If you are an AI enthusiast, you will agree that today nearly all of humanity's accumulated data, information, knowledge is available to anyone, instantly, for the price of a monthly subscription. Which should force every thinking person to ask a question: what is worth learning and knowing now for us humans? Because accumulating more data, information, knowledge is no longer the answer. AI already knows it all, and gives it back faster, and often better. Whatever knowledge-oriented skill you acquire, AI will sooner or later move into that area too. A naturally intelligent agent, a.k.a. humans, once earned their value by holding more degrees, reading more books, carrying more data, information and knowledge. That edge is gone. AI has more data, information and knowledge than any human could acquire in a hundred lifetimes, and it has only just begun.
So what is left that is ours? What can a naturally intelligent human still do that an artificially intelligent agent cannot? What is something that makes a human still relevant? The answer is our ability to think in systems. Our ability to think in systems is what allowed the ONLY species on this earth to grow and make progress. Not our opposable thumb, not our ability to stand erect, none of them are the reason for our prime differentiation. What differentiated us from the rest of the species is that we had a better ability to think in systems, and that differentiation of thinking in systems will still be our edge in the age of AI. But we have to work on bettering our understanding of systems to keep that edge. And let us not close this conversation so quickly and act like we already know how to think better in systems. There are nuances you need to understand, and nuances where the answer lies about what the role of humans will be in the age of AI.
Now, our ability to think in systems is a necessary condition but not a sufficient condition for our success. Why? Because our ability to think in systems not only enables us to create Knowledge of Systems, it also enables us to create pseudo-knowledge and non-sense. Because our inherent ability to think in systems creates a disproportionate amount of pseudo-knowledge and non-sense, historically humans themselves were the enemy of their own progress, because they had no method to segregate knowledge from pseudo-knowledge and non-sense. So homo sapiens for hundreds and thousands of years had the same brain as us today but made no or little progress, because we were living life equipped with the necessary condition, which is our ability to think in systems, and produced little knowledge and crazy amounts of pseudo-knowledge and non-sense, without the ability to segregate them.
Then around the 1500s or so, something fundamentally different happened. Humans, to be specific western philosophers, became aware that our lives are full of knowledge, pseudo-knowledge and non-sense, without anyone wanting it to be so. They asked themselves a question: how do we segregate knowledge from pseudo-knowledge and non-sense? And they worked towards it and ended up creating a new subject called epistemology, whose exact meaning is: how does one know that what they know is knowledge, and not some pseudo-knowledge or non-sense? In short, they developed a better System of Knowledge, using which they could develop better Knowledge of Systems. Meaning they said: before we seek knowledge of different systems, any kind of systems, political, social, environmental, managerial, we shall first seek a system of knowledge, so that we do not inadvertently add pseudo-knowledge and non-sense into our pursuit of knowledge. This subject that deals with System of Knowledge was called epistemology. Having a good epistemology is fundamentally about discarding the pseudo-knowledge and non-sense that our thinking in systems generates.
Here we should say something important, because it is where most people misunderstand us. When we say Knowledge of Systems, SystemsWay does not focus much on mechanical and technological systems. We focus on social systems, because that is exactly where our default epistemology starts to break. Our default way of knowing works beautifully on a machine, a bridge, a piece of code, and it falls apart on a team, a company, a market, a country. And this is the whole reason our scientific, mechanical and technological knowledge is growing exponentially while our social systems are not getting better, and in many cases are getting worse. Because almost every critical decision a human makes is a social decision. When a technologist needs to design a company, that is a social system. An SDLC is a social system. A political system, an economic system, all social. That is where the important decisions live, and that is exactly where our inherited epistemology is weakest.
There is no person that I know who has been taught or learned epistemology, and that included me too. What was taught to us was science, without anyone telling us that science is built on good epistemology. Even many, or most, Nobel laureates who pursue science effectively have no knowledge of the epistemology it stands on. So many of these scientists who do effective work, sending rockets to the moon, creating vaccines, quantum physics, the very same scientists produce crazy amounts of pseudo-knowledge and non-sense the moment they deal with social systems. And if scientists produce so much pseudo-knowledge and non-sense when it comes to designing social, political and business systems, think of how much non-scientists, who do not even know science, produce. This is why the world has gone through crazy exponential growth in scientific and technological knowledge, while our society does not get better as promised, and in many situations gets worse. Democracy is deteriorating, education is deteriorating, social discourse is falling apart. The same is true inside companies.
So in the age of AI, producing Knowledge of Systems is our edge over AI, but that edge can only be achieved if we learn a good System of Knowledge, or epistemology. And here is why it matters more now than ever. AI will never make a decision. Humans make decisions, and even when AI appears to make one, it is a human who delegated that decision to it. Those decisions have to be right, and getting them right is an act of epistemology. SystemsWay is what lets you ask the right questions of AI in the first place, and then judge whether the answers it gives you are knowledge, pseudo-knowledge or non-sense. AI will spit out pseudo-knowledge and non-sense if it is trained on wrong data, but it can spit out pseudo-knowledge and non-sense even when trained on right data. The ability to segregate the two remains in you. You can never blame a failure on AI, only on the human, because only a human can do that.
This is why SystemsWay is the rare school that, before teaching you how to think in systems or how to develop knowledge of different systems, first teaches System of Knowledge. Once you learn it, you will constantly see how much of what this world teaches is full of pseudo-knowledge and non-sense. We proudly call ourselves a thinking school, because we start by teaching people to think about thinking, so that you have a robust method for knowing that what you know is actually knowledge. Once you have that segregating power, you have an edge AI will never have.
So here is the whole of it. Our edge has always been thinking in systems, and using it we develop Knowledge of Systems, but that same ability also produces pseudo-knowledge and non-sense about systems. So seeking Knowledge of Systems is a necessary condition, but not a sufficient one. We also need a good System of Knowledge. Our Knowledge of Systems, especially our social systems, is not getting better because we are failing to discard the pseudo-knowledge and non-sense we ourselves produce. In the age of AI, developing a good System of Knowledge, a.k.a. epistemology, should be the prime job of any human and any thinker. And that is exactly what SystemsWay teaches.
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