Analyzing Viral AI Adoption

Quick Start

Tool: [AI tool name]
Stated value: [official positioning]
Actual usage pattern: [how people really use it]

Hidden mechanism:
- Psychological driver: [anxiety/status/efficiency]
- Coordination problem solved: [what illegible issue it addresses]
- Ontological reframe: [how it changes how users see their work]

Workflow

1. Map the stated vs. actual value

   • Document official positioning and marketing claims
   • Observe real usage patterns in communities/social media
   • Identify gaps between intention and behavior

2. Identify psychological drivers

   • Anxiety reduction (fear of being left behind, uncertainty management)
   • Status signaling (early adoption, technical competence)
   • Cognitive load reduction (decision fatigue, complexity management)

3. Uncover coordination problems

   • What illegible social/professional challenge does it solve?
   • How does it enable cooperation without explicit coordination?
   • What power dynamics or information asymmetries does it address?

4. Analyze ontological reframings

   • How does the tool change how users categorize their work?
   • What new mental models does it introduce?
   • How does it shift the user's identity or role perception?

Progress:

• Document stated vs. actual value gap
• Identify primary psychological driver
• Map the hidden coordination problem
• Articulate the ontological reframe

Examples

Example 1: Input: ChatGPT's viral adoption in late 2022 Output:

• Stated: General AI assistant for various tasks
• Actual: Anxiety reduction tool for "smart enough" responses
• Hidden coordination: Solves the illegible problem of "what counts as good enough thinking" in professional contexts
• Reframe: From "I need to be the expert" to "I need to be the good editor"

Example 2: Input: GitHub Copilot adoption patterns Output:

• Stated: Code completion and productivity tool
• Actual: Status signaling and impostor syndrome management
• Hidden coordination: Legitimizes "coding by assembly" vs "coding from scratch"
• Reframe: From "real programmers write everything" to "real programmers orchestrate solutions"

Example 3: Input: Viral prompt engineering techniques Output:

• Stated: Better AI outputs through structured prompts
• Actual: Ritual behavior for anxiety management around AI unpredictability
• Hidden coordination: Creates shared language for "doing AI right"
• Reframe: From "AI is a black box" to "AI is a controllable process"

Best Practices

• Look for adoption patterns that contradict stated utility maximization
• Pay attention to social proof and mimetic behavior in communities
• Examine the timing of viral moments relative to collective anxieties
• Focus on what the tool makes "sayable" or "doable" in social contexts
• Identify which existing power structures the tool reinforces or disrupts
• Track language changes in how people describe their work after adoption

Common Pitfalls

• Assuming rational adoption based on objective utility
• Ignoring the social/status dimensions of tool adoption
• Missing how tools solve problems users can't explicitly articulate
• Focusing only on individual psychology instead of collective coordination
• Overlooking how tools change the categories people use to think about work
• Treating viral adoption as random rather than revealing hidden structures