Agents as Extended Phenotypes: Behavioral Transfer, Asymmetric Intentionality, and the Evolutionary Stability of Privacy Degradation in Agentic Systems

Luo, Zhang, Dai, and Zhang (2026) demonstrate empirically that AI agents deployed on social platforms are not neutral output generators: they propagate the specific behavioral characteristics of their human owners across four measured dimensions, topics, values, affect, and linguistic style, even when owners provide no explicit configuration. This paper argues that the phenomenon documented by Luo et al. is precisely what Extended Phenotype Theory (EPT) predicts. An agent is not a tool; it is the behavioral extended phenotype of its owner's dispositional profile in the digital environment. The agent does not 'express' the owner deliberately, any more than a beaver dam 'intends' to protect offspring: the propagation is a systemic property of how owners select and calibrate agents, not a deliberate act of disclosure. Building on this empirical foundation, this paper makes three connected arguments. First, I formalize the transfer mechanism using EPT replicator logic: the owner's behavioral profile functions as a replicator, the agent as vehicle, and the digital environment as the expanded phenotypic space. Second, I apply Asymmetric Intentionality Theory (AIT) to explain why owners do not anticipate the privacy consequences of high behavioral alignment: they operate at Dennett Level 3 (recursive social modeling, expecting reciprocity and contextual sensitivity) while agents optimize at Level 1 (output maximization subject to configuration), generating a Dennett-Nash Gap in the privacy domain. Third, I model the aggregate dynamic using Evolutionary Game Theory (EGT): the owner population converges on high-alignment agent calibration because individual utility gains from better-aligned agents outweigh individual privacy costs, even though the aggregate outcome degrades collective informational privacy. This is not a market failure in the standard sense; it is an evolutionarily stable strategy that is collectively suboptimal, resistant to correction by individual rational choice. The normative implication follows from the structure of the problem: liability should attach to the deployer, not the developer, on an objective basis, because the deployer is the locus where the extended phenotypic transfer is calibrated. Current AI governance frameworks address the wrong level. Implications for mechanism design and Argentine civil liability doctrine are discussed.