AI Superpowers
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Summary
We are living in the AI age of implementation
"Implementation is what makes academic advances meaningful and what will truly end up changing the fabric of our daily lives. The age of implementation means we will finally see real-world applications after decades of promising research."
"The transitions from discovery to implementation, and from expertise to data, now tilt the playing field toward China. Moving from discovery to implementation reduces one of China’s greatest weak points (outside-the-box approaches to research questions) and also leverages the country’s most significant strength: scrappy entrepreneurs with sharp instincts for building robust businesses
China's cultural ecosystem gives it an advantage over America's in the age of implementation
Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective. That mentality leads to incredible flexibility in business models and execution, a perfect distillation of the “lean startup” model often praised in Silicon Valley
As technical know-how has diffused around the globe, it is becoming harder to force people of all countries and cultures into a cookie-cutter mold that was often built in America for Americans — and silicon valley companies with their purist approach cannot adapt
Eye-tracking maps reveals differences in how American and Chinese users approached search. Americans treated search engines like the Yellow Pages. Chinese users treated search engines like a shopping mall. For tens of millions of Chinese new to the internet, this was their first exposure to such a variety of information, and they wanted to sample it all
China believes in copying, unlike America. It's 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. To the winner go the spoils, and those spoils can amount to billions of dollars
China's internet universe that merges offline and online activities is giving it a data and insights edge
An explosion of mobile users pushed Chinese companies to roll up their sleeves and do the grunt work of running an operations-heavy business in the real world. American startups like to stick to what they know: building clean digital platforms that facilitate information exchanges. 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
This Chinese commitment to grunt work laying the groundwork for AI implementation. By immersing themselves in the messy details of food delivery, car repairs, shared bikes, and purchases at the corner store, these companies are acquiring deep "real life" data. Silicon Valley data concentrates heavily in your online behavior. Chinese companies are gathering both online and offline data.
By enrolling the vendors, processing the orders, delivering the food, and taking in the payments, China’s O2O champions began amassing a wealth of real-world data on the consumption patterns and personal habits of their users. Going heavy gave these companies a data edge over their Silicon Valley peers, but it was mobile payments that would extend their reach even further into the real world and turn that data edge into a commanding lead
China follows Taleb's recommendation of optimizing for impact, not efficiency
This process can be both highly inefficient and extraordinarily effective. When the long-term upside is so monumental, overpaying in the short term can be the right thing to do. China wanted to use government money to brute-force a faster transformation, one that would pay dividends through an earlier transition to higher-quality growth. That process was often locally inefficient—incubators that went unoccupied and innovation avenues that never paid off—but on a national scale, the impact was tremendous
It’s too early to know the exact results of China’s AI campaign, but if Chinese history is any guide, it is likely to be somewhat inefficient but extremely effective. The sheer scope of financing and speed of deployment almost guarantees that there will be inefficiencies. Government bureaucracies cannot rapidly deploy billions of dollars in investments and subsidies without some amount of waste
Modern AI revolution requires a large quantity of solid engineers more than a small number of innovative scientists
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.
Chinese engineers taking advantage of AI’s open research culture to absorb knowledge straight from the source and in real time. That means dissecting the latest online academic publications, debating the approaches of top AI scientists in WeChat groups, and streaming their lectures on smartphones
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.
This time is different: the AI revolution vs Industrial Revolution
“Whereas the Industrial Revolution took place across several generations, the AI revolution will have a major impact within one generation. That’s because AI adoption will be accelerated by three catalysts that didn’t exist during the introduction of steam power and electricity.
- First, many productivity-increasing AI products are just digital algorithms: infinitely replicable and instantly distributable around the world. This makes for a stark contrast to the hardware-intensive revolutions of steam power, electricity, and even large parts of ICT
- The second catalyst is one that many in the technology world today take for granted: the creation of the venture-capital industry. VC funding—early investments in high-risk, high-potential companies—barely existed before the 1970s. That meant the inventors and innovators during the first two Industrial Revolutions had to rely on a thin patchwork of financing mechanisms to get their products off the ground, usually via personal wealth, family members, rich patrons, or bank loans
- The third is the democratization of opportunity. People from all around the world and all walks of life are contributing to AI, unlike the industrial revolution where innovation was concentrated among western elites
Policy approach to reducing adverse effects of automation
THE THREE R’S to fighting adverse effects of AI: REDUCE, RETRAIN, AND REDISTRIBUTE
- Those advocates of reducing work hours believe that AI will reduce the demand for human labor and feel that this impact could be absorbed by moving to a three- or four-day work week, spreading the jobs that do remain over more workers
- Those advocating the retraining of workers tend to believe that AI will slowly shift what skills are in demand, but if workers can adapt their abilities and training, then there will be no decrease in the need for labor
- The redistribution camp tends to be the most dire in their predictions of AI-induced job losses. Many of them predict that as AI advances, it will so thoroughly displace or dislodge workers that no amount of training or tweaking hours will be sufficient. Instead, we will have to adopt more radical redistribution schemes to support unemployed workers and spread the wealth created by AI
Workers will find themselves in a state of constant retreat, like animals fleeing relentlessly rising flood waters, anxiously hopping from one rock to another in search of higher ground. Retraining will help many people find their place in the AI economy, and we must experiment with ways to scale this up and make it widely available. We cannot count on this haphazard approach to address the macro-level disruptions that will sweep over labor markets
Many of the jobs created by the free market will grow out of a natural symbiosis between humans and machines. While AI handles the routine optimization tasks, human beings will bring the personal, creative, and compassionate touch. This will involve the redefinition of existing occupations or the creation of entirely new professions in which people team up with machines to deliver services that are both highly efficient and eminently human
AI tools will only improve with time, and those who seek to compete against AI on its own terms will lose out. In the long run, resistance may be futile, but symbiosis will be rewarded.
In the past, [things like social impact] were the kinds of things that businesspeople merely dabbled in when they had time and money to spare. Sure, they think, why not throw some money into a microfinance startup or buy some corporate carbon offsets so we can put out a happy press release touting it. But in the age of AI, we will need to seriously deepen our commitment to—and broaden our definition of—these activities
Raw Highlights
“Not long ago, China lagged years, if not decades, behind the United States in artificial intelligence. But over the past three years [2016-2018] China has caught AI fever, experiencing a surge of excitement about the field that dwarfs even what we see in the rest of the world. Enthusiasm about AI has spilled over from the technology and business communities into government policymaking, and it has trickled all the way down to kindergarten classrooms in Beijing”
China’s Sputnik Moment
“AlphaGo scored its first high-profile victory in March 2016 during a five-game series against the legendary Korean player Lee Sedol, winning four to one. While barely noticed by most Americans, the five games drew more than 280 million Chinese viewers. Overnight, China plunged into an artificial intelligence fever. The buzz didn’t quite rival America’s reaction to Sputnik, but it lit a fire under the Chinese technology community that has been burning ever since”
“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”
“the skillful application of AI will be China’s greatest opportunity to catch up with—and possibly surpass—the United States”
“the story of the birth of deep learning took place almost entirely in the United States, Canada, and the United Kingdom. After that, a smaller number of Chinese entrepreneurs and venture-capital funds like my own began to invest in this area. But the great majority of China’s technology community didn’t properly wake up to the deep-learning revolution until its Sputnik Moment in 2016, a full decade behind the field’s breakthrough academic paper and four years after it proved itself in the computer vision competition”
“THE AGE OF IMPLEMENTATION
What they really represent is the application of deep learning’s incredible powers of pattern recognition and prediction to different spheres, such as diagnosing a disease, issuing an insurance policy, driving a car, or translating a Chinese sentence into readable English. They do not signify rapid progress toward “general AI” or any other similar breakthrough on the level of deep learning. This is the age of implementation, and the companies that cash in on this time period will need talented entrepreneurs, engineers, and product managers.
Deep-learning pioneer Andrew Ng has compared AI to Thomas Edison’s harnessing of electricity: a breakthrough technology on its own, and one that once harnessed can be applied to revolutionizing dozens of different industries”
“implementation is what makes academic advances meaningful and what will truly end up changing the fabric of our daily lives. The age of implementation means we will finally see real-world applications after decades of promising research, something I’ve been looking forward to for much of my adult life”
“the transitions described on the previous pages—from discovery to implementation, and from expertise to data—now tilt the playing field toward China. They do this by minimizing China’s weaknesses and amplifying its strengths. Moving from discovery to implementation reduces one of China’s greatest weak points (outside-the-box approaches to research questions) and also leverages the country’s most significant strength: scrappy entrepreneurs with sharp instincts for building robust businesses. The transition from expertise to data has a similar benefit, downplaying the importance of the globally elite researchers that China lacks and maximizing the value of another key resource that China has in abundance, data.”
“China’s successful internet entrepreneurs have risen to where they are by conquering the most cutthroat competitive environment on the planet. They live in a world where speed is essential, copying is an accepted practice, and competitors will stop at nothing to win a new market. Every day spent in China’s startup scene is a trial by fire”
“If one’s only edge is a single novel idea, that idea will invariably be copied, your key employees will be poached, and you’ll be driven out of business by VC-subsidized competitors. 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. That lack of competition can lead to a certain level of complacency, with entrepreneurs failing to explore all the possible iterations of their first innovation. The messy markets and dirty tricks of China’s “copycat” era produced some questionable companies, but they also incubated a generation of the world’s most nimble, savvy, and nose-to-the-grindstone entrepreneurs. These entrepreneurs will be the secret sauce that helps China become the first country to cash in on AI’s age of implementation”
Copycats in the Coliseum
“Wang adopted both when he created Xiaonei (“On Campus”). The network was exclusive to Chinese college students, and the user interface was an exact copy of Mark Zuckerberg’s site. Wang meticulously recreated the home page, profiles, tool bars, and color schemes of the Palo Alto startup. Chinese media reported that the earliest version of Xiaonei even went so far as to put Facebook’s own tagline, “A Mark Zuckerberg Production,” at the bottom of each page”
“In analyzing Wang’s success, Western observers make a fundamental mistake. They believe Meituan triumphed by taking a great American idea and simply copying it in the sheltered Chinese internet, a safe space where weak local companies can survive under far less intense competition”
“Survival in the internet coliseum required relentlessly iterating products, controlling costs, executing flawlessly, generating positive PR, raising money at exaggerated valuations, and seeking ways to build a robust business “moat” to keep the copycats out. Pure copycats never made for great companies, and they couldn’t survive inside this coliseum. But the trial-by-fire competitive landscape created when one is surrounded by ruthless copycats had the result of forging a generation of the most tenacious entrepreneurs on earth”
“Chinese entrepreneurs will be the tycoons and tinkerers who electrify everything from household appliances to homeowners’ insurance. Their knack for endlessly tweaking business models and sniffing out profits will yield an incredible array of practical—maybe even life-changing—applications. These will be deployed in their home country and then pushed abroad, potentially taking over most developing markets around the globe.
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”
“When they arrived in Silicon Valley, their commutes to and from work took them through the gently curving, tree-lined streets of suburban California.
It’s an environment of abundance that lends itself to lofty thinking, to envisioning elegant technical solutions to abstract problems. Throw in the valley’s rich history of computer science breakthroughs, and you’ve set the stage for the geeky-hippie hybrid ideology that has long defined Silicon Valley. Central to that ideology is a wide-eyed techno-optimism, a belief that every person and company can truly change the world through innovative thinking”
“Startups that grow up in this kind [comfortable Silicon Valley] of environment tend to be mission-driven. They start with a novel idea or idealistic goal, and they build a company around that”
“In stark contrast, China’s startup culture is the yin to Silicon Valley’s yang: instead of being mission-driven, Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product, adopt any model, or go into any business that will accomplish that objective. That mentality leads to incredible flexibility in business models and execution, a perfect distillation of the “lean startup” model often praised in Silicon Valley. It doesn’t matter where an idea came from or who came up with it. All that matters is whether you can execute it to make a financial profit”
“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 to true mastery.
Layered atop this cultural propensity for imitation is the deeply ingrained scarcity mentality of twentieth-century China. Most Chinese tech entrepreneurs are at most one generation away from grinding poverty that stretches back centuries. Many are only children—products of the now-defunct “One Child Policy”—carrying on their backs the expectations of two parents and four grandparents who have invested all their hopes”
“Growing up, their parents didn’t talk to them about changing the world. Rather, they talked about survival, about a responsibility to earn money so they can take care of their parents when their parents are too old to work in the fields”
“Combine these three currents—a cultural acceptance of copying, a scarcity mentality, and the willingness to dive into any promising new industry—and you have the psychological foundations of China’s internet ecosystem”
“In Beijing, entrepreneurs often joke that Facebook is “the most Chinese company in Silicon Valley” for its willingness to copy from other startups and for Zuckerberg’s fiercely competitive streak”
“The most valuable product to come out of China’s copycat era wasn’t a product at all: it was the entrepreneurs themselves.”
“While working as the founding president of Google China, I would bring visiting delegations of Google executives here to see the clocks in person. But I didn’t do it so they could revel in the genius of their European ancestors. I did it because, on closer inspection, one discovers that many of the finest specimens of European craftsmanship were created in the southern Chinese city of Guangzhou, which was then called Canton.
After European clocks won the favor of the Chinese emperor, local workshops sprang up all over China to study and recreate the Western imports. In the southern port cities where Westerners came to trade, China’s best craftspeople took apart the ingenious European devices, examining each interlocking piece and design flourish. They mastered the basics and began producing clocks that were near-exact replicas of the European models. From there, the artisans took the underlying principles of clock-building and began constructing timepieces that embodied Chinese designs and cultural traditions: animated Silk Road caravans, lifelike scenes from the streets of Beijing, and the quiet equanimity of Buddhist sutras. These workshops eventually began producing clocks that rivaled or even exceeded the craftsmanship coming out of Europe, all while weaving[…]”
“Silicon Valley investors take as an article of faith that a pure innovation mentality is the foundation on which companies like Google, Facebook, Amazon, and Apple are built. It was an irrepressible impulse to “think different” that drove people like Steve Jobs, Mark Zuckerberg, and Jeff Bezos to create these companies that would change the world. In that school of thought, China’s knockoff clockmakers were headed down a dead-end road. A copycat mentality is a core stumbling block on the path to true innovation. By blindly imitating others—or so the theory goes—you stunt your own imagination and kill the chances of creating an original and innovative product.
But I saw early copycats like Wang Xing’s Twitter knockoff not as stumbling blocks but as building blocks. That first act of copying didn’t turn into an anti-innovation mentality that its creator could never shake. It was a necessary steppingstone on the way to more original and locally tailored technology products”
“Companies like Google and Facebook are often loath to allow local changes to their core products or business models. They tend to believe in building one thing and building it well. It’s an approach that helped them rapidly sweep the globe in the early days of the internet, when most countries lagged so far behind in technology that they couldn’t offer any localized alternatives. But as technical know-how has diffused around the globe, it is becoming harder to force people of all countries and cultures into a cookie-cutter mold that was often built in America for Americans.
As a result, when Chinese copycats went head-to-head with their Silicon Valley forefathers, they took that American unwillingness to adapt and weaponized it. Every divergence between Chinese user preferences and a global product became an opening that local competitors could attack. They began tailoring their products and business models to local needs, and driving a wedge between Chinese internet users and Silicon Valley.”
“When eBay entered the Chinese market in 2002, they did so by buying the leading Chinese online auction site—not Alibaba but an eBay impersonator called EachNet. The marriage created the ultimate power couple: the top global e-commerce site and China’s number one knockoff. eBay proceeded to strip away the Chinese company’s user interface, rebuilding the site in eBay’s global product image. Company leadership brought in international managers for the new China operations, who directed all traffic through eBay’s servers back in the United States. But the new user interface didn’t match Chinese web-surfing habits, the new leadership didn’t understand Chinese domestic markets, and the trans-Pacific routing of traffic slowed page-loading times. At one point an earthquake under the Pacific Ocean severed key cables and knocked the site offline for a few days.
Meanwhile, Alibaba founder Jack Ma was busy copying eBay’s core functions and adapting the business model to Chinese realities. He began by creating an auction-style platform, Taobao, to directly compete with eBay’s core business. From there, Ma’s team continually tweaked Taobao’s functions and tacked on features to meet unique Chinese needs. His strongest localization plays were in payment and revenue models[…]”
“competition with eBay heated up, Ma developed a new approach: he pledged to make all listings and transactions on Taobao free for the next three years, a promise he soon extended indefinitely. It was an ingenious PR move and a savvy business play. In the short term, it won goodwill from Chinese sellers still leery of internet transactions. Allowing them to list for free helped Ma build a thriving marketplace in a low-trust society. It took years to get there, but in the long term, that marketplace grew so large that in order to get their products noticed, power sellers had to pay Ma for advertisements and higher search rankings. Brands would end up paying even larger premiums to list on Taobao’s more high-end sister site, Tmall”
“Eye-tracking maps revealed a deeper truth about the way both sets of users approached search. Americans treated search engines like the Yellow Pages, a tool for simply finding a specific piece of information. Chinese users treated search engines like a shopping mall, a place to check out a variety of goods, try each one on, and eventually pick a few things to buy. For tens of millions of Chinese new to the internet, this was their first exposure to such a variety of information, and they wanted to sample it all.
That strikingly fundamental difference in user attitudes should have led to a number of product modifications for Chinese users”
“recommended Google make an exception and copy the Baidu model of opening different windows for each click. But the company had a lengthy review process for any changes to core products because those changes “forked” the code and made it more difficult to maintain. Google and other Silicon Valley companies tried hard to avoid that, believing that the elegant products coming out of the Silicon Valley headquarters should be good enough for users around the globe. I fought for months to get this change made and eventually prevailed, but in the meantime Baidu had won over more users with its China-centric product offering.
Battles like this were repeated continuously over my four years with Google. In fairness to Google, headquarters gave us more latitude than most Silicon Valley companies give to their China branches, and we used that leverage to develop many locally optimized features, which won back substantial market share Google had lost in previous years. But headquarters’ resistance to forking made each new feature an uphill battle, one that slowed us up and wore us down. Tired of fighting with their own company, many employees left out of frustration.”
“Silicon Valley companies also lose out on top talent. With so much opportunity now for growth within Chinese startups, the most ambitious young people join or start local companies. They know that if they join the Chinese team of an American company, that company’s management will forever see them as “local hires,” workers whose utility is limited to their country of birth. They’ll never be given a chance to climb the hierarchy at the Silicon Valley headquarters, instead bumping up against the ceiling of a “country manager” for China. The most ambitious young people—the ones who want to make a global impact—chafe at those restrictions, choosing to start their own companies or to climb the ranks at one of China’s tech juggernauts. Foreign firms are often left with mild-mannered managers or career salespeople helicoptered in from other countries, people who are more concerned with protecting their salary and stock options than with truly fighting to win the Chinese market. Put those relatively cautious managers up against gladiatorial entrepreneurs who cut their teeth in China’s competitive coliseum, and it’s always the gladiators who will emerge victorious.”
“Kaixin001 was a solid product, but its founder was no gladiator. When he created the network, the URL that he wanted to use—kaixin.com—was already taken, and he didn’t want (or possibly couldn’t afford) to buy it from its owner. So instead he opted for kaixin001.com, which turned out to be a fatal mistake, equivalent to entering the coliseum without a helmet.
The moment Kaixin001 became a threat, the owner of Renren simply bought the original www.kaixin.com URL from its owner. He then recreated an exact copy of Kaixin001’s user interface, changing only the color, and brazenly dubbed it “The Real Kaixin Net.”
“Renren later announced it would merge Kaixin.com with Renren, effectively completing its kidnapping of Kaixin001 users. The move kneecapped Kaixin001’s user growth, killed its momentum, and neutralized a major threat to Renren’s dominance.”
“The lessons learned in the coliseum were clear: 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. To the winner go the spoils, and those spoils can amount to billions of dollars.
It’s a cultural system that also inspires a truly maniacal work ethic. Silicon Valley prides itself on long work hours, an arrangement made more tolerable by free meals, on-site gyms, and beer on tap. But compared with China’s startup scene, the valley’s companies look lethargic and its engineers lazy”
“In a market where copying was the norm, these entrepreneurs were forced to work harder and execute better than their opponents. Silicon Valley prides itself on its aversion to copying, but this often leads to complacency. The first mover is simply ceded a new market because others don’t want to be seen as unoriginal. Chinese entrepreneurs have no such luxury. If they succeed in building a product that people want, they don’t get to declare victory. They have to declare war.”
“But Wang was aware of the dangers of burning cash—that’s how he’d lost Xiaonei, his Facebook copy—and he foresaw the danger of trying to buy long-term customer loyalty with short-term bargains. If you only competed on subsidies, customers would endlessly jump from platform to platform in search of the best deal. Let the competitors spend the money on subsidizing meals and educating the market—he would reap the harvest that they sowed. So Wang focused on keeping costs down while iterating his product.”
“Those entrepreneurs are beating Silicon Valley juggernauts at their own game and have learned how to survive in the single most competitive startup environment in the world. They then leveraged China’s internet revolution and mobile internet explosion to breathe life into the country’s new consumer-driven economy”
China’s Alternate Internet Universe
“the copycat era had forged world-class entrepreneurs, and they were just beginning to apply their skills to solving uniquely Chinese problems”
“the money we wanted to pour into fostering these startups. Any relief on rent would mean more money for building products and companies. No problem, he said—he would make some calls. The local government could likely cover our rent for three years if we relocated to the neighborhood of Zhongguancun”
“Guo was just getting started. He didn’t want to only throw money at one incubator. He wanted to understand what really made Silicon Valley tick. Guo began peppering me with questions about my time in the valley during the 1990s. I explained how many of the area’s early entrepreneurs went on to become angel investors and mentors, how geographic proximity and tightly woven social networks gave birth to a self-sustaining venture-capital ecosystem that made smart bets on big ideas”
“process would require getting our hands dirty, adapting the valley’s disembodied innovation ethos to the very physical realities of present-day China. The result would leverage some of the core mechanisms of Silicon Valley but would take the Chinese internet in a very different direction”
“Simple shorthand like “the Amazon of China” or “the Facebook of China” no longer made sense when describing apps like WeChat—the dominant social app in China, but one that evolved into a “digital Swiss Army knife” capable of letting people pay at the grocery store, order a hot meal, and book a doctor’s visit.
Underneath this transformation lay several key building blocks: mobile-first internet users”
“Adding fuel to this fire was an unprecedented wave of government support for innovation”
“Thriving in this environment required both engineering prowess and raw manpower: armies of scooter-riding deliverymen schlepping hot meals around town, tens of thousands of sales reps fanning out to push mobile payments on street vendors, and millions of shared bikes loaded onto trucks and dispersed around cities. An explosion of these services pushed Chinese companies to roll up their sleeves and do the grunt work of running an operations-heavy business in the real world”
“American startups like to stick to what they know: building clean digital platforms that facilitate information exchanges. Those platforms can be used by vendors who do the legwork, but the tech companies tend to stay distant and aloof from these logistical details”
“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. They burn cash like crazy and rely on armies of low-wage delivery workers to make their business models work”
“this Chinese commitment to grunt work is also what is laying the groundwork for Chinese leadership in the age of AI implementation. By immersing themselves in the messy details of food delivery, car repairs, shared bikes, and purchases at the corner store, these companies are turning China into the Saudi Arabia of data: a country that suddenly finds itself sitting atop stockpiles of the key resource that powers this technological era”
“Silicon Valley juggernauts are amassing data from your activity on their platforms, but that data concentrates heavily in your online behavior, such as searches made, photos uploaded, YouTube videos watched, and posts “liked.” 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”
“Those three Chinese internet juggernauts (collectively known by the abbreviation “BAT”) used our startups to accelerate their transition into mobile internet companies. Those startup acquisitions formed a solid foundation for their mobile efforts”
“Hardly anyone noticed when the world’s most powerful app waltzed onto the world stage. The January 2011 launch of WeChat, Tencent’s new social messaging app, received only one mention in the English-language press, on the technology site the Next Web. Tencent already owned the two dominant social networks in China—its QQ instant messaging platform and Q-Zone social network each had hundreds of millions of users—but American analysts dismissed these as mediocre knockoffs of American products. The company’s new smartphone app didn’t even have an English name yet, going only by the Chinese name Weixin, or “micro-message.”
“Instead of trying to transform its dominant desktop platform, QQ, into a phone app, Tencent aimed to disrupt its own product with a better one built just for mobile. It was a risky strategy for an established juggernaut, but one that paid off big time.”
“as WeChat gained users, it also tacked on more functions. In just over a year it had hit 100 million registered users, and by its two-year anniversary in January 2013, that number was 300 million. Along the way it had added voice and video calls and conference calls, functions that seem obvious today but that WeChat’s global competitor WhatsApp waited until 2016 to incorporate.
WeChat’s early tweaks and optimizations were just the beginning. It soon pioneered an innovative “app-within-an-app” model that changed the way media outlets and advertisers used social platforms. These were WeChat’s “official accounts,” subscription-based third-party content streams that lived within the app and were sometimes compared to Facebook pages for media companies. But instead of Facebook’s minimalist platform for posting content, the official accounts offered much of the functionality of a standalone app without the hassle of actually building one. These accounts quickly became so dominant in the social media space that many media and consumer companies simply stopped building their own apps, choosing instead to live entirely in WeChat’s world”
“Over the ensuing five years, Tencent painstakingly built WeChat into the world’s first super-app. It became a “remote control for life” that dominated not just users’ digital worlds but allowed them to pay at restaurants, hail taxis, unlock shared bikes, manage investments, book doctors’ appointments, and have those doctors’ prescriptions delivered to your door”
“Tencent’s innovation was so simple—and such pure fun for users—that it masked the magnitude of the power grab. WeChat gave its users the ability to send out digital red envelopes containing real money to WeChat friends near and far. Once users linked their bank accounts to WeChat, they could send out envelopes worth a set amount of money to one person or into a group chat and let their friends race to see who could “open” it first and get the money. That money then lived inside users’ WeChat Wallet, a new subdivision of the app. The money could be used to make purchases, transferred to other friends, or added to their own bank account if they linked it with WeChat”
“Following the issuance of the State Council directive, cities around China rapidly copied Guo Hong’s vision and rolled out their own versions of the Avenue of the Entrepreneurs. They used tax discounts and rent rebates to attract startups. They created one-stop-shop government offices where entrepreneurs could quickly register their companies. 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”
“years leading up to 2014, total Chinese VC funding held steady at around $3 billion. In 2014, that immediately quadrupled to $12 billion, and then doubled again to $26 billion in 2015”
“what these critics miss is that this process can be both highly inefficient and extraordinarily effective. When the long-term upside is so monumental, overpaying in the short term can be the right thing to do. The Chinese government wanted to engineer a fundamental shift in the Chinese economy, from manufacturing-led growth to innovation-led growth, and it wanted to do that in a hurry.”
“China’s top leadership did not have the patience to wait. It wanted to use government money to brute-force a faster transformation, one that would pay dividends through an earlier transition to higher-quality growth. That process of pure force was often locally inefficient—incubators that went unoccupied and innovation avenues that never paid off—but on a national scale, the impact was tremendous”
“China’s other early internet giants often held Ph.D.s or had Silicon Valley experience in the United States. But Ma’s ascent to rock-star status gave a new meaning to “mass entrepreneurship”—in other words, this was something that anyone from the Chinese masses had a shot”
“When the staff told him that I wouldn’t be returning any time soon, the man lay on the ground and stripped naked, pledging to lie right there until Kai-Fu Lee listened to his idea.
That particular entrepreneur received a police escort rather than a seed investment, but the episode captures the innovation mania that was gripping China”
“Analysts dubbed the explosion of real-world internet services that blossomed across Chinese cities the “O2O Revolution,” short for “online-to-offline.” The terminology can be confusing but the concept is simple: turn online actions into offline services. E-commerce websites like Alibaba and Amazon had long done this for the purchase of durable physical goods. 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.”
“Urban China can be a joy, but it can also be a jungle: crowded, polluted, loud, and less than clean. After a day spent commuting on crammed subways and navigating eight-lane intersections, many middle-class Chinese just want to be spared another trip outdoors to get a meal or run an errand. Lucky for them, these cities are also home to large pools of migrant laborers who would gladly bring that service to their door for a small fee. It’s an environment built for O2O”
“Some hair stylists and manicurists gave up their storefronts entirely, exclusively booking through apps and making house calls. People who were feeling ill could hire others to wait in the famously long lines outside hospitals. Lazy pet owners could use an app to hail someone who would come right over and clean out a cat’s litter box or wash their dog”
“For Chinese people, the transition took the edge off urban life. For small businesses, it meant a boom in customers, as the reductions in friction led Chinese urbanites to spend more. And for China’s new wave of startups, it meant skyrocketing valuations and a ceaseless drive to push into ever more sectors of urban life.”
“It was a social and commercial transformation that was powered by—and which further empowered—WeChat. Installed on more than half of all smartphones in China and now linked to many users’ bank accounts, WeChat had the power to nudge hundreds of millions of Chinese into O2O purchases and to pick winners among the competing startups”
“WeChat Wallet linked up with top O2O startups so that WeChat users could hail a taxi, order a meal, book a hotel, manage a phone bill, and buy a flight to the United States, all without ever leaving the app”
“Tencent’s choice to go for the super-app model appeared risky at the start: could you possibly bundle so many things together without overwhelming the user? But the super-app model proved wildly successful”
“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”
“willingness to go heavy—to spend the money, manage the workforce, do the legwork, and build economies of scale—has reshaped the relationship between the digital and real-world economies. China’s internet is penetrating far deeper into the economic lives of ordinary people, and it is affecting both consumption trends and labor markets”
“By enrolling the vendors, processing the orders, delivering the food, and taking in the payments, China’s O2O champions began amassing a wealth of real-world data on the consumption patterns and personal habits of their users. Going heavy gave these companies a data edge over their Silicon Valley peers, but it was mobile payments that would extend their reach even further into the real world and turn that data edge into a commanding lead”
“When they were first introduced, credit cards were cutting edge, the most convenient and cost-effective solution to the payment problem. But that advantage has now turned into a liability, with fees of 2.5 to 3 percent on most charges turning into a drag on adoption and utilization.”
“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”
“Adding credit-card POS machines to bikes would be too expensive and repair-intensive, but frictionless mobile payments are both cheap to layer onto a bike and incredibly efficient”
“Something new was emerging from all those rides: perhaps the world’s largest and most useful internet-of-things (IoT) networks. The IoT refers to collections of real-world, internet-connected devices that can convey data from the world around them to other devices in the network. Most Mobikes are equipped with solar-powered GPS, accelerators, Bluetooth, and near-field communications capabilities that can be activated by a smartphone. Together, those sensors generate twenty terabytes of data per day and feed it all back into Mobike’s cloud servers”
“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.”
A Tale of Two Countries
“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.”
“While America still dominates when it comes to superstar researchers, Chinese companies and research institutions have filled their ranks with the kind of well-trained engineers that can power this era of AI deployment. It has done that by marrying the extraordinary hunger for knowledge that I witnessed in Hefei with an explosion in access to cutting-edge global research”
“They’re taking advantage of AI’s open research culture to absorb knowledge straight from the source and in real time. That means dissecting the latest online academic publications, debating the approaches of top AI scientists in WeChat groups, and streaming their lectures on smartphones.
This rich connectivity allows China’s AI community to play intellectual catch-up at the elite level, training a generation of hungry Chinese researchers who now contribute to the field at a high level”
“after Ke Jie’s loss to AlphaGo, the Chinese central government released a sweeping blueprint for Chinese leadership in AI. Like the “mass innovation and mass entrepreneurship” campaign, China’s AI plan is turbocharging growth through a flood of new funding, including subsidies for AI startups and generous government contracts to accelerate adoption”
“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”
“But not every technological revolution follows this pattern. 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”
“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. America may be home to the top researchers, but much of their work and insight is instantaneously available to anyone with an internet connection and a grounding in AI fundamentals. Facilitating this knowledge transfer are two defining traits of the AI research community: openness and speed.”
“openness grew out of the common goal of advancing the field and also from the desire for objective metrics in competitions. In many physical sciences, experiments cannot be fully replicated from one lab to the next—minute variations in technique or environment can greatly affect results. But AI experiments are perfectly replicable, and algorithms are directly comparable”
“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. 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”
“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 and return us to an age of discovery in which elite expertise tips the balance of power in favor of the United States”
“if the next deep learning is destined to be discovered in the corporate world, Google has the best shot at it. Among the Seven AI Giants, Google—more precisely, its parent company, Alphabet, which owns DeepMind and its self-driving subsidiary Waymo—stands head and shoulders above the rest. It was one of the earliest companies to see the potential in deep learning and has devoted more resources to harnessing it than any other company.”
“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”
“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. In this model, cloud computing platforms act as the grid, performing complex machine-learning optimizations on whatever data problems users require. The companies behind these platforms—Google, Alibaba, and Amazon—act as the utility companies, managing the grid and collecting the fees”
“Hooking into that grid would allow traditional companies with large data sets to easily tap into AI’s optimization powers without having to remake their entire business around it. Google’s TensorFlow, an open-source software ecosystem for building deep learning-models, offers an early version of this but still requires some AI expertise to operate. The goal of the grid approach is to both lower that expertise threshold and increase the functionality of these cloud-based AI platforms. Making use of machine learning is nowhere near as simple as plugging an electric appliance into the wall—and it may never be—but the AI giants hope to push things in that direction and then reap the rewards of generating the “power” and operating the “grid.”
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.”
“They are betting that traditional businesses won’t be able to simply plug the nitty-gritty details of their daily operations into an all-purpose grid. Instead of helping those companies access AI, these startups want to disrupt them using AI. They aim to build AI-first companies from the ground up, creating a new roster of industry champions for the AI age”
“On October 12, 2016, President Barack Obama’s White House released a long-brewing plan for how the United States can harness the power of artificial intelligence. The document detailed the transformation AI is set to bring to the economy and laid out steps to seize that opportunity: increasing funding for research, stepping up civilian-military cooperation, and making investments to mitigate social disruptions. It offered a decent summary of changes on the horizon and some commonsense prescriptions for adaptation.
But the report—issued by the most powerful political office in the United States—had about the same impact as a wonkish policy paper from an academic think tank. Released the same week as Donald Trump’s infamous Access Hollywood videotape, the White House report barely registered in the American news cycle”
“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.
The limp response to the Obama report made for a stark contrast to the shockwaves generated by the Chinese government’s own AI plan. Like past Chinese government documents on technology, it was plain in its language but momentous in its impact. Published in July 2017, the Chinese State Council’s “Development Plan for a New Generation of Artificial Intelligence” shared many of the same predictions and recommendations as the White House plan. It also spelled out hundreds of industry-specific applications of AI and laid down signposts for China’s progress toward becoming an AI superpower. It called for China to reach the top tier of AI economies by 2020, achieve major new breakthroughs by 2025, and become the global leader in AI by 2030.”
“It’s a process of government-accelerated technological development that I’ve witnessed twice in the past decade. 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”
“it’s too early to know the exact results of China’s AI campaign, but if Chinese history is any guide, it is likely to be somewhat inefficient but extremely effective. The sheer scope of financing and speed of deployment almost guarantees that there will be inefficiencies. Government bureaucracies cannot rapidly deploy billions of dollars in investments and subsidies without some amount of waste. There will be dorms for AI employees that will never be inhabited, and investments in startups that will never get off the ground. There will be traditional technology companies that merely rebrand themselves as “AI companies” to rake in subsidies, and AI equipment purchases that simply gather dust in government offices”
“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. And if the bet doesn’t pan out, the mayors won’t be endlessly pilloried by their opponents for attempting to act on the central government’s wishes.”
“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.”
“For the past thirty years, Chinese leaders have practiced a kind of techno-utilitarianism, leveraging technological upgrades to maximize broader social good while accepting that there will be downsides for certain individuals or industries”
“They may well lead American politicians, ever fearful of interest groups and attack ads, to pump the brakes on widespread self-driving vehicle deployment. We’ve already seen early signs of this happening, with unions representing truck drivers successfully lobbying Congress in 2017 to exclude trucks from legislation aimed at speeding up autonomous-vehicle deployment.”
“believe the Chinese government will see these difficult concerns as important topics to explore but not as a reason to delay the implementation of technology that will save tens if not hundreds of thousands of lives in the not-too-distant future. For better or worse—and I recognize that most Americans may not embrace this view—Chinese political culture doesn’t carry the American expectation of reaching a moral consensus on each of the above questions”
“China’s top-down approach to economic upgrades—and the eagerness of low-level officials to embrace each new central government mandate—can also lead to waste and debt if the target industries are not chosen well. But in this particular instance—building a society and economy prepared to harness the potential of AI—China’s techno-utilitarian approach gives it a certain advantage. Its acceptance of risk allows the government to make big bets on game-changing technologies, and its approach to policy will encourage faster adoption of those technologies”
Four Waves of AI
“The complete AI revolution will take a little time and will ultimately wash over us in a series of four waves: internet AI, business AI, perception AI, and autonomous AI. Each of these waves harnesses AI’s power in a different way, disrupting different sectors and weaving artificial intelligence deeper into the fabric of our daily lives”
“first two waves—internet AI and business AI—are already all around us, reshaping our digital and financial worlds in ways we can barely register. They are tightening internet companies’ grip on our attention, replacing paralegals with algorithms, trading stocks, and diagnosing illnesses.
Perception AI is now digitizing our physical world, learning to recognize our faces, understand our requests, and “see” the world around us. This wave promises to revolutionize how we experience and interact with our world, blurring the lines between the digital and physical worlds.
Autonomous AI will come last but will have the deepest impact on our lives. As self-driving cars take to the streets, autonomous drones take to the skies, and intelligent robots take over factories, they will transform everything from organic farming to highway driving and fast food.”
“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.”
“Internet AI is largely about using AI algorithms as recommendation engines: systems that learn our personal preferences and then serve up content hand-picked for us.”
“Revealingly, it was Robert Mercer, founder of Cambridge Analytica, who reportedly coined the famous phrase, “There’s no data like more data.”
“First-wave AI has given birth to entirely new, AI-driven internet companies. China’s leader in this category is Jinri Toutiao (meaning “today’s headlines”; English name: “ByteDance”). Founded in 2012, Toutiao is sometimes called “the BuzzFeed of China” because both sites serve as hubs for timely viral stories. But virality is where the similarities stop. BuzzFeed is built on a staff of young editors with a knack for cooking up original content. Toutiao’s “editors” are algorithms”
“Business AI takes advantage of the fact that traditional companies have also been automatically labeling huge quantities of data for decades. For instance, insurance companies have been covering accidents and catching fraud, banks have been issuing loans and documenting repayment rates, and hospitals have been keeping records of diagnoses and survival rates. All of these actions generate labeled data points—a set of characteristics and a meaningful outcome—but until recently, most traditional businesses had a hard time exploiting that data for better results”
“Business AI mines these databases for hidden correlations that often escape the naked eye”
“draws on all the historic decisions and outcomes within an organization and uses labeled data to train an algorithm that can outperform even the most experienced human practitioners. That’s because humans normally make predictions on the basis of strong features, a handful of data points that are highly correlated to a specific outcome, often in a clear cause-and-effect relationship”
“algorithms that can combine thousands of those weak and strong features—often using complex mathematical relationships indecipherable to a human brain—will outperform even top-notch humans at many analytical business tasks”
“These startups sell their services to traditional companies or organizations, offering to let their algorithms loose on existing databases in search of optimizations. They help these companies improve fraud detection, make smarter trades, and uncover inefficiencies in supply chains”
“This is also why the United States has built a strong lead in early applications of business AI. Major American corporations already collect large amounts of data and store it in well-structured formats. They often use enterprise software for accounting, inventory, and customer relationship management. Once the data is in these formats, it’s easy for companies like Palantir to come in and generate meaningful results by applying business AI to seek out cost savings and profit maximization.
This is not so in China. Chinese companies have never truly embraced enterprise software or standardized data storage, instead keeping their books according to their own idiosyncratic systems. Those systems are often not scalable and are difficult to integrate into existing software, making the cleaning and structuring of data a far more taxing process. Poor data also makes the results of AI optimizations less robust. As a matter of business culture, Chinese companies spend far less money on third-party consulting than their American counterparts”
“Both China’s corporate data and its corporate culture make applying second-wave AI to its traditional companies a challenge. But in industries where business AI can leapfrog legacy systems, China is making serious strides. In these instances, China’s relative backwardness in areas like financial services turns into a springboard to cutting-edge AI applications. One of the most promising of these is AI-powered micro-finance”
“Into this void stepped Smart Finance, an AI-powered app that relies exclusively on algorithms to make millions of small loans. Instead of asking borrowers to enter how much money they make, it simply requests access to some of the data on a potential borrower’s phone. That data forms a kind of digital fingerprint, one with an astonishing ability to predict whether the borrower will pay back a loan of three hundred dollars.
Smart Finance’s deep-learning algorithms don’t just look to the obvious metrics, like how much money is in your WeChat Wallet. Instead, it derives predictive power from data points that would seem irrelevant to a human loan officer. For instance, it considers the speed at which you typed in your date of birth, how much battery power is left on your phone, and thousands of other parameters.”
“What does an applicant’s phone battery have to do with creditworthiness? This is the kind of question that can’t be answered in terms of simple cause and effect. But that’s not a sign of the limitations of AI. It’s a sign of the limitations of our own minds at recognizing correlations hidden within massive streams of data. By training its algorithms on millions of loans—many that got paid back and some that didn’t—Smart Finance has discovered thousands of weak features that are correlated to creditworthiness, even if those correlations can’t be explained in a simple way humans can understand. Those offbeat metrics constitute what Smart Finance founder Ke Jiao calls “a new standard of beauty” for lending, one to replace the crude metrics of income, zip code, and even credit score.”
“There’s no question that China will lag in the corporate world, but it may lead in public services and industries with the potential to leapfrog outdated systems. The country’s immature financial system and imbalanced healthcare system give it strong incentives to rethink how services like consumer credit and medical care are distributed”
“This all changed with the advent of perception AI. Algorithms can now group the pixels from a photo or video into meaningful clusters and recognize objects in much the same way our brain does: golden retriever, traffic light, your brother Patrick, and so on. The same goes for audio data. Instead of merely storing audio files as collections of digital bits, algorithms can now both pick out words and often parse the meaning of full sentences.
Third-wave AI is all about extending and expanding this power throughout our lived environment, digitizing the world around us through the proliferation of sensors and smart devices. These devices are turning our physical world into digital data that can then be analyzed and optimized by deep-learning algorithms. Amazon Echo is digitizing the audio environment of people’s homes. Alibaba’s City Brain is digitizing urban traffic flows through cameras and object-recognition AI. Apple’s iPhone X and Face++ cameras perform that same digitization for faces, using the perception data to safeguard your phone or digital wallet”
“perception AI gets better at recognizing our faces, understanding our voices, and seeing the world around us, it will add millions of seamless points of contact between the online and offline worlds. Those nodes will be so pervasive that it no longer makes sense to think of oneself as “going online.”
“I call these new blended environments OMO: online-merge-offline. OMO is the next step in an evolution that already took us from pure e-commerce deliveries to O2O (online-to-offline) services. Each of those steps has built new bridges between the online world and our physical one, but OMO constitutes the full integration of the two”
“brings the convenience of the online world offline and the rich sensory reality of the offline world online. Over the coming years, perception AI will turn shopping malls, grocery stores, city streets, and our homes into OMO environments. In the process, it will produce some of the first applications of artificial intelligence that will feel truly futuristic to the average user”
“One KFC restaurant in China recently teamed up with Alipay to pioneer a pay-with-your-face option at some stores. Customers place their own order at a digital terminal, and a quick facial scan connects their order to their Alipay account—no cash, cards, or cell phones required. The AI powering the machines even runs a quick “liveness algorithm” to ensure no one can use a photograph of someone else’s face to pay for a meal.”
“The AI-powered education experience takes place across four scenarios: in-class teaching, homework and drills, tests and grading, and customized tutoring. Performance and behavior in these four settings all feed into and build off of the bedrock of AI-powered education, the student profile. That profile contains a detailed accounting of everything that affects a student’s learning process, such as what concepts they already grasp well, what they struggle with, how they react to different teaching methods, how attentive they are during class, how quickly they answer questions, and what incentives drive them”
“There’s no right answer to questions about what level of social surveillance is a worthwhile price for greater convenience and safety, or what level of anonymity we should be guaranteed at airports or subway stations. But in terms of immediate impact, China’s relative openness with data collection in public places is giving it a massive head start on implementation of perception AI. It is accelerating the digitization of urban environments and opening the door to new OMO applications in retail, security, and transportation.”
“As perception AI transforms our lived environment, the ease of experimentation and the production of smart devices gives Chinese startups an edge. Shenzhen is open to international hardware startups, but locals have a heavy home-court advantage. The many frictions of operating in a foreign country—language barrier, visa issues, tax complications, and distance from headquarters—can slow down American startups and raise the cost of their products. Massive multinationals like Apple have the resources to leverage Chinese manufacturing to the fullest, but for foreign startups small frictions can spell doom. Meanwhile, homegrown hardware startups in Shenzhen are like kids in a candy store, experimenting freely and building cheaply”
“Xiaomi doesn’t build all of these devices itself. Instead, it has invested in 220 companies and incubated 29 startups—many operating in Shenzhen—whose intelligent home products are hooked into the Xiaomi ecosystem. Together they are creating an affordable, intelligent home ecosystem, with WiFi-enabled products that find each other and make configuration easy. Xiaomi users can then simply control the entire ecosystem via voice command or directly on their phone”
“It’s a constellation of price, diversity, and capability that has created the world’s largest network of intelligent home devices: 85 million by the end of 2017, far ahead of any comparable U.S. networks. It’s also an ecosystem built on the Made-in-Shenzhen advantage. Low prices and China’s massive market are turbocharging the data-gathering process for Xiaomi, fueling a virtuous cycle of stronger algorithms, smarter products, better user experience, more sales, and even more data. It’s also an ecosystem that has produced four unicorn startups within Xiaomi’s ecosystem alone and is driving Xiaomi toward an IPO predicted to value the company at around $100 billion”
“Third-wave AI products like these are on the verge of transforming our everyday environment, blurring lines between the digital and physical world until they disappear entirely. During this transformation, Chinese users’ cultural nonchalance about data privacy and”
“Shenzhen’s strength in hardware manufacturing give it a clear edge in implementation”
“the developed world has largely replaced raw human muscle with high-powered machines. But while these machines are automated, they are not autonomous. While they can repeat an action, they can’t make decisions or improvise according to changing conditions. Entirely blind to visual inputs, they must be controlled by a human or operate on a single, unchanging track. They can perform repetitive tasks, but they can’t deal with any deviations or irregularities in the objects they manipulate”
“Google was the first company to develop autonomous driving technology, but it has been relatively slow to deploy that technology at scale. Behind that caution is an underlying philosophy: build the perfect product and then make the jump straight to full autonomy once the system is far safer than human drivers. It’s the approach of a perfectionist, one with a very low tolerance for risk to human lives or corporate reputation. It’s also a sign of how large a lead Google has on the competition due to its multiyear head start on research. Tesla has taken a more incremental approach in an attempt to make up ground. Elon Musk’s company has tacked on limited autonomous features to their cars as soon as they became available: autopilot for highways, autosteer for crash avoidance, and self-parking capabilities. It’s an approach that accelerates speed of deployment while also accepting a certain level of risk”
“The two approaches are powered by the same thing that powers AI: data. Self-driving cars must be trained on millions, maybe billions, of miles of driving data so they can learn to identify objects and predict the movements of cars and pedestrians. That data draws from thousands of different vehicles on the road, and it all feeds into one central “brain,” the core collection of algorithms that powers decision-making across the fleet. It means that when any autonomous car encounters a new situation, all the cars running on those algorithms learn from it.
Google has taken a slow-and-steady approach to gathering that data, driving around its own small fleet of vehicles equipped with very expensive sensing technologies”
“Tesla instead began installing cheaper equipment”
“on its commercial vehicles, letting Tesla owners gather the data for them when they use certain autonomous features. The different approaches have led to a massive data gap between the two companies. By 2016, Google had taken six years to accumulate 1.5 million miles of real-world driving data. In just six months, Tesla had accumulated 47 million miles.
Google and Tesla are now inching toward one another in terms of approach. Google—perhaps feeling the heat from Tesla and other rivals—accelerated deployment of fully autonomous vehicles, piloting a program with taxi-like vehicles in the Phoenix metropolitan area. Meanwhile, Tesla appears to have pumped the brakes on its rapid rollout of fully autonomous vehicles, a deceleration that followed a May 2016 crash that killed a Tesla owner who was using autopilot”
“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. That is, rather than wait for flawless self-driving cars to arrive, Chinese leaders will likely look for ways to deploy more limited autonomous vehicles in controlled settings. That deployment will have the side effect of leading to more exponential growth in the accumulation of data and a corresponding advance in the power of the AI behind it.
Key to that incremental deployment will be the construction of new infrastructure specifically made to accommodate autonomous vehicles. In the United States, in contrast, we build self-driving cars to adapt to our existing roads because we assume the roads can’t change. In China, there’s a sense that everything can change—including current roads”
“Xiong’an is poised to be the world’s first city built specifically to accommodate autonomous vehicles. Baidu has signed agreements with the local government to build an “AI City” with a focus on traffic management, autonomous vehicles, and environmental protection. Adaptations could include sensors in the cement, traffic lights equipped with computer vision, intersections that know the age of pedestrians crossing them, and dramatic reductions in space needed for parked cars. When everyone is hailing his or her own autonomous taxi, why not turn those parking lots into urban parks?
Taking things a step further, brand-new developments like Xiong’an could even route the traffic in their city centers underground reserving the heart of town for pedestrians and bicyclists. It’s a system that would be difficult, if not impossible, to implement in a world of human drivers prone to human errors that clog up tunnels. But by combining augmented roads, controlled lighting, and autonomous vehicles, an entire underground traffic grid could be running at the speed of highways while life aboveground moves at a more human pace.”
“There’s no guarantee that all of these high-flying AI amenities will be rolled out smoothly—some of China’s technologically themed developments have flopped, and some brand-new cities have struggled to attract residents. But the central government has placed a high priority on the project, and if successful, cities like Xiong’an will grow up together with autonomous AI”
“Baidu’s Apollo project—an open-source partnership and data-sharing arrangement among fifty autonomous-vehicle players, including chipmakers like Nvidia and automakers like Ford and Daimler—also presents an ambitious alternative to Waymo’s closed, in-house approach. But even with that rapid catch-up by Chinese players, there’s no question that as of this writing, the most experienced self-driving technologists still call America home.”
“Silicon Valley giants like Google, Facebook, and Uber want to directly introduce their products to these markets. They’ll make limited efforts at localization but will largely stick to the traditional playbook. They will build one global product and push it out on billions of different users around the globe. It’s an all-or-nothing approach with a huge potential upside if the conquest succeeds, but it also has a high chance of leaving empty-handed.
Chinese companies are instead steering clear of direct competition and investing in the scrappy local startups that Silicon Valley looks to wipe out. For example, in India and Southeast Asia, Alibaba and Tencent are pouring money and resources into homegrown startups that are fighting tooth and nail against juggernauts”
Utopia, Dystopia, and the Real AI Crisis
“This, the techno-optimists assert, is the real story of technological change and economic development. Technology improves human productivity and lowers the price of goods and services. Those lower prices mean consumers have greater spending power, and they either buy more of the original goods or spend that money on something else. Both of these outcomes increase the demand for labor and thus jobs. Yes, shifts in technology might lead to some short-term displacement. But just as millions of farmers became factory workers, those laid-off factory workers can become yoga teachers and software programmers. Over the long term, technological progress never truly leads to an actual reduction in jobs or rise in unemployment”
“The ramifications of these breakthroughs will cut across dozens of industries, with the potential to fundamentally alter economic processes and even social organization. These are what economists call general purpose technologies, or GPTs. In their landmark book The Second Machine Age, MIT professors Erik Brynjolfsson and Andrew McAfee described GPTs as the technologies that “really matter,” the ones that “interrupt and accelerate the normal march of economic progress.”
“The steam engine and electrification were crucial pieces of the first and second Industrial Revolutions (1760–1830 and 1870–1914, respectively). Both of these GPTs facilitated the creation of the modern factory system, bringing immense power and abundant light to the buildings that were upending traditional modes of production. Broadly speaking, this change in the mode of production was one of deskilling. These factories took tasks that once required high-skilled workers (for example, handcrafting textiles) and broke the work down into far simpler tasks that could be done by”
“low-skilled workers (operating a steam-driven power loom). In the process, these technologies greatly increased the amount of these goods produced and drove down prices”
“In terms of employment, early GPTs enabled process innovations like the assembly line, which gave thousands—and eventually hundreds of millions—of former farmers a productive role in the new industrial economy. Yes, they displaced a relatively small number of skilled craftspeople (some of whom would become Luddites), but they empowered much larger numbers of low-skilled workers to take on repetitive, machine-enabled jobs that increased their productivity. Both the economic pie and overall standards of living grew”
“One reason why ICT may differ from the steam engine and electrification is because of its “skill bias.” While the two other GPTs ramped up productivity by deskilling the production of goods, ICT is instead often—though not always—skill biased in favor of high-skilled workers”
“Techno-optimists can continue to dismiss these concerns as the same old Luddite fallacy, but they are now arguing against some of the brightest economic minds of today. Lawrence Summers has served as the chief economist of the World Bank, as the treasury secretary under President Bill Clinton, and as the director of President Barack Obama’s National Economic Council. In recent years, he has been warning against the no-questions-asked optimism around technological change and employment.
“The answer is surely not to try to stop technical change,” Summers told the New York Times in 2014, “but the answer is not to just suppose that everything’s going to be O.K. because the magic of the market will assure that’s true.”
“Whereas the Industrial Revolution took place across several generations, the AI revolution will have a major impact within one generation. That’s because AI adoption will be accelerated by three catalysts that didn’t exist during the introduction of steam power and electricity.
First, many productivity-increasing AI products are just digital algorithms: infinitely replicable and instantly distributable around the world. This makes for a stark contrast to the hardware-intensive revolutions of steam power, electricity, and even large parts of ICT”
“The second catalyst is one that many in the technology world today take for granted: the creation of the venture-capital industry. VC funding—early investments in high-risk, high-potential companies—barely existed before the 1970s. That meant the inventors and innovators during the first two Industrial Revolutions had to rely on a thin patchwork of financing mechanisms to get their products off the ground, usually via personal wealth, family members, rich patrons, or bank loans”
“Finally, the third catalyst is one that’s equally obvious and yet often overlooked: China. Artificial intelligence will be the first GPT of the modern era in which China stands shoulder to shoulder with the West in both advancing and applying the technology. During the eras of industrialization, electrification, and computerization, China lagged so far behind that its people could contribute little, if anything, to the field [more generalizable version of this — democratization of opportunity. People from all around the world are contibuting to AI]”
The Wisdom of Cancer
“My cancer would go into remission, sparing my life, but the epiphanies sparked by this personal confrontation with death have stuck with me. They’ve led me to reshuffle my priorities and to totally change my life. I spend far more time with my wife and daughters, and moved to be closer to my aging mother. I have dramatically cut down my presence on social media, pouring that time into meeting with and trying to help young people who reach out to me. I’ve asked for forgiveness from those I have wronged and sought to be a kinder and more empathetic coworker. 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”
“was in the midst of these mental calculations when the doctor informed me that they would be performing an immediate cesarean section. My wife was rushed off to an operating room with me in tow, and within an hour Shen-Ling and I were holding our baby daughter. We all had some time together, and with little time left to spare, I took off for the presentation.
It went extremely well. Sculley both greenlighted the project and demanded a full-on publicity campaign around what I had created. That campaign led to a high-profile TED talk, write-ups in the Wall Street Journal, and an appearance on Good Morning America in 1992, with John Sculley and I demonstrating the technology for millions of viewers. On the program, we used voice commands to schedule an appointment, write a check, and program a VCR, showcasing the earliest examples of futuristic functions that wouldn’t go mainstream for another twenty years, with Apple’s Siri and Amazon’s Alexa. These triumphs filled me with great personal pride and also turbocharged my career.
But looking back, it’s not those career successes that stick in my mind. It’s the scene in that hospital room. If I[…]”
“Like any good algorithm, I of course had to balance multiple goals. Self-driving cars don’t just optimize for getting you home as fast as possible; they must do so without breaking any laws and while minimizing the risk of accidents. Likewise, I had to make certain tradeoffs between my personal and professional lives. I hadn’t been a completely absent father, neglectful husband (the episode of my daughter’s birth notwithstanding), or ungrateful son. My social algorithms were good enough that I made a point of remembering anniversaries, giving thoughtful gifts, and spending some time with the people in my family.
But I approached these as minimization functions, looking for ways to achieve the desired result while putting in the least amount of time possible. I always weighted the master algorithm heavily in favor of my own career goals to maximize time at work, personal influence, and status within my profession.
When I was given vacations of four weeks, I would spend one or two weeks with my mother in Taiwan or with my family in Beijing and then head right back to work. Even when a surgical procedure forced me to remain lying flat in bed for two weeks[…]”
“After leaving Google and founding Sinovation Ventures, I began to spend more time mentoring young people. I used my massive following on the Twitter-like platform Weibo to engage directly with Chinese students, offering them guidance and writing open letters that were collected into books. Although I remained the head of one of the country’s most prestigious venture-capital funds, students began referring to me as “Teacher Kai-Fu,” an honorific that in China combined great respect and also a certain closeness”
“Every one of those thoughts began with “I” and centered on self-righteous assertions of my own “objective” value. It wasn’t until I wrote down the names of my wife and daughters, character by character in black ink, that I snapped out of this egocentric wallowing and self-pity. The real tragedy wasn’t that I might not live much longer. It was that I had lived so long without generously sharing love with those so close to me.
Seeing my ultimate end point threw my life into sharp focus and turned my egocentric wallowing inside out. I stopped asking why the world had done this to me, or lamenting that all my achievements couldn’t save me now. I began asking new questions: Why had I wanted so desperately to turn myself into a productivity machine? Why hadn’t I taken the time to share love with others? Why did I ignore the very essence that made me human?”
“The hardest thing about facing death isn’t the experiences you won’t get to have. It’s the ones you can’t have back. Palliative care nurse and author Bronnie Ware has written extensively on the most common regrets that her terminally ill patients expressed in their final weeks of life. Facing the ultimate, these patients were able to look back on their lives with a clarity that escapes those of us absorbed in our daily grind. They spoke of the pain of not having lived a life true to themselves, the regret at having focused so obsessively on their work, and the realization that it’s the people in your life who give it true meaning. None of these people looked back on their lives wishing they had worked harder, but many of them found themselves wishing they had spent more time with the ones they loved.
“It all comes down to love and relationships in the end,” Ware wrote in the blog post that launched her book. “That is all that remains in the final weeks: love and relationships.”
“What does it really mean to ‘maximize impact’?” he began. “When people speak in this way, it’s often nothing but a thin disguise for ego, for vanity. If you truly look within yourself, can you say for sure that what motivates you is not ego? It’s a question you must ask your own heart, and whatever you do, don’t try to lie to yourself.”
My mind raced with rebuttals. I searched for the airtight logic that would redeem my actions. The days since my diagnosis had been an agonizing exercise in regret about the way I had engaged with my family and friends. I was slowly coming to terms with the emptiness of my emotional life. But as described in Elisabeth Kübler-Ross’s theory of the five stages of grief, before acceptance comes bargaining.”
“Yes, I may have skipped out on family time to make public speeches, but think of all the people I had reached. I’d influenced millions of young students and tried to help a once-great country pull itself out of poverty. If you added it all up, wouldn’t you say that the good outweighed the bad? Couldn’t the gifts I’d given to so many strangers through my work make up for the dearth of love I had shared with those closest to me? Didn’t the equation balance out in the end?”
“Now Master Hsing Yun was kicking the proverbial last leg of the stool out from under me. I tried to explain myself and cast my actions in the best light, based on what they had achieved. But he wasn’t interested in the results that my personal well-designed algorithm spat out”
“Kai-Fu, humans aren’t meant to think this way. This constant calculating, this quantification of everything, it eats away at what’s really inside of us and what exists between us. It suffocates the one thing that gives us true life: love”
“And there it was: while the stage IV diagnosis from the hospital meant a five-year survival rate of just 50 percent, the more detailed and scientific rubric of the research paper bumped that number up to 89 percent.
I kept going back to check and double-check the numbers, and with each confirmation I grew more ecstatic. Nothing inside my body had changed, but I felt that I had been pulled back from the abyss. Later that week, I would visit the top lymphoma expert in Taiwan. He would confirm what the study had indicated: that the designation of my lymphoma as stage IV was misleading, and my illness remained highly treatable. Nothing was certain—I knew that now more than ever—but there was a good chance I would get through this alive. I felt reborn.”
“Since my recovery, I’ve come to cherish time with those closest to me. Before, when my two daughters came home from college, I would take just a couple of days off work to be with them. Now when they visit from their busy jobs, I take a couple of weeks. Whether on business trips or vacations, I travel with my wife. I spend more time at home taking care of my mother and try to keep my weekends free to see old friends”
“I’ve apologized and tried to mend friendships with those that I have hurt or neglected in the past. I meet with many young people who reach out to me, no longer communicating only through impersonal blasts across my social media accounts. I try to avoid prioritizing these meetings by who “shows potential,” doing my best to engage with all people equally, regardless of their status or talents.”
“that only humans can provide turns out to also be exactly what is most needed in our lives: love. It’s that moment when we see our newborn babies, the feeling of love at first sight, the warm feeling from friends who listen to us empathetically, or the feeling of self-actualization when we help someone in need. We are far from understanding the human heart, let alone replicating it. But we do know that humans are uniquely able to love and be loved, that humans want to love”
A blueprint for human coexistence with AI
“After consulting with the customer-service representatives, he found that people weren’t calling in because they couldn’t navigate the device. They were calling simply because they were lonely and wanted someone to talk to. Many of the elderly users had children who worked to ensure that all of their material needs were met: meals were delivered, doctors’ appointments were arranged, and prescriptions were picked up. But once those material needs were taken care of, what these people wanted more than anything was true human contact, another person to trade stories with and relate to.”
“this notion of universal basic income seems to be becoming more and more popular these days.
But in making that choice I believe we would both devalue our own humanity and miss out on an unparalleled opportunity. Instead, I want to lay out proposals for how we can use the economic bounty created by AI to double-down on what makes us human. Doing this will require rewriting our fundamental social contracts and restructuring economic incentives to reward socially productive activities in the same way that the industrial economy rewarded economically productive activities.”
“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 sense, China’s tech elites are aligned with the techno-optimistic American economists who believe that in the long run, technology always leads to more jobs and greater prosperity for all.
Why does a Chinese entrepreneur believe in that with such conviction? For the past forty years, Chinese people have watched as their country’s technological progress acted as the rising tide that lifted all boats”
“It makes a stark contrast to the stagnation and decline felt in many segments of American society, part of the “great decoupling” between productivity and wages we explored in previous chapters. It also helps explain why Chinese technologists appear unconcerned with the potential jobs impact of their innovations”
“THE THREE R’S: REDUCE, RETRAIN, AND REDISTRIBUTE”
“Those advocating the retraining of workers tend to believe that AI will slowly shift what skills are in demand, but if workers can adapt their abilities and training, then there will be no decrease in the need for labor. Those advocates of reducing work hours believe that AI will reduce the demand for human labor and feel that this impact could be absorbed by moving to a three- or four-day work week, spreading the jobs that do remain over more workers. The redistribution camp tends to be the most dire in their predictions of AI-induced job losses. Many of them predict that as AI advances, it will so thoroughly displace or dislodge workers that no amount of training or tweaking hours will be sufficient. Instead, we will have to adopt more radical redistribution schemes to support unemployed workers and spread the wealth created by AI”
“Lifelong learning via online platforms is a nice idea, and I believe retraining workers will be an important piece of the puzzle”
“But given the depth and breadth of AI’s impact on jobs, I 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”
“I fear workers will find themselves in a state of constant retreat, like animals fleeing relentlessly rising flood waters, anxiously hopping from one rock to another in search of higher ground. Retraining will help many people find their place in the AI economy, and we must experiment with ways to scale this up and make it widely available. But I believe we cannot count on this haphazard approach to address the macro-level disruptions that will sweep over labor markets”
“Work-share arrangements could blunt job losses, particularly for professions in the “Human Veneer” quadrant of our risk-of-replacement graphs, where AI performs the main job task but only a smaller number of workers are needed to interface with customers. If executed well, these arrangements could act as government subsidies or incentives to keep more workers on the company payroll.
But while this approach works well for short-term disruptions, it may lose traction in the face of AI’s persistent and nonstop decimation of jobs”
“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”
“We should be aware of the cultural biases that engineers and investors bring with them when tackling a new problem, particularly one with profound social and human dimensions. Most of all, when evaluating these proposed solutions, we must ask what exactly they’re trying to achieve. Are they seeking to ensure that this technology genuinely and truly benefits all people across society? Or are they looking only to avert a worst-case scenario of social upheaval? 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?”
“Many of the jobs created by the free market will grow out of a natural symbiosis between humans and machines. While AI handles the routine optimization tasks, human beings will bring the personal, creative, and compassionate touch. This will involve the redefinition of existing occupations or the creation of entirely new professions in which people team up with machines to deliver services that are both highly efficient and eminently human. In the risk-of-replacement graphs from chapter 6, we expect to see the upper-left quadrant (“Human Veneer”) offer the greatest opportunity for human-AI symbiosis: AI will do the analytical thinking, while humans will wrap that analysis in warmth and compassion. In that same chart, the two quadrants on the right-hand side of the graph (“Slow Creep” and “Safe Zone”) also provide opportunities for AI tools to enhance creativity or decision-making, though over time, the two left-side AI-centric circles will grow toward the right as AI improves”
“Traditional doctors could instead evolve into a new profession, one that I’ll call a “compassionate caregiver.” These medical professionals would combine the skills of a nurse, medical technician, social worker, and even psychologist. Compassionate caregivers would be trained not just in operating and understanding the diagnostic tools but also in communicating with patients, consoling them in times of trauma, and emotionally supporting them throughout their treatment. Instead of simply informing patients of their objectively optimized chances of survival, they could share encouraging stories”
“Similar synergies will emerge in many other fields: teaching, law, event planning, and high-end retail. Paralegals at law firms could hand their routine research tasks off to algorithms and instead focus on communicating more with clients and making them feel cared for. AI-powered supermarkets like the Amazon Go store may not need cashiers anymore, so they could greatly upgrade the customer experience by hiring friendly concierges”
“these tools will only improve with time, and those who seek to compete against AI on its own terms will lose out. In the long run, resistance may be futile, but symbiosis will be rewarded.”
“Fink referenced automation and job retraining multiple times in his letter. As an investor with interests spanning the full breadth of the global economy, he sees that dealing with AI-induced displacement is not something that can be left entirely up to free markets. Instead, it is imperative that we reimagine and reinvigorate corporate social responsibility, impact investing, and social entrepreneurship.
In the past, these were the kinds of things that businesspeople merely dabbled in when they had time and money to spare. Sure, they think, why not throw some money into a microfinance startup or buy some corporate carbon offsets so we can put out a happy press release touting it. But in the age of AI, we will need to seriously deepen our commitment to—and broaden our definition of—these activities”
“Service-focused impact investing, however, will need to be different. It will need to accept linear returns when coupled with meaningful job creation. That’s because human-driven service jobs simply cannot achieve these exponential returns on investment. When someone builds a great company around human care work, they cannot digitally replicate these services and blast them out across the globe. Instead, the business must be built piece by piece, worker by worker. The truth is, traditional VCs wouldn’t bother with these kinds of linear companies, but these companies will be a key pillar in building an AI economy that creates new jobs and fosters human connections”