Agree with a mild version of the author's statement: that for many competitions, the difference between top n spots is not statistically significant. However, the author's statement (as represented by this chart https://lukeoakdenrayner.files.wordpress.com/2019/09/ai-comp...) is far too strong.
The actual best model may not always win, but will typically be in the top 0.1%.
There are people on this thread who have poked holes in the author's sample size calculator (I'm not going to rehash that).
But an empirical observation: the same top ranked Kagglers consistently perform well in competition after competition.
You can see this by digging through the profiles of top ranked Kagglers (https://www.kaggle.com/rankings). Or by looking at competition leaderboards. For example the leaderboard screenshot the author shared in the post (https://lukeoakdenrayner.files.wordpress.com/2019/09/pneumo-...) shows 11 of the 13 top performers are Masters and Grandmasters, which puts them at the top ranked 1.5K members of our community of 3.4MM data scientists (orange and gold dots under the profile pictures indicate Master and Grandmaster rank).
I actually think the author's headline is often correct: there are many cases where machine learning competitions don't produce useful models. But for a completely different reason: Competitions sometimes have leakage.