The algorithmic layer refers to the meaning-quilting done by search engines, recommendation algorithms, and AI systems; Google, ChatGPT, Perplexity, and their equivalents. When someone searches for a solution to a problem you solve, or asks an AI what tool to use for a given task, the algorithmic layer produces an answer that carries significant weight. Algorithmic signals quilt meaning in two ways. First, they surface or suppress your product in relevant contexts; if you come up first when someone searches for your category, that visibility is itself a signal of legitimacy. Second, and increasingly importantly, they characterize your product when someone asks directly: "what is X," "what should I use for Y," "is X worth it." The answer these systems give is a meaning-forming event. Unlike the spectacle, the algorithmic layer is partially a byproduct of the spectacle; AI systems train on text produced by humans, including everything the spectacle has said about you. But it can also be influenced more directly through how your product is described in your own content, documentation, and the language you consistently use to characterize yourself. The practical implication is that meaning engineering needs to account for what the algorithmic layer says about you, not just what you and the spectacle say. In many categories, the algorithmic layer is now the first place a potential user encounters your product's meaning. See also: Quilting, Spectacle.