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

by Kai-Fu Lee

China has already vaulted far ahead of the United States as the world’s largest producer of digital data, a gap that is widening by the day.

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.

Chinese companies are instead gathering data from the real world: the what, when, and where of physical purchases, meals, makeovers, and transportation. Deep learning can only optimize what it can “see” by way of data, and China’s physically grounded technology ecosystem gives these algorithms many more eyes into the content of our daily lives.

In the span of two years, WeChat went from a no-name app to a powerhouse of messaging, media, marketing, and gaming.

This metastasizing functionality would blur the lines dividing our online and offline worlds, both molding and feeding off of China’s alternate internet universe.

The four years leading up to Tencent’s Pearl Harbor moment saw many of the pieces of China’s alternate internet universe fall into place. Gladiatorial competition between China’s copycat startups had trained a generation of street-smart internet entrepreneurs. Smartphone users had more than doubled between 2009 and 2013, from 233 million to a whopping 500 million. Early-stage funds were fostering a new generation of startups building innovative mobile apps for this market.

So when the central government sets a clear goal—a new metric on which lower-level officials can demonstrate their competence—ambitious officials everywhere throw themselves into advancing that goal and proving themselves capable.

The flood of subsidies created 6,600 new startup incubators around the nation, more than quadrupling the overall total. Suddenly, it was easier than ever for startups to get quality space, and they could do so at discount rates that left more money for building their businesses.

But if the portfolio companies succeed—say, double in value within five years—then the fund’s manager caps the government’s upside from the fund at a predetermined percentage, perhaps 10 percent, and uses private money to buy the government’s shares out at that rate. That leaves the remaining 90 percent gain on the government’s investment to be distributed among private investors who have already seen their own investments double. Private investors are thus incentivized to follow the government’s lead, investing in funds and industries that the local government wants to foster. During China’s mass innovation push, use of local government guiding funds exploded, nearly quadrupling from $7 billion in 2013 to $27 billion in 2015.

The campaign left a deep imprint on ordinary people’s perceptions of internet entrepreneurship, genuinely shifting the cultural zeitgeist.

The O2O revolution was about bringing that same e-commerce convenience to the purchase of real-world services, things that can’t be put in a cardboard box and shipped across country, like hot food, a ride to the bar, or a new haircut.

Uber may have given an early glimpse of O2O, but it was Chinese companies that would take the core strengths of that model and apply it to transforming dozens of other industries.

When looking to disrupt a new industry, American internet companies tend to take a “light” approach. They generally believe the internet’s fundamental power is sharing information, closing knowledge gaps, and connecting people digitally. As internet-driven companies, they try to stick to this core strength. Silicon Valley startups will build the information platform but then let brick-and-mortar businesses handle the on-the-ground logistics. They want to win by outsmarting opponents, by coming up with novel and elegant code-based solutions to information problems.

In China, companies tend to go “heavy.” They don’t want to just build the platform—they want to recruit each seller, handle the goods, run the delivery team, supply the scooters, repair those scooters, and control the payment. And if need be, they’ll subsidize that entire process to speed user adoption and undercut rivals. 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.

Cash has disappeared so quickly from Chinese cities that it even “disrupted” crime. In March 2017, a pair of Chinese cousins made headlines with a hapless string of robberies. The pair had traveled to Hangzhou, a wealthy city and home to Alibaba, with the goal of making a couple of lucrative scores and then skipping town. Armed with two knives, the cousins robbed three consecutive convenience stores only to find that the owners had almost no cash to hand over—virtually all their customers were now paying directly with their phones. Their crime spree netted them around $125 each—not even enough to cover their travel to and from Hangzhou—when police picked them up. Local media reported rumors that upon arrest one of the brothers cried out, “How is there no cash left in Hangzhou?”

That mobile payment data will prove invaluable in building AI-driven companies in retail, real estate, and a range of other sectors.

This shift forms a dramatic visual illustration of what China’s alternate internet universe does best: solving practical problems by blurring the lines between the online and offline worlds.

It’s been a messy, expensive, and disruptive process, but the payoff has been tremendous. China has built a roster of technology giants worth over a trillion dollars—a feat accomplished by no other country outside the United States.

Each dimension of that universe—WeChat activity, O2O services, ride-hailing, mobile payments, and bike-sharing—adds a new layer to a data-scape that is unprecedented in its granular mapping of real-world consumption and transportation habits.

China’s O2O explosion gave its companies tremendous data on the offline lives of their users: the what, where, and when of their meals, massages, and day-to-day activities. Digital payments cracked open the black box of real-world consumer purchases, giving these companies a precise, real-time data map of consumer behavior. Peer-to-peer transactions added a new layer of social data atop those economic transactions. The country’s bike-sharing revolution has carpeted its cities in IoT transportation devices that color in the texture of urban life. They trace tens of millions of commutes, trips to the store, rides home, and first dates, dwarfing companies like Uber and Lyft in both quantity and granularity of data.

But building an AI-driven economy requires more than just gladiator entrepreneurs and abundant data. It also takes an army of trained AI engineers and a government eager to embrace the power of this transformative technology.

As I laid out earlier, creating an AI superpower for the twenty-first century requires four main building blocks: abundant data, tenacious entrepreneurs, well-trained AI scientists, and a supportive policy environment.

As artificial intelligence filters into the broader economy, this era will reward the quantity of solid AI engineers over the quality of elite researchers. Real economic strength in the age of AI implementation won’t come just from a handful of elite scientists who push the boundaries of research. It will come from an army of well-trained engineers who team up with entrepreneurs to turn those discoveries into game-changing companies.

They’re also working to construct the “power grids” for the AI age: privately controlled computing networks that distribute machine learning across the economy, with the corporate giants acting as “utilities.”

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.

Fermi and the Manhattan Project embodied an age of discovery that rewarded quality over quantity in expertise.

Often, once a fundamental breakthrough has been achieved, the center of gravity quickly shifts from a handful of elite researchers to an army of tinkerers—engineers with just enough expertise to apply the technology to different problems. This is particularly true when the payoff of a breakthrough is diffused throughout society rather than concentrated in a few labs or weapons systems.

Mass electrification exemplified this process. Following Thomas Edison’s harnessing of electricity, the field rapidly shifted from invention to implementation. Thousands of engineers began tinkering with electricity, using it to power new devices and reorganize industrial processes. Those tinkerers didn’t have to break new ground like Edison. They just had to know enough about how electricity worked to turn its power into useful and profitable machines.

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

And for this technological revolution, the tinkerers have an added advantage: real-time access to the work of leading pioneers.

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.

Among the elite institutions, Tsinghua University even outnumbered places like Stanford University in total AI citations.

Artificial intelligence doesn’t touch on sensitive political questions, and China’s AI scientists are essentially as free as their American counterparts to construct cutting-edge algorithms or build profitable AI applications.

But if the next breakthrough on the scale of deep learning occurs soon, and it happens within a hermetically sealed corporate environment, all bets are off. It could give one company an insurmountable advantage over the other Seven Giants

So instead, many academic researchers are following Geoffrey Hinton’s exhortation to move on and focus on inventing “the next deep learning,” a fundamentally new approach to AI problems that could change the game.

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.

Of the top one hundred AI researchers and engineers, around half are already working for Google.

Tencent possesses perhaps the single richest data ecosystem of all the giants.

Alibaba has taken the lead on “City Brains”: massive AI-driven networks that optimize city services by drawing on data from video cameras, social media, public transit, and location-based apps. Working with the city government in its hometown of Hangzhou, Alibaba is using advanced object-recognition and predictive transit algorithms to constantly tweak the patterns for red lights and alert emergency services to traffic accidents. The trial has increased traffic speeds by 10 percent in some areas, and Alibaba is now preparing to bring the service to other cities.

It’s a contest between two approaches to distributing the “electricity” of AI across the economy: the “grid” approach of the Seven Giants versus the “battery” approach of the startups. How that race plays out will determine the nature of the AI business landscape—monopoly,

The “grid” approach is trying to commoditize AI. It aims to turn the power of machine learning into a standardized service that can be purchased by any company—or even be given away for free for academic or personal use—and accessed via cloud computing platforms.

AI startups are taking the opposite approach. Instead of waiting for this grid to take shape, startups are building highly specific “battery-powered” AI products for each use-case. These startups are banking on depth rather than breadth. Instead of supplying general-purpose machine-learning capabilities, they build new products and train algorithms for specific tasks, including medical diagnosis, mortgage lending, and autonomous drones.

The Chinese Ministry of Science and Technology is doling out large sums of money, naming as a specific goal the construction of a chip with performance and energy efficiency twenty times better than one of Nvidia’s current offerings.

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.

In fact, when President Trump took office just three months after the report’s debut, he proposed cutting funding for AI research at the National Science Foundation.

If AlphaGo was China’s Sputnik Moment, the government’s AI plan was like President John F. Kennedy’s landmark speech calling for America to land a man on the moon. The report lacked Kennedy’s soaring rhetoric, but it set off a similar national mobilization, an all-hands-on-deck approach to national innovation.

Between 2007 and 2017, China went from having zero high-speed rail lines to having more miles of high-speed rail operational than the rest of the world combined. During the “mass innovation and mass entrepreneurship” campaign that began in 2015, a similar flurry of incentives created 6,600 new startup incubators and shifted the national culture around technology startups.

But that’s a risk these Chinese government officials are willing to take, a loss they’re willing to absorb in pursuit of a larger goal: brute-forcing the economic and technological upgrading of their cities. The potential upside of that transformation is large enough to warrant making expensive bets on the next big thing.

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.

How should we balance the livelihoods of millions of truck drivers against the billions of dollars and millions of hours saved by autonomous vehicles?