We have always been approximating relevance. Every keyword list, every TF-IDF score, every editorial judgment about whether a page “covers the topic” has been an attempt to answer a single question: is this content about the thing the user is looking for? The tools changed. The question did not. What changed, meaningfully, is the resolution of the instrument. Keyword research approximated relevance through lexical overlap: If the words match, the topics probably align. Vector-based semantic analysis approximates it through meaning overlap: If the concepts are close in embedding space, the content is probably relevant regardless of whether the exact terms appear. That is a genuine, material upgrade, but it is not a move from guessing to knowing.
The reason that distinction matters is that a significant portion of the SEO and content strategy community is right now treating it as if it were. They are looking at alignment scores, cosine similarity outputs, and semantic proximity metrics and reading them as ground truth. A high score means aligned. A low score means not aligned. Optimize until the number goes up. And the number, because it is a number, feels like it has settled the question that keyword research always left open. It hasn’t. It has given you a higher-resolution version of the same approximation, and the higher resolution is exactly what makes it dangerous, because it removes the humility that low resolution used to enforce.
Precision Is Not Accuracy
Gerard Salton’s SMART system at Cornell introduced the vector space model for document retrieval in the 1960s. The core insight then was the same insight powering today’s embedding models: represent both the query and the document as vectors, measure the angle between them, and use that angle as a proxy for relevance. What has changed across 60 years is the sophistication of how those vectors are constructed. Salton used term frequency. Modern embedding models use transformer-derived representations that encode semantic relationships, contextual meaning, and conceptual proximity across hundreds or thousands of dimensions. The measurement got dramatically better. But the thing being measured, the angular distance between two vector representations, is still a proxy for a relationship that exists outside the math.
This is where the Netflix research team landed in their 2024 study on cosine similarity in embedding models. Steck, Ekanadham, and Kallus demonstrated that cosine similarity applied to learned embeddings can produce results that are, in their framing, arbitrary. The way an embedding model is trained, the regularization applied, the data it saw, all shape the geometry of the space in ways that make a raw cosine score unreliable as an absolute measure of semantic similarity. A high score in one embedding space is not equivalent to a high score in another. The score is real. The similarity it claims to represent may not be.
For practitioners optimizing content, the…
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