The Gig Economy of Machines
In addition to the previous factors that are pushing towards an increase of the Gig Economy, one should also consider the changes introduced by the Digital Transformation that lead to a major reshape of the business environment:
- The rise of globally reaching platforms playing the role of factory and marketplace across the world (or at least across broad areas of the world);
- The rise of ecosystems aggregated by these platforms that are conducive to the growth of the Gig Economy.
Furthermore, the growing importance of data and of the leveraging of data to create economic value has a twofold effect:
- The rapid evolution and obsolescence of knowledge that requires each person on the job market to continuously update her skills/knowledge to remain competitive;
- The increasing difficulty for humans to acquire the needed knowledge, in particular the one that is not direecosystemsctly represented in data but that can be derived through data correlation. This latter requires computation capacity that far exceeds the one of the human brain, calling for the use of machines (AI).
The problem with human skill and human knowledge is that they do not scale graciously both in increasing over time and in delivering. If the knowledge space increases beyond a certain rate it becomes impossible for a single person to match its growth with the growth of her knowledge. This issue has already caused a major impact on what is known as the loss of experience value.
In the past centuries experienced workers were more valuable than new workers because experience made a difference. In these last decades there has been a progressive loss of experience value and companies are better off in hiring new young people fresh of studies then leveraging on an outdated experience. The new hires are cheaper and even in those sectors where they can ask for higher wages that is cheaper/more effective than retraining existing staff.
The shift in value from experience to the knowledge owned by new hires will translate into a shift of value from human knowledge to machine knowledge. There are already first signs of this shift that by the end of this decade will become significant and impactful on companies and workers.
The volume of investment in 2020, worldwide, in AI in various sector is a first indicator:
- Manufacturing industry: 8B4
- Distribution and services: 9B$
- Public sector: 10B$ s
- Financial sector: 11B$.
It should also be considered that the evolution of AI has made possible for machines to acquire “experience”. This both in terms of Machine Learning and in terms of Digital Threads (an integral component of a Digital Twin). Actually, the analyses of several Digital threads can lead to a Machine Learning and identification of possible “causes” to a point that exceeds human capability of analyses and correlation.
This parallel evolution of Machines on one side and of the digitalisation of personal knowledge on the other (through cognitive digital twins) may lead to a Gig Economy of Machines:
- Use of machines for flexible, one demand access and exploitation of knowledge
- Mechanisation of human knowledge and its leverage as a separate entity from the human owner in form of Knowledge Digital Twins