An engineer is teaching a robot to use the company systems. The calendar needs one custom connector. The customer database needs another. The invoices need a third.
Then two more robots arrive. With three robots and 10 systems, that is 30 connectors to build and to maintain.
One Shared Plug
So the industry agreed on one shared plug. It is called the Model Context Protocol, MCP for short: a standard way to connect an AI model to tools and data.
Now each system builds one plug, and each robot builds one socket. That is 13 pieces instead of 30.
A Worked Example
The arithmetic uses the video’s round numbers. Without a standard, every robot needs its own connector to every system: 3 robots times 10 systems is 30.
With the standard, each of the 10 systems builds one plug and each of the 3 robots builds one socket: 10 plus 3 is 13. This publication’s arithmetic from the video’s example: that is 17 fewer pieces.
In the video’s words, custom connectors multiply, while a shared plug adds up.
Three robots times 10 systems is 30 connectors. One plug per system plus one socket per robot is 13. Round numbers for illustration.
Why It Matters
A tool built once works with every assistant that speaks the standard. That is the business point: the work of connecting is done once, on the standard, not again for each assistant.
So the question to ask is whether the tools we buy speak the shared standard.
MCP builds on Tool Use. MCP is a standard way to connect a model to tools and data, so each system builds one connector instead of many.
Computer Use contrasts with MCP. Computer use reaches systems with no connector through the screen, slower than a real connector such as MCP.
MCP is an example of Extensibility Is Control. MCP is a shared plug, and the extensibility lesson says whoever defines how others connect shapes what is built on top.
MCP constrains Switching Costs. Switching costs include rebuilt integrations; the lesson advises open standards, and a connect-once standard like MCP is one.
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.
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