MOAT : A hyper chronology of going from zero-to-one.

In zero-to-one, famed entrepreneur, Peter Thiel , writes about the idea of startups who will define a new era by generating unique paradigms and inventing new markets which don’t exist today. He argues, that because there is such novelty to this act, these companies have little to no competition and hence do not obey the conventional principals of economic modeling which are driven by competitive dynamics. He calls the process, ‘zero to one’. The idea is so radical, that one cant but help wonder: what exactly is happening inside a ‘zero-to-one furnace’. A baking oven if you will, of a zero-to-one venture. What processes must unfold in the innovators mind and surroundings that create such a unique and long lasting outcome.

Peter goes through a lot of ideas in identifying the markets ripe for zero-to-one innovations, on how to focus on things that matter, and how can investors identify the next batch of zero-to-ones. Focus of this article is different. It tries to answer the question : What processes (in thought and circumstances) get unleashed inside the mind of a zero-to-one factory. Here I provide a chronology of such events, by dividing them into three episodes. These don’t have discrete beginnings and ends. Rather they are meant as softly merging zones, which seep into each other like color droplets on a bloated paper. The point being that these processes still have a rough order of occurrence to them, while they are transgressing into each others boundaries. Within these three zones, there are other miniature processes unfolding which I sweep under a broad brush of that parent process. This is the reason its referred to as a hyper-chronology. A chronology of mini-chronologies if you want to call it. The processes listed below are not happening to individuals only. These processes describe the cumulative entropy inside the zero-to-one furnace.

Model Opacity

When people get together to create a unique paradigm, the first thing they encounter is an opacity of a model that hasn’t been sufficiently studied, nor adequately calibrated and mapped. They are in a no mans territory. An archetypal example from too far back would be Columbus and his men, on a voyage to chart another way to the land. Notice that sailing on long voyages was a known endeavor to them. But the longevity of this voyage was unique, the weathers and storms encountered on the other side of earth, the diseases that might befell them, the psychological toll it would take on the men, and any other number of factors, had enough uncertainty to them, to render the model sufficiently opaque. A more recent, and breathtaking example (from which we can learn a lot in our contexts) would be of Wrights, a little over hundred years ago. The Wrights, are an extreme example of disrupters, encountering model-opacity. The models of aerodynamics before them, were either grossly inaccurate, or did not exist in the first place. The Wrights, decided to build them from scratch, after the existing data repeatedly failed to hold up to experimental validation. This led them to build some of the most enduring ideas and test-rigs, whose principles and actual mechanics are used in designing and testing aerodynamics to this day. Model-opacity does not just hold for such disruptive scientific inventions. It holds for all zero-to-one endeavors. Thats what makes them hard. Imagine someone trying to build up a payment system for seamless transactions between a small Chinese vendor and a small US business/entity. The model here involves legal requirements of regions, support from banking infrastructure, managing of user expectations (returns and exchanges etc), exchange rate fluctuation, and a dozen other aspects that are not usually encountered in local transactions. All these need to be explored , understood to some degree, and then weaved into a synthesis of a product that offers an acceptable user experience. All new paradigms rest on opaque models that have not been probed to sufficient degree. The opacity of models will be the first thing to hit a zero-to-one bunch. They have to live through a period of uncovering, getting lost, probing, getting a hang of this opacity, while moving forward on execution.

Approximation

If you will build me a star-trek device that can look into my eyes and tell me the complete history of medical conditions that I have ever had, generate results of my blood work as it stands today, and warn me of any potential genetic disorders, of-course I will be able to sell it, and create a zero-to-one. That device does not exist, and cannot be built with technology in its current form. But this is an example to highlight a very critical aspect of zero-to-one life. Invariably what you will set out to do, its perfect form would be unachievable with technology/business tools of today. Thats the nature of the deal. So you have to approximate. Approximation, is an extreme skill. You want to approximate enough, such that the approximated problem becomes solvable, yet still remains relevant to the original problem. What you absolutely don’t want to do is to approximate away all the elements of the problem that matter, such that what remains is a triviality (a dud). So its a very fine line. All innovators struggle with it. Think of the cash-strapped bicycle mechanics. The Wrights. They did not have a spare staff, spare capital, spare anything for that matter, to build a big flying structure and risk loosing life and limb, or running their experimental capital dry. They approximated by building a large kite, that they can hold and run along the kitty Hawk beach, and observe and experiment with the amount of life the flaps were providing. A small kite wouldn’t have done the job because the size would be too unrepresentative of the actual aircraft to give the valuable lift-data . A large aircraft came with too much expense, risk, and inability to mould the structure and iterate. A kite, a very large kite, that can model the airflow over the aircraft wings, was just the perfect approximation to run (pun intended !!) with. Approximations face you, almost every day, in the middle of running a zero-to-one. You have to learn the fine art of approximation, build things that solve different , slightly modified versions of the original, with just the right tweaks and turns to make the approximation relevant and consequential to the ultimate object.

Tweaking

When you have approximated away the problem, you have to eventually match it to the audience. Some business schools would call it product-customer fit. It sounds flashy. Tweaking is a very different, and arguably much harder. You have your product, and you have your customer. You are not finding them with a product in hand. But you have certain settings within the product landscape. How to write the user-instruction. Whats the best time to prompt the user for something. How to get implicit data from user to improve your design every day. How to allow the user to update software to an improved version, without bogging him down in the middle of a download. All these are tweaks. Tweaks require intense focus and concentration within a startup to get right. Because tweaks matter. They can make or break a product. The settings you come up with, during the tweak, are everything. You product is as much differentiated by the technology that underlies is, as by the tweaks that go into its final form. They are your secrete sauce. They live at the intersection of technology, product engineering, human psychology, societal structures, historical contexts (we got a taste of it recently), etc etc. No human has the perfect knowledge to make those tweaks. But because tweaks are everything, as a leader, or a shepherd within the zero-to-one foundry, you have to be really perceptive to them. It requires a paranoia about the product. A paranoia about the details. A willingness to involve opinions, willingness to listen to gut and also to not listen at times !! , experiment with designs and options ruthlessly, and just be willing to grapple with the myriad of specifications to get the right mixture in place. Its hard. And its continuous. Its a process, that almost never ends. You have to be mindful of that.

There you are are. To know them, while living through them. MO (model opacity), A (approximation) and T (tweaking). Your MOAT for a zero-to-one. Happy creating !!!

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Seeking to identify a signal amidst noise. Interested in all patterns stochastic, dynamic, and emergent, and their applications to the world around us.

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Seeking to identify a signal amidst noise. Interested in all patterns stochastic, dynamic, and emergent, and their applications to the world around us.

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