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AI Power Exposure & Earnings Model

Translate announced data-center megawatts into probability-adjusted load, electricity spend, rate-base opportunity, and supplier exposure.

Investor scenario model

AI power exposure & earnings model

Translate announced data-center capacity into probability-adjusted merchant, regulated utility, and equipment exposure.

No company claims implied

Project load and timing

Announced MW is modeled as IT capacity. PUE converts IT load into total facility load.

Announced IT capacity

Completion probability

Capacity factor

Average utilization of available facility load.PUE

Total facility power divided by IT power.Operating in first year

months

Cancellation spread

pts

Added to and subtracted from completion probability.Delay stress

months

Downside loses the full delay; upside gains half.

Merchant power

Electricity price

$/MWh

Supplier share

Share of modeled spend captured by the merchant supplier.

Regulated utility

Utility capex

$/kW

Utility-funded share

Annual revenue requirement

Simplified return, depreciation, tax, and operating recovery rate.

Equipment and EPC

Equipment / EPC spend

$/kW

Addressable share

Relevant scope, not assumed market share for any company.

Probable IT capacity

700 MW

840 MW facility load at 1.20 PUE

First-year electricity

3.31 TWh

6.62 TWh annualized

Merchant revenue

$109M

$182M total modeled spend

Utility annual revenue

$13.8M

$250M probability-adjusted rate base

Probability-adjusted equipment / EPC opportunity

$221M

Represents addressable project scope. It is not backlog, revenue, or market share for a named supplier.

Delay and cancellation scenario bands
Scenario	Completion	Year-one timing	TWh	Merchant	Utility revenue	Equipment / EPC
Downside	50%	2 mo	0.79	$26M	$3.28M	$158M
Base	70%	6 mo	3.31	$109M	$13.8M	$221M
Upside	90%	8 mo	5.68	$187M	$23.6M	$284M

Formula and interpretation

Energy: announced MW × completion probability × PUE × capacity factor × 8,760 hours × first-year timing.

Merchant: first-year MWh × power price × supplier share.

Utility: announced kW × capex/kW × completion probability × utility-funded share. Annual revenue applies the selected requirement and timing.

Equipment / EPC: announced kW × spend/kW × completion probability × addressable scope.

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A headline megawatt announcement is not the same as operating load or public-company earnings. This model adjusts capacity for completion probability, utilization, PUE, and commercial timing, then estimates electricity demand, merchant-power revenue, utility rate-base economics, and equipment or EPC opportunity across delay scenarios.

How this ai power exposure & earnings model works

The model probability-adjusts announced capacity, phases it by commercial timing, and converts IT load through utilization and PUE into annual electricity demand. Separate assumptions translate that load into merchant revenue, regulated rate-base opportunity, and equipment or EPC exposure.

Formula

Annual TWh = announced MW × completion probability × utilization × PUE × operating-year share × 8,760 ÷ 1,000,000

Grid queues, permitting, PPAs, fuel costs, regulation, financing, and ownership decide who captures the economics. The output sizes a scenario; it does not identify a winning stock by itself.

Primary specifications

Before you use the result

Assumptions

• Announced megawatts are not treated as operating capacity until adjusted for completion and timing.
• Merchant power, regulated utility, and supplier economics use separate editable margins and capital assumptions.
• The result does not assign a project to a company without contract and service-territory evidence.

Quick start

1. Enter the announced capacity and its current probability of completion.
2. Set utilization, PUE, power price, and the share of a full year expected online.
3. Stress delay, cancellation, rate-base, and supplier-content assumptions independently.

Frequently asked questions

Why probability-adjust announced megawatts?

Projects can be delayed, resized, or cancelled before they become operating load. Multiplying announced capacity by an explicit completion probability keeps the scenario from treating every press release as certain revenue.

What does PUE change?

Power usage effectiveness compares total facility electricity with electricity used by computing equipment. A higher PUE increases total site demand for the same IT load.

Does electricity spend equal utility profit?

No. Revenue, fuel cost, regulated returns, transmission spending, contract structure, and ownership determine who earns the economics. The model keeps merchant, utility, and equipment cases separate.

Can this identify a winning power stock?

Not by itself. It sizes an exposure scenario. Investors still need project-level contracts, service territory, regulatory treatment, backlog quality, financing, and valuation evidence.

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