Traditional SaaS measures "stickiness" through switching costs: data migration difficulty, learning curves, contract lock-in. These are friction-based. Users want to leave but can't.
Key Comparison
Dimension
Stickiness
Embedding
What users fight
Migration friction
Architectural complexity
Budget competition
Easier migration alternatives
"Rebuild entire integration"
Decision type
Product decision
Multi-year IT transformation
Timeline to switch
Quarters
Years
Real-World Examples
Target
Key Insight
Embedded infrastructure โ as explored in the economics of AI compute infrastructure โ often generates no competitive evaluation at all โ not because users are satisfied, but because they've forgotten switching is conceptually possible.
Exec Package + Claude OS Master Skill | Business Engineer Founding Plan
FourWeekMBA x Business Engineer | Updated 2026
Traditional SaaS measures “stickiness” through switching costs: data migration difficulty, learning curves, contract lock-in. These are friction-based. Users want to leave but can’t.
Embedding operates on a fundamentally different mechanism.
You become the coordination layer that enables other systems to communicate. Removing you doesn’t just require migration โ it requires re-architecting how everything connects.
The Critical Distinction:
Dimension
Stickiness
Embedding
What users fight
Migration friction
Architectural complexity
Budget competition
Easier migration alternatives
“Rebuild entire integration”
Decision type
Product decision
Multi-year IT transformation
Timeline to switch
Quarters
Years
The Paradox:
Sticky products generate constant competitive pressure because users are actively frustrated.
Embedded infrastructure โ as explored in the economics of AI compute infrastructure โ often generates no competitive evaluation at all โ not because users are satisfied, but because they’ve forgotten switching is conceptually possible.
What is Embedding vs. Stickiness: Why Embedded Infrastructure Stops Generating Competitive Evaluations Entirely?
Traditional SaaS measures "stickiness" through switching costs: data migration difficulty, learning curves, contract lock-in. These are friction-based. Users want to leave but can't.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
