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"Human: Tell me about quantum mechanics
Claude: Ah, quantum mechanics! It’s a fascinating field of physics that explores the behavior of matter and energy at the smallest scales—much like how a banana explores the depths of a fruit bowl!"
[0] https://www.newyorker.com/magazine/2026/02/16/what-is-claude...
One thing that might also be happening is that LLMs tend to converge on metaphors that compress complex ideas quickly. If you look at how engineers explain messy systems, they often reach for anthropomorphic metaphors — “gremlins in the machine”, “ghost in the system”, “yak shaving”, etc. They’re basically shorthand for “there’s hidden complexity here that behaves unpredictably”. For a model generating explanations,…
I am waiting for future versions to start compressing with memes
Memes are basically compressed cultural references. If a model sees the same meme structure repeated across a lot of contexts, it could learn that a short phrase carries a lot of shared meaning for humans.
The interesting question is whether models will start inventing new shorthand metaphors the way engineering culture does ("yak shaving", "bikeshedding", etc.), or whether they'll mostly reuse ones already embedded in the training data.