The 1982 DeLorean is coming back. Thanks to a warehouse full of never-used parts from the original run, the remnant of the Humble, Texas-based company will produce a few hundred new replicas of the 34-year-old sports cars with their distinctive stainless steel exterior and gull-wing doors.
It is life imitating art — a Back to the Future move to redress past wrongs and mistakes. And we were reminded of those missed predictions in considerable detail last October on the date predicted in one of the movie’s sequels. The nostalgia was tinged with regret and the question: Why didn’t we get that stuff done? The market responded by rushing “hoverboards” to store shelves in time for the holiday gift-giving season. They were, in fact, self-balancing scooters with lithium-ion batteries. And the cheap ones came with an undocumented feature — a tendency to overheat, ignite or even explode.
As for the “new” DeLoreans, the replicas of cars that originally sold for $50,000 in 1982 will now demand $100,000. Compare that to $80,000 for a Tesla Model X. Both are spendy and, arguably, out of reach in many cases, but in this instance, the past is selling at a 20 percent premium.
And there you have it: cautionary analogies for how we seem so much better at confronting yesterday’s future than the future as it faces us today. Go fast, go cheap and get burned, or choose something that seems familiar but is obsolete, and forego the advantages that come with innovation.
We are well into a season of innovation and technological advancement, much of which is well known: the cloud and the Internet of Things; big data and analytics; and ubiquitous networks and the Semantic Web. Together, they have helped usher in what one major player calls an era of cognitive computing, combining thinking networks and thinking machines. You know some of the members of this cognitive family by their first names — Watson, Alexa, Cortana and a social animal known by its initial, M.
Machines that think, reason and understand can (and are) coming alongside public servants who have invested their careers to realize operational efficiencies (finance, administration and eligibility), improve service delivery (including mobile and social), and open government (transparency and open data).
These machines are even disrupting the disruptors. The movement that grew up around open data is in transition. Civic hackathons have largely run their course, and the catalytic forces behind them — including the Sunlight Foundation and Code for America — are working to reimagine the civic tech community for what comes next.
According to research by Silicon Valley analyst Nadia Eghbal, public data projects and data science are, for the most part, not venture backable. Writing in Medium, Eghbal reports, “Funders were skeptical. They didn’t understand why these projects mattered.”
Against that backdrop, it’s worth noting that more than half of the inaugural GovTech 100, published here in January, have overcome venture capitalist skepticism. Fifty-seven of them have attracted venture funding because they are combining and interrogating formerly disparate data to solve problems for government in ways that government is not doing or cannot do for itself. With an average age of 9 years, the companies on the index also demonstrate a stick-to-itiveness in maturing ideas that may have been sparked during pizza- and caffeine-fueled weekends of code cutting into viable solutions. In a volatile market, time and track record also have a way of winnowing out the equivalent of cut-rate hoverboard makers.
Public officials and policymakers have options. It would be a pity for them to revert to DeLorean-style thinking and double down on the familiar at the cost of missing a larger, future-leaning opportunity. Combine thinking machines with thoughtful people who bring curiosity, entrepreneurial drive and a determination to make things better — and it may just be enough to outthink and outhustle the vexing challenges of governing in the second decade of the 21st century.