Exploring this channel's material, you will encounter embedding space as the setting where language models place words and ideas as points — so that similarity becomes distance, and meaning becomes geometry. Novelist Robin Sloan describes spending years tinkering with early language models and how these spaces reshaped his understanding of language and creativity: embedding spaces feel poetic, he says, because a model's way of relating words turns out to reveal something about how meaning itself hangs together [^01a0f15b-0cce-7b92-8d60-bff9931bdd8b@52]. In this material, embedding space is not presented as dry math but as a lens — a way to understand why great writing sits at the edge of a probability distribution, and how books, dreams, and what happens next connect to language models [^01a0f15b-0cce-7b92-8d60-bff9931bdd8b@495]. As you explore the conversations on this page, you will be able to explain what a model's embedding space implies about human language — and form your own position on what that implies about us.