53 pages • 1 hour read
AI Superpowers
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Chapters 4-6Chapter Summaries & Analyses
Chapter 4 Summary: “A Tale of Two Countries”
In 1999, Lee gave a lecture on speech and image recognition at the University of Science and Technology of China. He recalls that, while the computer science students were attentive and hardworking, they were hindered by limited access to tech and high-quality learning materials. In the span of just 30 years, China evolved from this to an AI superpower. Lee writes:
[C]reating an AI superpower for the twenty-first century requires four main building blocks: abundant data, tenacious entrepreneurs, well-trained AI scientists, and a supportive policy environment […] This chapter assesses the balance of power in the two remaining ingredients—AI expertise and government support (92).
Throughout this chapter, he argues that China has all of these elements in greater abundance than any other country. He believes that, up to this point, genius “elite researchers” were the key to leading the world in AI. Now, the economy will favor larger workforces of “solid AI engineers.” While the US has the former, China’s high population and cultural infatuation with AI ensures the latter.
Lee also notes that, where America has a “combative political system [that] aggressively punishes missteps or waste in funding technological upgrades, China’s techno-utilitarian approach rewards proactive investment and adoption” (93). While the Chinese government sometimes restricts innovation in the social sciences, Lee writes that its control over all aspects of digital life within its borders incentivizes it to nurture AI research.
Lee introduces the so-called “Seven Giants of the AI age”; four American companies and three Chinese ones: Google, Facebook, Amazon, Microsoft, Baidu, Alibaba, and Tencent. These companies are extremely powerful and “closed off” from the rest of AI research, which is typically open with information. While Lee believes that China is “winning” the AI race and will continue to do so, “if the next breakthrough on the scale of deep learning occurs soon, and it happens within a hermetically sealed corporate environment, all bets are off” (100). He believes the odds are “slightly against” this outcome. However, of the giants, Google’s parent company Alphabet is the most likely to achieve it.
Roughly half of the world’s top 100 AI experts work for Google. Lee describes the Seven Giants’ competition to hire “elite AI talent” as an “arms race.” Tech startups are also vying for talent and scientific breakthroughs. As Lee writes, “It’s a contest between two approaches to distributing the ‘electricity’ of AI across the economy: the ‘grid’ approach of the Seven Giants versus the ‘battery’ approach of the startups” (104). While the grid approach seeks to build infrastructure to facilitate large-scale integration of AI into every aspect of daily life—akin to the power grid that brings electricity into homes and businesses—“AI startups are taking the opposite approach. Instead of waiting for this grid to take shape, startups are building highly specific ‘battery-powered’ AI products for each use-case” (104). Lee believes it is too early to determine which approach will win.
Lee compares and contrasts the US and China in terms of governmental tech policies and cultural attitudes toward these policies. While President Obama’s 2016 “commonsense” plan to harness AI’s power garnered little domestic attention and was later undone by President Trump, a similar 2017 plan published by the Chinese State Council sparked a “national mobilization.”
Lee describes the Chinese government as techno-utilitarian: “leveraging technological upgrades to maximize broader social good while accepting that there will be downsides for certain individuals or industries” (110). Lee believes this makes them likely to adopt new AI-powered innovations quickly, where the US government might hesitate.
Chapter 5 Summary: “The Four Waves of AI”
Liu Qingfeng was one of the students who went to see Lee’s lecture at the University of Science and Technology of China. He later went on to found iFlyTek, a startup that created digital voices for presidents Trump and Obama. This technology translated the presidents’ speeches from English to Mandarin in real time while replicating their actual voices. Lee believes that this translator and products like it will “revolutionize” business and daily life. However, he also reminds us that this revolution will be gradual. Lee contextualizes the development of AI as a process that will occur in four waves: internet AI, business AI, perception AI, and autonomous AI.
The first wave, Internet AI, is already underway. This wave includes elements like the recommendation algorithms that power social media, ecommerce, and digital entertainment. Lee notes that, while the US and China are on “equal footing” in this arena, he believes that China will take a slight advantage within five years.
The second wave is business AI and data mining, which is also already underway. Business AI “uses labeled data to train an algorithm that can outperform even the most experienced human practitioners” (120). Business AI fuels algorithms that can make small loans, perform medical diagnosis, and advise courtroom judges. Lee writes that, while the US has a strong lead in business AI, China is making “serious strides” to catch up.
The third wave, perception AI, is in its early stages. Unlike other AI, which can only label and evaluate data, perception AI can actually process sensory data. Current uses of perception AI include the “pay-with-your-face” scanners pioneered by KFC and Alipay. Lee predicts that technologies like this will continue to develop and turn our everyday lives into online-merge-offline (OMO) environments. He presents a speculative version of the real-life chain Yonghui Superstore, powered by perception AI tools that already exist. He also discusses the privacy concerns this technology provokes.
The fourth wave is autonomous AI, also in its infancy and it “represents the integration and culmination of the three preceding waves, fusing machines’ ability to optimize from extremely complex data sets with their newfound sensory powers” (140). Lee points out the difference between automated vs autonomous technology: while some repetitive tasks can be automated, an autonomous machine would be able to make “decisions” and take actions without being directed. While Lee believes that autonomous AI is not likely to enter our homes “anytime soon,” he notes that companies like Amazon and Google are already implementing this technology. He also predicts that China will “almost certainly take the lead in autonomous drone technology” (143).
Lee returns to comparing and contrasting China with the US, using autonomous cars as an example. “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?” (147). Lee believes that the Chinese approach to self-driving cars will be to shape their infrastructure around AI, while American governments are more likely to try to fit self-driving technology into current US infrastructure. While the US’s self-driving car technology is years ahead of China’s, Lee predicts China will use the technology to better effect.
Chapter 6 Summary: “Utopia, Dystopia, and the Real AI Crisis”
Instead of worrying about AI developing consciousness and going rogue, Lee directs us to consider a more realistic AI-driven crisis: widespread job loss. Lee predicts that, as AI becomes more refined and reliable, it will replace both white- and blue-collar workers. This will negatively impact economics on national and international scales, rendering entire fields obsolete, consigning “developing” nations to poverty and exacerbating class resentments.
Lee introduces the concept of general purpose technologies (GPTs): inventions that alter culture and the way most people live their lives, like electricity, steam engines, and information and communication technology (ICT). He predicts that AI will be considered a GPT and compares its potential impact to that of the Industrial Revolution, itself brought on by GPTs. While the Industrial Revolution resulted in the deskilling of laborers, AI will “simply take over” certain jobs. Lee argues that this will increase productivity but not wages. He cites “the great decoupling”: “While productivity has continued to shoot upward, wages and jobs have flatlined or fallen” (162).
Lee believes that AI will be adopted more quickly than other GPTs, due to “three catalysts” unique to the post-industrial world: 1) AI are digital products that can be proliferated instantly; 2) they can be funded by venture capital; 3) the Chinese workforce is prepared to contribute.
Lee presents x- and y-axes predicting which modern-day jobs will be at risk of replacement and to what extent. The four quadrants are: “human veneer” (jobs that are necessarily interpersonal, like teacher, doctor, or bartender), “safe zone” (jobs that AI won’t disturb, like VC investor, dog trainer, or CEO), “slow creep” (jobs that will be impacted slowly over time, like graphic designer, plumber, or construction worker), and “danger zone” (jobs that will be impacted by AI first, like line cook, radiologist, or cashier).
Lee explains that predicting AI-driven job losses is extremely popular among economists and consulting firms. While some return “terrifying” estimates, others suggest negligible losses. Lee presents a few of these studies. He personally predicts a “grim picture”: 38% of workers will lose their jobs to machines; another 10% will be impacted by “ground-up disruptions,” wherein roles will be created specifically for AI instead of people. This will exacerbate the problems of the gig economy. However, Lee believes the unemployment rate can be slowed or even cut in half through the creation of new jobs.
Lee predicts that AI will increase both interpersonal and global inequities. Disparity between China and the US will widen. China and the US will be titanic forces in the global economy, creating an even wider gulf between them and “AI-poor” nations. Lee asks: “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?” (185). Lee ends this chapter by alluding to a personal revelation that helped him answer questions about how to mitigate this impending crisis.
Chapters 4-6 Analysis
Much of Lee’s argumentation is based on historical analogs. Chapter 6, “Utopia, Dystopia, and the Real AI Crisis” relies on the Industrial Revolution to explain the potential impact of AI. Chapter 5, “The Four Waves of AI” uses historical figures in industrial science, such as Thomas Edison and Enrico Fermi, to demonstrate Lee’s concept of an “age of discovery.”
Lee contrasts the “age of discovery”—in which innovation is driven by individual visionaries who can “singlehandedly tip the scales of scientific power” (94)—with what he calls the “age of implementation,” in which profit-driven organizations take the reins, building infrastructure and finding applications for the new technologies created in the age of discovery. As he defines them, ages of discovery rely upon gifted individuals to produce revolutionary ideas and inventions. His primary example of this phenomenon is Enrico Fermi, a nuclear physicist who fled fascist Italy for America, where he developed the world’s first self-sustaining nuclear reactor and contributed to the Manhattan Project.
This top-secret project was the largest industrial undertaking the world had ever seen, and it culminated in the development of the world’s first nuclear weapons for the US military. Those bombs put an end to World War II in the Pacific and laid the groundwork for the nuclear world order. […] In nuclear physics, the 1930s and 1940s were an age of fundamental breakthroughs, and when it came to making those breakthroughs, one Enrico Fermi was worth thousands of less brilliant physicists (94).
Lee implies that Fermi’s presence in the United States almost single-handedly won World War II for the allies and that only Fermi could have achieved this. For Lee, the crucible of World War II created the perfect conditions for a genius-driven age of discovery. This is a slightly altered version of Entrepreneurship as a Driving Force of Progress. As always, competition is the spur that drives innovation, but in this case the competition is not for profit but for national survival. In highlighting a moment of American innovation that occurred at the behest of the US government, Lee shores up another of his key arguments—that government control is not always a hindrance to innovation, and can in fact be a powerful advantage.
Lee argues that different scientific fields experience genius-driven ages of discovery in waves. Gifted individuals have “fundamental breakthroughs,” and that knowledge trickles down to competent “tinkerers” who spark ages of implementation en masse. In Lee’s conception, these tinkerers are usually businesspeople developing and selling products.
Lee exemplifies the age of implementation by shifting from nuclear physics to electrical engineering. Thomas Edison exemplifies electrical engineering’s age of discovery: “Following Thomas Edison’s harnessing of electricity, the field rapidly shifted from invention to implementation. Thousands of engineers began tinkering with electricity, using it to power new devices and reorganize industrial processes” (95). Lee presents the ages of discovery and implementation as discrete events. He argues that—while “the Enrico Fermis of AI” (95) are still providing valuable insight today, “the real action today is with the tinkerers” (95). The Importance of Hard Work and Competition is clearest in the age of implementation, as hundreds or thousands of tinkerers across many different organizations compete to capture as much of the nascent industry as possible.
In Lee’s vision of the emerging AI industry, the division between the age of discovery and the age of implementation maps across the division between the US and China, or between Silicon Valley and the Avenue of the Entrepreneurs. Throughout AI Superpowers, Lee highlights the US’s elite universities and high-minded, individualistic entrepreneurs. He also frequently revisits China’s huge and industrious workforce. America’s discovery-fueled lead in the AI economy, he argues, will be eclipsed by China’s persistent efforts in implementation.
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