Hence the flood of ML grads which will only get bigger and bigger as time goes on.
As a general observation of business, returns per unit of labor can range from completely linear (factories, hospitals) to more nonlinear in things like software or art. ML researcher is on the far end of this. Yes, Bengio and Goodfellow get paid 7 figures, but that's because even if you pay 100 amateurs 1/100th of that, they will still produce worse work.
If you want to be a good ML researcher, the time to get in was 10 years ago.
Deep learning brings some new paradigms to the table but it’s not fundamentally different from other optimization / ML methods, so with a background in statistics, math, natural sciences or quantitative economics (to name only a few) and a good understanding of programming (which most people from these fields have) you can do just fine. If you start out with zero knowledge of university-level math and no programming ability it will be much harder of course.
The order of magnitude point is just wrong. You might argue that it's harder because it's essentially software development and a lot of applied math. But that certainly doesn't account for orders of magnitude difference.
I'm a java developer and during my first 3 years I was able to investigate bugs not only in my code, but inside jboss or hibernate ORM; I can look up core java code and understand it just fine.
How many of the ML self-taught crowd can write framework-level code, or debug a ML algorithm bug?
Fooling management wanting to get into ML with some scikit code is easy, mastery of ML is orders of magnitudes harder than mastering a programming language/frameworks.
This isn't specific to ML though - I don't see why the number should be different from the number of self-taught web developers who can write framework-level code.
As someone who spent more than a few months on a couple courses on various platforms, a few months is NOTHING in this field.
ML is not something where most people out of bootcamps can make a living out of.
ML is the ultimate winner-take all technology.
Sure, there might be some fast.ai examples of self taught ML scientists, but it is orders of magnitude harder than software development, mainly because of much longer learning feed-back -- you can't just printf or debug your code and expect to learn something new every 5 minutes.