The AI creativity conversation has shifted from "Can it do this?" to "Should it be doing this?"

Over the last two years, generative AI has moved from a novelty to a daily utility for millions of people. Tools that can write essays, generate images, compose music, and even design interior spaces are now widely available. Users are decorating their homes with AI-generated layouts and turning family photos into custom coloring pages. The technology is clearly capable of producing novel outputs that humans find useful and entertaining.

But the question that nags at artists, writers, and thinkers is older than the current boom: Is this actually creativity? Or is it just sophisticated pattern-matching dressed up in a convincing costume?

Dr. Lindsey Brainerd, a professor in the UAB Department of Philosophy, has been researching precisely this question. Her specialty lies at the intersection of philosophy and cognitive science, and she has spent recent years examining whether machines can meaningfully replicate what we call human creativity.

"My research is primarily about creativity," Brainerd said in a recent interview. "I'm interested in whether this thing that we like so much — creativity — can be meaningfully replicated in an artificial system."

Her conclusion, so far, is cautious. "I argue that even though these models give us new, interesting things, they haven't actually achieved that special..." she said, trailing off before completing the thought. The implication is clear: novelty alone is not enough.

What counts as creative output?

Creativity has long been defined by two criteria: originality and value. A creative product must be new, and it must be useful or meaningful in some context. By that standard, AI-generated images and text often qualify. A coloring page derived from a real photograph is certainly new, and it may be genuinely valuable to a parent looking for a rainy-day activity.

But philosophers like Brainerd are pushing for a third criterion: intentionality. Human creativity involves a conscious agent who selects goals, makes judgments, and invests meaning into the act of creation. When a person chooses a color palette for a living room, they are not just picking random hex codes — they are thinking about mood, function, personal history, and aesthetic tradition. The choice reflects a mind with a history.

AI models, by contrast, have no intention behind their outputs. They generate statistically likely arrangements of pixels or words based on patterns in training data. The result may appear creative, but the process is fundamentally different.

This distinction matters because the value we place on human creativity is tied to the human story behind the work. A painting created by an artist who struggled with technique, experimented with materials, and finally broke through to a new style carries a weight that a generated image cannot replicate, no matter how visually striking.

The practical reality: AI as a creative partner

None of this means AI is useless for creative tasks. On the contrary, the technology is already proving itself as a powerful tool for augmenting human creativity. Users are employing it to generate ideas, overcome creative blocks, and automate repetitive parts of the design process.

For example, a homeowner might use an AI interior design tool to generate several layout options based on their room dimensions and style preferences. They still make the final decision, but the AI saves them time by producing alternatives they might not have thought of themselves. Similarly, a non-artist can create a custom coloring page for their child by uploading a family photo and letting the AI trace outlines. That act is not deeply creative in the philosophical sense, but it produces a meaningful result.

Brainerd's research does not dismiss these applications. Instead, she asks whether over-reliance on AI could erode the skills and habits that make human creativity valuable. If people outsource too much of the thinking and making to machines, they may lose the practice of coming up with their own ideas, making their own judgments, and enduring the struggle that often leads to breakthroughs.

The limits of machine creativity

Even the most advanced AI models today operate within strict boundaries. They cannot set their own goals, feel genuine inspiration, or experience the emotional highs and lows that accompany creative work. They also cannot take credit for their outputs in any meaningful sense, because they lack a sense of self.

Brainerd's argument aligns with a growing body of philosophical work suggesting that true creativity requires a subject — a "who" behind the "what." A machine can produce a poem, but it cannot mean the poem. It can generate a painting, but it cannot care about the painting.

This does not make AI-generated work worthless. But it does mean that the technology sits in a different category from human creation. We might call it "generative output" rather than creativity, reserving the latter term for processes that involve conscious agency.

What this means for the future

The fear that AI will replace human creativity is understandable but probably misplaced, at least in the near term. What is more likely is a reshaping of creative industries. Jobs that involve high-volume, low-uniqueness content — stock photography, basic copywriting, template design — are already being disrupted. But roles that require deep originality, complex judgment, and meaningful human connection will likely remain resistant to full automation.

For individuals, the smartest approach is to treat AI as a collaborator, not a replacement. Use it to expand options, test ideas, and speed up the grunt work. But maintain the habits of practicing your own craft, making your own decisions, and developing your own creative voice.

Brainerd summed up the challenge neatly: AI can give us new and interesting things. But whether it can give us the thing we call creativity — that special combination of intention, meaning, and human experience — remains an open question. Her research suggests the answer is no, at least for now. And that might be a relief.

Because if creativity were reducible to algorithms, it would lose some of the mystery and value we attach to it. The fact that machines can produce novel outputs does not diminish the human capacity for creation — it clarifies what makes that capacity unique.