In practicality the OP is right. You won't be on the same level as people with a PhD in a corporate or applied setting. The hardest parts are feature engineering, researching and statistical analysis (presenting research to team). It's hard to gain all those skills without years of experience researching in an academic setting.

As an undergrad, I was doing all those easy ML tutorials and took an undergrad level ML course. I thought I would be useful in actual practice, but knowing the whats/hows of neural nets/clustering/etc. is not enough. Feature engineering/math is the most difficult part. In a corporate setting, if it was a straight forward solution, you wouldn't be doing that work because the solution would be trivial and already implemented.

As an engineer with only a bachelors on a ML team full of PhDs there is a definite difference in skill. I've been reduced to a monkey (a content one) that works on the data pipeline. Learning to deal with real world ML problems would take me years of work that I am not sure I would be willing to do, especially when the pay increases per effort expended learning ML is much lower than with regular software/distributed systems/etc..

On the interest part, you're right that I would never have tried to learn ML if I had known the amount of work that is required to actually be good or if I tried learning the math first. That's the real world though. The useful ML engineers did learn the math. The efficient way to learn ML is to learn the math/statistics first.