AI Superpowers: China, Silicon Valley, and the New World Order

by Kai-Fu Lee

The engineering know-how and design sensibility needed to create a world-class technology product don’t just appear out of nowhere. In the United States, universities, companies, and engineers have been cultivating and passing down these skillsets for generations. Each generation has its breakout companies or products, but these innovations rest on a foundation of education, mentorship, internships, and inspiration.

I work at a startup mentored and managed by others who have worked in their youth at startups and so on. Such is the Silicon Valley legacy.

To Chinese startups, the deeper they get into the nitty-gritty—and often very expensive—details, the harder it will be for a copycat competitor to mimic the business model and undercut them on price.

After driving Uber out of the Chinese ride-hailing market, Didi has begun buying up gas stations and auto repair shops to service its fleet, making great margins because of its understanding of its drivers and their trust in the Didi brand. While Airbnb largely remains a lightweight platform for listing your home, the company’s Chinese rival, Tujia, manages a large chunk of rental properties itself.

Chinese political culture: while America’s combative political system aggressively punishes missteps or waste in funding technological upgrades, China’s techno-utilitarian approach rewards proactive investment and adoption. Neither system can claim objective moral superiority, and the United States’ long track record of both personal freedom and technological achievement is unparalleled in the modern era.

Fermi and the Manhattan Project embodied an age of discovery that rewarded quality over quantity in expertise. In nuclear physics, the 1930s and 1940s were an age of fundamental breakthroughs, and when it came to making those breakthroughs, one Enrico Fermi was worth thousands of less brilliant physicists. American leadership in this era was built in large part on attracting geniuses like Fermi: men and women who could singlehandedly tip the scales of scientific power.

In reality, we are witnessing the application of one fundamental breakthrough—deep learning and related techniques—to many different problems.

To some observers in the West, these research achievements fly in the face of deeply held beliefs about the nature of knowledge and research across political systems. Shouldn’t Chinese controls on the internet hobble the ability of Chinese researchers to break new ground globally? There are valid critiques of China’s system of governance, ones that weigh heavily on public debate and research in the social sciences. But when it comes to research in the hard sciences, these issues are not nearly as limiting as many outsiders presume.

It will be interesting to see how this plays out. I feel only someone who has studied and done research in both the United States and in China would be able to speak truthfully and usefully about the impact the differences between the two countries' differences have on the propagation and exchange of free ideas. Guess that's precisely Kai Fu Lee. Though, perhaps some would cast him as a PRC shill? I don't know, but I definitely wouldn't rely on Reddit armchair commentators.

In terms of funding, Google dwarfs even its own government: U.S. federal funding for math and computer science research amounts to less than half of Google’s own R&D budget.

This is either a "Go capitalism!" kind of fact or a "What's wrong with the U.S. government — we're going to fall behind!" kind of fact. I lean more towards the former, but I have little basis on which to make this statement.

That’s because when economic disruption occurs on the scale promised by artificial intelligence, it isn’t just a business question—it’s also a major political question.

China’s AI plan originated at the highest levels of the central government, but China’s ambitious mayors are where the real action takes place.

One of the recipients of those loan guarantees was Solyndra, a California solar panel company that initially looked promising but then went bankrupt in 2011. President Obama’s critics quickly turned that failure into one of the most potent political bludgeons of the 2012 presidential election. They hammered the president with millions of dollars in attack ads, criticizing the “wasteful” spending as a symptom of “crony capitalism” and “venture socialism.” Never mind that, on the whole, the loan guarantee program is projected to earn money for the federal government—one high-profile failure was enough to tar the entire enterprise of technological upgrading.

Obama survived the negative onslaught to win another term, but the lessons for American politicians were clear: using government funding to invest in economic and technological upgrades is a risky business. Successes are often ignored, and every misfire becomes fodder for attack ads. It’s far safer to stay out of the messy business of upgrading an economy.

The sentencing assistant starts with the fact pattern—defendant’s criminal record, age, damages incurred, and so on—then its algorithms scan millions of court records for similar cases. It uses that body of knowledge to make recommendations for jail time or fines to be paid.

Kind of a scary thing tbh

By empowering judges with data-driven recommendations, they can help balance the scales of justice and correct for the biases present in even well-trained judges. American legal scholars have illustrated vast disparities in U.S. sentencing based on the race of the victim and the defendant.

One might gasp in horror at the concept of letting an AI decide someone's sentence. Another might gasp in horror at the amount of bias in the current U.S. justice system. AI-powered recommendations to correct for this bias? What about AI bias itself? These are complicated questions.

When managing a country of 1.39 billion people—one in which 260,000 people die in car accidents each year—the Chinese mentality is that you can’t let the perfect be the enemy of the good.

While all of this may sound exciting and innovative to the Chinese landscape, the hard truth is that no amount of government support can guarantee that China will lead in autonomous AI. When it comes to the core technology needed for self-driving cars, American companies remain two to three years ahead of China.

In America, we sometimes fail to appreciate just how advanced and ahead of the world we are in high technology.

AI has a much higher localization quotient than earlier internet services. Self-driving cars in India need to learn the way pedestrians navigate the streets of Bangalore, and micro-lending apps in Brazil need to absorb the spending habits of millennials in Rio de Janeiro.

looking out on a world all their own. These are visions of Hao Jingfang, a Chinese science-fiction writer and economics researcher. Hao’s novelette “Folding Beijing” won the prestigious Hugo Award in 2016 for its arresting depiction of a city in which economic classes are separated into different worlds.

Always love new book recs

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.

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.

In essence, AI is great at thinking, but robots are bad at moving their fingers.

For most of my adult life, I have been driven by an almost fanatical work ethic. I gave nearly all my time and energy to my job, leaving very little for family or friends. My sense of self-worth was derived from my achievements at work, from my ability to create economic value and to expand my own influence in the world.

Most of all, I’ve stopped viewing my life as an algorithm that optimizes for influence. Instead, I try to spend my energy doing the one thing I’ve found that truly brings meaning to a person’s life: sharing love with those around us.

Sounds like he had a spiritual experience

But looking back, it’s not those career successes that stick in my mind. It’s the scene in that hospital room. If I had been forced to choose between the birth of my first child and that Apple meeting, I likely would have chosen the meeting. Today, I must confess that I find this deeply embarrassing but not entirely baffling.

But the truth is, there exists no algorithm that could replace the role of my family in my healing process. What they shared with me is far simpler—and yet so much more profound—than anything AI will ever produce.

This is the synthesis on which I believe we must build our shared future: on AI’s ability to think but coupled with human beings’ ability to love.

To date, China’s tech elite have said very little about the possible negative impact of AI on jobs. Personally, I don’t believe this silence is due to any desire to hide the dark truth from the masses—I think they genuinely believe there is nothing to fear in the jobs impact of AI advances.

In this envisioned world of fluid retraining, unemployed insurance brokers can use online education platforms like Coursera to become software programmers. And when that job becomes automated, they can use those same tools to retrain for a new position that remains out of reach for AI, perhaps as an algorithm engineer or as a psychologist.

Only SV bubble would think this will work

fear this approach will be far from enough to solve the problem. As AI steadily conquers new professions, workers will be forced to change occupations every few years, rapidly trying to acquire skills that it took others an entire lifetime to build up.

From my perspective, I can understand why the Silicon Valley elite have become so enamored with the idea of a UBI: it is a simple, technical solution to an enormous and complex social problem of their own making. But adopting a UBI would constitute a major change in our social contract, one that we should think through very carefully and most critically. While I support certain guarantees that basic needs will be met, I also believe embracing a UBI as a cure-all for the crisis we face is a mistake and a massive missed opportunity. To understand why, we must truly look at the motivations for the frenzy of interest in UBI and also think hard about what kind of a society it may create.

Are they willing to put in the legwork needed to build new institutions or merely looking for a quick fix that will assuage their own consciences and absolve them of responsibility for the deeper psychological impacts of automation?

It tends to envision that all problems can be solved through a tweaking of incentives or a shuffling of money between digital bank accounts.

Like devon

Private companies already create plenty of human-centered service jobs—they just don’t pay well. Economic incentives, public policies, and cultural dispositions have meant that many of the most compassion-filled professions existing today often lack job security or basic dignity.

To do this, I propose we explore the creation not of a UBI but of what I call a social investment stipend. The stipend would be a decent government salary given to those who invest their time and energy in those activities that promote a kind, compassionate, and creative society. These would include three broad categories: care work, community service, and education.

But these distinctions are easily blurred by those more interested in political posturing than in human flourishing. If we are not careful, this single-minded rhetoric around an “AI race” will undermine us in planning and shaping our shared AI future. A race has only one winner: China’s gain is America’s loss, and vice-versa. There is no notion of shared progress or mutual prosperity—just a desire to stay ahead of the other country, regardless of the costs.

Instead of seeking to outperform the human brain, I should have sought to understand the human heart.

If AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved.

An interesting sentiment at the end. I have a desire to call it sappy and sentimental and naive, but, well, why not? Every AI optimist envisions a future in which automation takes care of everything, so what else would humans be left with?