Description This paper applies the foundational-layer constraint and emergence-direction methodology of the SAE (Self-as-an-End) framework to analyze the structural migration of AI inference capability from centralized cloud infrastructure to edge devices. Starting from the concrete technical milestone of KV cache compression (turboquant_plus, based on Google's TurboQuant, ICLR 2026), the paper identifies a two-tier architecture in which local models handle everyday inference and meta-cognitive routing (12DD), while cloud models recede to on-demand peak services, with non-automatable value judgments (13DD) necessarily retained by human users. Five historical technology migrations (computing, electricity, printing, packet switching, GPS) are used as structural validation. Six nontrivial observations are derived: the competitive shift from model capability to meta-cognition, partial commoditization of cloud services, structural advantage of full-stack hardware platforms, privacy as architectural constraint, stratification as steady state, and the decoupling of training and inference value. Keywords SAE framework; Self-as-an-End; artificial intelligence; local inference; KV cache compression; TurboQuant; foundational-layer constraint; emergence direction; technology migration; 12DD; 13DD; meta-cognition; Apple Silicon; edge computing; distributed architecture License Creative Commons Attribution 4.0 International (CC BY 4.0) Access Right Open Access Related Identifiers Is supplement to (SAE foundational papers) DOI: 10.5281/zenodo.18528813 (SAE Paper 1: Systems, Emergence, and the Conditions of Personhood) DOI: 10.5281/zenodo.18666645 (SAE Paper 2: Internal Colonization and the Reconstruction of Subjecthood) DOI: 10.5281/zenodo.18727327 (SAE Paper 3: The Complete Self-as-an-End Framework) References DOI: 10.5281/zenodo.18842450 (SAE Methodological Overview) Subjects Philosophy of Technology Artificial Intelligence SAE (Self-as-an-End) Framework Technology Forecasting Edge Computing Authors Han Qin (秦汉) ORCID: 0009-0009-9583-0018 Affiliation: Independent Researcher Email: han.qin.research@gmail.com