This article is part of the THNK EVERGREENS series. In the Evergreen series, we bridge seminal works on innovation management and works by influential thinkers, by extracting key implications and offering new insights to innovation practitioners. This article builds on the book Post-Capitalist Society by Peter F. Drucker.
Much has been glorified about the societal changes in the twentieth century. In the past century, the wealth generated by industrialization caused unprecedented drops in infant mortality rates, which in turn led to the rise of the blue-collar worker. Subsequent labor movements, (civil) rights movements, and widespread formal education paved the way for the current rise of the knowledge worker – a term first depicted by Drucker in 1959. According to him, “the most valuable asset of a 21st-century institution, whether business or non-business, will be its knowledge workers and their productivity”, envisioning a post-capitalist “society of organizations”. For the first time in history, knowledge has become the key resource and most important factor of production.
The truth about knowledge
Through much of his later life, Drucker saw the knowledge economy emerge and understood three truths lying within it:
- True value lies not in machines and tools but in the knowledge of workers, as value is created by productivity and innovation – both applications of knowledge to work.
- The knowledge society puts the individual at the center, as knowledge is always carried, used or misused by a person.
- This poses organizational challenges of making knowledge productive. Either organizations increase productivity of knowledge workers or risk stagnation.
Drucker saw these changes as positive, placing the individual and management back to the social core. He dismissed fears about automated work, stating that “all automation might do is shift employment from fairly low-pay manual to much more highly-paid professional or technical work.”
Much of Drucker’s statements have proven to be in the past: knowledge work has grown faster than any other sector and many low-paid jobs have indeed been replaced by higher-paid knowledge requiring jobs. Developed countries are spending most on knowledge formation and as of 2012, there were over 230 million knowledge workers, according to McKinsey estimates. Social media, the Internet and technological advancements are already driving new forms of knowledge innovation focused on “peer-to-peer” knowledge sharing and collaboration and machine learning, posing new social challenges on issues such as intellectual property and job creation. What is more, emerging technologies such as mobile Internet, Internet of Things, cloud technology, advanced robotics, 3D printing, energy stories and renewable energies are evolving quickly, with rapidly dropping costs and vast potential for global implementation. In 2025, McKinsey estimates 2-3 billion more people with access to Internet. Combine this with a $5-7 trillion dollar potential economic impact of automation of knowledge work, and it appears that our current knowledge society is about to change more drastically than ever before – for better or worse.
COMPUTERIZATION OF WORK
On a political level, these developments have once again given rise to fears of joblessness in the face of computerization. A recent study by LSE suggests that “nearly 50 percent of occupations in the US are under threat of computerization.” High-risk job categories include jobs in transportation and logistics, as a result of e.g., Internet of Things, and autonomous vehicle developments. Many sales and service jobs, a major source of growth these past decades, also risk computerization. “The market for personal and household service robots is growing some 20% annually.” LSE’s research group says that low-risk jobs are those that “require human-level social intelligence and specialist occupations involving the development of novel ideas and artifacts”, which include management jobs, and occupations in education, healthcare, the arts and media, engineering, and science occupations – all of which requires a high degree of “creative intelligence”. At the medium-level risk are maintenance and repair jobs, deemed “safe for the immediate future”.
“Machines may be reaching new heights of intelligence, but they are no match for human resourcefulness, imagination, and interaction.” Or are they?
Though potentially problematic, it is likely that jobs will shift towards more knowledge-demanding work once more. Historically, technological advancement has been a net creator of jobs, simply changing the nature of work to be done. When this happens, Drucker argues that organizations and governments have a joint responsibility of ensuring that everyone have access to education when jobs and skills become obsolete – something he deems “easy enough to anticipate”, and act upon. Rather than pointing fingers at technology, this sheds light on the importance of repairing our educational system, allowing people to be educated in ways that offer future employment opportunities.
FROM COMPUTERIZATION TO ARTIFICIAL SUPERINTELLIGENCE
Less known and potentially greatly disruptive, however, are current developments in artificial intelligence and machine learning. So far, the brain’s neural infrastructure has been used as a model for machine intelligence – largely disregarding “all other complexities inherent in the brain”. This is why jobs that require social and creative intelligence are still deemed to be safe from computerization. According to Neurobiology and Computational biology Professor Dennis Bray: “Brains are the source of emotions, motivation, creativity and consciousness. [They] differ from computers in a number of key respects. They operate in cycles rather than in linear chains of causality, sending and receiving signals back and forth. Unlike the hardware and the software of a machine, the mind and brain are not distinct entities. Ad then there is the question of chemistry. Living cells process incoming sensory information and generate not just electrical signals but subtle biochemical changes. Cells are soft, malleable and built form an essential infinite variety of macromolecular species quite unlike silicon chips. Organisms encode past experience.
Yet, emulating these aspects of the human brain’s bio-systems, called “deep learning” is exactly what start-ups like DeepMind (acquired by Google last year) and MetaMind are doing. Also companies like Facebook, IBM, and Microsoft are buying up deep learning talent, and investing heavily in artificial intelligence. One Google executive said: “everything in the company driven by machine learning”.
As explained by a recent article, there are three major AI categories: narrow AI, general AI and artificial super-intelligence, defined as an “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Narrow AI specializes in one area, general AI refers to computers that can perform any “intellectual task a human being can do”, i.e., the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience. Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter all around. It means that machines learn to ability of recursive self-improvement, allowing computers to improve its own intelligence smarter and faster than human brains ever could, reaching the superintelligent level. This is commonly referred to as Intelligence Explosion by computer scientists, and would be the “ultimate example of the Law of Accelerating Returns.”
Running the risk of sounding like a bad sci-fi movie, this latter category of deep learning is the reason that leading AI scientists have come together to make sure AI systems are put to humanity’s common benefit. It is also the reason that Elon Musk donated some $10 million to the Future of Life Institute, whose aim it is to keep Artificial Intelligence “beneficial to humanity”. In doing so, he joined many other leading tech entrepreneurs and scientists that have expressed concerns about current developments in machine learning.
FUTURE OF EMPLOYMENT
What does this tell us about the future of technology vis-à-vis human employment? Will it be a future of leisure time and meaningful work? Will countries have to focus on creating flexible and adaptive labour markets, to accommodate entrepreneurial, creative and self-employed citizens to “balance human needs with technological advances”?
Some hope that emerging technology will allow us to define our relationship with “work” in a more positive and socially beneficial way. First coined “technological unemployment” by economist John Maynard Keynes, Future of Work author Jeffrey Rifkins believes that technological advancement and machine learning will “free people from the need to earn a living and people will get closer to the thing that really matters: collaboration and empathizing with others.” Proponents of such a future of work call for universal basic income, to sustain basic levels of subsistence, while allowing people to focus on doing what truly matters to them, and “redefine our relationship with work in a more positive and socially beneficial way”. They cite recent experiments with money to prove their case. In London, homeless people were given a one-time grant. This solution proved less costly then other social provisions available, and more effective, as participants invested their money into education and developing skills to become economically self-sufficient again.
Though this might seem a utopic social ideal, Finland recently elected a party that supports these ideas – which may allow us to witness such experiments on a societal level soon. Others are crowd-sourcing their salary to create such a future for themselves, supported by Gittip, a financial innovation start-up. They argue basic incomes have the added benefit of doing away with our current welfare provisions – currently vast, inefficient and highly bureaucratic in nature.
Emerging technologist and futurist Michell Zappa reminds us that “all technology is human-made.” Truth is, when it comes to the future, all of these options are perfectly possible. As Rifkins says: “it is up to us to decide which one will become reality.” The real issue at stake is not technological advancement in and of itself – but our societal response to these emerging technologies and changes. “Our educational system is not adequately preparing us for work of the future, and our political and economic institutions are poorly equipped to handle these hard choices,” says Drucker. If anything, we need to tackle these issues to ensure that technology and artificial superintelligence will continue to be a social benefit.
[Images: header: Vice.com / 1 Da Vinci Robot 2 Robotworx.com]
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