AI Superpowers

AI Superpowers Plot Summary

Introduction

In May 2017, a nineteen-year-old Chinese Go champion named Ke Jie sat across from a computer, ready to face off against Google's AlphaGo in a three-game match. Go, an ancient board game with more possible positions than atoms in the universe, had long been considered a final frontier for artificial intelligence. When AlphaGo defeated Ke Jie, it wasn't just a technological milestone – it was China's "Sputnik Moment." While barely noticed in the West, the match drew over 280 million Chinese viewers and ignited an AI fever across the country. Overnight, Chinese entrepreneurs, investors, and government officials mobilized to pursue AI dominance. This pivotal moment represents the beginning of a new global order shaped by artificial intelligence. The technology promises to transform everything from healthcare to transportation, creating unprecedented wealth while potentially eliminating millions of jobs. The coming AI revolution will be driven by two superpowers – the United States and China – with vastly different approaches to innovation, data collection, and governance. Understanding this new dynamic is crucial for anyone who wants to comprehend how AI will reshape global economics, politics, and society in the coming decades.

Chapter 1: China's Sputnik Moment: The AI Awakening

In 2017, the world of artificial intelligence reached a turning point that few in the West fully appreciated. When Google's AlphaGo defeated China's top Go player, it sparked a national awakening in China that would fundamentally alter the global AI landscape. The Chinese government responded with remarkable speed, issuing an ambitious national AI plan just two months after the match. This comprehensive strategy set clear benchmarks: reach the top tier of AI economies by 2020, achieve major breakthroughs by 2025, and become the undisputed global leader by 2030. What makes this AI revolution different from previous technological waves is the transition from the "age of discovery" to the "age of implementation." While the fundamental breakthroughs in deep learning originated primarily in North America, the application of these technologies across industries doesn't require genius-level researchers. Instead, it demands abundant data, determined entrepreneurs, and supportive government policies – areas where China excels. Chinese entrepreneurs, forged in the world's most cutthroat competitive environment, bring a relentless drive that often surpasses their Silicon Valley counterparts. China's advantage extends beyond entrepreneurial spirit to its unparalleled data ecosystem. With over 800 million internet users generating vast amounts of information through mobile payments, food deliveries, bike sharing, and countless other digital interactions, China has become what the author calls "the Saudi Arabia of data." This wealth of real-world information provides the essential fuel for training AI algorithms, creating a virtuous cycle where better data leads to better products, which attract more users, generating even more data. The Chinese government's role cannot be understated. Unlike the United States, where AI initiatives receive limited federal support, China's central and local governments are pouring massive resources into AI development. Cities across China compete to attract AI companies with subsidies, preferential policies, and government contracts. This techno-utilitarian approach allows China to implement AI applications more rapidly, accepting certain risks to achieve broader societal benefits. This combination of entrepreneurial talent, data abundance, and government support positions China to potentially surpass the United States in practical AI implementation. While America maintains an edge in fundamental research, China's advantages in the age of implementation could lead to economic gains on a scale not seen since the Industrial Revolution. PricewaterhouseCoopers estimates that AI will add $15.7 trillion to the global economy by 2030, with China capturing nearly half of those benefits – a shift that would dramatically alter the global balance of power.

Chapter 2: Copycats to Innovators: China's Entrepreneurial Evolution

Between 2005 and 2015, China's internet ecosystem underwent a remarkable transformation from imitation to innovation. The journey began with entrepreneurs like Wang Xing, once derided as "The Cloner" for creating Chinese versions of Friendster, Facebook, Twitter, and Groupon. Silicon Valley viewed these copycats with disdain, believing they represented China's fundamental inability to innovate. But this perspective misunderstood what was actually happening in China's entrepreneurial landscape. The copycat era served as a crucial apprenticeship for Chinese entrepreneurs. Without access to the decades of accumulated knowledge and mentorship available in Silicon Valley, Chinese founders had to learn by doing – and initially, that meant imitating successful models. However, copying wasn't enough to survive. China's internet market quickly became a coliseum where thousands of nearly identical startups fought to the death. In this environment, entrepreneurs had to constantly iterate, localize, and optimize their products to survive against waves of competitors. Take Jack Ma's battle against eBay as an example. When the American e-commerce giant entered China in 2002, it seemed poised to dominate. But Ma's Alibaba created Taobao, a platform specifically designed for Chinese users. While eBay maintained its global approach and refused to adapt, Taobao added features like instant messaging between buyers and sellers and an escrow payment system (Alipay) that addressed local trust issues. Most crucially, Ma made listings free, declaring "free is a business model" – a strategy eBay dismissed as unsustainable. Within a few years, eBay retreated from China entirely. This pattern repeated across the Chinese internet. Foreign giants like Google struggled because they resisted localizing their products, while domestic companies thrived by obsessively tailoring their offerings to Chinese users' preferences. The competition became increasingly fierce, with companies employing tactics that would be unthinkable in Silicon Valley – from smear campaigns to forced software uninstallations and even reporting rivals to police. By 2015, this crucible had forged a generation of battle-hardened entrepreneurs with skills perfectly suited for the AI age. Wang Xing's Meituan evolved from a Groupon clone into a $30 billion super-app handling everything from food delivery to hotel bookings. These entrepreneurs had learned to move with incredible speed, iterate rapidly based on user feedback, and build comprehensive solutions that addressed all aspects of a problem – what the author calls "going heavy" versus Silicon Valley's preference for "going light." This entrepreneurial transformation represents China's greatest asset in the AI revolution. While Silicon Valley excels at breakthrough innovations, China's gladiatorial entrepreneurs excel at the relentless implementation and optimization that will characterize the AI age. They stand ready to apply AI across countless industries, creating practical applications that could transform everything from healthcare to transportation.

Chapter 3: China's Alternate Internet Universe and Data Advantage

Around 2013, something remarkable happened in China – its internet ecosystem stopped merely catching up to the West and instead veered off in an entirely different direction. What emerged was what the author calls an "alternate internet universe," one with unique products, business models, and user behaviors that had no direct parallels in Silicon Valley. This divergence would ultimately give China a decisive advantage in the AI era: unprecedented access to real-world data. The foundation of this alternate universe was laid by several key developments. First, hundreds of millions of Chinese users leapfrogged over desktop computers entirely, experiencing the internet primarily through smartphones. Second, WeChat evolved from a simple messaging app into an all-encompassing "super-app" where users could chat, pay bills, book services, and access countless mini-programs without ever leaving the platform. Third, mobile payments exploded in popularity, with Alipay and WeChat Pay creating a largely cashless society where even street vendors and beggars accepted digital payments. These developments enabled an "online-to-offline" (O2O) revolution that deeply integrated the internet into everyday life. Chinese urbanites began using apps to order food deliveries, book massages, hail taxis, and access countless other services. Unlike their Silicon Valley counterparts who preferred to build lightweight digital platforms, Chinese companies "went heavy" – they hired armies of delivery people, built out logistics networks, and handled every aspect of the customer experience. By 2017, China's mobile payment market was 50 times larger than America's, and its food delivery volume was ten times greater. The Chinese government played a crucial role in accelerating this transformation. In 2014, Premier Li Keqiang launched the "mass entrepreneurship and mass innovation" campaign, which unleashed unprecedented support for technology startups. Local governments created thousands of incubators, simplified business registration, and established "guiding funds" that channeled billions into venture capital. This initiative wasn't just about funding – it represented a cultural shift that elevated entrepreneurs from risky outliers to national heroes. The result of these converging forces was an explosion of data-generating services that captured the minutiae of daily life. While Silicon Valley companies primarily collected data on online behaviors (searches, likes, posts), Chinese apps were gathering information about physical-world activities: what people ate, where they traveled, how they commuted, and what they purchased in brick-and-mortar stores. This real-world data would prove invaluable for training AI algorithms. By 2017, China had created the world's richest data ecosystem – a crucial advantage as AI entered the age of implementation. With deep learning algorithms improving in direct proportion to the data they're trained on, China's vast and detailed datasets positioned its companies to develop increasingly sophisticated AI applications. As the author puts it, "In the age of AI, there's no data like more data" – and China had become the Saudi Arabia of this essential resource.

Chapter 4: The Four Waves of AI: Technology Transformation Roadmap

The AI revolution won't arrive as a single tsunami but rather as four distinct waves, each transforming different sectors of the economy in unique ways. Understanding this progression is crucial for anticipating how AI will reshape our world in the coming decades. These four waves – internet AI, business AI, perception AI, and autonomous AI – are already washing over us at different speeds, with varying implications for China and the United States. The first wave, internet AI, is already deeply embedded in our digital lives. It powers the recommendation engines that determine what content we see on platforms like YouTube, Amazon, and Toutiao (China's AI-driven news aggregator). By analyzing our clicks, views, and purchases, these algorithms create personalized feeds that maximize engagement. Chinese companies like Toutiao have pushed this approach to extremes, creating addictive platforms where users spend over 70 minutes daily. In this wave, China and the United States are roughly equal competitors, though China's massive user base and frictionless payment systems give it a slight edge going forward. Business AI represents the second wave, applying algorithms to the vast troves of structured data that companies have accumulated over decades. These systems can analyze loan applications, insurance claims, and medical records to make predictions that outperform human experts. The United States currently leads in this domain thanks to its well-organized corporate datasets and established enterprise software systems. However, China is rapidly catching up by leapfrogging legacy systems in areas like micro-lending, where companies like Smart Finance use unconventional data points (such as smartphone battery levels) to assess creditworthiness for those without traditional credit histories. The third wave, perception AI, is where the digital and physical worlds begin to merge. Through devices equipped with cameras, microphones, and sensors, AI can now see, hear, and understand our physical environment. This creates what the author calls "online-merge-offline" (OMO) experiences – smart stores that recognize shoppers, AI-powered education systems that monitor student engagement, and homes filled with intelligent devices. China has a significant advantage in this wave due to its hardware manufacturing ecosystem centered in Shenzhen and its citizens' greater comfort with data collection in public spaces. Autonomous AI constitutes the final and most transformative wave, combining the pattern recognition of deep learning with the ability to take physical actions in the world. Self-driving cars represent the most visible example, but autonomous technology will also revolutionize factories, warehouses, and drone operations. The United States currently leads in core autonomous vehicle technology, but China's government is taking a more aggressive approach to implementation, building new infrastructure specifically designed for autonomous vehicles and creating entire cities optimized for this technology. Across these four waves, we see a pattern emerging: while America excels at technological breakthroughs, China's advantages in data, implementation speed, and government support give it an edge in practical applications. As these technologies spread globally, both countries are pursuing different strategies, with American companies attempting to conquer foreign markets directly while Chinese firms invest in local startups fighting against Silicon Valley giants. This battle for global AI influence will reshape economies and power structures far beyond these two superpowers.

Chapter 5: The Real AI Crisis: Jobs and Inequality

While much public discourse about AI focuses on far-future scenarios of superintelligent machines or robot uprisings, a more immediate and concrete crisis looms on the horizon: the massive displacement of human labor and the resulting economic inequality. This isn't just another cycle of technological disruption – AI represents a fundamental shift that could permanently alter the relationship between work and human value. Unlike previous technological revolutions that primarily automated physical tasks, AI will impact workers across the entire economic spectrum. The algorithms powering this transformation excel at tasks involving pattern recognition and prediction – capabilities that underpin many white-collar professions. Radiologists, financial analysts, legal researchers, and countless other highly educated workers will find their expertise challenged by systems that can process vastly more information and spot subtle correlations invisible to the human mind. Meanwhile, advances in robotics will continue to automate blue-collar jobs in manufacturing, transportation, and retail. Based on a comprehensive analysis of existing research and his own expertise, the author estimates that within 10-20 years, AI will be technically capable of replacing 40-50% of jobs in the United States. While social friction and regulatory barriers may slow actual job losses, the impact will still be enormous – potentially 20-25% of workers displaced, with wages suppressed for many others. This disruption will occur far more rapidly than previous technological transitions, leaving workers and institutions with little time to adapt. The economic consequences will extend beyond job losses to exacerbate inequality on multiple levels. Within countries, AI naturally concentrates wealth in the hands of those who own the algorithms and data. The technology creates winner-take-all markets where a few dominant companies capture most of the value, while requiring far fewer employees than traditional businesses. Between countries, the gap between AI superpowers (China and the United States) and the rest of the world will widen dramatically. Developing nations that once relied on low-cost manufacturing to climb the economic ladder will find this path blocked as production reshores to automated factories closer to consumers. Perhaps most troubling is the psychological impact of this transition. For centuries, human beings have derived meaning and self-worth largely from their work. As AI increasingly outperforms humans at tasks we once mastered, many will face not just economic hardship but an existential crisis of purpose. The author quotes an unemployed electrician who poignantly expressed this loss: "I lost my sense of worth, you know what I mean? Somebody asks you 'What do you do?' and I would say, 'I'm an electrician.' But now I say nothing. I'm not an electrician anymore." This multifaceted crisis demands a response that goes beyond traditional economic policies. The author argues that we must fundamentally rethink our social contract, moving away from defining human value primarily through economic productivity. Rather than simply implementing a universal basic income as many Silicon Valley figures advocate, we need to create new structures that recognize and reward the uniquely human contributions of care, creativity, and community-building – areas where AI cannot compete. The coming AI revolution will test not just our economic adaptability but our conception of what it means to be human.

Chapter 6: Finding Human Purpose in the AI Era

The author's personal journey provides a powerful lens for understanding the deeper implications of the AI revolution. For decades, he had lived as what he calls an "Ironman" – obsessively optimizing his time to maximize professional achievement while minimizing personal connections. This algorithmic approach to life brought him tremendous success as an AI researcher, technology executive, and venture capitalist, but left him emotionally disconnected from family and friends. Everything changed in 2013 when he was diagnosed with stage IV lymphoma. Facing mortality, he experienced a profound realization: the metrics he had used to measure his life's value – professional accomplishments, influence, efficiency – suddenly seemed hollow. What mattered most were the relationships he had neglected and the love he had failed to share. Though his cancer eventually went into remission, this brush with death fundamentally transformed his perspective on what gives human life meaning. This personal awakening parallels the collective challenge we face as AI increasingly outperforms humans at economically productive tasks. If we continue to define human worth primarily through economic output, we risk creating a society where most people feel increasingly worthless as machines surpass their capabilities. Instead, we must recognize and elevate what makes us uniquely human – our capacity for love, compassion, and connection. The author illustrates this principle through a revealing anecdote about a friend's startup that created a touchscreen device to help elderly people order food, watch TV, and contact doctors. When the product launched, the company discovered something unexpected: users were primarily using the device not for its intended functions but to call customer service representatives simply to have someone to talk to. Despite all their material needs being met, what these elderly users craved most was human connection. This story encapsulates the fundamental insight that should guide our approach to the AI era: machines may increasingly outperform us at economically productive tasks, but they cannot love or be loved. While AI can diagnose diseases with superhuman accuracy, it cannot hold a patient's hand or provide emotional support during treatment. It can optimize financial portfolios but cannot give meaning to the wealth it generates. It can drive a car but cannot appreciate the beauty of the journey. By recognizing this essential truth, we can begin to envision a future where AI handles routine optimization tasks while humans focus on activities centered around care, connection, and creativity. This shift would require not just new economic policies but a profound cultural reorientation – moving from a society that primarily values efficiency and productivity to one that celebrates compassion and human relationships. The author's personal transformation from a machine-like optimizer to someone who prioritizes love and connection offers a template for how society might navigate this transition. Just as his cancer diagnosis forced him to confront what truly matters in life, the AI revolution challenges us to rediscover the essence of our humanity. In this light, AI becomes not a threat to human purpose but a catalyst for its redefinition – freeing us from routine tasks to focus on what makes us uniquely human.

Chapter 7: A Blueprint for Human Coexistence with AI

How do we build a society that harnesses AI's tremendous productive capacity while ensuring human flourishing? The author proposes a multifaceted approach that goes beyond the technical fixes typically suggested by Silicon Valley – retraining workers, reducing work hours, or implementing a universal basic income (UBI). While these measures have merit, they treat the symptoms rather than addressing the fundamental challenge: redefining human value in an age when machines increasingly outperform us economically. The first pillar of this new approach involves fostering human-AI symbiosis in the private sector. Rather than competing directly with algorithms, workers should partner with them, focusing on the uniquely human elements of their professions. For example, as AI becomes better at medical diagnosis, doctors could evolve into "compassionate caregivers" who combine technical knowledge with the emotional intelligence to support patients through illness. Similar transformations could occur across industries, with humans providing the empathetic interface while AI handles optimization tasks. The second pillar involves redirecting investment capital toward human-centric service jobs. The author envisions a new ecosystem of "service-focused impact investing" that would fund businesses creating meaningful employment in areas like elder care, education, and community building. Unlike traditional venture capital that seeks exponential returns from scalable technology, these funds would accept linear growth in exchange for significant job creation. This approach acknowledges that many of the most socially valuable activities – caring for children, supporting the elderly, fostering community – have traditionally been undervalued by markets. The third and most ambitious pillar is what the author calls a "social investment stipend" – a government salary for those who engage in care work, community service, or education. Unlike a UBI, which provides unconditional payments, this stipend would reward socially beneficial activities that machines cannot perform. It would recognize that in an age of AI-driven abundance, human contributions should be measured not just by economic output but by their impact on social cohesion and well-being. Implementing this vision would require overcoming significant challenges. Funding such programs would demand substantial resources, likely requiring higher taxation of the enormous profits generated by AI. Cultural shifts would be necessary to elevate the status of care work and service. And political will would be needed to enact bold policies in the face of entrenched interests. Yet the alternative – allowing AI to create unprecedented wealth while leaving millions economically and psychologically adrift – poses far greater risks. The author draws on his experience with cancer to emphasize that confronting existential challenges often leads to profound reevaluation of priorities. Just as his illness forced him to recognize the centrality of love in human life, the AI revolution could prompt a collective reorientation toward what truly matters. This blueprint represents not just a response to technological disruption but an opportunity to create a more humane society – one that values connection over optimization, compassion over efficiency, and love over productivity. By embracing these values, we can ensure that AI serves as a tool for human flourishing rather than a force for displacement and alienation.

Summary

AI Superpowers presents a compelling narrative of how artificial intelligence is reshaping the global order through the lens of the U.S.-China technology rivalry. The central tension throughout this historical transformation is not simply a competition between nations, but a profound shift in how we understand human value in an age when machines increasingly outperform us at economically productive tasks. The rise of China as an AI superpower, fueled by its entrepreneurial ecosystem, data advantage, and government support, challenges the assumption that the United States will automatically lead this technological revolution. More importantly, it forces us to confront the deeper implications of AI for employment, inequality, and human purpose. The lessons from this technological inflection point extend far beyond geopolitics. First, we must recognize that the AI revolution differs fundamentally from previous technological transitions in its speed, scope, and skill bias – potentially displacing 40-50% of jobs within two decades. Second, our response must go beyond technical fixes like universal basic income to address the crisis of meaning that will accompany widespread automation. Finally, we have an unprecedented opportunity to harness AI's productive capacity to create a more humane society – one that values care, creativity, and community over optimization and efficiency. By focusing on what makes us uniquely human – our capacity for love and connection – we can ensure that artificial intelligence enhances rather than diminishes our humanity in the decades ahead.

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Kai-Fu Lee

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