Generative Systems Theory is a foundational inquiry and metaphysics concerning systems theory and complexity science. It focuses on how concepts regarded as basic elements—such as nodes, relations, compatibility, attractors, information, and so on—come into being in the first place, and on what grounds they can be said to exist. If we do not simply assume that they already exist, can they instead be derived from fewer premises and more parsimonious conditions? It is also committed to integrating different, scattered domains into a single generative genealogy: how influence generates constraints; how constraints generate interactions and coupling; how uneven influence generates differences in state; how states and constraints generate evolutionary trajectories; how evolutionary trajectories generate compatibility and attractors; how compatibility and attractors serve as preconditions for nodes and systems; how nodes are represented; what hidden coupling conditions lie behind representation; how synchronicity should be explained; what distinguishes a system from a node; whether relations can be divided into fundamentally different types at the most basic level; why some systems possess robustness; into how many types robustness can be further classified; how a form of information that does not depend on bits can be derived and defined purely from systemic logic; how cognitive systems maximize information; what the most fundamental difference is between living systems and other systems; why gene-centered theories in biology are difficult to sustain; what two opposite extremes animals and artificial intelligence occupy, and why humans lie in the intermediate zone; what the core cognitive functions of human beings are besides embodiment; how the most distinctive and difficult-to-articulate human cognitive functions can be connected with neural networks; what common information-theoretic foundation underlies theories such as Archetype, predictive processing, and generative grammar; how that information-theoretic foundation can be used to derive the optimal forms of human–computer interaction and human–machine symbiosis; how modern Pythagoreanism relates to academic institutions and historical change; and, rather than dividing explanations into top-down and bottom-up, what kind of general explanation can come closer to the underlying logic of predictive processing, and so on and so forth. All of these are derived from the foundational theory of Generative Systems Theory.