The human brain is organized as a network of interconnected components in the form of neural units, ensembles, areas, and regions. Across a range of spatial scales, that network is neither perfectly ordered nor perfectly random. Its heterogeneous organization supports complex activity dynamics while simultaneously constraining such dynamics. How does this constraint affect the cost of activity flow? In this talk, I will discuss the notion of network economy: the idea that the brain's network organization partially determines the cost of reaching a brain state, maintaining a brain state, and transitioning between brain states. I will draw ideas and examples from the field of network control theory, which provides a framework for calculating energy costs associated with network systems reaching, maintaining, and transitioning among brain states. The discussion will focus on basic principles and intuitions, and will point listeners to code repositories, primers, and methodological studies they might find useful in implementing the approach. I will close by broadening out to discuss how principles of network economy can inform our study of cognitive effort and executive function, as well as cognitive changes associated with neurodevelopment.