AI-Mediated Generative Relations: Contribution, Control, Capability, and Benefit

Once artificial intelligence enters an economy as something more than a tool, the natural question is who is exploiting whom. With capital, labor, the system, and users there are twelve directed pairs; adding the firm that builds the system and separating it from the firm that deploys one gives twenty. This paper argues that the question in that form cannot be answered, and that the reason is structural rather than evidentiary. A diagnosis of exploitation assigns four roles — who controls, who acts, whose capability falls, and who benefits through the fall — and a directed pair names at most two of them. The paper proves that topologies can agree on every quantity such a table is usually populated from — which node acted, whose capability changed, whose return rose — and still disagree about who, if anyone, exploited whom. It then proves an aggregation result explaining why the entity list had to be revised twice: merging a controller with the system it governs manufactures an action attribution that belonged to neither, and merging a provider with an adopter internalizes a benefit path and can convert a positive diagnosis into a negative one, so a merge is admissible for a question only when no edge of that question's witness crosses or is internalized by the merge boundary. A third result concerns opacity. Where corpus membership, the counterfactual capability, and the benefit path are each unavailable, the observation set consistent with what is seen contains both positive and negative cases, so the audit is unresolved; and every observation that would resolve it is held by the party whose conduct is at issue. Disclosure is therefore a condition on the framework's applicability rather than a policy preference, and reading an unresolved audit as a negative finding is an error with an identifiable beneficiary. A fourth result concerns speed: two parties with identical holdings and different expansion rates have different reachable sets at every finite horizon, so equalizing holdings does not equalize reachability. The paper also argues that the training corpus is a less diffuse object than the collective inheritance it is said to condense, since a corpus is finite, assembled, and bounded by decisions someone made; that the standing of the system itself is open, and that the framework must remain expressible under both answers. It classifies no system, provider, or firm, and makes no empirical claim. A closing section states, item by item, the evidence that any such claim would require and that this paper does not have.