Argument

Then They Came for the Lawyers

Technology has already driven blue-collar workers into the underclass. Professionals may be next.

John Tomac illustration for Foreign Policy
John Tomac illustration for Foreign Policy

Strange though it may sound, there was a time when manufacturing work resembled professional work today. In the 18th century, on the cusp of the Industrial Revolution, life wasn’t bad for skilled tradespeople, who enjoyed a remarkable level of freedom and flexibility in performing their work. They represented a relatively well-off, aspirational class.

Then came the machines. The mechanization of manufacturing transformed industrial work. Because the new machines cost lots of money, bosses kept a close eye on workers to make sure they were doing their jobs and taking good care of the equipment. Over time, this monitoring allowed industrialists to rejigger production in efficiency-enhancing ways. Where manufacturing work was once something of an art that relied on knowledge built up through apprenticeship and experience, it became a highly scripted slog, broken down into repetitive tasks. Anyone could do it, and so the special status and relatively high wages once enjoyed by manufacturing workers disappeared. Laborers became mere extensions of the machines, handling tasks the machines could not — until, eventually, they could.

Across advanced economies, the professional class — white-collar workers in business management, technology, law, finance, and medicine — has largely escaped the ill effects of the recent changes, including rapid globalization and automation. Those changes have disproportionately hurt workers engaged in routine sorts of tasks — running machines on factory floors or carrying out back-office jobs — and those without a college (or especially a graduate) degree. Conventional wisdom long held that this immunity was likely to continue. A paper published by Carl Benedikt Frey and Michael A. Osborne in 2013, which famously estimated that 47 percent of job categories would be vulnerable to automation in coming decades, ranked positions such as manager, engineer, and lawyer among those at lowest risk of displacement.

But the forecast for highly skilled workers is starting to look less sunny. The professional world is about to be transformed by artificial intelligence. As that process unfolds, it could reshape white-collar work much as industrialization transformed manufacturing.

Anyone who has ever interacted with companies such as Amazon, Google, and Facebook is already familiar with AI, whether they know it or not. Every time we like a friend’s photograph, send an email, or search for a good nearby restaurant, we provide massive amounts of data to those firms. The tech giants use that data to train machine-learning programs to provide us a more customized experience. That process, in turn, allows the firms to sell us more stuff or to sell more advertisements to others who want to sell us more stuff.

But the same techniques that generate the ads that follow us around the web are increasingly finding their way into the workplace, as Ajay Agrawal, Joshua Gans, and Avi Goldfarb write in their new book, Prediction Machines. Many firms already rely on AI to help them assess business risks (to tell banks which borrowers are most likely to default, for instance) or anticipate consumer demand. And new applications are appearing all the time.

Employers already amass piles of human resources data on their workers: the roles they have held in the company, for instance, and the way both they and colleagues around them performed in each. Machines that are fed that information on a large scale can divine what sorts of worker characteristics are most associated with high team performance — and, therefore, which job applicants are most likely to thrive within the company. By chewing on reams of detailed career histories, machine-learning programs can predict whether a worker on a particular career arc should be promoted or directed toward the exit — or whether a valued employee is about to quit. They can be given sales data and asked to predict which accounts can be made to generate more business and which are dead ends.

Yet this is only a start. According to Agrawal, Gans, and Goldfarb, machine prediction will get steadily better and vastly cheaper. Those advances will give companies incentives to more aggressively find ways to deploy AI — and more concertedly collect information on their employees to feed into those systems. The result will likely be much closer monitoring of workers of all sorts.

As consumers, we have grown accustomed to being constantly observed by Silicon Valley. Our GPS-enabled phones keep tabs on every place we go, and the apps we use register our every communication and online purchase. But the same constant background tracking we tolerate as the price of a modern digital existence will soon follow professionals into their offices. Companies can be expected to more closely monitor their high-value workers, as well as their digital communications. AI promises to make profitable use of the information companies already collect from workers’ email and internal office messages. Business processes not conducted in digital formats will be pushed in that direction, the better to mine them for data. That includes even physical movements and interactions within the office — perhaps subtly at first, through the tracking of key fobs, for instance, but more overtly as the value of such data demonstrates itself.

Though creepy, this intrusiveness may yield important benefits for workers themselves. If too much overwork — or even simply too much sitting — reduces productivity, the machines could perceive it. Algorithms could pick up risk factors for harassment and abuse or detect biases in a company’s hiring and promotion practices. Systematic analysis of performance should help weed out those skilled at free-riding on others and could nip the Peter Principle — whereby workers tend to be promoted past the level of their competence — in the bud. AI analyses will prod companies to abandon bureaucratic practices that do not actually improve workers’ performance.

The better companies become at understanding what their employees are doing, the more interested they will be in applying lessons. Digital assistants — picture the workplace equivalent of Amazon’s Alexa — might initially offer suggestions to workers: background reading relevant to a worker’s task, for example, or suggested edits to shared documents. But over time, digital guidance will apply to a greater share of professional work and will become more insistent.

Just as assembly lines transformed artisanal work into a series of rote tasks, the AI revolution will allow companies to analyze and deconstruct white-collar work into simpler, more efficient pieces. The discretion workers now enjoy to decide when and how to meet with clients, brainstorm with colleagues, or manage employees will erode, first as companies push for increased structure in such settings to gather better data and then as AI analyses begin specifying how often to schedule client interactions and what form they should take, how best to run a meeting, and how to match workers with tasks. This process, once begun, will take on a life of its own, much as the mechanization of industry did. The more systematized office work becomes, the more opportunities there will be to gather data and further hone what was once an amorphous, freewheeling, high-status business.

White-collar jobs will then bifurcate into a smaller, better-compensated group of elite professionals and managers who oversee the deployment of new technologies and a larger, less differentiated professional class. Good jobs in finance that now require the flexibility to shift among different sorts of tasks — from interacting with clients to building statistical analyses to writing reports and presenting ideas to managers — will become less cognitively demanding and less exclusive as AI takes over some tasks and sets detailed guidelines for others. Those a rung or two up the ladder, overseeing how new technologies are used or faced with complex judgment calls too tricky to be put to an AI, will find their skills in high demand and will be able to bargain for an outsize share of the profits generated by this transformation. Not all professions will be affected equally. Fields such as law and medicine, which are more highly regulated and protected by powerful professional organizations, may be slower to change. But the pressure to keep down costs, and to limit liability risks, will help push the use of AI in such places.

Less fortunate professionals, like the once-proud craftsmen and women of the 18th century, will find their jobs less cognitively and financially rewarding. They will become human cogs in a broader machine — like the dismal laborers on assembly lines repeatedly affixing the same part to the same place on car after car after car. This shift will come as a shock to many professionals, for whom technology has up until now been an empowering force. Past innovations, from database and statistical software to email and cloud computing, helped eliminate tedium and annoyance from professional lives, leaving such workers better able to do the more interesting and challenging parts of their jobs. Now, the good bits of such work, including broad strategic thinking and interacting with others, will be made more tedious so as to raise productivity and cut costs. White-collar workers who might previously have scoffed at the utility of trade unions or fundamental critiques of capitalism may change their tunes.

Ironically, professionals will push the process along in their capacity as consumers. Just as the mechanization of manufacturing made things such as clothing far more affordable, the AI revolution should result in a world of cheaper, better, more accessible professional services. These advances will come at a cost, however. First, everything will get a little stranger, such as when you meet your friendly doctor or accountant and then learn that she may be little more than the puppet of an AI, which keeps tabs on the meeting and whispers guidance into her ear — or handheld tablet — from what substantive questions to ask about an individual’s case to the names of family pets to mention during an allegedly casual conversation. Office technology will also track your reactions to help determine, for instance, which sorts of verbal probes elicit more expansive or useful responses. The economy has always rewarded those who are good at projecting, or faking, empathy and sincerity. The fakery will soon take on a scientific precision.

The most intriguing consequences, however, will be political. The mechanization of industry took a class of fairly content petits bourgeois, who had served as a source of political stability, and turned them into a class of machine-smashing radicals: the source material for trade unions and revolutionary political parties. Professionals, similarly, have been a bulwark against economic radicalism in recent decades — none too fussy so long as stock and home prices keep moving upward and politics remain relatively predictable. Should the spread of AI deprive them of their autonomy, and of what many of us see as a basic right to job security and growing incomes, the political world could be turned on its head.

That is something to consider the next time you ask Alexa to order more dog food. Before long, it will join you at the office. And before you know it, the antics of the Luddites, who smashed mechanized weaving machines in a futile effort to save their livelihoods, may not seem so foolish after all.

This article originally appeared in the July 2018 issue of Foreign Policy magazine.

Ryan Avent is an economics columnist for the Economist. @ryanavent

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