We can still create computer programs displaying only the most rudimentary natural language processing capabilities. One of the greatest barriers to advanced natural language processing is our inability to overcome the linguistic knowledge acquisition bottleneck. In this paper, we describe recent work in a number of areas, including grammar checker development, automatic question answering, and language modeling, where state of the art accuracy is achieved using very simple methods whose power comes entirely from the plethora of text currently available to these systems, as opposed to deep linguistic analysis or the application of state of the art machine learning techniques. This suggests that the field of NLP might benefit by concentrating less on technology development and more on data acquisition.