Does the value of a job lie in how long it resists automation?
Over the course of the pandemic, I saw a growing wave of mealtime deliveries: riders whizzing by silently on electric bicycles, ferrying takeout meals to folks in my urban neighborhood who don’t want to venture out of their homes. Under constant pressure to pick up and deliver meals before they go cold, these delivery workers toil for some of the lowest wages on offer.
In the past, delivery was an entry-level position, a way to get a foot into the door, like working in the mail room. Today, it’s a business all on its own, with gigantic public companies such as Uber and Deliveroo providing delivery services for restaurant owners. With that outsourcing, delivery has become a dead-end job. Success means only that you get to work the day shift.
Just a few years ago, we believed these jobs would be gone—wiped out by Level 4 and Level 5 autonomous driving systems. Yet, as engineers better understand the immense challenges of driving on roads crowded with some very irrational human operators, a task that once seemed straightforward now looks nearly intractable.
Other tasks long thought to be beyond automation have recently taken great leaps forward, though. At the end of June, for example, GitHub previewed its AI pair programmer, Copilot: a set of virtual eyes that works with developers to keep their code clean and logically correct. Copilot falls short of a complete solution—it wouldn’t come up with a sophisticated algorithm on its own—but it shows us how automation can make weak programmers stronger.
It won’t be long before massive AI language models like Microsoft and Nvidia’s Megatron-Turing Natural Language Generation (MT-NLG) make short work of basic business copywriting. Other writing jobs—digesting materials to extract key details, expressing them in accessible language, then preparing them for publication—are also surrendering to automation. The elements for this transformative leap are already falling into place.
While it’s unlikely that most programming or copywriting will be done by machines anytime soon, an increasing portion will. Those professions now face real competition from automation. Paradoxically, bicycle-based deliveries look likely to need a human mind behind the handlebars for at least the next several years.
In a world where software eats everything in sight, those bits that can’t be digested continue to require human attention. That attention requires people’s time—for which they can earn a living. What we pay people for performing their jobs will increasingly be measured against the cost of using a machine to perform that task. Some white-collar workers will, no doubt, suffer from these new forms of competition from machines.
A century ago, farm labor faced a similar devaluation, as agriculture became mechanized. And while countless manufacturing jobs have succumbed to factory automation over the decades, Tesla production hiccups reveal what happens when you try to push automation too far on the factory floor. As the history of the Luddites so aptly demonstrates, the tension between machines and human labor isn’t new—but it’s growing again now, this time striking at the heart of knowledge work.
To stay one step ahead of the machines, we’ll need to find the hard bits and maintain the skills required to keep crunching on them. Creativity, insight, wisdom, and empathy—these aptitudes are wholly human and look to remain that way into the future. If we lean into these qualities, we can resist the competitive rise of the machines.