Assuming that you're comfortable with 2d histograms (hexagonal binning is the standard) I completely agree. Otherwise scatterplots are pretty crucial.
For dense data, scatterplots can obscure more than they show:
http://www.chrisstucchio.com/blog/2012/dont_use_scatterplots...
Even an author who was well aware of the problem made the same mistake:
http://garyrubinstein.teachforus.org/2013/01/09/the-50-milli...
Secondly, the data you throw away is usually just sampling noise. Most of the time the interesting object is the underlying probability distribution - individual points are only useful to infer that.
In the cases where individual data points are actually of interest (e.g. http://cl.ly/GvnM ), go ahead and use them. But they are terrible default choice.
Whether scatterplots make a "terrible default choice," then, depends on what you believe about the distribution of data sets out there. Based on your advice, you must believe that most people have data sets that are dense.
For a lot of people, however, that's not true. For them, "plot density, not points" is the terrible default choice. But you're telling them to make it their default anyway.