AI Superpowers: China, Silicon Valley, and the New World Order – Summary

By Frank Diana | 14 November 2018
Reimagining the Future

(Credit: Dreamstime)

I just finished a fantastic book on artificial intelligence and the evolutionary path of China and the U.S.. Author Kai-Fu Lee inspires, as he focuses on the astounding capabilities of AI, and the one thing that only humans can provide; love. The journey includes the author’s own brush with mortality, and proposes a path forward: the synthesis on which we must build our shared future is AI’s ability to think, coupled with a human’s ability to love. He believes this synergy harnesses the undeniable power of artificial intelligence to generate prosperity, while also embracing our essential humanity. His hope for our future lies both in this new synergy between artificial intelligence and the human heart, and an AI-fueled age of abundance that fosters love and compassion in our societies.

I recommend reading this book from cover to cover. In the meantime, here is a summary organized by several key themes.

Side note: an AI-driven economy as depicted by our author falls under the Automated Society future scenario as described through this Blog.

The Evolution of Artificial Intelligence

Although the first three phases of the industrial revolution were highly transformative, what comes next is likely larger in scale and faster to broad impact. Whereas the Industrial Revolutions took place across several generations, the AI revolution will drive major impact within one generation. The author effectively describes the prior transformations induced by general purpose technology (GPT) and AI’s emergence as a GPT. This emergence is accelerated by three catalysts that didn’t exist during the introduction of steam power, electricity and Information Technology:

  1. Digital enables replication and distribution at near zero marginal cost. This is in stark contrast to the hardware-intensive revolutions of steam power, electricity, and the information technology-fueled third industrial revolution
  2. The presence of venture capital; an industry that did not exist in the prior phases of the industrial revolution
  3. China: Artificial intelligence will be the first GPT of the modern era in which China plays a significant role in both advancing and applying the technology. China’s entrance to the field of AI constitutes a major accelerant that was absent for previous revolutions.

Where the innovations of prior revolutions altered the nature of manual labor and some aspects of cognitive labor, AI cuts across both. The first and second revolutions changed the mode of production and drove deskilling; what once required high-skilled workers was now done by low-skilled workers. This phenomenon drove job creation and improved our standard of living; effectively described by Economist Robert J. Gordon in The Rise and Fall of American Growth. However, a third revolution beginning in the late 1960s did not follow that same pattern. The technology of this era – and the innovation of the emerging era – has a skill bias in favor of high-skilled workers. As the author describes, the main thrust of AI’s employment impact is not one of job creation through deskilling but of job replacement through increasingly intelligent machines.

Although there is plenty of speculation about impact; AI has arrived. Enabled by large amounts of data, increases in computing power, and the invention of deep learning, the author sees AI evolving in four waves:

  • The first two waves have hit the shores; Internet AI and business AI. They are reshaping our digital and financial worlds in very profound ways.
  • The third wave is Perception AI, which digitizes the physical world and learns to interact with, understand, and see the world around us.
  • The final and most impactful wave is Autonomous AI. The self-driving cars, autonomous drones, and intelligent robots of our future.

The Superpowers

Compelling content on AI makes this a must read. Perhaps more eye opening however, is the education on China provided by the author. A peek into the history and culture of China helps us to understand their journey, mindset, and aspirations. It then becomes easy to understand the authors assertion that China is quickly becoming an AI super power. Driven by the accomplishments of AlphaGo in 2016, China embarked on a plan to become an AI leader. At the heart of the China supremacy argument is a global shift driven by two transitions: from the age of discovery to the age of implementation, and from the age of expertise to the age of data. The U.S. dominated in the age of expertise, but China stands to dominate in the age of implementation.

These two transitions tilt the playing field towards China. Battle tested entrepreneurs, incomparable amounts of data, and a supportive policy environment are the reasons AI leadership now favors China. We learn how Chinese startups embraced a copycat mentality, took inspiration from American business models, and then fiercely competed against each other to adapt and optimize for Chinese users. Through that process, world-class entrepreneurs were born. From a data standpoint, the immersion in the details of the offline world enabled Chinese companies to create the Saudi Arabia of data: the fossil fuel that powers this emerging era. Their technology ecosystem in the physical world gives algorithms eyes into the content of daily lives, while mobile payments generate the richest maps of consumer activity the world has ever known. WeChat, the all-in-one super-app, gives Tencent perhaps the single richest data ecosystem of all the giants.

Beyond entrepreneurs and data, China has a favorable policy environment. They embraced a mantra of mass entrepreneurship and mass innovation and fostered a startup ecosystem to support this innovation. The Chinese government sought to drive a fundamental shift in their economy, from manufacturing-led growth to innovation-led growth. To get a sense for the policy differences, here is an example articulated by our author: American politicians, weary of the societal implications of autonomous vehicles may pump the brakes on deployment. The Chinese government will see these difficult concerns as important topics to explore but not as a reason to delay the implementation of technology that will save tens if not hundreds of thousands of lives. As the western world looks to Balance the Opposing Forces of Innovation, does China’s techno-utilitarian political culture pave the way for faster deployment of game-changing innovation?

The author envisions that over the coming decade, China’s world class entrepreneurs will apply deep learning to any problem that shows the potential for profit across hundreds of industries. They are likely aided by a Silicon Valley weakness: an unwillingness and resistance to localization. As the author describes, AI has a much higher localization quotient than earlier internet services. Every divergence between Chinese preferences and a standard global product has and will become an opening that local competitors attack. In addition, U.S. and Chinese companies take very different approaches to global markets: while U.S. giants seek to conquer these markets for themselves, China is arming the local startups. The China approach, built more on cooperation than conquest, may prove better suited to globalizing a technology that requires both experts and local data collection. For example, self-driving cars in India need to learn the way pedestrians navigate the streets of Bangalore, and micro-lending apps in Brazil need to absorb the spending habits of millennials in Rio de Janeiro.

A Chinese leadership position will alter the balance of power. Real-world applications of AI will translate into productivity gains on a scale not seen since the Industrial Revolution. Among other lofty estimates, PricewaterhouseCoopers estimates that these real-world applications will add $15.7 trillion to global GDP by 2030. Seven trillion of that is predicted to go to China, nearly double North America’s $3.7 trillion in gains.

Societal Implications

As opposed to terminator-based apocalyptic scenarios, the author focuses on the real underlying threat posed by artificial intelligence: large-scale social disorder and political collapse stemming from widespread unemployment and gaping inequality. While the author feels our present AI capabilities can’t create a super-intelligence that destroys our civilization, he fears that we humans may prove more than up to that task ourselves. In the coming decades, AI’s greatest potential to disrupt and destroy lies with its impact on labor markets and social systems. The industrial age conditioned us to view our primary role and identity in society as productive, wage-earning work; but the emerging age requires a shift in our mindset, culture and values. Cultural change can only come through different policies that nudge our behaviors in different directions. As the author points out, this will require rewriting our fundamental social contracts and restructuring economic incentives to reward socially productive activities in the same way that the industrial economy rewarded economically productive activities.

While a path to socially productive activities is desired, emerging platforms leveraging an AI foundation have a natural affinity for winner-take-all economics, eroding the competitive mechanisms of markets in the process. This is likely to play out across many industries with a skill bias that divides the job market and squeezes out the middle class. Along the way, we could see the rapid emergence of a new class of AI-powered oligarchies. American antitrust laws may be ineffective due to the need to prove that a monopoly is harming consumers, when in reality, AI monopolists would likely be delivering better services at cheaper prices. Inequality is likely to rise as a greater concentration of wealth lands in the hands of a few. The gap between the global haves and have-nots will widen, with no known path toward closing it. This gap widens between individuals and nations. As robot-operated factories relocate closer to customers in large markets, the ladder that developing countries climbed up on their way to prosperity disappears.

People will struggle. Many of us are 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. Our author stresses that lurking beneath this social and economic turmoil will be a psychological struggle. As we are displaced by intelligent machines, we will face a deeper question: what does it mean to be human?


Consensus on the AI impact to jobs is elusive. The author effectively presents all the common arguments on both sides of this polarized discussion. His conclusions are logical and compelling; within 15 years AI will be capable of eliminating upwards of 50% of U.S. Jobs. It will pose two distinct threats: one-to-one replacements and ground-up disruptions. An autonomous-vehicle that provides mobility services is a one-to-one replacement of a taxi driver. Finding new ways to satisfy a fundamental human need via algorithms versus employees is a ground-up disruption. This latter impact to jobs is not accounted for in the various projections. As these disruptors enable lower cost and superior service, their market share gains apply pressure to their employee-heavy rivals. Those companies will be forced to adapt; restructuring to leverage AI and reduce employees.

Author Kai-Fu Lee focuses the job discussion on the human element. He describes how economic incentives, public policies, and cultural dispositions have disadvantaged compassion-filled professions. According to the U.S. Bureau of Labor Statistics, home health aides and personal care aides are the two fastest growing professions in the country, with an expected growth of 1.2 million jobs by 2026. Average annual income: just over $20,000. Our labors of love like stay-at-home parenting or caring for aging or disabled relatives aren’t even considered jobs. A true shift in culture requires converting these compassion-filled endeavors into careers with respectable pay and greater dignity.

Existing high paying professions take a different path towards the same end. For example, as intelligent machines surpass a human’s ability to diagnose disease and recommend treatments, the Doctor may become a compassionate caregiver; needed for activities requiring emotional intelligence, but not their ability to memorize facts or optimize treatment regimens. This reflects a natural symbiosis between humans and machines: AI handles optimization, humans bring empathy, creativity, and a compassionate touch. Existing occupations are likely redefined and entirely new professions created.

The journey from here to this possible future is complex. Our author digs into some of the popular policy suggestions for adapting to this future. He views them largely as technical fixes or tweaks to policy and business models that seek to smooth the transition but do not actually shift the culture. He lumped these fixes into three buckets: retraining workers, reducing work hours, or redistributing income. The folks in the retraining camp believe that a slow shift in the needed skills will drive workers to adapt their abilities through training – mitigating the risk of job loss. Others believe that reducing work hours is the answer, spreading the jobs that do remain over more workers. Lastly, the redistribution camp believes in more radical redistribution schemes (like UBI) to support unemployed workers and spread the wealth created by AI.

Throughout the book, we learn about China. In the context of jobs, this learning helps us understand the issue through their lens, and how their culture and history contribute to this perspective. Not every region of the world tipped from agriculture to industry as aggressively as the West. Over one-quarter of Chinese workers are still on farms, with another quarter involved in industrial production. That compares with less than 2 percent of Americans in agriculture and around 18 percent in industrial jobs. Although China will face a wrenching labor-market transition due to automation, our author believes there is a difference in timing. Intelligent automation of the twenty-first century operates differently than the physical automation of the twentieth century. AI algorithms will impact jobs before intelligent robots do, therefore knowledge work in the west is likely impacted before blue collar work in China.

Creating Our Future

A letter said it all. BlackRock founder Larry Fink posted a letter to CEOs demanding greater attention to social impact. Imagine the Chief Executive in the shareholder value era being told to focus on purpose. That letter read:

We see many governments failing to prepare for the future on issues ranging from retirement and infrastructure to automation and worker retraining. As a result, society increasingly is turning to the private sector and asking that companies respond to broader societal challenges. Society is demanding that companies, both public and private, serve a social purpose. Companies must benefit all their stakeholders, including shareholders, employees, customers, and the communities in which they operate.

As the world moves towards its third tipping point, profit must shift to purpose. As our author states, blindly pursuing profits without any thought to social impact won’t just be morally dubious; it will be downright dangerous. In creating our future, Kai-Fu Lee foresees a venture ecosystem emerging to focus on the creation of humanistic service-sector jobs – steering money into human-focused service projects that can scale up and hire large numbers of people. This type of impact investing will need to be different in his eyes – it will need to accept linear returns when coupled with meaningful job creation. But the private sector can’t do it alone. Orchestrating a fundamental change in social and economic structures often requires government. Devising a new social contract for this emerging future requires public policy. For example, our author suggests a social investment stipend. The stipend would be a government salary given to those involved in activities that promote a kind, compassionate, and creative society, including: care work, community service, and education.


The book is a fascinating journey into Chinese history, culture and current perspectives. We get a vantage point from an AI expert, a venture capitalist, a Father, Son, and Brother. Through this journey, we learn that the future does not have to be an either/or proposition. Humans and machines can indeed create a world of great human flourishing. As our author states: never has the potential for human flourishing been higher – or the stakes of failure greater. 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.

Reprinted with permission from the author.

Frank DianaFrank Diana is a recognized futurist, thought leader and frequent keynote speaker. He has served in various executive roles throughout his career and has over 30 years of leadership experience. At Tata Consultancy Services (TCS), he is a thought leader and advisor in the context of business, societal and economic evolution. He blends a futurist perspective with a pragmatic, actionable approach – leveraging horizon scanning and story telling to see possible futures. His leadership experience obtained through various executive roles connects practical realities with the need to focus on an emerging future filled with complexity and change.

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Presentation Content – Reimagining the Future

How AI can save our humanity | Kai-Fu Lee

The Race to Win at AI: A Cross-Border View with Kai-Fu Lee (Sinovation Ventures)

Digital Expert Interviews: Futurist Frank Diana

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