The AI-Identity Loop: How Computational Capabilities and Cognitive Offloading Reconstruct Professional Identity

The proliferation of Large Language Models (LLMs) has disrupted traditional human-computer interaction paradigms. Moving beyond the conceptualization of technology as a passive instrument, this conceptual paper introduces "The AI-Identity Loop," a theoretical framework that maps how specific bundles of computational capabilities may contribute to the reconstruction of users' professional identities. By synthesizing human-computer interaction (HCI), cognitive psychology, and the sociology of work, the framework outlines a four-phase mechanistic progression: (1) Computational Capability, (2) Frictionless Delegation, (3) Miscalibrated Agency Attribution, and (4) Identity Reconstruction and Skill Atrophy. To substantiate this framework and avoid platform essentialism, a comparative analysis isolates three archetypal capability bundles—Generative Execution, Macro-Synthesis, and Epistemic Discovery—using contemporary platforms as functional exemplars. The analysis suggests how distinct computational environments and workflows may cultivate specific professional archetypes (The Augmented Creator, The Knowledge Director, and The Researcher), each with characteristic patterns of cognitive trade-off. The paper concludes by examining the structural tensions of this paradigm. While acknowledging the substantial potential of role elevation, it identifies the risk of "perishable abilities"—foundational human cognitive and technical skills that may systematically degrade without deliberate practice as a consequence of habitual cognitive offloading. Boundary conditions, moderating variables, and counter-arguments are discussed to delineate the scope and limitations of the framework.