Beyond One-Click Images: Rethinking What Creative AI Tools Can Be
When many people think of AI art, they picture a text prompt, a loading bar, and a flood of almost interchangeable images. Platforms like Infini.Gallery show how easy it has become to generate stylised, derivative artworks at scale: users upload an image, and AI models instantly transform it into a new visual variant for a vast, shared online wall. The emphasis is on frictionless participation and automated reinterpretation rather than on individual process or authorship. That kind of generative art process fuels fears that AI is flattening creativity into mass-produced content. But it also obscures another trajectory: artists who treat AI not as a black-box factory, but as a medium to think with. For them, algorithms and datasets are raw material—tools to interrogate, remix, and push against—rather than a shortcut around the slow, uncertain work of making art.
Sougwen Chung’s AI Drawing Robots and the Art of Co-Creation
Sougwen Chung’s practice is a prominent counterexample to push-button AI art. Known for blending drawing, robotics, and machine learning, she designs systems where AI-driven drawing robots respond to her gestures in real time. Instead of typing prompts, she performs alongside machines that have been trained on her own marks, turning AI artist collaboration into something more like a duet than a delegation. Chung has described collaboration with AI as fueling creative growth and offering a new lens on her work, framing algorithms as partners that expand perception rather than replace human intent. At the same time, she stresses how important it is to center humanity and human agency, especially amid pressures “to eliminate the human hand and just automate our creativity to death.” Her installations highlight process—movement, feedback, and negotiation—making the generative art process visible rather than hiding it behind an interface.
AI as Instrument, Not Author: A Spectrum of Creative Use
Debates about AI in art are often framed as a zero-sum clash between artists and technologists, swinging between moral panic and hype. Yet, as critics note, this framing misses the nuances of how AI actually enters creative workflows. On one end of the spectrum are end-to-end generators that churn out derivative content, optimizing for speed and volume. On the other are hybrid practices where AI functions more like an instrument or brush—powerful, but still shaped by human choices. Writers have argued that the value of art rests in intentionality and a series of decisions, which pure automation cannot fully replicate. Sougwen Chung’s work sits firmly in this second camp: translating her drawings into machine-readable forms, then responding to what the system sends back. Rather than claiming AI can produce fully formed, meaningful representations of human experience, this approach uses AI to probe, distort, and reflect the artist’s own inner world.
Why Serious Artists Care About Ethics, Data, and Transparency
As generative tools spread into advertising, entertainment, and anonymous online galleries, the stakes move beyond aesthetics. Mass-market systems often rely on opaque training data and large-scale remixing of existing imagery, raising unresolved questions about consent, authorship, and ownership. Platforms that host algorithmically generated collections can reconfigure how value and visibility are distributed, sometimes sidelining the very artists whose work fed the models. In contrast, artists working closely with AI—as in Sougwen Chung’s practice—tend to foreground where their datasets come from, how their models are built, and what role the machine plays in the final piece. This transparency is not just ethical posture; it is part of the work’s meaning. By acknowledging AI drawing robots, training corpora, and algorithmic limits, they invite audiences to see the piece as a collaboration across human and non-human agents, rather than a mysterious output that appeared from nowhere.
Intentional Experimentation: How Creators Can Collaborate With AI
For curious creators, the lesson from artists like Chung is not “never use AI,” but “use it with intention.” One practical approach is to keep human sketches, storyboards, or photographs at the center, and let AI models transform or extend them instead of generating everything from scratch. Another is to treat AI tools as suggestion engines: iterate on color studies, compositional variations, or motion tests, then selectively refine what resonates. Paying attention to how a model was trained—and what biases that implies—helps avoid overclaiming originality and encourages more responsible usage. Even simple experiments, such as feeding your own drawings into an image-to-image system or working alongside robotic plotters, can reframe AI as a responsive material. The aim is not to outsource creativity, but to discover new constraints, accidents, and perspectives that you can still ultimately direct and own.