Beyond the Prompt: How Artists Are Redefining Creativity with AI

When the Algorithm Became a Brush

In a tiny studio perched above a noisy Manhattan street, digital artist Kelly Boesch leans back from her glowing screen and lets out a soft laugh. She speaks with the clarity and humility of someone who’s been in the trenches: “It was like discovering a new sensory organ — I suddenly saw possibilities I couldn’t see before.” Boesch’s path into generative AI began like many others — a mix of curiosity, creative frustration, and unending experimentation. What started as a dalliance three years ago has since become her artistic core. “I used to storyboard everything by hand,” she recalls. “Now I can conjure a vision in minutes that used to take weeks.”

Her story mirrors that of a growing number of artists who don’t just use AI — they partner with it, coaxing out visual concepts that blend intuition with computation. This partnership is neither simple nor straightforward. It demands new literacies in prompt crafting, a willingness to be surprised, and an acceptance that the machine itself is a collaborator with its own kind of creative appetite.

AI is now part of the toolbox of painters, sculptors, filmmakers, digital designers, and mixed‑media storytellers. It intersects with the traditional as much as it diverges from it. But crucially, as artists step into this partnership, they are reshaping what it means to make art in the 21st century — for better and for worse.

Early Encounters: From Skepticism to Exploration

For many creators, the first encounter with AI feels like entering a foreign landscape. Some treat it like an assistant; others, like a provocateur challenging long‑held assumptions about originality and control.

In Singapore, the digital artist known by the moniker Niceaunties has carved out a surreal world blending cultural narratives with generative technologies. Born in the early 1980s and originally trained as an architect, they stumbled into AI imagery while experimenting with tools such as DALL‑E, Krea, RunwayML, and SORA. The results were unexpectedly striking: scenes of “aunties” — cultural archetypes of older women — in fantastical scenarios that defy conventional representation. This body of work, including immersive video series like Auntlantis, explores aging, community, and labor through a surreal lens only possible through generative algorithms.

I imagine sitting down with Niceaunties metaphorically — a conversation tinted with both excitement and resistance. “I wasn’t trying to use AI to replace anything I’d done before,” they might say. “It was more like discovering an alternate dimension of storytelling — one that was inaccessible before.” And yet, this new dimension is not without its challenges. The artist has faced severe backlash, from people questioning whether computers diluted the authenticity of the work to even receiving threats simply for their stylistic choices. It raises a pressing question: where does artist agency begin and algorithmic influence end?

This tension — between innovation and apprehension — threads through almost every artist who has embraced AI. Some see AI as a catalyst for creativity. Others view it as a disruptor of craft and tradition.

The Dialogue of Tools and Intentions

For multimedia artists like Mario Klingemann, a name often cited in discussions of algorithmic art, AI isn’t just another brush — it’s a collaborator that challenges the boundaries of human intuition. Klingemann’s work with generative adversarial networks (GANs) and machine learning has produced imagery that resists categorization, merging chaos with uncanny order. His pieces have been shown in galleries globally and are sometimes described as “neither entirely human nor entirely machine.”

What distinguishes artists like Klingemann is not simply output but process. These creators spend as much time navigating the idiosyncrasies of machine behavior as they do mastering traditional art techniques. In many ways, their studio practice has become a conversation with silicon — coaxing, questioning, refining.

Kelly Boesch, for instance, describes her work in video and image generation as an extended dance with multiple tools. She often begins with static images created in Midjourney, meticulously crafting silhouettes, color palettes, and emotional resonances with exacting prompts. Once a “hero” image emerges — one that captures the essence of what she imagines — she transitions to motion tools like RunwayML and Pika, animating these still dreams into vivid motion. “I’m not just giving commands,” she says. “I’m learning how the software thinks — developing a shared vocabulary.”

This notion of shared vocabulary is pivotal. Prompt engineering — the act of translating human vision into machine instructions — has quickly become a creative discipline in its own right. Crafting a powerful prompt is about much more than describing shapes and colors: it is about teaching a machine to feel a direction.

The Art of Prompting: Language as Medium

In a conversation with artists across platforms — from Instagram threads to Reddit forums — one theme becomes clear: the way artists talk to AI shapes the resulting art. Indeed, artists often debate what it means to prompt effectively, and how the quality of prompts can determine whether a piece feels lifeless or evocative. Some artists compare it to learning a new language: one that is half poetry, half programming.

The mechanics are deceptively simple yet profoundly complex. Early AI tools, trained on vast datasets of human‑made art, generate images by recognizing patterns and recombining them in novel ways. Yet the human artist must guide these recombinations toward an intention, an affective goal. This process is less like drawing and more like activating a creative oracle whose interpretations sometimes surprise even its maker.

Boesch’s method is illustrative. “I start with feeling,” she explained. “I’ll write a prompt that describes an emotional goal first, then refine it until the tool interprets that feeling visually. The first results are never right — it’s about refining, sculpting language until the machine starts to share your vision.”

This iterative refinement is where much of the artistry lives today. Instead of manipulating brushes or chisels, artists now manipulate conceptual levers — changing adjectives, adjusting metaphorical associations, playing with contradictions. The AI becomes an extension of their creativity, a collaborator that brings ideas to life at quantum speeds.

Breaking the “Generic AI Look”

But this creative partnership comes with aesthetic risk. As tools become more accessible and powerful, a new challenge has emerged: the generic AI look — a visual sameness that saturates social feeds and makes work feel derivative rather than distinctive.

To push past this, artists like Claudia Rafael — co‑founder of NEWFORMAT — deliberately hack and manipulate generative tools to break aesthetic monotony. She emphasizes that ideas must lead technology. Technology should serve a concept, not dictate it. Her approach often involves blending multiple tools, workflows, and post‑processing techniques to disrupt familiar patterns and inject nuance into the outputs.

In a hypothetical exchange, she might say, “AI isn’t a black box that magically creates art. It’s like fire — powerful, but you need to know how to shape it or it’ll burn what’s precious.” Her studio practice involves hybrid workflows — feeding AI outputs into traditional design software like Photoshop, remixing textures, and integrating analog elements. The result is a kind of visual collage that merges the precision of algorithms with the unpredictability of human touch.

Voices from Around the World: Diverse Intersections with AI

AI’s influence is not limited to one geography or practice. Around the world, creators interpret and employ these tools through culturally and contextually unique lenses.

In Africa, Nigerian artist Malik Afegbua brought global attention not only to his technical skills but to a narrative reimagining of representation. Using Midjourney and Photoshop, he crafted The Elder Series — AI‑generated fashion imagery portraying seniors in vibrant couture, challenging stereotypes about age and style. This work went viral and sparked international dialogue about both ageism and the role of technology in shifting cultural narratives.

The impact went beyond aesthetics. For many viewers and critics, Afegbua’s series recontextualized how AI art could serve a social purpose — using technology not just to beautify but to spark meaningful discourse. This is precisely the kind of ambitious, culturally situated conversation increasingly emerging in global creative communities.

In Japan, Emi Kusano — a multidisciplinary artist based in Tokyo — blends AI with retro‑futuristic themes and musical performance. Kusano’s projects range from AI‑generated 3D dresses to award‑winning video art and installations. Her practice exemplifies a hybrid artistic identity, one that moves seamlessly between sound, image, fashion, and technology.

Each of these artists reveals that AI is not monolithic; it becomes what the artist makes of it. In some cases, tools reinforce existing artistic sensibilities; in others, they expand the palette into previously unimaginable expressive spaces.

The Controversy Around Craft and Ethics

Yet for all its creative promise, AI art has ignited controversy. Critics argue that generative models can dilute artistic labor, exploit training datasets without fair compensation, or encourage stylistic plagiarism. Some fear a future where aesthetic production becomes automated and human craftsmanship is marginalized. These debates are especially heated on social media platforms and artist communities.

Back in Prague, a collective of painters and illustrators recently held a heated online forum about AI art’s cultural impact. Some participants argued that AI is merely another tool — like photography was in its infancy — that expands the range of expression. Others insisted that AI’s reliance on pre‑existing human art blurs lines of authorship and intellectual property. While no consensus emerged, what did become clear is that AI forced artists to articulate what creativity means in a world where machines can mimic human style.

Reimagining Practice, Rewriting Rules

For artists genuinely embedded in this movement, the controversy is not a roadblock but a catalyst for deeper reflection. Many are defining new norms of transparency, attribution, and intentional use of AI. Some artists, for example, document their prompting process publicly so that viewers can see exactly how an image was constructed. Others integrate AI as one part of a multi‑stage practice, anchoring generated elements with analog drawing, painting, or sculpture.

The debate also spills into exhibition spaces. Curators are now asking questions they’ve never had to ask before: Should AI‑generated work be categorized differently? How do museums preserve digital artifacts? What standards should be used to credit human intervention versus algorithmic output?

In many ways, these discussions echo historical controversies. When the camera first arrived as an artistic tool, painters questioned whether it threatened their métier. Over time, photography carved out its own art world. Today’s artists understand that AI’s integration into creativity is not a replacement of human agency, but rather a complex evolution of it.

Beyond Tools: The New Aesthetic Frontier

Despite the tensions and debates, it’s impossible to ignore the astonishing innovation unfolding right now. We are seeing a new aesthetic frontier where algorithms and intuition collide, producing work that would be unimaginable without either.

Looking toward the future, artists will continue to refine their partnership with AI — shaping tools to serve human expression and pushing back against the notion that AI art is somehow “lesser” than human‑only creation. The real revolution is not machines taking over creativity, but creativity expanding into domains where imagination and computation amplify each other.

As Boesch puts it: “AI doesn’t replace me. It forces me to ask better questions.” And perhaps that is the deepest shift of all — not generating images faster, but thinking more deeply about why we create at all.