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Comment by AlecSchueler | Hacker News Reader

Any idea why there's so much convergence on the fish in a basket idea?

Same reason they're all facing right with the same composition, they all have three motion lines, they all have a yellow sun and 2-3 blobby clouds, they're all on two-lane pavement with a white dashed lane separator.

And the same reason Claude has all its notorious Claudisms in prose.

The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training. These are ley lines that the models gravitate towards. Diffusion models too.

It's why lazy AI use for creative work always comes across as lazy in the wild and always will.

When you use AI for "creative" purposes yourself, it feels like magic to see a pelican riding a bicycle at all, and you're impressed by all those cool details it added. But then 10, or 100, or 10M people throw more or less the same trivial prompts at it and we start to recognize all those "ley line" associations fill in the gaps the same way again and again and again.

This is interesting. There's a balance between:

- simple prompt for complex output (bad)

- complex prompt for simple output (bad)

- simple prompt for simple output (good)

- complex prompt for complex output (.... is this good or bad?)

To be fair, people have a genuine preference for images with things facing to the right. And lots of drawings will do 3 motion lines. You'd likely draw a dashed rather than solid separator too, or it would look like a random line. Lots of the AI habits are there because of choices we make.

Exactly, but that doesn't explain the fish.

Leave these poor pelicans and their choice of lunch alone, sheesh.

Doesn't it, or do we not know the answer? I'd ask people to start adding elements to the picture and see how quickly the fish appears.

There aren't that many associations left once we're primed with a bike and a pelican. It's like that old mental trick "What's the colour of the clouds? What's the colour of the snow? What do cows drink?"

> The shorter and vaguer a prompt you give to an LLM, the more it fills in further details using associations they've developed through training.

I can see that for stuff like facing right because characters in images generally do face right, and roads generally do have dashed white line separators. But putting a fish in a basket seems like a stretch of creativity—one that I would rather it not make unless prompted to—so it's surprising that every single model now seems to insist on it.

The models do all seem to converge in ways that, on occasion, wouldn’t seem obvious. But I suppose since they’re all products of the same core training data it’s inevitable that when they’re given the opportunity to fill in the blanks they come to the same conclusions. This seems to be true regardless of what modality you’re working with.

If there isn’t a concerted effort against it the world is going to start to look and feel ever more homogenous.

There's only one average in a set.

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