Derrida’s notion of iterability

When an artificial intelligence reads a sentence, it does not puzzle over hidden meanings or cultural jokes the way a human does. Instead, computer programs process language using massive statistical maps. They look at billions of sentences to calculate how often certain words appear next to each other. Think of an AI as an extraordinarily fast mosaic artist. It does not know what a horse actually feels like, but it has looked at a billion pictures of horses. When it sees the word "stable," it checks its mathematical map to see if the surrounding words look more like a farm or more like a person's mood. ## The Illusion of a Fixed Code Because human words constantly shift their flavor in new contexts, a static computer program faces a fascinating puzzle. To handle this, modern AI systems use a trick called "attention mechanisms." This allows the computer to dynamically adjust what a word means based on the other words currently sitting beside it in a specific sentence. However, a notable limitation remains. While the computer can shift a word's numerical profile to match a new context, it does so purely through math, not through lived experience. It knows that the word "cold" can describe weather or a person's greeting because numbers tell it those patterns frequently occur. This creates a peculiar tension. The AI successfully mimics human communication by riding the wave of iterability, yet it anchors none of those shifting words to actual physical sensations. It masters the dance of language without ever stepping onto the dance floor.