I actually strongly agree with him, In fact the progress in Deep Learning has stalled due to inefficiencies in sharing data and code between researchers. Today to replicate a study you have to spend several days downloading data, making sure all dependencies are installed etc. This is worsened by lack of standard formats for even simple things such as representing 2D bounding box on an image.
People forget that the reason Deep Learning came to prominence was engineering that enabled use of GPU's (Thanks to Alex K.) and large dataset from ImageNet competition (Thanks to Fei-Fei Li).
Importance of software engineering in moving the field forward is often under-appreciated. This blogpost beautifully illustrates several instances where a great implementation made it easier for researcher to speed up experimentation and lead to breakthroughs in Computer Vision. [1]
I am building Deep Video Analytics which aims to become the Rail/Django/MySQL/(favorite analogy) of Visual data Analytics [2,3].
[1] http://www.computervisionblog.com/2015/01/from-feature-descr...