Companies need fewer and fewer employees to generate each dollar of profit. This trend is likely to continue. The best thing you can do is focus on areas which are less likely to be automated away: entrepreneurship, art, creation, ideas, and innovation. Let’s explore what is happening and what you can do about it.
Martin Ford begins his solid new book Rise of the Robots with the following anecdote:
Sometime during the 1960s, the Nobel laureate economist Milton Friedman was consulting with the government of a developing Asian nation. Friedman was taken to a large-scale public works project, where he was surprised to see large numbers of workers wielding shovels, but very few bulldozers, tractors, or other heavy earth-moving equipment. When asked about this, the government official in charge explained that the project was intended as a “jobs program.” Friedman’s caustic reply has become famous: “So then, why not give the workers spoons instead of shovels?”
It was nice of Ford to start the book with a chuckle, because the rest of the book made me progressively more anxious. I am not a political guy, but the question of how to deal with more and more technology, automation, and robots in the workplace is intriguing and important, especially since there is already great progress being made in Intelligent Automation in what many people are calling the ‘Age of the Digital Workforce’.
In this piece I highlight interesting trends in the amount of sales and net income earned by U.S. companies per employee. The bottom line is that fewer people are needed to earn each dollar of profit—and this relevant to profit margins, investors, and employees of all stripes.
Here are the sales and earnings generated by each employee since 1963. I only include companies that 1) reported data for the number of employees and 2) reported sales data. This covers roughly 38 million employees today. Key point: all data is inflation adjusted to 2011 dollars. Since the early 1990s, U.S. companies have generated significantly more earnings per employee.
As we saw with profit margins within sectors, there are very interesting trends for earnings-per-employee at the sector level. Between 1990 and 2015, earnings per employee grew by 100% or more in all sectors except materials. Technology companies earn 348% more per employee today than in 1990. At the end of the post, you will find the time series charts for all sectors.
This is all good for business owners and ongoing employees, but could be quite awful for those being replaced (in part or in full) by technology of various types.
This isn’t just an issue for blue-collar workers:
One widely held belief that is certain to be challenged is the assumption that automation is primarily a threat to workers who have little education and lower-skill levels. That assumption emerges from the fact that such jobs tend to be routine and repetitive. Before you get too comfortable with that idea, however, consider just how fast the frontier is moving. At one time, a “routine” occupation would probably have implied standing on an assembly line. The reality today is far different. While lower-skill occupations will no doubt continue to be affected, a great many college-educated, white-collar workers are going to discover that their jobs, too, are squarely in the sights as software automation and predictive algorithms advance rapidly in capability… The fact is that “routine” may not be the best word to describe the jobs most likely to be threatened by technology. A more accurate term might be “predictable.” Could another person learn to do your job by studying a detailed record of everything you’ve done in the past? Or could someone become proficient by repeating the tasks you’ve already completed, in the way that a student might take practice tests to prepare for an exam? If so, then there’s a good chance that an algorithm may someday be able to learn to do much, or all, of your job.
One of my favorite automation examples in the book was Japan’s Kura sushi restaurant chain:
In the chain’s 262 restaurants, robots help make the sushi while conveyor belts replace waiters. To ensure freshness, the system keeps track of how long individual sushi plates have been circulating and automatically removes those that reach their expiration time. Customers order using touch panel screens, and when they are finished dining they place the empty dishes in a slot near their table. The system automatically tabulates the bill and then cleans the plates and whisks them back to the kitchen. Rather than employing store managers at each location, Kura uses centralized facilities where managers are able to remotely monitor nearly every aspect of restaurant operations. Kura’s automation-based business model allows it to price sushi plates at just 100 yen (about $1), significantly undercutting its competitors.
But this automation moves up the chain quickly:
Narrative Science’s technology is used by top media outlets, including Forbes, to produce automated articles in a variety of areas, including sports, business, and politics. The company’s software generates a news story approximately every thirty seconds, and many of these are published on widely known websites that prefer not to acknowledge their use of the service. At a 2011 industry conference, Wired writer Steven Levy prodded Narrative Science co-founder Kristian Hammond into predicting the percentage of news articles that would be written algorithmically within fifteen years. His answer: over 90 percent.
Most of the proposed solutions to job and wage stagnation are of the top-down variety (re-distribution of wealth, guaranteed income, granting of capital (shares in a mutual fund), etc.). But I am more interested in the bottom-up solutions, in what individuals can do to better their financial health.
Our options are to spend less and/or earn more (or at least earn steadily).
The former is simple: embrace material minimalism. I find this can be quite liberating. This isn’t a personal finance website, so maybe check this out.
To achieve the latter, two things stand out.
First, we should be investors and strive for more widely dispersed ownership of financial assets. We should buy shares in the corporations which have benefited from these trends in efficiency and profitability. Wages have basically gone nowhere since I was born in 1985. The stock market is up 2,000% (nominally, but still).
Second, we should focus on developing talent in areas that machines will lag. Machines are fantastic at answering questions, but humans are still much better at asking and discovering the questions in the first place. Humans are still better innovators, creators, and artists. But even this advantage is in doubt:
Most of us quite naturally tend to associate the concept of creativity exclusively with the human brain, but it’s worth remembering that the brain itself—by far the most sophisticated invention in existence—is the product of evolution. Given this, perhaps it should come as no surprise that attempts to build creative machines very often incorporate genetic programming techniques. Genetic programming essentially allows computer algorithms to design themselves through a process of Darwinian natural selection. Computer code is initially generated randomly and then repeatedly shuffled using techniques that emulate sexual reproduction. Every so often, a random mutation is thrown in to help drive the process in entirely new directions. As new algorithms evolve, they are subjected to a fitness test that leads to either their survival, or—far more often—their demise. Computer scientist and consulting Stanford professor John Koza is one of the leading researchers in the field and has done extensive work using genetic algorithms as “automated invention machines.” Koza has isolated at least seventy-six cases where genetic algorithms have produced designs that are competitive with the work of human engineers and scientists in a variety of fields, including electric circuit design, mechanical systems, optics, software repair, and civil engineering. In most of these cases, the algorithms have replicated existing designs, but there are at least two instances where genetic programs have created new, patentable inventions. Koza argues that genetic algorithms may have an important advantage over human designers because they are not constrained by preconceptions; in other words, they may be more likely to result in an “outside-the-box” approach to the problem.
I am no Luddite. I cannot wait to see what the future of technology holds–we will no doubt benefit in innumerable ways. But we will be better off if we spend less, invest the savings, and seek jobs that are about art, creation, ideas, and new combinations. Doing so, we stand the best chance of being protected as the robots continue to rise.
p.s. I will be writing more about the act of creation and innovation in the near future, so stay tuned.