[erikb]

It seems like the dissonance you felt from reading comes from the article being written more in a business style of writing and thinking rather than a technical style which you are more used to.

For instance the "velocity data" and "but over a decade it makes a difference" parts. From a technical perspective it's awesome if you know that thanks to the data science you can outcompete others over a decade. But in business terms thinking is quarter based (i.e. three month) for short term and yearly for long term. The current leader that has to spend his budget on data science or other stuff has to find an advantage in it in the next few weeks after implementation, because usually making it happen takes already a quarter or two.

You might think they are idiots for thinking that short term, but their goals are also set in this way. So if they invest heavily in a topic and don't see any results for 3 quarters they might be replaced.

So if you say algorithm X will cost him 80% of his budget to implement but only shows results ten years later, for him that's the same as "no results". It's just the game he has to play to stay in the game.

I personally think this is part of why companies will not be able to make a drastic change in their DNA and instead will be replaced by the next generation of companies who comes out of start-ups, or be replaced by companies who already actively participate in the market in other areas and have the right DNA to take on the market. For instance Amazon is already more data driven than the traditional super market chains, therefore they now can attack that space with their modern technology.

> And again he is contradicting himself. Because if you make an analogy from student to a learning algorithm he now gives TWO orthogonal metrics to optimize for.

Loving that part. I bet few people have combined his advise for student learning with his advise on how to do ML earlier.