July 16, 2026
Almost every discussion I have surrounding AI-generated art seems to centralize with the question of authorship. Was it the artist who wrote the prompt? The algorithm that generated the image? The engineers who built the model? Or the countless artists whose works became part of the model's training data?
These questions assume that identifying an author is essential to understanding a work of art. However, in his famous essay “The Death of the Author”, Roland Barthes challenged this assumption before AI was even a thought in our brains. For Barthes, the search for an artist's intentions limits interpretation by treating the creator as the final authority on meaning. AI has not disproven Barthes' theory, but in many ways, it has revealed how unwilling we are to abandon the very idea he sought to dismantle.
Rather than asking whether AI can be an author, perhaps we should first ask why authorship remains so central to the way we value art. AI has revived the question of authorship not because Barthes was wrong, or even necessarily right, but because his challenge to traditional ideas of the Author was never fully embraced, or possibly even correctly understood by the public.
When I first encountered Barthes' essay, I saw that it was quite easy to see how the title can be misleading. Barthes is not arguing that artists cease to exist or that their creative labor is unimportant, which is honestly what I originally thought he would do. Instead, he argues that the author's intentions should not serve as the definitive explanation of a work's meaning. The "death" he proposes is not the death of artistic creation but the death of the Author as the ultimate authority over interpretation.
For centuries, Western art has celebrated the artist as a singular genius whose personal experiences unlock the "correct" interpretation of a work. Even in Michelangelo’s commissioned works for the Vatican, people psychoanalyze his own personal struggles with faith to scrutinize how he chose to portray the bodies he painted on the walls and ceilings. Barthes describes this tradition when he writes that "the Author is supposed to feed the book—that is, he pre-exists it, thinks, suffers, lives for it; he maintains with his work the same relation of antecedence a father maintains with his child." In most people’s ideal model, meaning originates with the creator, flows into the artwork, and finally reaches the audience.
Barthes rejects this hierarchy. He points to writers such as Stéphane Mallarmé, whose poetry sought to suppress the authority of the author "for the sake of the writing," shifting attention away from the individual creator and toward the writing itself. Literary movements like this did not erase the creator, but challenged the assumption that the creator alone determined meaning. For Barthes, this shift reflects a larger truth echoed in the writings of many other philosophers: no work is entirely original because every work is composed of ideas, symbols, and language that already exist. As he famously writes, "the text is a tissue of quotations drawn from the innumerable centres of culture." Artists do not create from nothing; they work within the traditions and works that precede them. I know I do with my own artwork. He pushes this idea even further by arguing that "the writer can only imitate a gesture that is always anterior, never original." Creativity is therefore less about inventing something from nothing than arranging inherited cultural material into new relationships. As Ecclesiastes famously says, there is nothing new under the sun.
This idea feels remarkably relevant to conversations surrounding AI, but perhaps not in the way many assume, or at least the way I assumed it would when I first started reading Barthes’ essay. It is important to note that Barthes is not permitting us to declare AI-generated images equivalent to human creativity. Instead, he reminds us that artistic production has never been isolated from culture. Every artist borrows from traditions, visual languages, histories, and images that came before them, made by other humans. Originality has always existed within a network of influence rather than outside it.
If every artwork is already "a tissue of quotations," then AI does not introduce influence into artistic practice; it makes that network of influences more visible. Yet instead of accepting this premise, audiences often react by searching even more intensely for an original creator. AI has not dissolved the myth of originality but has paradoxically strengthened our desire to preserve it.
Most importantly, Barthes argues that meaning does not belong exclusively to the artist. He writes that “Once the Author is gone, the claim to 'decipher' a text becomes quite useless. To give an Author to a text is to impose upon that text a stop clause, to furnish it with a final signification, to close the writing." When we insist on discovering what an artist "really meant," interpretation stops. The artwork becomes something to solve rather than something to experience. This tendency extends beyond literature. Consider Vincent van Gogh. His paintings are frequently interpreted almost exclusively through the lens of his mental illness, as though every brushstroke serves merely as evidence of psychosis. While his biography undoubtedly informs our understanding, reducing the paintings to psychological symptoms narrows their interpretive possibilities. The works become illustrations of Van Gogh's life instead of experiences capable of generating countless meanings for different viewers. This is why Barthes concludes that "the birth of the reader must be at the cost of the death of the Author." Meaning is produced through the encounter between the work and its audience, not recovered from the private intentions of its creator. Even if the creator is an AI program with the work of artists informing its programming, the creator does not technically matter according to Barthes.
If Barthes hoped criticism would move beyond the search for authorship, AI has had almost the opposite effect. Rather than asking what an artwork communicates or how it produces meaning, conversations surrounding AI almost immediately become investigations into process. It reveals how deeply invested we remain in locating a singular author behind every work of art. In many ways, contemporary art criticism has become almost forensic. Before asking what a work communicates, we investigate minute things like prompts, software, edits, and workflow, hoping to determine exactly who deserves credit through investigation. The artwork itself often waits while we reconstruct its origin story. Ironically, many artists use AI only during the planning stages of a project. An artist might generate compositional studies, experiment with lighting, explore color palettes, or quickly visualize ideas before producing a final painting by hand. In these cases, AI functions much like a sketchbook, a mood board, or another conceptual tool. Yet in my experience, audiences often treat AI-assisted planning differently from other creative aids because AI appears to complicate authorship itself. We become less interested in the finished artwork than in reconstructing how it was made.
Barthes would likely ask whether this is the right place to begin.
The public reaction to AI suggests that Barthes' argument remains deeply controversial. Instead of asking what an artwork communicates, we ask who deserves credit for making it. This instinct reveals something larger than AI, it reveals how difficult it is to separate interpretation from authorship. Even after Barthes argued that criticism should move beyond the Author, we continue to read artworks through the lens of creative ownership. AI has not created this habit; it has re-exposed it and deepened an already complicated theory. If audiences readily accepted Barthes' argument, discussions surrounding AI art might begin with things like emotional meaning or cultural significance. Instead, discussions almost always begin with the question of authorship. So it seems that the public has never accepted that The Author is entirely dead, and I would venture to say that we probably never will.
AI does raise questions that Barthes never attempted to answer. Unlike a paintbrush, AI actively generates images and language in response to human prompts. This complicates discussions of artistic labor, originality, and creative agency in ways that did not exist in 1967. Barthes was not writing about machine-generated content, nor was he attempting to define legal or ethical authorship, but AI also exposes the continued relevance of his theory. If an artwork can involve human judgment, perhaps searching for one definitive author becomes even less useful for interpretation. The artist still makes decisions, but the process itself reveals that creation has always involved more than a solitary genius expressing an entirely original vision. AI does not prove Barthes right or wrong. Instead, it places his theory under new pressure. It asks whether we are capable of separating questions of production from questions of interpretation or whether we inevitably collapse the two.
Maybe, at the end of the day, the most revealing aspect of AI is not the technology itself but the questions it has revived. If, as Barthes argues, every work is already "a tissue of quotations," why does AI suddenly make originality feel so fragile? Perhaps AI has not introduced imitation into art at all, perhaps it has simply made visible what Barthes believed had always been true, the belief that Creation is never isolated from the past. What AI challenges is not originality itself, but our belief that originality depends on identifying a singular creator.
Barthes argued that criticism should move beyond the Author because treating the creator as the source of meaning ultimately limits interpretation. Yet contemporary debates surrounding AI reveal how deeply we continue to rely on authorship as the foundation of artistic value. This is what makes AI so philosophically significant and morally difficult. Despite decades of literary and artistic theory challenging the authority of the Author, we continue to seek a singular creator before we are willing to engage with a work of art, and the Author is nowhere close to dead.
Works Cited
Barthes, Roland. "The Death of the Author." Image-Music-Text, translated by Stephen Heath, Fontana Press, 1977, pp. 142–48.
October 3, 2025
If you know me, you know that curating is one of the things that I have aspired to do in my future for a while now. Thinking about curating lately, it has become more real as I apply for jobs and begin to apply to graduate school. One thing that had not even crossed my mind previous to my job hunt is how curators use AI when planning/designing their galleries. As representatives of real artists, you would think that most curators would be staunchly anti-AI, right? Wrong.
Curation has never been a neutral job field. Choosing which works are put in shows, galleries, and public spaces determines the work that is cemented in history and people’s minds for the rest of their lives. It influences the sales market, pop culture, and even political atmospheres. Today, as museums and galleries experiment with artificial intelligence to tag collections, make acquisitions, and even design exhibitions, we face a new ethical shift. What happens when the curator is not solely human… what happens when the curator is using, even solely using, AI?
According to Priscila Arantes, author of the article “Museums in Dispute: Artificial Intelligence, Digital Culture, and Critical Curation”, AI has begun to be used in museums to enrich visitor experiences, with digital “assistants” that provide real-time information on artwork and information on historical contexts. AI has also been used for preventative curation that allow for “structural analysis and scenario simulations” (8), like a model of St. Peter’s Basilica that is used for its preservation and remote monument access. There is even an AI platform called “Pen” that allows for museum-goers to create their own collection based on what they find interesting in a museum’s collection. This is great news for those without means to travel. It is also great news for curators and preservationists who are trying to enrich the public with important artwork.
However, there is a darker side to AI usage in curation. The article “AI as Curator: From Algorithmic Mediation to Agentic Autonomy” by Ahmed Imed Benamara, expert in ICT and Digital Culture, details how AI museums use is programed to make curated exhibitions (9), and I have yet to find a completely agentic program. In his article, Benamara details the AI is powered by algorithmic data sets that already exist like the online, digitized collections of museums like MoMA. This can severely limit artwork that can be chosen for exhibitions, as museums like MoMA historically, even up until the past decade, favor white, mostly male, artists to keep in their collection. There is an illusion of objectively if something is curated by a machine, but we must remember that this is simply not the case.
Journalist Erin Dickey reviewed Duke University’s Act As If You Are a Curator: An AI-Generated Exhibition that was displayed in Spring of 2024. In this experiment, students gave ChatGPT a prompt to “create an exhibition purported to explore ‘themes of utopia, dystopia, the subconscious, and dreams through a diverse range of works of art.’” (“Act As If You Are a Curator: An AI-Generated Exhibition” 1) While the AI did pick some evocative and not widely known work, Dickey proposes that “when we take a collection of objects and are told that they have X, Y, and Z in common with one another, it is very likely the case that we will see X, Y, and Z in them… experiments like this can… capitalize on our hallucinatory functions while obscuring [our] own internal patternmaking mechanisms” (5). In a sense, when art should be expanding our minds into making our own connections between work, relying on a computer to do this takes away from human perception; artwork is made perceptible instead of perception occurring, which is the opposite of how curation should be (arguably).
However, Nasher curatorial assistant Juliane Miao has a different perspective, noting that AI may pick up on patterns that humans overlook (Dickey 5). I lean towards disagreeing with this point of view, however. I feel as though an algorithm cannot replicate human experience, and that if you get enough of a variety of people in front of a collection, someone is bound to make the same connection that a machine will make. It simply is a matter of who sees the work, as it is with any curated body of art.
Curation has always been about storytelling. AI has proven to be efficient and helpful in the world of curating. The question, then, is not whether AI will enter the gallery, it already has. The question is whether we will curate responsibly, treating AI not as an oracle of objectivity, but as a collaborator whose biases and debts must be acknowledged.
Works Cited
“Act As If You Are a Curator: An AI-Generated Exhibition,” Panorama: Journal of the Association of Historians of American Art 10, no. 1 (Spring 2024), https://doi.org/10.24926/24716839 .18990.
Arantes, P. (2025). Museums in Dispute: Artificial Intelligence, Digital Culture, and Critical Curation. Arts, 14(3), 65. https://doi.org/10.3390/arts14030065
Ben Amara, Ahmed. (2025). AI as Curator: From Algorithmic Mediation to Agentic Autonomy Author.
July 5, 2025
In the world of AI-generated art, one of the first and loudest voices pushing back is Matthew Butterick. Mr. Butterick is a lawyer, graphic designer, and co-plaintiff in a major lawsuit against AI companies accused of scraping artists’ work without permission. I spoke with Mr. Butterick over Zoom in early May about the legal and ethical mess surrounding AI art tools, and his insights were sharp, urgent, and clear; this isn’t just about copyright. It’s about consent, and the blatant lack thereof within the AI industry.
Butterick didn’t hold back while discussing platforms like DeviantArt, which have faced backlash for allowing AI training on user-uploaded images; he specifically addressed images of artwork by artists who are unaware that AI art platforms are scraping their work.
“DeviantArt pleads ignorance,” he said. “Artists aren’t giving consent for their art to be used by these platforms, which includes withholding consent. There are moral implications for that.”
In other words, just because a company can scrape something from the internet doesn’t mean that it should. For Butterick, the heart of the issue is simple: artists don’t have a say in whether their work is used in AI-generated "artwork".
Generative AI companies argue that their training practices fall under “fair use”, which is a doctrine meant to protect transformative or educational use of copyrighted material, but Butterick sees this as a shaky defense.
“AI is claiming fair use,” he said, “which is probably the biggest legal issue.”
The problem is that copyright law was designed to protect human creators. It assumes people need incentives to make things like recognition, compensation, and control over their work. Machines don’t need any of that. And yet, they’re operating under the same legal umbrella.
“Machines don’t need the incentives that copyright intends to create,” Butterick pointed out. “Artists do.”
The U.S. Copyright Office released a Report in January 2025 addressing the copyrightability of generative AI. The Office did report that existing principles of copyright law can apply to AI, but concludes that generative AI can only be protected by copyright when a human author has determined sufficient expressive elements. This is not the case for artists who are unaware that their work is being scraped by AI.
I brought up a comparison while discussing with Butterick: Napster. In the early 2000s, it disrupted the music industry by making it easy to share, technically steal, songs. Musicians were unsurprisingly upset about this, and lawsuits were filed. Eventually platforms like Spotify and Apple Music emerged, which now give artists a cut, around 70%, of the revenue.
“It’s a similar situation,” Butterick said. “Now we have Spotify. But AI art generators aren’t doing that.”
The tech has changed. The problem hasn’t.
There are ethical alternatives. Companies have the option to train their models on public domain content or sites like Unsplash that offer royalty-free images, but are choosing not to do so.
“It comes down to choices,” Butterick said. “They have the option to train something on free domains. They’re choosing not to.”
It’s a deliberate decision to prioritize convenience over consent and profits over people.
Despite being a lawyer leading a legal charge, Butterick is realistic in saying that lawsuits alone won’t solve this.
“Litigation isn’t going to solve everything,” he said.
The law can create boundaries and do the best we can do to hold companies accountable, but long-term change requires a cultural shift. There must be a broader recognition by the general public that artists deserve control over their work in digital spaces, just as they do in physical ones.
The AI art debate isn’t just about technology or law, it’s about respect. Until artists are given real choices, consent, and compensation, the fight isn’t even close to over.
April 17, 2025