The Suits Ate Fashion. Now the Robots Get Dessert.

Fashion is currently having one of its periodic nervous breakdowns about artificial intelligence. Designers will disappear. Algorithms will decide what we wear. TikTok will identify a microtrend at breakfast, artificial intelligence will design it before lunch, and somewhere outside Guangzhou a factory will have 40,000 units ready before anyone has worked out whether we actually wanted the thing.

The anxiety is understandable. It is also slightly late.

Long before generative AI learned how to make a handbag with seven handles and an inexplicable third zipper, fashion had already begun removing the troublesome human ingredients from its business model: intuition, eccentricity, patience, experimentation, obsession and, worst of all, people with opinions.

AI did not invent fashion slop.

It arrived after several decades of excellent preparatory work.

First, Remove the People Who Care

There was a time when fashion companies were built around people who were frankly inconvenient.

Yves Saint Laurent thought fashion belonged somewhere in the vicinity of art. Halston went to Studio 54, stayed out until indecent hours, then wandered into the studio and somehow translated the whole nocturnal circus into clothes. Karl Lagerfeld treated it as an expression of the present and appeared constitutionally incapable of being bored.

None of this would survive a contemporary efficiency review.

Imagine presenting Halston to a private-equity operating partner. His hours are irregular. His personal brand creates key-person risk. His creative process cannot be entered into Excel. Studio 54 appears to be producing no measurable return on investment whatsoever.

The modern corporation prefers a different kind of genius: predictable, scalable, measurable and ideally available as a software subscription.

Fashion's gradual corporatization did not eliminate creativity, of course. Wonderful designers still work inside enormous companies. But the balance of power changed. The designer increasingly became one component inside a machine whose real language was no longer silhouette, proportion, fabric or desire. It was growth, margin, inventory turns, customer acquisition, conversion and exit multiple.

Then private equity arrived with an especially seductive proposition: what if we made capitalism even more capitalist?

Private equity is often described as sophisticated finance. Sometimes it is. It can provide capital, restructure failing businesses, professionalize operations and help companies grow. Even the Bank of England acknowledges its role in providing long-term capital to British businesses.

But there is another version, the one fashion knows rather well: buy something somebody else built, load it with expectations and frequently debt, extract efficiencies, improve the numbers, and eventually sell it to somebody else.

It is entrepreneurship with the awkward entrepreneurial bit removed.

The Lazy Genius of Financial Engineering

This is where neoliberalism performed one of its better magic tricks.

For decades, we were told that markets would make companies more efficient. Often they did. Unfortunately, nobody specified what they were supposed to become efficient at.

Making better products is difficult. Building a culture is difficult. Training craftspeople takes years. Creating something nobody knew they wanted is horribly unreliable. Maintaining factories, investing in employees, developing materials and allowing designers to make mistakes all involve money disappearing today in the vague hope that something worthwhile might happen tomorrow.

Financial engineering is much tidier.

The private-equity model did not create short-term capitalism, but it perfected one particularly elegant version of it: treat the company itself as an asset whose financial performance can be optimized during an ownership window.

Suddenly the brand has another customer.

Not the woman buying the coat.

The next owner buying the company.

That distinction changes everything.

The transcript that prompted this article points to EVERLANE, a company built around "radical transparency" and conscious consumption, which later took investment from L Catterton and eventually landed at SHEIN after accumulating substantial debt. Whatever one thinks of the individual companies involved, the trajectory contains a wonderful piece of contemporary poetry: a brand whose identity was transparency ending up inside the empire most associated with hyper-fast, algorithmically driven fashion.

You couldn't workshop a better metaphor.

Britain, Incorporated

If you want to see what happens when financialization becomes a national hobby, look at Britain.

Britain spent several decades enthusiastically selling, outsourcing, privatizing, securitizing and generally discovering that almost anything becomes more exciting once somebody puts it into a holding company.

Water. Railways. Care homes. Veterinary practices. Supermarkets. Infrastructure. Housing. Essential services acquired the intoxicating glamour of the spreadsheet.

Private equity is now deeply embedded in the British economy. The Bank of England estimates that PE-backed companies account for roughly 10 percent of UK private-sector employment and around 15 percent of corporate debt. It also points to significant leverage and is actively stress-testing the private-markets ecosystem.

That does not mean private equity "caused" Britain's economic problems. Britain's famous inability to build things, invest enough, maintain infrastructure, resolve planning constraints or think beyond the next political weather system has many parents. The Bank of England notes that UK capital investment has remained low relative to other G7 economies for years.

But private equity belongs beautifully to the same cultural moment.

It represents an economy increasingly brilliant at owning things and strangely less interested in making them.

Which brings us neatly back to fashion.

Then the Algorithm Discovered Taste

Once fashion had been converted from a cultural business into an increasingly optimized consumer-products business, the algorithm did not need to stage a coup.

The throne was vacant.

Companies had already learned to watch what sold, measure what clicked, copy what moved and eliminate what couldn't justify itself quickly enough. Social media then provided the missing ingredient: a gigantic real-time laboratory containing billions of people voluntarily broadcasting what they desired.

Why employ somebody to imagine what people might want next year when TikTok can tell you what 19-year-olds wanted seventeen minutes ago?

The fashion company becomes less creator than seismograph.

The transcript describes Addicted as explicitly more algorithm-based than design-based, using search behavior, social-media trends and celebrity outfits to determine assortments before rapidly producing hundreds of inexpensive styles.

This sounds technologically advanced until you notice that the machine is essentially looking backward at enormous speed.

That is the central joke of algorithmic fashion. It presents itself as futuristic while being structurally nostalgic. It can only identify what has already generated a signal. Yesterday's novelty becomes today's data point, tomorrow's product and Thursday's landfill.

AI makes this spectacularly efficient.

It can generate more designs, more imagery, more variations, more descriptions, more campaigns and eventually more synthetic influencers wearing more synthetic clothes in synthetic Tuscan villas.

Infinite content meets infinite product.

At last, supply can become as bored as demand.

AI Slop Is a Business Model

We tend to discuss AI slop as an aesthetic problem: ugly images, generic prose, derivative music, soulless clothes.

That misses the more interesting point.

Slop is an economic achievement.

It is what happens when a system becomes extraordinarily good at producing adequate things cheaply, quickly and continuously while becoming progressively worse at explaining why those things should exist.

Private equity and generative AI therefore make oddly compatible bedfellows. One asks: how can we extract more measurable value from an existing asset? The other asks: how can we generate more plausible output from existing material?

Neither question is inherently evil. Both can produce useful results.

But neither is the question from which culture normally begins.

Culture asks something much more financially irritating: What if we tried this?

There may be no data. There may be no demonstrated demand. The customer may initially hate it. The board may not understand it. The first collection may fail.

This is how genuinely new things tend to enter the world: without a five-year revenue history.

AI doesn't destroy that possibility. Private equity doesn't automatically destroy it either. But an economic culture obsessed with optimization gradually makes such irrational behavior harder to defend.

And creativity is gloriously inefficient.

The Future Needs Worse Businesspeople

Perhaps fashion's next revolution will not be technological at all.

Perhaps it will involve rediscovering the value of people who are slightly bad at optimization.

Designers who make something because they cannot stop thinking about it. Founders who refuse to sell. Companies willing to remain small. Manufacturers who keep expensive skills alive. Customers who buy fewer things and keep them long enough to develop an emotional relationship with a stain.

People, in other words, behaving irrationally.

Because the great irony of algorithmic capitalism is that everyone is desperately searching for differentiation while using increasingly identical systems to find it.

The same trend reports. The same consumer data. The same platforms. The same consultants. The same AI models trained on the same cultural archive, producing variations informed by the same engagement signals, presented to consumers through the same recommendation engines.

And then everyone holds a strategy meeting to discuss why nothing feels distinctive anymore.

Fashion does not have an AI problem so much as it has an imagination-accounting problem.

We spent decades teaching businesses that anything difficult to measure was suspicious. Private equity accelerated the lesson. Algorithms automated it. Artificial intelligence may now industrialize it.

So perhaps we have the chronology backward.

The robots did not arrive and turn fashion into a spreadsheet.

Fashion turned itself into a spreadsheet.

The robots simply learned how to dress for the office.