Generative AI (GenAI) often reproduces and amplifies societal stereotypes, such as portraying software engineers predominantly as young, white males. These outputs reflect biases embedded in the training data and are rooted in historical discrimination and systemic inequalities. Addressing such biases requires more than technical fixes; it demands a socio-technical perspective that combines algorithmic improvements with a critical understanding of the social contexts and ideologies in which these technologies operate. In this vision paper, we argue that software engineering (SE) researchers and practitioners should engage with constructs and theories from the social sciences to better motivate the study, explain the presence, and initiate the mitigation of bias in GenAI. We collate and discuss social theories relevant to SE research and practice, and call for greater interdisciplinary collaboration between SE and social sciences. We also discuss risks associated with blind copying and recommend more careful approaches. By carefully grounding GenAI research into relevant social perspectives that fit the SE context in the age of AI, the SE community will be better equipped to design inclusive tools, develop equitable solutions, and articulate the societal impacts of their work.