I don't think MOOCs can come even close to be a replacement for a PhD (and from what I understand, neither do you). If a candidate who learned with MOOCs can apply for a job which previously "required" a PhD, then that requirement was simply misguided in the first place. A PhD in (AI, CS, stats or whatever else) does not teach you how to be a good all-around data-scientist, it teaches you how to conduct scientific research. In AI this means either developing or improving algorithms, theory work or applying AI to one particular problem for four years. That kind of expertise is not needed for most DS jobs and never was.
In my opinion however it is a good rule of thumb to assume that someone with a PhD in a relevant field will become a good data scientist after an adjustment period.
> If a candidate who learned with MOOCs can apply for a job which previously "required" a PhD, then that requirement was simply misguided in the first place.
Therein lies the problem. A lot of people I've talked with in leadership positions but without a statistical background believe that data science/AI requires a PhD, and since there's a healthy supply of candidates with the PhDs, there isn't much reason to reevaluate that position.
(that's more for traditional job positions; obviously research positions will benefit more from a PhD.)
The problem is that when you put people without formal training in charge of building and training models, you get also results that match - models with horrible biases, models that don't reflect the reality at all, models that are worse than taking random guesses.
Running through a bunch of tutorials and then taking a deep learning toolkit (e.g. Tensorflow or Keras) and starting to feed data into it won't teach you squat about the importance of having a representative sample, about removing biases from the data, about correctly handling outliers, about doing some basic statistical analysis on the data to see whether they are even relevant to the problem at hand and so on and so forth.
Or even how to build a questionnaire/experiment so that you don't get only a load of expensive garbage instead of data out of it.
This is what a doctorate and the associated research training generally give you. Of course, it is not anything that couldn't be learned without doing a formal PhD, publishing research papers and defending a thesis but it is typically the background you won't get from these MOOCs or various online (and offline) "data science" trainings.
In my opinion however it is a good rule of thumb to assume that someone with a PhD in a relevant field will become a good data scientist after an adjustment period.