Scientific software is at the heart of data-intensive research projects in nearly every domain. With the increasing availability of generative AI tools like GitHub Copilot and ChatGPT, the practice of programming for research is sure to be affected. Because scientific conclusions frequently depend on code for data analysis, collection, visualization and simulation, the potential impacts of these tools on data-intensive research may be substantial. In this structured discussion, we'll together address the question: How does the use of generative AI code tools change the work involved in writing, using, and maintaining code for data-intensive science? We'll explore how the skills needed to develop, maintain and use high-quality scientific code may be changing. We'll discuss the potential impacts of increased reliance on generative AI code tools on the validity, correctness, and maintainability of scientific code. Finally, we'll also brainstorm what kind of training, practices, and tooling might help scientists best take advantage of generative AI software tools while mitigating risks.