Latent space

Plain English. The internal space a model represents meaning in — a high-dimensional map where every input becomes a vector of numbers, and where nearness stands for similarity. "Cat" and "kitten" sit close together; a translation is a short move in one direction. The model never works with words or pixels directly, only with positions in this space.

Why it moves money. The latent space is where a model's real capability and its defensibility live. Two systems can share an architecture and still differ entirely in the geometry they learn, and that geometry is what training buys. It is also where the leaks are: because these spaces turn out to be more universal than assumed, a competitor who captures embeddings can often reconstruct sensitive attributes, which turns a vector store into a liability an investor should price.

What to watch. Whether "our data is our moat" survives contact with the finding that embeddings translate across models. If one lab's latent geometry can be aligned to another's without the original data, the moat is thinner than the pitch deck claims.

From the signals. Embeddings translate across model spaces, exposing vector databases — the universal-geometry result, and why a latent space is not the private asset it looks like. See also /invest/embeddings.