In the current discourse on artificial intelligence (AI), generative AI, and chatbots, the boundaries between human subjects and sophisticated technology are increasingly and often intentionally blurred. The following contribution argues that the conception of the human subject and how this is historically, socially and discursively produced is central to critical contemporary AI debates.

In business narratives, computer science, and popular media, the achievement of “general artificial intelligence” is presented as almost inevitable. Accordingly, new tests and benchmarks are constantly emerging the claim to make “intelligence” measurable. These practices often draw uncritically on dualistic psychological traditions that separate subject and world, mind and materiality, cognition and practice. It is argued that these divisions make it possible to describe machines through anthropomorphic analogies and, at the same time, to conceptualize humans through machine metaphors. Terms such as “understanding,” “reasoning,” and “learning” circulate as seemingly context-free abilities of generative AI systems, rather than being understood as deeply relational, embodied, and socially situated practices. This conceptual distortion is politically and economically situated: It is closely interwoven with promises of growth, funding logics, and capitalist narratives of the future, while questions of subjectivity and sustainability are systematically treated as secondary.

This contribution advocates for a critical, dialectical research practice in current debates on AI’s capabilities. Only through conceptual transparency and reflexive research practices will it be possible to adequately investigate both the actual technical potential and the societal significance of AI.