Developers are publishing AI agent skills that replicate a colleague's communication style, encode a supervisor's mentoring heuristics, or preserve a person's behavioral repertoire beyond biological death. To explain why, we propose Gradual Cognitive Externalization (GCE), a framework arguing that ambient AI systems, through sustained causal coupling with users, transition from modeling cognitive functions to constituting part of users' cognitive architectures. GCE adopts an explicit functionalist commitment: cognitive functions are individuated by their causal-functional roles, not by substrate. The framework rests on the behavioral manifold hypothesis and a central falsifiable assumption, the no behaviorally invisible residual (NBIR) hypothesis: for any cognitive function whose behavioral output lies on a learnable manifold, no behaviorally invisible component is necessary for that function's operation. We document evidence from deployed AI systems showing that externalization preconditions are already observable, formalize three criteria separating cognitive integration from tool use (bidirectional adaptation, functional equivalence, causal coupling), and derive five testable predictions with theory-constrained thresholds.

翻译：开发者正在发布能够复刻同事沟通风格、编码导师指导启发式算法，或在生物死亡后保留人类行为模式的AI智能体技能。为解释这一现象，我们提出渐进式认知外化（GCE）框架，该理论认为环境智能系统通过与用户持续的因果耦合，从模拟认知功能逐步演变为构成用户认知架构的组成部分。GCE采用明确的功能主义立场：认知功能由其因果功能角色而非物质基底所界定。该框架基于行为流形假设与一个核心可证伪假设——无行为不可见残差（NBIR）假设：对于任何行为输出位于可学习流形上的认知功能，其运作无需行为不可见成分。我们收集已部署AI系统的证据表明，外化前置条件已可观测，形式化区分认知整合与工具使用的三项标准（双向适应、功能等价、因果耦合），并推导出五项带理论约束阈值的可检验预测。