It’s tough to make predictions
“It’s tough to make predictions, especially about the future.”
Yogi Berra
There are three ways to predict future weather. The first is to assume tomorrow will be the same as today. The second is to assume tomorrow will be the historical average of weather on the same day in previous years. The other is to use commercial predictive models.
According to this chart by Randall Olson, the first approach, to assume tomorrow is the same as today, is only the most accurate for a single day. Beyond that, the other two approaches are more accurate. Commercial forecasts are more accurate for about a week or so but finally lose to climatology, the historical average.
My takeaway is that when predicting the future, it is tempting to assume that everything is going to be roughly the same as it has always been.
Plus ça change?
There is nothing so old as warnings about modernity.
The Guardian. “We have always been modern, and it has often scared us”
Plato complained that the introduction of text would result in people knowing nothing (much like we now hear people complain that children know nothing without Googling it). The Christian church complained that the printing press would allow false information to be distributed.
So vexed was he by the interruptions of modern life in 1890 that in a Christmas letter Mark Twain wished “a heaven of everlasting rest and peace and bliss” to everyone but the inventor of the telephone.
It can be easy to conclude that the notion of accelerating change is bunk and a tune we’ve heard since before Darwin. Since we can look back at their quaint notions of rapid change and shake our heads, the same must be true of assertions of accelerating change now.
Is the world really changing faster than ever?
“We’re living in a world right now where disruption is constant and the pace of change is unrelenting. We can’t simply manage what is known, we actually have to lead into the unknown.”
Sara Kalick, Leadfully VP and General Manager
It is a common refrain these days: the world is changing faster and faster. Many say the world is changing exponentially. This idea underpins a lot of my motivation for starting this blog. My goal was to study innovation because of the unprecedented opportunity and peril that our rapidly changing world creates.
But is the world really changing faster than ever? And if so, can we predict the changes?
The impact on jobs
McKinsey, a global consulting firm, has studied the impact of technological change on jobs.
McKinsey found that in the United States in the years since 1960 both employment and productivity have gone up together 79 percent of the time. If one looks at longer periods of time, in three year periods since 1960 in the US, there has never been a period where employment drops as productivity rises.
The introductions of computers and the internet closed 3.5 million jobs in the US since 1980. But in the same period of time these technologies created jobs for over 19 million new jobs.
This makes the case that technological change has, to date, been a net positive in terms of employment. New technologies improve productivity which reduces the cost of goods and increases demand.
But is it different this time?
The statistics cited by McKinsey may bring a sigh of relief, but what happens to the displaced individuals. Do they all get jobs? Do they pay as well? And is it possible that the new age of artificial intelligence and automation will displace so many people that there won’t be enough new jobs to be found?
Could it be different this time?
The transition from an agrarian society to an industrial one took decades. The risk to individuals in the face of technological change really hinges on how fast the change happens.
McKinsey sees threats to jobs in manufacturing and retail and jobs that involve simple information processing like assessing credit risk for mortgage applications.
Seventy-five million to 375 million may need to switch occupational categories and learn new skills.
McKinsey Global Institute, Jobs lost, jobs gained, 2017
Ray Kurzweil, exponential vs s-curves
Our forebears expected the future to be pretty much like their present, which had been pretty much like their past. Although exponential trends did exist a thousand years ago, they were at that very early stage where an exponential trend is so flat that it looks like no trend at all. So their lack of expectations was largely fulfilled.
Ray Kurzweil, The Law of Accelerating Returns, 2001
Kurzweil’s argument is that any exponential curve, looked at for a short duration, appears to be approximately flat. Humans, being mortal, have a short perspective relative to history, so we can look at the pace of change and assume it is linear, when in fact it is exponentially greater than it was for our ancestors.
Kurzweil has used this premise to predict many technological advances, based primarily on the anticipation that processing power, data storage and transmission will get cheaper, faster and more abundant exponentially. This is a broad interpretation of Moore’s law.
Interestingly, Moore’s law has taken a bit of a hit lately, failing to predict the recent struggles the computer industry has had keeping up with historical doubling of the number of transistors in a dense integrated circuit every two years.
Kurzweil highlights the risk of assuming a curve is a straight line simply because it looks straight. Looking at the s-curve below, we can see that between -6 and -4 we could be forgiven for thinking that things were basically a straight line. Between -4 and -2 things look like they are going exponential. And then later between 0 and 2 (where we might be with semiconductors) things are stop obeying the exponential predictions.
Whether exponential or logarithmic or other, these mathematical curves we use to describe reality are only as good as long as they trace reality. We can’t assume that any kind of change will match a curve in the future simply because it has in the past.
More curves than a mountain road
u.s. technology adoption Rates, 1900–2014
I talked about the experience curve recently. There is often an explosive adoption of new technology once demand is recognized and manufacturers drive the price of it down with innovation and volume. As the BlackRock chart illustrates, many new technologies are being adopted far faster than they were in the early 20th century.
These curves are not exponential, as they round off as they reach the top, but they are rapid and one could argue that they are happening in faster succession.
The future is here. Well actually there.
“The future is already here — it’s just not evenly distributed.”
William Gibson
Not only is the shape of the future uncertain, but its distribution is also uneven. Yes, we may have self driving cars in ten years, but where? Yes, we may have robotic building construction in five years, but where?
This makes it doubly hard to know how how and when change will affect us – or the organizations we form – directly.
Call me a coward but there are too many cross currents for me to say whether the world is changing exponentially or not. Even the premise itself is vague. It implies the changing world can be described by some rise over run, like the pitch of a roof, curving ever faster into the sky.
What is the rise? What is the run? If we get specific, like we do with Moore’s law, we can be accused of cherry picking to support our argument. Nah, I’m going to keep things simple.
The world is changing. I think we can agree on that. So what do we do?
Leashing the wolves at the door
New technologies are being deployed that will change our world. This is a fact. Based on this fact, McKinsey and others see a future where many kinds of work are disrupted, perhaps to the tune of 375 million people worldwide who will not only lose their job but also lose the prospect of finding the same job again. These job losses will be ushered in by technologies and process that improve our productivity, promising greater abundance – the good with the bad.
This creates a huge opportunity but also a huge risk to our existing jobs and organizations.
Innovation is the answer to this challenge. Innovation paves the way for that abundant future. As individuals and organizations, we need to be innovative, which means making useful changes.
I define innovation as making changes that make improvements. There are two components to this. The first is iteration – making small steps without assuming that the result is obvious or easily achievable. The second component is getting feedback at regular intervals. Both of these impulses are reactions to classical project management that assumed that the outcome could be known and planned for for the entirety of a project. The longer the duration of a project and the larger its scope, the less likely these assumptions can be, particularly in a world that is changing rapidly.
The techniques described in these pages, Design Thinking, Lean Startup, etc, all embrace the need for speed and feedback. This is the purpose of this website, to allow me to explore and share the techniques of innovation.
Together we can leash the wolves at the door.
Download a free printable guide to SCAMPER
Developing the quantity and quality of ideas needed to innovate in a rapidly changing world can be daunting. I’ve prepared a free guide to producing more ideas than you thought possible to initiate the innovations you need in your job, your business and your life.
Click here to get your free downloadable guide to the SCAMPER’s 7 Powerful Idea Generators, which includes seven examples of idea generation involving Reese’s Peanut Butter Cups. Delicious!
Photo credit
Photo by stephan sorkin on Unsplash
