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Don’t Be Afraid of Robots: Technology is What We Make of It

Rapid technological change, if it is even happening, does not necessarily need to lead to mass unemployment or even major disruptions in people’s lives. In all cases, new technology is what society makes of it — that is, it should be used to broadly improve lives and work, not reorient the world around the technology itself or redistribute wealth upwards. Ride-hailing services like Uber and the promise of self-driving cars illustrate both sides of this point; polices like a job guarantee provide a path forward.

Illustration: Heske van Doornen

Don’t Be Afraid of Robots: Technology is What We Make of It

By Kevin Cashman

There is a lot of talk of the rapid development of technology leading to changes in the way people work as well as mass automation and thus mass unemployment. However, the data generally don’t support this story (the most recent data being a notable, but very limited, exception). Nevertheless, the story has currency among the public and politicians, in part due to the novelty and allure of technology — and the political power of its promoters. Throughout recent history, the promises of revolutionary technology have captivated imaginations but also come up far short. Instead of flying cars, there are apps for refrigerators and ordering cat food over the internet.

It is important to note that the gains from technological advancements do not necessarily need to go to the rich or lead to mass unemployment. If shared fairly, the gains could lead to social benefits, such as increased social services, and broad individual gains, such as more leisure time. And there can be concerted action to help those directly affected by technological change. While there are many policies that could be implemented, a job guarantee — where the government provides jobs to all those that need them — is the simplest and most straightforward way to deal with job loss. If people lose their jobs due to factors outside of their control, why not simply provide them with new jobs?

If the gains go to the top, it is important to point out that this is because of deliberate policy. It is not a natural outcome. The rich and their allies in politics promote this redistribution to the top as inevitable — as the “future of work,” for example — whether or not advancements in technology pan out or not. Since advancements in technology do not fundamentally necessitate a change in social relations, this is intentionally deceptive at worst and wishful thinking at best. To see this dynamic, looking at particular jobs and industries is instructive, for example, in taxis and buses and trucking.

The ability to use smartphones and the internet to mediate services is not particularly revolutionary or unique but it does provide some benefits. Uber, the ride-hailing company, brought investment and these ideas to the taxi industry and quickly took over a large part of the market, despite many issues with its service and sustainability. In Uber’s case, appealing to the political power of affluent residents in cities and the supposed innovation of its app was enough to negate its blatant disregard for regulations, questionable safety record, exploitation of drivers, and unprofitability. In this sense, Uber’s investment allowed it to provide some benefits to its relatively wealthy passengers at the expense of the disabled, regular taxi drivers, and others. Most importantly, because it subsidizes every ride (Uber loses money on every ride taken), it was able to undercut the regulated taxi industry. The government’s lack of interest in maintaining fairness in the taxi industry effectively led to Uber being handed the market.

How could this have been different? The taxi industry on a whole is not an industry with large margins or much investment. In part, this is due to underlying characteristics of the industry as well as regulation, including those aimed at limiting the number of taxis operating in a city (which is good policy). To realize the benefits of technology, taxi commissions or groups of taxi drivers in various places could have developed their own app and infrastructure to facilitate ordering of cabs on the internet. This would have required substantial organization and money, which could have been facilitated and provided, respectively, by the government. The result could have been an app that allowed taxi authorities to continue to maintain standards for safety and operation and also provide the seamless service that certain groups of consumers desire. Indeed, competitor apps are being developed this way and existed before Uber, but they must now claw market share away from Uber. This is quite difficult because Uber is still subsidizing rides and keeping prices artificially low.

Let us now assume that rapid technological advancement is inevitable: self-driving cars and buses are finally right around the corner, as has been promised for years. (Indeed society could be on the cusp of this sort of technology, although the challenges shouldn’t be understated.) There would be massive benefits if self-driving vehicles are implemented successfully: increased mobility for the elderly, many fewer accidents, lower operating costs, increased productivity when in transit, etc.

Along with these benefits, there would be significant disruptions to the labor market. Ideas around how to approach these changes were discussed in a recent report, Stick Shift: Autonomous Vehicles, Driving Jobs, and the Future of Work.¹ It discusses two questions that are central to evaluation rapid disruptions to the labor market: How fast will the technology develop? How much of an impact will it have?

Regarding the first question, and assuming that these technological hurdles are overcome,² the report notes:

If the technology is successfully developed, the rate of the adoption and popularization of autonomous vehicles will depend greatly on whether necessary infrastructure is built, and whether and how regulation responds to these advances in technology. One of the inevitable debates will be between those who wish to ensure that autonomous vehicles are safe and reliable and those who want to get them to market as soon as possible. The outcome of this debate could greatly determine how the labor market is affected. Thorough vetting of the technology, along with phased rollouts, would allow time for workers to adjust to incoming shocks, and would dampen those shocks as well.

If the government were to assume the costs of building infrastructure for self-driving vehicles instead of the companies that are selling them, it would be fair for the government to also take a pro-active approach and develop a process to adequately assess the safety of those vehicles. This would somewhat mitigate the effects on the taxi industry and on bus drivers, especially in the early years of their use.

Proponents of self-driving vehicles also often forget to mention that technology will replace individual activities of workers but not necessarily all of the activities that encompass their jobs. Truckers, for instance, perform many other activities besides simply driving:

…in the trucking industry, there are many tasks that are difficult to imagine autonomous-vehicle technology being able to manage, which may limit their adoption or consign them or the driver to a secondary role. This includes many things that truck drivers are required to know, such as how to inspect the vehicle and cargo, perform maintenance and fix emergency problems, put on tire chains and deal with unpredictable weather, refuel the vehicle safely, and carry dangerous materials safely, to name a few.

If self-driving trucks took over the trucking industry, this suggests there would be many more support jobs in the trucking industry.

The other question is more pertinent considering our assumptions. How much of an impact technology will have on society is entirely up to society. The question is then not how much of an impact will self-driving cars have on society but where does society need self-driving cars and how do self-driving cars fit with social goals? There is a convincing argument that cars — self-driving or not — should have much less of a role in cities in the future. While taxis could have a role to play in the future, for example, public transportation and good urban design should be the focus, thus eliminating much of the need for taxis. In this vein, employment in the taxi industry could decline, but in addition to more social benefits from less vehicle use, employment would increase in association with an increase in investment in public transportation.

The social aspects of occupations are also important to consider when asking whether it might be desirable to transition to self-driving vehicles:

There is also the question of more socially oriented driving jobs. Bus drivers are one example. City bus drivers preserve order and safety on buses, provide information, ensure payment, and are generally considered community members and authority figures. School bus drivers have specific responsibilities related to the safety of the children they supervise. For these reasons, it may not be desirable or necessary to replace bus drivers, completely at least, even if the buses were fully autonomous.

In this sense, the elimination of these jobs would be akin to cuts in public services, and they would also eliminate some social benefits. Social aspects of jobs are rarely considered — but they are very important.

Here, a jobs guarantee would be useful, since it is a policy that prioritizes the social aspects of jobs and since social benefits are not prioritized in the private job market. Returning to the example of bus drivers, buses could be self-driving in the future but the “driver” need not be replaced. Rather, the position could be reoriented in a purely social role.

Whether technology will bring small changes, as in the case of Uber, or large changes, as in the case of self-driving vehicles, who benefits is entirely up to society. Gains from technology can be shared broadly with the right policies — just a few of which were described here — so there is no need to inherently fear the robots. A jobs guarantee is one of those policies, and it is perhaps the most important. (And it’s gaining traction in the mainstream.) A broad coalition, focused on the appropriate use of technology and promoting a job guarantee, could keep the actual threat — those wanting to harness the benefits of technology for themselves — at bay. Whether or not robots and mass automation are around the corner, it’s good policy, too.

The Automation Grift: From Flying Cars to Ordering Cat Food on the Internet – Part 2

It’s conventional wisdom among pundits that automation will cause mass unemployment in the near future, fundamentally changing work and the social relations that underpin it. Part 1 of this series contrasted this extreme rhetoric with the data that should support the inevitable robot apocalypse, and found that these predictions are likely motivated by politics or outlandish assessments of technology, not data. Part 2 assesses the technology behind these predictions, and follows a thread from the mid-20th century onwards. Subsequent parts will examine the political economy of automation in both general and specific ways, and will also discuss what the future should look like — with or without the robots.

The Automation Grift: From Flying Cars to Ordering Cat Food
on the Internet – Part 2
By Kevin Cashman

Part 1 of this article made a case that macroeconomic data does not suggest that there is rapid automation occurring broadly in the economy nor in large industries or sectors. Other indicators, like slack in the labor market, support that assertion. It pointed to periods of rapid automation in the past as well, and found these were times with generally low unemployment and healthy job growth.

Regardless of the data past or present, there are still claims that society is on a precipice, facing mass unemployment due to wide-scale automation. Many say that the technology in the near future is different than developments that occurred in the past, and that instead of slow or moderate change that the economy can adapt to, the rate of change will be so profound that suddenly millions will be out-of-work.

There are good reasons to be suspicious of this narrative. First, it is very difficult to predict how technology will develop and affect the world, and if it will be viable or even necessary in the first place. Second, adopting new technology — for example, automating a process and replacing workers — and more importantly, the threat of adopting new technology, gives power to employers and capital instead of workers. This weaponization of technology needs to be credible in order to be taken seriously; hence, it relies on the broader narrative that rapid automation is happening. The first point will be considered now; the second, in Part 3.

The (False) Promises of Technology

Predicting how technology affect the future is a difficult endeavor. The flying cars, spaceships, and moon bases that many were sure would arrive by the year 2000 never materialized. Anthropologist David Graeber posits that technological progress did not keep up with imaginations because capitalism “systematically prioritize[s] political imperatives over economic ones.” In a capitalist system like that in the U.S., if political threats do not align with technological advancement like they did during part of the Cold War, flying cars will stay in science fiction books, he says. As the perceived threat from the Soviet Union fell away, neoliberalism’s project shifted to cementing itself as the only viable political system, at the “end of history.”

More recent predictions have remained as bold as they were in the past, but reflect this change in focus. Audrey Watters, an education technology writer, details many in her excellent presentation, “The Best Way to Predict the Future is to Issue a Press Release.” She makes the case that narratives are spun about technology for mostly political reasons or for self-interest, rather than around higher, collective ideals. Bold predictions today are about the destruction and privatization of educational institutions, technology as consumption, or mass unemployment as human labor fades into obsolescence. Pointing to the dismal track record of those who analyze technological trends — based on methods that include opaque and ill-suited taxonomies and graphs, like the one-way hype cycle — she suggests that we are actually in a period of technological stagnation. “[T]he best way to resist this future,” she says, “is to recognize that, once you poke at the methodology and the ideology that underpins it, a press release is all that it is.”

Recent evidence from the dot-com bubble lends itself to these observations. Over-enthusiastic predictions of how the Internet would fundamentally change nature of shopping — not quite a lofty aspiration to begin with — led in large part to the bubble, which popped when it became clear that these companies’ business models did not work. (For example, individually shipping very heavy bags of pet food is expensive, a fact lost on the “innovative” owners of, and “savvy” investors in, Pets.com.) As neoliberalism was busy fashioning itself as the only ideology left standing, it served as the basis for allocating capital in unproductive ways. Whereas the ballooning of the finance sector over the last forty years is sustainable inasmuch as bankers are able to make money by creating and protecting the illusion of their usefulness, the dot-com era was a hard landing for companies that tried the same approach but ultimately could not drum up enough business to survive.

But even if past predictions are incorrect and past technological advances were limited (or had an economic potential that was much less than anticipated), the technology that is developing today could still could be extraordinary and kick off a period of very rapid automation, right? Before going further it is important to define what sort of technological developments could lead to these sorts of changes in the labor market. Often general advances in technology, or things like Moore’s law or speculation about the singularity, are used as evidence that the conditions that underlie the economy are shifting today. Here it is worth quoting directly from Economic Policy Institute’s State of Working America:

“We are often told that the pace of change in the workplace is accelerating, and technological advances in communications, entertainment, Internet, and other technologies are widely visible. Thus it is not surprising that many people believe that technology is transforming the wage structure. But technological advances in consumer products do not in and of themselves change labor market outcomes. Rather, changes in the way goods and services are produced influence relative demand for different types of workers, and it is this that affects wage trends. Since many high-tech products are made with low-tech methods, there is no close correspondence between advanced consumer products and an increased need for skilled workers. Similarly, ordering a book online rather than at a bookstore may change the type of jobs in an industry — we might have fewer retail workers in bookselling and more truckers and warehouse workers — but it does not necessarily change the skill mix.”

The takeaway from this should be that some technological advances that seem significant are not necessarily things that threaten jobs, change their pay or working conditions, or point to a jobless future. Technology can create new consumer products — let’s say smartphones — that seem like they fundamentally change the foundation of the economy. But they actually only shift jobs to the companies making smartphones, and don’t mean that workers making consumer products are somehow unnecessary. More significant developments like the technology behind the car or airplane can make entire industries obsolete but also can create an entire ecosystem of industries that generate wealth. Still other advancements can reduce the costs of products to a large degree so that they are increasingly used as inputs in other industries, benefiting both supplier and buyer.

These sorts of technological development are usually conflated with each other, and with the kind that is supposed to lead to mass automation and job loss. That kind of development is when very expensive robots or software replace humans completely, without spawning new industries and jobs. Two commonly cited examples are self-driving cars and delivery services. Delivery robots and drones might capture imaginations (and make for good PR) but that doesn’t mean that the economics behind them lead to a situation where workers will be replaced anytime soon.¹ Self-driving car technology is massively hyped, but many think they won’t arrive in even a lifetime. Labor platforms, like TaskRabbit, a marketplace to find help with errands or odd jobs, or Uber, the taxi app, are other Silicon Valley “innovations” often lumped in with this discussion. But they don’t threaten to reduce the total number of jobs at all: they shift jobs to their platforms.

This doesn’t mean that more original uses for technology couldn’t significant impact specific sectors. However, it’s likely that, in general, technology that does affect jobs will complement those positions, replacing or changing the specific tasks that workers do, but not going as far as replacing them in all cases. For jobs that are replaced wholesale, it shouldn’t be assumed that they will disappear overnight. There still need to be decisions, investment, and planning involved in replacing workers with (usually expensive) alternatives, which are all things that take time. This has certainly been the case in manufacturing. One interesting table from the Bureau of Labor Statistics that supports this point details the fastest declining occupations. Even extrapolating out ten years, the BLS assumes that there will be significant employment in these occupations. And any changes will vary by specific industry and occupation. Even then, many “low-skill” or low-paying jobs, especially in the service sector, are not conducive to automation very much at all. (And the robots must have forgotten that those were their targets, since many of the fastest growing jobs require no formal education or only a high school degree.)

There’s really no definitive way to tell either way if the robot apocalypse is upon us. But the precedence for wildly inaccurate predictions; the history of technology companies being unable to deliver on extravagant promises; the fact that the technology that would threaten jobs today is more suited toward slow, incremental changes like in the past; and that the orientation of our political system is toward prioritizing political, rather than economic imperatives, strongly suggests that the robots are probably much farther off than is conventionally accepted.

Is the recent deluge of talk of disruptive technological change, ubiquitous automation, and mass unemployment a continuation of the trends and mistakes that Graeber and Watters have highlighted? It seems so, and might even be approaching the lunacy of the dot-com era. Venture capitalists pour billions of dollars into unprofitable companies with questionable business models, which are in turn valued at billions of dollars. Many of the most popular and “innovative” businesses are simply delivery services, transportation companies, or in the consumer goods industry. How many different delivery services does society need? How many different taxi apps does it need? Does anyone really need a $700 juicer, especially if it isn’t even necessary? How are these ways of doing business adding value to the economy, let alone the beginning of a jobless future? More ambitious technology has proven to been a bust, especially in biotechnology.² One also has to question the value of recent technological assessments and predictions when many of the economic and political commentators that are doing that prognosticating couldn’t see the dot-com bubble or even the massive housing bubble that preceded the Great Recession.

The reality is that companies that are seen as the forebearers of mass automation are often unoriginal, repackaging old ideas and existing technology and using political power, venture capital money, and a lot of press releases to survive. Like Graeber said, these “innovations” seem to be more in line with boosting the prevailing economic and political ideology. Old, obsolete ideas³ like flying cars have been resurrected; for example, as part of a public relations and investment strategy to distract from Uber’s myriad scandals and disastrous finances.

If anything, the novelty of this new era of technology seems to come from the lessons business have learned from the survivors of the dot-com bubble, like eBay, Google, and Amazon:⁴ mainly, that business models don’t need to make sense as long as a company is able to take over a big slice of the market and change the terms of that market. In this way, vague ideas about technology and the usefulness of Silicon Valley — promoted by neoliberal icons like Elon Musk, Steve Jobs, and Mark Zuckerberg — are used as a smokescreen for anti-competitive and anti-worker practices that seek to change the economic landscape.

Part 3 will explore an underexamined consequence of this debate: how it affects the social relations between employers, workers, and the government that are a foundation of the economy.

About the Author
Kevin Cashman lives in Washington, DC, and researches issues related to domestic and international policy at the Center for Economic and Policy Research. Follow him on Twitter: @kevinmcashman.

The Automation Grift: Robots Are Hiding From The Data But Not From The Pundits –Part 1

It’s conventional wisdom among pundits that automation will cause mass unemployment in the near future, fundamentally changing work and the social relations that underpin it. But the data that should support these predictions do not. Part 1 of this article contrasts this extreme rhetoric and the data that should support the inevitable robot apocalypse, and finds that these predictions are likely motivated by politics or outlandish assessments of technology, not data. Part 2 assesses the technology behind these predictions, and follows a thread from the mid-20th century onwards. Subsequent parts will examine the political economy of automation in both general and specific ways, and will also discuss what the future should look like — with or without the robots.

The Automation Grift: The Robots Are Hiding From The Data But Not From The Pundits – Part 1

By Kevin Cashman

The Rhetoric

Few things are more breathlessly written about than automation and how it will affect society. In the mainstream discourse, technology writers, policy wonks, public relations hacks, self-stylized “futurists,” and others peddle their predictions and policy prescriptions, as if they are letting the rest of us in on a secret rather than following in a long history of over-enthusiastic predictions and misplaced priorities. Others view automation as a panacea for social problems. Either way, mass unemployment is usually at the center of this narrative and how workers, especially poorer workers, will become outmoded in the age of robots. In the waning days of the Obama administration, the White House joined the frenzy, publishing a report warning about the dangers automation posed to workers as well as the benefits of technology.

This report cited (and further legitimized) a 2013 report that boldly claimed that 47 percent of occupations were at risk from automation in the next two decades. Since its release, this study has been cited close to 900 times. Other predictions are just as bold. One is that the entire trucking industry will be automated in the next ten or so years. “Visionaries” like Bill Gates, Stephen Hawking, and Elon Musk use their stardom to add to the fears of these claims — and push for policies that don’t make much sense, like taxing robot workers or creating a basic income that is an excuse to eviscerate our other social programs and do other bad things. Still others blame automation for causing past problems, like the loss of manufacturing jobs in the U.S., when they are easily explained by political decisions, not economic realities.

With all this interest and all these forecasts, you’d think there would be evidence that automation is affecting the economy in a significant way. Indeed, economists have determined a measure for “automation”: productivity growth. As productivity growth expresses the relationship between inputs (e.g. robots, people, machines) and outputs (i.e. goods and services), it should be a decent and measurable proxy for automation. More automation and robots would result in greater outputs for fewer inputs, which would show up clearly in the data. This is because replacing humans with robots only makes economic sense if it saves money or increases output. In both of these scenarios, productivity would increase.

The Data

So what do the data points say? They show that productivity growth on an economy-wide scale has been very low for the past ten or so years, at a rate that is a bit over 1 percent annually. (In fact, multifactor productivity — productivity of all combined inputs — decreased 0.2 percent in 2016, the first decline since 2009.) The previous ten years — the mid-1990s to mid-2000s — was a period of moderate productivity growth, or just over 3 percent annual productivity growth. From the mid-1970s to mid-1990s, there was another period of slow growth. And before that, there was a sustained period of moderate growth post war until the mid-1970s: the so-called “Golden Age” of prosperity. These data points do not support the assertion that automation is happening on a large scale.

It is important to note that productivity growth and automation are constantly happening, and that automation can affect small industries or occupations in big ways. It can also replace individual tasks but not entire jobs themselves; for example, you may order your food on a computer at a restaurant rather than talk to a waiter, who would still deliver your food. These things may not show up in the data because they do not represent fundamental changes to the entire economy. In other words, automation on a small scale is not evidence that automation will cause a sea change in how work is done: it is normal.

Other macroeconomic indicators support the low rate of productivity growth seen today. The labor market has still not recovered to pre-recession levels, levels which were depressed compared to the highs of the late 1990s and early 2000s. Growth in wages and employment costs have also been relatively low. Since these indicate that there is still considerable slack in the labor market (i.e. in general it is easy to fill open positions, and there are many more applicants than open positions) there is less pressure to automate. After all, why would businesses en masse invest in automation on a significant scale if they can find desperate workers willing to be paid minimum wage?

History also provides useful data points. Technological change and its effects on the labor market have been consistently overstated in the past, which is acknowledged by even mainstream economists. If anything, this is evidence that automation is good for the economy because it creates jobs, in net, and it creates new sectors of the economy. It also can increase living standards by, for example, shortening work weeks or improving conditions of work (and together with organized labor, this happened in the “Golden Age,” which is how it got its moniker).

Supporters of the robots-are-taking-all-of-our-jobs myth usually ignore this evidence. They’ll say that productivity growth cannot take into account the changes that are happening and that automation will have catastrophic effects on the labor market either way. While there are legitimate debates to be had on how to measure automation, the reality is that despite all the spilled ink, the robot boosters do not have history or the data on their side. It is only their analysis of the technology that supports their assertions. They think that there is something extraordinary about the technological change that is happening now and it will be transformative, in contrast to the slow and steady automation that occurred in the past, where benefits were realized over a long horizon.

Part 2 assesses the technology behind these predictions.