Artificial, neurobiological, and social networks are three distinct complex adaptive systems (CAS), each containing discrete processing units (nodes, neurons, and humans respectively). Despite the apparent differences, these three networks are bound by common underlying principles which describe the behaviour of the system in terms of the connections of its components, and its emergent properties. The longevity (long-term retention and functionality) of the components of each of these systems is also defined by common principles. Here, I will examine some properties of the longevity and function of the components of artificial and neurobiological systems, and generalise these to the longevity and function of the components of social CAS. In other words, I will show that principles governing the long-term functionality of computer nodes and of neurons, may be extrapolated to the study of the long-term functionality of humans(or more precisely,of the noemes, an abstract combination of 'existence' and 'digital fame'). The study of these phenomena can provide useful insights regarding practical ways that can be used in order to maximize human longevity.The basic law governing these behaviours is the 'Law of Requisite Usefulness', which states that the length of retention of an agent within a CAS is proportional to the agent's contribution to the overall adaptability of the system.