AI Superpowers | Reflections & Notes

Kai-Fu Lee. AI Superpowers: China, Silicon Valley, and the New World Order.Houghton Mifflin Harcourt, 2018. (253 pages)


An amazing read; surprisingly simple, compellingly human, and quite hopeful.

For a book on the geopolitics and business of advanced technology, Lee actually covers the wide range of related and relevant subjects–culture, history, economics (UBI), and ethics–which makes this an incredible read, not just for those interested in artificial intelligence, but for anyone wishing to understand the scope, sequence, and trajectory of the human condition. To address AI merely on technical terms would be technically informative, but stale. To address AI merely on philosophical or religious terms would be evocative, but parochial. To address both in one synergistic framework would be brilliant, and this is what Lee accomplishes in AI Superpowers. Especially for the lay reader, this book brings a greater depth of understanding and a compelling exhortation to consider the real and critical implications of AI but mostly from a human perspective.


As a vocational minister, it was an astounding experience to read a book on AI that speaks so boldly of love and even ends with an exact quote from Jesus (“love one another”), a core central principle to Judaism and Christianity. This was extremely provocative for three reasons.

First, regardless of Lee’s immense influence in the field, I wonder how much traction his clarion call will actually get. As all technologies have a bias, so too, the “technologies” of “Silicon Valley” and “religion” have biases that are at odds with each other. Silicon Valley tends toward technocratic solutions. Religion tends towards introspective and theological solutions. While there are several attempts at melding the two as “sympatico,” most expressions I have seen are minority voices on the margins (usually expressed as “workplace ministries” or “faith and work” by employees of big technology companies). Moreso, few have been successful in homogenizing “religion” and “technology” in a way that is mutually wholistic. I have hardly seen an expression of technology and religion as one cohesive unit. I sense that the relationship between the two is more accurately described as a “battle/wrestling,” than a “balance/symbiosis.”

This perennial dilemma is most famously captured in Tertullian’s frequently quoted, “What has Athens to do with Jerusalem?” (Prescription Against Heretics) We could perhaps formulate its inverse here, “What has The Church to do with Silicon Valley?”

Second, and related, the articulation of love as synergistic with AI, authored by someone who does not claim any religious identity–though Lee did mention he lived “as a Christian” for a little time while living in the US–is the closest articulation to a “solution” to the perennial debate of technology and human experience I have seen. And it is compelling and beautiful. I have only read one book on AI from a “Christian” perspective, and it was extremely disappointing. Perhaps later publications will captivate our religious imagination in a more thoughtful way. Until then, Lee’s articulation is fully in alignment with the commitments that are articulated in the sacred traditions and texts of Judaism and Christianity.

Third, philosophically, I simply wonder if “love” is a religious word that has emerged to describe the most comprehensive and complicated algorithm in the universe. While reading through Lee’s exhortation for us to understand what “humans” can do versus “robots” or “algorithms,” his writing had very subtle undercurrents related to Cartesian dualism, the idea that there is something separate, special, and immaterial to humanity. It is fair to say that at this point in our philosophical context, the jury may still be out deliberating the merits of the arguments, but the weight of “evidence” stands heavily on the side of materialism. So, while Lee may say that love “separates us from machines,” he is wading out into philosophical waters that have a steep continental shelf that dives into the abyss.

Mere observation can evince that not all humans love, and they don’t all love in the same way (especially as articulated by Lee). To sum, how humans love is highly contingent upon the trillions upon trillions of data points that have been calculated by someone’s life experience. The “inputs” determine (or should I say “greatly influence,” lest I fall into another philosophical rut?) the “outputs” of human behavior. Could we say that “love” (whatever that is) is perhaps one of life’s most complicated and intricate algorithms? Put another way, perhaps it is not love which makes us human, but rather it’s being human that makes us love?

This is obviously barely scratching the surface of what could be a book-length response. For this review, it is simply sufficient to reflect. And, to consider carefully, why we are even considering carefully!

And that is exactly what Lee does in his book. Fantastic.



…when it comes to understanding our AI future, we’re all like those kindergartners. We’re all full of questions without answers, trying to peer into the future with a mixture of childlike wonder and grownup worries. We want to know what AI automation will mean for our jobs and for our sense of purpose. We want to know which people and countries will benefit from this (x) tremendous technology. We wonder whether AI can vault us to lives of material abundance, and whether there is space for humanity in a world run by intelligent machines. (xi)

| No one has a crystal ball that can reveal the answers to these questions for us. But that core uncertainty makes it all the more important that we ask these questions and, to the best of our abilities, explore the answers. (xi)

Part of why predicting the ending to our AI story is so difficult is because this isn’t just a story about machines. It’s also a story about human beings, people with free will that allows them to make their own choices and to shape their own destinies. Our AI future will be created by us, and it will reflect the choices we make and the actions we take. In that process, I hope we will look deep within ourselves and to each other for the values and wisdom that can guide us. (xi)

| In that spirit, let us begin this exploration. (xi)

1 China’s Sputnik Moment



During the Ke Jie match, it wasn’t the AI-driven killer robots some prominent technologists warn of that frightened me. Itw as the real-world demons that could be conjured up by mass unemployment and the resulting social turmoil. The threat to jobs is coming far faster than most experts anticipated, and it will not discriminate by the color of one’s collar, instead striking the highly trained and poorly educated alike. On the day of that remarkable match between AlphaGo and Ke Jie, deep learning was dethroning humankind’s best Go player. That same job-eating technology is coming soon to a factory and an office near you. (5)


But in that same match, I also saw a reason for hope. (6)

Over the course of these three matches, Ke had gone on a roller-coaster of human emotion: confidence, anxiety, fear, hope, and heartbreak. It had showcased his competitive spirit, but I saw in those games an act of genuine love: a willingness to tangle with an unbeatable opponent out of pure love for the game, its history, and the people who play it. Those people who watch K’s frustration responded in kind. AlphaGo may have been the winner, but Ke became the people’s champion. In that connection–human beings giving and receiving love–I caught a glimpse of how humans will find work and meaning in the age of artificial intelligence. (6)

| I believe that the skillful application of AI will be China’s greatest opportunity to catch up with–and possibly surpass–the United States. But more important, this shift will create an opportunity for all people to rediscover what it is that makes us human. (6)


…the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. (7)

The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself. … Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon–pictures, chess games, sounds–into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better. (8)

In 1988, I used a technique akin to neural networks (Hidden Markov Models) to create Sphinx, the world’s first speaker-independent program for recognizing continuous speech. (8)

What ultimately resuscitated the field of neural networks–and sparked the AI renaissance we are living through today–were changes to two of the key raw ingredients that neural networks feed on, along with one major technical breakthrough. Neural networks require large amounts of two things computing power and data. The data “trains” the program to recognize patterns by giving it many examples, and the computing power lets the program parse those examples at high speeds. (9)


Fundamentally, these algorithms use massive amounts of data from a specific domain to make a decision that optimizes for a desired outcome. It does this by training itself to recognize deeply buried patterns and correlations connecting the many data points to the desired outcome. This pattern-finding process is easier when the data is labeled with that desired outcome–“cat” versus “no cat”; “clicked” versus “didn’t click”; “won game” versus “lost game.” It can then draw on its extensive knowledge of these correlations–many of which are invisible or irrelevant to human observers–to make better decisions than a human could. (10)

| Doing this requires massive amounts of relevant data, a strong algorithm, a narrow domain, and a concrete goal. If you’re short any one of these, things fall apart. Too little data? The algorithm doesn’t have enough examples to uncover meaningful correlations. Too broad a goal? The algorithm lacks clear benchmarks to shoot for in optimization. (10)

| Deep learning is what’s known as “narrow AI”–intelligence that takes data from one specific domain and applies it to optimizing one specific outcome. (10)


The West may have sparked the fire of deep learning, but China will be the biggest beneficiary of the heat the AI fire is generating. That global shift is the product of two transitions: from the age of discovery to the age of implementation, and from the age of expertise to the age of data. (12)


Much of the difficult but abstract work of AI research has been done, and it’s now time for entrepreneurs to roll up their sleeves and get down to the dirty work of turning algorithms into sustainable businesses. (13)

The age of implementation means we will finally see real-world applications after decades of promising research,… (13)

During the age of discovery, progress was driven by a handful of elite thinkers, virtually all of whom were clustered in the United States and Canada. (13)


In deep learning, there’s no data like more data. (14)


Realizing the newfound promise of electrification a century ago required four key inputs: fossil fuels to generate it, entrepreneurs to build new businesses around it, electrical engineers to manipulate it, and a supportive government to develop the underlying public infrastructure. Harnessing the power of AI today–the “electricity” of the twenty-first century–requires four analogous inputs: abundant data, hungry entrepreneurs, AI scientists, and an AI-friendly policy (14) environment. By looking at the relative strengths of China and the United States in these four categories, we can predict the emerging balance of power in the AI world order. (15)

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, like a day spent as a gladiator in the Coliseum. The battles are life or death, and your opponents have no scruples. (15)

| The only way to survive this battle is to constantly improve one’s product but also to innovate on your business model and build a “moat” around your company. (15)

These entrepreneurs will have access to the other “natural resource” of China’s tech world: an overabundance of data. (16)


…on top of this natural rebalancing, China’s government is also doing everything it can to tip the scales. (17)

I believe that China will soon match or even overtake the United States in developing and deploying artificial intelligence. In my view, that lead in AI deployment will translate into productivity gains on a scale not seen since the Industrial Revolution> PricewaterhouseCoopers estimates AI deployment will add $15.7 trillion to global GDP by 2030. China is predicted to take home $7 trillion of that total, nearly double North America’s $3.7 trillion in gains. As the economic balance of power tilts in China’s favor, so too will political influence and “soft power,” the country’s cultural and ideological footprint around the globe. (18)

For as far back as many of us can remember, it was American technology companies that were pushing their products and their values on users around the globe. As a result, American companies, citizens, and politicians have forgotten what it feels like to be on the receiving end of these exchanges, a process that often feels akin to “technological colonization.” China does not intend to use its advantage in the AI era as a platform for such colonization, but AI-induced disruptions to the political and economic order will (18) lead to a major shift in how all countries experience the phenomenon of digital globalization. (19)


Significant as this jockeying between the world’s two superpowers will be, it pales in comparison to the problems of job losses and growing inequality–both domestically and between countries–that AI will conjure. (19)

I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon. (19)


China and the United States are currently incubating the AI giants that will dominate global markets and extract wealth from consumers around the globe. (20)

| At the same time, AI-driven automation in factories will undercut the one economic advantage developing countries historically possessed: cheap labor. (20)

The AI world order will combine winner-take-all economics with an unprecedented concentration of wealth in the hands of a few companies in China and the United States. This, I believe, is the real underlying threat posed by artificial intelligence: tremendous social disorder and political collapse stemming from widespread unemployment and gaping inequality. (21)

Tumult in jobs markets and turmoil across societies will occur against the backdrop of a far more personal and human crisis–a psychological loss of one’s purpose. For centuries, human beings have filled their days by working: trading their time and sweat for shelter and food. We’ve built deeply entrenched cultural values around this exchange, and many of us have been conditioned to derive our sense of self-worth from the act of daily work. The rise of artificial intelligence will challenge these values and threatens to undercut that sense of life-purpose in a vanishingly short window of time. (21)

Tackling these problems will require a combination of clear-eyed analysis and profound philosophical examination of what matters in our lives, a task for both our minds and our hearts. (21)

2 Copycats in the Coliseum

In creating his early clones of Facebook and Twitter, Wang was in fact relying entirely on the Silicon Valley playbook. This first phase of the copycat era–Chinese startups cloning Silicon Valley websites–helped build up baseline engineering and digital entrepreneurship skills that were totally absent in China at the time. But it was a second phase–Chinese startups taking inspiration from an American business model and then fiercely competing against each other to adapt and optimize that model specifically for Chinese users–that turned Wang Xing into a world-class entrepreneur. (24)

The battle royal for China’s group-buying market was a microcosm of what China’s internet ecosystem had become: a coliseum where hundreds of copycat gladiators fought to the death. Amid the chaos and bloodshed, the foreign first-movers often proved irrelevant. It was the domestic combatants who pushed each other to be faster, nimbler, leaner, and meaner. They aggressively copied each other’s product innovations, cut prices to the bone, launched smear campaigns, forcibly deinstalled competing software, and even reported rival CEOs to the police. For these gladiators, no dirty trick (24) or underhanded maneuver was out of bounds. They deployed tactics that would make Uber founder Travis Kalanick blush. They also demonstrated a fanatical around-the-clock work ethic that would send Google employees running to their nap pods. (25)

| Silicon Valley may have found the copying undignified and the tactics unsavory. In many cases, it was. But it was precisely this widespread cloning–the onslaught of thousands of mimicking competitors–that forced companies to innovate.  … 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. (25)

Wang Xing didn’t succeed because he’d been a copycat. He triumphed because he’d become a gladiator. (26)


Startups and the entrepreneurs who found them are not born in a vacuum. Their business models, products, and core values constitute an expression of the unique cultural time and place in which they come of age. (26)

China’s startup culture is the yin to Silicon (26) 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. The core motivation for China’s market-driven entrepreneurs is not fame, glory, or changing the world. Those things are all nice side benefits, but the grand prize is getting rich, and it doesn’t matter how you get there. (27)

Jarring as that mercenary attitude is to many Americans, the Chinese approach has deep historical and cultural roots. Rote memorization formed the core of Chinese education for millennia. Entry into the country’s imperial bureaucracy depended on world-for-word memorization of ancient texts and the ability to construct a perfect “eight-legged essay” following rigid stylistic guidelines. 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. (27)

| Layered atop this cultural propensity for imitation is the deeply ingrained scarcity mentality of twentieth-century China. (27)




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. … A copycat mentality is a core stumbling block ont he 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. (33)

| But I saw early copycats like Wang Xing’s Twitter knockoff not as stumbling blocks but as building blocks. (33)



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. (38)


American companies treat China like just any other market to check off their global list. They don’t invest the resources, have the patience, or give their Chinese teams the flexibility needed to compete with China’s world-class entrepreneurs. They see the primary job in China as marketing their existing products to Chinese users. In reality, they need to put in real work tailoring their products for Chinese users or building new products from the ground up to meet market demands. Resistance to localization slows down product iteration and makes local teams feel like cogs in a clunky machine. (39)

[via: This reminded me of how Starbucks failed and succeeded in China.]



A “mission” makes for a strong narrative when pitching to media or venture-capital firms, but it can also become a real burden in a rapidly changing market. What does a founder do when there’s a divergence between what the market demands and what a mission dictates? (45)

If they succeed in building a product that people want, they don’t get to declare victory. They have to declare war. (45)



The dawn of the internet in China functioned like the invention of the telegraph, shrinking distances, speeding information flows, and facilitating commerce. The dawn of AI in China will be like the harnessing of electricity: a game-changer that supercharges industries across the board. The Chinese entrepreneurs who sharpened and honed their skills in the coliseum now see the power that this new technology holds, and they’re already seeking out industries and applications where they can turn this energy into profit. (50)

3 China’s Alternate Internet Universe


In my view, that willingness to get one’s hand dirty in the real world separates Chinese technology companies from their Silicon Valley peers. 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. They aspire to the mythology satirized in the HBO series Silicon Valley, that of a skeleton crew of hackers building a billion-dollar business without ever leaving their San Francisco loft. (55)

| 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. It’s a defining trait of China’s alternate internet universe that leaves American analysts entrenched in Silicon Valley orthodoxy scratching their heads.


But 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. (55)

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. As AI begins to “electrify” new industries, China’s embrace of the messy details of the real world will give it an edge of Silicon Valley. (56)




But building an alternate internet universe that reaches into every corner of the Chinese economy couldn’t be done without hte country’s most important economic actor: the Chinese government. (61)





…the “O2O Revolution,” short for “online-to-offline.” (68)

With the rise of O2O, WeChat had grown into the title bestowed on it by Connie Chan of leading VC fund Andreesen Horowitz: a remote control for our lives. (70)

Tencent’s choice to go for the super-app model appeared (70) risky at the start: could you possibly bundle so many things together without overwhelming the user? But the super-app model proved wildly successful for WeChat and has played a crucial role in shaping this alternate universe of internet services. (71)


The terms refer to how involved an internet company becomes in providing goods or services. They represent the extent of vertical integration as a company links up the on- and offline worlds. (71)

| When looking to disrupt a new industry, American internet companies tend to take a “light” approach. They generally believe the internet’s fundamental power is sharing information, closing knowledge gaps, and connecting people digitally. As internet-driven companies, they try to stick to this core strength. Silicon Valley startups will build the information platform but then let brick-and-mortar businesses handle the on-the-ground logistics. They want to win by outsmarting opponents, by coming up with novel and elegant code-based solutions to information problems. (71)

| 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. Going heavy means building walls around your business, insulating yourself from the economic bloodshed of China’s gladiator wars. These companies win both by outsmarting their opponents and by outworking, outhustling, and outspending them on the street. (71)



By contrast, Apple Pay and Google Wallet have tread lightly in this arena. They theoretically offer greater convenience to users, but they haven’t been willing to bribe users into discovering that method for themselves. Reluctance on the part of U.S. tech giants is understandable: subsidies eat into quarterly revenue, and attempts to “buy users” are usually frowned on by Silicon Valley’s innovation purists. (76)

| But that American reluctance to go heavy has slowed adoption of mobile payments and will hurt these companies even more in a data-driven AI world. Data from mobile payments is currently generating the richest maps of consumer activity the world has ever known, far exceeding the data from traditional credit-card purchases or online activity captured by e-commerce players like Amazon or platforms like Google and Yelp. That mobile payment data will prove invaluable in building AI-driven companies in retail, real estate, and a range of other sectors. (77)



But building an AI-driven economy requires more than just gladiator entrepreneurs and abundant data. It also takes an army of trained AI engineers and a government eager to embrace the power of this transformative technology. These two factors–AI expertise and government support–are the final pieces of the AI puzzle. (80)

4 A Tale of Two Countries


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. (83)

…Seven Giants of the AI age… Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent,… (83)

Behind these efforts lies a core difference in American and Chinese political culture: while America’s combative political system aggressively punishes missteps or waste in funding technological upgrades, China’s techno-utilitarian approach rewards proactive investment and adoption. Neither system can claim objective moral superiority, and the United States’ long track record of both personal freedom and technological achievement is unparalleled in the modern era. But I believe that in the age of AI implementation the Chinese approach will have the impact of accelerating deployment, generating more data, and planting the seeds of further growth. It’s a self-perpetuating cycle, one that runs on a peculiar alchemy of digital data, entrepreneurial grit, hard-earned expertise, and political will. (84)


cf. Enrico Fermi

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. (85)

| 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 army of tinkerers–engineers with just enough expertise to apply the technology to different problems. This is particularly true when the (85) payoff of a breakthrough is diffused throughout society rather than concentrated in a few labs or weapons systems. (86)

A constant stream of headlines about the latest task tackled by AI gives us the mistaken sense that we are living through an age of discovery,… In reality, we are witnessing the application of one fundamental breakthrough–deep learning and related techniques–to many different problems. (86)


Many AI scientists aren’t trying to make fundamental breakthroughs on the scale of deep learning, but they are constantly making marginal improvements to the best algorithms. (87)




But while the global AI research community has blossomed into a fluid and open system, one component of that ecosystem remains more closed off: big corporate research labs. Academic researchers may rush to share their work with the world, but public technology companies have a fiduciary responsibility to maximize profits for their shareholders. That usually means less publishing and more proprietary technology. (91)



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 (94) problems users require. (95)

…”battery-powered” AI products…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. (95)

It’s far too early to pick a winner between the grid and batter approaches. While giants like Google steadily spread their tentacles outward, startups in China and the United States are racing to claim virgin territory and fortify themselves against incursions by the Seven Giants. How that scramble for territory shakes out will determine the shape of our new economic landscape. It could concentrate astronomical profits in the hands of the Seven Giants–the super-utilities of the AI age–or diffuse those profits out across thousands of vibrant new companies. (95)


High-performance chips are the unsexy, and often unsung heroes of each computing revolution. …but…they remain largely hidden to the end user. But from an economic and security perspective, building those chips is a very big deal: the markets tend toward lucrative monopolies, and security vulnerabilities are best spotted by those who work directly with the hardware. (96)




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. It, like all political structures, is a highly imperfect system. Top-down government mandates to expand investment and production can also send the pendulum of public investment swinging too far in a given direction. In recent years, this has led to massive gluts of supply and unsustainable debt loads in Chinese industries ranging from solar panels to steel. But when national leaders correctly channel those mandates toward new technologies that can lead to seismic economic shifts, the techno-utilitarian approach can have huge upsides. (101)

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. Promotion of a broader social good–the long-term payoff in lives saved–is a good enough reason to begin implementation, with outlier cases and legal intricacies to be dealt with in due time. (102)

5 The Four Waves of AI

cf. iFlyTek


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. (105)


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.






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. (113)


cf. RXThinking



These applications of second-wave AI have immediate, real-world impacts, but the algorithms themselves are still trafficking purely in digital information mediated by humans. Third-wave AI changes all of this by giving AI two of humans’ most valuable information-gathering tools: eyes and ears. (117)



I call these blended environments OMO: online-merge-of-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. It 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. (118)

Pay-with-your-face applications are fun, but they are just the tip of the OMO iceberg. (118)


“Based on what’s in your cart and fridge at home, it looks like your diet will be short on fiber this week. Shall I add a bag of almonds or ingredients for a split-pea soup to correct that?” (119)



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. (125)


Today, the greatest advantage of manufacturing in China isn’t the cheap labor–countries like Indonesia and Vietnam offer lower wages. Instead, it’s the unparalleled flexibility of the supply chains and the armies of skilled industrial engineers who can make prototypes of new devices and build them at scale. (126)


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. Today, China’s edge is slight (60-40), but I predict that in five years’ time, the above factors will give China a more than 80-20 chance of leading the United States and the rest of the world in the implementation of perception AI. (128)


…by giving machines the power of sight, the sense of touch, and the ability to optimize from data, we can dramatically expand the number of tasks they can tackle. (129)


cf. Traptic




When managing a country of 1.39 billion people–one in which 260,000 people die in car accidents each year–the Chinese mental-(132)ity 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 vehicle sin 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. (133)


cf. Momenta; JingChi;; Baidu’s Apollo project

Predicting which country takes the lead in autonomous AI largely comes down to one main question: will the primary bottleneck to full deployment be one of technology or policy? (135)




Scanning the AI horizon, we see waves of technology that will soon wash over the global economy and tilt the geopolitical landscape toward China. Traditional American companies are doing a good job of using deep learning to squeeze greater profits from their businesses, (138) and AI-driven companies like Google remain bastions of elite expertise. But when it comes to building new internet empires, changing the way we diagnose illnesses, or reimagining how we shop, move, and eat, China seems poised to seize global leadership. (139)

This analysis sheds light on the emerging AI world order, but it also showcases one of the blind spots in our AI discourse: the tendency to discuss it solely as a horse race. Who’s ahead? What are the odds for each player? Who’s going to win? (139)

| This kind of competition matters, but if we dig deeper into the coming changes, we find that far weightier questions lurk just below the surface. When the true power of artificial intelligence is brought to bear, the real divide won’t be between countries like the United States and China. Instead, the most dangerous fault lines will emerge within each country, and they will possess the power to tear them apart from the inside. (139)

6 Utopia, Dystopia, and the Real AI Crisis

With superintelligent computers that understand the universe on levels that humans cannot even conceive of, these machines become not just tools for lightening the burdens of humanity; they approach the omniscience and omnipotence of a god. (141)

| Not everyone, however, is so optimistic. Elon Musk has called superintelligence “the biggest risk we face as a civilization,” comparing the creation of it to “summoning the demon.” (141)

For the most part, members of the dystopian camp arent’ worried about the AI takeover as imagined in films like the Terminator series, with human-like robots “turning evil” and hunting down people in a power-hungry conquest of humanity. Superintelligence would be the product of human creation, not natural evolution, and thus wouldn’t have the same instincts for survival, reproduction, or domination that motivate humans or animals. Instead, it would likely just seek to achieve the goals given to it in the most efficient way possible. (141)


When utopian and dystopian visions of the superintelligent future are discussed publicly, they inspire both awe and a sense of dread in audiences. Those all-consuming emotions then blur the lines in our mind separating these fantastical futures from our current age of AI implementation. The result is widespread popular confusion over where we truly stand today and where things are headed. (142)

| To be clear, none of the scenarios described above–the immortal digital minds or omnipotent superintelligences–are possible based on today’s technologies; there remain no known algorithms for AGI or a clear engineering route to get there. The singularity is not something that can occur spontaneously, with autonomous vehicles running on deep learning suddenly “waking up” and realizing that they can band together to form a superintelligent network. (142)

| Getting to AGI would require a series of foundational scientific breakthroughs in artificial intelligence, a string of advances on the scale of, or greater than, deep learning. These breakthroughs would need to remove key constraints on the “narrow AI” programs that we run today and empower them with a wide array of new abilities: multidomain learning; domain-independent learning; natural-language understanding; commonsense reasoning, planning, and learning from a small number of examples. Taking the next step to emotionally intelligent robots may require self-awareness, humor, love, empathy, and appreciation for beauty. These are the key hurdles that separate what AI does today–spotting correlations in data and making predictions–and artificial general intelligence. (142)

The mistake of many AGI forecasts is to simply take the rapid rate of advance from the past decade and extrapolate it outward or launch it exponentially upward in an unstoppable snowballing of computer intelligence. Deep learning represents a major leveling up in machine learning, a movement onto a new plateau with a variety of real-world uses: the age of implementation. But there is no proof that this upward change represents the beginning of exponential growth that will inevitably race toward AGI, and then superintelligence, at an ever-increasing pace. (143)

I cannot guarantee that scientists definitely will not make the breakthroughs that would bring about AGI and then superintelligence. In fact, I believe we should expect continual improvements to the existing state of the art. But I believe we are still many decades, if not centuries, away from the real thing. There is also a real possibility that AGI is something humans will never achieve. (143)

I believe that civilization will soon face a different kind of AI-induced crisis. (144)

In short, this is the coming crisis of jobs and inequality. Our present AI capabilities can’t create a superintelligence that destroys our civilization. But my fear is that we humans may prove more than up to that task ourselves. (144)



In most developed countries, economic inequality and class-based resentment rank among the most dangerous and potentially explosive problems. The past few years have shown us how a caul-(146)dron of long-simmering inequality can boil over into radical political upheaval. I believe that, if left unchecked, AI will throw gasoline on the socioeconomic fires. (147)

| Lurking beneath this social and economic turmoil will be a psychological struggle, one that won’t make the headlines but that could make all the difference. As more and more people see themselves displaced by machines, they will be forced to answer a far deeper question: in an age of intelligent machines, what does it mean to be human? (147)


Ever since the Industrial Revolution, people have feared that everything from weaving looms to tractors to ATMs will lead to massive job losses. But each time, increasing productivity has paired with the magic of the market to smooth things out. (148)

| Economists who look to history–and the corporate juggernauts who will profit tremendously from AI–use these examples from the past to dismiss claims of AI-induced unemployment in the future. They point to millions of inventions–the cotton gin, lightbulbs, cars, video cameras, and cellphones–none of which led to widespread unemployment. Artificial intelligence, they say, will be no different. It will greatly increase productivity and promote healthy growth in jobs and human welfare. So what is there to worry about? (148)


If we think of all inventions as data points and weight them equally, the techno-optimists have a compelling and data-driven argument. But not all inventions are created equal. Some of them change how we perform a single task (typewriters), some of them eliminate the need for one kind of labor (calculators), and some of them disrupt a whole industry (the cotton gin). (148)

| And then there are technological changes on an entirely different scale. … 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 mat-(148)ter,” the ones that “interrupt and accelerate the normal march of economic progress.” (149)

Economic historians have many quibbles over exactly which innovations of the modern era should qualify…but surveys of the literature reveal three technologies that receive broad support: the steam engine, electricity, and information and communication technology (such as computers and the internet). (149)

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. (149)

Both the economic pie and overall standards of living grew. (150)

| But what about the most recent, GPT, information and communication technologies (ICT)? So far, its impact on labor markets and wealth inequality have been far more ambiguous. As Brynjolfsson and McAfee point out in The Second Machine Age, over the past thirty years, the United States has seen steady growth in worker productivity but stagnant growth in median income and employment. Brynjolfsson and McAfee call this “the great decoupling.” After decades when productivity has continued to shoot upward, wages and jobs have flatlined or fallen. (150)

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. Digital communications tools allow top performers to efficiently manage much larger organizations and reach much larger audiences. By breaking down the barriers to disseminating information, ICT empowers the world’s top knowledge workers and undercuts the economic role of many in the middle. (150)

one thing is increasingly clear: there is no guarantee that GPTs that in-(150)crease our productivity will also lead to more jobs or higher wages for workers. (151)


I am confident that AI will soon enter the elite club of universally recognized GPTs, spurring a revolution in economic production and even social organization. The AI revolution will be on the scale of the Industrial Revolution, but probably larger and definitely faster. (151)

Steam power fundamentally altered the nature of manual labor, and ICT did the same for certain kinds of cognitive labor. AI will cut across both. It will perform many kinds of physical and intellectual tasks with a speed and power that far outstrip any human, dramatically increasing productivity in everything from transportation to manufacturing to medicine. (151)


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. (152)

| First, many productivity-increasing AI products are just digital algorithms: infinitely replicable and instantly distributable around the world. (152)

The second catalyst is one that many in the technology world today take for granted: the creation of the venture-capital industry. (153)

Finally, the third catalyst is one that’s equally obvious and yet often overlooked: China. (154)

Reviewing the preceding arguments, I believe we can confidently state a few things. First, during the industrial era, new technology has been associated with long-term job creation and wage growth. Second, despite this general trend toward economic improvement, GPTs are rare and substantial enough that each one’s impact on jobs should be evaluated independently. Third, of the three widely recognized GPTs of the modern era, the skill biases of steam power and electrification boosted both productivity and employment. ICT has lifted the former but not necessarily the latter, contributing to falling wages for many workers int he developed world and greater inequality. Finally, AI will be a GPT, one whose skill biases and speed of adoption–catalyzed by digital dissemination, VC funding, and (154) China–suggest it will lead to negative impacts on employment and income distribution. (155)

| Iff the above arguments hold true, the next questions are clear: What jobs are really at risk? And how bad will it be? (155)


…AI creates a mixed bag of winners and losers depending on the particular content of job tasks performed. While AI has far surpassed humans at narrow tasks that can be optimized based on data, it remains stubbornly unable to interact naturally with people or imitate the dexterity of our fingers and limbs. It also cannot engage in cross-domain thinking on creative tasks or ones requiring complex strategy, jobs whose inputs and outcomes aren’t easily quantified. What this means for job replacement can be expressed simply through two X-Y graphs, one for physical labor and one for cognitive labor. (155)

For physical labor, the X-axis extends from “low dexterity and structured environment” on the left side, to “high dexterity and unstructured environment” on the right side. The Y-axis moves from “social” at the bottom to “highly social” at the top. The cognitive labor chart shares the same Y-axis (social to highly social) but uses a different X-axis: “optimization-based” on the left, to “creativity- or strategy-based” on the right. Cognitive tasks are categorized as “optimization-based” if their core tasks involve maximizing quantifiable variables that can be captured in data (for example, setting an optimal insurance rate or maximizing a tax refund). (156)

| These axes divide both charts into four quadrants: the bottom-left quadrant is the “Danger Zone,” the top-right is the “Safe-Zone,” the top-left is the “Human Veneer,” and the bottom right is the “Slow Creep.” Jobs whose tasks primarily fall in the “Danger Zone” (dish-washer, entry-level translators) are at a high risk of replacement in the coming years. Those in the “Safe Zone” (psychiatrist, home-care nurse, etc.) are likely out of reach of automation for the foreseeable future. The “Human Veneer” and “Slow Creep” quadrants are less clear-cut: while not fully replaceable right now, reorganization of work tasks or steady advances in technology could lead to wide-spread job reductions in these quadrants. As we will see, occupations (156) often involve many different activities outside of the “core tasks” that we have used to place them in a given quadrant. This task-diversity will complicate the automation of many professions, but for now we can use these axes and quadrants as general guidance for thinking about what occupations are at risk. (157)

| For the “Human Veneer” quadrant, much of the computational or physical work can already be done by machines, but the key social interactive element makes them difficult to automate en masse. The name of the quadrant derives from the most likely route to automation: while the behind-the-scenes optimization work is overtaken by machines, human workers will act as the social interface for customers, leading to a symbiotic relationship between human and machine. Jobs in this category could include bartender, school-teacher, and even medical caregiver. How quickly and what percentage of these jobs disappear depends on how flexible companies are in restructuring the tasks done by their employees, and how open customers are to interacting with computers. (157)

| The “Slow Creep” category (plumber, construction worker, entry-level graphic designer) doesn’t rely on human beings’ social skills but instead on manual dexterity, creativity, or ability to adapt to unstructured environments. These remain substantial hurdles for AI, but ones that technology will slowly chip away at in the coming years. The pace of job elimination in this quadrant depends less on process innovation at companies and more on the actual expansion in AI capabilities. But at the far right end of the “Slow Creep” are good opportunities for the creative professionals (such as scientists and aerospace engineers) to use AI tools to accelerate their progress. (157)


Predicting the scale of AI-induced job losses has become a cottage industry for economists and consulting firms the world over. De-(157)pending on which model one uses, estimates range from terrifying to totally not a problem. (158)

cf. Organization for Economic Cooperation and Development (OECD)

The OECD team instead proposed a task-based approach, breaking down each job into its many component activities and looking at how many of those could be automated. In this model, a tax preparer is not merely categorized as one occupation but rather as a series of tasks that are automatable (reviewing income documents, calculating maximum deductions, reviewing forms for inconsistencies, etc.) and tasks that are not automatable (meeting with new clients, explaining decisions to those clients, etc.) (159)

cf. PwC; McKinsey Global Institute


While I respect the expertise of the economists who pieced together the above estimates, I also respectfully disagree with the low-end estimates of the OECD. That difference is rooted in two disagreements: one in terms of the inputs of their equations, and one major difference in the way I envision AI disrupting labor markets. The quibble causes me to go with the higher-end estimates of PwC, and the difference in vision leads me to raise that number higher still. (160)

…few, if any, experts predicted that deep learning was going to get this good, this fast. (161)


But beyond that disagreement over methodology, I believe using only the task-based approach misses an entirely separate category of potential job losses: industry-wide disruptions due to new AI-empowered business models. Separate from the occupation- or task-based approach, I’ll call this the industry-based approach. (162)

But then there exists a completely different breed of AI startups: those that reimagine an industry from the ground up. These companies don’t look to replace one human worker with one tailor-made (162) robot that can handle the same tasks; rather, they look for new ways to satisfy the fundamental human need driving the industry. (163)


Putting together percentages for the two types of automatability–38 percent from one-to-one replacements and about 10 percent from ground-up disruption–we are faced with a monumental challenge. Within ten to twenty years, I estimate we will be technically capable of automating 40 to 50 percent of jobs in the United States. (164)

This–and I cannot stress this enough–does not mean the country will be facing a 40 to 50 percent unemployment rate. Social frictions, regulatory restrictions, and plain old inertia will greatly slow down the actual rate of job losses. Plus, there will also be new jobs created along the way, positions that can offset a portion of these AI-induced losses, something that I explore in the coming chapters. These could cut actual AI_induced net unemployment in half, to between 20 and 25 percent, or drive it even lower, down to just 10 to 20 percent. (164)

…if left unchecked, it could constitute the new normal: an age of full employment for intelligent machines and enduring stagnation for the average worker. (165)


In my opinion, the conventional wisdom on this is backward. While China will face a wrenching labor-market transition due to automation, large segments of that transition may arrive later or move slower than the job losses wracking the American economy. While the simplest and most routine factory jobs–quality control and simple assembly-line tasks–will likely be automated in the coming years, the remainder of these manual labor tasks will be tougher for robots to take over. This is because the intelligent automation of the twenty-first century operates differently than the physical automation of the twentieth century. Put simply, it’s far easier to build AI algorithms than to build intelligent robots. (166)

| Core to this logic is a tenet of artificial intelligence known as Moravec’s Paradox. Hans Moravec was a professor of mine at Carnegie Mellon University, and his work on artificial intelligence and robotics led him to a fundamental truth about combining the two: contrary to popular assumptions, it is relatively easy for AI to mimic the high-level intellectual or computational abilities of an adult, but it’s far harder to give a robot the perception and sensorimotor skills of a toddler. Algorithms can blow humans out of the water when it comes to making predictions based on data, but robots still can’t perform the cleaning duties of a hotel maid. In essence, AI is great at thinking, but robots are bad at moving their fingers. (166)

…the fine motor skills of robots–the ability to grasp and manipulate objects–still lag far behind humans. While AI can beat the best humans at Go and diagnose cancer with extreme accuracy, it cannot yet appreciate a good joke. (167)

[via: While I’m persuaded, I can’t help but think that Boston Dynamics may challenge the assumptions.]


This hard reality about algorithms and robots will have profound effects on the sequence of AI-induced job losses. The physical automation of the past century largely hurt blue-collar workers, but the coming decades of intelligent automation will hit white-collar workers first. The truth is that these workers have far more to fear from the algorithms that exist today than from the robots that still need to be invented. (167)

| In short, AI algorithms will be to many white-collar workers what tractors were to farmhands: a tool that dramatically increases the productivity of each worker and thus shrinks the total number of employees required. (167)


Whatever gaps exists between China and the United States, those differences will pale in comparison between these two AI superpowers and the rest of the world. Silicon Valley entrepreneurs love to describe their products as “democratizing access,” “connecting people,” and, of course, “making the world a better place.” That vision of technology as a cure-all for global inequality has always been something of a wistful mirage, but in the age of AI it could turn into something far more dangerous. If left unchecked, AI will dramatically exacerbate inequality on both international and domestic levels. It will drive a wedge between the AI superpowers and the rest of the world, and may divide society along class lines that mimic the dystopian science fiction of Hao Jingfang. (168)

| As a technology and an industry, AI naturally gravitates toward monopolies. (168)

I fear this process will exacerbate and significantly grow the divide between the AI haves and have-nots. While AI-rich countries rake in astounding profits, countries that haven’t crossed a certain technological and economic threshold will find themselves slipping backward and falling farther behind. With manufacturing and services increasingly done by intelligent machines located in the AI superpowers, developing countries will lose the one competitive edge that their predecessors used to kick-start development: low-wage factory labor. (169)

| Large populations of young people used to be these countries’ (169) greatest strengths. But in the age of AI, that group will be made up of displaced workers unable to find economically productive work. This sea change will transform them from an engine of growth to a liability on the public ledger–and a potentially explosive one if their governments prove unable to meet their demands for a better life. (170)

| Deprived of the chance to claw their way out of poverty, poor countries will stagnate while the AI superpowers take off. I fear this ever-growing economic divide will force poor countries into a state of near-total dependence and subservience. Their governments may try to negotiate with the superpower that supplies their AI technology, trading market and data access for guarantees of economic aid for their population. Whatever bargain is struck, it will not be one based on agency or equality between nations. (170)


…also exacerbate inequality within the AI superpowers. (170)

Driving income inequality will be the emergence of an increasingly bifurcated labor market. The jobs that do remain will tend to be either lucrative work for top performers or low-paying jobs in tough industries. (171)

Pushing more people into these jobs while the rich leverage AI for huge gains doesn’t just create a society that is dramatically un-(171)equal. I fear it will also prove unsustainable and frighteningly unstable. (172)



The resulting turmoil will take on political, economic, and social dimensions, but it will also be intensely personal. In the centuries since the Industrial Revolution, we have increasingly come to see our work not just as a means of survival but as a source of personal pride, identity, and real-life meaning. Asked to introduce ourselves or others in a social setting, a job is often the first thing we mention. It fills our days and provides a sense of routine and a source of human connections. A regular paycheck has become a way not just of rewarding labor but also of signaling to people that one is a valued member of society, a contributor to a common project. (173)

The winners of this AI economy will marvel at the awesome power of these machines. But the rest of humankind will be left to grapple with a far deeper question: when machines can do everything that we can, what does it mean to be human? (174)

7 The Wisdom of Cancer

…mesmerized by my quest to create machines that thought like people, I had turned into a person that thought like a machine. (176)

DECEMBER 16, 1991


As a young man, computer science and artificial intelligence resonated with me because the crystal logic of the algorithms mirrored my own way of thinking. At the time, I processed everything in my life–friendships, work, and family time–as variables or inputs in my own mental algorithm. They were things to be quantified and metered out in the precise amounts required to achieve a specific outcome. (179)




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. (185)


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. (186)


cf. Master Hsing Yun, founder of Fo Guang Shan in 1967

cf. Elisabeth Kübler-Ross

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.

Many people understand it, but it’s much harder to live it. For that we must humble ourselves. We have to feel in our bones just how small we are, and we must recognize that there’s nothing greater or more valuable in this world than a simple act of sharing love with others. If we start from there, the rest will begin to fall into place. It’s the only way that we can truly become ourselves.

– Master Hsing Yu

During my time as a researcher, I had stood on the absolute frontier of human knowledge about artificial intelligence, but I had never been further from a genuine understanding of other human beings or myself. That kind of understanding couldn’t be coaxed out of a cleverly constructed algorithm. Rather, it required an unflinching look into the mirror of death and an embrace of that which separated me from the machines that I built: the possibility of love. (190)


Ranking stages based on such simple characteristics of a complex disease is a classic example of the human need to base decisions on “strong features.” Humans are extremely limited in their ability to discern correlations between variables, so we look for guidance in a handful of the most obvious signifiers. (191)

These so-called strong features really don’t represent the most accurate tools for making a nuanced prognosis, but they’re simple enough for a medical system in which knowledge must be passed down, stored, and retrieved in the brains of human doctors. (191)


I wouldn’t seek to be a productivity machine. A loving human being would be enough. (193)

I have great respect and deep appreciation for the medical professionals who led my treatment. (194)

And yet, that was only half of the cure for what ailed me. (194) … I wouldn’t be sharing this story with you if it weren’t for Shen-Ling, my sisters, and my own mother, who through quiet example showed me what it means to lead a life of selflessly sharing love. (195)

Without these unquantifiable, nonoptimizable connections to other people, I would never have learned what it truly means to be human. (195)

The reality is that it will not be long until AI algorithms can perform many of the diagnostic functions of medical professionals. … In some cases, the algorithms may replace the doctor entirely. (195)

| But the truth is, there exists no algorithm that could replace the role of my family in my healing process. What they shared with me is far simpler–and yet so much more profound–than anything AI will ever produce. (195)

For all of AI’s astounding capabilities, the one thing that only humans can provide turns out to also be exactly what is most needed in our lives: love. … 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 and (195) be loved, and that loving and being loved are what makes our lives worthwhile. (196)

| This is the synthesis on which I believe we must build our shared future: on AI’s ability to think but coupled with human beings’ ability to love. If we can create this synergy, it will let us harness the undeniable power of artificial intelligence to generate prosperity while also embracing our essential humanity. (196)

8 A Blueprint for Human Coexistence with AI

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. (198)

It is in this uniquely human potential for growth, compassion, and love where I see hope. I firmly believe we must forge a new synergy between artificial intelligence and the human heart, and look for ways to use the forthcoming material abundance generated by artificial intelligence to foster love and compassion in our societies. (199)

| If we can do these things, I believe there is a path toward a future of both economic prosperity and spiritual flourishing. Navigating that path will be tricky, but if we are able to unite behind this common goal, I believe humans will not just survive in the age of AI. We will thrive like never before. (199)


We must proactively seize the opportunity that the material wealth of AI will grant us and use it to reconstruct our economies and rewrite our social contracts. The epiphanies that emerged from my experience with cancer were deeply personal, but I believe they also gave me a new clarity and vision for how we can approach these problems together. (200)

Building societies that thrive in the age of AI will require substantial changes to our economy but also a shift in culture and values. Centuries of living within the industrial economy have conditioned many of us to believe that our primary role in society (and even our identity) is found in productive, wage-earning work. Take that away and you have broken one of the strongest bonds between a person and his or her community. As we transition from the industrial age to the AI age, we will need to move away from a mindset that equates work with life or treats humans as variables in a grand productivity optimization algorithm. Instead, we must move toward (200) a new culture that values human love, service, and compassion more than ever before. (201)

| No economic or social policy can “brute force” a change in our hearts. But in choosing different policies, we can reward different directions. (201)



Many of the proposed technical solutions for AI-induced job losses coming out of Silicon Valley fall into three buckets: retraining workers, reducing work hours, or redistributing income. Each of these approaches aims to augment a different variable within the labor markets (skills, time, compensation) and also embodies different assumption [sic] about the speed and severity of job losses. (203)

| 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 (203) adopt more radical redistribution schemes to support unemployed workers and spread the wealth created by AI. (204)

Uncertainty over the pace and path of automation makes things even more difficult. … Can we really expect a typical worker choosing a retraining program to accurately predict which jobs will be safe a few years from now? (204)

Workers may accept this knock to their income during a temporary economic crisis, but no one desires stagnation or downward mobility over the long term. Telling a worker making $20,000 a year that they can now work four days a week and earn $16,000 is really a non-starter. (206)


An alternate proposal, often called a guaranteed minimum income (GMI), calls for giving the stipend only to the poor, turning it into an “income floor” below which no one could fall but without the universality of a UBI. (206)

The bleak predictions of broad unemployment and unrest have put many of the Silicon Valley elite on edge. People who have spent their careers preaching the gospel of disruption appear to have suddenly woken up to the fact that you disrupt an industry, you also disrupt and displace real human being with it. Having founded and funded transformative internet companies that also contributed to gaping inequality, this cadre of millionaires and billionaires appear determined to soften the blow in the age of AI. (207)

From my perspective, I can understand why the Silicon Valley elite have become so enamored with the idea of a UBI: it is a simple, technical solution to an enormous and complex social problem of their own making. But adopting a UBI would constitute a major change in our social contract, one that we should think through very carefully and most critically. While I support certain guarantees that basic needs will be met, I also believe embracing a UBI as a cure-all for the crisis we face is a mistake and a massive missed opportunity. (208)


In observing Silicon Valley’s surge in interest around UBI, I believe some of that advocacy has emerged from a place of true and genuine concern for those who will be displaced by new technologies. But I worry that there’s also a more self-interested component: Silicon Valley entrepreneurs know that their billions in riches and their role (208) in instigating these disruptions make them an obvious target of mob anger if things ever spin out of control. With that fear fresh in their minds, I wonder if this group has begun casting about for a quick fix to problems ahead. (209)

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? (209)

[via: wow.]

I fear that many of those in Silicon Valley are firmly in the latter camp. They see UBI as a “magic wand” that can make disappear the myriad economic, social, and psychological downsides of their exploits in the AI age. UBI is the epitome of the “light” approach to problem-solving so popular in the valley: stick to the purely digital sphere and avoid the messy details of taking action in the real world. It ends to envision that all problems can be solved through a tweaking of incentives or a shuffling of money between digital bank accounts. (209)

| Best of all, it doesn’t place any further burden on researchers to think critically about the societal impacts of the technologies they build; as long as everyone gets that monthly dose of UBI, all is well. (209)

Seen in this manner, UBI isn’t a constructive solution that leverages AI to build a better world. It’s a painkiller, something to numb and sedate the people who have been hurt by the adoption of AI. And that numbing effect goes both ways: not only does it ease the pain for those displaced by technology: it also assuages the conscience of those who do the displacing. (210)

| As I’ve said before, some form of guaranteed income may be necessary to put an economic floor under everyone in society. But if we allow this to be the endgame, we miss out on the great opportunity presented to us by this technology. Instead of simply falling back on a painkiller like a UBI, we must proactively seek and find ways of utilizing AI to double-down to that which separates us from machines: love. (210)

…if we commit to doing the hard work now, I believe we have a shot at not just avoiding disaster but of cultivating the same humanistic values that I rediscovered during my own encounter with mortality. (210)


Human-AI coexistence in the labor market

In the long run, resistance may be futile, but symbiosis will be rewarded. (213)

There may come a day when we enjoy such material abundance that economic incentives are no longer needed. But in our present economic and cultural moment, money still talks. Orchestrating a true shift in culture will (214) require not just creating these jobs but turning them into true careers with respectable pay and greater dignity. (215)

| Encouraging and rewarding these prosocial activities means going beyond the market symbiosis of the private sector. We will need to energize these industries through service sector impact investing and government policies that nudge forward a broader shift in cultural values. (215)


cf.: A Sense of Purpose

…publicly traded companies are in it to win it, bound by fiduciary duties to maximize profits. But in the age of AI, this cold logic of dollars and cents simply can’t (215) hold. Blindly pursuing profits without any thought to social impact won’t just be morally dubious; it will be downright dangerous. (216)

[via: Sounds like Donella Meadows, The Limits To Growth.]

If we can pull together these different strands of socially conscious business, I believe we’ll be able to weave a new kind of employment safety net, all while building communities that foster love and compassion. (217)


I have a different vision. I don’t want to live in a society divided into technological castes, where the AI elite live in a cloistered world of almost unimaginable wealth, relying on minimal handouts to keep the unemployed masses sedate in their place. I want to create a system that provides for all members of society, but one that also uses the wealth generated by AI to build a society that is more compassionate, loving, and ultimately human. (218)



…I believe it is incumbent on us to use the economic abundance of the AI age to foster these same values ane encourage this same kind of activity. To do this, I propose we explore the creation not of a UBI but of what I call a social investment stipend. The stipend would be a decent government salary given to those who invest their tie and energy in those activities that promote a kind, compassionate, and creative society. These would include three broad categories: care work, community service, and education. (220)

| These would form the pillars of a new social contract, one that valued and rewarded socially beneficial activities in the same way we currently reward economically productive activities. The stipend would not substitute for a social safety net–the traditional welfare, healthcare, or unemployment benefits to meet basic needs–but would offer a respectable income to those who choose to invest energy in these socially productive activities. (221)

…the beauty of human beings lies in our diversity, the way we each bring different backgrounds, skills, interests and eccentricities. (222)

Providing a stipend in exchange for participation in prosocial activities reinforces a clear message: It took efforts from people all across society to help us reach this point of economic abundance. We are now collectively using that abundance to recommit ourselves to one another, reinforcing the bonds of compassion and love that make us human. (222)



The AI superpowers of the United States and China may be the countries with the expertise to build these technologies, but the paths to true human flourishing in the AI age will emerge from people in all walks of life and from all corners of the world. (225)

9 Our Global AI Story


…our current AI boom shares far more with the dawn of the Industrial Revolution or the invention of electricity than with the Cold War arms race. (228)

A clear-eyed look at the technology’s long-term impact has revealed a sobering truth: in the coming decades, AI’s greatest potential to disrupt and destroy lies not in international military contests but in what it will do to our labor markets and social systems. Appreciating the momentous social and economic turbulence that is on our horizon should humble us. It should also turn our competitive instincts into a search for cooperative solutions to the common challenges that we all face as human beings, people whose fates are inextricably intertwined across all economic classes and national borders. (228)



…when it comes to shaping the future of artificial intelligence, the single most important factor will able the actions of human beings. (230)

| We are not passive spectators in the story of AI–we are the authors of it. That means the values underpinning our visions of an AI future could well become self-fulfilling prophecies. (230)

If we believe that life has meaning beyond this material rat race, then AI just might be the tool that can help us uncover that deeper meaning. (230)


…if the original goal was to truly understand myself and other human beings, then these decades of “progress” got me nowhere. In effect, I got my sense of anatomy mixed up. Instead of seeking to outperform the human brain, I should have sought to understand the human heart. (231)

Let us choose to let machines be machines, and let humans be humans. Let us choose to simply use our machines, and more importantly, to love one another.

About VIA

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  1. Pingback: The Age of AI | Critical Review & Notes | vialogue

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