The world we live in may be crazy awesome, but it’s not nearly as futuristic as people fifty or a hundred years ago thought it might be.
In fact, in many sectors it seems like progress has somewhat stalled, and products and services look much like they did thirty years ago—this process, or lack thereof, has been called the great stagnation. But there’s one area where innovation continues to accelerate: technology, specifically computer science. If the great stagnation is indeed real, it’s computer science and software engineering that’s going to end it and get us back on track towards the future. But, we need more geeks!
If you look at the history of computer science, it’s been limited to solving computational problems—that is, hard math problems that we need computers to help us solve. As processors got faster and the field became better-developed, we solved harder and bigger problems. Sites like Google or Amazon couldn’t exist without both powerful processors and skilfully written code. But the problems that both sites solve are fundamentally computational—that is “how do we do stuff to these big sets of digital data that we already have?”
We have so many big problems in society begging to be digitized and computed, but only so many computer scientists.
It’s said that to a man with a hammer everything looks like a nail but now that we’ve begun to master solving computational problems in the digital world, we’ve got to find solutions by thinking beyond only computation. Take the human genome project: when Watson and Crick discovered the structure of DNA, they were doing science with a microscope and lab coats. But the people analyzing DNA spend much of their time writing software and performing computational analysis while lacking the imagination of Watson and Crick who started it all.
Take public policy: it used to be that believing a course of action was the correct one was sufficient, but now evidence-based policy is (or should be) making governance decisions computable problems—when we collect so much data and have specific goals, we can do some computer science to lead the way forward. This trend, where old problems turn digital and are solved at a fraction of the cost, has been called de-materialization.
At T4G, we’re very excited about this process in the context of marketing. Companies like Google, Facebook, and LinkedIn are making billions by looking at advertising as a computational problem. It’s absolutely the right step, but it’s also the first step. These companies have engineering cultures through and through, so it follows that the ads they display are very engineer-like. Google’s ads are just-the-facts, based on what you’re searching for, facebook’s ads (rightly or wrongly) correspond to whatever you’ve been chatting about, and LinkedIn’s ads (job postings) map to the tags you use to describe yourself on your profile. These ads are starkly different than some of the brilliant and touching ads that we’re used to.
Clearly, we need to split the difference.
Traditionally it’s been argued that this wouldn’t really be possible: that software engineers and creative types are fundamentally different, and we’re not going to find people with both skillsets in equal measure. Fortunately, the world has proven this position wrong: as shown by a blog about statistical analysis of online dating. Really. Think of the skills required to put this together? Deep computational and mathematical expertise married with creative and funny writing—exactly the combination we should be looking for.
These people—or small teams capable of producing the same results—are currently in short supply. But that’s going to change. It has to. We have so many big problems in society begging to be digitized and computed, but only so many computer scientists. Further, our current computer scientists are computer scientists to the bone. What we need are people (or groups) with computer science fundamentals, but also other expertise. For example, bioinformatics needs computer science married with biology, public policy needs computer science mixed with political science and policy administration, and marketing needs computer science mixed with a customer point of view.
These are the types of people who will understand not just what sectors in the midst of stagnation need to do, but how to apply technology to best solve old problems anew. There’s already a name for this type of person, even if they’re in short supply: growth hackers. If we had perfect educational foresight and students with balanced left brain/right brain capabilities we could graduate a generation of very employable growth hackers. It turns out however that real humans tend to be more comfortable with one type of thinking or another. In the real world, individual growth hackers may be few and far between but interdisciplinary teams—groups of teams with a mix of hard skills and soft skills are easy to create and have the potential to drive economic, technological, and social growth for the benefit of us all.