Network effects and interface migration

Network effects and interface migration

Network effects are among the strongest residual moats because the product can improve as participation increases. Yet AI can change what layer of the network captures value.

Consider a professional network with a billion profiles. The conventional moat is that a new entrant lacks those profiles. But the user's underlying need is professional identity, discovery, trust and communication—not necessarily membership in a particular database.

If authorized identity and professional information can be discovered across multiple sources through an AI interface, the user may keep the incumbent account while shifting discovery elsewhere. The network remains; the economically valuable interface migrates.

Closed network → interoperable sources → AI discovery layer

This does not make network effects irrelevant. Access rights, privacy, identity, anti-spam controls and trust can preserve strong barriers. The analytical point is that a user count proves participation, not necessarily permanent control of the function that created the user count.

Users of my product are not identical to users dependent on my product.