On a recent afternoon, Lauren Leek stood on a typical London shopping street, in front of a Victorian-era pub and a predictable array of corporate-owned food, drink and retail outlets: Pret a Manger, Starbucks, Joe & the Juice, Subway.
“What we see in front of us used to be culturally diverse. Now it is culturally generic,” said Leek, a Dutch Australian data scientist who is an assistant professor at Leiden University in the Netherlands. “Most of what we see is the big chains we see everywhere,” she said. “It creates a sense of placelessness. We could be anywhere.”
Leek recently posted a Substack article, with the headline “
Temperature Zero for Culture: Why Everything Is Starting to Look the Same,” that analyzed the “flattening” effect that predictive algorithms have had on music, movies, hospitality and almost every aspect of today’s consumer culture. The post was shared and discussed by widely read technology commentators such as
Cory Doctorow and
John Naughton.
Taking 640 shopping streets in London as a test case, Leek used advanced data-analysis tools to examine how predictive algorithms have helped create what she calls a homogeneous “monoculture” in which, she writes in her Substack post, “we lose the ability to tell the difference between what people wanted and what the system made easy to want.”
Leek’s insight into everyday consumer behavior is, in some ways, reminiscent of
Roland Barthes’s classic 1957 collection of essays, “Mythologies.” Back then, Barthes used the recently developed discipline of semiotics, the study of signs and their meanings, to turn things as disparate and as seemingly banal as soap powder, wrestling, margarine and automobiles into subjects of serious cultural consideration.
But unlike Barthes, who was steeped in the French philosophical tradition, Leek is a London School of Economics-educated data cruncher. She uses her own algorithms, digital mapping and text analysis to show how
large language models are making consumer culture, wherever it happens to be, feel the same.
A recent excursion with Leek in southwest London to experience her analysis in action began in the Prince of Wales pub. The bar is owned by one of
Britain’s biggest brewers and hospitality retailers, which runs 2,600 sites across the country. This British company is in turn owned by a Hong Kong-listed
property development and investment conglomerate.
It was just after noon, and the old-fashioned wall décor and wooden furniture lent the pub a traditional feel. Multiple TV screens were showing the buildup to a Premiere League soccer match. Generic mood music played in the background.
“Often these pub chains have playlists that artificial intelligence automatically adjusts according to the sound level,” Leek said. “If the pub isn’t very full, it will play chilled background music,” she explained. “When it gets louder, the music gets louder.”
Pop music is an area where Leek’s research has revealed how sharply culture has “flattened” in recent years. Her Substack post includes a graph that tracks the verbal richness of lyrics from over 4,000 songs in Billboard magazine’s Hot 100 hits from 1950 to 2015. Since the early 1960s, Leek found, the range of distinct words has contracted more than a quarter, while the number of repeated lines has increased nearly a third.
“Streaming has created very concrete incentives — pay starts after 30 seconds,” Leek said. “A repeated hook in a song is the rational response.”
Leek studied the pub’s menu. It included staples such as chicken wings, burgers, and fish and chips, all of which could be ordered via a QR code. “Pub chains get the data from people who have previously ordered. They typically aggregate ordering data across their sites to optimize their menus. Everything has been analyzed,” Leek said. “Local differences have been taken away. Quirkiness is taken out.”
Leek’s tour moved on to an upmarket cafe and bakery nearby. It is one of a chain of more than 150 in London majority owned by an
American private equity company that manages more than $200 billion of assets. A cappuccino costs 4.40 pounds, about $5.90.
As a steady stream of customers ordered coffees and pastries, Leek explained that chain stores use specialized
site-section algorithms to choose the optimum locations for their branches.
“They use public, open socio-economic data, like small-area census statistics. They have their own data, which neighborhood restaurants don’t have access to. And they have their own data analysis teams,” Leek said.
For Leek, the key to the flattening of consumer culture lies in the mathematics of algorithms and their methodology of
next-token prediction. Language models like ChatGPT write one small chunk of text, called a “token,” at a time and predict and select the most likely next token at each step. The models have a “
temperature” setting, a set of parameters that control how adventurous that pick is. At “temperature zero,” the model always chooses the most likely next token and gives the same answer every time.
“It’s predictable and safe,” Leek said. “When the model is uncertain, it still has to make a prediction, so it always falls back on the most expected answer.” That means there is no randomness, no surprises. Leek argues that algorithms are pushing culture toward a blandly predictable, mathematically determined temperature zero.
“The algorithm is making predictions for the average person, but the average person doesn’t exist,” she said. “Each person has some niche preferences, and the niches differ. A system that optimizes for the average of everyone’s tastes produces something that nobody particularly dislikes but nobody particularly loves,” she added. “It’s built for a person who doesn’t exist.”
by Scott Reyburn, NY Times |
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Image: Joanna Yee[ed. I can't blame it all on algorithms. This process has been going on for decades. Every street (save for whatever underlying unique architecture used to exist), mall, airport, hotel, restaurant, clothing store, bar, etc. all looks the same. Why go to Paris or London, Japan, Dublin, or even China, if you're just going to see the same chains everywhere? In fact, it's probably harder than ever to find a city that still retains its unique character unpolluted by the likes of Starbucks, KFC, McDonalds, Taco Bell, 7-11, Louis Vitton and the rest of their ilk these days. Corporations have the deep pockets to buy the best trafficked locations. See also:
We already live in 'clone towns' - AI is making it worse (Observer).]
*****
In this terminology, a “home town” is a place that retains its individual character and is instantly recognisable and distinctive to the people who live there, as well as those who visit. A “clone town”, in contrast, is a place that has had the individuality of its high street shops replaced by a “monochrome strip of global and national chains”. Which meant that its retail heart could easily be mistaken for dozens of other bland town centres across the country. Of the towns surveyed, the NEF concluded: 41% were clone towns, 33% were home towns and 26% were “border towns”, meaning somewhere in between.
This lack of diversity is not just confined to towns. It’s everywhere. You see it in property ads for new flats, or houses that have been modernised for resale. Kitchens like operating theatres (and upmarket ones with the obligatory island); sitting rooms featuring white settees with overstuffed cushions, polished wood flooring and oversized TVs. Or cafes with exposed brickwork, and an expectation that oat milk with flat whites and smashed avocado are what you want. Or the legion of young influencers on Instagram who seem increasingly undistinguishable.