Issue no. 189 of October devotes a substantial Questionnaire on Art and Machine Learning to twenty-four artists, theorists, and critics, offering a wide-ranging overview of the debate on the relationship between art and artificial intelligence. The majority of contributors come from, or work within, the United States (around 70 – 75%): a sample that is not globally representative, but nonetheless indicative of the current Anglo-American discourse. The positions expressed fall broadly into two main attitudes:
1) Artists who “use AI in order to hate it better” (as Nam June Paik once said of technology) – this is the slightly predominant group, accounting for more than half of the responses. These are artists who engage with AI tools mainly to deconstruct them: exposing bias, extractivism, surveillance logics, venture-capital rhetoric, and so on. This critique is extensive, sophisticated, and forms essential knowledge, even if not all arguments are equally convincing. Personally, I find it useful but also somewhat repetitive and transitional, since it can only represent a temporary phase in the face of a technology that is already part of our cultural and social infrastructure. The refusal to use the technology—except to criticize it—is an attitude bound to fade away.
2) Artists who acknowledge its problems but also explore its positive or innovative aspects – slightly less than half. Here we find more cautious and pragmatic approaches: risks and distortions are recognized, but artistic and theoretical possibilities are explored as well. In this group, critique becomes preparatory or parallel to experimentation—a bridge toward a more creative and constructive use of these systems. As mentioned, this stance is somewhat in the minority, though it is worth noting that the journal’s editorial board itself belongs to the first category.
Among the contributions I found most compelling: Stephanie Dinkins emphasizes the role of narratives as cultural algorithms and proposes that marginalized communities might “gift” their stories to AI systems, thereby inoculating them with alternative perspectives—guiding them toward care rather than control. Noam M. Elcott examines latent space as a new kind of “cultural grid,” heir to historical techniques of organizing the visible, highlighting both its opacity and its aesthetic potential. Lev Manovich situates generative AI within the long history of digital media and graphic algorithms, defining it as a form of “predictive media” capable of separating and recombining visual patterns in unprecedented ways. Antonio Somaini interprets latent spaces as new epistemic conditions linking what is visible and what is sayable, urging artists not to submit to them but to construct antagonistic and alternative ones. Finally, Christopher Kulendran Thomas offers a hybrid reflection spanning politics, economics, and artistic practice: he critiques Big Tech’s instrumental alarmism and imagines scenarios in which AI could foster post-capitalist models of cooperation.
A noteworthy detail in observing AI’s cultural reception is that the wave of hostility toward visual artists using generators appears to be subsiding. In 2022 – 2023, the debate was dominated by the alarm of illustrators and comic artists who felt their practice was under threat—I recall this vividly, having been caught in a shitstorm for daring to publish Sunyata—but that climate has now eased somewhat. On one hand, because the curve of outrage tends to follow a familiar cycle of panic and normalization (a “Sisyphean cycle,” as scholar Amy Orben puts it); on the other, because the use of AI images has rapidly become normalized, flowing into the everyday practice of many creatives. Conversely, suspicion has now shifted toward writing: the use of language models in journalistic, academic, or literary texts provokes reactions that echo the initial paranoia over images (“They don’t understand! Don’t use them!”).
This may simply reflect a historical asynchrony: the visual wave arrived first, the textual one is emerging now. As soon as AI becomes capable of doing something, we seem to enter what Benjamin Bratton has described as the five stages of grief. First comes denial—the belief that the machine cannot produce anything worthwhile and (paradoxically) that it is a threat. Then anger, manifesting as collective indignation and accusations of betraying human creativity. Next comes bargaining—attempts to delineate acceptable and forbidden uses. Then depression, when it becomes clear that the technology will not disappear and that certain traditional roles are bound to change (I think that’s the stage many are at with images). Finally, albeit slowly, comes acceptance: integrating AI as part of the cultural and creative landscape—something to critique, but also to inhabit.
A concept I find particularly useful for understanding the relationship between art and AI is that of distributed creativity, developed among others by psychologist Vlad Gl?veanu. From this perspective, creation is never the result of a solitary genius but arises from the interplay of actors, tools, audiences, and contexts. The author becomes a node within a network that includes materials, techniques, institutions, expectations—and now algorithms. Seeing AI as part of this creative ecology helps to temper both fear and hype: it is not a rival, but a new actor woven into a web of actions, constraints, and possibilities that already included brushes, cameras, software, archives, and economic structures. Rather than rejecting it, we should demand that it not remain in the hands of a few monopolies.
Perhaps the time has come to follow Dinkins’s appeal, when she writes:
“Instead of stubbornly clinging to old practices, labor hierarchies, claims of intellectual property or privacy, we should adapt our thinking and regulatory frameworks to learn to ride the exponential change introduced by intelligent technologies—while also redirecting their benefits. This does not mean ceasing to hold the tech sector and policymakers accountable, but ensuring that AI is built around care and social openness. The guiding ethic should be: always be learning—to keep expanding knowledge and understanding in the face of AI’s rapid evolution.”
One thing we should certainly learn is not to fear the machines themselves,
but those who own them.
Francesco D’Isa, trained as a philosopher and digital artist, has exhibited his works internationally in galleries and contemporary art centers. He debuted with the graphic novel I. (Nottetempo, 2011) and has since published essays and novels with renowned publishers such as Hoepli, effequ, Tunué, and Newton Compton. His notable works include the novel La Stanza di Therese (Tunué, 2017) and the philosophical essay L’assurda evidenza (Edizioni Tlon, 2022). Most recently, he released the graphic novel “Sunyata” with Eris Edizioni in 2023. Francesco serves as the editorial director for the cultural magazine L’Indiscreto and contributes writings and illustrations to various magazines, both in Italy and abroad. He teaches Philosophy at the Lorenzo de’ Medici Institute (Florence) and Illustration and Contemporary Plastic Techniques at LABA (Brescia).