This is really not about interpolation or mipmapping; it's about creating a data structure that allows you to quickly answer queries with which to build visualizations. For example, you might want to see a heatmap of all tweets that happened in 2013, but you might want to see only the count of a particular week, or a month of tweets from Instagram. You might also want to see all of these things above over the entire world, or over a single metro region. What nanocubes enables is answering these queries in time essentially proportional to the size of the output (we take a polylog hit, but that's kind of to be expected). We don't even touch all of the input data, which, if you ask me, is kind of nice.
I wish I had bigger public datasets to show you, but we keep this fast experience with datasets in the billions of points.
About your interpolation point: yes, we could (and do sometimes) use interpolation and smoothing. We choose here not do it here so that it looks the same when you go to the webgl-less mode and run it on an iPhone or iPad, there isn't a big visual difference.