Some thoughts on the Next Generation Statistical Computing Environment Duncan Temple Lang R has been terifically successful for a variety of reasons. However, some of the design is based on work from almost 40 years ago. As the information technology landscape has changed so dramatically and continues to evolve, it is important to ask whether we are still on the right path. We need to plan not just for the immediate needs of the statistical computing community, but also build infrastructure for the future. We need this to enable us and others to experiment with new paradigms and innovate rather than simply program. R is being used for different purposes than the original interactive EDA environment. Developing software with these tools is probably suboptimal. While one can extend R through the R language, it is very difficult to extend the system itself. This makes it difficult to introduce new data structures at the system level and leaves them as second class objects. We need extensible data types to be able to take advantage of application-specific information to do complex, efficient computations. Additionally, we continue to need to interface to other languages, extending the notion of interface and relying on meta-data, be it dynamic/run-time or static and treat external objects natively. Given the limited resources we have in our community, we need to be intelligent in how we leverage the work of other communities in shared infrastructure. I'll discuss some of the possible approaches we might consider for evolving or building the next generation system and discuss some of the tradeoffs and sociological aspects of such development. The key notions are the traditional staples - extensibility, components, meta-data. But importantly, they need to be applied at the right level and different audiences need to be identified and characterized.