AI Superpowers: China, Silicon Valley, and the New World Order — Chapter-by-Chapter Outline

Author: Kai-Fu Lee

First published: 2018

Edition covered: Current U.S. Mariner Books / Harper Business paperback, 2021, ISBN 978-0-358-10558-9, xi + 261 pages. This outline covers the publisher-listed current U.S. edition, whose contents were checked against the Ingram table of contents reproduced by Better World Books, the Houston Community College Library.Link record, WorldCat's 2018 first-edition record, and the Perlego e-book table of contents. The edition retains the Introduction and nine numbered chapters from the 2018 Houghton Mifflin Harcourt first edition and includes a short 2021 Afterword; no added numbered chapters were identified.

Central thesis

Kai-Fu Lee argues that modern artificial intelligence has moved from a research-centered age of discovery into an application-centered age of implementation. In that new phase, the winners are not necessarily the countries with the most elegant theories or the most celebrated laboratories. They are the countries and companies with enough data, engineers, entrepreneurs, capital, and policy support to apply deep learning repeatedly to real-world problems.

The book's first claim is geopolitical: China and the United States are the two likely AI superpowers. The United States begins with elite research talent, leading universities, major cloud platforms, and global technology firms. China has a different advantage: a huge mobile-first user base, dense offline commerce, intense entrepreneurial competition, a willingness to collect and use data, and a state that can push infrastructure and adoption at scale.

The book's deeper claim is social rather than nationalist. Lee argues that the question of who wins the AI race is less important than the coming disruption to jobs, income, dignity, and meaning. AI will not need general consciousness to cause upheaval. Narrow systems that recognize patterns, predict outcomes, and optimize decisions can still automate large portions of routine cognitive and physical work. The response, in Lee's view, must center human capacities that machines cannot supply: love, empathy, trust, care, creativity, and moral responsibility.

If AI makes machines better at routine optimization, what should humans choose to become better at?

Introduction

Central question

What does the current AI revolution change, and why should readers understand it through both geopolitics and human meaning?

Main argument

A personal and geopolitical frame. Lee writes from an unusual position: he trained as an AI researcher, worked in U.S. technology companies, led Google China, and later invested in Chinese startups through Sinovation Ventures. The introduction uses that vantage point to frame AI as a technological, economic, and moral transition rather than a purely technical subject.

The new power of deep learning. The book focuses on the practical breakthrough of deep learning: systems trained on large bodies of labeled data can detect patterns and make predictions in narrow domains. Lee stresses that this is not the same as human-level intelligence, but it is powerful enough to reshape industries.

From competition to coexistence. The introduction previews the book's two-part movement. First, it explains why China has caught up quickly in AI implementation. Then it turns to the more difficult question of how people can preserve dignity and purpose when machines perform more economically valuable tasks.

Key ideas

• AI is presented as a general-purpose technology whose effects spread across many industries.
• The U.S.-China race matters, but it is not the book's final concern.
• Deep learning rewards data, deployment, and iteration as much as academic discovery.
• The book treats AI's job impact as a near-term problem, not a distant science-fiction scenario.
• Human coexistence with AI will require new social values, not only better software.

Key takeaway

The introduction frames AI as a contest over implementation capacity and as a test of whether societies can redefine human worth beyond routine work.

Chapter 1 — China's Sputnik Moment

Central question

Why did AlphaGo's 2017 victory over Ke Jie become a turning point for China's AI ambitions?

Main argument

AlphaGo as public shock. Lee opens with Google's AlphaGo defeating Ke Jie, then the world's leading Go player. In the West, the match was often treated as one more proof of DeepMind's technical excellence. In China, Lee argues, it became a national awakening: a moment when policymakers, entrepreneurs, students, and ordinary viewers recognized that AI would be a strategic technology.

From AI winters to deep learning. The chapter sketches AI's earlier cycles of enthusiasm and disappointment. Rule-based systems tried to encode explicit human logic; neural approaches tried to learn patterns from data. Deep learning changed the balance because large neural networks, trained on enough examples and computing power, could outperform handcrafted rules in tasks such as speech recognition, image recognition, and game play.

Two global shifts. Lee argues that deep learning changes the geography of AI power. First, it shifts value from a few elite discoveries toward widespread implementation. Second, it increases the importance of data. Countries with large, active, digitized populations can generate training data that helps products improve, which attracts more users and produces still more data.

Key ideas

• AlphaGo's Go victory served as a symbolic trigger for China's AI mobilization.
• Lee uses "Sputnik moment" to describe a public shock that accelerates national competition.
• Deep learning relies on large data sets, computing power, and narrow optimization.
• The book treats AI as entering an implementation phase after a major technical breakthrough.
• China's advantage begins with data, scale, and practical urgency rather than original research alone.
• The chapter sets up the book's recurring contrast between U.S. discovery and Chinese deployment.

Key takeaway

AlphaGo made AI vivid to China, and Lee uses that moment to argue that the future race will be won through mass implementation of deep learning.

Chapter 2 — Copycats in the Coliseum

Central question

How did China's much-criticized copycat internet era become training for AI-era entrepreneurship?

Main argument

Wang Xing as case study. Lee uses Wang Xing, who built Chinese versions of Facebook, Twitter, and Groupon before founding Meituan, as a representative of China's early internet entrepreneurs. The point is not that copying is inherently admirable. It is that copying exposed entrepreneurs to live competition, rapid iteration, user acquisition battles, and the discipline of localizing products for Chinese behavior.

The coliseum model. The chapter contrasts Silicon Valley's stated ideals of originality and mission with China's harsher market culture. In Lee's telling, Chinese founders learned to fight for users through price wars, operational intensity, local relationships, feature copying, subsidies, and direct confrontation. Some tactics were ethically questionable, but the competitive pressure created founders who could move quickly and survive thin margins.

Copying as apprenticeship. Lee argues that imitation can be a stage in capability building. Copying Western products let Chinese teams learn product design and business mechanics, but success required changing those models for Chinese users. Alibaba's battle with eBay illustrates the point: a foreign product transplanted into China lost to a local competitor that understood payments, trust, communication, and seller behavior in the Chinese market.

Key ideas

• Chinese internet entrepreneurship developed under scarcity, speed, and intense local competition.
• Copying lowered the cost of learning but did not guarantee success.
• Local execution mattered more than abstract originality.
• The copycat era trained founders in iteration, monetization, logistics, and user warfare.
• Lee sees China's "gladiatorial" founders as an input into later AI implementation.
• The chapter asks readers to separate moral approval from strategic explanation.

Key takeaway

China's copycat period produced entrepreneurs who were unusually prepared to commercialize AI quickly and relentlessly.

Chapter 3 — China's Alternate Internet Universe

Central question

Why did China's internet ecosystem diverge from Silicon Valley's model, and why does that matter for AI?

Main argument

WeChat as super-app. Lee presents WeChat as the clearest example of China's alternate internet. Rather than remaining a messaging app, it became a portal for payments, appointments, transportation, games, commerce, official services, and daily coordination. This concentration of activity created dense behavioral data and made the phone a bridge between online and offline life.

Mobile-first leapfrogging. China did not pass through the same PC and credit-card stages as the United States. Many users came online through smartphones and moved from cash directly to mobile payments. That difference made Chinese consumers more willing to use QR codes, digital wallets, app-based services, and embedded commerce.

Going heavy. Lee contrasts Silicon Valley's preference for light, scalable software with Chinese companies' willingness to build operationally heavy systems: delivery fleets, offline merchant networks, local subsidies, call centers, and physical infrastructure. These hard-to-copy operations created more real-world data and positioned Chinese firms for perception AI, autonomous logistics, and online-merge-offline services.

Government-backed startup infrastructure. The chapter also describes Beijing's Avenue of the Entrepreneurs and the national slogan of "mass innovation and mass entrepreneurship." Lee is not claiming every incubator or government fund was efficient. His claim is that the campaign normalized startup ambition and flooded the ecosystem with capital, office space, and political attention.

Key ideas

• China's internet became a distinct ecosystem rather than a delayed copy of the U.S. internet.
• WeChat shows how a super-app can consolidate payments, identity, communication, and services.
• Mobile-first adoption made Chinese users comfortable with digital wallets and QR-code commerce.
• Offline operations generated data that pure software companies often lacked.
• Government campaigns accelerated startup formation even when individual projects wasted money.
• The chapter connects consumer behavior, platform design, and AI-ready data.

Key takeaway

China's alternate internet created dense, practical, real-world data streams that gave Chinese companies an implementation advantage in AI.

Chapter 4 — A Tale of Two Countries

Central question

How do the United States and China compare across the core ingredients needed for AI leadership?

Main argument

Four building blocks. Lee identifies four ingredients for an AI superpower: abundant data, tenacious entrepreneurs, well-trained AI scientists, and a supportive policy environment. The previous chapters focused on data and entrepreneurs. This chapter weighs talent and policy.

Discovery versus implementation. The United States has a large lead in elite AI research, top universities, and major corporate labs. Lee compares this to an age of discovery, where a small number of exceptional scientists can matter disproportionately. But once deep learning becomes a broadly usable technique, the bottleneck shifts toward implementation: many capable engineers applying known methods across industries. Lee argues that China is well positioned for this second phase.

The Seven Giants and the grid. Lee names Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent as the major AI giants. These firms have data, talent, cloud resources, and distribution. They can build AI "grids" that make machine learning available throughout the economy, while startups build narrower "battery" products for specific use cases.

Policy as accelerator. The chapter contrasts U.S. political hesitation with China's national AI plan and local-government enthusiasm. Lee's controversial claim is not that Chinese policy is always wise, but that a state willing to coordinate infrastructure, funding, and adoption can accelerate applied AI in fields such as transportation, security, finance, and smart cities.

Key ideas

• AI leadership depends on data, entrepreneurs, engineers, and policy support.
• The United States is strongest in elite research and corporate labs.
• China is strongest in implementation pressure, engineering volume, and policy mobilization.
• The Seven Giants may dominate AI infrastructure in both countries.
• Startup "battery" products can still compete by solving narrow problems quickly.
• Policy differences matter because AI often requires data access, infrastructure, and deployment permission.

Key takeaway

The U.S. may lead in AI discovery, but Lee argues that China's system is built for the implementation phase.

Chapter 5 — The Four Waves of AI

Central question

What are the main stages of AI deployment, and where does China or the United States hold an advantage?

Main argument

Internet AI. The first wave uses online behavior to recommend, rank, target, and personalize. Search, news feeds, e-commerce, advertising, and entertainment platforms all turn clicks, purchases, views, and dwell time into labeled data. Lee sees the United States and China as roughly comparable here, with China gaining from its huge mobile platforms.

Business AI. The second wave applies machine learning to structured business data: loans, insurance, fraud, medical records, legal documents, customer service, and enterprise workflows. The United States has an early edge because its firms have cleaner legacy data and mature enterprise systems. China can catch up where new digital platforms generate data natively.

Perception AI. The third wave gives machines eyes, ears, and other sensors. Voice recognition, face recognition, smart speakers, cashierless stores, city cameras, and industrial sensors merge digital intelligence with physical environments. Lee uses examples such as iFlyTek, facial payments, and smart retail to show how perception AI can create online-merge-offline environments.

Autonomous AI. The fourth wave combines perception, prediction, and physical action. Self-driving vehicles, drones, warehouse robots, and automated factories operate in the world rather than merely advising humans. Lee argues that adoption will depend on both technical maturity and policy choices. China may gain where it can redesign roads, districts, or logistics zones around autonomous systems.

Key ideas

• The four waves are Internet AI, business AI, perception AI, and autonomous AI.
• AI applications arrive unevenly; all four waves can overlap in time.
• Internet AI feeds on online user behavior.
• Business AI depends on high-quality structured organizational data.
• Perception AI expands the data frontier into voice, face, image, gesture, and environment.
• Autonomous AI is hardest because it must act safely in messy physical settings.
• China's advantage increases where offline deployment, sensors, and infrastructure coordination matter.

Key takeaway

Lee's four-wave model shows AI moving from screens and databases into bodies, streets, stores, factories, and vehicles.

Chapter 6 — Utopia, Dystopia, and the Real AI Crisis

Central question

What is the real near-term danger of AI if human-level superintelligence is not imminent?

Main argument

The wrong fear. Lee argues that fears of conscious machines or runaway artificial general intelligence distract from a more immediate problem. Today's AI is narrow, domain-bound, and dependent on human-defined objectives. It does not need self-awareness to transform labor markets.

Automation of routine work. AI is especially strong where work involves pattern recognition, prediction, optimization, or repetitive action within a defined setting. That includes white-collar tasks such as loan approval, translation, radiology triage, tax preparation, and customer service, as well as physical tasks such as inspection, cooking, driving, warehouse sorting, and harvesting.

The job-risk map. Lee organizes work by traits that make it more or less automatable: social interaction, creativity, dexterity, and unstructured environments reduce risk; asocial optimization in structured settings increases it. He describes zones such as the danger zone, slow creep, human veneer, and safe zone to show that jobs will not disappear at the same speed.

Inequality and meaning. The crisis is not only unemployment. AI can concentrate profits in the companies and countries that own the best systems, widening class and global inequality. It can also strip people of identity, status, and purpose when work has been the main way they prove value to society.

Key ideas

• Narrow AI is enough to cause large social disruption.
• Automation pressure will affect both blue-collar and white-collar work.
• The pace may be faster than earlier industrial transitions because software scales globally.
• UBI may address income but not identity, community, and dignity.
• The countries that lead AI may pull further away from AI-poor countries.
• The chapter prepares the turn from geopolitics to human values.

Key takeaway

The real AI crisis is not machines becoming human; it is humans losing work, bargaining power, status, and meaning in an economy optimized by machines.

Chapter 7 — The Wisdom of Cancer

Central question

How did Lee's cancer diagnosis change his understanding of work, success, and what AI cannot replace?

Main argument

The limits of optimization. Lee shifts into memoir. Before his lymphoma diagnosis, he describes himself as organized around achievement, productivity, influence, and professional visibility. The diagnosis made that model of life feel inadequate. His regret was not that he had failed to optimize his career, but that he had undervalued love, family, care, and presence.

Mortality as reorientation. The chapter recounts his diagnosis, fear, search for medical clarity, and reassessment of priorities. He draws the lesson that human life cannot be reduced to output metrics. The traits he had previously treated as secondary became central once death was no longer abstract.

The bridge to AI. This personal turn is not separate from the book's argument. Lee uses it to answer the question raised by automation. If machines can outperform humans at many optimizing tasks, humans should not respond by trying to become more machine-like. They should build institutions that honor the forms of value machines cannot experience or reciprocate: love, compassion, trust, and moral care.

Key ideas

• The chapter is the book's autobiographical pivot.
• Lee contrasts career optimization with relational meaning.
• Cancer forces the question of what remains valuable when productivity is interrupted.
• Love becomes the book's name for a specifically human capacity.
• The chapter supplies the ethical basis for the blueprint in Chapter 8.

Key takeaway

Lee's cancer experience turns the book from a story about winning the AI race into a story about protecting human value from an optimization-centered economy.

Chapter 8 — A Blueprint for Human Coexistence with AI

Central question

What social design could let humans live with AI without reducing people to obsolete workers?

Main argument

Why simple fixes are insufficient. Lee reviews responses such as retraining, shorter workweeks, job sharing, and universal basic income. He treats each as partially useful but incomplete. Retraining cannot convert everyone into AI-proof workers. UBI may preserve consumption but risks leaving the deeper wound of lost meaning untouched.

Human-plus-AI work. Lee argues that many future jobs should pair machine competence with human care. In medicine, for example, AI can help diagnose, triage, and detect patterns across massive data sets, while humans provide trust, explanation, empathy, and moral judgment. Similar pairings can exist in education, elder care, social work, mental health, hospitality, and community services.

The social investment stipend. The chapter's central policy idea is to reward socially valuable human activity: caregiving, volunteering, mentoring, education, community service, and personal development. The stipend is meant to be different from a no-strings income floor. It signals that society values care and service as real contributions, not as sentimental leftovers after machines handle the "productive" work.

A change in status. Lee's goal is cultural as much as economic. If societies keep treating income-maximizing market labor as the main measure of worth, AI will intensify humiliation for displaced workers. Coexistence requires elevating care work and community work into respected, supported roles.

Key ideas

• Coexistence requires more than technical retraining.
• AI should handle routine optimization where it is genuinely better.
• Humans should specialize in trust, compassion, creativity, judgment, and care.
• UBI addresses money but not necessarily purpose.
• A social investment stipend would fund caregiving, service, and learning.
• The chapter proposes a moral economy in which love becomes institutionally supported work.

Key takeaway

Lee's blueprint asks societies to use AI-generated abundance to make care, service, and human connection economically visible.

Chapter 9 — Our Global AI Story

Central question

How should countries write a shared future when AI power is likely to be concentrated in the United States and China?

Main argument

From superpowers to responsibility. The closing chapter broadens the frame from domestic job disruption to global inequality. AI profits will flow disproportionately to the companies and countries with data, talent, platforms, and capital. Many countries may become dependent consumers of AI systems built elsewhere.

The risk for AI-poor nations. Lee argues that developing countries could lose the labor-cost advantages that helped them industrialize. If manufacturing, customer support, translation, logistics, and clerical work are automated by AI systems owned elsewhere, the ladder of economic development narrows. This makes AI a development problem as well as a technology problem.

Human agency in the story. The chapter resists fatalism. Lee argues that the U.S. and China can choose whether AI becomes a zero-sum arms race, a source of corporate concentration, or a source of broader human flourishing. Other societies can also contribute models of education, caregiving, civic service, redistribution, and public trust.

A final return to meaning. The book ends by tying policy to personal values. The story of AI is not only about where algorithms are built. It is about what people decide to honor once machines can perform more economically useful tasks.

Key ideas

• AI leadership may deepen global inequality between AI-rich and AI-poor countries.
• The U.S. and China have responsibilities that follow from their technological power.
• Automation threatens some development paths that relied on low-cost labor.
• International cooperation is necessary but politically difficult.
• Societies still have agency over taxation, education, care, labor, and technology governance.
• The book's final emphasis is on authorship: humans shape the institutions around AI.

Key takeaway

The final chapter argues that AI's global story is not predetermined; it will be written through political choices about power, dignity, and shared responsibility.

Afterword

Edition note

The Afterword appears in the current paperback/e-book structure after Chapter 9 and before the acknowledgments. It is not an added numbered chapter.

Central question

What changed after the 2018 publication, and how does Lee adjust the book's closing hopes?

Main argument

A more difficult geopolitical setting. The Afterword updates the book after several years of worsening U.S.-China tension. Lee acknowledges that the cooperative posture hoped for in the first edition became harder to imagine as trade, technology controls, national-security suspicion, and political mistrust intensified.

The core claim remains social. The update does not replace the book's structure. It reinforces the argument that AI will keep advancing and that societies must still address work displacement, inequality, concentration, and human meaning. The practical question remains how to channel AI's productivity toward human welfare rather than only toward strategic rivalry or corporate profit.

Key ideas

• The 2021 edition adds a short update rather than a new argument.
• The afterword recognizes that U.S.-China cooperation became less plausible than the 2018 ending hoped.
• AI's social consequences remain the book's central concern.
• The humanistic solution is presented as more urgent, not less.

Key takeaway

The Afterword narrows the book's optimism about geopolitics while preserving its argument that AI policy should be judged by its effect on human dignity.

The book's overall argument

The Introduction frames AI as both a technological race and a human-values problem. The Afterword later updates that frame for a more tense geopolitical climate. Between them, the nine numbered chapters build the argument as follows:

1. Chapter 1 (China's Sputnik Moment) — AlphaGo's defeat of Ke Jie turns AI into a national priority in China and signals the shift from research prestige to implementation capacity.
2. Chapter 2 (Copycats in the Coliseum) — China's copycat entrepreneurs learn speed, localization, and ruthless execution, creating a founder class suited to applied AI.
3. Chapter 3 (China's Alternate Internet Universe) — China's mobile-first, payment-heavy, super-app ecosystem generates practical data and offline integration that feed AI deployment.
4. Chapter 4 (A Tale of Two Countries) — The U.S. leads in elite discovery, but China has strong advantages in engineers, policy support, entrepreneurs, and implementation.
5. Chapter 5 (The Four Waves of AI) — AI will spread through internet platforms, business data, perception systems, and autonomous machines, with national advantages varying by wave.
6. Chapter 6 (Utopia, Dystopia, and the Real AI Crisis) — The near-term danger is not conscious machines but job displacement, inequality, and the loss of work-based meaning.
7. Chapter 7 (The Wisdom of Cancer) — Lee's illness reorients the book from optimization and success toward love, care, and mortality.
8. Chapter 8 (A Blueprint for Human Coexistence with AI) — Societies should pair machine optimization with human care and fund socially valuable work through new institutions.
9. Chapter 9 (Our Global AI Story) — The U.S., China, and the rest of the world must decide whether AI deepens hierarchy or supports a broader human project.

Common misunderstandings

Misunderstanding: The book says China is ahead in every dimension of AI.

Lee's claim is narrower. He gives the United States a major lead in elite research, universities, corporate labs, cloud infrastructure, and some business-data applications. His argument is that China has structural advantages in implementation, data generation, entrepreneurial competition, and policy mobilization.

Misunderstanding: The book treats copying as morally good.

Lee does not present all copycat tactics as ethical. He argues that the copycat era functioned as harsh training for founders. The analytical claim is about capability formation, not a blanket defense of deception, cloning, or unfair competition.

Misunderstanding: The real AI danger is artificial general intelligence.

The book explicitly redirects attention away from near-term fear of conscious superintelligence. Lee's crisis is narrower and more immediate: narrow AI systems automating tasks, concentrating wealth, and undermining people's sense of social value.

Misunderstanding: Universal basic income is the book's solution.

Lee treats UBI as insufficient because it addresses income more directly than status, purpose, and human connection. His proposed social investment stipend is designed to reward caregiving, service, education, and community contribution.

Misunderstanding: Human work survives only where AI is technically weak.

The book's point is not merely that humans should retreat to leftover tasks. Lee argues that societies should actively elevate human strengths: empathy, trust, moral judgment, creativity, service, and love.

Misunderstanding: The U.S.-China race is the final subject of the book.

The race is the hook and the first half's organizing structure. The final subject is the social contract humans build around AI-generated abundance and AI-driven displacement.

Central paradox / key insight

The book's central paradox is that AI can make societies materially richer while making many people feel less necessary. Productivity, convenience, and national power can rise at the same time that workers lose income, status, and identity. The technology that performs routine tasks better than humans also forces a question that economics alone cannot answer: what counts as a valuable human life?

Lee's resolution is to stop measuring human value only by market productivity. If AI excels at optimization, then human institutions should recognize and reward the capacities AI lacks: care, compassion, trust, creativity, moral responsibility, and love.

The more machines take over optimization, the more societies must decide whether human care is treated as central work or sentimental residue.

Important concepts

Afterword

The short update in the current paperback/e-book edition, placed after Chapter 9. It revisits the book's U.S.-China cooperation hopes in a more tense geopolitical environment.

Age of discovery

Lee's term for a phase of technology in which fundamental breakthroughs by elite researchers matter most. He contrasts it with an age of implementation.

Age of implementation

The phase in which known AI techniques are applied across industries by engineers, entrepreneurs, platforms, and governments. Lee argues this is the phase deep learning has entered.

AI-poor countries

Countries without leading AI platforms, data sets, capital, talent, or deployment capacity. Lee worries they may lose development opportunities as automation erodes the value of low-cost labor.

Autonomous AI

The fourth wave of AI: systems that perceive, decide, and act in the physical world, such as self-driving vehicles, drones, robots, and automated logistics systems.

Business AI

The second wave of AI: machine learning applied to structured organizational data such as loans, insurance, medicine, fraud, legal documents, and enterprise operations.

Data advantage

The compounding benefit of having large, relevant, frequently updated data sets. In Lee's account, more users create more data, which improves products, which attracts more users.

Deep learning

A machine-learning approach using multi-layered neural networks trained on large data sets to detect patterns and make predictions in narrow domains.

Four waves of AI

Lee's framework for AI deployment: Internet AI, business AI, perception AI, and autonomous AI.

General-purpose technology

A technology that spreads across many sectors and reorganizes economic life, such as electricity, steam power, information technology, or, in Lee's argument, AI.

Gladiatorial entrepreneurship

Lee's description of China's intensely competitive startup culture, where founders fight through copying, subsidies, price wars, rapid iteration, and operational pressure.

Internet AI

The first wave of AI: recommendation, ranking, targeting, search, advertising, and personalization based on online behavioral data.

Online-merge-offline

The blending of digital services with physical environments through sensors, payments, delivery networks, cameras, smart retail, and location-based services.

Perception AI

The third wave of AI: systems that interpret sensory data such as faces, voices, images, speech, movement, and environmental signals.

Seven Giants

The major AI platform companies Lee highlights: Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent.

Social investment stipend

Lee's proposed public support for caregiving, service, education, volunteering, and other socially valuable activities that markets often underpay.

Sputnik moment

A public shock that mobilizes a nation around technological competition. Lee applies the term to China's reaction to AlphaGo's victory over Ke Jie.

References and Web Links

Primary book and edition information

• Kai-Fu Lee. AI Superpowers: China, Silicon Valley, and the New World Order. Houghton Mifflin Harcourt, 2018; current U.S. Mariner Books / Harper Business paperback, 2021, ISBN 978-0-358-10558-9.
  • HarperCollins current U.S. edition page
  • Better World Books / Ingram table of contents for ISBN 9780358105589
  • Houston Community College Library.Link record for the 2021 paperback
  • WorldCat record for the 2018 Houghton Mifflin Harcourt edition
  • Google Books bibliographic page for the 2021 reprint
  • Perlego e-book page with table of contents

Background and overview

• Wikipedia overview of AI Superpowers
• IEEE Spectrum Q&A with Kai-Fu Lee on AI, jobs, and the human heart
• TED Talk: Kai-Fu Lee, "How AI can save our humanity"
• National Committee on U.S.-China Relations video: AI Superpowers
• Thinking Ahead Institute review and discussion questions
• Asian Cha review discussing Lee's China and labor claims

Author's framing and key ideas

• Kai-Fu Lee, "The Four Waves of A.I."
• Kai-Fu Lee, "How AI can coexist with humans"
• NYU Shanghai event summary: "Love Is What Differentiates Us From AI"
• Peter Diamandis overview of Lee's four-wave framework

Additional chapter summaries and study resources

These are secondary summaries and should be used alongside, rather than instead of, the original book.

• SuperSummary: Introduction-Chapter 3 summary and analysis
• SuperSummary: Chapters 4-6 summary and analysis
• SuperSummary: Chapters 7-9 summary and analysis
• SuperSummary: themes and recurring concepts
• RMG chapter-by-chapter summary
• Muhan Zhang notes and cheatsheet
• Vialogue notes and reflections
• What Do I Buy chapter summary