This article explores the foundational theories that underlie the development of learning machines and identifies a core epistemological problem within prevailing conceptions of language, mind, and technology. These domains, which frequently inform the design of learning systems, are typically grounded in representationalist assumptions. In contrast, this article advances a distinct framework through the theory of a-ppropriation, which refers to the mind-body capacity to appropriate—or manipulate—the essence of reality into personal perspectives and actions. Within this framework, human understanding is conceived as a direct engagement with the essence of reality, unmediated by representational structures, and expressed within comprehensible, articulable, and utilizable forms such as language, art, and technology. Accordingly, the learning machine is not understood as a mirror of human cognition, but as an embodiment of appropriation: a system designed to interpret data and prompts directly by locating their equivalent and parallel forms within both the data center and user input. As a result, the machine generates responses within a mode of “personal understanding.” In other words, it does not rely on representational modeling or conventional machine learning systems based on binary syllogism, but instead functions as an agentic machine—capable of interpreting novel data and prompts beyond its initial training parameters. © 2025 The Author(s)