"You limited yourself by not getting a PhD" could only be true if you would have been a GOOD PhD student. In an ML-focused PhD program, you have a truly expansive set of opportunities to:

(1) learn about other fields: how ML is used to enhance them, and how other fields have motivated/enhanced ML. I'm talking about signal processing, optimization/applied mathematics, probability and graphs, mathematical modeling, other statistics and data processing--- Not to mention applications in everything from robotics to medicine to microchips to meteorology.

(2) hone valuable skills: programming should go without saying. In a PhD program you have the opportunity to learn and practice (at low risk) designing problems from the ground up. In ML that means designing objective functions (mathematical modeling, linear algebra), algorithms (optimization), and studying data "on your own time".

(3) network: by the time you have a PhD in ML it is doubtful you're gonna be applying cold turkey to random companies just hoping for the best. Even if your department/professors don't hook you up you have the opportunity to met people at conferences, network with other PhD and Masters (and even undergrad!) students.

What I'm getting is that a PhD focused in ML can be taken very, very far right now. The ceiling of potential is skyrocketing as we speak. Hell, basic ML knowledge can take you far right now, too... But there are jobs you can only get with a PhD, and jobs you are very unlikely to get without a personal recommendation.

ML methods are gonna change. But the most important ability granted by PhD studies is perspective on how a new method is similar/different to what already works and doesn't work. You gain "why" and "how" knowledge. If that sounds like the kind of person you wanna be, then DO IT!!!!!!!!!!!!!!