The behavioral selection model predicts AI motivations by distinguishing between identical behaviors that stem from different internal drivers. This distinction matters because similar training patterns often lead to divergent outcomes during deployment. The author admits the framework is useful for short-term predictions but lacks critical dynamics. Practitioners must disambiguate these motivations to ensure long-term AI alignment.