Transductive game: machine learning as the process of transindividualization

The relevance of the work is determined by a profound crisis of understanding in the era of widespread implementation of large artificial intelligence models. Dominating public narratives, oscillating between the utopia of total automation and the apocalypse of machine superiority, prove helpless in the face of real problems: the unpredictable behavior of algorithms, their rootedness in social prejudices, and the emergence of new forms of dependence and labor. Existing analytical frameworks, whether legal, ethical, or purely engineering, are inadequate for comprehending this new technology. It manifests not as a passive tool, but as an active agent, transforming the foundations of communication, creativity, and cognitive activity. In this regard, there is a need to develop a new conceptual language capable of describing this new technosocial reality of joint creativity and co-evolution of humans and machines. The methodological foundation consists of a synthesis of philosophical and technical analysis in the tradition of Gilbert Simondon and critical genealogy of technologies. The conceptual reconstruction of such notions as "transduction" and "transindividual" allows for a problematization of the metanarratives about automation. Genetic analysis traces the co-evolution of algorithms, data, and social practices. Problem-oriented comparison is applied to the paradigms of play and labor, while the hermeneutic approach reveals machine learning as a form of joint becoming of humans and machines within a common operational environment. The novelty of the proposed approach lies in the implementation of a radical ontological shift, achieved through the synthesis of the social history of machine learning and Simondon's philosophical concept of technology. Unlike critical studies focusing on the external social consequences of artificial intelligence, this work addresses the internal logic of its emergence as a technical object. The concept of transductive play allows for rethinking the genealogy of machine learning not as a linear sequence of succeeding paradigms, but as a single continuous process of transindividualization. In this process, the technological system and human collective mutually condition and generate each other within the framework of an emerging common environment. This approach enables a reformulation of key ethical dilemmas: the problem of algorithmic bias becomes a question of the quality and representativeness of the initial environment of emergence, while the problem of control over technology transforms into the issue of designing conditions for open divergent play.