Instrumental goals such as resource acquisition, power-seeking, and self-preservation are key to contemporary AI alignment research, yet the phenomenon's ontology remains under-theorised. This article develops an ontological account of instrumental goals and draws out governance-relevant distinctions for advanced AI systems. After systematising the dominant alignment literature on instrumental goals we offer an exploratory Aristotelian framework that treats advanced AI systems as complex artefacts whose ends are externally imposed through design, training and deployment. On a structural reading, Aristotle's notion of hypothetical necessity explains why, given an imposed end pursued over extended horizons in particular environments, certain enabling conditions become conditionally required, thereby yielding robust instrumental tendencies. On a contingent reading, accidental causation and chance-like intersections among training regimes, user inputs, infrastructure and deployment contexts can generate instrumental-goal-like behaviours not entailed by the imposed end-structure. This dual-aspect ontology motivates for governance and management approaches that treat instrumental goals as features of advanced AI systems to be managed rather than anomalies eliminable by technical interventions.

翻译：资源获取、权力追求与自我保存等工具性目标是当代人工智能对齐研究的核心议题，然而该现象的本体论基础仍缺乏系统理论阐释。本文构建了工具性目标的本体论框架，并提炼出适用于先进人工智能系统治理的关键区分维度。在系统梳理现有对齐文献关于工具性目标的主流论述后，我们提出一个探索性的亚里士多德式分析框架，将先进人工智能系统视为通过设计、训练与部署过程从外部赋予目标的复杂人工制品。从结构视角解读，亚里士多德的"假言必然性"概念揭示了当特定环境中长期追求既定目标时，某些使能条件如何成为条件性必需，从而产生稳定的工具性倾向。从偶然性视角解读，训练机制、用户输入、基础设施与部署环境之间偶然的因果关系及类随机交汇，可能催生并非由既定目标结构衍生的类工具性目标行为。这种双重本体论主张，治理与管理方法应将工具性目标视为先进人工智能系统需调控的结构特征，而非可通过技术干预消除的异常现象。