I'm concerned deep networks are being overhyped. They're certainly exciting, but they haven't seen much use in industry yet; it's too early to make claims about how they have impacted data science.

Also, data science involves a lot more than building predictive models. In my experience >95% of effort goes into something other than building a model. In kaggle contests you usually concentrate on that <5%, which IMO is the fun part but it's not the reality of industry. There are many big breakthroughs in data science that don't involve model building.

edit: I haven't listened to the podcast yet (at work), my comment is more about the title.