Stagnation is a known problem in reinforcement learning and similar methods. It's very easy to get stuck at a local maximum. My favorite fun example is https://gym.openai.com/envs/BipedalWalkerHardcore-v2/ where a standard DDPG(https://arxiv.org/abs/1509.02971) will get stuck at pits in the environment. Although it could get a higher score if it learned to jump, there is a penalty with falling in that makes it stabilize on standing still and running out the timer. Video: https://www.youtube.com/watch?v=DEGwhjEUFoI

I suspect there is something similar going on with video/music recommendations. When a bad novel suggestion is made the penalty is likely too high to overcome (User immediately clicks off) with traditional reinforcement methods.

The author is pretty much attacking a strawman he cherry picked. He took one toy example of someone learning ML and used it to attack the newest trend. Instead of taking the time to dive into the topic he's trying to discount it as less relevant. It's obvious he's stuck in the C/Unix era of programming, which has long since passed it's heyday.

Basic programming is quickly becoming antiquated and articles like this, and many of the commenters are stuck in the past.

The efficiency of ML is in cost, not overall computation. Throwing machine resources at a problem is cheaper than hiring some guru with the necessary math and CS background to solve the problem.

We've seen the same thing with frameworks/libraries. Before it took specialized knowledge to do basic things like networking, media creation, etc. in code. Now there are existing tools that do everything for you.

The same has happened with optimization/algorithmic knowledge, and the genie is not going back in the bottle. There will definitely be specialized cases where that particular expertise is needed, but that is no longer the norm.