Temperature Zero for Culture: Why Everything Is Starting to Look the Same
Lauren’s data Substack
SubscribeSign in
Temperature Zero for Culture: Why Everything Is Starting to Look the Same<br>What 640 London shopping streets, 4,000 Billboard hits, a million films and synthetic AI personas tell us about why everything is flattening into sameness and how to regulate the algorithms behind it.
Lauren Leek<br>Aug 10, 2026
Share
I’ve spent most of the last decade unable to answer the simplest question anyone asks: where’s home? I’ve lived in multiple countries, with a passport situation that makes border guards frown. Now I’m moving again, to The Hague, and it was house-hunting that finally made me see it. Every flat I scrolled, in a city I barely know, I already recognised: the same refinished parquet, matte-black taps, rewilded pot plant on the same windowsill. Same with the café near every place I’ve lived, flat white and exposed brick and oat milk assumed. I’m at home everywhere because everywhere is converging on the same few templates, and I’m exactly who those templates were optimised for. The recommender’s algorithm’s ideal user, basically.<br>Here is what puzzled me. We have never had more individual data and naive intuition says all that granular personal data should fragment us, splinter the world into a billion niches. And yet the opposite is happening. The more the machines learn about us as individuals, the more alike our worlds look. That paradox, more personalisation producing more sameness, is what this piece is about, and by the end I want to convince you this is a political economy story that needs and has an easy intervention.<br>The theoretical mechanism
Imagine forty options, cuisines, genres, shop types, and a standard algorithm: predict what people want, show them more of it, watch what they pick, update. Then let the loop run.
The mode gets shown more, so it gets picked more, so the model grows more confident the mode is what people want, and the tails starve. Within a few dozen rounds, a catalogue that began almost perfectly even collapses onto one dominant option. But why does it collapse toward the mode rather than fan out across all that individual data?<br>Almost every recommender is trained to minimise a prediction error, get the rating wrong by as little as possible, or maximise the chance you click. But the problem is that under squared-error loss, the prediction that minimises your expected error is the conditional mean. So an algorithm that is uncertain about you, and it is always at least a little uncertain, hedges toward the average. The more uncertain it is, the harder it pulls you toward the crowd. This is why more data doesn’t save us. Personalisation under a standard loss function is regression to the collective mean with extra steps. Variance, the technical word for the stuff that makes you you, is expensive to predict. Basically, diversity is variance, and optimisers are built to minimise variance.<br>What makes this political economy (and hence why I decided to write about it) is that the system doesn’t have to be right, it only has to be listened to. A recommender that’s a mediocre predictor of what you’d love in a rich, diverse world becomes an excellent predictor of what you’ll click in the impoverished one it creates.<br>This is also where I should say plainly that I didn’t invent any of this. Economists would call it performativity, the way a model, once acted on, bends the world into agreement (MacKenzie’s markets that reshaped themselves to fit their own equations). Machine-learning researchers have started calling a version of it model collapse: train a system on its own outputs for long enough and the variance drains away until everything converges on the mode. Urbanists have their own word, placelessness, for what’s left when everywhere optimises toward the same template. What I’m going to show here though is that these are one mechanism seen at four different scales. Think of it as temperature zero for culture: always take the most likely next token, and watch the surprise drain out of the sequence. Let’s make it concrete.<br>Test 1: physical environment
As a first test, I took the Greater London Authority's map of 640 high streets, mapped 18,000 London food places onto it, tagged each by cuisine, and flagged the chains, meaning any name recurring three or more times across the city (Subway, Pret, PizzaExpress, the M&S Food-to-Gos). Then I asked a question you can only ask with the whole city at once: how similar is any one high street to any other? For every pair I compared their cuisine mix, scored 0 for nothing shared, 1 for identical for all sixty thousand pairings.
Pick two London high streets at random and, on average, they already share almost half their food profile. For a city like London that sells itself on the variety of its neighbourhoods, that's a lot of similarity. But the average is just a snapshot, what drives it is even more interesting.
So, I sorted...