Yann Davidson Vossah (SanJuan AI) has posted Local AI: Artificial Intelligence that Understands Place, Culture, and Daily Life on SSRN. Here is the abstract:
Contemporary artificial intelligence systems have achieved substantial gains through scale, centralization, and general-purpose optimization. However, when deployed in real-world environments such as public institutions, regulated industries, and local communities, these systems frequently exhibit systematic failures. These failures include legally invalid guidance, operationally infeasible recommendations, and outputs that lack institutional accountability. This work argues that such failures are not incidental, but architectural in nature.
We introduce Local AI, a system-level framework for embedded intelligence in which locality (encompassing place, culture, institutions, and daily-life constraints) is treated as a structural component of reasoning rather than as auxiliary metadata. The framework formalizes locality through the Local Context Frame (LCF) and integrates governance, privacy, and feasibility constraints directly into system behavior.
We present conceptual definitions, architectural patterns, and implementation considerations that distinguish Local AI from existing approaches such as retrieval-augmented generation, edge computing, and federated learning. We further examine policy implications, ethical risks, and governance requirements, and illustrate the framework through a case study of SanJuan AI, a pilot public-interest Local AI system. The paper concludes by outlining open research directions and positioning Local AI as a potential next layer of intelligence infrastructure.