Switching costs are everything you lose or have to redo when you change supplier, not the price of the new product.
They come in four kinds: your data, which has to be moved; your integrations, which have to be rebuilt; your people, who have to be retrained; and your processes, which grew around the old tool.
A Worked Example
The video uses round numbers. A new supplier is $20,000 a year cheaper, but moving would cost $100,000 in work and disruption. It takes five years to break even, so most customers stay.
Five years to break even, so most customers stay. Round numbers for illustration.
What Happens in AI
In AI something interesting happens. Swapping one model for another is often easy. But everything built around the model is not: the prompts, the tests, the connections to your data, the habits of your team.
This is why suppliers work so hard to become part of your workflow. The deeper a product sits in your daily work, the more expensive it is to remove, and the more pricing power the supplier holds.
What a Buyer Can Do
As a buyer, you can lower these costs in advance. Keep your data in formats you can export. Prefer open standards. And test.
MCP constrains Switching Costs. Switching costs include rebuilt integrations; the lesson advises open standards, and a connect-once standard like MCP is one.
Switching Costs constrains Churn. The churn lesson says switching AI products is often as easy as changing one setting, which widens the hole.
Extensibility Is Control builds on Switching Costs. Leaving the product everything connects to means rebuilding every connection.
Model Deprecation(lesson unlocks soon) builds on Switching Costs. Deprecation forces a model switch, and the switching-costs lesson says the prompts, tests and connections around the model are the costly part.
Exit Fee(lesson unlocks soon) contrasts with Switching Costs. An exit fee is the written price of leaving; switching costs are the unwritten work of moving data, integrations and people.
The Business Engineer framework maps where each part of the AI stack sits and who holds the leverage. These pills are short lessons from the same library.
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.
