INTELLIGENCE MARKET LAB

A rising curve is not yet a commodity.

Predict what happens when capability compounds. Then watch deployment, review cost, and demand decide what useful intelligence is worth.

Your prediction
Space to run or pause
YEAR0.0
RAW
CAPABILITY
REVIEW
FLOOR
DEMAND
Raw capability1.0×
Useful capability1.0×
Price / useful task$102.00
Demand index100
Total spend$10,200
Illustrative sensitivity model, not a forecast. The supplied source is clipped after “which is used in”.

READ THE TRANSMISSION

The curve passes through a market.

Raw capability is only the input. Deployment determines how much becomes useful; compute and review determine price; elasticity determines whether cheaper tasks shrink or expand the market.

CapabilityTask priceTotal spend

FOLLOW THE CAUSE

Same capability.
Different economy.

The model isolates what the original curve exercise leaves implicit: capability must be deployed, deployment must reduce useful-task cost, and lower prices must meet responsive demand.

C(t)

Capability compounds

Both scenarios reach the same 12.2× raw capability at year ten. This variable alone cannot explain the price difference.

A(t)

Deployment opens the gate

At 99.8% deployment, raw capability becomes 12.15× useful capability. At 25.9%, it becomes only 3.90×.

P(t)

Review sets a floor

Automated cost can fall quickly, but an irreducible $100 review step keeps the delivered task at $125.65.

D(t)

Demand can rebound

With elasticity 1.4, a lower unit price creates proportionally more usage. Total spending rises even as each task becomes cheaper.

TRANSFER THE MODEL

Price falls 10×. Demand elasticity is above 1. What happens to total spend?

Complete a simulation, then transfer the reasoning to a new market claim.

MODEL EQUATIONS

Transparent by design.

Raw capability
C(t) = egrowth × t
Deployment
A(t) = 1 − e−integration × t
Useful capability
U(t) = 1 + (C(t) − 1) × A(t)
Task price
P(t) = compute cost ÷ U(t) + review floor
Demand
D(t) = 100 × (P(0) ÷ P(t))elasticity
Total spend
R(t) = P(t) × D(t)

These equations expose assumptions for learning. They are not fitted to a specific market or offered as a forecast.

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