Zen Book Club

Book 34: Nexus Chapter 11 - Session Plan

Wed, 04 Feb 2026 09:14:00 +0800

The Silicon Curtain: Global Empire or Global Split?

Duration: 60 minutes
Date: February 4, 2026
Host: Jizu

Session Structure

Chapter Overview

Chapter 11 examines how AI will reshape global power dynamics, arguing that the greatest AI dangers come not from the technology itself but from human divisions. The chapter presents two dystopian scenarios:

Global Digital Empire: A few powers (or one) use AI and data control to dominate the world, creating “data colonies” without military occupation
The Silicon Curtain: Rival digital empires develop incompatible AI systems, dividing humanity into camps that cannot communicate or cooperate

Key argument: Unlike the Industrial Revolution (which took decades for governments to recognize), the AI revolution saw governments wake up quickly after AlphaGo’s 2016 victory. The race is now between government-corporate teams, with China and the US leading. The stakes: whoever controls AI and data “will become the ruler of the world” (Putin).

The chapter traces how the most valuable asset evolved from land (Roman Empire) → machines (British Empire) → information (AI Empire). Unlike land or factories, information can be concentrated in a single hub at the speed of light, making this potentially the most unequal empire in history.

Key Quotes Needing Discussion

“The rise of AI, then, poses an existential danger to humankind not because of the malevolence of computers but because of our own shortcomings.” (p. 362)

Why this matters: Shifts blame from technology to human nature. The danger isn’t Terminator-style AI rebellion, but paranoid dictators, terrorists, bad actors, and inability of good actors to cooperate. This reframes the entire AI safety debate.

Discussion point: Is this reassuring or more terrifying? Can we fix human shortcomings faster than we develop AI?

“Imagine a situation—in twenty years, say—when somebody in Beijing or San Francisco possesses the entire personal history of every politician, journalist, colonel, and CEO in your country: every text they ever sent, every web search they ever made, every illness they suffered, every sexual encounter they enjoyed, every joke they told, every bribe they took. Would you still be living in an independent country, or would you now be living in a data colony?” (p. 370-371)

Why this matters: Makes data colonialism concrete and personal. Not abstract geopolitics but blackmail material on every leader. Independence becomes meaningless without information sovereignty.

Discussion point: Is this already happening? What about Cambridge Analytica, NSA surveillance, TikTok data collection?

“All those cat images that tech giants had been harvesting from across the world, without paying a penny to either users or tax collectors, turned out to be incredibly valuable. The AI race was on, and the competitors were running on cat images.” (p. 368)

Why this matters: The absurd becomes sinister. Cute cat photos → facial recognition → Israeli Red Wolf app for Palestinians → Iranian hijab enforcement. We provided the training data for our own surveillance, for free.

Discussion point: What other “harmless” data are we providing that could be weaponized?

“Unlike cotton and oil, digital data can be sent from Malaysia or Egypt to Beijing or San Francisco at almost the speed of light. And unlike land, oil fields, or textile factories, algorithms don’t take up much space. Consequently, unlike industrial power, the world’s algorithmic power can be concentrated in a single hub.” (p. 373)

Why this matters: Explains why AI colonialism could be worse than any previous empire. Romans couldn’t move the Nile to Italy. British couldn’t move oil wells to Yorkshire. But ALL the world’s algorithms CAN concentrate in one place.

Discussion point: Is this concentration inevitable? Can decentralization (blockchain, open source) prevent it?

“In 2017, China’s government released its ‘New Generation Artificial Intelligence Plan,’ which announced that ‘by 2030, China’s AI theories, technologies, and application should achieve world-leading levels, making China the world’s primary AI innovation center.’” (p. 370)

Why this matters: Shows how China’s historical trauma (“century of humiliations”) drives current AI ambitions. This isn’t just economic competition - it’s existential. China will not be late to this revolution.

Discussion point: Does this make US-China AI cooperation impossible? Is an AI arms race inevitable?

“A world of rival empires separated by an opaque Silicon Curtain would also be incapable of regulating the explosive power of AI.” (p. 365)

Why this matters: The paradox - division might prevent single tyranny, but also prevents cooperation on existential threats (climate change, AI safety, pandemics). No good options.

Discussion point: Which is worse - one AI empire or multiple competing ones? Is there a third option?

Session Structure

Opening (5 min)

Brief recap: AI as a global problem, not just national
Key question: Will AI lead to new digital empires or a divided world?

Part 1: The Imperial Threat (15 min)

Discussion prompts:

How does the chapter compare the Industrial Revolution’s impact on imperialism to AI’s potential impact?
The AlexNet (2012) and AlphaGo (2016) moments - why were these turning points?
What does “data colonialism” mean? How is it different from traditional colonialism?

Part 2: Data as the New Cotton (15 min)

Discussion prompts:

The progression: land → machines → information as the most valuable asset
Why is information concentration more dangerous than industrial concentration?
Real examples: China banning Western apps, US debating TikTok ban, India blocking Chinese apps
Social credit systems going global - realistic or alarmist?

Part 3: Winners and Losers (15 min)

Discussion prompts:

The $15.7 trillion projection (70% to China & North America)
What happens to countries like Pakistan, Bangladesh when textile production automates?
Can small nations like Qatar, Tonga, Tuvalu maintain independence in an AI-dominated world?
The “century of humiliations” - China’s motivation to lead in AI

Closing: The Silicon Curtain (10 min)

Two scenarios: single global empire vs. rival digital empires separated by a “Silicon Curtain”
Which is more dangerous?
Can humanity regulate AI without global unity?
Personal reflection: Are we already living in data colonies?

Key Quotes to Reference

“Whoever becomes the leader in this sphere will become the ruler of the world”
— Putin, 2017

“The one who control [sic] the data will control the world”
— Modi, 2018

“We’re really making an AI”
— Larry Page to Kevin Kelly, 2002

Key Concepts

Data Colonialism: Control through information rather than military force
The Cat Images Paradox: From cute kittens to facial recognition weapons
Digital Empires: Concentration of algorithmic power in single hubs
The Silicon Curtain: Potential division of humanity into incompatible digital networks

Discussion Questions for Participants

Are we already experiencing data colonialism in our daily lives?
Should countries prioritize digital sovereignty over global connectivity?
Is the comparison to 19th-century imperialism fair or overblown?
What role should international institutions play in AI governance?

Proposed Answers & Discussion Points

Part 1: The Imperial Threat

Q: How does the chapter compare the Industrial Revolution’s impact on imperialism to AI’s potential impact?

Key parallels:

Both started with private entrepreneurs (railways in 1830s, tech companies in 2000s)
Governments initially slow to recognize geopolitical significance
By mid-century, became tools of empire building (steamships/railways then, data/AI now)
Those who missed the revolution became colonized (China’s “century of humiliations”)

Key difference:

Industrial tech required physical presence (gunboats, railways)
AI enables control through information alone - no troops needed

Q: Why were AlexNet (2012) and AlphaGo (2016) turning points?

AlexNet (2012):

Jumped from 75% to 85% accuracy in image recognition
Proved neural networks could rapidly improve
Showed value of harvested data (cat images)
Tech industry woke up

AlphaGo (2016):

Defeated world champion Lee Sedol at Go
Governments woke up, especially China
Go is culturally significant in East Asia (training for strategists)
China remembered being late to Industrial Revolution, vowed “never again”

Q: What does “data colonialism” mean? How is it different from traditional colonialism?

Traditional colonialism:

Required military force, physical occupation
Extracted raw materials (cotton, rubber, oil)
Visible, overt control

Data colonialism:

Control through information, not force
Extracts data (personal histories, behaviors, preferences)
Invisible, algorithmic control
Example: Someone in Beijing/San Francisco has complete personal history of every politician, journalist, CEO in your country - are you still independent?

Part 2: Data as the New Cotton

Q: The progression: land → machines → information. Why is information concentration more dangerous?

Land era (Roman Empire):

Wealth stayed distributed (can’t move Nile valley to Italy)
Provincial landowners retained power
Eventually emperors moved to the wealth (Rome → Constantinople)

Machine era (British Empire):

More centralized (factories in Birmingham, raw materials from India)
But still physical limits (can’t move oil wells from Kirkuk to Yorkshire)

Information era (AI Empire):

Data travels at speed of light
Algorithms take up no physical space
ALL algorithmic power CAN concentrate in one hub
Even traditional industries (textiles) now controlled by information (Amazon became #1 US clothing retailer in 2021)

Q: Real examples of digital sovereignty battles - what do they tell us?

Current bans:

China: banned Facebook, YouTube, Western social media
Russia: banned Western social media, some Chinese apps
India (2020): banned TikTok, WeChat (citing “sovereignty and integrity”)
US: TikTok banned on federal devices, debating full ban

What this reveals:

Countries already see apps as sovereignty threats
The Silicon Curtain is already forming
Each superpower building separate digital ecosystems
Small countries forced to choose sides

Q: Social credit systems going global - realistic or alarmist?

Arguments for realistic:

We already use global scores (Tripadvisor, Airbnb, credit ratings)
US dollar used globally for transactions
If dominant player creates social credit system, foreigners can’t ignore it (affects visas, jobs, scholarships, flight tickets)
Network effects favor monopoly

Arguments for alarmist:

Requires massive global data collection
Different cultures have different values
Regulatory pushback likely
But chapter suggests this is already happening through corporate platforms

Part 3: Winners and Losers

Q: The $15.7 trillion projection (70% to China & North America) - what does this mean?

The math:

AI adds $15.7 trillion to global economy by 2030
China + North America take $11 trillion (70%)
Rest of world shares $4.7 trillion (30%)
Gap widens over time

The mechanism:

Digital leaders profit → invest in retraining workforce → profit more
Left-behind countries: workers become redundant → no money to retrain → fall further behind
Positive feedback loop of inequality

Q: What happens to Pakistan and Bangladesh when textile production automates?

Current situation:

Textile = 40% of Pakistan’s labor force
Textile = 84% of Bangladesh’s exports
Both economies heavily dependent

Automation scenario:

Robots/3D printers make European production cheaper
Millions lose jobs
New jobs require retraining (factory worker → data analyst)
Where do they get money for retraining?
Economic collapse possible

Broader implication:

Developing countries’ traditional path (cheap labor → industrialization → wealth) may be closed
The ladder is being pulled up

Q: Can small nations maintain independence in an AI-dominated world?

Chapter’s examples:

Qatar, Tonga, Tuvalu, Kiribati, Solomon Islands currently have leverage
They play superpowers against each other
This works in “postimperial era” with distributed power

AI future:

Power concentrates in few hubs
Small nations become data colonies
“Tigers allow fat chickens to live” - but will AI-empowered tigers stay vegetarian?
Without control of digital infrastructure, independence is illusion

Q: China’s “century of humiliations” - why does this matter?

Historical context:

China was world’s greatest superpower for centuries
Missed Industrial Revolution
Repeatedly defeated, partially conquered, exploited
National trauma

Current motivation:

“Never again to miss the train”
2017: “New Generation AI Plan” - world leader by 2030
Massive resources poured into AI
By early 2020s, leading in several AI fields
This isn’t just economic competition - it’s existential for China

Closing: The Silicon Curtain

Q: Which is more dangerous - single global empire or rival digital empires?

Single empire scenario:

Total surveillance possible
No escape, no alternatives
Dictator’s dilemma: might hand nuclear weapons to fallible AI
But: unified humanity could regulate AI, address climate change

Rival empires scenario:

Different networks, different alignment solutions
Humans in different empires can’t communicate or agree
Arms races, wars likely
Cannot cooperate on existential threats (climate, AI regulation)
Chapter suggests this is MORE dangerous

The paradox:

Unity enables tyranny but also cooperation
Division enables freedom but also destruction
No good options?

Q: Can humanity regulate AI without global unity?

Chapter’s argument:

AI is global problem like climate change
One country’s good regulations don’t protect it from others’ bad actors
Even handful of irresponsible societies endanger everyone
Examples: dictator giving AI nuclear launch codes, terrorists using AI for pandemic

The challenge:

Humanity has never been united
Bad actors always exist
Good actors disagree with each other
AI poses existential danger “not because of malevolence of computers but because of our own shortcomings”

Q: Are we already living in data colonies?

Evidence we are:

Personal data harvested by foreign corporations
Algorithms we don’t control shape our information diet
Social media from other countries upended politics (Myanmar, Brazil)
Economic dependence on foreign digital infrastructure
Cat images → facial recognition weapons (we provided training data for free)

Evidence we’re not (yet):

Still have some regulatory power
Can ban apps (though at cost of connectivity)
Physical sovereignty intact
Traditional power structures still function

Middle ground:

We’re in transition period
The colonization is subtle, algorithmic, invisible
By the time it’s obvious, might be too late to resist

Book 34: Nexus Chapter 11 - The Silicon Curtain

Wed, 04 Feb 2026 08:06:00 +0800

Chapter 11: The Silicon Curtain: Global Empire or Global Split?

The previous two chapters explored how different human societies might react to the rise of the new computer network. But we live in an interconnected world, where the decisions of one country can have a profound impact on others. Some of the gravest dangers posed by AI do not result from the internal dynamics of a single human society. Rather, they arise from dynamics involving many societies, which might lead to new arms races, new wars, and new imperial expansions.

Computers are not yet powerful enough to completely escape our control or destroy human civilization by themselves. As long as humanity stands united, we can build institutions that will control AI and will identify and correct algorithmic errors. Unfortunately, humanity has never been united. We have always been plagued by bad actors, as well as by disagreements between good actors. The rise of AI, then, poses an existential danger to humankind not because of the malevolence of computers but because of our own shortcomings.

Thus, a paranoid dictator might hand unlimited power to a fallible AI, including even the power to launch nuclear strikes. If the dictator

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trusts his AI more than his defense minister, wouldn’t it make sense to have the AI supervise the country’s most powerful weapons? If the AI then makes an error, or begins to pursue an alien goal, the result could be catastrophic, and not just for that country.

Similarly, terrorists focused on events in one corner of the world might use AI to instigate a global pandemic. The terrorists might be more versed in some apocalyptic mythology than in the science of epidemiology, but they just need to set the goal, and all else will be done by their AI. The AI could synthesize a new pathogen, order it from commercial laboratories or print it in biological 3-D printers, and devise the best strategy to spread it around the world, via airports or food supply chains. What if the AI synthesizes a virus that is as deadly as Ebola, as contagious as COVID-19, and as slow acting as AIDS? By the time the first victims begin to die, and the world is alerted to the danger, most people on earth might have already been infected.[1]

As we have seen in previous chapters, human civilization is threatened not only by physical and biological weapons of mass destruction like atom bombs and viruses. Human civilization could also be destroyed by weapons of social mass destruction, like stories that undermine our social bonds. An AI developed in one country could be used to unleash a deluge of fake news, fake money, and fake humans so that people in numerous other countries lose the ability to trust anything or anyone.

Many societies—both democracies and dictatorships—may act responsibly to regulate such usages of AI, clamp down on bad actors, and restrain the dangerous ambitions of their own rulers and fanatics. But if even a handful of societies fail to do so, this could be enough to endanger the whole of humankind. Climate change can devastate even countries that adopt excellent environmental regulations, because it is a global rather than a national problem. AI, too, is a global problem. Countries would be naive to imagine that as long as they regulate AI wisely within their own borders, these regulations will protect them from the worst outcomes of the AI

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revolution. Accordingly, to understand the new computer politics, it is not enough to examine how discrete societies might react to AI. We also need to consider how AI might change relations between societies on a global level.

At present, the world is divided into about two hundred nation-states, most of which gained their independence only after 1945. They are not all equal. The list contains two superpowers, a handful of major powers, several blocs and alliances, and a lot of smaller fish. Still, even the tiniest states enjoy some leverage, as evidenced by their ability to play the superpowers against each other. In the early 2020s, for example, China and the United States competed for influence in the strategically important South Pacific region. Both superpowers courted island nations like Tonga, Tuvalu, Kiribati, and the Solomon Islands. The governments of these small nations—whose populations range from 740,000 (Solomon Islands) to 11,000 (Tuvalu)—had substantial leeway to decide which way to tack and were able to extract considerable concessions and aid.[2]

Other small states, such as Qatar, have established themselves as important players in the geopolitical arena. With only 300,000 citizens, Qatar is nevertheless pursuing ambitious foreign policy aims in the Middle East, is playing an outsized rule in the global economy, and is home to Al Jazeera, the Arab world’s most influential TV network. One might argue that Qatar is able to punch well above its weight because it is the third-largest exporter of natural gas in the world. Yet in a different international setting, that would have made Qatar not an independent actor but the first course on the menu of any imperial conqueror. It is telling that, as of 2024, Qatar’s much bigger neighbors, and the world’s hegemonic powers, are letting the tiny Gulf state hold on to its fabulous riches. Many people describe the international system as a jungle. If so, it is a jungle in which tigers allow fat chickens to live in relative safety.

Qatar, Tonga, Tuvalu, Kiribati, and the Solomon Islands all indicate that we are living in a postimperial era. They gained their independence from the British Empire in the 1970s, as part of the final

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demise of the European imperial order. The leverage they now have in the international arena testifies that in the first quarter of the twenty-first century power is distributed between a relatively large number of players, rather than monopolized by a few empires.

How might the rise of the new computer network change the shape of international politics? Aside from apocalyptic scenarios such as a dictatorial AI launching a nuclear war, or a terrorist AI instigating a lethal pandemic, computers pose two main challenges to the current international system. First, since computers make it easier to concentrate information and power in a central hub, humanity could enter a new imperial era. A few empires (or perhaps a single empire) might bring the whole world under a much tighter grip than that of the British Empire or the Soviet Empire. Tonga, Tuvalu, and Qatar would be transformed from independent states into colonial possessions—just as they were fifty years ago.

Second, humanity could split along a new Silicon Curtain that would pass between rival digital empires. As each regime chooses its own answer to the AI alignment problem, to the dictator’s dilemma, and to other technological quandaries, each might create a separate and very different computer network. The various networks might then find it ever more difficult to interact, and so would the humans they control. Qataris living as part of an Iranian or Russian network, Tongans living as part of a Chinese network, and Tuvaluans living as part of an American network could come to have such different life experiences and worldviews that they would hardly be able to communicate or to agree on much.

If these developments indeed materialize, they could easily lead to their own apocalyptic outcome. Perhaps each empire can keep its nuclear weapons under human control and its lunatics away from bioweapons. But a human species divided into hostile camps that cannot understand each other stands a small chance of avoiding devastating wars or preventing catastrophic climate change. A world of rival empires separated by an opaque Silicon Curtain would also be incapable of regulating the explosive power of AI.

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The Rise of Digital Empires

In chapter 9 we touched briefly on the link between the Industrial Revolution and modern imperialism. It was not evident, at the beginning, that industrial technology would have much of an impact on empire building. When the first steam engines were put to use to pump water in British coal mines in the eighteenth century, no one foresaw that they would eventually power the most ambitious imperial projects in human history. When the Industrial Revolution subsequently gathered steam in the early nineteenth century, it was driven by private businesses, because governments and armies were relatively slow to appreciate its potential geopolitical impact. The world’s first commercial railway, for example, which opened in 1830 between Liverpool and Manchester, was built and operated by the privately owned Liverpool and Manchester Railway Company. The same was true of most other early railway lines in the U.K., the United States, France, Germany, and elsewhere. At that point, it wasn’t at all clear why governments or armies should get involved in such commercial enterprises.

By the middle of the nineteenth century, however, the governments and armed forces of the leading industrial powers had fully recognized the immense geopolitical potential of modern industrial technology. The need for raw materials and markets justified imperialism, while industrial technologies made imperial conquests easier. Steamships were crucial, for example, to the British victory over the Chinese in the Opium Wars, and railroads played a decisive role in the American expansion west and the Russian expansion east and south. Indeed, entire imperial projects were shaped around the construction of railroads such as the Trans-Siberian and Trans-Caspian Russian lines, the German dream of a Berlin-Baghdad railway, and the British dream of building a railway from Cairo to the Cape.[3]

Nevertheless, most polities didn’t join the burgeoning industrial arms race in time. Some lacked the capacity to do so, like the

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Melanesian chiefdoms of the Solomon Islands and the Al Thani tribe of Qatar. Others, like the Burmese Empire, the Ashanti Empire, and the Chinese Empire, might have had the capacity but lacked the will and foresight. Their rulers and inhabitants either didn’t follow developments in places like northwest England or didn’t think they had much to do with them. Why should the rice farmers of the Irrawaddy basin in Burma or the Yangtze basin in China concern themselves about the Liverpool–Manchester Railway? By the end of the nineteenth century, however, these rice farmers found themselves either conquered or indirectly exploited by the British Empire. Most other stragglers in the industrial race also ended up dominated by one industrial power or other. Could something similar happen with AI?

When the race to develop AI gathered steam in the early years of the twenty-first century, it too was initially spearheaded by private entrepreneurs in a handful of countries. They set their sights on centralizing the world’s flow of information. Google wanted to organize all the world’s information in one place. Amazon sought to centralize all the world’s shopping. Facebook wished to connect all the world’s social spheres. But concentrating all the world’s information is neither practical nor helpful unless one can centrally process that information. And in 2000, when Google’s search engine was making its baby steps, when Amazon was a modest online bookshop, and when Mark Zuckerberg was in high school, the AI necessary to centrally process oceans of data was nowhere at hand. But some people bet it was just around the corner.

Kevin Kelly, the founding editor of Wired magazine, recounted how in 2002 he attended a small party at Google and struck up a conversation with Larry Page. “Larry, I still don’t get it. There are so many search companies. Web search, for free? Where does that get you?” Page explained that Google wasn’t focused on search at all. “We’re really making an AI,” he said.[4] Having lots of data makes it easier to create an AI. And AI can turn lots of data into lots of power.

By the 2010s, the dream was becoming a reality. Like every major historical revolution, the rise of AI was a gradual process involving

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numerous steps. And like every revolution, a few of these steps were seen as turning points, just like the opening of the Liverpool–Manchester Railway. In the prolific literature on the story of AI, two events pop up again and again. The first occurred when, on September 30, 2012, a convolutional neural network called AlexNet won the ImageNet Large Scale Visual Recognition Challenge.

If you have no idea what a convolutional neural network is, and if you have never heard of the ImageNet challenge, you are not alone. More than 99 percent of us are in the same situation, which is why AlexNet’s victory was hardly front-page news in 2012. But some humans did hear about AlexNet’s victory and decoded the writing on the wall.

They knew, for example, that ImageNet is a database of millions of annotated digital images. Did a website ever ask you to prove that you are not a robot by looking at a set of images and indicating which ones contain a car or a cat? The images you clicked were perhaps added to the ImageNet database. The same thing might also have happened to tagged images of your pet cat that you uploaded online. The ImageNet Large Scale Visual Recognition Challenge tests various algorithms on how well they are able to identify the annotated images in the database. Can they correctly identify the cats? When humans are asked to do it, out of one hundred cat images we correctly identify ninety-five as cats. In 2010 the best algorithms had a success rate of only 72 percent. In 2011 the algorithmic success rate crawled up to 75 percent. In 2012 the AlexNet algorithm won the challenge and stunned the still minuscule community of AI experts by achieving a success rate of 85 percent. While this improvement may not sound like much to laypersons, it demonstrated to the experts the potential for rapid progress in certain AI domains. By 2015 a Microsoft algorithm achieved 96 percent accuracy, surpassing the human ability to identify cat images.

In 2016, The Economist published a piece titled “From Not Working to Neural Networking” that asked, “How has artificial intelligence, associated with hubris and disappointment since its earliest

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days, suddenly become the hottest field in technology?” It pointed to AlexNet’s victory as the moment when “people started to pay attention, not just within the AI community but across the technology industry as a whole.” The article was illustrated with an image of a robotic hand holding up a photo of a cat.[5]

All those cat images that tech giants had been harvesting from across the world, without paying a penny to either users or tax collectors, turned out to be incredibly valuable. The AI race was on, and the competitors were running on cat images. At the same time that AlexNet was preparing for the ImageNet challenge, Google too was training its AI on cat images, and even created a dedicated cat-image-generating AI called the Meow Generator.[6] The technology developed by recognizing cute kittens was later deployed for more predatory purposes. For example, Israel relied on it to create the Red Wolf, Blue Wolf, and Wolf Pack apps used by Israeli soldiers for facial recognition of Palestinians in the Occupied Territories.[7] The ability to recognize cat images also led to the algorithms Iran uses to automatically recognize unveiled women and enforce its hijab laws. As explained in chapter 8, massive amounts of data are required to train machine-learning algorithms. Without millions of cat images uploaded and annotated for free by people across the world, it would not have been possible to train the AlexNet algorithm or the Meow Generator, which in turn served as the template for subsequent AIs with far-reaching economic, political, and military potential.[8]

Just as in the early nineteenth century the effort to build railways was pioneered by private entrepreneurs, so in the early twenty-first century private corporations were the initial main competitors in the AI race. The executives of Google, Facebook, Alibaba, and Baidu saw the value of recognizing cat images before the presidents and generals did. The second eureka moment, when the presidents and generals caught on to what was happening, occurred in mid-March 2016. It was the aforementioned victory of Google’s AlphaGo over Lee Sedol. Whereas AlexNet’s achievement was largely ignored by politicians, AlphaGo’s triumph sent shock waves through government

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offices, especially in East Asia. In China and neighboring countries go is a cultural treasure and considered an ideal training for aspiring strategists and policy makers. In March 2016, or so the mythology of AI would have it, the Chinese government realized that the age of AI had begun.[9]

It is little wonder that the Chinese government was probably the first to understand the full importance of what was happening. In the nineteenth century, China was late to appreciate the potential of the Industrial Revolution and was slow to adopt inventions like railroads and steamships. It consequently suffered what the Chinese call “the century of humiliations.” After having been the world’s greatest superpower for centuries, failing to adopt modern industrial technology brought China to its knees. It was repeatedly defeated in wars, partially conquered by foreigners, and thoroughly exploited by the powers that did understand railroads and steamships. The Chinese vowed never again to miss the train.

In 2017, China’s government released its “New Generation Artificial Intelligence Plan,” which announced that “by 2030, China’s AI theories, technologies, and application should achieve world-leading levels, making China the world’s primary AI innovation center."[10] In the following years China poured enormous resources into AI so that by the early 2020s it was already leading the world in several AI-related fields and catching up with the United States in others.[11]

Of course, the Chinese government wasn’t the only one that woke up to the importance of AI. On September 1, 2017, President Putin of Russia declared, “Artificial intelligence is the future, not only for Russia, but for all humankind…. Whoever becomes the leader in this sphere will become the ruler of the world.” In January 2018, Prime Minister Modi of India concurred that “the one who control [sic] the data will control the world."[12] In February 2019, President Trump signed an executive order on AI, saying that “the age of AI has arrived” and that “continued American leadership in Artificial Intelligence is of paramount importance to maintaining the economic and national security of the United States."[13] The United

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States at the time was already the leader in the AI race, thanks largely to efforts of visionary private entrepreneurs. But what began as a commercial competition between corporations was turning into a match between governments, or perhaps more accurately, into a race between competing teams, each made of one government and several corporations. The prize for the winner? World domination.

Data Colonialism

In the sixteenth century, when Spanish, Portuguese, and Dutch conquistadors were building the first global empires in history, they came with sailing ships, horses, and gunpowder. When the British, Russians, and Japanese made their bids for hegemony in the nineteenth and twentieth centuries, they relied on steamships, locomotives, and machine guns. In the twenty-first century, to dominate a colony, you no longer need to send in the gunboats. You need to take out the data. A few corporations or governments harvesting the world’s data could transform the rest of the globe into data colonies—territories they control not with overt military force but with information.[14]

Imagine a situation—in twenty years, say—when somebody in Beijing or San Francisco possesses the entire personal history of every politician, journalist, colonel, and CEO in your country: every text they ever sent, every web search they ever made, every illness they suffered, every sexual encounter they enjoyed, every joke they told, every bribe they took. Would you still be living in an independent country, or would you now be living in a data colony? What happens when your country finds itself utterly dependent on digital infrastructures and AI-powered systems over which it has no effective control?

Such a situation can lead to a new kind of data colonialism in which control of data is used to dominate faraway colonies. Mastery of AI and data could also give the new empires control of people’s

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attention. As we have already discussed, in the 2010s American social media giants like Facebook and YouTube upended the politics of distant countries like Myanmar and Brazil in pursuit of profit. Future digital empires may do something similar for political interests.

Fears of psychological warfare, data colonialism, and loss of control over their cyberspace have led many countries to already block what they see as dangerous apps. China has banned Facebook, YouTube, and many other Western social media apps and websites. Russia has banned almost all Western social media apps as well as some Chinese ones. In 2020, India banned TikTok, WeChat, and numerous other Chinese apps on the grounds that they were “prejudicial to sovereignty and integrity of India, defense of India, security of state and public order."[15] The United States has been debating whether to ban TikTok—concerned that the app might be serving Chinese interests—and as of 2023 it is illegal to use it on the devices of almost all federal employees, state employees, and government contractors.[16] Lawmakers in the U.K., New Zealand, and other countries have also expressed concerns over TikTok.[17] Numerous other governments, from Iran to Ethiopia, have blocked various apps like Facebook, Twitter, YouTube, Telegram, and Instagram.

Data colonialism could also manifest itself in the spread of social credit systems. What might happen, for example, if a dominant player in the global digital economy decides to establish a social credit system that harvests data anywhere it can and scores not only its own nationals but people throughout the world? Foreigners couldn’t just shrug off their score, because it might affect them in numerous ways, from buying flight tickets to applying for visas, scholarships, and jobs. Just as tourists use the global scores given by foreign corporations like Tripadvisor and Airbnb to evaluate restaurants and vacation homes even in their own country, and just as people throughout the world use the U.S. dollar for commercial transactions, so people everywhere might begin to use a Chinese or an American social credit score for local social interactions.

Becoming a data colony will have economic as well as political and

page_372

social consequences. In the nineteenth and twentieth centuries, if you were a colony of an industrial power like Belgium or Britain, it usually meant that you provided raw materials, while the cutting-edge industries that made the biggest profits remained in the imperial hub. Egypt exported cotton to Britain and imported high-end textiles. Malaya provided rubber for tires; Coventry made the cars.[18]

Something analogous is likely to happen with data colonialism. The raw material for the AI industry is data. To produce AI that recognizes images, you need cat photos. To produce the trendiest fashion, you need data on fashion trends. To produce autonomous vehicles, you need data about traffic patterns and car accidents. To produce health-care AI, you need data about genes and medical conditions. In a new imperial information economy, raw data will be harvested throughout the world and will flow to the imperial hub. There the cutting-edge technology will be developed, producing unbeatable algorithms that know how to identify cats, predict fashion trends, drive autonomous vehicles, and diagnose diseases. These algorithms will then be exported back to the data colonies. Data from Egypt and Malaysia might make a corporation in San Francisco or Beijing rich, while people in Cairo and Kuala Lumpur remain poor, because neither the profits nor the power is distributed back.

The nature of the new information economy might make the imbalance between imperial hub and exploited colony worse than ever. In ancient times land—rather than information—was the most important economic asset. This precluded the overconcentration of all wealth and power in a single hub. As long as land was paramount, considerable wealth and power always remained in the hands of provincial landowners. A Roman emperor, for example, could put down one provincial revolt after another, but on the day after decapitating the last rebel chief, he had no choice but to appoint a new set of provincial landowners who might again challenge the central power. In the Roman Empire, although Italy was the seat of political power, the richest provinces were in the eastern Mediterranean. It was impossible to transport the fertile fields of the Nile valley to the Italian

page_373

Peninsula.[19] Eventually the emperors abandoned the city of Rome to the barbarians and moved the seat of political power to the rich east, to Constantinople.

During the Industrial Revolution machines became more important than land. Factories, mines, railroad lines, and electrical power stations became the most valuable assets. It was somewhat easier to concentrate these kinds of assets in one place. The British Empire could centralize industrial production in its home islands, extract raw materials from India, Egypt, and Iraq, and sell them finished goods made in Birmingham or Belfast. Unlike in the Roman Empire, Britain was the seat of both political and economic power. But physics and geology still put natural limits on this concentration of wealth and power. The British couldn’t move every cotton mill from Calcutta to Manchester, or shift the oil wells from Kirkuk to Yorkshire.

Information is different. Unlike cotton and oil, digital data can be sent from Malaysia or Egypt to Beijing or San Francisco at almost the speed of light. And unlike land, oil fields, or textile factories, algorithms don’t take up much space. Consequently, unlike industrial power, the world’s algorithmic power can be concentrated in a single hub. Engineers in a single country might write the code and control the keys for all the crucial algorithms that run the entire world.

Indeed, AI makes it possible to concentrate in one place even the decisive assets of some traditional industries, like textile. In the nineteenth century, to control the textile industry meant to control sprawling cotton fields and huge mechanical production lines. In the twenty-first century, the most important asset of the textile industry is information rather than cotton or machinery. To beat the competitors, a garment producer needs information about the likes and dislikes of customers and the ability to predict or manufacture the next fashions. By controlling this type of information, high-tech giants like Amazon and Alibaba can monopolize even a very traditional industry like textile. In 2021, Amazon became the United States’ biggest single clothing retailer.[20]

Moreover, as AI, robots, and 3-D printers automate textile

page_374

production, millions of workers might lose their jobs, upending national economies and the global balance of power. What will happen to the economies and politics of Pakistan and Bangladesh, for example, when automation makes it cheaper to produce textiles in Europe? Consider that at present the textile sector provides employment to 40 percent of Pakistan’s total labor force and accounts for 84 percent of Bangladesh’s export earnings.[21] As noted in chapter 9, while automation might make millions of textile workers redundant, it will probably create many new jobs, too. For instance, there might be a huge demand for coders and data analysts. But turning an unemployed factory hand into a data analyst demands a substantial up-front investment in retraining. Where would Pakistan and Bangladesh get the money to do that?

AI and automation therefore pose a particular challenge to poorer developing countries. In an AI-driven economy, the digital leaders claim the bulk of the gains and could use their wealth to retrain their workforce and profit even more. Meanwhile, the value of unskilled laborers in left-behind countries will decline, and they will not have the resources to retrain their workforce, causing them to fall even further behind. The result might be lots of new jobs and immense wealth in San Francisco and Shanghai, while many other parts of the world face economic ruin.[22] According to the global accounting firm PricewaterhouseCoopers, AI is expected to add $15.7 trillion to the global economy by 2030. But if current trends continue, it is projected that China and North America—the two leading AI superpowers—will together take home 70 percent of that money.[23]

Book 34: Nexus Chapter 3 - How Information Flows

Wed, 03 Dec 2025 10:23:43 +0800

English Reading Club - Host Notes

Chapter 3: How Information Flows

Book: Nexus: A Brief History of Information Networks from the Stone Age to AI
Author: Yuval Noah Harari
Date: December 3, 2025
Pages: 177-191

Chapter Overview

This chapter explores how information flows differently in democratic vs totalitarian systems, using historical examples to illustrate the advantages and disadvantages of each approach.

Key Themes

1. Information Network Types

Democratic networks: Information flows through many independent channels
Totalitarian networks: All information passes through central hub

2. Historical Examples

Chernobyl (1986) vs Three Mile Island (1979)

Chernobyl: Information suppressed, delayed response, health consequences
Three Mile Island: Rapid information flow, quick public awareness, lessons learned

Stalin’s Soviet Union

Lysenkoism disaster in agriculture
Pavel Rychagov case - truth-telling punished
WWII initial failures due to fear-based culture
Stalin’s death delayed by fear of doctors

3. Trade-offs

Totalitarian advantages: Quick decisions, order during emergencies
Totalitarian disadvantages: Blocked information channels, no self-correction
Democratic advantages: Multiple information sources, self-correction
Democratic disadvantages: Slower decisions, potential chaos

Discussion Questions

Information Suppression: Why do you think totalitarian regimes prioritize order over truth? Can you think of modern examples?
The Chernobyl Quote: “Americans grow up with the idea that questions lead to answers, but Soviet citizens grew up with the idea that questions lead to trouble.” How does this mindset affect society?
Democratic Chaos: The 1960s brought social upheaval to Western democracies as more voices joined the conversation. Was this chaos worth the inclusivity?
Technology’s Role: How do you think modern technology (internet, social media, AI) changes the balance between democratic and totalitarian information systems?
Self-Correction: What mechanisms do democratic societies have for correcting mistakes? Are they effective?
The Silicon Curtain: Harari suggests future division might be “humans vs algorithms” rather than “democracy vs totalitarianism.” What do you think this means?

Key Quotes for Discussion

Quote 1: Questions vs Trouble (Page 179)

“Americans grow up with the idea that questions lead to answers, but Soviet citizens grew up with the idea that questions lead to trouble.”

Discussion guidance:

How does this mindset affect innovation and problem-solving in organizations?
What creates a culture where questioning is encouraged vs discouraged?
Can you think of modern examples where asking questions leads to trouble?

Quote 2: Truth and Order (Page 185)

“Information systems can reach far with just a little truth and a lot of order.”

Discussion guidance:

Is this statement about Stalinism’s “success” disturbing? Why or why not?
How much truth is necessary for a system to function?
What are the moral costs of prioritizing order over truth?

Quote 3: The Silicon Curtain (Page 190)

“The main split in twenty-first-century politics might be not between democracies and totalitarian regimes but rather between human beings and nonhuman agents.”

Discussion guidance:

What does “nonhuman agents” refer to in our current world?
How might AI change the traditional democracy vs totalitarianism debate?
Are we already seeing signs of this “Silicon Curtain” today?

Quote 4: Flying Coffins (Page 181)

“He flatly told Stalin that pilots were being forced to operate hastily designed and badly produced airplanes, which he compared to flying ‘in coffins.’”

Discussion guidance:

Why was Rychagov executed for telling the truth?
How do fear-based cultures discourage honest feedback?
What happens to organizations that punish truth-telling?

Quote 5: Self-Correcting Mechanisms (Page 180)

“Since they believe they are infallible, they see little need for such mechanisms, and since they are afraid of any independent institution that might challenge them, they lack free courts, media outlets, or research centers.”

Discussion guidance:

What are examples of self-correcting mechanisms in democratic societies?
Why do authoritarian systems resist these mechanisms?
How can organizations build better feedback systems?

Reflection Points

How do we balance truth-seeking with social stability?
What happens when information networks become too centralized or too distributed?
Are we seeing signs of the “Silicon Curtain” today?

Next Steps: Consider how these historical lessons apply to our current information landscape and the role of AI in shaping future political systems.

Background Information for Chinese Readers

Major Historical Events

Chernobyl Nuclear Disaster (1986)

Location: Pripyat, Ukrainian SSR, Soviet Union
What happened: Nuclear reactor explosion at Chernobyl Nuclear Power Plant
Soviet response: Complete information blackout for 2 days, denied severity
International discovery: Swedish scientists detected radiation 1,200km away
Consequences: Massive radiation exposure, long-term health effects
Significance: Shows how totalitarian systems suppress bad news to maintain order

Three Mile Island Accident (1979)

Location: Pennsylvania, United States
What happened: Partial nuclear meltdown at power plant
US response: Information flowed quickly through multiple channels
Timeline: Accident at 4am → Radio report at 8:25am → Public knew within hours
Contrast: Democratic system’s transparency vs Soviet secrecy

Key Historical Figures

Joseph Stalin (1878-1953)

Role: Leader of Soviet Union (1924-1953)
Relevance: Example of totalitarian information control
Key policies mentioned:
- Lysenkoism: Rejected Darwin’s evolution, promoted pseudoscience
- Great Terror (1936-1938): Purged military officers, intellectuals
- Collectivization: Forced agricultural reorganization, caused famines

Pavel Rychagov (1911-1941)

Role: Soviet Air Force commander
Background: War hero in Spanish Civil War, fought Japanese in China
Fate: Executed for telling Stalin that Soviet planes were “flying coffins”
Significance: Shows how totalitarian systems punish truth-telling

Trofim Lysenko (1898-1976)

Role: Soviet agronomist who promoted pseudoscientific theories
Lysenkoism: Rejected genetics, claimed environment could change heredity
Impact: Set back Soviet agriculture and science for decades
Significance: Example of how political ideology can override scientific truth

Cultural References

“Good Soldier Švejk” by Jaroslav Hašek

Context: Satirical novel about Austro-Hungarian Empire in WWI
Relevance: Shows how bureaucrats report fake good news to avoid punishment
Loyalty grades: I.a, I.b, I.c system - all police reported perfect I.a morale
Lesson: Fear makes subordinates hide bad news from superiors

The Doctors’ Plot (1951-1953)

What it was: Fabricated conspiracy theory about Jewish doctors
Claim: Jewish doctors allegedly murdering Soviet leaders
Reality: Anti-Semitic campaign, hundreds arrested and tortured
Stalin’s death: Ironically, fear of doctors delayed Stalin’s medical treatment
Significance: Shows how conspiracy theories can backfire on their creators

Political Systems Context

Context: Previously marginalized groups gained voice (women, minorities, LGBTQ)
Result: More viewpoints = harder to reach consensus = social instability
Examples: 1968 protests in Paris, Chicago; assassinations of JFK, MLK Jr.
Lesson: Democratic inclusion can cause temporary chaos but leads to progress

Soviet Collapse (1980s)

Problem: Centralized system couldn’t handle rapid technological change
Example: Soviet Union got personal computers in 1984, US had 11 million by then
Cause: “Secretive, top-down, military-oriented” approach vs open competition

Key Concepts

Information Network Trade-offs

Order vs Truth: Totalitarian systems prioritize stability over accuracy
Speed vs Accuracy: Centralized systems decide faster but may be wrong
Self-correction: Democratic systems have mechanisms to fix mistakes

Modern Relevance: “Silicon Curtain”

Harari’s prediction: Future division may be humans vs AI, not democracy vs totalitarianism
Question: How do you see technology changing information control in modern societies?

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Zen Book Club

Book 34: Nexus Chapter 11 - Session Plan

The Silicon Curtain: Global Empire or Global Split?

Session Structure

Chapter Overview

Key Quotes Needing Discussion

Session Structure

Opening (5 min)

Part 1: The Imperial Threat (15 min)

Part 2: Data as the New Cotton (15 min)

Part 3: Winners and Losers (15 min)

Closing: The Silicon Curtain (10 min)

Key Quotes to Reference

Key Concepts

Discussion Questions for Participants

Proposed Answers & Discussion Points

Part 1: The Imperial Threat

Part 2: Data as the New Cotton

Part 3: Winners and Losers

Closing: The Silicon Curtain

Book 34: Nexus Chapter 11 - The Silicon Curtain

Chapter 11: The Silicon Curtain: Global Empire or Global Split?

The Rise of Digital Empires

Data Colonialism

Book 34: Nexus Chapter 3 - How Information Flows

English Reading Club - Host Notes

Chapter 3: How Information Flows

Chapter Overview

Key Themes

1. Information Network Types

2. Historical Examples

Chernobyl (1986) vs Three Mile Island (1979)

Stalin’s Soviet Union

3. Trade-offs

Discussion Questions

Key Quotes for Discussion

Quote 1: Questions vs Trouble (Page 179)

Quote 2: Truth and Order (Page 185)

Quote 3: The Silicon Curtain (Page 190)

Quote 4: Flying Coffins (Page 181)

Quote 5: Self-Correcting Mechanisms (Page 180)

Reflection Points

Background Information for Chinese Readers

Major Historical Events

Chernobyl Nuclear Disaster (1986)

Three Mile Island Accident (1979)

Key Historical Figures

Joseph Stalin (1878-1953)

Pavel Rychagov (1911-1941)

Trofim Lysenko (1898-1976)

Cultural References

“Good Soldier Švejk” by Jaroslav Hašek

The Doctors’ Plot (1951-1953)

Political Systems Context

1960s Western Social Upheaval

Soviet Collapse (1980s)

Key Concepts

Information Network Trade-offs

Modern Relevance: “Silicon Curtain”

Contact

Search