Why every AI-built landing page looks the same (and how to stop shipping it)

You know this page. Inter, or the system font. A hero section, centred, under a small pill-shaped badge that says something like "Now in beta." A gradient running from indigo to purple. Three feature cards in a row, icon on top. Rounded corners, a shadow at ten percent opacity, shadcn components straight out of the box.

You may have shipped this page. I'm in no position to judge. But in April 2026 somebody finally counted, and the count is why I wrote this.

The measurement

In August 2025 Adam Wathan, who created Tailwind, posted a half-serious apology. Five years earlier he had picked bg-indigo-500 as the default button colour in Tailwind UI. The default was copied into so many tutorials that the models learned it as what a button looks like, and now it's the complexion of the machine-made web: one person's offhand choice, laundered through a training set into the taste of a generation of startups. When a parody site of vibe-coded landing pages reached the Hacker News front page, the top comment was two words long. Mode collapse.

In April 2026 the joke became a measurement. A developer named Adrian Krebs pointed a headless browser at 1,590 recent Show HN launch pages and ran sixteen deterministic checks for exactly these tells. Twenty-two percent of the pages had four or more. Another 32 percent had two or three. (He puts his false-positive rate at five to ten percent.) So more than half of the startups launching on Hacker News that spring wore at least two fingerprints of machine-made design, and they wore them on the one page where a company tries hardest to look like nobody else.

The benchmark followed in October. Fifteen frontier models, eighty design briefs, 2,400 generated interfaces. Every model scored above 90 percent on appropriateness, so these are not bad pages. But on how distinct different models' answers to the same brief were, the models scored 0.59 where human designers scored 0.76. On creative range, meaning how differently one designer handles different briefs, the models managed 0.58 against the humans' 0.90. Fifteen different AIs, asked for fifteen different apps, produced work more alike than human designers given the same brief. I should say that the authors build UI-generation tooling, so hold the decimals loosely. The direction matches everything else here.

Where the look came from

It was converging long before the models showed up. A CHI paper from 2021 measured how visually different website layouts were from one another and found the distance had fallen 43 percent between 2010 and 2019. Blame the frameworks. Bootstrap sits under three out of four sites whose CSS framework can be identified, and Tailwind is downloaded 176 million times a week. The interaction designer Boris Müller had the diagnosis in 2018, in one line: templates are content-agnostic, and that's the problem.

A note on the running example: the full essay opens with three friends who each asked ChatGPT to render their future apartment and each got the same beige room. That room is the living room mentioned below.

Why the model keeps giving you this page

The picture everyone reaches for, and the one I used myself, is the bell curve. The model learns the distribution of human work and hands you its middle. Everyone uses the same curve, so everyone gets the same middle. It's tidy, it's wrong, and the way it's wrong is the whole story.

Try it on my friends' living room. If you really averaged every living room on the internet you'd get a brown smear. The mean of a red sofa and a blue sofa is mud, and the mean of a thousand floor plans is fog. What ChatGPT drew was nothing like that. It was crisp and specific, a room you could order from a catalogue by Friday. An average is a blur of everything, and this was one particular thing. It wasn't averaged. It was picked.

The picking happens late. Pre-training teaches a model the whole messy distribution of what people have made, every cluster, with the empty space between them. Then comes post-training, the stage where people rate outputs and the model is tuned toward the ratings. That stage is an election, and it's held under a brutal rule. A 2025 paper from Meta's FAIR lab wrote the rule down: for the standard preference objective, putting all of the probability on the single highest-rated answer is an optimal solution. Winner takes all. And when two answers tie, moving everything onto one of them is still optimal, because the loss never looks at what was thrown away. (Real systems add a regularizer that stops the collapse from being total. It's a brake. The engine still pulls one way.)

So the oat-milk sofa is the candidate that won, under a system that gave the winner every seat. Averaging had nothing to do with it. The runner-up rooms, the ones with green velvet and terrible wallpaper, are probably still in the weights somewhere. They just never get called on.

The part that made me sit up was the fix. The same authors changed the objective to reward answers for being good and rare, and got about 75 percent more variety in stories and up to 46 percent more in invented personas, while winning head-to-head quality comparisons about as often as before. Which means the sameness is a default, the kind that stays put until somebody has a reason to change it.

Other groups have reached the same place from other directions. Take five models and compare each with its "aligned" chat version on a test of conceptual variety: alignment narrows every one of them, and none gets near the human baseline. Change nothing but the chat template, the role markers wrapped around your prompt, and variety drops anyway. And there's a finding that explains why switching vendors doesn't help, under the grand name of the Platonic Representation Hypothesis (ICML 2024): as vision and language models get stronger, their internal representations converge on one another. Tolstoy would have recognised it. All strong models are alike; each weak model is weak in its own way.

The model is not averaging us. It is agreeing with itself.

So the honest version of my thesis is narrower than "AI averages culture," and I think worse: a handful of systems, finished by similar methods toward similar ratings, settle on the same winners, and then everybody draws their first draft from those winners. Tyler Cowen called his 2013 book Average Is Over. He was writing about the labour market. For the work itself I'd put it differently. The average isn't over. It's free, and nobody pays for what's free.

It isn't only CSS

Ask a language model for a metaphor about time. Now ask it again, fifty times, with the randomness turned up. Then do the same with twenty-four other models, from other labs, trained on other data by people who compete with each other for a living. That's 1,250 attempts. A team ran exactly this for a paper called Artificial Hivemind, an oral at NeurIPS 2025, and what came back was two ideas. Time is a river. Time is a weaver. Two of the Qwen models produced the identical motto, word for word.

The full study threw 26,000 open-ended prompts at more than seventy models. A single model mostly repeated itself, and different models mostly repeated each other: cross-model similarity ran from 71 to 82 percent, and a typical cluster of fifty look-alike answers held the work of about eight different models. You can change your vendor. You'll get the same river.

None of this is new, only better measured. In January 2023 two researchers in Germany asked ChatGPT for a joke 1,008 times. More than 90 percent of what came back was the same 25 jokes, and one of them, about a scarecrow who won an award for being outstanding in his field, turned up 140 times. (Go and ask the chatbot in your other tab for a joke. I'll wait. If a scarecrow, a tomato or an atom is involved, you've just replicated a paper.)

The obvious reply is that people are no better. Ask a hundred humans about time and you'll hear about plenty of rivers. So a second team ran the comparison. Twenty-two models and 102 people took the same standard creativity tests. On the alternate-uses task, the one where you list things to do with an everyday object, the average distance between two humans' answers was 0.738. Between two models' answers it was 0.459. That gap is about two standard deviations, and a system prompt begging the models to "be very creative" closed less than half of it.

The detail I can't shake is that the models scored slightly higher than the people on individual originality. Any one answer was good, often better than yours or mine would have been. It was the set that was a monoculture.

The part you can't see from your desk

The study that got to me came out of Wharton in 2025, in Nature Human Behaviour. Five experiments on brainstorming with ChatGPT. In one, people had to invent a toy using a brick and a fan. With the chatbot's help the individual ideas got better, as they nearly always do. But 94 percent of them overlapped with somebody else's, and nine participants, working independently, gave their toy the same name: "Build-a-Breeze Castle."

Think about those nine people for a second. Each of them, as far as they knew, had named a toy. Each had seen one answer. Only the researchers saw nine.

Across the five experiments, the pool of ideas was narrower with ChatGPT in 37 of 45 comparisons. The most varied ideas in the whole study came from people using nothing but their own heads and a web search.

One lab could be a fluke. The meta-analysis is harder to wave away: 28 studies, 8,214 participants. People working with generative AI do somewhat better work than people working alone (effect size +0.27). The variety of ideas across those same people falls off a cliff (effect size −0.86, and in social science you rarely see a number that size with a minus sign in front of it). If you keep one sentence from this essay, I'd like it to be the next one.

The tool makes each person better and the group narrower.

That is also why it's so hard to notice. Your own work really did improve, and you have the evidence in front of you. The cost turns up somewhere you can't see from your desk. It turns up in the set.

What to do in your next build

None of this is an argument for closing the tab. The tools are good and I use them daily. The expensive habit is accepting the first draft, so the fixes are all about where in the process the model gets a say.

1. Decide before you prompt. Write three sentences about what the page should feel like and who it's for, however clumsy. If the model speaks first, its answer becomes your anchor.
2. Give it a reference that isn't its own default. A screenshot of something you admire, a typeface, a palette from outside tech. A model with no reference falls back on the winner of its own election.
3. Ask for twenty directions and bin the first five. The first five are everybody's.
4. Change the tokens, not the pixels. Swap the font, the radius scale, the shadow and the accent colour at the design-token level before generating components. Indigo-500 is a default somebody picked on an ordinary afternoon; you can pick too.
5. Run the checks on yourself. Krebs published his sixteen tells. If your launch page trips four of them, you look like 22 percent of Show HN.
6. Keep one deliberate oddity. One thing on the page that no panel of raters would have agreed on, and that still makes sense. Airbnb data on 481,747 listing photos shows the shape of the reward: clones are penalized, so are the truly strange, and legible distance from the pack wins.

The numbers

The full essay goes further than the web: living rooms, car colours, music, the history, the strongest counter-evidence I could find, and three bets with stated confidence. It's here.

Originally published at piszczek.pl, where every number is linked to its source.