The catchiest (and wrong) assertion is this:
"even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners"
Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you are designing the network for new problem domain, there are thousands of decisions to make - everything from hyperparameters to network architecture to distributions in data. Making these decisions without having developed lots of intuition and good foundations is very hard. I often find usual developers without ML background and experience completely lost in these cases. Debugging a model that doesn't work is super hard. There are no IDEs, no breakpoints, no watches and in fact not even error messages. Its purely statistical debugging and probabilistic fixes.