The article hopefully addressed this question... I would say the role of the PM in my opinion is not about taking their time to think forever to take action. They are the ones to put the whole team in a better position to take action while strategizing so they move forward and make better decisions as time goes on. As there is no way you can know everything ahead of time before making 100% correct action. That is impossible.
In my opinion, those are PM who are incompetent because they can't pull and affect those people around them to come to an agreement that everyone is ok with. This type of PM (which is everywhere) is afraid of confrontation to their superior and often can't ask the right questions of their peers and ultimately pull the right resources (in or outside companies) to help out the rest of the team to move faster or done things right.
I would say a incompetent PM wouldn't be able to make their own deadlines but to only comply deadline set by their superior who of course has no intention or knowledge on how the system actually works and the time it takes to accomplish them. A good PM would be able to fight back and make a much better deadline that both engineer, sales, marketing, design and business all would be happy about. In my mind, a great PM pushes tech to meet new business goals and pull business to be aware of technical pitfalls. A great PM is in control of all sides of the project and able to pull them together without being expert in any of the fields.
How does it know that web is the one to built with linking from other commands? Also if there is no Dockerfile the web in the README is not running in docker, how is the env being filled by the linking process? Is it hardcoded?
Reasoning from analogy is the wrong way of saying what people have in mind when they provide examples to explain what it is. To me, it sound more like reasoning form what already works. In most examples I have seen, it is mostly about solving a problem using existing solutions/packages available and add your own flavor to it. That's why the "analogy" thinking because that's how the idea is presented, people pitch the idea using other's solutions. But what Elon Musk is different from others is that his thinking is stemming from scientific research more than you think it is. It is asking about the right question by digging deep into the core of the problem until you cannot dig any further. For example, in Elon Musk's example of battery, you first start with a goal: how to build an electric cars that is cost effective. You break the goals down into smaller problems, and you could recognize that battery is the biggest contributing factor in providing the solution to this goal, then you ask, why is the cost of battery so high yet it yield so little? Then you break the problem down into further smaller problems such as the cost for each component that comprised the battery, then you keep reasoning from there until you can find a ground where you can start reasoning back up. Personally I think the name "First principle" is also misleading since it is not mainly about principles, but more about asking the right questions.
This author has no real deep understanding of current AI research and the what semantic web actually means. Throughout the article, the author not only ignores and reference any authentic websites for defining semantic web, but also trying to make fun of it by unrelated example such as Google's Knowledge Graph.... Comparing semantic web with the Knowledge Graph is like comparing graphs with oranges. Those are completely different beast and for different purposes. This goes to show the understanding of the author and his/her intent. And ironically, everything that the author tried to sell us on are spot on part of semantic web. For example, the Stream data he/she defined is precisely what semantic web is trying to do, is to label and give meaning to a certain data instead of a big chunk of text/binary. The author also mentioned about pushing and pulling, I argue that the way information/data is disseminated has nothing to do with the ontology or the semantics of data.
And there you go, such a shallow article with a very aggressive link title bait, just pathetic.