Modern Artificial Intelligence (AI) systems lack human-like consciousness or culpability, yet they exhibit fluid agency: behavior that is (i) stochastic (probabilistic and path-dependent), (ii) dynamic (co-evolving with user interaction), and (iii) adaptive (able to reorient across contexts). Fluid agency generates valuable outputs but collapses attribution, irreducibly entangling human and machine inputs. This fundamental unmappability fractures doctrines that assume traceable provenance -- authorship, inventorship, and liability -- yielding ownership gaps and moral "crumple zones." This Article argues that only functional equivalence stabilizes doctrine. Where provenance is indeterminate, legal frameworks must treat human and AI contributions as equivalent for allocating rights and responsibility -- not as a claim of moral or economic parity but as a pragmatic default. This principle stabilizes doctrine across domains, offering administrable rules: in copyright, vesting ownership in human orchestrators without parsing inseparable contributions; in patent, tying inventor-of-record status to human orchestration and reduction to practice, even when AI supplies the pivotal insight; and in tort, replacing intractable causation inquiries with enterprise-level and sector-specific strict or no-fault schemes. The contribution is both descriptive and normative: fluid agency explains why origin-based tests fail, while functional equivalence supplies an outcome-focused framework to allocate rights and responsibility when attribution collapses.

翻译：现代人工智能系统虽缺乏类人的意识或可归责性，却展现出流动能动性：其行为具有（i）随机性（概率性且路径依赖）、（ii）动态性（与用户互动协同演化）以及（iii）适应性（能跨情境重新定向）。流动能动性产生有价值输出，却消解了可归因性，使人机输入不可分割地相互纠缠。这种根本上的不可映射性瓦解了以可追溯来源为前提的学说——作者身份、发明人身份与责任归属——从而形成所有权真空与道德“缓冲地带”。本文主张唯有功能等效原则能稳定法律学说。当来源无法确定时，法律框架必须将人类与人工智能的贡献视为等效以分配权利与责任——这并非主张道德或经济对等，而是一种务实的默认规则。该原则为跨领域学说提供稳定基础，形成可执行的规则：在版权领域，将所有权赋予人类协调者而无需解析不可分割的贡献；在专利领域，将记录发明人身份与人类协调及实践实施相绑定，即使人工智能提供了关键洞见；在侵权法领域，以企业层面与行业特定的严格或无过错责任方案取代难以处理的因果关系调查。本文贡献兼具描述性与规范性：流动能动性解释了为何基于来源的检验标准必然失效，而功能等效性则在归因消解时提供了以结果为导向的权利与责任分配框架。