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Technological Progress and the Perpetual Learning Curve

As technology evolves at warp speed, so must engineers

3 min read

Technological Progress and the Perpetual Learning Curve
Photo-illustration: Ian Spanier/Getty Images

opening image Spectral LinesPhoto-illustration: Ian Spanier/Getty Images

I talk to my iphone 6 Plus to compose text messages, and I’m not alone. Many people are abandoning typing, which can be hard on the hands. Why let your fingers take a pounding when you can get what you want by speaking?

Thanks to steady improvements in voice recognition, orality—vocal expression—is unexpectedly reviving. But we aren’t speaking more with other people; telephony between humans is collapsing. Instead, we speak to our digital devices as never before.

The shift is part of a broader transformation in everyday life. Our artifacts are in upheaval. Compact discs are being creatively destroyed by streaming music. Tickets to sports and other entertainment events are now images on a smartphone screen. Keys and ID cards are vanishing, replaced by biometrics.

Even learning to drive an automobile is on the endangered list. Neither of my two 20-something children has a driver’s license. My son says he may never get one. “Between Uber and driverless cars,” he says, “I won’t ever learn to drive.”

Typing and driving—both courses I took in high school in the 1970s—are no longer part of the skill set that defines adulthood. For engineers who design and build the platforms upon which human life depends, rapid and radical changes demand a response. Engineers, to stay relevant and employable, must change along with the rest of us.

Consider the shifts in coding, a lucrative field that, despite high pay and status, seems addicted to change. Take the size of coding teams. When I wrote a book 20 years ago about the making of Windows NT, the 250-person team that wrote the massive program at Microsoft seemed to foretell the size and shape of software work to come. Success as a programmer in megateams depended on finding a niche, on hyperspecializing and fitting in. Today smaller, agile teams of as few as 15 coders instead value versatility.

Solo coding, meanwhile, was once the stuff of legends. Gates, Brin, and Zuckerberg all wrote initial versions of popular programs. Today, the lone-wolf coder, fueled by a me-against-the-world sensibility, is an endangered species.

Pivotal, a large software house in San Francisco, has halted solo programming, opting instead for two-person teams that enable each programmer to monitor the other. The buddy system seems to boost reliability and reduce the tendency of coding “divas” to hold projects hostage.

Programming languages also go in and out of style and shape career trajectories. Some languages rise and fall on the fortunes of particular hardware platforms, but whatever the cause of the fluctuation in popularity, it’s vital to engineers to be able to see the trends. It’s no surprise that IEEE Spectrum’s Top Programming Languages app has tens of thousands of views each month. Coders want to know what languages are hot.

Development paradigms also shift. Consider the DevOps paradigm, which seeks to overcome barriers between development and operations. Since its debut in 2009, DevOps has rapidly gained followers.

The imperative to keep pace with change unites engineers with other humans. Change isn’t always a journey into the unknown; at times, the experience returns us to the familiar, to older, even dormant practices. By enabling our new digital tools to harvest best practices from the past, we all benefit.

Whatever the speed, we can still go home again—at least until the next adaptation is impossible to resist.

About the Author

G. Pascal Zachary, a professor of practice at Arizona State University’s School for the Future of Innovation in Society, is the author of Showstopper! (1994), on the making of Windows NT.

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