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Automation NPV

Every automation has two numbers that matter: what it costs to construct, and the risk-adjusted value of the process it creates. The spread between them is the entire investment thesis.

The Construction Spread

A factory costs $X to build and generates $Y/year in risk-adjusted value. The spread Y/X tells you how fast the asset pays for itself and where it ranks against alternatives. Knowledge assets work the same way - with the added property that the value stream can appreciate rather than decay.

# construction spread
S = (annual_value × P(success)) / build_cost
# deploy capital where S is highest
rank = sort(candidates, by=S, descending)

You can only allocate that which has been assessed. Assess the spread on each opportunity individually, then rank to decide where the next dollar goes.

Medium volume, high error cost. The verifier investment is expensive but the compliance risk justifies it. Data moat appreciates as regulatory knowledge accumulates.

Investment Inputs

$

Development + integration + verifier

How many items run through the pipeline

$

Manual processing cost per unit

$

Compute + API + verification labor

$

From Dollarized Confusion Matrix: P(error) x cost

years

How far out to project

Risk & Asset Dynamics

75%

Accounts for technical risk, integration complexity, and verification difficulty. A novel task with no gold standard might be 50%. A well-understood process with existing verifiers might be 90%.

15%

Model drift, competitive catch-up, tech obsolescence

10%

Data moat growth, verification learning, process knowledge

The Spread

Construction Cost
$80.0k
Risk-Adjusted Annual Value
$36.0k
$48.0k × 75%
Construction Spread
0.45x
Slow payback - check assumptions

Valuation

NPV
$-1.6k
IRR
11%
Payback
30mo
ROI
22%
Annual Cash Flows (risk-adjusted savings, drift-adjusted)
Y0
Y1
Y2
Y3
Net asset drift:-5%/yr(depreciating - model decay outpaces learning)

Compare Candidates

Assess each soft spot individually, save it, then compare. The ranking tells you where the next dollar goes.

The Physical Capital Mapping

Purchase price
Often lower than physical, but hidden costs in verification design
Build cost (dev + integration + verifier)
Operating cost
Scales sub-linearly (unlike physical). Marginal cost approaches zero.
Compute + API + verification labor per item
Revenue / savings
Quantifiable via Tools 1-2. Not a guess.
Labor cost displaced - AI cost - error cost
Risk adjustment
Physical assets have construction risk too. Knowledge assets have an additional axis: will the AI actually work?
P(success) - probability the system reaches target performance
Depreciation
Often cliff-shaped, not gradual. Major version releases reset the curve.
Model drift + competitive erosion
Appreciation
The unique property of knowledge assets. Physical assets almost never appreciate.
Data moat + verifier learning + process knowledge
Salvage value
Can exceed original build cost. The data outlives the model.
Transferable training data + learned verifiers
Replacement cost
This IS the moat. Models are commodity. Data and verifiers are not.
Cost for a competitor to replicate your data + verifiers

The Punchline

The construction spread is the same metric a PE fund uses to rank deals: risk-adjusted return on deployed capital. The only difference is the asset class. A warehouse robot depreciates from day one. A knowledge asset with a strong data moat appreciates.

The model depreciates like a truck. The data and verifiers appreciate like land under the depot. Invest in the appreciating side.

Use commodity models (they are the truck - replaceable, depreciating). Build proprietary verifiers and data pipelines (they are the land - unique, compounding). The Quadrant Shifting playbook tells you what to build. The construction spread tells you whether it pencils out and where it ranks.