Most AI stack diagrams stack like a layer cake. We treat it as a manufacturing line: raw data on the left, a shipped product on the right, with seven stations in between. Each station ships a concrete artifact to the next. The rest of the site — comp bubbles, tag networks, company fingerprints — is built on top of this model.
Layer-cake diagrams (a16z, Coatue, etc.) are five rectangles labelled Hardware / Compute / Foundation Models / Tooling / Apps. They’re fine for taxonomy. They miss the asymmetry we care about: the same engineer can’t flow up and down a layer cake, but they very much flow down a pipeline. Comp tilts with the flow — upstream stages are scarcer and pay more. The curve above tells that story directly.
We anchor every station on its ship artifact — what you actually hand to the next station. That makes classification sharp. A role that “does training infra” isn’t ambiguous: if the artifact is a base model, it’s Pretrain; if the artifact is a tuned variant, it’s Post-train; if the artifact is the GPU cluster others use, it’s Serve. The classifier prompt enforces this.
Two questions get sharp answers from this model that a layer cake muddles. Who’s doing real foundation training? — count Pretrain + Post-train roles per company. Who’s wrapping someone else’s model? — high Ship roles, sparse Pretrain. The asymmetry between “owns the model” and “owns the product” is the core hiring-market signal we publish. The rest of the site lets you slice it.
Customer / internal AI product — recsys, code, fraud, multimodal