[Author: Dr. Li Wei, CEO, Transfong Ventures]
A Technology venture project is to have a team to create using technology. And, to create, there are two philosophies: To make and To grow. The team is the carrying body of the philosophy. Teams with these two different philosophies may be doing the same projects with the same team structure, but they are fundamentally different organizations with different behaviors and capabilities to capture the opportunities that exist in the world.
The team with the “to make” philosophy focuses on the final target, the plan and the implementation. It demands accurate pre-project intelligence and sophisticated project planning and management to increase the efficiency of implementation and lower the risk. The team with the “to make” philosophy is like a machine. It is designed and controlled from without.
The team with the “to grow” philosophy focuses on the team members, the growing environment and the evolvement. It demands strong connections among the team members and the ability to acquire knowledge and resources to increase its vitality and adaptivity. The team with the “to grow” philosophy is like a life. It is found and influenced from within.
A well-designed machine can be efficient, controllable and predictable, but inflexible and fragile. A vital life can be resilient, open to change and creative, but inefficient and unpredictable. Our choice of team to create using technologies comes out of how we perceive the world. If the world is stable and predictable, we would choose a “machine” to make the result with efficiency. If the world is full of unknown opportunities, we would choose a “life” to grow and evolve for surprise. We are all aware of the uncertainties in the tech startup space and in the world. The wrong choice of the type of team is not just about the high risk of failure, but more importantly, the missed opportunities during the project’s implementation. The opportunity cost is too high and the chance of losing is exponentially greater.
To have a life-like project team with the “to grow” philosophy can be counter-intuitive or even incomprehensible. We are educated and used to the philosophy, from the industrial era, which is “to make.” The vitality of the life-like team comes from its capability to hold error rather than scuttle it. The error is not the flexibility designed within a boundary, but something out of control. The error can be an active team member, deeply involved in the project, connected with resources and members internally and externally, and sensitive to new opportunities. He is redundant from a project manpower point of view, but if something grabs his interest, he has the ability to re-direct or gather new resources to pursue it. He will make the team non-optimal and inefficient, but able to learn, adapt and evolve. The result is totally unpredictable. It can be an evolution that brings the team unimageable opportunity. It can also be a mutation that sucks all the resources from the original project by redirecting it. The error can bring new superpower through evolution, but it can also cause a cancer due to malicious mutation. Most of us, in the tech startup ecosystem, know that we need a life-like team, but without the error. What we will have, at most, is a terminator T800 or many of them. We can never control an error, but we can intentionally put it in the right environment, hold it and influence it, and then balance the tradeoff and hope for the best.
Similar to a technology venture project, a company has to make the choice of the best type of organization for their survival in this rapidly changing industry. And an individual also has to make the choice of their own philosophy for their development in this society full of uncertainties. The question is never whether we need to introduce the error, but how to hold an error, influence it and balance its cost/benefit to make the organization or ourselves more life-like.
It is similar creating in the Artificial Intelligence sector. Do you choose to write a thoroughly considered and hard coded execution program (“to make”)? Or do you choose to focus on the basic interaction and mimic a neural network algorithm, in which case you need to train the program to learn and adapt to its application (“to grow”)?
Our discussion on organizational structure, Artificial Intelligence, or even an individual development process shares the same underlying philosophy. Are you a machine “to make” or a life “to grow?”