When the Scaffold Stays On: AI, Practice Style, and Screening in Elite Skill Formation

Generative AI raises short-term productivity by completing tasks that learners would otherwise practice on their own. Whether this exchange erodes frontier skill depends on the mode of use: substitute-users let AI stand in for deliberate practice and fail to develop skill, while complement-users use it to accelerate skill development. For institutions that train and certify talent, the design question is not whether to allow AI but how to govern the mode of its use. We ask whether AI-prohibited evaluation gates can separate the two modes. In elite competitive programming, the International Collegiate Programming Contest (ICPC) and the International Olympiad in Informatics (IOI) prohibit AI under in-person proctoring, with qualification-round entry, whereas Codeforces (CF) practice is unproctored and open to all. From CF submission histories we build an AI-prompt signature, more first-attempt acceptances, fewer attempts, fewer debugging retries, consistent with AI-assisted practice. CF practice has shifted toward this signature across entry cohorts spanning two AI rollouts. In CF contests, a stronger signature predicts smaller rating gains for users with no ICPC-IOI affiliation, but not for those who qualified. Inside the AI-prohibited ICPC environment, a shift toward AI-style practice predicts higher non-AI-aided scores for AI-era entrants. The same signature carries opposite signs across the two environments, exactly the pattern a type-separating gate predicts. The message is constructive: AI-style practice is compatible with frontier skill; the erosion risk links to the substitute mode; and that mode is separable by gates standard at credential boundaries, from medical and legal boards to professional certification.