Traditional network effects are weakening. Multi-homing is easy, platform fragmentation accelerates, and API-mediated access reduces lock-in. Memory networks operate on entirely different physics – and they create moats that only widen over time.
Traditional networks: Value comes from connections between users. If you and I both join LinkedIn, we might connect. That connection has fixed value.
Memory networks: Value comes from accumulated intelligence about and for users. If we both use an AI platform, we’re contributing to collective intelligence that learns from both of us. That contribution compounds over time without additional effort.
The mathematics differ fundamentally:
Traditional network effect: Value is proportional to n squared (Metcalfe’s Law)
Memory network effect: Value is proportional to n times d squared (where n = users, d = average memory depth)
The exponent is on depth, not breadth. This is the inversion.
Three Types of Memory Networks
Type 1 – Parallel Memory: Each user develops individual memory with the platform. No cross-pollination. Network effect is weak – just individual switching costs.
Type 2 – Pooled Memory: Individual usage contributes to collective platform intelligence benefiting all users. Your debugging session improves suggestions for others. Strong network effects through intelligence accumulation.
Type 3 – Recursive Memory: Individual and collective memory layers interact. The platform learns from everyone but applies learning through each user’s personal context. This creates exponential lock-in and is the new moat.
Why Recursive Networks Win
As the Startup Defensibility framework shows, once recursive memory networks cross minimum thresholds, defensibility becomes nearly absolute:
Time-based moats: You can’t replicate accumulated memory. Competitors can copy features, not years of reasoning patterns.
Depth-based moats: Users with deep individual memory can’t switch without losing context AND access to platform intelligence shaped by equally deep users.
Interaction moats: The magic at the intersection of individual and platform memory is hardest to copy – it requires excellence in both simultaneously.
Key Takeaway
The moat widens over time rather than saturating. Early leadership becomes insurmountable leadership. In memory networks, the winner’s advantage accelerates rather than diminishes with scale.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
