This essay argues that large language models (LLMs), such as GPT-type transformer architectures, actualize Jacques Derrida’s concept of différance. Originally introduced within the context of poststructuralist theory and semiotics, différance designates the way meaning is produced through a system of differences and deferrals, rather than stable reference. Drawing on this framework, the essay examines how LLMs generate meaningful content by calculating statistical differences across massive textual corpora—foregrounding processes of spacing, temporalization, and trace. It proposes that LLMs can be understood as “différance engines” that computationally enact the very mechanisms Derrida theorized. In addition to tracing these points of intersection, the essay reflects on the philosophical consequences of this alignment, including challenges to logocentrism, authorship, and the metaphysics of presence. It then addresses three potential criticisms of this approach, arguing that the use of Derrida’s work in this context is not a misappropriation, but a continuation and reiteration of its logic. And it concludes by identifying three systemic limitations and by charting opportunities for future research in this domain. The essay thus shows, on the one hand, how LLMs can be read through poststructuralist theory, and on the other, how poststructuralist theory can be clarified and rendered accessible through the technical operations of contemporary AI.

New materialism and object-oriented ontology have recently gained widespread attention. Taking as exemplary the work of Jane Bennett and Graham Harman (yet also drawing on other figures within these ....

Abstract Digital and analog: What do these terms mean today? The use and meaning of such terms change through time. The analog, in particular, seems to go through various phases of popularity and disu...

In this paper, I reflect on the puzzle that machine learning presents to social theory to develop an account of its distinct impact on social reality. I start by presenting how machine learning has pr...

For the official version of record, see here: Díaz Alva, A. (2023). Technics and Contingency: Ontological Productivity in Computation. Media Theory, 7(2), 37–76. Retrieved from Technics a…

Despite widespread consensus that China's digital revolution was sure to bring about massive democratic reforms, such changes have not come to pass. While scholars and policy makers alternate between ...

We observe a recent trend toward applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent ...