PhDs are one especially crappy way to prove you have the intellectual chops to engage with ML. There are far more direct, practical, and expedient alternative paths to get there.
Importantly, the number of people who were perfectly capable of doing a PhD but chose not to (because, frankly, it's a very bad deal) vastly dwarfs the number of people who stuck around in academia and obtained one. Additionally, my observation at several major tech companies is that PhDs have a bent of mind that is roughly orthogonal to the pursuit of real business value.
Getting a PhD is a fine deal if you have good reasons.
The phrase "bent of mind" roughly implies "the way someone thinks". Its usage is declining I suppose but there's nothing connotatively nefarious there.
Just because the man who can see is king among blind men doesn't mean they're qualified to call him visionary.
Ehhh, French? I like Chollet(many people dont) but he is not in the same league of Yan LeCun at all.
It seems both the author and you have a naive view of expertise in academia.
We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statistics only in this map) can be called an Expert in a statistical/mathematical field, what do we even call somebody with all the same applied software engineering & exploratory analysis, and business experience, but also a PhD in theoretical topics (math, stats, etc)? A 'super expert'? What do we call Francis Chollet, or LeCun, or anybody else? What's the differentiation between an expert from the roadmap and the team of individuals deploying GPT-3 into Google Assistant? Are they the same?
As a hiring manager and team lead for a large fintech firm in London, I would happily see an individual who had really mastered the above path(s) as a strong candidate for an intermediate or upper junior role in applied data science/machine learning. But ... it's not enough to be a senior, and certainly not an expert. Just my two cents.