From User Work to Compensated Contribution

This research note proposes an opt-in architecture that connects ordinary AI-assisted work to independently validated and potentially compensated contribution. With separate user authorization, selected work evidence supports a task-specific capability hypothesis. The user can inspect and contest that hypothesis, receive an accessible developer-relevant challenge, and decide whether validated evidence may enter an internal contributor registry. Attribution begins with evidence intake and remains linked to the contributor through review, transformation, internal use and documented outcomes. Repeated useful contribution can then support an accountable decision about a paid task, expert engagement or other formal collaboration; neither a high score nor a registry entry creates an entitlement to employment. The contribution is a system-level integration hypothesis and an evaluation agenda, not a claim to have invented expert search, work-sample assessment, provenance or paid external collaboration. A targeted review identifies close antecedents, including activity-derived skill profiles, expertise-based AI-work platforms and AI learning-to-employment initiatives. These sources document substantial overlap but do not establish the complete pathway examined here; that documentary limit proves neither worldwide absence nor technical novelty. The primary empirical question is whether selected, authorized ordinary-use evidence and targeted validation predict later useful performance beyond strong alternatives under comparable resources. A separate organizational study must establish internal usefulness, attribution retention, actual payment and total cost. No prototype, participant study, product improvement or economic return is reported.