Copyright

Nicolas Malevé

Published On

2026-05-27

Language

  • English

Print Length

20 pages

THEMA

  • JP
  • JPA
  • JHB
  • JBCT
  • UY
  • UT

BISAC

  • POL063000
  • POL050000
  • SOC026000
  • SOC052000
  • COM079000
  • COM060000

Keywords

  • Open knowledge infrastructures
  • Digital governance
  • Digital commons
  • Politics of technology
  • Open source and open access
  • Epistemic justice

14. Controversial Openness, an Exploration of Open Image Generation Assemblages

Open infrastructures exist in a world already invested with property. Openness is not antinomic to property, it is conditioned by its relations to it. This chapter endeavours to map these paradoxical relations in order to think open infrastructures as alternatives within a world regulated by structures of ownership rather than as alternatives to it. It examines the generative image platform Stability AI that take up such a challenge, exploring ways of decentralizing the compute infrastructure and testing the limits it imposes on those who depart from the infrastructural template set by hegemonic AI companies. For Stability AI, curating images offers a means to produce better models whilst reducing their dependency on hardware. This brings them into an economy of attention where aesthetic judgements become a currency. And it opens their flanks to legal controversies as right owners claim authority over the images they appropriate. These controversies are symptomatic of a general condition that tests the mutability of the structures of intellectual ownership, the principle that guides the strategies with which platforms capture an economy of collaboration. From open to closed source, from image as data to image as property, open infrastructures are involved in a game of assimilation and appropriation that they both resist and embrace. Turning to the example of Stable Horde, a generative image distributed network which radicalizes Stability AI’s politics, the text argues that this mutability opens the door to the creation of alternatives that diverge but never break from the model they want to reconfigure.

Contributors

Nicolas Malevé

(author)
Postdoctoral Researcher at Sciences Po, Paris

Nicolas Malevé is a researcher, visual artist, and computer programmer investigating the socio-technical networks of machine learning and their artistic and epistemic implications. His doctoral research, Algorithms of Vision, was developed with the Center for the Study of the Networked Image (CSNI) at London South Bank University and The Photographers’ Gallery, London; he is currently a postdoctoral researcher at the SciencesPo School of Law and Medialab, Paris.