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by
Kai-Fu Lee
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November 4 - December 23, 2020
We wonder whether AI can vault us to lives of material abundance, and whether there is space for humanity in a world run by intelligent machines.
It was the real-world demons that could be conjured up by mass unemployment and the resulting social turmoil.
Neural networks require large amounts of two things: computing power and data.
Deep learning’s big technical break finally arrived in the mid-2000s, when leading researcher Geoffrey Hinton discovered
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.
China’s technology community didn’t properly wake up to the deep-learning revolution until its Sputnik Moment in 2016,
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.
AI is the impression that we are living in an age of discovery, a time in which elite AI researchers are constantly breaking down old paradigms and finally cracking longstanding mysteries. This impression has been fed by a constant stream of breathless media reports announcing the latest feat performed by AI:
beating human champions at the bluff-heavy game
The age of implementation means we will finally see real-world applications after decades of promising research,
Since the dawn of deep learning, no other group of researchers or engineers has come up with innovation on that scale.
big data, computing power, and the work of strong—but
Silicon Valley’s entrepreneurs have earned a reputation as some of the hardest working in America, passionate young founders who pull all-nighters in a mad dash to get a product out, and then obsessively iterate that product while seeking out the next big thing. Entrepreneurs there do indeed work hard. But I’ve spent decades deeply embedded in both Silicon Valley and China’s tech scene, working at Apple, Microsoft, and Google before incubating and investing in dozens of Chinese
startups. I can tell you that Silicon Valley looks downright sluggish compared to its competitor across the Pacific.
Every day spent in China’s startup scene is a trial by fire, like a day spent as a gladiator in the Coliseum. The battles are life or death, and your opponents have no scruples.
This rough-and-tumble environment makes a strong contrast to Silicon Valley, where copying is stigmatized and many companies are allowed to coast on the basis of one original idea or lucky break.
Chinese urbanites began paying for real-world purchases with bar codes on their phones, part of a mobile payments revolution unseen anywhere else. Armies
But it does have the ability to pick out certain long-term goals and mobilize epic resources to push in that direction.
PricewaterhouseCoopers estimates AI deployment will add $15.7 trillion to global GDP by 2030. China is predicted to take home $7 trillion of that total, nearly double North America’s $3.7 trillion in gains.
accountants, assembly line workers, warehouse operators, stock analysts, quality control inspectors, truckers, paralegals, and even radiologists, just to name a few.
predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon.
That combination of data and cash also attracts the top AI talent to the top companies, widening the gap between industry leaders and laggards.
industry profits across different companies and regions, we will begin to see greater and greater concentration of these astronomical sums in the hands of a few, all while unemployment lines grow longer.
AI research labs staffed with great talent, but they lack the venture-capital ecosystem and large user bases to generate the data that will be key to the age of implementation.
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.
myself faced a mortal threat and a crisis of purpose in my own personal life.
To the Silicon Valley elite, Wang was shameless. In the mythology of the valley, few things are more stigmatized than blindly aping the establishment. It was precisely this kind of copycat entrepreneurship
is now the fourth most valuable startup in the world, valued at $30 billion, and Wang sees Alibaba and Amazon as his main competitors going forward.
Over five thousand companies did the exact same thing, including Groupon itself.
a coliseum where hundreds
of copycat gladiators fought to the death.
The dramatic transformation that deep learning promises to bring to the global economy won’t be delivered by isolated researchers producing novel academic results in the elite computer science labs of MIT or Stanford.
Instead, it will be delivered by down-to-earth, profit-hungry entrepreneurs teaming up with AI experts to bring the transformative power of deep learning to bear on real-world industries.
Corporate America is unprepared for this global wave of Chinese entrepreneurship because it fundamentally misunderstood the secret to The Cloner’s success. Wang Xing didn’t succeed because he’d been a copycat. He triumphed because he’d become a gladiator.
It’s an environment of abundance that lends itself to lofty thinking, to envisioning elegant technical solutions to abstract problems.
detached from earthly concerns or financial motivations.
Chinese companies are first and foremost market-driven.
Those things are all nice side benefits, but the grand prize is getting rich, and it doesn’t matter how you
While Socrates encouraged his students to seek truth by questioning everything, ancient Chinese philosophers counseled people to follow the rituals of sages from the ancient past. Rigorous copying of perfection was seen as the route
“let some people get rich first” in order to develop.
Silicon Valley inspiration but also droves of similar copycats. They learned what worked and what didn’t with Chinese
Alibaba founder Jack Ma was busy copying eBay’s core functions and adapting the business model to Chinese realities.
Within a year, eBay fully retreated from the Chinese market.
believing that the elegant products coming out of the Silicon Valley headquarters should be good enough for users around the globe.
American companies treat China like just any other market to check off their global list.
Battles with Silicon Valley may have created some of China’s homegrown internet Goliaths, but it was cutthroat Chinese domestic competition that forged a generation of gladiator entrepreneurs.
kill or be killed. Any company that can’t fully insulate itself from competitors—on a technical, business, or even personnel level—is a target for attack.
The pace is incredible in China. While I was leading teams in China, I’d just call a meeting on a Saturday or Sunday, or whenever I felt like it, and everyone showed up and there’d be no complaining. If I sent a text message at 7:00 PM over dinner and they haven’t responded by 8:00 PM, I would wonder what’s going on. It’s just a constant pace of decision-making. The market does something, so you better react. That, I think, has made the China ecosystem incredible at figuring out innovations, how to take things to market. . . . I was in the US working with a vendor. I won’t use any names, but a
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That methodology was first explicitly formulated in Silicon Valley and popularized by the 2011 book The Lean Startup. Core to its philosophy is the idea that founders don’t know what product the market needs—the market knows what product the market needs. Instead of spending years and millions of dollars secretly creating their idea of the perfect product, startups should move quickly to release a “minimum viable product” that can tease out market demand for different functions. Internet-based startups can then receive instant feedback based on customer activity, letting them immediately begin
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