When Network Effects are good moats for AI startups (blog.tryresearchly.com)

The author analyzes when network effects remain durable moats for AI startups and offers a practical framework to assess defensibility. They define network effects as features where each additional user improves the product for others and where the network is hard to re-create. They categorize effects as local (team-level), global (platform-wide), and semi-local, and list core sources: physical interactions, community and templates, APIs/integrations, and data loops—highlighting that the strongest loops rely on proprietary, product-related data. The piece argues that AI weakens many traditional network advantages by making it easy to replicate templates, migrate data, or automate roles that sustained seat-based networks. Examples include AI-powered template generation in Notion, AI-assisted data migration (Attio), and design agents that could reduce reliance on Figma’s user density. Conversely, genuinely proprietary signals—company-specific ontologies or usage data that produce bespoke models—are harder for rivals to copy and thus form stronger moats. The recommended assessment framework asks three concrete questions: can AI replicate your assets, can it migrate them elsewhere, or can it render them redundant? Practically, founders should prioritize local, hard-to-copy data and rethink pricing/business models (e.g., seat-based) as AI changes the economics of network-driven software.