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AI Superpowers

(My personal highlights on Kindle @ AI Superpowers)

These internet juggernauts had given the United States a dominance of the digital world that matched its military and economic power in the real world. With AlphaGo—a product of the British AI startup DeepMind, which had been acquired by Google in 2014—the West appeared poised to continue that dominance into the age of artificial intelligence.

Today, Zhongguancun is the beating heart of China’s AI movement. To people here, AlphaGo’s victories were both a challenge and an inspiration. They turned into China’s “Sputnik Moment” for artificial intelligence.

The event sparked widespread U.S. public anxiety about perceived Soviet technological superiority, with Americans following the satellite across the night sky and tuning in to Sputnik’s radio transmissions. It triggered the creation of the National Aeronautics and Space Administration (NASA), fueled major government subsidies for math and science education, and effectively launched the space race. That

When Chinese investors, entrepreneurs, and government officials all focus in on one industry, they can truly shake the world. Indeed, China is ramping up AI investment, research, and entrepreneurship on a historic scale. Money for AI startups is pouring in from venture capitalists, tech juggernauts, and the Chinese government. Chinese students have caught AI fever as well, enrolling in advanced degree programs and streaming lectures from international researchers on their smartphones. Startup founders are furiously pivoting, reengineering, or simply rebranding their companies to catch the AI wave.

Deep Blue had essentially “brute forced” its way to victory—relying largely on hardware customized to rapidly generate and evaluate positions from each move. It had also required real-life chess champions to add guiding heuristics to the software. Yes, the win was an impressive feat of engineering, but it was based on long-established technology that worked only on very constrained sets of issues. Remove Deep Blue from the geometric simplicity of an eight-by-eight-square chessboard and it wouldn’t seem very intelligent at all. In the end, the only job it was threatening to take was that of the world chess champion.

The threat to jobs is coming far faster than most experts anticipated, and it will not discriminate by the color of one’s collar, instead striking the highly trained and poorly educated alike. On the day of that remarkable match between AlphaGo and Ke Jie, deep learning was dethroning humankind’s best Go player. That same job-eating technology is coming soon to a factory and an office near you.

Those tears triggered an outpouring of sympathy and support for Ke. Over the course of these three matches, Ke had gone on a roller-coaster of human emotion: confidence, anxiety, fear, hope, and heartbreak. It had showcased his competitive spirit, but I saw in those games an act of genuine love: a willingness to tangle with an unbeatable opponent out of pure love for the game, its history, and the people who play it. Those people who watched Ke’s frustration responded in kind. AlphaGo may have been the winner, but Ke became the people’s champion. In that connection—human beings giving and receiving love—I caught a glimpse of how humans will find work and meaning in the age of artificial intelligence.

Doing this requires massive amounts of relevant data, a strong algorithm, a narrow domain, and a concrete goal. If you’re short any one of these, things fall apart. Too little data? The algorithm doesn’t have enough examples to uncover meaningful correlations. Too broad a goal? The algorithm lacks clear benchmarks to shoot for in optimization. Deep learning is what’s known as “narrow AI”—intelligence that takes data from one specific domain and applies it to optimizing one specific outcome.

People are so excited about deep learning precisely because its core power—its ability to recognize a pattern, optimize for a specific outcome, make a decision—can

As I demonstrate in the following chapters, that analysis is wrong. It is based on outdated assumptions about the Chinese technology environment, as well as a more fundamental misunderstanding of what is driving the ongoing AI revolution. The West may have sparked the fire of deep learning, but China will be the biggest beneficiary of the heat the AI fire is generating. That global shift is the product of two transitions: from the age of discovery to the age of implementation, and from the age of expertise to the age of data.

Today, successful AI algorithms need three things: big data, computing power, and the work of strong—but not necessarily elite—AI algorithm engineers.

The Chinese government’s sweeping plan for becoming an AI superpower pledged widespread support and funding for AI research, but most of all it acted as a beacon to local governments throughout the country to follow suit. Chinese governance structures are more complex than most Americans assume; the central government does not simply issue commands that are instantly implemented throughout the nation.

For as far back as many of us can remember, it was American technology companies that were pushing their products and their values on users around the globe. As a result, American companies, citizens, and politicians have forgotten what it feels like to be on the receiving end of these exchanges, a process that often feels akin to “technological colonization.” China does not intend to use its advantage in the AI era as a platform for such colonization, but AI-induced disruptions to the political and economic order will lead to a major shift in how all countries experience the phenomenon of digital globalization.

Further concentrating those profits is the fact that AI naturally trends toward winner-take-all economics within an industry. Deep learning’s relationship with data fosters a virtuous circle for strengthening the best products and companies: more data leads to better products, which in turn attract more users, who generate more data that further improves the product. That combination of data and cash also attracts the top AI talent to the top companies, widening the gap between industry leaders and laggards.

China and the United States are currently incubating the AI giants that will dominate global markets and extract wealth from consumers around the globe.

The AI world order will combine winner-take-all economics with an unprecedented concentration of wealth in the hands of a few companies in China and the United States. This, I believe, is the real underlying threat posed by artificial intelligence: tremendous social disorder and political collapse stemming from widespread unemployment and gaping inequality.

This kind of analysis, however, is the result of a deep misunderstanding of the dynamics at play in the Chinese market, and it reveals an egocentrism that defines all internet innovation in relation to Silicon Valley.

China’s early copycat internet companies looked harmless from the outside, almost cute. During China’s first internet boom of the late 1990s, Chinese companies looked to Silicon Valley for talent, funding, and even names for their infant startups. The country’s first search engine was the creation of Charles Zhang, a Chinese physicist with a Ph.D. from MIT. While in the United States Zhang had seen the early internet take off, and he wanted to kick-start that same process in his home country. Zhang used investments from his professors at MIT and returned to China, intent on building up the country’s core internet infrastructure.

I’ve found Silicon Valley’s approach to China to be a far more important reason for their failure. American companies treat China like just any other market to check off their global list. They don’t invest the resources, have the patience, or give their Chinese teams the flexibility needed to compete with China’s world-class entrepreneurs. They see the primary job in China as marketing their existing products to Chinese users. In reality, they need to put in real work tailoring their products for Chinese users or building new products from the ground up to meet market demands. Resistance to localization slows down product iteration and makes local teams feel like cogs in a clunky machine.

They aspire to the mythology satirized in the HBO series Silicon Valley, that of a skeleton crew of hackers building a billion-dollar business without ever leaving their San Francisco loft. Chinese companies don’t have this kind of luxury. Surrounded by competitors ready to reverse-engineer their digital products, they must use their scale, spending, and efficiency at the grunt work as a differentiating factor.

But of those three, it is the volume of data that will be the most important going forward. That’s because once technical talent reaches a certain threshold, it begins to show diminishing returns. Beyond that point, data makes all the difference.

Our present phase of AI implementation fits this latter model. A constant stream of headlines about the latest task tackled by AI gives us the mistaken sense that we are living through an age of discovery, a time when the Enrico Fermis of the world determine the balance of power. In reality, we are witnessing the application of one fundamental breakthrough—deep learning and related techniques—to many different problems. That’s a process that requires well-trained AI scientists, the tinkerers of this age. Today, those tinkerers are putting AI’s superhuman powers of pattern recognition to use making loans, driving cars, translating text, playing Go, and powering your Amazon Alexa.

And for this technological revolution, the tinkerers have an added advantage: real-time access to the work of leading pioneers. During the Industrial Revolution, national borders and language barriers meant that new industrial breakthroughs remained bottled up in their country of origin, England. America’s cultural proximity and loose intellectual property laws helped it pilfer some key inventions, but there remained a substantial lag between the innovator and the imitator.

Not so today. When asked how far China lags behind Silicon Valley in artificial intelligence research, some Chinese entrepreneurs jokingly answer “sixteen hours”—the time difference between California and Beijing.

The speed of improvements in AI also drives researchers to instantly share their results. Many AI scientists aren’t trying to make fundamental breakthroughs on the scale of deep learning, but they are constantly making marginal improvements to the best algorithms. Those improvements regularly set new records for accuracy on tasks like speech recognition or visual identification. Researchers compete on the basis of these records—not on new products or revenue numbers—and when one sets a new record, he or she wants to be recognized and receive credit for the achievement. But given the rapid pace of improvements, many researchers fear that if they wait to publish in a journal, their record will already have been eclipsed and their moment at the cutting edge will go undocumented. So instead of sitting on that research, they opt for instant publication on websites like,

For more academic discussions, I’m part of the five-hundred-member “Weekly Paper Discussion Group,” just one of the dozens of WeChat groups that come together to dissect a new AI research publication each week. The chat group buzzes with hundreds of messages per day: earnest questions about this week’s paper, screen shots of the members’ latest algorithmic achievements, and, of course, plenty of animated emojis. But Chinese AI practitioners aren’t just passive recipients of wisdom spilling forth from the Western world. They’re now giving back to that research ecosystem at an accelerating rate.

Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent. These Seven Giants have, in effect, morphed into what nations were fifty years ago—that is, large and relatively closed-off systems that concentrate talent and resources on breakthroughs that will mostly remain “in house.”

The seals around corporate research are never airtight: team members leave to found their own AI startups, and some groups like Microsoft Research, Facebook AI Research, and DeepMind still publish articles on their most meaningful contributions. But broadly speaking, if one of these companies makes a unique breakthrough—a trade secret that could generate massive profits for that company alone—it will do its best to keep a lid on it and will try to extract maximum value before the word gets out. A groundbreaking discovery occurring within one of these closed systems poses the greatest threat to the world’s open AI ecosystem.

Competition, however, won’t play out in just these two countries. AI-driven services that are pioneered in the United States and China will then proliferate across billions of users around the globe, many of them in developing countries. Companies like Uber, Didi, Alibaba, and Amazon are already fiercely competing for these developing markets but adopting very different strategies. While Silicon Valley juggernauts are trying to conquer each new market with their own products, China’s internet companies are instead investing in these countries’ scrappy local startups as they try to fight off U.S. domination. It’s a competition that’s just getting started, and one that will have profound implications for the global economic landscape of the twenty-first century.

business AI can be about more than dollars and cents. When applied to other information-driven public goods, it can mean a massive democratization of high-quality services to those who previously couldn’t afford them.