Astrophysics is undergoing a structural transformation as artificial intelligence and large language models rapidly integrate into core research workflows. At the Center for Astrophysics in Cambridge, Massachusetts, the AstroAI group led by Cecilia Garraffo is coordinating institutional efforts to apply machine learning to astronomical datasets. Industry partnerships with NVIDIA, Anthropic, and OpenAI are further accelerating these initiatives. The efficiency gains are already evident. Researcher Alyssa Goodman utilized generative AI to resolve a complex data-fitting challenge regarding galactic spiral arm kinematics in minutes, a task that had previously stalled her team for years. Similar automated workflows are enabling faster literature reviews, code generation, and preliminary data analysis across major research hubs. Despite these capabilities, the acceleration of AI adoption has sparked intense debate regarding scientific integrity and workforce development. A primary concern centers on deskilling and the potential displacement of early-career researchers. Graduate students and postdocs traditionally build analytical competencies through foundational computational and theoretical tasks. Experts warn that over-reliance on automated agents could erode the methodological training required for independent discovery. David Hogg of New York University and the Flatiron Institute emphasizes that astrophysics must remain a human-centered endeavor. He argues that the iterative process of questioning, failing, and interpreting results holds intrinsic value that cannot be replicated by speed-optimized algorithms. Practical challenges are already manifesting in academic publishing. Editors at the American Astronomical Society report a surge in submissions, many generated by autonomous AI pipelines. This influx complicates peer review and raises serious questions about transparency and academic integrity. The barrier to producing polished manuscripts has dropped significantly, threatening to flood journals with low-quality or hallucinated research. Without robust verification standards, the field risks normalizing automated outputs and forcing stricter gatekeeping that could stifle legitimate innovation. Furthermore, current large language models demonstrate clear limitations in rigorous mathematical reasoning and theoretical physics. In documented tests, state-of-the-art models failed to solve complex equations in general relativity, confirming that human oversight remains essential for validating probabilistic results. The technology sector and academic community hold divergent perspectives on the trajectory of these tools. AI developers view astrophysics as a high-value proving ground, with some executives projecting capabilities that could soon rival postdoctoral researchers. Conversely, scientists at the Flatiron Institute and Harvard remain divided on policy responses. While some institutions are experimenting with AI-assisted mentorship and code repositories, others are drafting strict usage guidelines to preserve academic rigor and protect junior researchers from being replaced by automated pipelines. As computational systems reshape the discipline, the astrophysics community is navigating a critical threshold. The field must establish clear boundaries for AI integration that balance automation with the preservation of human intuition, mentorship, and critical inquiry. The outcome will likely dictate not only the future of astronomical research but also set precedents for AI governance across all foundational sciences.