We live in an age of simplistic explanations. We build simple systemic models and crude abstractions. As a result, both our sense making and our decisions are built on an inadequate appreciation of the complex systems we are part of.
We have seen what it can lead to: industrial farming has caused a radical reduction of variety in nature in order to meet the goals of productivity. The simplification of crops was economically very efficient, allowing specialization in machinery and lowering the cost of learning, but it often damaged the local ecology in an irreversible way. The result was a fragile ecosystem, with a growing dependency on artificial fertilizers.
Every time we replace natural, complex systems with simplified mono-cultures we gain in short-term productivity, but at the cost of long-term resilience and viability. The less diverse a system is, the more vulnerable it is, and the more unsustainable it becomes.
Farming is now changing. New voices within agriculture say that “all farming takes place in a unique space and time”. These scholars claim that a mechanical application of generic rules and principles that ignore these contextual particularities is an invitation to catastrophic failure.
The principles of simplification still apply to the social systems of work: most of our firms can be described as mono-cultures. We also do our best to productize humans to fit the job markets. Many organizations are productive in the short term, but fragile in the long term. As long as the environment remains the same, simplified systems are very efficient, but they immediately become counterproductive when the environment changes even slightly. And it always will.
Our view of efficiency in firms still follows the line of thinking of efficiency on farms.
Job markets need standardized workers who are uniform in their skills and motivations. People are interchangeable labor. These people have no uniqueness. They have no original ideas to contribute to work. The focus is on the price of work; supply and demand.
In classical economic theory, markets are assumed to tend to a state of equilibrium. If there is an increase in demand, prices rise to encourage a reduction in demand and/or an increase in supply to match the demand. This is the principle behind Uber’s surge pricing. A market, then, is a simple cybernetic system: any significant change is self-regulating adaptation. There is no learning.
One-dimensional social designs have the same inbuilt risks as simplified natural designs. Simplified social systems can cause the same kind of damage to the human ecology as simplified farming systems have caused to the natural ecology. People become dependent on artificial motivation systems, the human equivalents of fertilizers. We call them incentives.
Just as all sustainable farming is now seen as taking place in a unique context, all human work takes place in a unique space and at a unique time. Human work is situated and context-dependent. It just hasn’t been understood that way. The digital architecture of this kind of work might resemble Amazon Dash buttons more than Uber.
Technological intelligence helps farmers to be more context-aware. Technological intelligence can do the same for human work. Mass systems were built on general knowledge and generic competences. Perhaps post-mass systems are going to be built more on situated knowledge and contextual competences.
An example of this might be the difference between the general knowledge of seamanship in open waters and the contextual knowledge of piloting. When a ship approaches land, the captain often hands over control to a local pilot, who then navigates the ship to the port. Pilots know well the dynamic peculiarities of the area, the winds and the currents. Much of this situated knowledge would be irrelevant somewhere else, at another harbor entrance.
A job market, as a concept, is a radical abstraction of human work. Every time we replace practical, local knowledge with general, standardized knowledge we gain in productivity, but at the cost of more environmental adaptation in the future. Learning debt is created and the whole system (of jobs) is less resilient and may even become dysfunctional. Short-term gains turn out to be extremely expensive in the long run!
The post-industrial era is too complicated to boil down into a single slogan describing work, but three scenarios seem to be emerging: (1) processes are automatized and robotized, leading to an algorithmic economy: (2) generic work is found through platforms, or turned into tasks circling the world, leading to a platform economy, and (3) context-specific value creation takes place in interaction between interdependent people, leading to an entrepreneurial economy.
I believe that the future of human work is contextual. Even after the captains are automated, the pilots may still be human beings. Even after the surgeons are robots, the nurses may still be human beings. Some people doubt this because there is some very advanced research going on that explores sensor technologies and responsive algorithms. The collaboration between sensors and actuators is getting better and better. Despite that, if you are a human being, it is better to be a tour guide than a travel agent.
It is a more profound change in work patterns than what the present platforms offer. It is not about employees becoming contractors. It is about generic, mass solutions becoming contextual and about interchangeable people who are now, perhaps for the first time, being seen as unique. The case for networked small units, such as human beings working together in responsive interaction, is stronger than ever. Local, contextual knowledge is needed not only for sustainability in farming but also at work.
What is most desperately needed is a deeper understanding of the complexity of life.
Farming is starting more and more with a true understanding of the particularities of the land. Work should also start with an understanding of the particularities of human beings.
Thank you Doug Griffin and James Scott