Last year, I’ve joined Entrepreneur-First (EF) program, 5th Paris cohort. The idea is simple: they gather a group of people with potential in technology or business, they give some money and sometime, and…starting dating. The objective is to form a team of two, CEO and CTO. Lectures and mentorship are provided during 3-4 months period, and this culminates in the big event, the Investment Committee (IC), which decides whether to invest a pre-seed money with you are not. With that, congratulations, the road is open.

During this intense period of time, I came to learn a lot of new things. It was a self-discovery journey, but also full of dark parts about humans and how they behave. In this post, I will focus mainly about these dark aspects.

To give a context, so far in my life, either by unconscious design or by chance, I’ve been isolated from large segments of the society. The people around me were either engineers or scientists, beautiful minds in both cases, and I’ve got used to this company. However, my exposure for different domains (business for example) was limited.

During this period, what I came to recognize is the following:

  1. There is a big labeling crisis all over the place. In EF for example, starting from day one: this is a tech edge (aka: you have a PhD), catalyst talker (business background), catalyst doer (aka: engineer without a PhD), and a domain edge (a vague label that includes those unclassified yet). Unfortunately, with this, the expectations are set from you. If you are an engineer wanting to be a CEO, it is a long fight.
  2. There is a general hidden labeling for people with PhD as “those academia guys”. This implies aspects like the insistence on certainty. None of that is true. A decision to do a PhD is one of the riskiest decision ever: you start with a topic that may, after sheer efforts and intelligence from you, leads to a dead-end. You can not be too stubborn, you have to “pivot” from your current convictions, otherwise, you risk not getting your degree (or at least having a super hard time graduating). My PhD is machine learning (lots of statistics), which means that I do deal with uncertainty on…daily bases! The difference is that there are ways to assess uncertainty, and while some prefer Voodoo-magic to perform this such assessment.
  3. PhD holders are too smart/intelligent: well, thank you, but the simple reality is…no. We are just people. We worked in domains probably judged by society as “the smart people domain”, like math or AI and so on. I personally feel envy for those who have the “street smart” or emotional intelligence, or any other kind of socially beneficial intelligence. What I guess we are good at is to dive deep into problems, by asking question after the other, with skills/an eye for quantifying the observations to those questions, and select an answer. But this is a skill everyone can learn (and really, in the current times we are in, where we are bombarded with lots of information, everyone should), it is useful from time to time.

Status-quo and thinking model

What is always surprising for me is the acceptance of the status-quo as a given: X people start companies, only 0.01X succeed (for example), and that is life. This reminds me of similar situation with the percentage of PhD students getting depressed (alarmingly high), or the PhD drop rate (also very high), but everyone in the academia was taking this as…life; it is what it is.

One thing that really affected my thinking in life was reading a book about the flight computer used for Apollo moon program. The computer contract was given to MIT, a very unconventional thing to this day (normally it will go for a company like IBM, not a university). The quality of semi-conductor electronics at the time were not great. In order to reach the required performance (reliability of the computer), a typical route would be by using redundancy (by adding backup computers, you increase the quality of the system). The MIT guys said no to redundancy! Redundancy means more mass, and more complexity of its own (to manager this system). They decided they will investigate all the factors affecting the quality, all the way down to the factory floor, and how workers and machines are manufacturing those electronics. They decided to increase the quality by going to the fundamentals, to the elementary factors of the problem. What they end up with is a master-piece of engineering. To my knowledge, there was not even a backup to this computer! It was just…as good as it gets! That was a huge contribution to the electronics and computer industry, something that we are all benefiting from to this day.

This leads to fundamental questions about how do we perceive and reason about the world. By perception I mean getting the data about the problem, and by reasoning I mean analyzing the data and deciding the plan to address this problem. There are several patterns I could observe:

  1. Voodoo-magic: like in astrology. Quite common. Those who gets lucky enough are qualified by society as gurus (aka: influencers or thinkers…). Their path is heavily studied and analyzed by those young eager minds seeking success. Many videos and interviews will be made, recommendations will be given, …etc.
  2. Mixing correlation and causation: sometimes it is a continuation for the Voodoo-magic, where people seeking “success” want to extract a “system” out of this Voodoo-magic, a formula to follow. This somehow creates the illusion of logic, analysis and criticality, but it far from it. So they start with a “deep segmentation” of the guru’s behavior (he wash his teeth in the morning, sleeps only 3 hours, run for 5 miles, check his emails at 06:42:28 AM…etc). It gets even better if multiple gurus share some habits. It is like: eureka!
  3. Flat-out lie: well, yes. You see a hansom guy, well dressed, making this wonderful speech/pitch, with numbers and all, and you feel amazing! It was wonderful pitch, enjoyed the cocktails and food after that, but…he is so full of shit! Maybe he doesn’t recognize it, I don’t know. I saw stats and visualizations about funding percentages and success rates that use one metric here, and a different metric there (e.g., the relative risk vs the absolute risk), in an inconsistency manner. Why you may ask? Enters the power of visualization to deliver a story. Visualization is one of those important arts needed for understanding and for storytelling…it is a very human thing. Yet, time and time again, it is abused to deliberately provide a lie. It is a nice way to say: looks, it looks scientific: The are numbers and graphs, thus, it must be true. People tend to believe that more. It is all about mental tricks.
  4. Information overload: a traditional technique to bury the truth is to send you tons of documents, knowing in advance that the truth is in one line in a single document. This became an art in itself, with its own experts. This is a common technique now, with the social media and news doing this already.
  5. Analogy: Now we are getting closer to things that make sense. Reasoning by analogy is quite a powerful technique, but does require that you have a good library of examples in your knowledge arsenal, and good eye for observation. As an example, you can draw on the similarity between one problem (that you don’t know the solution for) and another problem - maybe in another domain - that you know the solution for. This will give you greater insight about tackling the problem. Care is required to understand both the problems and their context on a deep level, then find connections on the causalities. Experience at this level can be extremely valuable.
  6. Elementary/first principles: this is the ultimate form of reasoning in my opinion. Break the problem into its elementary pieces (like the MIT guys for the Apollo guidance computer), go beneath the assumptions and what everyone assume to be right, and start asking questions about these basic elements (why does this component exist? what is the relationship between this and that? how did we get to that point?…).

With that being said, the first 4 forms are the most common ones. To recognize what is happening (which form of reasoning is being used), it is essential to ask yourself the question: who wants what from whom? This is a foundational problem. People hide their true intentions and needs (no necessarily out of bad intention, sometimes, the norms of the society dictates that), thus, it makes it hard to determine what is happening (e.g., if entertaining the emotional aspect is a result of true care for you or a manipulation technique).

Ask questions about what you suspect, or what you don’t know. However, if you ask too many questions, you may raise suspicion, and it is easy for others to label you as the trouble maker, the cynical guy,…etc, then you become an outcast. Choose your questions carefully. But, most importantly, do your personal investigation and your own homework. Do these numbers make sense? What is that doing? what is everyone’s interest? It does help a lot to put some strategic question in during a conversation, dropped casually.

Even though I clearly have my biases, it is interesting to look at these patterns from a different perspective, as a human phenomena, as colors of rainbow. It is easy to fall into the trap of labeling things as good and bad, and being entrapped by the words. Food for thought :)