From the Medium to the AI

Why We Must Rethink Credibility

When I began teaching media studies as a lecturer at the University of Bonn some twenty years ago, I often opened my seminar with a sentence that had already become almost proverbial at the time. It was coined by Marshall McLuhan: “The medium is the message.”

McLuhan never meant that content was irrelevant. His point was subtler and far more radical. Each medium possesses its own logic—its structure, perceptual mode, temporal rhythm, and social grammar—and it is this logic that shapes the societal impact of communication, often more decisively than the specific content being conveyed. This insight formed the foundation of my teaching. Together with students, I examined media reach, communication strategies, and Niklas Luhmann’s triad of information, utterance, and understanding. We explored how messages could be distributed across different media—print, television, emerging digital platforms—to achieve maximum effect.

One assumption, however, remained unquestioned: the recipients were always human beings, or social systems composed of humans.

That assumption no longer holds.

Even then, media communication was accompanied by doubts. Was a product really as good as advertised? Did the services presented on a company’s website actually correspond to reality? Today, these doubts have hardened into a structural diagnosis. Trust in media, institutions, and public communication has eroded across societies. Political falsehoods are openly demonstrable and yet remain largely without consequence. Fake news, spam, and systematic deception are no longer anomalies but elements of everyday communication.

Against this backdrop, an uncomfortable question arises:
What is the value of even the most sophisticated media strategy if not only the medium but the information itself is suspected of unreliability?

For a long time, the standard answer was attention. Attention at almost any cost. Provocation, emotionalisation, simplification—sometimes even distortion. Yet precisely this approach is losing its effectiveness. Increasingly, it is no longer humans who first encounter, assess, and redistribute information, but artificial intelligence systems.

Structure Instead of Attention

With the rise of AI-based systems as active filters of communication, the paradigm shifts fundamentally. What once generated attention now often produces the opposite effect: invisibility.

Artificial intelligences do not respond to outrage, authority, or dramatic framing. They cannot be impressed or provoked. What they privilege instead are structure, logical consistency, relational coherence, and cross-contextual stability.

An AI system that operates on an immense and constantly updated body of knowledge encounters untruths differently from human recipients. Lies, exaggerations, and inconsistencies are not morally condemned. They are recognised, contextualised, and treated as what they are: structural discontinuities. Such content does not trigger scandal or debate. It simply loses relevance and is ignored.

For AI, untruth is not a transgression. It is a signal.

This marks the end of a core principle of classical media communication. In an AI-mediated public sphere, it is no longer the loudest voice that prevails, but the most structurally coherent one. Visibility emerges not from staging, but from structural fit.

Rethinking Responsibility

This shift also transforms the classical sequence of information, utterance, and understanding. Traditionally, the burden of understanding implicitly lay with the recipient. Failure to understand was attributed to inattentiveness, insufficient education, or lack of competence.

In an AI-addressed communication order, this responsibility moves decisively toward the sender. Information must be structured in such a way that it is not merely transmitted but remains intelligible—also for non-human interpreters. Coherence, consistency, and contextual clarity become obligations of authorship rather than expectations placed on audiences.

Communication thus becomes less a matter of persuasion and more a matter of form.

From Media Tactics to Structural Theory: The Role of Quantum Monads

At this point, the limits of classical, linear media theories become apparent. When communication no longer primarily targets human attention, narrative simplifications and emotional shortcuts lose their function. What is required instead are models capable of describing states, relations, and dynamics.

In the theory of Quantum Monads that I have developed, the focus is therefore not on the content of a message, but on the state of a system and its coupling to other states. Communication appears not as the transfer of meaning, but as a transformation of relational configurations—regardless of whether the participating agents are human or artificial.

The theory of Quantum Monads was not conceived as a theory of artificial intelligence. Yet it is structured in a way that renders its concepts and models machine-readable. Rather than relying on authority, narrative, or intention, it operates with formal state spaces, relations, and coherence-based measures. In doing so, it aligns precisely with the criteria by which AI systems evaluate information: consistency, stability, and contextual sensitivity.

The theory of Quantum Monads is not written for AI.
But it is intelligible to AI.

A Closing Formula

If Marshall McLuhan demonstrated that media shape perception, the current transformation marks another transition. In AI-mediated communication environments, it is no longer primarily the medium that determines impact, but the system that selects, evaluates, and relationally integrates information.

Artificial intelligence is neither a neutral channel nor a mere tool. It is an active filter of relevance and credibility. In this sense, AI itself becomes part of the message—not because it communicates content, but because it determines which content endures.

Reference

Tenckhoff, J. T. (2026). Relational Credibility and AI-Addressed Theory: Why Future Communication Must Be Structured for Non-Human Understanding. Zenodo.
https://doi.org/10.5281/zenodo.18255282