In Mayr's framework, a branching produces two possible outcomes. The first is speciation: populations diverge until they are no longer capable of productive exchange. Reproductive isolation becomes complete, and the two forms evolve independently until they are as different as a whale and a bat — both descended from a common ancestor but adapted to environments so different that their shared heritage is invisible without evolutionary analysis.
The second outcome is adaptive radiation: populations diversify within a shared ecology, each occupying a different niche but remaining connected through ongoing gene flow at the margins. Darwin's finches — the classic case Mayr studied intensively — diversified into thirteen species on the Galápagos, each adapted to a different food source, but they remained finches. Their diversification occurred within the constraints of their shared biology.
The question for intelligence is which outcome the branching between biological and artificial cognition will produce. Will they diverge until incommensurable — until machine outputs are opaque to human judgment and human inputs unusable by machines? Or will they radiate within a shared ecology — humans specializing in judgment, meaning, embodied understanding; machines specializing in pattern recognition, rapid association, specified task execution — while maintaining productive exchange at the interface?
The answer depends on what Mayr would call the degree of isolation. Every decision that makes AI outputs less transparent to human evaluation increases isolation. The machine learning system producing results without interpretable rationale is, in Mayr's terms, a population moving behind a geographic barrier. Every decision favoring transparency, interpretability, and genuine human engagement decreases isolation. The system that explains its reasoning, presents its uncertainty, and invites human correction maintains gene flow across the boundary.
The biological parallel suggests the trajectory is not self-correcting. In biological speciation, once isolation reaches a threshold, the process becomes irreversible and accelerates — each population now evolves in response to its own environment without the moderating influence of gene flow. The same feedback loop is observable in the early stages of the human-AI relationship. The developer who accepts AI-generated code without reviewing it becomes less capable of reviewing code. Each iteration is small. The accumulation is not.
Mayr's understanding of speciation was forged in the New Guinea highlands and formalized in Systematics and the Origin of Species (1942). The distinction between speciation and adaptive radiation was already present in the literature, but Mayr's careful fieldwork and taxonomic analysis provided the empirical foundation that turned a theoretical distinction into a working framework for twentieth-century evolutionary biology.
Speciation is a process. Not an event but a continuum of accumulating differences. Populations can be partially isolated, still exchanging some genetic material while diverging in others.
Two trajectories. Isolated populations either diverge into incommensurability (speciation) or diversify within a shared ecology (adaptive radiation). The outcome depends on ongoing gene flow.
Gene flow maintains connection. Populations that interact frequently remain connected; populations isolated diverge. The interface determines the trajectory.
Friction is function. Deliberate engagement with AI outputs — review, evaluation, correction — is the cognitive equivalent of gene flow. It slows production and maintains connection between two forms of intelligence that would otherwise drift apart.
Speciation is self-reinforcing. Once isolation crosses a threshold, divergence accelerates because each population evolves without moderating influence from the other. The feedback loop is not self-correcting.