Artificial intelligence (AI) is reshaping how AI itself is developed. From designing learning algorithms to automating research workflows and evaluation, this expanding role has fueled the rapid growth of AI for AI (AI4AI). Yet a fundamental question remains: as AI takes on more research tasks, which decisions does it actually control? Even systems that execute entire research workflows may still rely on humans to define research goals, allowable modifications, and criteria for success. Existing taxonomies, typically organized by technique or target artifact, offer limited insight into these differences in research responsibility. This survey presents a unified, control-centered perspective on AI4AI, viewing its evolution as a progressive transfer of control over three key decisions in the discovery loop: search direction, search space, and evaluation signal. We introduce an L0–L3 control-source framework, complemented by full responsibility profiles, to characterize different forms of human–AI research responsibility without conflating control with overall capability or end-to-end autonomy. This perspective unifies research across AI4AI Learning, Inference, Data, and Infrastructure, while motivating an evaluation framework that goes beyond downstream performance to assess decision quality, discovery efficiency, transfer, evaluator independence, and robustness under optimization. Looking further ahead, AI4AI may enable recursive self-improvement, where advances in AI enhance the process of making subsequent discoveries. If sustained and independently validated, such improvements could offer a pathway toward superintelligence. By connecting research autonomy with the conditions for reliable and cumulative progress, this survey provides a unified foundation for understanding the evolution and long-term potential of AI4AI.