In a recent interview conducted by Katrina Sluis from the Computational Culture Lab of the School of Art and Design of the ANU , Hito Steyerl, a prominent theorist and artist, explores the intersection of machine learning, artificial intelligence (AI), and the evolving role of photography. The conversation centers around the ways in which AI-generated imagery is reshaping aesthetics and the politics of visual culture. Speaking with a focus on her ongoing research, Steyerl unpacks how photography, once considered a tool for documenting reality, is now entangled with digital systems of data extraction, algorithmic bias, and speculative finance.
Steyerl’s analysis challenges our understanding of photography in the age of AI, where the image is no longer a product of the human eye and camera, but rather the result of complex, automated processes. Her reflections on “derivative images” and “subprime visibility” illuminate the profound shifts taking place in the way images are produced, circulated, and valued in the digital economy.
From Photography to Derivative Imagery
Steyerl introduces the concept of “derivative images,” a term coined by Jonathan Beller, which situates photography and image generation within the framework of financial speculation. Like financial derivatives, which are bets on the future value of assets, AI-generated images become wagers on visual culture. These images are not original; they are composites, statistically averaged from vast datasets, often scraped without consent from millions of online sources.
In traditional photography, the relationship between the subject, the photographer, and the viewer maintained a certain sense of integrity and authorship. However, Steyerl argues that AI image generation severs this relationship, producing what she calls “subprime visibility.” Just as subprime mortgages bundled risky loans together, AI-generated images amalgamate diverse, sometimes conflicting data, producing images that reflect a “structural mediocrity.” These images, often hollow and statistically derived, lack the depth and intentionality associated with traditional photographic practices.
The Politics of Image Training and Data Extraction
Steyerl delves deeply into the politics of AI-generated imagery, revealing the labor and data infrastructures that make these processes possible. A central component of her argument is the exploitative nature of data extraction. Much like the raw materials used in earlier industrial economies, data — often scraped without permission — becomes the fuel for AI models like Stable Diffusion. Images and text from platforms, creators, and users are looted en masse, and this raw data is then refined, repackaged, and used to generate new visual content.
Steyerl also highlights the labor of so-called “ghost workers” — underpaid, often invisible workers tasked with cleaning, annotating, and processing data to make it suitable for machine learning. This invisible workforce, many of whom come from disadvantaged backgrounds, forms the backbone of AI image generation. The “subprime” quality of the images is not just a reflection of the datasets but also of the exploitative systems of labor that produce them.
The Collapse of Meaning
A key point Steyerl raises is the potential collapse of meaning in the age of AI-generated images. As models are increasingly trained on their own output — a recursive loop of AI-generated content feeding back into the machine — the probability space narrows. The result is what Steyerl likens to “machine dementia,” where the diversity and richness of image production are reduced to a set of statistical probabilities. This leads to repetitive, shallow visual outputs that fail to resonate with human viewers in the same way as traditional photographs.
Steyerl’s notion of “model collapse” parallels the 2008 financial crisis, where derivative markets failed catastrophically because they were built on flawed assumptions and speculation. In the same vein, she warns that AI-generated imagery risks a similar collapse, not in financial terms, but in terms of cultural and aesthetic value. The endless recycling of existing data threatens to erode trust in images, as they increasingly reflect algorithmic biases rather than human vision or experience.
Toward a New Aesthetic Paradigm
Despite her critical stance, Steyerl does not entirely dismiss the potential of AI-generated images. She hints at the possibility of a new “thermodynamic” paradigm of image-making, one that moves away from the traditional optical and electronic frameworks of photography and video. This shift emphasizes the energy, heat, and entropy involved in data processing and image generation. In this new aesthetic paradigm, images are no longer just representations but are part of a larger, energy-consuming system of information production.
Steyerl also notes that AI image generation has sparked widespread protests, particularly from artists and content creators whose work has been appropriated without consent. She views these protests as a form of recursive action, where the very technologies used to produce and distribute images are also catalysts for resistance. In her view, the social impact of these technologies lies not just in their output, but in the counteractions they provoke, as artists and communities fight to reclaim control over their creative labor and data.
Conclusion: Rethinking Photography in the Age of AI
Steyerl’s insights invite us to rethink photography not as a static medium but as a dynamic, evolving practice caught in the crosscurrents of technological change, labor exploitation, and financial speculation. In the age of AI, the act of creating an image is no longer a simple matter of capturing light on film or a sensor. Instead, it involves navigating vast, opaque networks of data, algorithms, and labor, where images are produced not by individuals but by systems.
In this context, the aesthetics of photography must evolve to account for the ethical, political, and ecological implications of AI image generation. Steyerl’s concept of “subprime visibility” offers a framework for understanding how these new forms of visual production undermine the integrity of the image, while her focus on the political economy of data underscores the urgent need for new forms of resistance and collective action.
As we move further into the digital age, Steyerl’s work serves as a crucial reminder that photography is no longer just about seeing — it’s about understanding the hidden structures that produce what we see.