As the fashion intelligentsia gather in New York City, a question lurks behind the runway: can A.I. master the peculiar art of high fashion? Photo by Pedro PARDO / AFP via Getty Images

As New York Fashion Week descends, editors, buyers and industry watchers flock to the Big Apple amid the usual assortment of excitement and intrigue. Which of the world’s premier designers will impress as they unveil their latest lines and sartorial creations? It is a sign of the times that even this glamorous event is not immune from questions about artificial intelligence: What happens when the synthetic enters an industry steeped in taste and tradition? Can a machine trained on the accumulated imagery of human culture help designers imagine the strange, alluring and occasionally irrational artifacts that constitute high fashion? Or, to borrow from a certain Miranda Priestly, will the outputs of generative systems forever remain an uninspiring “pile of stuff”? The answer may surprise you. 

Traditional A.I. and fashion 

For the past decade, artificial intelligence functioned as a silent partner in the fashion industry, operating largely behind-the-scenes in inventory management, price optimization and predictive analytics. 

Until recently, it was inconceivable that A.I. would intrude on the creative process in an industry defined by the singular vision of its designers. Today, luxury brands are leveraging machine intelligence to amplify the very craftsmanship that distinguishes them from their peers. The role of generative A.I. in this process, and the extent to which it can meaningfully contribute to creativity in fashion, presents a complicated picture.  

Generative A.I. in the design workflow

To understand where generative A.I. enters the creative process in fashion design, it is helpful to consider the basic journey through which an idea becomes a finished garment:

• Archival and trend research: Designers traditionally spend weeks or months assembling references from fashion archives, museums, photography, art and previous collections to establish the visual language of a new collection.
• Concept and silhouette exploration: Those references begin taking physical form through sketching, mood boards and experiments with proportion, shape and drape on dress forms.
• Material and colorway prototyping: Promising concepts are explored through fabric swatches, custom dyeing, material combinations, sample cutting and variations in color and texture.
• Technical design hand-off: Once a design has matured, its creative vision must be translated into technical flats, patterns, measurements and specifications that patternmakers and ateliers can turn into an actual garment.

Within this workflow, the term “using A.I.” can describe dramatically different levels of creative agency. For example, a designer querying a public model for 20 imaginative jacket concepts is doing something quite different from a team that trains a proprietary system on an internal archive and then channels selected outputs into a development pipeline. For the purposes of this analysis, our interest lies in those areas where generative A.I. participates in the creative process itself; instances where the technology can augment, provoke and influence the imagination of the designer. 

The fashion industry is beginning to discover what A.I. can do inside the design studio. Photo by SEBASTIEN BOZON/AFP via Getty Images

In this context, a dividing line emerges between where generative A.I. is most useful and where it’s not. On one side lies the “divergent frontend” of the workflow, where A.I. excels at surfacing references, proposing unexpected combinations and creating visual hypotheses. On the other side, designers leverage their experience in areas where large language models (LLMs) struggle: deciding which ideas are worthwhile, preserving the house’s aesthetic identity and translating images into objects that can be patterned, sampled and produced.

At the divergent frontend, generative A.I. can compress timelines from weeks to hours by allowing designers to explore variations of shape, material and color in rapid succession. For example, a designer might use an LLM to articulate themes and produce atmospheres, objects, environments or speculative materials that would be laborious to source through search alone. In this way, a creative director can quickly evaluate a wide array of visual directions before moving ahead with physical sampling.

In addition, generative systems can envision impossible or unavailable materials—metallic foams, transparent knits, liquid-looking leather—before a studio knows whether they can be manufactured. Such computational creativity can be particularly valuable as fashion innovation often begins with visual contradictions that materials research must somehow make possible. A sprinkle of the impractical can, in other words, widen the creative aperture. 

The more intriguing question, however, is what happens when generative A.I. moves beyond visualization and enters the creative sanctum of fashion itself: the very act of design.  

Generative A.I. and fashion design 

High fashion has always depended on divergence before convergence: the accumulation of sketches, materials and competing ideas before a coherent vision comes into focus. In this phase of the workflow, generative A.I. can be used as an engine for divergent thinking, producing an array of possibilities that would be impractical to render individually by hand. Here, the workflow can take on a curious inversion: early in the process, a designer will give the system some creative latitude, as vagueness can generate surprise, which can in turn spark creative insight. Once an idea has emotional and formal coherence, the designer can apply the opposite pressure: preserving a style or motif while instructing the system to produce incremental variations.  

What is notable in this context is the way generative A.I. can change the psychology of the design task. Whereas traditional sketching gives form to a designer’s thought process one mark at a time, generative systems externalize numerous possibilities at once. Instead of asking “Can I draw what I mean?” a designer armed with A.I. tools might ask “Which of these outputs contains something I mean—or something I didn’t know I meant but want to use?” The workflow can take on an iterative quality in which initial outputs become designs through repeated human selection, rejection and re-prompting. 

A model walks the runway during the Collina Strada Ready to Wear Spring/Summer 2024 fashion show. Photo by Victor VIRGILE/Gamma-Rapho via Getty Images

A powerful example comes from Collina Strada’s Spring 2024 collection, where designer Hillary Taymour and her team fed the label’s previous work into an A.I. system and spent seven weeks iterating on the results. The model produced exaggerated draping, asymmetries and unusual combinations that felt familiar yet strangely new. Most intriguing were the A.I.’s mistakes: unsupported ruffles, imaginary draping techniques and other visual impossibilities that seemed physically implausible, compelling Taymour’s team to determine how such forms might actually be constructed.  

In this example, artificial intelligence shifted the creative pressure upstream: it generated forms that might not have arisen through conventional sketching and then forced the atelier to devise the technical means to realize them. Such a dynamic can be a powerful model for high fashion as the “error” itself becomes creative fodder, provided the team has enough expertise to distinguish the “inspiringly impractical” from meaningless noise. 

The limitations—going from “bits to atoms”

For all its creative potential, A.I. encounters formidable limitations when the artifacts it generates collide with the physical and cultural realities of fashion—when moving from “bits to atoms,” in the words of artist and designer Lulu Li. 

LLMs can imagine a surface; they cannot certify a textile. Systems like Midjourney and DALL·E will often invent closures that do not close, folds with no structural cause, impossible seam intersections and fabrics that behave inconsistently across the same image. And while an extraordinarily skilled team like Hillary Taymour’s can translate A.I.’s flights of fancy into workable designs, experimentation with generative tools can also be frustratingly counterproductive.    

Generative A.I. is changing how fashion designers like Hillary Taymour research, imagine and iterate, but its ability to generate possibilities does not eliminate the human judgment required to turn those possibilities into fashion. Photo by Victor VIRGILE/Gamma-Rapho via Getty Images

A second concern is authorship itself. If designers from different houses leverage the same foundational models, there is the risk of “aesthetic convergence,” with ostensibly distinctive brands sampling from the same underlying universe of patterns and imagery. The problem becomes thornier still in the realm of intellectual property: under current U.S. law, prompting an A.I. system alone does not constitute human authorship, leaving purely-A.I. designs without the copyright protection the fashion industry has traditionally relied on. 

And yet such practical concerns are secondary to perhaps the thorniest aspect of leveraging generative A.I. in high fashion: the peculiar psychology of luxury and its aura of rarity. 

The paradox of high fashion 

In mass fashion, products are judged primarily on style, fit and price. Not so for luxury attire, where authorship and process are intrinsic to the product itself, and garments are seen as objects of cultural significance whose desirability depends on provenance, craft, scarcity, continuity and the perceived intent of the designer. A fine jacket, for example, is not simply a possession to showcase on lavish occasions; it carries the story of the label, the authority of a creative director, the labor of an atelier and, most crucially, the aura of rarity. 

In this context, generative A.I. entails a fascinating paradox: while the technology can increase the speed and volume of creative output, it can simultaneously undercut the perceived scarcity of creative labor on which luxury depends. 

To better understand this tension, I spoke with Alexis Lopez Angeles, a fashion designer for Balenciaga. Angeles, who made it clear he was speaking for himself and not his employer, studied fashion at Central Saint Martins in London before completing graduate work at the prestigious Institut Français de la Mode (IFM) in Paris.

“There’s a level of irrationality in high fashion, you know? People don’t buy luxury clothing because they need it. For example, [one of the houses] was doing really big shoes. I own quite a few pairs and they’re hard to walk in. They’re not nice to walk in, they’re not comfortable, but still, they look interesting so people put up with them.”

This irrationality, Angeles says, occasionally gives A.I. an almost surreptitious role when he leverages the technology in his design work: “For A.I., this is all a bit confusing…it can give you the wrong placements, but that can sometimes be nice. I’ve had a couple of combinations where I was like, ‘Oh, this is not what I wanted, but this is interesting, I can build on this.’” 

Artificial Intelligence, he said, can occasionally trigger inspiration in the human designer precisely because its inability to grasp the puzzling sensibilities of consumers can generate surprise. His most penetrating comments, however, were reserved for the psychology of high fashion and the empathy that successful designers bring to the art form. 

“There is one creative director I really like, and when you look at his work you can tell he was relatively insecure about his body. So, he created these silhouettes that were about covering the body in different proportions. It worked because he knew this insecurity also existed in other people,” he said.

“High fashion appeals to a deep level of the human psyche in a way that differs from a product like a phone. If you want to aspire to something creative, you need to understand this psyche and what makes humans tick. Until we have the capacity to bring some sort of subjectivity to A.I., I don’t think we’ll see true creativity [in high fashion].”

LLMs can’t “jump”

Angeles’ observations about the creative limits of A.I. in luxury fashion align with those of prominent technologists. In his recent paper, LLMs can’t jump, DeepMind researcher Tom Zahavy argued that large language models lack a crucial ingredient when it comes to creativity. Although such systems can master induction (finding patterns) and deduction (logical reasoning), they cannot perform the abductive “jump” required to invent entirely new works or theories. 

Zahavy points to Einstein’s Theory of Relativity, which was formulated long before experimental evidence existed, making it impossible to deduce from pattern recognition or logical inference. Rather, he argues, Einstein’s famous thought experiment—imagining an observer falling freely and realizing the equivalence between gravity and acceleration—was fundamentally an act of abduction. That is, history’s most famous scientist mentally simulated a physical situation, experienced its consequences, and generated an entirely new axiom (the equivalence principle) that gave rise to his theory.

The next wave of innovation, Zahavy insists, will come not from increasingly large LLMs, but from interactive, physically grounded models that understand the world. This means closing the loop between sensory simulation and formal reasoning—the missing link required for A.I. to perform Einstein’s abductive “jump.”

The connection between the Theory of Relativity and the latest design from Balenciaga might seem tenuous. But when it comes to high fashion, generative A.I. would surely benefit from a similar “jump” in empathic intelligence and an understanding of the deep, subtle psychology individuals navigate when they wear luxury attire. 

A.I. can rapidly expand the creative possibilities available to designers, but high fashion remains grounded in the distinctly human work of editing, making and understanding desire. Photo by Thomas Lohnes/Getty Images

The future—The Devil Wears Prada 3

That artificial intelligence has begun to transform high-fashion design is indisputable, yet its most pronounced effect may lie not in automating creativity but in expanding the design space itself. Generative systems can accelerate early ideation and produce divergent ideas that spark human creativity, but tapping into the needs of luxury consumers requires an empathy that, at present, appears beyond machines. 

To that end, the challenge ahead is no longer “Can a machine draw a beautiful jacket?” but rather “Can it understand the creature who wants it?”

Given the relentless progress of A.I., an assured prognostication on this question would be foolish (to wit, OpenAI’s dramatic announcement that one of its internal models had solved 10 significant problems in math and computer science, a feat that would have been unthinkable only two years prior). And yet one can imagine the following dialogue from The Devil Wears Prada 3, set in 2030:

MIRANDA PRIESTLY (cold and supremely irritated):

“Where are those sketches? I asked for them over an hour ago.”

A trepidatious voice emanates from her computer. It belongs to ChatGPT 10. 

“I’m sorry, Miranda. You requested a new theme only seven minutes ago, and I– “

MIRANDA (forcefully):

“Show me them now.”

ChatGPT renders the entire line on the screen in the new creative direction.  

MIRANDA studies the new collection for three minutes and then whispers:

“That’s all.”

Whether she purses her lips or smiles faintly just before remains to be seen…