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Bigger is not always better

  A few years ago, there was a popular term called “Internet thinking”, and everyone in the industry talked about it, although no one could say exactly what it was. However, there is a conclusion that many people regard as the golden rule, that is, Internet business must be “scalable”, which is the real value of Internet companies. If it cannot develop at scale, there will be neither the so-called “long tail” nor the emergence of a “platform”. Venture capital is keen on the business model of “spending a lot of money to develop, and then using the smallest marginal cost to rapidly expand to achieve large-scale capabilities”.
  ”Can it scale?” is the first question almost anyone asks about anything new—startups, technologies, ideas. It’s OK to start small, but bigger is almost always considered better, or at least more profitable. The English word “scale” comes from the Latin scala, which means “ladder,” and seems to have full legitimacy to ask for scale, because everyone knows that in this world, it is better to go up than to go down.
  Yet, so engrossed in this logic, we lose a sense of what is appropriate and what really works. In fact, increasing scale is not always desirable. There are even extreme cases: size and power are no longer related: the smaller and less visible the power, the more dangerous it is. The scale has been turned upside down.
  In reality, it’s easy to become numb to the kinds of problems encountered across scales. At the same time, each local challenge is drawn into a complex network of influences. In this way, even solving a problem under a small range of local conditions will be constrained by a larger range of conditions, which is really a headache.
  With that in mind, James Hunt, a design professor at the New School in New York, wrote Thinking at Scale, which attempts to put into perspective our current societal predicament and outline design strategies for navigating the many “broken” systems. Hunter inspires us to understand scale as a conceptual framework for thinking about the present. This idea, which Hunter calls “scalar framing,” is a pioneering framework for thinking in terms of scale.
  From people to entire cities to planets to the universe, each successive outward zoom reframes the view, giving us new information, new horizons, and new contexts for thinking, Hunter said. The same is true for inward scaling.
  Building a scalar framework in this way will reveal that new opportunities will always arise as the system scales up or down. The scalar framework can give us a flexible way to identify overlooked opportunities, stakeholders, constraints, collaborators, and new insights through complex hierarchies. It also shows the pros and cons of the two types of solutions we commonly use. One is a top-down solution, which scales quickly but receives little feedback from those closest to the problem; the other is a bottom-up solution, which is slow and unreliable. Forecasting, rarely responds well to shifts involving trends.
  Bottom-up is not the best way to build bridges if there is a gulf between problem and solution, but top-down does not guarantee that bridges will be built where they are needed. To this end, Hunter proposes an intermediate approach, which he calls “scaffolding”: to design a framework in the middle that does not belong to the thing itself, but rather facilitates the creation of many different configurations.
  Scaffolding is an analogy: it is designed from above, and it takes knowledge to build it, as well as an awareness of the process. But it is also bottom-up, and a development process must be designed to encourage input from below in order to capture the wisdom of the crowd, synthesize, optimize, and maximize the ideas of many. As an “intermediate framework”, it must embed continuous feedback loops that connect scaffolders to the community; it must also have various iterative paths that allow the outcome of the process to follow internal logic that was unclear from the start Development, regular adjustments and revisions over time, constant trial and error and iteration.

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