“It is difficult to make predictions, especially about the future.” - Karl Kristian Steincke
Predicting the future of technological change has always been an inexact science at best. One of the more infamous, recent examples was Steve Ballmer’s ill-fated dismissal of the iPhone: “there is no chance that the iPhone is going to get any significant market share” while poking fun at it because “it doesn’t even have a keyboard”. Ballmer is in good company. There is a long list of serious predictions made by authoritative figures that now seem rather quaint. In 1903 the New York Times wrote: “it might be assumed that the flying machine which will really fly might be evolved by the combined and continuous efforts of mathematicians and mechanicians in from one million to ten million years.” That same year, the Wright Brothers went on to make their historic Kitty Hawk flight. But the history of prediction is also replete with examples that hugely overstated technological progress. AI pioneers Herbert Simon and Adam Newell predicted in 1958 that computers will beat the world chess champion within ten years; in fact they had to wait another 40 years.. In 1955, Alex Lewyt, the president of a vacuum cleaner company stated that "nuclear-powered vacuum cleaners will probably be a reality within ten years."
So, why are future predictions so much more inaccurate than our predictions about chemical reactions? The answer is complexity. Even if we were to put probabilistic quantum physics aside, it’s impossible to create the all-knowing demon of Laplace[MM1] , which could calculate the future, because our society of intelligent beings is highly interconnected, meaning that “the present determines the future, but the approximate present does not approximately determine the future.”
However, chaos theory does not mean that all future predictions are a waste of time. As William Gibson put it beautifully: “the future is already here, it is just not very evenly distributed”. There is always the risk of a “black swan” event and we don’t know which technologies will develop and how fast, and how they will interact with each other and society. Predicting the future is not magic; the seeds of future technologies and developments are laying their roots as we speak, but not all may grow to maturity. Complexity just means that there is a space of possible future scenarios rather than THE future.
The Power of Visions
“Anything that one man can imagine, other men can make real” - Jules Verne
What do geostationary satellites, submarines, waterbeds, debit cards, tasers, tablet computers, tanks and video chats all have in common? They are all examples of technologies that were first created in Science Fiction before they became reality. Our expectations about the future influence our own behavior, which shapes the future we end up with. It is difficult to estimate the influence of Sci-Fi visions on real technological developments, however, they have definitely served as a model and inspired many innovators and inventors. For example, the word “Taser” is actually an acronym of “Tom A. Swift’s Electronic Rifle” named after its original Sci-Fi inspiration. Igor Sikorsky, the inventor of the helicopter, explicitly referenced the quote from Jules Verne above and stated that the French author's “Clipper of the Clouds” was his inspiration for building such a machine in real life. For disruptive rather than incremental innovation, it can be easier to start at a vision, a future scenario, and work backwards. Instead of trying to make your horse run faster, you begin by asking yourself what the transportation of the future might look like. New technologies alone do not make a vision yet.
A failure of imagination
Even if the arrival of a new technology is common knowledge, the difficulty is to tease out how society, business models and everyday life may change, because many consequences remain hidden and depend on the convergence of other developments. Israeli author Yuval Harari (2016, Homo Deus, p.50) recalls his first encounter with the Internet, less than twenty-five years ago:
“It was back in 1993, […]. Ido was already a huge computer fan, and before opening the ping-pong table he insisted on showing us the latest wonder. He connected the phone cable to his computer and pressed some keys. For a minute all we could hear were squeaks, shrieks and buzzes, and then silence. It didn’t succeed. We mumbled and grumbled, but Ido tried again. And again. And again. At last he gave a whoop and announced that he had managed to connect his computer to the central computer at the nearby university. “And what’s there on the central computer” we asked. “Well”, he admitted, “there’s nothing there yet. But you could put all kinds of things there”. “Like what?” we questioned. “I don’t know”, he said, “all kinds of things.” It didn’t sound very promising. We went to play ping-pong, and for the following weeks enjoyed a new pastime, making fun of Ido’s ridiculous idea.”
This applies to almost every technology. Take autonomous cars: Everyone agrees by now that they will come and some societal changes, such as the hope that regular road accidents and traffic jams will go down drastically, are quite straightforward. However, we are not automatically aware of any potential impact beyond that, such as changes in interior and exterior car design, ownership and access models, policing, suburbanization, hotels, alcohol consumption, organ donations, advertising, radio consumption, youth and elderly mobility, digital nomadism, mobile business or nightly traffic.
This is why we at UBS Y, rather than to predict the future, often assume that certain technological capabilities will be available at some point and then use creativity techniques and various methodologies, such as the vision cone, to detect a wide range of possible opportunities, threats and white spots.