by Kai-Fu Lee
Over one-quarter of Chinese workers are still on farms, with another quarter involved in industrial production. That compares with less than 2 percent of Americans in agriculture and around 18 percent in industrial jobs. Pundits such as Rise of the Robots author Martin Ford have argued that this large base of routine manual labor could make China “ground zero for the economic and social disruption brought on by the rise of the robots.” Influential technology commentator Vivek Wadhwa has similarly predicted that intelligent robotics will erode China’s labor advantage and bring manufacturing back to the United States en masse, albeit without the accompanying jobs for humans. “American robots work as hard as Chinese robots,” he wrote, “and they also don’t complain or join labor unions.”
These predictions are understandable given the recent history of automation. Looking back at the last hundred years of economic evolution, blue-collar workers and farmhands have faced the steepest job losses from physical automation. Industrial and agricultural tools (think forklifts and tractors) greatly increased the productivity of each manual laborer, reducing demand for workers in these sectors. Projecting this same transition out into the age of AI, the conventional wisdom views China’s farm and factory laborers as caught squarely in the crosshairs of intelligent automation. In contrast, America’s heavily service-oriented and white-collar economy has a greater buffer against potential job losses, protected by college degrees and six-figure incomes.
In my opinion, the conventional wisdom on this is backward. While China will face a wrenching labor-market transition due to automation, large segments of that transition may arrive later or move slower than the job losses wracking the American economy. While the simplest and most routine factory jobs—quality control and simple assembly-line tasks—will likely be automated in the coming years, the remainder of these manual labor tasks will be tougher for robots to take over. This is because the intelligent automation of the twenty-first century operates differently than the physical automation of the twentieth century. Put simply, it’s far easier to build AI algorithms than to build intelligent robots.
Core to this logic is a tenet of artificial intelligence known as Moravec’s Paradox. Hans Moravec was a professor of mine at Carnegie Mellon University, and his work on artificial intelligence and robotics led him to a fundamental truth about combining the two: contrary to popular assumptions, it is relatively easy for AI to mimic the high-level intellectual or computational abilities of an adult, but it’s far harder to give a robot the perception and sensorimotor skills of a toddler. Algorithms can blow humans out of the water when it comes to making predictions based on data, but robots still can’t perform the cleaning duties of a hotel maid. In essence, AI is great at thinking, but robots are bad at moving their fingers.
Moravec’s Paradox was articulated in the 1980s, and some things have changed since then. The arrival of deep learning has provided machines with superhuman perceptual abilities when it comes to voice or visual recognition. Those same machine-learning breakthroughs have also turbocharged the intellectual abilities of machines, namely, the power of spotting patterns in data and making decisions. But the fine motor skills of robots—the ability to grasp and manipulate objects—still lag far behind humans. While AI can beat the best humans at Go and diagnose cancer with extreme accuracy, it cannot yet appreciate a good joke.
THE ASCENT OF THE ALGORITHMS AND RISE OF THE ROBOTS
This hard reality about algorithms and robots will have profound effects on the sequence of AI-induced job losses. The physical automation of the past century largely hurt blue-collar workers, but the coming decades of intelligent automation will hit white-collar workers first. The truth is that these workers have far more to fear from the algorithms that exist today than from the robots that still need to be invented.
In short, AI algorithms will be to many white-collar workers what tractors were to farmhands: a tool that dramatically increases the productivity of each worker and thus shrinks the total number of employees required. And unlike tractors, algorithms can be shipped instantly around the world at no additional cost to their creator. Once that software has been sent out to its millions of users—tax-preparation companies, climate-change labs, law firms—it can be constantly updated and improved with no need to create a new physical product.
Robotics, however, is much more difficult. It requires a delicate interplay of mechanical engineering, perception AI, and fine-motor manipulation. These are all solvable problems, but not at nearly the speed at which pure software is being built to handle white-collar cognitive tasks. Once that robot is built, it must also be tested, sold, shipped, installed, and maintained on-site. Adjustments to the robot’s underlying algorithms can sometimes be made remotely, but any mechanical hiccups require hands-on work with the machine. All these frictions will slow down the pace of robotic automation.
This is not to say that China’s manual laborers are safe. Drones for deploying pesticides on farms, warehouse robots for unpacking trucks, and vision-enabled robots for factory quality control will all dramatically reduce the jobs in these sectors. And Chinese companies are indeed investing heavily in all of the above. The country is already the world’s top market for robots, buying nearly as many as Europe and the Americas combined. Chinese CEOs and political leaders are united in pushing for the steady automation of many Chinese factories and farms.
But the resulting blue-collar job losses in China will be more gradual and piecemeal than the sweeping impact of algorithms on white-collar workers. While the right digital algorithm can hit like a missile strike on cognitive labor, robotics’ assault on manual labor is closer to trench warfare. Over the long term, I believe the number of jobs at risk of automation will be similar for China and the United States. American education’s greater emphasis on creativity and interpersonal skills may give it an employment edge on a long enough time scale. However, when it comes to adapting to these changes, speed matters, and China’s particular economic structure will buy it some time.
THE AI SUPERPOWERS VERSUS ALL THE REST
Whatever gaps exist between China and the United States, those differences will pale in comparison between these two AI superpowers and the rest of the world. Silicon Valley entrepreneurs love to describe their products as “democratizing access,” “connecting people,” and, of course, “making the world a better place.” That vision of technology as a cure-all for global inequality has always been something of a wistful mirage, but in the age of AI it could turn into something far more dangerous. If left unchecked, AI will dramatically exacerbate inequality on both international and domestic levels. It will drive a wedge between the AI superpowers and the rest of the world, and may divide society along class lines that mimic the dystopian science fiction of Hao Jingfang.
As a technology and an industry, AI naturally gravitates toward monopolies. Its reliance on data for improvement creates a self-perpetuating cycle: better products lead to more users, those users lead to more data, and that data leads to even better products, and thus more users and data. Once a company has jumped out to an early lead, this kind of ongoing repeating cycle can turn that lead into an insurmountable barrier to entry for other firms.
Chinese and American companies have already kick-started this process, leaping out to massive leads over the rest of the world. Canada, the United Kingdom, France, and a few other countries play host to top-notch talent and research labs, but they often lack the other ingredients needed to become true AI superpowers: a large base of users and a vibrant entrepreneurial and venture-capital ecosystem. Other than London’s DeepMind, we have yet to see groundbreaking AI companies emerge from these countries. All of the seven AI giants and an overwhelming portion of the best AI engineers are already concentrated in the United States and China. They are building huge stores of data that are feeding into a variety of different product verticals, such as self-driving cars, language translation, autonomous drones, facial recognition, natural-language processing, and much more. The mo
re data these companies accumulate, the harder it will be for companies in any other countries to ever compete.
As AI spreads its tentacles into every aspect of economic life, the benefits will flow to these bastions of data and AI talent. PwC estimates that the United States and China are set to capture a full 70 percent of the $15.7 trillion that AI will add to the global economy by 2030, with China alone taking home $7 trillion. Other countries will be left to pick up the scraps, while these AI superpowers will boost productivity at home and harvest profits from markets around the globe. American companies will likely lay claim to many developed markets, and China’s AI juggernauts will have a better shot at winning over Southeast Asia, Africa, and the Middle East.
I fear this process will exacerbate and significantly grow the divide between the AI haves and have-nots. While AI-rich countries rake in astounding profits, countries that haven’t crossed a certain technological and economic threshold will find themselves slipping backward and falling farther behind. With manufacturing and services increasingly done by intelligent machines located in the AI superpowers, developing countries will lose the one competitive edge that their predecessors used to kick-start development: low-wage factory labor.
Large populations of young people used to be these countries’ greatest strengths. But in the age of AI, that group will be made up of displaced workers unable to find economically productive work. This sea change will transform them from an engine of growth to a liability on the public ledger—and a potentially explosive one if their governments prove unable to meet their demands for a better life.
Deprived of the chance to claw their way out of poverty, poor countries will stagnate while the AI superpowers take off. I fear this ever-growing economic divide will force poor countries into a state of near-total dependence and subservience. Their governments may try to negotiate with the superpower that supplies their AI technology, trading market and data access for guarantees of economic aid for their population. Whatever bargain is struck, it will not be one based on agency or equality between nations.
THE AI INEQUALITY MACHINE
The same push toward polarization playing out across the global economy will also exacerbate inequality within the AI superpowers. AI’s natural affinity for monopolies will bring winner-take-all economics to dozens more industries, and the technology’s skill biases will generate a bifurcated job market that squeezes out the middle class. The “great decoupling” of productivity and wages has already created a tear between the 1 percent and the 99 percent. Left to its own devices, artificial intelligence, I worry, will take this tear and rip it wide open.
We already see this trend toward monopolization in the online world. The internet was supposed to be a place of freewheeling competition and a level playing field, but in a few short years many core online functions have turned into monopolistic empires. For much of the developed world, Google rules search engines, Facebook dominates social networks, and Amazon owns e-commerce. Chinese internet companies tend to worry less about “staying in their lane,” so there are more skirmishes between these giants, but the vast majority of China’s online activity is still funneled through just a handful of companies.
AI will bring that same monopolistic tendency to dozens of industries, eroding the competitive mechanisms of markets in the process. We could see the rapid emergence of a new corporate oligarchy, a class of AI-powered industry champions whose data edge over the competition feeds on itself until they are entirely untouchable. American antitrust laws are often difficult to enforce in this situation, because of the requirement in U.S. law that plaintiffs prove the monopoly is actually harming consumers. AI monopolists, by contrast, would likely be delivering better and better services at cheaper prices to consumers, a move made possible by the incredible productivity and efficiency gains of the technology.
But while these AI monopolies drive down prices, they will also drive up inequality. Corporate profits will explode, showering wealth on the elite executives and engineers lucky enough to get in on the action. Just imagine: How profitable would Uber be if it had no drivers? Or Apple if it didn’t need factory workers to make iPhones? Or Walmart if it paid no cashiers, warehouse employees, and truck drivers?
Driving income inequality will be the emergence of an increasingly bifurcated labor market. The jobs that do remain will tend to be either lucrative work for top performers or low-paying jobs in tough industries. The risk of replacement cited in the earlier figures reflects this. The most difficult jobs to automate—those in the top-right corner of the “Safe Zone”—include both ends of the income spectrum: CEOs and healthcare aides, venture capitalists and masseuses.
Meanwhile, many of the professions that form the bedrock of the middle class—truck drivers, accountants, office managers—will be hollowed out. Sure, we could try to transition these workers into some of the highly social, highly dexterous occupations that will remain safe. Home healthcare aide, techno-optimists point out, is the fastest-growing profession in America. But it’s also one of the lowest paid, with an annual salary of around $22,000. A rush of newly displaced workers trying to enter the industry will only exert more downward pressure on that number.
Pushing more people into these jobs while the rich leverage AI for huge gains doesn’t just create a society that is dramatically unequal. I fear it will also prove unsustainable and frighteningly unstable.
A GRIM PICTURE
When we scan the economic horizon, we see that artificial intelligence promises to produce wealth on a scale never before seen in human history—something that should be a cause for celebration. But if left to its own devices, AI will also produce a global distribution of wealth that is not just more unequal but hopelessly so. AI-poor countries will find themselves unable to get a grip on the ladder of economic development, relegated to permanent subservient status. AI-rich countries will amass great wealth but also witness the widespread monopolization of the economy and a labor market divided into economic castes.
Make no mistake: this is not just the normal churn of capitalism’s creative destruction, a process that has previously helped lead to a new equilibrium of more jobs, higher wages, and a better quality of life for all. The free market is supposed to be self-correcting, but these self-correcting mechanisms break down in an economy driven by artificial intelligence. Low-cost labor provides no edge over machines, and data-driven monopolies are forever self-reinforcing.
These forces are combining to create a unique historical phenomenon, one that will shake the foundations of our labor markets, economies, and societies. Even if the most dire predictions of job losses don’t fully materialize, the social impact of wrenching inequality could be just as traumatic. We may never build the folding cities of Hao Jingfang’s science fiction, but AI risks creating a twenty-first-century caste system, one that divides the population into the AI elite and what historian Yuval N. Harari has crudely called the “useless class,” people who can never generate enough economic value to support themselves. Even worse, recent history has shown us just how fragile our political institutions and social fabric can be in the face of intractable inequality. I fear that recent upheavals are only a dry run for the disruptions to come in the age of AI.
TAKING IT PERSONALLY: THE COMING CRISIS OF MEANING
The resulting turmoil will take on political, economic, and social dimensions, but it will also be intensely personal. In the centuries since the Industrial Revolution, we have increasingly come to see our work not just as a means of survival but as a source of personal pride, identity, and real-life meaning. Asked to introduce ourselves or others in a social setting, a job is often the first thing we mention. It fills our days and provides a sense of routine and a source of human connections. A regular paycheck has become a way not just of rewarding labor but also of signaling to people that one is a valued member of society, a contributor to a common project.
Severing these ties—or forcing people into downwardly mobile careers—will damage so
much more than our financial lives. It will constitute a direct assault on our sense of identity and purpose. Speaking to the New York Times in 2014, a laid-off electrician named Frank Walsh described the psychological toll of intractable unemployment.
“I lost my sense of worth, you know what I mean?” Walsh observed. “Somebody asks you ‘What do you do?’ and I would say, ‘I’m an electrician.’ But now I say nothing. I’m not an electrician anymore.”
That loss of meaning and purpose has very real and serious consequences. Rates of depression triple among those unemployed for six months, and people looking for work are twice as likely to commit suicide as the gainfully employed. Alcohol abuse and opioid overdoses both rise alongside unemployment rates, with some scholars attributing rising mortality rates among uneducated white Americans to declining economic outcomes, a phenomenon they call “deaths of despair.”
The psychological damage of AI-induced unemployment will cut even deeper. People will face the prospect of not just being temporarily out of work but of being permanently excluded from the functioning of the economy. They will watch as algorithms and robots easily outperform them at tasks and skills they spent their whole lives mastering. It will lead to a crushing feeling of futility, a sense of having become obsolete in one’s own skin.
The winners of this AI economy will marvel at the awesome power of these machines. But the rest of humankind will be left to grapple with a far deeper question: when machines can do everything that we can, what does it mean to be human?
That’s a question that I found myself grappling with in the depths of my own personal crisis of mortality and meaning. That crisis brought me to a very dark place, one that pushed my body to the limit and challenged my deepest-held assumptions about what matters in life. But it was that process—and that pain—that opened my eyes to an alternate ending to the story of human beings and artificial intelligence.