That's because they have the platforms and applications that people are using at scale. So ML is a force multiplier if you already have a consistent and strong user base for a good product.
If you're trying to get a product or company started, unless you're a pure ML research company like Clarifai (and arguably even then), ML is probably going to cost you more than you gain.
You don't have to invent or implement the algorithm yourself. You can use AI/ML intelligent/cognitive services provided by the big-3 cloud companies to reduce your starting cost significantly.
To have positive ROI, your problem complexity should have crossed the threshold where common-sense traditional solutions don't work any more.
If you do embark on ML research yourself, then be sure to walk the path from simpler models to complex ones while carefully establishing performance metrics.
You have to be very selective about what you consider "ML" to come to that conclusion. There has been a constant parade of incredible, mind-blowing results out of ML over the past decade, advancing the state of the art by leaps and bounds both in research and in real applications.
Do you not remember how terrible speech recognition and speech synthesis were just a few short years ago? Did you not see DeepMind finally crack Go? Check out BigGAN [1]. Try search in Google Photos. See translation getting better every year.
Yes, there are quacks and charlatans and people who are just plain wrong. But ML is real, and it solves real problems that people failed to solve any other way despite decades of concerted effort.
[1] https://medium.com/syncedreview/biggan-a-new-state-of-the-ar...