Futurism is a means to see beyond COVID-19. Here's how to time travel

This piece originally appeared on The Innovator News.

Famed futurist and author, Arthur C. Clarke, said: “Any sufficiently advanced technology is indistinguishable from magic.” This gem has been used in board rooms, server rooms and classrooms alike to help open minds and inspire curiosity, to challenge orthodoxies and overcome inertia and incrementalism.

Wide-eyed optimists will tell you that the future is clear. Transparent and entirely predictable. Cynics and skeptics, on the other hand, see the future as opaque. Unknowable.

Neither is quite correct.

The future is translucent. We can make out the shapes and shadows, but not the fine details. The pragmatist sees futurism as a strategic discipline: a means of harnessing tailwinds, dodging headwinds and setting a more intentional course towards a preferred tomorrow.

The COVID-19 crisis is shining a Klieg light on the immense challenge leaders face in planning for the future amid extreme uncertainty. In parallel, new technologies of the Fourth Industrial Revolution, such as artificial intelligence (AI), cloud solutions and robotics are changing the way we live, learn and do business at a rate unprecedented in human history. These historic changes, considered within the increasingly urgent context of shifting political landscapes and environmental instability, suggest that now, more than ever, leaders need tools to help them understand the future beyond the near-term and plan accordingly.

But where to get started?

As it turns out, futurists are closet historians. We look back to make sense of our journey to the current, and more specifically, to plot the trendlines that can help us chart our course towards what might be coming next. The trick with trendlines, though is that they are not created equal.

Linear: these trends are the province of traditional strategists and forecasters. It’s tempting, in futurism, to over-index towards linear trendlines because they’re intuitive and typically characterize already sizeable phenomena. For instance, the cost of housing, worldwide is “high, and getting higher”, which makes for attention-grabbing headlines.

Exponential: The thing is that most complex adaptive systems, whether they be natural or human-made, aren’t linear. They’re curved, or more precisely, exponential. In plain terms, exponential trends start small, sometimes imperceptibly so, but repeatedly self-reinforce so as to become material, and eventually, game-changers. The forces that have most shaped society tend to follow exponentials: compound interest, population growth, Moore’s law and climate change, to name a few.

Cyclical: From a too-close vantage point, everything looks like a linear or an exponential. Pull the lens back, however, and some trendlines show themselves to ebb and flow over time between local maxima and minima. Consider petroleum, the price of which fluctuates not just due to cyclical demand, but to sporadic breakthroughs in exploration, extraction and refinement.

Taken together, we can use these three types of trendlines to broadly characterize virtually any natural, social or technological trend.

Armed with an understanding of trends to date, we can project forward, considering relatively non-controversial probabilities and debatable possibilities.

As available data (and the trendlines able to be drawn) increases, we find ourselves with a conundrum: a single “general relativity” model accounting for all historical data and future projections becomes an impossibility. Consider this illustration below.

In 1971, economist Herbert Simon argued that as information approaches infinity, our ability to meaningfully attend to it approaches zero. Or in other words: nobody likes an eye-chart.

To resolve this problem, a domain-specific filter can serve as a liberating constraint, reducing noise and resulting in a projection space that is both more understandable and, more importantly, more useful. To be clear: rather than attempt to see a single holistic future, we can apply, say, an education filter to focus our attention on the subset of trends and projections most relevant to education.

Taken together, these three tools ­– trendlines, projections and filters – create what we call a Longitudinal Emergence Scatterplot (LEnS), which provides us with a clear and compelling rubric for thinking about where domain-specific futures are headed.

As with most things future-focused, there is no “one right filter” for any given foresight exercise. Just as different business models, industries and cultures produce richness and resilience, multiple filters move us beyond homogeneity and towards a more nuanced understanding of what may lie ahead.

Futures discussions can often feel like a blizzard of buzzwords – many of them technical and most of them faddish. The long lens shows us that, since Charles Babbage and Ada Lovelace’s first computer design in the 1840s, the entire history of information technology has been a surprisingly clear story of progress along these three enduring layers: interaction, information and computation. We would assert that the future of information technology will continue to be a story of progress along these same three enduring layers.

Simplicity: mind the (digital) gap: Computer users once required PhDs. Today, basic language skills suffice. While underlying enabling technologies grow more complicated, the reach, accessibility and usability of information technology continues to grow exponentially. Leaders would be wise to plan for a world where every interaction is mediated through a technological interface. Will tomorrow’s public services be designed in such a way as to require digital IDs? AR glasses? It may be critical to ensure that access to human necessities don’t gradually begin to presume, much less require, the availability of commercial technological niceties.

Intelligence: teach your (digital) children well: The rise of machines is already well under way and accelerating. Popular science fiction tends to make this a story about malevolent sentience – mechanical minds as supervillains with dark agendas. In truth, software has always been neutral, manifesting the explicit orders and tacit biases of its developers. How can we develop artificial intelligences that embody our explicitly shared financial, social and ethical values? By training them to do as we say, not necessarily as we’ve done.

Abundance: invest in moonshots: Though the challenges we face are indeed becoming more complex, our collective ingenuity appears to be evolving faster still. Our historical, and projected, capacity to create game-changing solutions – from stone tools through to quantum computing – gives us an edge in responding to emergent perils. Leaders should consider allocating time, mindshare and money for moonshots – projects that might not help us compete today, but rather, create better tomorrows.

That’s not just stewardship. That’s leadership.