Who controls the image? AI and the shifting power of visual culture

Artificial intelligence has radically transformed the way images & visuals are produced, perceived, and valued. No longer just tools for editing or enhancement, AI models such as Midjourney, Stable Diffusion, and DALL-E have become autonomous image generators, blurring the boundaries between photography, illustration, and computational aesthetics. The emergence of AI-generated visuals raises profound questions: How does AI alter artistic creativity? What happens to realism when images are synthesised from statistical models rather than captured from reality? Can AI-generated images develop their own aesthetic identity, or are they trapped in an endless loop of algorithmic remixing? And who takes visual narrative control in midst of AI proliferation of visuals?

At the heart of these discussions is a fundamental shift: AI-generated visuals no longer function as mere representations of the world but as complex outputs shaped by data-driven processes. Unlike traditional photography, which has long been associated with indexicality and evidence, AI-generated images exist in a liminal space where meaning is inferred rather than captured. The “latent space” of AI—where images are mapped according to probabilistic associations—has become a new site of aesthetic exploration, challenging our historical understanding of images as records of reality. One of the most striking aspects of AI-generated images is the way they redefine the very concept of meaning in visual culture. Unlike traditional images—whether painted, photographed, or digitally manipulated—AI images are not direct representations but statistical reconstructions of patterns found in vast datasets. This has profound implications for how we understand meaning, interpretation, and communication in an era where images are no longer simply captured or created by human hands but instead synthesised by algorithms operating within a latent space.

This raises a key question: Do AI-generated images truly introduce new visual forms, or do they merely remix existing ones? Multiple reports suggests that the latent space of AI models plays a crucial role in this process, acting as a vast computational archive where meaning is not simply stored but mathematically measured; language plays an increasingly dominant role in image production, as text prompts shape what is visualised. This suggests a major shift in visual culture theory, where words become the structuring force behind images, reversing the historical relationship between text and image in traditional media.

One of the most profound disruptions introduced by AI-generated visuals is the crisis of realism in digital aesthetics. Historically, images—whether painted, photographed, or rendered—have been assessed in relation to their ability to represent reality. Even with the advent of digital manipulation, photography retained its indexical link to the physical world, making it a dominant form of visual evidence . However, with AI image synthesis, the very notion of realism is destabilised, transforming it from an epistemic standard into a mere stylistic effect. This shift raises critical questions: Can AI-generated images still be considered realistic if they are simulations rather than records of reality? What happens to the concept of “photographic truth” when realism is no longer tied to material causality but to algorithmic approximation?

The declining authority of photography in the face of AI-generated realism is not just a technological shift—it also represents a cultural transformation in how we perceive images. Experts argue that we are witnessing the final stages of a long transition from the pictorial to the post-pictorial condition, in which images are no longer judged by their connection to physical reality but by their effectiveness in constructing aesthetic and conceptual experiences. This contrast raises important questions about the future of visual culture:

• Will AI-generated realism eventually replace traditional photography, making all images suspect?

• Will aesthetic imperfection—grain, blur, compression artifacts—become new indicators of truth and authenticity?

• How will photographic realism evolve as AI continues to redefine the boundaries between representation and simulation?

As AI tools become more integrated into artistic workflows, the role of the artist is shifting. Rather than being the sole creator of an image, the artist now acts as a curator, editor, and prompt engineer, guiding AI systems to produce desired outputs. However, not all scholars view this shift positively. Few of them warn that as AI-generated content becomes more prevalent, the skills associated with traditional artistic creation may become devalued. If AI can generate high-quality images in seconds, what incentive remains for artists to develop their own unique styles and techniques? This echoes long-standing concerns about automation and labor in creative industries, where AI threatens to replace human workers rather than assist them.

The automation of creativity also represents the battle for visual narrative control for artistic production. While AI democratises access to visual creation, it also raises concerns about aesthetic homogenisation, artistic devaluation, and the limits of generative systems. As we move into a future where AI-generated images become increasingly ubiquitous, the challenge will be to balance automation with artistic agency, ensuring that generative tools serve as instruments of creative exploration rather than mere machines of repetition. As AI-generated images become increasingly prevalent, their socio-political and ethical dimensions come under scrutiny. While AI offers new creative possibilities, it also raises profound concerns about bias, labor exploitation, surveillance, and the reinforcement of dominant power structures. AI-generated images are not just aesthetic artifacts—they are products of algorithmic mediation shaped by social, economic, and political forces.

Moving forward, artists, researchers, and theorists must develop a critical aesthetics of AI-generated visuals —one that goes beyond technological fascination to interrogate the cultural, social, and political implications of this emerging visual paradigm. AI is not merely an artistic tool; it is a contested space of knowledge production, automation, and ideological struggle. How we engage with AI-generated visuals today will shape the future of visual culture for generations to come.

(Sameer Dhanrajani is the CEO of Bengaluru-based AI advisory and consulting firms AIQRATE & 3AI)