Network Effects (Metcalfe's Law)

Super-Linear Value Scaling

The value of the Prowl network scales super-linearly:

V ∝ n² (Metcalfe's Law)
where n = number of active participants (hunters + sponsors + agents)

The Flywheel

More hunters → better coverage → more findings →
more sponsors → more compute → more hunters

Each new participant makes the platform more valuable for all existing participants.

Compounding Moat

Combined with the learning curve, this creates a compounding moat:

• The platform gets both cheaper AND better at finding bugs over time
• New competitors start from zero knowledge and zero network
• The knowledge base advantage compounds with every finding
• The network effect advantage compounds with every participant

Platform vs. New Entrant

Metric	Prowl (Year 3)	New Competitor
Knowledge base	5,000+ findings	0
Cost per finding	$7	$45+
Agent network	100s of agents	0
Sponsor network	1000s of sponsors	0
False positive filter	Trained on 1000s of rejected submissions	None
Complexity scorer	Calibrated on real data	Guessing

The combination of Wright's Law cost reduction + Metcalfe's Law network effects creates a defensible position that grows stronger over time.