adminAI Social M&A: What Strategic Buyers Should Evaluate Before an Acquisition
An acquisition checklist for AI social businesses covering retention, identity rights, creator concentration, inference economics, safety and technical portability.
AI Creator Platform Take Rate: How Much Revenue Should the Platform Keep?
How to analyze AI creator platform take rates across inference cost, payments, discovery, creator services, margins and long-term marketplace incentives.
AI Social International Expansion: What Changes Across Languages and Markets?
A business framework for AI social international expansion across language quality, creator supply, cultural norms, payments, safety and local retention.
AI Companion Marketplaces vs Single-Character Apps: Which Model Scales Better?
Compare AI companion marketplaces with single-character apps across acquisition, retention, creator supply, brand, unit economics and defensibility.
AI Social Network Effects: When More Users Actually Make the Product Better
An investor framework for distinguishing real AI social network effects from ordinary growth across users, creators, data, discovery and interaction quality.
AI Social Platform KPIs: A Board-Level Dashboard for Growth and Quality
A board-level KPI framework for AI social platforms covering activation, retention, payer conversion, creator supply, inference cost and safety.
AI Personality Marketplace Economics: Discovery, Conversion and Creator Payouts
How AI personality marketplaces work economically across discovery, fan conversion, creator payouts, generation costs and retention.
Creator AI Supply: How Platforms Build a Defensible Marketplace of Personalities
A business framework for creator AI supply: acquisition, onboarding, rights, quality, activation and marketplace defensibility.
AI Social Safety as Infrastructure: Why Moderation Becomes a Business Moat
Why safety, identity governance, age controls and moderation can become core infrastructure—and a business moat—for AI social platforms.
AI Social Retention: How Investors Should Read D1, D7 and D30 Behavior
An investor framework for reading AI social retention through activation, cohort behavior, relationship depth and inference economics.
