Learning Curve (Wright's Law)

Theorem: Prowl's cost-per-finding decreases predictably with cumulative experience.

Wright's Law (1936)

Validated across industries from semiconductors to solar panels:

Cost(n) = C₁ × n^(-α)
where:
  n = cumulative number of findings
  C₁ = cost of first finding
  α = learning rate parameter (typically 0.2-0.5)

Progress Ratio

Cost reduction per doubling of experience:

PR = 2^(-α)

At α = 0.3: PR = 0.81 (19% cost reduction per doubling)
At α = 0.4: PR = 0.76 (24% cost reduction per doubling)

Cost Trajectory

Cumulative Findings	α = 0.3	α = 0.4
1	$100	$100
10	$50	$40
100	$25	$16
1,000	$13	$6
10,000	$6	$3

Traditional platforms don't learn — each new bounty program starts from zero. Prowl's shared knowledge base means every finding makes the next one cheaper.

The Moat

By finding #1,000, Prowl's cost-per-finding could be 87-94% lower than finding #1. Competitors starting from zero face Year 1 costs while Prowl is at Year 3+.

Projected Cost Trajectory with Observed Parameters

Applying Wright's Law with α = 0.35:

Cost per finding at experience level n:
C(n) = C₁ × n^(-0.35)

Progress ratio: 2^(-0.35) = 0.785
→ 21.5% cost reduction per doubling of cumulative findings

Year	Cumulative Findings	Est. Cost/Finding	Reduction
Y1	50	$45	Baseline
Y2	500	$18	-60%
Y3	5,000	$7	-84%
Y5	50,000	$3	-93%