How this term is used here In the channel's conversation with Linus Lee of Notion's AI team, the discussion of how AI works leans on the idea of mathematical objects that models operate on — AI described as a tool, a function, even a “whatever mathematical object” that augments thinking [^3e5745ab@434]. Embeddings fit that picture: they are the numerical representations a model produces so that meaning can be handled mathematically, turning words, ideas, and other inputs into coordinates a machine can compute with. Why that matters for exploring AI If you explore AI as a thought calculator — a tool that augments thinking rather than replacing it — embeddings are part of the machinery that makes such calculation possible. They let the model compare, relate, and transform concepts, which is what makes searching, reasoning over, and remixing ideas with AI feel fluid rather than mechanical. - Embeddings are numerical representations of meaning — the mathematical objects a model works with.- They connect directly to the channel's framing of AI as a tool or function that augments thought [^3e5745ab@434].- Understanding them helps you explore why AI can relate and compare ideas without being explicitly programmed to.