In Rise of the Robots, Martin Ford describes how smart machines are starting to replace human workers in more and more kinds of work, raising the threat of a “jobless future.” He sees no alternative but a government-guaranteed basic income to support the millions of people who will have trouble finding jobs.
Although I have described Ford’s outlook as bleak, I am not opposed to letting machines do the work they can do better and more efficiently than humans. Nor am I opposed to strengthening the social safety net to assist workers who are most hurt by this transformation. What I do want to suggest is that Ford’s focus on job destruction alone may miss some of the more positive possibilities of the information economy.
Where does displaced labor go?
In the late nineteenth and early twentieth centuries, the United States experienced a dramatic decline in farm jobs, as we transitioned from a predominantly agricultural society to an urban industrial society. That was a hardship for many former farmworkers, who had to move to the city and compete with other displaced workers for whatever wages they could get. The “Great Migration” of African Americans from the rural South and waves of mostly Southern and Eastern European immigrants heightened the competition and provoked racial and ethnic conflict.
Nevertheless, the situation did not result in protracted high unemployment. The expansion of manufacturing and service industries absorbed most of the displaced workers. In addition, a lot of human activity was shifted away from employment by shortening the work week, phasing out child labor, and encouraging retirement. The Fair Labor Standards Act of 1938 reduced the standard work week to 40 hours and established minimum wages and overtime pay for many workers. These measures distributed the available work more widely, while keeping incomes up. The expansion of leisure also helped create job opportunities in leisure industries such as travel.
Ford would say that the ways we have avoided high unemployment in the past are no longer relevant, since we now face worker displacement on a much larger scale. As an artificial intelligence expert, he is focused more on how machines will work than on how humans can create and distribute work for themselves. He is very imaginative when it comes to what robots might do, but less imaginative when it comes to how humans might adapt. He seems to be making a couple of simplifying assumptions: first, that there’s only a fixed amount of work to be done; and second, that whatever work remains for humans after the robots move in must be hogged by the few rather than shared by the many. If these assumptions are wrong, then the job outlook isn’t as bleak as he makes it out to be.
Will humans still work?
IBM used to have a slogan: “Machines should work; humans should think.” Now that the machines are getting smarter, that distinction is harder to defend. Martin Ford’s slogan might be: “Machines should work and think, and a lot of humans should just consume.”
That’s a little unfair however, since Ford himself acknowledges that computers think only in a very limited and specialized sense. “Even IBM’s Watson, perhaps the most impressive demonstration of machine intelligence to date, doesn’t come close to anything that might reasonably be compared to general, human-like intelligence. Indeed, outside the realm of science fiction, all functional artificial intelligence technology is, in fact, narrow AI.” Ford is also more reluctant than other computer scientists to assume that general machine intelligence is inevitable or even possible, or to speculate that robots will soon have the ability to rebel against their creators.
Some brilliant philosophers and scientists have questioned whether a dead machine could ever have the capabilities of any living organism, let alone a human one. (See, for example, Robert Rosen’s Life Itself.) Arguments for continuing to distinguish ourselves from machines are not confined to religious traditionalists or Cartesian dualists who look for an immaterial soul within an otherwise mechanical system. They are made by scientists like Stuart Kauffman, who have a less reductionist and mechanistic conception of nature itself. Many thinkers reject the idea that the universe in general and human thought in particular is reducible to algorithm (computational procedure). Philosophers in the Whitehead tradition argue that each human experience is a unique creation that synthesizes a multitude of past experiences. We should be careful about extrapolating from what we know well–our own machines–to aspects of the natural world that remain deeply mysterious to us.
Granted that computers can simulate human work by detecting patterns in what humans have already done. That includes existing works of art. A computer has “already produced millions of unique compositions in the modernist classical style.” But humans invented that style by experiencing and expressing modernity. The human creative work is a synthesis and expression of lived experience. The machine simulation is a meaningless exercise because the dead machine has no lived experience to express.
Ford says that narrow intelligence is all that the machines need in order to do most human work, since “the tasks that occupy the majority of the workforce are, on some level, largely routine and predictable.” But maybe the reason why so many jobs have been routine and predictable is precisely because we haven’t had anyone besides humans to do the boring work until now. Now that we can automate more of what we’ve been doing, how do we know there isn’t some new frontier of creative activity for humans to explore?
The distribution of human work
Suppose Ford is correct that half of the existing work could be done by machines. We can still imagine the future economy in more than one way. At one extreme, half of the human workers keep their existing jobs, while the other half become unemployed. At the other extreme, the human work is distributed among the same number of human workers so that each works half as much as before. The benefits of the new technology are taken the same way they were in the twentieth century–in the form of higher productivity and more leisure.
Because Ford is focused on replacement of the human worker, he plays down the possibility of productive collaboration between human and machine. The machines are out to get your job, and if you work with them you will be helping them learn to do so. “If you find yourself working with, or under the direction of, a smart software system, it’s probably a pretty good bet that–whether you’re aware of it or not–you are also training the software to ultimately replace you.” But that is true only to the extent that the work is predictable, general intelligence is irrelevant, and the human touch is dispensable. Ford seems to vacillate between admitting that machines cannot do everything and talking as if they can.
A recent article in the New York Times was titled, “Meet the People Who Train the Robots (to Do Their Own Jobs).” It reported that some companies are asking their employees to train artificial intelligence systems to act more like humans. However, the workers who told their stories did not see their human role as very endangered. A travel agent who used A.I. to book hotels said, “It made me feel competitive, that I need to keep up and stay ahead of the A.I.” Using the system “frees me up to do something creative.” A customer service rep who was training a system to answer customer questions said that “she doesn’t foresee a future where she’s out of a job. Too many questions still require a level of human intuition to know the appropriate answer. There are also times when rules need to be broken, like when customers ask for an extension on their account because of some circumstances beyond their control.” The executive who developed a system for searching and analyzing legal documents said that he “doesn’t think A.I. will put lawyers out of business, but it may change how they work and make money. The less time they need to spend reviewing contracts, the more time they can spend on, say, advisory work or litigation.” As for myself, I have been using technology all my professional life to become more creative and productive, and I have trouble imagining any occupation where such collaboration couldn’t occur.
If most occupations allow for both human and technological input, the benefits of that collaboration could appear in some combination of higher output and reduced work hours. As with the twentieth-century technological advances, many workers could produce more while working less, and that would spread the available work to more people. How exactly this would be accomplished in our time I don’t know. It probably would not be as simple as legislating a new standard work week. But if the alternative is mass unemployment and paying people not to work, I think society will find a way.
Productivity and income reconsidered
The potential for human-machine collaboration calls into question Ford’s most basic contention, that artificial intelligence is a worker replacement and not a tool for raising worker productivity. In the “golden age” of the American economy, “As the machines used in production improved, the productivity of the workers operating those machines likewise increased, making them more valuable and allowing them to demand higher wages.” But those days are gone, along with a big chunk of the labor force. Are they really, or is it that we have not yet seen the social changes that would translate new technological capacities into worker benefits? Perhaps we are living in a period like the 1920s, just after the introduction of the assembly line but before the New Deal regulated wages and hours and recognized labor’s right to organize.
I return to a fundamental economic problem I raised in the last post. If technology can make us richer in output, then why should people settle for being poorer in consumption? In Ford’s imagined world of massive unemployment, government taxes the winners to provide a basic income to the losers. In an alternative vision, workers are typically technologically assisted, highly productive, and employed fewer hours so that many can work. They can become better off in two ways: winning a fair share of the benefits of their own productivity, and consuming goods and services that have become less expensive. Recall that assembly-line technology led to both higher wages and less expensive cars, a win-win for ordinary people. As in the past people will have to fight for such gains, and they will have to use the tools of democracy to get government on their side.
Another possibility explored by some futurists, such as Jeremy Rifkin, is that people who spend less of their time in paid employment will use new technologies to create goods and services to share for free. (If you find that idea absurd, you can start paying me for my posts right now!) The abundance of goods and services produced in the “collaborative commons” could reduce people’s need for money, softening the economic impact from reducing paid work hours.
In my final post on this book, I’ll explore possibilities for creating new jobs that robots are unlikely to do.