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Averting a Technology-Driven Social Crisis
Technology Briefing |
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TranscriptBroadly speaking, technology impacts the economy via two pathways. One pathway is referred to as automation, which replaces human activity with technology-based alternatives. The second, is referred to as augmentation, which enables people themselves to be more productive. The good news according to Erik Brynjolfsson, Director of the Stanford Digital Economy Laboratory, is that both automation and augmentation can boost labor productivity, which is simply the ratio of value-added output to labor-hours worked. As history shows, when productivity increases, so do average incomes and living standards, as well as our capabilities for addressing challenges ranging from environmental degradation to poverty and longevity. The bad news is that no economic law ensures that everyone will share this growing pie. In practice, technological change can disproportionately hurt some groups, even though it is beneficial, on average. The way the benefits of technology are distributed depends to a great extent on how the technology is deployed and thereby impacts the economic rules and norms which influence the allocation of goods, services, and incomes. Specifically, when technologies automate human labor, they tend to reduce the marginal value of workers’ contributions, so more of the gains go to the owners, entrepreneurs, inventors, and architects of the new systems. In contrast, when technologies augment human capabilities, more of the gains go to human workers. Observers readily assume that all or most productivity-enhancing innovations fall into the automation category. However, according to Brynjolfsson, the augmentation category has been far more important throughout most of the past two centuries. One key indicator of this fact is that the economic value associated with an hour of human labor has risen sharply. Specifically, real median wages have grown more than ten-fold since 1820. Why? An entrepreneur is willing to pay far more for a worker whose capabilities are amplified by a bulldozer than one who can only work with a shovel, let alone with his bare hands. In many cases, not only wages but also employment opportunities grow with the introduction of new technologies. For example, the invention of jet engines, dramatically increased pilot productivity in terms of passenger-miles per pilot-hour. And rather than reducing the number of employed pilots, the technology spurred demand for air travel so much that the number of pilots also grew. Obviously, such outcomes are comforting in an era of accelerating innovation. But past performance does not guarantee future results. Modern technologies ― and, more importantly, the ones now under development ― are different from those that were important in the past. In recent years, we have seen growing evidence that not only is labor’s share of the economy declining, but some groups of workers are beginning to fall farther behind relative to others. That’s why, over the past forty years, the numbers of millionaires and billionaires grew while the average real wages for Americans with only a high school education fell. And while many phenomena including new patterns of global trade contributed to this trend, changes in technology deployment seem to be the single biggest contributor. Why is this true? When we focus on human-like AI which automates jobs, technology will frequently replace many non-superstar workers, driving down their market wages, while it amplifies the market power of a few superstar workers. This realization has created a growing fear that AI and related advances will lead to a burgeoning class of unemployable people, with “zero marginal value.” Social stress is rapidly increasing because the postwar consensus which defines our world, was developed at the middle of the mass-production techno-economic revolution. Mass manufacturing, mass markets, and mass media were mutually reinforcing institutions driven by analog technologies. State-of-the-art breakthroughs such as the Manhattan Project relied on mechanical calculators and slide rules, while digital computers existed only as mathematical models. As the Trends editors have long insisted, a social contract optimized for the realities of the mass production world, must adapt to the realities of a digital economy based on ubiquitous computing and networking. And while the flashpoints of this crisis are multi-dimensional, none is more important than the complex task of facilitating and encouraging augmentation of human performance via Artificial Intelligence. As explained in the January 2017 Trends issue, commercialization of AI has the potential to double the economic growth rate through 2035. However, in a complex modern economy, decisions regarding the deployment of a transformative technology like AI has ripple effects which extend beyond the immediate confines of a single industry and impact entrepreneurs, investors, consumers and workers across an entire eco-system. Previous general-purpose technologies like factories, railroads, steel and electricity had broad-based and even revolutionary implications for labor and capital. And like AI, these revolutionary technologies not only impacted the world directly via the innovation itself, but by triggering cascades of complementary innovations, from new products to new production systems. Perhaps more importantly, those technologies, much like AI, had profound effects of on work and were rife with what economist call externalities. Consider what that means. When a worker loses opportunities to earn a labor-based income, the costs go beyond the newly unemployed to affect many others in their communities and in the broader society. With fading opportunities often come the dark forces of alcoholism, crime, and opioid abuse. Recently, the United States has experienced the first decline in life expectancies in its recorded history, a result of increasing deaths from suicide, drug overdose, and alcoholism, which economists call “deaths of despair.” This spiral of marginalization can grow because concentration of economic power often begets concentration of political power. In contrast, when humans are indispensable to value creation, economic power will tend to be more decentralized. Historically, most economically valuable knowledge — what economist Simon Kuznets called “useful knowledge”— resided within human brains. But no human brain can contain even a small fraction of the useful knowledge needed to run even a medium-sized business, let alone a whole industry or economy, so knowledge had to be distributed and decentralized. As Brynjolfsson observes, “the decentralization of useful knowledge, in turn, decentralized economic and political power.” Unlike nonhuman assets such as property and machinery, much of a person’s knowledge is inalienable, both in the practical sense that no one person can know everything that another person knows and in the legal sense that its ownership cannot be legally transferred. In contrast, when knowledge becomes codified and digitized, it can be owned, transferred, and concentrated very easily. Thus, when knowledge shifts from humans to machines, it opens the possibility of concentration of power. When historians look back on the first two decades of the twenty-first century, they will note the striking growth in the digitization and codification of information and knowledge. In parallel, machine learning models are becoming larger, with hundreds of billions of parameters, using more data and getting more accurate results. Why is this important? Ownership of key assets provides bargaining power in relationships between economic agents (such as employers and employees, or business owners and subcontractors). To the extent that a person controls an indispensable asset (like useful knowledge) needed to create and deliver a company’s products and services, that person can command not only higher income but also a voice in decision-making. When useful knowledge is inalienably locked in human brains, so too is the power it confers. But when it is made alienable, it enables greater concentration of decision-making and power. What’s the bottom line? Decisions made in the next few years will determine how artificial intelligence and related capabilities are developed and commercialized. The future is not preordained. Humans control the extent to which AI either expands human opportunity through augmentation or replaces humans through automation. The first option means prioritizing challenges that are easy for machines and hard for humans, rather than hard for machines and easy for humans. And it offers the opportunity of growing and sharing the economic pie by augmenting the workforce with tools and platforms. The second option risks dividing the economic pie among an ever-smaller number of people by creating automation that displaces ever-more types of workers. While both approaches can and do contribute to progress, too many technologists, businesspeople, and policymakers have been putting a finger on the scales in favor of replacement. Moreover, the tendency of a greater concentration of technological and economic power to beget a greater concentration of political power risks trapping a powerless majority into an unhappy equilibrium which leads to an increasingly dysfunctional society. As a cautionary tale, consider the backlash against free trade. Economists have long argued that free trade and globalization tend to grow the economic pie through the power of comparative advantage and specialization. They have also acknowledged that market forces alone do not ensure that every person in every country will come out ahead. So, they proposed a grand bargain: maximize free trade to maximize wealth creation and then distribute the benefits broadly to compensate any injured occupations, industries, and regions. Unfortunately, this hasn’t worked as they hoped. As the economic winners gained power, they reneged on the second part of the bargain, leaving many workers worse off than before. The result helped fuel a populist backlash which created import tariffs and other barriers to free trade. Some of the same dynamics are already at work with respect to AI. More and more Americans, as well as a cross-section of workers around the world, argue that while technology is expanding a new billionaire class, it is not working for them. The more technology is used to replace rather than augment labor, the worse the disparity may become, and the greater the resentments that feed destructive political instincts and actions. More fundamentally, the moral imperative of treating people as ends, and not merely as means, calls for everyone to share in the gains of automation. Given this trend, we offer the following forecasts for your consideration. First, ensuring that technology serves the needs of the broader society will occupy center-stage by mid-decade. The incredible flowering of affluence over the past 250 years occurred because of the emergence of a broad-based consumer economy where nearly everyone played a role. And that system has underpinned the American Dream since at least World War II. But, despite its overarching importance, this issue has received little formal attention from policy makers, business gurus and even social scientists because the trade-offs are so difficult. That will change and it could come to the forefront in the 2024 election. Second, the solution is not to slow down technology, but rather to eliminate or reverse the excess incentives for automation over augmentation. Tax policies, subsidies and regulations tend to favor capital over labor and makes no distinction between augmentation and automation. The new American Consensus will explicitly favor incentives for building and augmenting human capital as well as delivering technologies that do what has never been done before. Third, the United States will build political and economic institutions that are robust in the face of the growing power of AI. Short-term, this will manifest itself mostly in increased regulation of socalled "big tach." With both the right and left advocating constraints, expect dramatic change by 2025. Fourth, left unchecked, the tech-based concentration of wealth will create a huge backlash against automation. This nascent backlash can be reversed by creating the kind of prosperous society that inspires discovery, boosts living standards, and offers political inclusion for everyone. And, Fifth, over the long haul, research and capital will flow applications of technology doing important tasks that people aren’t naturally well-suited to perform. Most of the value that our economy has created since ancient times comes from new goods and services that not even the kings of ancient empires had, rather than making cheaper versions of existing goods. To produce and deliver these new products and services myriad new tasks were required; in fact, fully 60 percent of people are now employed in occupations that did not exist in 1940. That’s because automating labor ultimately unlocks less value than augmenting it to create something new. Sixth, by the end of this decade we’ll have exhausted most of the opportunities to cost-effectively, replace people with machines. Recent research on the suitability for machine learning found many occupations in which machines could perform some tasks, but zero occupations out of 950 in which machine learning could do 100 percent of the necessary tasks. |
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