ChatGPT's Retention Curves Resemble Past Winners

Situation

Market context: A new AI-driven distribution platform is emerging, with multiple competitors (ChatGPT, Claude, Gemini) battling for dominance
Strategic challenge: Companies need to identify which platform will likely win to make appropriate integration bets
Historical pattern: New distribution platforms follow a predictable cycle - competitive market conditions, identification of a moat, platform opening, and eventual closing for monetization
Current state: We're at the inflection point where a clear winner is emerging but not yet fully established

Retention analysis: Brian examined retention data published by VC Didi Doss comparing all major AI platforms
Engagement patterns: Looked beyond raw user numbers to focus on depth of engagement and retention curves
Historical comparison: Applied lessons from previous platform wars (Google vs. Yahoo, Facebook vs. MySpace) where winners showed superior retention
Pattern recognition: Identified the rare "smile curve" in ChatGPT's retention data - where retention initially drops but then increases over time

Context and memory: Determined that the key moat in AI platforms is context accumulation and memory capabilities
User experience quality: Assessed which platform was delivering consistently better outputs through context
Flywheel effects: Analyzed how increased usage creates better personalization, which drives further usage
Scale assessment: Considered ChatGPT's estimated 10x user advantage over competitors like Claude

Clear leader identification: ChatGPT emerged as the platform with significantly higher retention rates
Upward trajectory: Data showed ChatGPT's retention curves shifting upward dramatically over time
Rare pattern emergence: The "smile curve" in retention data matched patterns seen only in major platform winners like Slack
Competitive advantage: Despite competitors having potential distribution advantages (like Google with Chrome), the retention data signaled stronger user loyalty to ChatGPT

Look beyond vanity metrics: Raw user numbers (MAUs) are less predictive of platform success than retention and engagement depth
Study retention curves: The shape and trajectory of retention curves provide early signals of which platform will achieve escape velocity
Recognize the "smile curve": A retention curve that initially drops but then increases over time is an extremely rare and powerful indicator of network effects
Apply historical patterns: Previous platform wars consistently show that superior retention beats initial distribution advantages
Make focused bets: For startups with limited resources, analyzing retention data helps identify where to place your single platform integration bet
Act quickly: The window for capitalizing on emerging platforms is shrinking with each cycle, requiring faster decision-making