It’s a cliché that we live in a time of ‘unprecedented change.’ Every sector has been impacted and even more disruptive change is coming. ISN works with organizations and communities who are being bold in dealing with these challenges. Regardless of their sector, they have been proactive in identifying emerging threats and opportunities, imagining possibilities, choosing a desired future, and then mobilizing to create it.
Technology has been the principal driving force for change over the last two centuries. As technology became more complex, so did the economy and society. Change in all three has accelerated in lockstep. In The Nature of Technology: What It Is and How It Evolves (2009), complexity theorist Brian Arthur described how deeply these systems are intertwined.
“The economy forms an ecology for its technologies, it forms out of them, and this means it does not exist separately. And as with an ecology, it forms opportunity niches for novel technologies and fills these as novel technologies arise.
This way of thinking carries consequences. It means that the economy emerges — wells up — from its technologies. It means that the economy does more than readjust as its technologies change. It continually forms and re-forms…”
Deep coupling of technology and the economic system, Arthur says, creates tipping points where the economy goes critical, suffers the shock of an avalanche of change, and moves to a new attractor.
“The arrival of the automobile in the early 1900s caused the replacement of horse transportation. The death of horse transportation eliminated the needs for blacksmithing and carriage making. The collapse of blacksmithing in turn eliminated the need for anvil making. Collapses caused further collapses in a backward succession.”
Computers, cellphones and the Internet were adopted much more rapidly by American households than electrical appliances in the century before (Michael Felton, The New York Times). Each new technology triggered a wave of change in the economy.
There were only 15 computers in the United States in 1954, and seventeen thousand in 1964. By 1986 the number was greater than 30 million. By 2000 there were more than 168 million. In 2014 the number of PCs alone was estimated at 2 billion. There were 16 million Internet users globally in 1995, 36 million in 1996, 70 million in 1997, 147 million in 1998 and 240 million in 1999. In December 2017 the number was estimated at more than 4 billion.
New devices are now being connected to the Internet, creating an ‘Internet of Things.’ By 2020 the number of connected devices is expected to be almost 31 billion worldwide. By 2025 this number is expected to grow to more than 75 billion.
Technology change has been accompanied by economic dislocation. Companies have had to adapt constantly to survive. In 1960 S&P 500 companies had a lifespan of about 60 years. Corporate life expectancy declined rapidly in subsequent decades, to 33 years in 1964 and 24 years by 2016. It is expected to shrink to just 12 years by 2027. The following chart from Innosight shows the trend.
Since 2000, 52% of companies in the Fortune 500 have either gone bankrupt, been acquired, or ceased to exist. In 2015, more than half of the Fortune 500 failed to make a profit.
High corporate mortality leads to reduced job stability. In 2012 the Bureau of Labor Statistics reported people remained in a job on average for 4.4 years. Ninety-one percent of Millennials expect to stay in a job for less than three years. (“Job Hopping Is the ‘New Normal’ for Millennials: Three Ways to Prevent a Human Resource Nightmare.” Forbes, August 14, 2012.) Full-time jobs are being replaced by part-time and temporary work.
In his book The Precariat: The New Dangerous Class (2014), Guy Standing described increasing job uncertainty. “Every year, about a third of employees in OECD countries leave their employer for one reason or another. In the United States, about 45 per cent leave their jobs every year. The image of long-term employment is misleading, even though a minority still have it. A third of the job turnover is accounted for by the creation and ending of firms.”
At the same time there has been a drop in employee loyalty. According to the Center for Work-Life Policy loyalty declined from 95 to 39 percent between June 2007 and December 2008. Trust in employers fell from 79 to 22 percent in the same period.
The Tipping Point
Waves of change are now happening more frequently and the complexity of systems is increasing. Growing connectivity (the number of links within and between complex systems) and tight coupling (rapid propagation of a disturbance) are making them more prone to failure.
Nassim Nicholas Taleb writes in Antifragile: Things That Gain from Disorder (2012) that the world we have constructed is ripe for unpredictable, high-impact, Black Swan events.
“Man-made complex systems,” he says, “tend to develop cascades and runaway chains of reactions that decrease, even eliminate, predictability and cause outsize events. So the modern world may be increasing in technological knowledge, but, paradoxically, it is making things a lot more unpredictable. Now for reasons that have to do with the increase of the artificial, the move away from ancestral and natural models, and the loss in robustness owing to complications in the design of everything, the role of Black Swans is increasing. Further, we are victims to a new disease, called in this book neomania, that makes us build Black Swan-vulnerable systems — ‘progress.’”
The next wave of change may be much more consequential.
Machine intelligence is expected to become a new destabilizing force. We are teetering on the edge of a new tipping point — entering a period Erik Brynjolfsson and Andrew McAfee called the “Second Machine Age,” when smart machines will be capable of performing complex tasks that until now have required human intelligence (The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, 2014).
Massive wealth will accrue to those who create and fund these machines. This will deepen inequality as millions of people are displaced from middle-class jobs. John Maynard Keynes called this “technological unemployment” — displacement of workers through the use of cost-saving technologies at a faster rate than new jobs can be created.
In a paper published by the Oxford Martin School in September 2013 — “The Future of Employment: How Susceptible are Jobs to Computerisation?” – Carl Benedikt Frey and Michael Osborne suggest that 47 per cent of jobs will be automated. They looked at the probability of job losses due to automation, by occupational category. At the highest end of the scale this was 99% for telemarketers, 94% for accountants and auditors, 92% for retail salespeople, 89% for technical writers, and 86% for real estate agents.
The Economist magazine wrote in January 2014 (“The Onrushing Wave”): “Evidence is mounting that rapid technological progress, which accounted for the long era of rapid productivity growth from the 19th century to the 1970s, is back.” Jobs that aren’t easily computerized may still be impacted. “New data-processing technology could break ‘cognitive’ jobs down into smaller and smaller tasks.”
In his book Average is Over: Powering America Beyond the Age of the Great Stagnation (2013), economist Tyler Cohen says the gap between rich and poor is widening. The world is dividing into two camps: those who have the skills required to create and apply the new intelligent technologies and those who do not. In the future, he says, there will be more limited opportunities for the middle class.
Many people believe history will repeat itself, mirroring the economic and social dislocations of the Industrial Revolution — until, as happened then, the benefits of machine intelligence become more widely distributed. Some believe this will cause such fundamental change in the economy that we will have to rethink how income is earned and distributed.
This article is an excerpt from a book in progress on complexity, collaboration, and the challenges of transformative change. It was first posted on April 10, 2018, on LinkedIn.