On the most basic level, its asking the ai to generate valid svg code for a picture of a pelican riding a bicycle, as a way of checking its intelligence. Popularized (invented?) by simonw, its been used as part of his reviews of new models as they come out since oct 2025.

It used to be a very difficult task for models, see [2,3,4]

it cuts across several tasks that AI used to be very bad at, but now has improved quite a bit. Namely, spatial reasoning (because it has to manually place the points of the svg such that they make sense and form what it says it forms. This used to not work very well, with random shapes floating around that it would mark things like "eyebrows" but were nowhere near the "eyes", etc.

It also tests the model's world knowledge (what do pelicans look like? sure they have wings, feet, beaks etc, but what shape are they? how to get proportions roughly right? this isn't a given from text data about the bird. This goes doubly for a bike, which is a quite complex shape that most humans fail to draw correctly[1] (many draw the frame or chain connecting in impossible ways that would not ever function mechanically)

Before it was pelican on a bicycle there were people having it do horses/unicorns making the rounds - gpt4.0 or whatever would often make hideous abominations of legs and mouths

[1] https://www.gianlucagimini.it/portfolio-item/velocipedia/

[2] https://static.simonwillison.net/static/2026/mistral-small-4...

[3] https://static.simonwillison.net/static/2025/codex-hacking-m...

[4] https://static.simonwillison.net/static/2025/gemini-2.5-flas...

If your school mascot is a pelican and you need to make a flyer for the upcoming bike safety event, then this precise thing becomes useful. Most things that are useful in the real world seem not useful out of context.