced.

Support is the piece we would flag for anyone modeling the financing question. Roughly $19 billion of advance-billed, high-renewal, high-margin revenue functions as a permanent working capital float sitting alongside a $90 billion annual capital program. It is nearly flat and easy to overlook, and it is the reason Oracle can fund a build of this size without a capital structure resembling its independent competitors.

Compared structurally against a dedicated AI infrastructure provider, the difference reduces to options. Oracle can slow optional capex, draw on software cash flow, shift the contract mix toward customer-supplied hardware, cross-sell into the installed base, and monetize the same megawatt through several layers. An independent provider carrying comparable leverage against comparable leases has one lever, which is filling capacity at whatever price clears.

Revenue into earnings, and where the EPS target leaks

OCI segment margin expands from 25.0% in fiscal 2026 to 38.5% in 2030 on our management execution path. Early years carry costs arriving ahead of revenue, since facilities incur expense before acceptance, staffing precedes utilization, depreciation begins at commissioning, and power contracts start on the utility's schedule. Those margins already include power, data-center expense, networking, operations, maintenance, ordinary depreciation, and operating lease expense, so depreciation is not deducted a second time below operating income.

Our locked model broadly supports management's fiscal 2030 revenue target of $225 billion. It does not reach the associated $21 of non-GAAP EPS, arriving at $19.02 on the management path and $18.92 in our independent Base Case. The gap sits almost entirely in the capital structure.

Hold the diluted share count at the fiscal 2026 level of 2.91 billion and the same non-GAAP net income produces $22.03 per share, so dilution accounts for roughly $3.00 of it. Fiscal 2030 interest expense of $9.1 billion costs approximately $2.13 per share after tax. Neither item requires an operating disappointment, and neither is visible in a revenue target.

The financing schedule is where fiscal 2027 does the most damage. Oracle raises $40.0 billion of external capital that year across debt, equity, and mandatory convertible preferred, then $38.3 billion in 2028 and $11.0 billion in 2029. Funded debt peaks near $155 billion in fiscal 2029 before falling to $127.7 billion as cash flow inflects, and diluted shares rise 15.8% on the management path and 30% in Stress.

There is also a liability most coverage skips. Oracle carries approximately $260 billion of uncommenced data-center lease payments, undiscounted and not on the balance sheet in that form. Lease-equivalent obligations in our model reach $186 billion by fiscal 2029 against $155 billion of funded debt, which makes leases the larger of the 2 for most of the forecast. Anyone watching only the debt figure is watching the smaller number.

We also classify customer prepayments as financing throughout and never as earnings. Cash arrives, deferred revenue rises, capacity gets built, revenue is recognized, and deferred revenue unwinds. Treating the inflow as permanent free cash flow and the later unwind as a surprise working capital loss misreads both periods.

The 4 scenarios

Stress assumes slower recognition, $13.5 million per megawatt, expensive financing, more Oracle-owned hardware, and dilution to 3.78 billion shares. Management responds rationally by finishing near-term contracted projects, stopping optional phases, and cutting gross capex to $35.0 billion, well below the $46.0 billion the fleet needs to sustain itself. The company stays independent, profitable, and large. The equity is weak, since $175 billion of debt and $13.8 billion of annual interest were committed before the weaker economics became visible.

Bear is the case we think deserves the most study. It delivers 8.0 GW of exit capacity, $195.0 billion of revenue, a 33.5% OCI margin, and $30.5 billion of free cash flow, which is an excellent business result. It requires no scandal, no downturn, and no execution failure. It requires competitors to arrive with capacity on schedule, sophisticated customers to negotiate the way sophisticated customers negotiate, and Oracle to fund the build at ordinary rather than favorable terms.

Base assumes Oracle executes the operating plan and pays a normal price for the capital funding it, broadly hitting management's revenue target and missing the earnings target entirely through financing and dilution. Bull requires the enterprise attach argument to convert into genuine pricing power at $20.5 million per megawatt, with capacity only 8% above Base. A modest capacity difference producing an enormous economic difference is the whole argument of the density section expressed as an outcome.

Metric	Stress	Bear	Base	Bull
Exit delivered capacity	7.4 GW	8.0 GW	8.6 GW	9.3 GW
Average billable AI capacity	6.30 GW	6.90 GW	7.45 GW	8.10 GW
AI revenue density	$13.5M/MW	$15.5M/MW	$18.0M/MW	$20.5M/MW
Total OCI revenue	$114.1B	$137.5B	$166.6B	$201.1B
Total revenue	$170.1B	$195.0B	$225.6B	$263.1B
OCI margin	29.0%	33.5%	38.0%	42.0%
Non-GAAP operating income	$56.5B	$70.6B	$88.6B	$111.4B
Non-GAAP EPS	$9.04	$13.40	$18.92	$25.93
Operating cash flow	$67.0B	$75.5B	$88.5B	$107.5B
Gross capex	$35.0B	$45.0B	$58.0B	$70.0B
Reported free cash flow	$32.0B	$30.5B	$30.5B	$37.5B
Normalized sustaining capital	$46.0B	$42.0B	$35.5B	$38.0B
Funded debt	$175B	$155B	$130B	$95B
Interest expense	$13.8B	$11.6B	$9.2B	$7.2B
Diluted shares	3.78B	3.55B	3.38B	3.23B

Read the first row against the eighth. Exit capacity varies by 26% and earnings per share varies by 187% in the same direction.

Dividing by average billable capacity strips out scale and shows the quality of each outcome directly.

Per billable megawatt	Stress	Bear	Base	Bull
AI revenue	$13.50M	$15.51M	$18.00M	$20.51M
OCI segment profit	$5.25M	$6.68M	$8.50M	$10.43M
Funded debt	$27.78M	$22.46M	$17.45M	$11.73M
Replacement capital	$7.30M	$6.09M	$4.77M	$4.69M
Normalized free cash flow	$3.33M	$4.86M	$7.11M	$8.58M

Revenue per megawatt varies by 52%. Normalized free cash flow per megawatt varies by 158%, and funded debt per megawatt varies by 137% in the opposite direction. The top rows describe the operating story and the bottom rows decide the shareholder outcome.

What we reconstructed vs What is Disclosed

Fiscal 2026 revenue, OCI, RPO, debt, and capex are company reported, and fiscal 2027 revenue is company guidance. The capacity delivery schedule is our reconstruction. Revenue density, the split between prepaid and customer-supplied funding, and the replacement reserve are our assumptions and constructs.

Oracle does not disclose the prepaid versus customer-supplied split, customer-level backlog, site-level capex, second-contract pricing, hardware residual values, or the lag between commissioning and billing. Those are precisely the variables determining per-share returns, which forces indirect estimation of the items carrying the most weight.

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Hey Everyone,

We are back with another Nebius report.

As always if you get value out of our research, please consider checking out our free newsletter as a way to get Nebius updates, modeling, and as a way support the work.

Every AI company is announcing gigawatts right now and almost none of it is verifiable. A press release costs nothing to write.

So when Nebius disclosed a 1.2 GW campus in Schuylkill County this past May, we went looking for the parts of the project the company does not control: county property records, township ordinances, state environmental permits, and regional grid filings.

The grid filings turned out to be the most useful, and it is worth explaining why if you have not worked with them before. Power at this scale does not arrive overnight. A facility that wants hundreds of megawatts has to be physically tied into the transmission system, which requires the utility to bring the request to the regional grid operator years ahead of the load actually showing up.

In Pennsylvania that operator is PJM, which runs the grid across 13 states and maintains a public planning process where these requests get presented, engineered, and costed. The resulting documents are engineering material with no promotional function, and the requesting party is usually identified only as "a customer."

One of them covers Gordon, Pennsylvania, which sits a few miles from the land Nebius bought.

Filing detail
Need number	PPL-2025-0008
Presented to PJM	May 6, 2025
Service requested	230 kV source near Frackville, PA
Requested in-service date	May 2027
2027 load	290 MW
2028 load	300 MW
2029 load	600 MW

Nebius has publicly targeted 260 MW by October 2027 and roughly 660 MW by January 2029.

Line those up against the filing and you get the same town, the same years, and nearly the same megawatts, with the company's own dates sitting about 5 months behind the requested utility energization.

That gap is roughly what commissioning, GPU installation, and customer acceptance would consume, so the schedule holds together on its own engineering terms. No party has named the customer in any public document, which is why we describe the request as strongly consistent with the Nebius site and stop short of calling it confirmed.

The dates are where it gets interesting. That filing went to PJM on May 6, 2025, twelve months before Nebius said anything publicly.

Date	What happened
March 2025	A NorthPoint vehicle reportedly acquires key Butler Township parcels from the county development corporation for about $6 million
May 2025	PPL brings the 600 MW Gordon load request to PJM's advisory committee
September 2025	Butler Township rezones the land and adopts a data center overlay covering 5 parcels
May 2026	Land closing reported near $187.5 million
May 2026	Nebius publicly discloses the Pennsylvania project
June 2026	Pennsylvania DEP issues the site stormwater permit to Highridge DC Propco LLC

By the time the announcement landed, the land was bought, the ground was rezoned, and a 600 MW request had already moved through the opening stage of regional grid planning. The company was disclosing work that was finished. That generalizes past this one project, since utility filings and municipal ordinances are searchable, free, and roughly a year ahead of corporate communications, which makes them the earliest honest read on whether an infrastructure company is building what it says.

The other thing that came out of the filings is a number mismatch nobody seems to have flagged. PJM's documentation covers approximately 600 MW of load through 2029. Nebius announced up to 1.2 GW. Half of this campus has a grid operator, a utility, a cost estimate, and a construction schedule behind it, while the back half currently rests on a company commitment and a master site plan. We are not calling the second half doubtful, and later transmission phases may simply not have surfaced yet. The point is that the two halves carry different evidence quality and anyone modeling the site should price them differently.

The full writeup goes through the rest of what we pulled: the land trail from that $6 million county sale to the $187.5 million closing that still has unresolved acreage in public records, the permit-holding entity that no filing has yet connected to Nebius on paper, why the campus has 30 minutes of batteries and no generation of its own, what owning infrastructure changes for a company that historically leased it, and what could realistically break the ramp.

Highridge is at 0 MW today and will take years to ramp. Everything in the report covers how likely it is to remain on schedule.

Hey everyone, I run a small independent research firm.

We specialize on neoclouds and ai infrastructure and recently took a position in CRWV. We just finished our full 2030 model and report and wanted to share it here.

If you get value out of it, consider checking out our free newsletter for updates and other AI infra research.

The Report

CoreWeave has already established itself as the leading independent AI cloud. Revenue reached $5.13 billion in 2025, backlog climbed from $66.8 billion to $99.4 billion during the first quarter of 2026, and active power has surpassed 1 GW across 49 data centers.

The demand case has become increasingly clear.

The shareholder case remains far more complicated.

CoreWeave is attempting to build one of the largest infrastructure platforms in the world through a combination of leased data centers, financed hardware, customer prepayments, debt, convertibles, and equity issuance. The business can scale successfully while landlords, lenders, equipment financiers, and new shareholders capture much of the resulting value.

Our model focuses on that gap.

Step 1: CoreWeave bought speed through leasing

CoreWeave leases most its physical estate and finances the compute installed inside it.

That structure allowed the company to avoid years of land development, grid studies, permitting, and substation construction. During an acute compute shortage, speed carried enormous value. CoreWeave reached more than 1 GW of active power ahead of competitors that control more of their underlying infrastructure.

The tradeoff appears later.

The landlord retains the building, power connection, and substation. CoreWeave retains the accelerators, networking equipment, storage systems, and software that depreciate much faster.

CoreWeave’s true assets are its contracts, deployment experience, software, customer integrations, supplier relationships, and financing machinery. Those advantages are meaningful. They also have to overcome a very expensive physical and financial structure.

Step 2: backlog proves demand, while delivery determines returns

CoreWeave ended Q1 2026 with $99.4 billion of backlog, up $32.6 billion in one quarter.

That backlog shows that major customers want the capacity and will commit years before delivery. It gives CoreWeave extraordinary revenue visibility.

It also requires an enormous amount of capital to fulfill.

Every contracted dollar runs through a leased facility, financed hardware, depreciation, interest expense, employee compensation, and eventual replacement spending. Backlog sits near the top of the income statement. Shareholder value sits at the bottom of the entire capital stack.

Our bear case illustrates the distinction. CoreWeave reaches nearly $60 billion of annual revenue and more than $30 billion of adjusted EBITDA, yet produces only about $500 million of normalized unlevered free cash flow.

The platform succeeds. The economics available to the common shareholder remain weak.

Step 3: contracted power has to become billable power

CoreWeave reported more than 3.5 GW of contracted power at the end of Q1 2026, including the 1 GW already active.

From there, capacity still has to move through installation, networking, commissioning, customer testing, and formal acceptance before it produces revenue.

A megawatt activated late in the year contributes heavily to the exit run rate and very little to annual revenue. Our model therefore separates year-end active power from average billable power.

2030 capacity	Stress	Bear	Base	Bull
Year-end active power	4.20 GW	6.50 GW	7.50 GW	8.20 GW
Average billable power	3.52 GW	5.40 GW	6.53 GW	7.13 GW

Our base case reaches 7.5 GW of active power by 2030. Management’s target of more than 8 GW sits in the bull case.

Reaching base requires roughly 4 GW of future capacity beyond the currently contracted estate. That makes physical execution and access to new power central assumptions rather than background details.

Step 4: revenue density matters more than raw megawatts

Management’s 2026 guidance implies roughly $10.5 million to $11.2 million of annual revenue per average billable megawatt. Our base model begins at $10.9 million.

Later generations should produce more compute from the same electrical footprint. Storage, networking, inference, orchestration, and managed services can also raise the revenue attached to each megawatt.

Older contracts still retain the economics of the period in which they were signed.

For that reason, we model revenue density by deployment vintage rather than applying the newest hardware economics across the entire fleet.

By 2030, our base case assumes the newest cohort earns $19 million per megawatt, while the full fleet averages $14.01 million. In bull, the newest cohort reaches $24 million while the fleet averages $17.04 million.

That distinction reduces base revenue by more than $30 billion compared with a model that applies 2030 economics to every installed megawatt.

The resulting terminal revenue outcomes are:

2030	Stress	Bear	Base	Bull
Revenue	$30.79B	$59.42B	$91.46B	$121.58B
Adjusted EBITDA	$10.78B	$30.90B	$58.08B	$82.67B

Even the bear case represents an extraordinary operating expansion from the $5.13 billion generated in 2025.

Step 5: building the cloud consumes almost $270 billion in base

Our base case estimates approximately $34 million of deployment capital per active megawatt, with about 82% directed toward technology equipment.

Cumulative capital expenditure from 2026 through 2030 reaches:

Scenario	Cumulative capex
Bear	$246.7B
Base	$269.8B
Bull	$286.1B

Across the same period, cumulative base revenue reaches approximately $227.9 billion.

CoreWeave therefore spends around $42 billion more building the platform than the platform generates in revenue through 2030. In bear, the gap approaches $83 billion.

Customer prepayments and supplier financing provide meaningful support, but our base assumptions cover only around 15% to 18% of total capital needs.

The remaining funding has to come from debt, convertibles, equity issuance, or internally generated cash.

Step 6: adjusted EBITDA leaves out the most important costs

CoreWeave’s first-quarter results capture the central accounting issue.

The company generated $2.08 billion of revenue and $1.16 billion of adjusted EBITDA at a 56% margin. It also reported a $144 million operating loss, $536 million of net interest expense, and a $740 million net loss.

Adjusted EBITDA provides a useful view of operating performance before capital costs. CoreWeave’s capital costs happen to define the business.

Our terminal model produces the following:

2030	Stress	Bear	Base	Bull
Adjusted EBITDA	$10.78B	$30.90B	$58.08B	$82.67B
Depreciation and amortization	$28.5B	$36.0B	$39.0B	$41.0B
GAAP operating income	$(18.92)B	$(7.50)B	$17.18B	$39.57B
Normalized unlevered FCF	$(10.42)B	$0.50B	$25.57B	$47.66B

Bear consumes more value through depreciation than it creates through adjusted EBITDA.

The company still operates a massive platform with contracted customers. The hardware, financing, and replacement burden absorb the economics.

Step 7: leases and debt compete with the shareholder

CoreWeave carried $25.1 billion of principal debt at the end of Q1 2026.

Its lease commitments extend much further. Disclosed commenced and uncommenced lease payments, together with a separate 525 MW arrangement, bring contractual rent toward approximately $77 billion.

Leasing accelerated the buildout, but rent continues according to contractual schedules even when customer acceptance slips or project returns weaken.

Our 2030 net debt assumptions range from $95 billion in bull to $145 billion in bear. At an illustrative 7% rate, bear would face more than $10 billion of annual interest expense.

Dilution also plays a major role. The basic share count stood near 545.6 million in April 2026. Our operating model reaches roughly 1.05 billion shares in bear, approximately 836 million in base, and approximately 754 million in bull after residual convertible dilution.

Better execution produces less dilution because stronger cash generation, customer funding, and equity pricing reduce the number of shares required to finance each new gigawatt.

Where we currently stand

CoreWeave possesses real advantages in deployment, financing, cluster operations, customer relationships, and software integration. Those strengths could support one of the largest independent compute platforms in the world.

The investment outcome depends on how efficiently the company converts that scale into cash after rent, depreciation, hardware replacement, interest, and dilution.

Bear and base differ by only 1 GW of active power and roughly $3 million of realized revenue per megawatt. That relatively narrow operating gap produces a 51-fold difference in normalized free cash flow.

We have recently built a sizable position in CRWV after waiting some time for the proper risk to reward.

Hey everyone,

I am back with a brand new Iren model updated for the recent deal announcements, compensation plan, and power expansions.

I have broken down the model and procedures in a series of steps below.

If you get value out of it, consider checking out our free newsletter for updates on our IREN work, as well as on other neoclouds and sectors.

Step 1: the asset is power, and power is not fast

IREN controls approximately 5 GW of secured power globally, with 810 MW operational today. Roughly 65% of the North American portfolio is operational or under construction.

North American development stage:

Status	Capacity	Share
Operational	810 MW	18.0%
Under construction	2,100 MW	46.6%
In development	1,600 MW	35.5%
Total	4,510 MW	100%

A new entrant can raise capital and order GPUs tomorrow. It cannot conjure a grid queue position, a completed interconnection study, or an energized substation, and it cannot buy back the years those things took. That is the entire physical case, and it comes from bitcoin mining having forced IREN to solve industrial power problems years before power became the binding constraint in AI.

Step 2: a secured megawatt is nowhere near a dollar

There are 13 gates between a power agreement and collected cash: site control, grid application, connection agreement, long-lead procurement, substation construction, data hall construction, critical IT commissioning, hardware installation, cluster integration, customer testing, acceptance, utilization, cash conversion.

Capital exposure rises as uncertainty falls, which produces the governing paradox. An early failure wastes deposits and time. A late failure strands a completed building, a full compute system, and the debt behind both.

One measurement problem sits inside all of it. IREN reports capacity in gross megawatts. Customer and hardware economics attach to critical IT megawatts. The Microsoft deployment gives the clearest bridge at roughly 300 MW gross supporting roughly 200 MW of critical IT, or 66.7%. Apply revenue on one basis and capex on the other and project returns move by whatever margin the modeler prefers.

Step 3: July 20 cleared the commercial gates, not just the physical ones

IREN announced $2.8 billion of new multiyear cloud contracts, raised the year-end 2026 AI Cloud ARR target from $3.7 billion to more than $4 billion, and disclosed that approximately 85% of the revised target is under contract.

The $2.8 billion is not the important number. The 85% is. At the $4 billion threshold, roughly $3.4 billion of annualized revenue now sits behind executed contracts, leaving about $600 million dependent on further contracting.

The customer list runs from Microsoft and NVIDIA through Perplexity, Figure AI, Together AI, Fluidstack, Fireworks AI, Fal AI, Hume AI, and one unnamed developer. Weighted-average term is near 4 years. Prepayments cover approximately 45% of GPU capital expenditure on contracts signed since June 1.

The 480 MW gross program did not move, so the target increase implies at least $8.33 million of annualized revenue per gross MW against roughly $7.71 million previously. The 8% uplift came from pricing, mix, storage, or managed-cloud attachment.

Step 4: which means the bear case changed shape rather than disappearing

The old commercial bear said IREN builds enormous infrastructure, never develops the software and relationships to move past bulk capacity, and finds itself undifferentiated the moment supply catches up. That version is now much harder to defend.

The revised bear assumes IREN proves the product at small scale and fails to preserve the economics as the platform expands. Initial contracts commission, managed cloud attracts real customers, density starts above expectations, capacity keeps growing. Then additional supply enters, hyperscalers finish internal campuses, hardware availability improves, and customers ask for lower pricing, smaller prepayments, shorter terms, and stronger guarantees.

Revenue rises while revenue per MW falls. Customer count climbs while two counterparties keep the leverage. July 20 lowered the probability that IREN fails to find customers. It left untouched the probability that IREN fails to capture enough value from them.

Step 5: so the question becomes what a megawatt has to earn

This is the load-bearing step. A base-case equipped gross megawatt costs approximately $42.7 million fully loaded.

Scenario	Fully loaded capex per equipped gross MW
Bear	$46.5M
Base	$42.7M
Bull	$40.0M

Base composition:

Component	Cost per gross MW
Infrastructure	$14.5M
Compute, networking, storage, installation	$28.2M
Total	$42.7M

By development type:

Development type	Approximate cost per gross MW
Existing conversion and expansion	$34M to $40M
Texas greenfield	$36M to $42M
Kiowa	$39M to $44M
Spain	$46M to $52M
Bundey	$49M to $55M
Unannounced or acquired	$42M to $48M

Against that cost stack, the required revenue is:

Target sponsor return	Required annual revenue per equipped gross MW
10%	$10.94M
15%	$11.73M
20%	$12.51M
25%	$13.29M

Our reconstruction of the newer AI-native contracts puts the upper end near $12 million of ARR per gross MW. That figure is inferred from the disclosed 2026 program, known anchor contracts, and residual capacity required to reach the revised target. IREN did not disclose the megawatts assigned to the new deals. Treat it as an upper-end signal on newer cohorts and never as a fleet average.

At roughly $12 million, a marginal deployment clears 10% comfortably, clears 15% narrowly, approaches 20%, and falls short of 25%.

Step 6: the prepayment number is smaller than it reads

Compute is approximately 66% of the project. A 45% prepayment against $28.2 million of compute equals roughly $12.7 million per gross MW, or 29.7% of the $42.7 million build.

Customers are funding most of a compute system, not a third of a data center. Our base funding assumption therefore uses 30% customer prepayments and not 45%.

Funding source	Bear	Base	Bull
Customer prepayments	20%	30%	35%
Project and GPU debt	35%	40%	40%
IREN sponsor funding	45%	30%	25%

Scenario	Capex	Prepayment	Project debt	Sponsor funding
Bear	$46.5M	$9.3M	$16.3M	$20.9M
Base	$42.7M	$12.8M	$17.1M	$12.8M
Bull	$40.0M	$14.0M	$16.0M	$10.0M

Across 1 GW the sponsor requirement runs to roughly $20.9 billion at bear economics, $12.8 billion at base, and $10 billion at bull. A modest move in prepayment terms shifts required equity by billions.

Microsoft shows the efficient extreme. Roughly $1.94 billion of prepayment plus roughly $3.65 billion of GPU financing against roughly $5.81 billion of GPU capex covers about 96% of the compute layer at a blended debt cost near 6%. A lower-priced hyperscaler megawatt can beat a higher-priced AI-native megawatt on the only metric that reaches shareholders.

Step 7: which is why the model does not equip everything it energizes

Base energizes 6.0 GW by 2030 and equips 4.6 GW. The 1.4 GW gap is the most important discipline in the thesis.

Read it as an option position. IREN pays construction cost as the premium and buys the right, without the obligation, to install compute later at a strike set by hardware cost and contract terms. Substations, land, and shells have decades of useful life. GPUs have 5 accounting years and shorter technological ones.

Waiting has value whenever pricing is inadequate, prepayments are weak, debt is expensive, or a new architecture is approaching. Equipping low-return capacity locks in depreciation, interest, principal, and dilution against revenue that never clears the hurdle.

Step 8: run it forward and the answer is a distribution, not a binary

Year-end physical capacity:

Year	Bear	Base	Bull
2026	430 MW	480 MW	500 MW
2027	1,050 MW	1,210 MW	1,300 MW
2028	1,700 MW	2,410 MW	3,210 MW
2029	2,450 MW	4,210 MW	5,500 MW
2030	3,400 MW	6,000 MW	7,100 MW

By site:

Site or region	Bear	Base	Bull
British Columbia	160 MW	160 MW	160 MW
Childress	750 MW	750 MW	750 MW
Sweetwater	1,100 MW	2,000 MW	2,000 MW
Kiowa	550 MW	1,400 MW	1,600 MW
Secured Spain	350 MW	490 MW	490 MW
Additional Spain	0 MW	0 MW	300 MW
Bundey	190 MW	600 MW	800 MW
Unannounced	300 MW	600 MW	1,000 MW
Total	3,400 MW	6,000 MW	7,100 MW

Bull deliberately does not exceed the secured 2 GW at Sweetwater, so bull upside comes from faster fit-out and better economics instead of invented capacity.

Equipped capacity by vintage:

Vintage	Bear	Base	Bull
2026	400 MW	480 MW	500 MW
2027	450 MW	470 MW	600 MW
2028	500 MW	900 MW	1,500 MW
2029	600 MW	1,450 MW	1,900 MW
2030	500 MW	1,300 MW	1,700 MW
Total equipped	2,450 MW	4,600 MW	6,200 MW

Revenue density by deployment vintage, expressed as exit ARR attached to each newly equipped gross MW:

Vintage	Bear	Base	Bull
2026	$8.0M	$8.4M	$8.7M
2027	$8.2M	$9.2M	$10.0M
2028	$8.4M	$10.5M	$12.5M
2029	$8.8M	$12.5M	$16.0M
2030	$9.2M	$14.5M	$20.0M

The 2026 row is anchored to a live company target. Bear still rises over time, given that newer hardware delivers more compute per megawatt and validated demand does not vanish. The bear case lives in the spread, with $9.2 million against $14.5 million and $20.0 million by 2030, on the assumption that technical progress gets handed to customers through lower pricing and thinner service attachment.

Cohort-weighted exit ARR:

Scenario	Exit ARR before acceptance	Acceptance factor	Accepted exit ARR
Bear	$20.97B	90%	$18.87B
Base	$54.78B	95%	$52.04B
Bull	$93.50B	97%	$90.70B

Fleet average, which is the number to watch:

Scenario	Accepted exit ARR	Equipped capacity	Accepted ARR per equipped MW
Bear	$18.87B	2,450 MW	$7.70M
Base	$52.04B	4,600 MW	$11.31M
Bull	$90.70B	6,200 MW	$14.63M

The model never reprices a 2026 Microsoft or NVIDIA deployment to 2030 economics. Cohort treatment is what stops the most favorable marginal economics from quietly infecting the whole fleet.

Step 9: the outcome variable is the denominator

Depreciation is where the EBITDA conversation ends.

Asset class	Modeled accounting life
Buildings and structural infrastructure	20 years
Electrical, cooling, plant equipment	10 years blended
HPC and GPU hardware	5 years

Working the base project through those lives produces roughly $0.4 million of building D&A, $0.7 million on electrical and plant, and $5.6 million on compute, for approximately $6.7 million per equipped gross MW.

Annual revenue per MW	EBITDA at 65%	D&A	Unit EBIT
$8.4M	$5.5M	$6.7M	Negative $1.2M
$12.5M	$8.1M	$6.7M	Positive $1.4M
$14.5M	$9.4M	$6.7M	Positive $2.7M

A megawatt earning 2026 economics at an excellent EBITDA margin is unprofitable at the EBIT line. Only later cohorts cross over.

Replacement burden:

Scenario	Economic GPU life	Fleet share replaced	Cumulative refresh through 2030	Normalized annual reserve
Bear	7.0 years	70%	$1.0B	$9.63B
Base	7.5 years	65%	$2.0B	$13.72B
Bull	8.0 years	60%	$3.5B	$14.84B

Cumulative refresh looks small against the fleet due to timing. Most equipped capacity installs during 2028 through 2030 and remains young at the valuation date, so the 2030 income statement can show excellent EBITDA immediately before the largest refresh burden arrives.

Capital structure:

Scenario	2030 fully diluted shares
Bear	1.40B
Base	900M
Bull	775M

Scenario	2030 net debt	Exit EBITDA	Implied leverage
Bear	$22B	$8.49B	2.6x
Base	$35B	$33.83B	1.0x
Bull	$40B	$63.49B	0.6x

The share count falls as execution improves, given that better outcomes bring more customer funding, more project debt, higher issuance prices, and stronger internal cash generation. Bear carries the least debt and the most dangerous debt, since lower EBITDA, weaker project returns, and softer hardware collateral make each dollar heavier.

The $3 billion convertible carries a 1% coupon, a 2033 maturity, an initial conversion price near $73.07, capped-call protection to approximately $110.30, and initial conversion exposure near 54.4 million shares. Until converted or settled it is a senior claim. In a poor outcome it behaves like debt and in a strong one like dilution, and shareholders pay one of the two.

Where we currently stand

IREN can build part of the announced platform, equip less than it energizes, keep meaningful concentration, dilute substantially, carry tens of billions of debt, and still create an enterprise many times larger than the company that entered this cycle.

An investment resting on unproven demand is speculative. An investment resting on proven demand with unresolved capital returns is underwritable. The risk here is still high and has become measurable.

However, enough of our questions have been answered to see IREN as very much worth the risk reward at this price level.

We are building our first significantly sized position to date.

Hey everyone,

I run a small independent equity research firm. We used to cover RKLB in the past, but haven't touched it in quite some time due to what we believed was a bit too hot of a run in space names in 2025-2026.

We believe RKLB is a fantastic opportunity, but are interested in getting into it longterm for the right price (congrats to those who nailed early entries).

After the 55% drawdown, we finally see RKLB as nearing appealing for us and decided to take a crack at a full guide and model.

We do gate and charge for our actual price targets and action plan, but our full financial model and research is completely free and public.

A Little Background

Rocket Lab stopped being primarily a launch company a while ago. Space Systems did roughly two thirds of the $602M in 2025 revenue, and most people still model RKLB completely off of launches.

Electron is genuinely profitable at the gross line. Q1 2026 came in above a 44% gross margin, close to $4M of gross profit per mission. Small launch was supposed to be a loss leader.

The Iridium deal is widely misread. Rocket Lab is paying about 16x EBITDA for globally coordinated L-band spectrum, 66 satellites with inter-satellite links, 2.5M+ subscribers, deep DoD relationships, and Aireon (the global aircraft surveillance platform).

One thing almost no coverage mentions: when Rocket Lab eventually builds and launches Iridium's replacement satellites internally, that creates zero consolidated revenue. The internal invoice disappears in accounting. The value shows up as lower constellation capex, so any model stacking "internal launch revenue" on top is double counting.

The 2030 scenario outputs

We modeled 6 outcomes. Standalone assumes the Iridium deal breaks. Stress through Exceptional scenarios assume it closes with progressively better execution.

Our base case assumes Electron/HASTE reach 55 missions, Neutron reaches 18 external missions with reuse actually working economically, and Iridium grows through IoT, PNT, and the government contract renewal (not a satellite phone revival, that segment stays flat).

Metric	Standalone	Stress	Bear	Base	Bull	Exceptional
Revenue	$3.93B	$3.53B	$4.56B	$5.63B	$7.95B	$10.40B
Adj. EBITDA	$0.71B	$0.70B	$1.24B	$1.96B	$3.09B	$4.40B
GAAP net income	$0.40B	($0.21B)	$0.40B	$1.06B	$1.92B	$2.95B
Owner FCF	$0.27B	($0.30B)	$0.33B	$1.00B	$1.74B	$2.64B
Net debt / (cash)	($0.50B)	$4.00B	$2.50B	$0.80B	($0.50B)	($1.50B)
Diluted shares	720M	860M	810M	770M	750M	735M
GAAP EPS	$0.55	($0.24)	$0.50	$1.38	$2.56	$4.01

Owner FCF treats stock comp as a real cost, which is why it runs below reported free cash flow.

Notable results from the model

Stress is a legitimate case and risk scenario. The deal closes, Iridium keeps operating fine, and shareholders still lose: $4B of net debt, 860M shares, negative owner cash flow. You don't need Iridium to fail for this acquisition to disappoint. Overpaying and financing it badly is enough.

The share count is reflexive to the stock price. Our bull case ends with fewer shares than Base since a stronger stock improves the merger exchange ratio, makes equity raises cheaper, and lets debt get repaid with cash instead of shares. Weak execution runs that loop in reverse, which is how Stress hits 860M. That circularity is one of the most underappreciated features of the Iridium deal.

Standalone isn't a disaster case. Deal breaks, Rocket Lab keeps a clean balance sheet and $500M of net cash, just a smaller and less complete company. The market would probably sell the headline and the balance sheet would quietly improve.

Why it's on the watchlist

The architecture is genuinely rare. Launch, components, spacecraft manufacturing, an owned network, spectrum, and recurring services under one roof exists almost nowhere else in public markets. The drawdown compressed the price while the operating evidence (backlog above $2.2B, 100% Electron mission success, $1.3B+ of SDA prime awards, new HASTE contracts) mostly kept improving.

The execution burden is also enormous. Rocket Lab is simultaneously qualifying a new rocket, scaling fixed-price government programs, integrating half a dozen acquisitions, and closing an $8B deal with a bridge loan behind it. Plenty of ways this goes sideways, which is exactly what the Stress column describes.

Hey everyone,

In preparation for developing my new IREN financial model, I have updated my full coverage of Iren's power pipeline and individual data centers.

If you'd like to check out the full resource detailing every known Iren data center project, you can find it at the link here.

I am still updating the individual data center articles that the hub links out to and will release those over the coming weeks.

If you get value out of the coverage and would like to receive updates to the site deepdives and receive the new 2030 financial model, check out my free newsletter here!

Thanks guys! IREN has finally entered what I consider my own by zone and have been building out a larger position for the first time since it was in the low 30s earlier this year.

Hey everyone,

For those who don't know me, I run a small boutique research firm that has posted on the site quite a bit this year regarding IREN's individual data centers.

We also have an IREN model that is generally kept private for our members (how we fund the research).

Since we are developing a new model for release in the next few weeks, we have decided to open our current model for free to everyone as an appreciation for the support we have received this year from the sub.

The current model needs quite a bit of updating from earnings to the announced new capacity and NVDA partnership which we are excited about releasing soon.

The model has a weighted price target of $270 per share in 2030 with a present fair value of around $150 depending on risk tolerance and required rate of return.

Bear: $24.69

Base: $272.85

Acquisition Case: $130

Bull: $670.49

Please let me know if you have any questions, and what you would like to see in the upcoming report.

If you enjoyed the report and would like to follow our research updates, consider checking out our free newsletter!

Link for our full site by site research reports!

Hey everyone, I have posted a lot the last few months with my individual site reports for Nebius data centers across the world.

I also use the research to create full energization schedules and models for Nebius and derive price targets based off of them and a combination of ARR per MW.

I generally gate my price targets and full models (this is what pays for all the free research), but have decided to open up my full model and price targets for the week as I finish up my new model and updates.

Please let me know if you have any comments, feedback, or questions you would like included in the new report coming soon.

It took a lot of confidence to post a $1,250 price target ($644 present value) price target when Nebius was below a hundred a share in February, but the research and numbers spoke for themselves.

If anything, the most surprising thing I learned is that I was too conservative in most of my model and Nebius is executing cleaner than I truly thought to be possible. The new model will reflect higher energization expectations, higher revenue per mw, quicker shift to enterprise mix, and much more.

Thanks again for your incredible support this year and if you would like to stay up to date with my nebius and other ai infra research, check out my free newsletter here!

Hey everyone,

We just posted our final site research analysis for KEEL for now!

We hope you have enjoyed this series and gotten a lot of value and understanding of where Keel is at in its developmental pipeline.

If you want to stick around for more of our AI infrastructure research, consider checking out our free newsletter for weekly updates. We will have more Keel reporting in the future!

We will also be updating our full model and forecast shortly after earnings.

Scrubgrass is a 750-acre site in Venango County, Pennsylvania, with options on an additional 1,100 acres.

The land carries an industrial legacy that creates an unusual starting position.

An 85 MW waste coal generating plant from the original buildout, a Stronghold-era bitcoin mining phase that proved on-site compute could operate continuously, and a Bitfarms acquisition that consolidated ownership before redomiciling and rebranding as Keel Infrastructure in early 2026.

The site holds 63 MW of secured grid capacity today, inherited from the legacy plant and existing FirstEnergy interconnection.

FirstEnergy is currently processing an active load study for an additional 750 MW, with visibility expected in the second half of 2026.

That study is not firm capacity. It sits in the pipeline category until it converts to an executed Electricity Service Agreement.

Separate from grid expansion, Keel is evaluating a 550 MW behind-the-meter natural gas plant using combined-cycle gas turbines.

The site sits near the Tennessee Gas Pipeline Zone 4 200 Line trading hub, providing direct access to competitively priced fuel.

If both the 750 MW grid study and the 550 MW gas option advance, the site's theoretical ceiling approaches 1,363 MW, large enough to qualify as a genuine gigacampus.

The most visible environmental overhang is the legacy coal ash pile.

A March 2025 settlement with Earthjustice requires Keel, through the Scrubgrass Reclamation Company, to complete removal by September 2026, a deadline accelerated 14 months from the original regulatory schedule.

A January 2025 FERC settlement also closed out approximately $1.4 million in penalties tied to Stronghold-era market violations between 2021 and 2022.

PJM matters structurally. The grid is facing a supply-demand imbalance driven by traditional generation retirements and surging data center demand concentrated in markets like Northern Virginia.

Available, interconnected power within PJM has become scarce. Sites with grid access and expansion room sit in a meaningfully different competitive position than comparable acreage outside the constrained zone.

The financing model is the powered shell with credit-wrapped lease structures. Keel signs a long-term lease with an investment-grade or near-investment-grade tenant, then uses that contracted cash flow to secure project-level financing.

What the market is missing and why this site matters

The bottleneck in AI infrastructure is power delivery.

Grid queue timelines in many U.S. markets stretch beyond five years for new interconnection requests, and available power in core PJM markets is increasingly spoken for.

Sites that already hold grid-connected capacity, industrial zoning, transmission infrastructure, and pipeline access skip a meaningful portion of that timeline.

Scrubgrass holds an unusual combination of those ingredients.

Most coverage either treats the site as a legacy waste coal asset that may eventually pivot, or extrapolates the full 1,363 MW ceiling as if it were already firm capacity.

Both framings collapse a real conditional opportunity into a single point estimate.

The site's actual position sits in the middle. 63 MW of operational capacity is real.

The 750 MW load study is credible given the existing FirstEnergy relationship and interconnection, but it is not yet firm.

The 550 MW gas plant is real optionality given confirmed pipeline proximity, but turbine selection, permitting, and capital commitments remain in evaluation.

The cleanup is on an accelerated schedule that has to land on time. None of these are speculative. None of them are settled either.

This is where the report does its work. It separates secured capacity from pipeline capacity from optionality.

It traces how the site got to its current starting position, what each phase of ownership left behind, and why the FERC enforcement history matters for how future co-location must be structured.

It maps the customer fit between hyperscaler deployment requirements and neo-cloud powered-shell economics, and it explains why western Pennsylvania latency to northeastern population centers becomes more relevant as AI deployment shifts toward inference.

Three things make this worth tracking. The starting position is genuinely difficult to replicate from scratch.

Power access, land scale, PJM location, and gas pipeline proximity are individually valuable and collectively rare.

The path forward is legible with specific milestones that can be observed over the next 12 to 18 months. And the financing mechanism does not require Keel to self-fund. It requires a creditworthy anchor tenant to make the capital stack work.

The risks are equally specific. The load study may not convert to firm capacity on the implied timeline.

The gas option may stall in permitting.

Cleanup slippage past September 2026 would create institutional diligence problems.

The absence of a publicly announced anchor tenant means the lease-driven financing model remains theoretical.

And FERC enforcement history requires that any future co-location maintain clear physical and contractual separation between generation and data center loads.

Scrubgrass is one of the more interesting raw setups in the AI infrastructure transition pipeline.

Whether the ceiling gets reached depends on execution across several fronts in a compressed timeline.

The next 12 to 18 months will determine whether the site becomes a credible AI campus or remains an unusual collection of unrealized inputs.