CONCEPT
Equifinality
Bertalanffy’s principle that the same final state can be reached from different initial conditions and by different paths—the defining property of self-organizing open systems, and the most structurally revealing fact about how large AI systems develop their capabilities.
Equifinality is
Ludwig von Bertalanffy’s most counterintuitive insight about living systems: the same mature form can develop from a half, a doubled, or a rearranged beginning, because the end is not coded into the initial conditions but is an attractor the whole system finds. A machine is path-dependent: disturb its starting conditions and you get a different output. An organism is equifinal: disturb its starting conditions and, within limits, it still converges on the same mature form. This property, Bertalanffy argued, is one of the deepest signatures distinguishing a self-organizing open system from a closed mechanism. Its application to contemporary AI is direct and structurally precise. Train the same architecture twice from different random seeds on shuffled data: you get two different sets of weights, internally dissimilar in their details, that nonetheless converge on near-identical capabilities. Train two different architectures—different number of layers, different attention scheme, different tokenizer—and they too, given sufficient scale and data, arrive at strikingly similar competence.