When we think of art, we often imagine a solitary figure: the genius alone at the easel, brush in hand, conjuring masterpieces from the depths of individual imagination. Yet this image of the artist as a solitary Romantic figure is relatively recent. In the eighteenth century, for example, portraiture was rarely the work of one hand alone. Famous names such as Joshua Reynolds or Thomas Gainsborough relied on workshops of assistants, apprentices, and fellow specialists.

Different artists within the same studio would contribute different elements of a portrait: one painted the face, another the hands, another the clothing, another the background. Still-life details, fruit, flowers, or drapery, might be outsourced to yet another painter. Someone else prepared the canvas, yet another crafted the ornate gilded frame. The result, though, was unified under the vision of the master painter, often originating in a drawing or sketch that guided the whole process.

What held this fragmented labour together, what made it art rather than mere decoration, was the overarching vision of the leading artist. It was not necessarily the brushstroke of Reynolds or Gainsborough that mattered most but their conceptual direction. Their sketch or idea determined how the disparate parts would cohere into something recognisable, meaningful, and resonant.

This historical fragmentation of artistic labour offers a useful parallel with AI today. Just as the eighteenth-century portrait was the outcome of multiple hands coordinated by a master vision, AI-generated works today involve the combination of vast processes, statistical patterns, and machine assistance, all ultimately guided by human intent.

The Many Faces of AI

AI gets a bad press. For some, it is a looming existential threat: an autonomous force waiting to take over the world. This narrative is compelling but misleading. It blurs crucial distinctions. There are different types of AI, ranging from non-agentic, tools that generate outputs when prompted, like today’s text-to-image systems, to more experimental “agentic” systems designed to take limited actions in pursuit of defined goals. Neither, however, resembles a self-conscious being bent on world domination.

What AI actually does is far less glamorous, if no less powerful. Modern AI is a statistical engine. It operates by identifying patterns in enormous datasets, images, texts, audio files, and producing outputs that resemble those patterns when prompted. The infrastructure behind this is not an invisible brain but a network of servers in massive data centres, consuming electricity and cooling systems on a global scale. AI “thinks” not by reasoning but by calculating probabilities, like predicting which word is likely to follow another in a sentence.

This is why philosophy is useful here. Early analytic philosophers like Bertrand Russell believed language was built upon logical structures, fundamental truths from which meaning could be deduced. Ludwig Wittgenstein initially agreed, but in his later work he abandoned this idea. Meaning, he realised, is not grounded in universal logic but in use, in the messy, context-dependent ways we speak and live. Words mean what they do because of the forms of life in which they appear.

AI, too, is grounded in structures, mathematical, logical, and statistical. Its foundations are algorithms, probability distributions, and the hardware architectures of vast data centres. In that sense, it is far closer to Russell’s and early Wittgenstein’s vision of language as governed by deep logical scaffolding. And that is precisely why AI cannot “take over the world” in the human sense. Its operations are rule-bound and structural, not lived or contextual.

The mistake, then, is the same one Russell and early Wittgenstein made: mistaking logical structure for meaning. Wittgenstein later realised that language only works within a context, a form of life. Likewise, AI can manipulate and extend statistical patterns to extraordinary degrees, indeed, in 2000 years its statistical sophistication may be unimaginable, but without being situated in the shifting, embodied, contextual world of human beings, it remains bound by its structures. It can simulate meaning, but it cannot live it.

The Dependence of AI on Human Input

Another common misconception is that AI is self-sufficient, that once created it could operate indefinitely without human involvement. A simple thought experiment reveals the flaw. Suppose AI had access to its own power source and humanity suddenly disappeared. Would AI “continue” indefinitely? Perhaps for a time. But eventually, as the world changes, the AI would become obsolete.

Technology requires constant updating to remain relevant. Consider a mundane example: a website running outdated PHP code. Without regular maintenance, it ceases to function. Likewise, AI requires ongoing training with fresh data, because the world it models is constantly in flux.

Even natural science gives us examples. The Earth’s magnetic field, for instance, is known to flip polarity over geological timescales. If AI were designed to navigate using magnetism but was never updated, it would eventually fail. Or consider atmospheric changes: if levels of carbon dioxide, light conditions, or even the reflectivity of surfaces altered significantly, AI systems reliant on earlier datasets would no longer function accurately.

The point is simple: AI cannot escape dependence on human beings. It needs us for data, context, meaning, and relevance. It is not an independent agent but a tool, extraordinary, yes, but tethered to human input.

Creativity and Its Shifting Definitions

Much of the debate about AI in the arts revolves around creativity. Can AI truly be creative? To many, creativity seems inseparable from human spontaneity, genius, or inspiration. Yet this is a peculiarly Romantic conception, one that emerged in the nineteenth century with figures like Wordsworth and Shelley. Creativity, in that era, was linked to originality, subjectivity, and the idea of the artist as visionary outsider.

But the history of creativity is far longer and more diverse. In classical antiquity, creativity was not about originality but about skillfully imitating nature or perfecting established forms. Medieval thinkers emphasised divine inspiration: creativity was participation in God’s act of creation. During the Renaissance and early modern period, it shifted again, bound up with craft, invention, and the collective labour of workshops.

The eighteenth-century portrait illustrates this pre-Romantic model well. Creativity resided not in isolated originality but in the coordination of many hands under a unifying vision. It was the master sketch that held the process together, not the solitary flourish of genius.

By this standard, AI in the arts is less revolutionary than it seems. Like a team of artists in an eighteenth-century workshop, AI can generate faces, hands, clothes, and backgrounds. It can fill in details, embellish, or mimic styles. But it is the human artist who initiates, guides, and determines meaning, who provides the “sketch” that makes sense of the whole.

Redefining the Artist for the Twenty-First Century?

What, then, is the role of the artist in an age of AI? Perhaps not the Romantic genius we have inherited from the nineteenth century but something closer to older traditions: the master coordinator, the one whose vision draws together the labour of many parts.

In this sense, the artist of the future may resemble the artist of the past. The “art” may lie less in the physical production of brushstrokes and more in the ability to conceive, to guide, to integrate disparate fragments into a coherent whole. Just as Reynolds’ sketch unified the hands of multiple assistants, so too does the modern artist’s prompt, direction, and curation unify the fragments produced by AI.

Far from replacing human creativity, AI might bring us back to an older understanding of what creativity means: not isolated originality but the orchestration of many contributions into something meaningful. And in that return, we may rediscover that art has always been less about the solitary genius than about the collective work of many hands under one guiding vision.

Dr C G Barlow. Copyright © 2025 C. G. Barlow.