Where René Magritte questioned representation, this iteration questions origin, ownership, and the conditions of production. The work no longer resides within a fixed category but operates as tension between individuality – collectivity, protection – access and originality – transformation.


Origin – Cultures and Context
All images are produced within a social context. They are shaped by shared knowledge, visual culture, and historical references. No image emerges in isolation.
Every act of creation is embedded within a network of references, influences, and shared visual languages.
This becomes evident when compared to scientific knowledge, which depends on openness and reuse. If knowledge were treated with the same rigidity as copyrighted material, progress is barely possible, suspended by restrictions.
Cultural production, like scientific inquiry, relies on continuity and transformation.


Source – The Collective Archive
The data that underpins AI systems is also inseparable from the history of the internet as a space of shared knowledge production.
Much of this content has been created and distributed freely by individuals contributing to a collective resource. Software, Tutorials, explanations, creative works, and everyday expressions form an huge archive of human knowledge and specific experience.
This collective effort, initially driven by participation rather than profit, has become a foundational resource for computational systems.

The transformation of shared knowledge into a form of extractable data shifts the relationship between contribution, ownership, and value.


Creation – Data aggregation
AI models are trained on extensive datasets composed of images, texts, and other cultural artifacts (often also without direct attribution or compensation).
The outputs generated by these systems appear novel, yet they are fundamentally dependent on pre-existing material. This creates a recursive loop in which images are continuously remodeled from prior inputs.
By operating through large-scale aggregation artificial intelligence intensifies existing dynamics.


Ownership & Copyright
The copyright is hacked and the original is questioned. The gesture echoes René Magritte, yet it moves into the terrain of ownership and collective input.
Copyright establishes a framework that defines ownership, authorship, and the conditions under which images may circulate.
It protects artistic production by granting exclusive rights, yet simultaneously possibly removing works from open cultural exchange. The image and the image of the image becomes enclosed, regulated through permissions and licenses, and integrated into systems of economic value.
At the same time, AI enables the production of works that can circumvent traditional copyright frameworks by generating new variations, raising questions about the limits and relevance of ownership in such contexts.


Markets & Value
Artistic production is often dependent on institutional structures—galleries, publishers, music labels—that form networks of visibility and circulation. These intermediaries shape desire and construct value, often requiring artists to relinquish rights in exchange for visibility.
In many cases, artists relinquish rights to their work in exchange for access to these networks, resulting in a redistribution of ownership. The work becomes part of an economic system where its value is determined not only by its content but by its circulation.
Attempts by artists (eg. Damien Hirst, Taylor Swift,…) to bypass these structures highlight the instability of ownership and the ongoing negotiation between creation and control.


Transfer– Knowledge Systems
Approaches to authorship and ownership are not universal. Western models tend to emphasize individual rights and the protection of intellectual property, while other traditions—found largely in Asia—prioritize continuity, adaptation, collective development. In these frameworks the transfer of knowledge works through reenactment of the given instructions, where copying the gestures (e.g. writing) or the master is how you learn.

In these contexts, cultural production is seen less as isolated output and more as an ongoing process.
The strict separation between original and copy becomes less relevant, replaced by a model in which knowledge evolves through repetition and variation.
This creates a fundamental collision between systems that restrict and those that enable the flow of cultural material.


Transfer – Copying, Repetition and Embodiment
In many cultural contexts, Copying is not only reproduction—it is a method of learning. Repetition is used to internalize knowledge, to translate observation into embodied understanding.

Repetition functions as a means of internalization, where knowledge is not only conceptual but embodied through practice. Drawing, writing, or performing the same gesture repeatedly allows for a deeper form of understanding that exceeds surface-level comprehension.
This stands in contrast to rigid frameworks of restriction, where engagement with existing works is limited.
The tension between copying as learning and copying as infringement reveals differing conceptions of how knowledge and culture are transmitted.


Authorship
AI-generated images produce outputs by the act of prompting reshaping the act of making. This produces a condition in which authorship is diffused across datasets, algorithms, and users. The image can no longer be clearly categorized as original or copy, owned or shared. Instead, it exists within overlapping systems that define and contest its status.