AbstractWhile the benefits of Model‐Based System Engineering (MBSE) are recognized, its adoption is hampered by seemingly pragmatic problems, such as a harsh learning curve and lack of mature tooling and integration. While these perceived drawbacks are often described, they are only rarely explained – it is unclear what exactly about MBSE is challenging to use and learn, and how to determine whether new approaches mitigate this challenge. It this work, we propose a theory for explanation for three observations: (1) MBSE is more appreciated by non‐system engineers, than by those that create the models, (2) system models, particularly in the early stage, are used for communication and rarely for automation, and (3) graphical notation is seen as both the major advantage of MBSE and a big drawback when learning it. We use a cognitive framework based on semiotics, conceptual spaces and naturalness and provide a first explanation of these observations: A graphical model can be interpreted either in terms of the domain it models, or in terms of the language it is expressed in. These two interpretations are in parallel when a user interprets a model and can interfere, which leads to problems in understanding.