Questions worth thinking about in this article
What is a complexity system?
What is biological thinking?
What is the relationship between biological thinking and the underlying logic of a complex world?
How to use biological thinking in business?
Ma Huateng mentioned in the open letter that Tencent has always adhered to the “grayness rule” for creating bio-type organizations when building organizations and product innovations, just as it allows mutations in evolution and allows “imperfections”.
Today’s headline Zhang Yiming said when talking about enterprise systems, living things are rich in cell and ecology, and the species are diverse, but the rules behind them are very simple and elegant, which has many analogies for us to design enterprise systems.
In fact, not only Ma Huateng, Zhang Yiming, including Fu Sheng, Wang Xiaochuan, etc., more and more Internet front-line entrepreneurs have emphasized the inspiration that biology brings to them in interviews, and they have become obsessives and firm practices of [biological thinking] . Wang Xiaochuan even said that it is biological thinking that makes Sogou live to this day.
Why do Internet gangsters use biological thinking? In fact, the logic behind it is simple, they have to solve the problems caused by [complex systems] .
What is a complexity system?
One of the most important changes facing Internet companies and our generation is: ultra-complex
Yes, we are in a new era and are building a new system, and this system cannot be mastered as a whole, or it is too complicated for anyone. In the modernization process of the past 200 years, complexity is a common thing, and human society has always tended to become more and more complicated.
Take the machine, from the earliest dozens of parts to hundreds of tens of thousands of parts, and now a Boeing 747 aircraft has 6 million parts, 275 kilometers of various pipelines. No one in the world can fully figure out what is going on with a Boeing 747.
At the beginning, humans also evolved a method to deal with this complex system. The solution mentioned by Mr. Wu Jun is modularity and hierarchical collaboration. Each person is only responsible for one of these modules, and then they are layered on top of each other to form a huge system. Don’t be afraid if something goes wrong, you can find the problem by breaking it down.
So, even if something as complex as a chip has hundreds of millions of transistors integrated on it, humans can still design it, make it, and control it. Complexity doesn’t look that scary.
Now, in the age of the critical point of technological complexity, human beings face the problem of super complexity.
A bunch of things are put there, even if many are messy, it’s called “complex”. But when a bunch of things reach a certain amount, they also affect each other, and a “cascade effect” occurs, which is called “complexity”.
Once this complexity is reached. It was an uncontrollable, even incomprehensible system.
In 2015, Google counted 2 billion lines of code for all its products.
In 2018, programmers at Alibaba wrote 1.2 billion lines of code a year.
Sounds okay? This number is about the same as the number of transistors on a chip. So a chip we humans can control. That software we humans can also control. Note, however, that their complexity is completely different.
why? Because code and code are going to affect each other. This is called the “ cascading effect “. Once there are more than one billion lines of code that interact and interact with each other, and there are layers of interaction at different levels, how many kinds of situations are there? There must be countless more than the number of particles in the entire universe.
On March 6, 2019, the BNP Paribas system went down for more than 24 hours, and users could not perform online banking related operations and could not access his official website or applications. In January of this year, this happened once.
If you are familiar with the banking software system, you will know very well that with the development of banking business, the banking software system is continuously updated and has been constantly patched. Basically, the banking system is developed and built on the basic platform. It has become so complicated that no one can explain the system clearly.
In addition to the Internet software system, all aspects of human society are shrouded in complexity and fog. For example, the federal tax law of the United States has more than 74,000 pages. In fact, no one in the United States knows how this country collects taxes.
Another example is the financial industry, a financial wealth management product, after repeated packaging and sales, no one can understand what it is. No one knows when this product may ignite the financial crisis. Not even financial experts know.
Internet companies that are at the forefront of the times, they have to face such a super complicated internal and external system environment.
Samuel Abesman wrote in “Why Biological Thinking Is Needed”:
Living things are complicated when they are alive, and at most they can be said to be complicated.
Man is also one of the creatures and the most complicated one to live.
Human beings are not only complex themselves, they have also created a complex world, and in the complex world have created various complex systems.
There are inextricable relationships between people, between people and all living things in the world, between people and various systems created by themselves, and between systems. As long as one is alive, one has to face up to complex relationships. Because, as long as you are alive, there is no way to not be associated with other things.
It can be said that human technology, from websites to transaction systems, from urban infrastructure to scientific models, and even supply chain systems and logistics systems that provide supporting services for large enterprises, have become too complex and intertwined.
Why do we need biological thinking?
Human cognition is ultimately limited. With the passage of time, the various technical systems we build have become more and more complex, and the relationships between the systems have become stronger and more difficult to understand. .
No matter how smart humans are and how good their memory is, it doesn’t help, because these systems are constructed differently from the way humans [think] . Humans do not have the ability to cope with the millions of components and the numerous interactions between them at the same time, keeping all the results in their heads. Our brains are “seriously overloaded” and then fail.
Therefore, while we enjoy the efficiency and convenience brought by technology, we are often troubled by these overly complex systems. The world is becoming more and more complex, and complexity continues to grow at a rapid rate. Paying attention to all designable, controllable, and predictable physics thinking becomes powerless, and the pursuit of adaptation, connection, and symbiotic biological thinking Has gradually been used as a cognitive framework for coping with complex variable environments.
Similarly, an advanced system cannot exist independently if it is to function normally. Among many system problems, biology is the most complicated system problem. If you accept the way you think about solving complex problems, you can develop the ability to solve complex problems.
In the process of creating complex machinery, mankind has repeatedly returned to nature to find guidance. We often lament the power of nature. Complex systems such as hive, ant colony, forest, and sea are formed naturally, cyclically, and constantly evolve.
There is a profound “kinship” between biological systems and technological systems, which means that we can learn a lot from biological thinking. Attention to detail and diversity of biological thinking will provide a vital perspective for understanding cluttered evolutionary systems.
Therefore, biological thinking is the golden key to understanding the complex world.
The biological world is a world from 0 to 1. Biology emerges, lives, and evolves in a complex and changeable environment.
Biological thinking is about the coexistence of errors and the coexistence of risks. As long as it is effective, it does not require precision, or even understanding, which is the normal state of all successful species. This is almost the same as the underlying logic of the enterprise, and the company’s path is from nothing to existence, from survival to competition, and then to evolution.
Therefore, Fu Shengcai of Cheetah lamented that biology is a discipline that can open up a lot of cross-border knowledge. Compared with natural sciences such as physics, biology reveals the underlying laws of the world more deeply, and its ideas are universal. Understanding the world with biological thinking is actually nothing more than returning to the state of human existence.
How to use biological thinking to solve problems?
So, in the face of complex systems, how can we use biological thinking to solve problems?
1. Use to cope with risks.
The physical method is to avoid risks by precision. The biological approach is redundant. Redundancy means that instead of providing increased accuracy to ensure security, alternative methods are used to ensure stability. Insects and fish have spawned a large number of offspring by spawning a large number, and few survived, but the genetic safety of the species is guaranteed.
The human body has two lungs, two kidneys, and two eyes. Any one of them has a problem. The other one can ensure the continued function. Even if there are problems, other functional organs will provide compensation methods to make up for the missing functions. . People with damaged left brains have particularly developed right brains, and blind people have particularly sensitive hearing.
The most direct strategy in the business world is: Don’t put eggs in a basket. We know the giants Kodak, Motorola, Nokia, their business is too “focused” is very easy to subvert. Therefore, the current actions of Internet giants after occupying traffic are often to seek investment in multiple fields, or to “heavy” the entire industry chain.
Alibaba has Alipay, rookie, Ant Financial, and Flying Pig. It has developed Hema fresh products offline, and invested in hundreds of companies in artificial intelligence, travel, finance, social and other fields. In product innovation, Tencent also uses this kind of biological thinking. In a chaotic world, only believe in probability, calmly face the failure in the process, keep throwing dice, and look for opportunities in the change.
“Don’t put eggs in a basket” is not only manifested in competing for the layout of multiple industries, deepening and heavy operation, but also starting to seek a more dynamic and resilient team management method in terms of organization.
Enterprises represented by Haier have gradually realized “flattening” and “decentralization” in management, which is actually an active transformation of “anti-fragile” organizational management. Inamori Kazuo’s original “Amoeba” business model, by dividing the company organization into small “Amoeba” small collectives, while maintaining vitality, the profit center has sunk, and has never lost money for more than 50 years.
2. Gray rule: use “imperfect” rule to correct errors
The physics method is to figure out the principle first, then correct the mistakes, and clear the original. The biological method is to survive a variety of environmental mutations. As long as you can pass through the evolutionary scissors, the fittest has survived. As for whether there are no errors at all and biology does not care, this is not important.
Ma Huateng has always adhered to the “imperfect” rule in Tencent’s product innovation, that is, to achieve a single point of breakthrough, it is necessary to allow imperfections, but to quickly approach perfection. It is precisely some defects and imperfections of the product that have laid a very important foundation for the next development and evolution.
Cheetah has also emphasized “continuing to benefit in a volatile world.” Fu Sheng believes that benefiting from large fluctuations is the ultimate ability of biology.
In fact, genetics and mutations in biology are one that keeps stability and one looks for opportunities in fluctuations; one side is the death of an individual and the other is the evolution of a population; and the corresponding enterprise is that the environment is harsh on the one hand and the life is stronger on the other. These practices of all enterprises are actually imitating the nature, imitating the scissors of evolution, and creating various extreme environments, depending on who can survive. It’s good to survive.
As for whether the company is completely free of bugs and defects, it is neither possible nor important. This is biological thinking.
3, humble heart + iterative perspective
In the face of mistakes and failures, while admitting our ignorance and incompetence, we must also maintain an open and calm mind. For example, there is widespread synergy in ecosystems. It does not mean acting directly on the problem and influencing it, but rather making up for individual deficiencies by connecting others.
Existing knowledge cannot solve contemporary problems and can be left to future generations to deal with. For a complex world, maintain a humility, awe, and generosity, and use continuous biological evolution theory to deal with it. The power to iterate with time and change will be endless.
to sum up
If biological thinking is a golden key to understanding the complex world, biological thinking can be expressed as: don’t be confused by appearances, chaos and unpredictability; enjoy the process, and look at things you do n’t understand with a developmental mindset; With a constant iterative vision, we firmly believe in probability and look for opportunities in change.
We must therefore strive to maintain two opposing states:
The first state requires us to work hard to overcome our ignorance, and we must not be obsessed with it; (insist on exploration)
The second state means that once we understand something, don’t take it for granted. This is scientific thinking and biological thinking. It is our necessary ability and prerequisite for learning new things and solving problems. (Keep iterating)
