Different approaches suit different people and PHD is a relatively specialized route. It's good to have people targeting similar goals with different approaches.
For an anecdote, I recall hearing one of the Kaggle founders mention that many of their bounties are won by non-statisticians/ML-ists. Producing novel (in the academic sense) stuff is unlikely outside of an academic setting, but producing products or solving problems is do-able.
Edit/comment: no need to downvote rsrsrs86 people. He's putting forward a position and defending it, not trolling. If you disagree, then disagree. The whole point of a thread like this is hearing people's take. Surely, PHD is a valid suggestion.
Kaggle competitions are very restricted in the sense that they are supervised learning problems. This typically results in applications in analytics. This should o.k. be easier to get into.
But ML can do much more than analytics., and much more than supervised problems. And the great problems to be solved are not supervised problems. They involve learning as you go, without a clean database with examples to learn from. They are adaptive problems.
You might optimize prices in an online retail player by trying to estimate supply and demand curves, but you will fail, and the best way to do it is not much different than teaching a neural network to play video games, but is fundamentally different from supervised learning and regressions.
ML can do self-driving cars, it can build drones that learn to fly, it can translate horses to zebras, it can play defeat humans at Go, it can make guitars sound like pianos.
There is a lot of technique and theory into framing any problem as a problem that can be solved by machine learning. Machine learning is generally not feasible unless you restrict the problem properly.
I think if the problem is figuring out an ML solution to an already existing large dataset (as in the case of Kaggle and big companies), you do not need an advanced education. However, if you are doing a start-up one needs to answer questions like when should I stop collecting data, when should I give up trying this algorithm, when should I conclude this problem is impossible in its current form etc. These questions require crazy amount of experience of solving novel problems. During your PhD, you try to solve many novel problems and it makes you an expert to answer these questions. I also think you need an advisor/mentor to develop these skills. This is the real knowledge you can learn in PhD and that's why they are valuable and hard to replace
For an anecdote, I recall hearing one of the Kaggle founders mention that many of their bounties are won by non-statisticians/ML-ists. Producing novel (in the academic sense) stuff is unlikely outside of an academic setting, but producing products or solving problems is do-able.
Edit/comment: no need to downvote rsrsrs86 people. He's putting forward a position and defending it, not trolling. If you disagree, then disagree. The whole point of a thread like this is hearing people's take. Surely, PHD is a valid suggestion.