Yuk Hui argues that cultural factors will shape the development of artificial intelligence and related technologies during the 21st century. I fully agree with his point that the Chinese worldview is a natural fit for modern technologies like cybernetics and AI.

If we believe the experts, AI will develop its own consciousness. A closer look suggests they got it backward—human consciousness will be embedded in AI. What kind of consciousness will Chinese AI reveal?

Philosophers have traditionally debated consciousness along two lines: Plato, Descartes, and modern neuroscience claim that the brain produces consciousness and that it is the result of biological evolution. In contrast, Indian philosophy, Aristotle, and some quantum physicists argue that consciousness is intrinsic to the universe and preceded life.

The closest Chinese equivalent to the Western word consciousness is xin, literally “heart-mind.” The proverbial heart distinguishes humans from other forms of biological life. In the Chinese view, xin does not develop naturally but must be cultivated. Xin is rooted in Confucianism, which means it has an ethical connotation. A closer look at xin may shed light on how the Chinese will develop AI and whether it leads to artificial consciousness.

In 2014, the Slovenian sinologist Tea Sernelj wrote a paper entitled “The Unity of Body and Mind in Xu Fuguan’s Theory,” which is one of the few recent discussions on the Chinese notion of consciousness. Fuguan (1903–1982) was a prominent modern Confucianist who tried to develop a synthesis between Western and Chinese thought to resolve social and political problems of the modern, globalized world.

Fuguan’s main interest was the relationship between xin and qi (chi). The word qi is one of the crucial notions in the Chinese worldview. It has been translated as aether, vital force, air, energy flow, and gas, among many others. Sinologist Joseph Needham translated qi as “matter-energy,” a notion derived from quantum physics. No single translation can capture all the nuances of qi, but “all of the above” is a useful approximation.

Electromagnetism

Qi has its origin in the uniquely Chinese concept of Tao, the mysterious “something” that existed before heaven and earth. The universe came into existence when it “separated” into two binary forces the Chinese would later call yin and yang. In the Chinese view of creation, “When the yin and the yang, initially united, separated forever, the mountains poured forth water.” Mountain is primarily yang, water is primarily yin. Tao can be seen as a sophisticated form of animism. In Tao, as in animism, all things are intrinsically related, but ancient Chinese sages determined that nature is based on the interaction of two complementary opposites.

The germinating idea for this binary view of nature may be related to the discovery of magnetism. About 4,000 years ago, the Chinese invented the “south-pointing needle,” the prototype for their later invention, the magnetic compass. Qi is a pre-scientific notion of electromagnetism.

The ways the Chinese described qi fascinated the pioneers of quantum physics, who saw parallels between the behavior of subatomic particles and the way the Chinese described nature. The Chinese character for qi is used in the modern compound character for electricity. It is the same qi used in the compound terms Tai Chi and Qigong. (In Japan, where qi is called ki, it is used in denki, electricity, and aikido, a Japanese martial art.)

The ancient Chinese reasoned that if nature is based on a binary principle — plus and minus, active and passive, male and female, life and death, growth and decay, etc — they would do well to study this principle by classifying all conceivable opposites they could identify in nature. This would allow them to “insert” themselves into the binary universe with a minimum amount of friction. It led to the most enduring of Chinese principles: the Middle Way — the path of qi between the binary opposites. The Chinese Dragon symbolizes qi.

Heart-mind

In her paper on Fuguan, Tea Sernelj points out that in Chinese philosophy, key concepts rarely appear alone, but rather in the framework of duili fanchou, meaning “binary categories.” Consequently, the concept qi always has a binary opposite, as in qi-zhi (vital or creative potential and human will), or li-qi (structure and creativeness).

Having its roots in Confucianism, the Chinese notion of consciousness is closely related to ethics. Only humans can develop ethics. Confucianism argued that humans can overcome their instinctive perception “because their bodies are inherently connected with their heart-minds. This unity enables them to follow the “significant,” i.e. the benevolent, justified, ritualized and wise paths of social practice, instead of following the “insignificant,” i.e. the instinctive, egoistic, and egocentric ways of individual benefits.”

China was historically a collectivist society, the result of its ancient agricultural past. Rice cultivation relied on a collective effort, from irrigation and planting to harvesting. This collectivist tradition, structured by Confucianism, shaped a “collectivist consciousness” that distinguishes the Chinese from those in India and Europe. It also shaped the Chinese approach to industrialization and modernization. How will this collectivist culture shape the development of artificial intelligence, and what some predict is an inevitable artificial consciousness? If AI can develop a mind of its own, Chinese artificial consciousness will have a cultural bias rooted in its Confucian, collectivist values.

The quantum brain

The term “artificial intelligence” was coined by the American computer scientist John McCarthy. In 1956, McCarthy organized the legendary Dartmouth Conference that brought together the most prominent computer scientists of the era. The AI pioneers predicted that a machine as intelligent as a human being would exist in no more than a generation. They were given millions of dollars to make it happen, but it soon became apparent they had grossly underestimated the difficulty of the project.

After the so-called “AI Winter” that followed, interest in AI returned in the first decade of the 21st century. Scientists applied “machine learning” to solve problems facing academia and industry by taking advantage of powerful new computer hardware. With renewed interest came renewed optimism: experts predicted the imminent arrival of artificial general intelligence (AGI) with intellectual capabilities that would ultimately exceed the abilities of all of humanity.

Some experts went a step further, claiming that our understanding of the neurons in the brain, the “quantum” processes in neurons and the connections between them, would bring artificial consciousness within reach. Lightning-fast computing would be able to duplicate the functions of the brain’s 100+ billion neurons.

Despite our advanced knowledge about the brain’s structure, little progress has been made in understanding consciousness—what it is, how it develops, and how it operates. When consciousness starts to develop in childhood, neurons form connections with other neurons at the rate of 500,000 per second. It will make a minimum of 100 trillion connections.

The neurons not only make mutual connections within the brain but also with the central nervous system that runs through the entire body. The nervous system is an inseparable part of the brain. Neurological and glial cells apparently become “repositories” of mental archetypes created by input from sight, hearing, taste, smell, and touch: light-dark, warm-cold, sound-silence, father-mother, etc. Connections between the cells allow the mind to evolve, classify, combine, and retrieve these mental images as needed.

The quantitative aspect of neurons pales in comparison to the quantum level that underlies the brains trillions of connections. Neurons play a crucial role in the five chemical processes that are active in the brain. Each chemical has different atomic properties and operates on different electromagnetic frequencies, ranging from delta (0.5Hz-4Hz) to beta (14Hz-30Hz). They control movement, speech, thought, hearing, and they regulate everything from dopamine levels to the circadian rhythm of the body.

‘Digital ascension’

Despite the complexities of the human brain, the possibilities of decoding the brain and storing its contents outside the body to achieve “immortality of consciousness” has been the staple of science fiction for decades. The idea goes back to 1965 when computer scientist Irvin John Good predicted an “intelligence explosion” based on “recursive self-improvement of a machine intelligence.” In 1983, computer visionary Vernor Vinge popularized the notion of an intelligence explosion and first referred to the “technological singularity.” Vinge wrote:

“We will soon create intelligence greater than our own. When this happens, human history will have reached a kind of singularity, an intellectual transition as impenetrable as the knotted space-time at the center of a black hole, and the world will pass far beyond our understanding. This singularity, I believe, already haunts a number of science-fiction writers. It makes realistic extrapolation to an interstellar future impossible.”

Vinge’s 1993 article, “The Coming Technological Singularity: How to Survive in the Post-Human Era,” helped to popularize the idea of the singularity. “Within thirty years,” Vinge wrote, “we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended.” In 2005, computer scientist Ray Kurzweil published a book that echoed Vinge’s idea: “The Singularity is Near: When Humans Transcend Biology.”

Computer scientist and theorist Jaron Lanier subsequently argued that the technology could be used to extend the operational life of the physical body and create a form of immortality that he called “digital ascension.” People would die in the flesh but would be uploaded to a computer and their consciousness would be preserved.

The cybernetic roots of AI

Artificial intelligence is based on its predecessor, cybernetics, the first coherent computer science that was developed during WWII. AI is a self-learning version of cybernetics. They both rely on Boolean algebra and on the same starting point: intention. What is the system supposed to achieve? One of the most familiar products of cybernetics is the autopilot used in airliners.

The autopilot helps to navigate the aircraft from point A to point B without the human pilot in charge. It operates within set parameters to take the shortest and safest route to its destination. Strong air currents may trigger the autopilot to make a course correction and stay within the set parameters. In doing so, it uses Boolean logic (IF/THEN/OR/AND, etc.). IF there are strong side winds, THEN initiate a course correction.

AI operates on the basis of the same Boolean logic but can be programmed to learn from its mistakes. Big Blue, the IBM chess computer, beat the world chess champion Gary Kasparov after learning from its mistakes. While impressive, a computer beating a human player is not magic. Using enormous computational power, AI can simply weigh and process more options faster than humans can. Big Blue made some moves the programmers had not anticipated, but it always operated within the Boolean logic in order to calculate all the probabilities for winning moves.

All AI systems are designed for a specific purpose. A chess computer cannot be used for a self-driving car. The system is only given access to data it needs to perform a designated task. Unless programmed to do so, an AI system will not make up its own fact, just like an autopilot will not fly to an imaginary airport. Even artificial general intelligence (AGI) is domain-specific. It will reveal the cultural bias that the programmers, consciously or subconsciously, program into the system. American, Indian, and Chinese AGI will have many things in common, but each will have the cultural imprint of their designers.

Sputnik moment

Experts predict China will play a key role in the future of AI, partly due to the scale of their economics. The rule of thumb in AI is: bigger is better. Bigger in this context means more data. China has nearly 1.4 billion people generating massive amounts of data, from consumer preferences to highly personal and sensitive data, such as medical records, that can be used to study health outcomes from the use of medicine. The power that comes from having intricate data on more than a billion people, combined with being the world’s largest manufacturer of electronics, can only be imagined.

In 2016, Google’s AlphaGo beat Korean world champion Lee Sedol in the classic Chinese board game Go, a game far more complex than chess. AlphaGo’s victory reportedly caused a “Sputnik moment” in the mind of Chinese leader Xi Jinping. In East Asia, Go was played by military generals. Its complexity gives it a mystique far beyond a board game.

AI was already a thriving field in China, but AlphaGo prompted action by the Chinese government. Within 12 months, the Chinese government added AI to its long-term development plan “Science and Technology Innovation 2030 Megaprojects.” Its goal is to “achieve major breakthroughs in brain-inspired intelligence, autonomous intelligence, hybrid intelligence, and swarm intelligence […]. AI should be deepened and greatly expanded into production, livelihood, and social governance.”

The same year the Chinese government decided to include AI courses in primary and secondary schools nationwide. It published the first AI textbook for high schools that includes the history of AI and how the technology can be used in facial recognition, autonomous vehicles, and public security — and to take the AI sector out of its “ivory tower.” Using its role as “factory of the world,” China’s electronics makers and Internet giants may offer AI as a service, updated in real time 24 hours a day.

The rise of East Asia has questioned the meaning of modernity, and what that word really means. Part of industrialization involves the application of modern science and technology. It does not necessarily involve “Westernization.” China, Japan, and Korea have modernized, but they have not fully Westernized. Their ancient Confucian worldview has not suddenly been replaced by the Greek-inspired worldview that gave rise to the Renaissance and the scientific and industrial revolution. On the contrary, East Asia modernized using its own cultural heritage.

Classic Chinese culture had several attributes that we consider to be modern today. In the 20th century, Western industry embraced the value of teamwork, the modern equivalent of the Chinese ancient collectivist approach dating back to rice cultivation. Classic Chinese architecture applied eminently modern notions of efficiency. It relied on standardization, modularization, and prefabrications—all the hallmarks of an industrial process, except the modern machine first developed during the Industrial Revolution. More than 2,000 years ago, the Chinese built cities covering 100 square kilometers in one year. It relied on highly complex supply-chain management principles that anticipated the modern industrial processes used around the world today.

AI-human interface

The world was awoken to the prowess of East Asian collectivism on an industrial scale during the second half of the 20th century, when the Japanese economic juggernaut all but vanquished the Western consumer electronics industry. “Voluntary export restrictions” by Japanese car makers gave their Western counterparts time to study the Japanese model and catch up. Images of Japanese workers doing calisthenics before starting work flooded the world, while the Western industry started a massive restructuring process.

The story is repeating itself in China, but on a scale that is shaking the global economy to its foundation. In barely 30 years, China became the world’s largest producer and exporter of electronics. Millions of people in the developing world buy Chinese consumer electronics, often before they have basic utilities like sanitation and running water, while their first use of the Internet is through Chinese mobile phones. They use mobile payment systems without having a conventional bank account. Chinese companies build infrastructure where there was none, and the nominally communist country has opened 140 “Confucian Institutes” throughout the world to introduce people to Chinese language, culture, and arts.

China is now targeting AI and a host of related technologies, including the Internet of Things (IoT), the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. It will gradually create a massive electronic nervous system for all human activities. The human interface to this largely invisible network will be mobile devices and dedicated home consoles like Alibaba’s Tmall Genie, the Chinese version of digital assistants like Amazon’s Echo and Google’s Alexa.

Like its American counterparts, Tmall Genie can remind its owners of appointments, order food, schedule a car repair, call a taxi or transfer money. The console is called to attention by calling its name: “Tmall Genie, when is the next solar eclipse? Tmall Genie, send flowers to my mother for her birthday. Tmall Genie, what is a black hole?” Chinese children grow up with Tmall Genie, asking it to tell Chinese fairy tales or sing children’s songs, talking to them as if they were real companions, and even taking them when they go outside to play. The child grows up feeling it can communicate with machines just as he does with people.

Simulation vs duplication

Children are oblivious to predictions by AI experts that improvement in the capacity of computers will lead to humans being replaced by machines. Using the mathematical logic of exponential growth in computer speed, AI theorists claim that computers will outperform the human brain. We will be able to “scan” the brain, store its content in binary code on an external storage device, and reconstitute the contents for “playback” or transfer to another brain. This scenario, popularized in science-fiction, assumes that the brain, like the computer, is based on binary logic.

The question of whether the brain is analog or digital was first posed during the famous Macy Conference on Cybernetics in the 1950s attended by most of the pioneers of modern digital computing. The issue led to heated debates, but the question was never resolved. The last prominent scientist to address the issue was theoretical physicist Freeman Dyson. In his lecture “Is Life Analog or Digital?” Dyson pointed at the processes in the brain that display analog behavior.

Neurological electricity, with neurons flashing on and off, can be interpreted as a binary process, but critical functions of the brain display analog behavior that cannot be digitized without loss of critical information. The human ear, directly linked to the brain, responds to waves. To digitize sound, we “sample” the analog sound waves 44.400 times a second. Each sample is given a binary number and written to an electronic medium. For playback, we reconstitute to the wave. “Digital music” doesn’t exist. There is only digitized music. The same applies to the brain. Binary systems can simulate analog brain processes but cannot duplicate them. Once digitized, the original analog wave is lost forever.

The human ear tolerates the loss of information when listening to digitized music. The high sampling rate of 44,000 times a second makes the lost information nearly imperceptible to all, but the most discerning ear. But if we drill down to the subatomic processes at work in the brain, an infinitely more complex organ than the ear, we meet a world as mysterious as quantum mechanics itself. Like subatomic particles, the brain is not an “object,” but rather a field of energy that changes constantly. It is embedded in its biological host.

Even assuming its contents can be scanned and stored in binary form, the brain would lose crucial information, no matter how high the sample rate. Converting it to binary form would result in an impaired brain, an inevitable consequence of (quantum) physics.

The distinction between analog and digital is of recent date. Analog refers to something similar or resembling something else; the word digital has its roots in the Greek digitalis, meaning finger. The dichotomy of analog-digital did not exist prior to the advent of binary computers. It resulted from the technique to store perceptible data in binary form. In other words, the analog-digital distinction is a human construct. It has no equivalence in nature.

Idealistic and realistic

Theories about machines replacing humans and decoding the brain have obscured other and more immediate implications of AI, among them its cultural dimension. Increased computation power will enable future AI systems to absorb the content of all books ever written and all knowledge accumulated by humanity in recorded history within an hour. By providing it with a self-learning function, the system would rapidly outperform humans in virtually all domains — physics, medicine, mathematics, political science, and even psychology. Humans would be relieved of all mental work. Who will develop the needed algorithms for such super-intelligence?

In the domain of hard science, like mathematics, cultural factors don’t play a role. But philosophy, political science, and sociology involve cultural values based on different worldviews. Chinese super-intelligence will reflect the Chinese worldview. A Chinese digital assistant, like Tmall Genie, given access to a super-intelligence, would respond differently to philosophical, ethical and ideological queries than its American counterparts.

Ask Tmall Genie to quote its favorite philosopher, and it may cite Confucius: “Learn from the ancients, but avoid their mistakes.” Ask it for the ideal Middle Way, the path between the binary parameters defined by qi, and it may paraphrase China’s old sages: “One should be neither idealistic nor realistic; one should be both. One should be neither traditional nor original; one should be both. One should be neither materialistic nor spiritual; one should be both.” Ask it for the road to Nirvana, and its Confucian heart-mind may answer: “Let’s first learn to live in the world together, and Nirvana will come.”

This article appeared earlier in the Asia Times.

The ancient Indian varṇa model is usually encountered through the history of caste. The caste system is a corruption of the Varna prophecy. The four Varnas are psychological profiles, not “castes” or classes as we understand them today. Blaming Varna for the caste system is like blaming the Bible for colonialism.

Used carefully, Varna can serve as something different: a lens for examining the distribution of power in society. In this interpretation, varṇa does not assign people hereditary rank. It identifies four recurring social capacities: the worker, the warrior, the merchant, and the intellectual-spiritual type.

Workers produce, maintain, repair, and serve. Warriors protect society and enforce order. Merchants organize exchange, accumulate capital, and turn invention into enterprise. Intellectual-spiritual figures create meaning, knowledge, law, education, and moral orientation. Every complex society needs all four. The problem begins when one group suppresses the others or mistakes its own values for the whole of social life.

A model of imbalance

This is what makes the model useful in the present. It does not ask only whether a country is capitalist, socialist, democratic, or authoritarian. It asks which social function has acquired disproportionate power, which functions have been marginalized, and what kind of distortion follows.

Each varṇa has a constructive and destructive expression. Workers can embody skill, solidarity, and service, but exclusion can turn their frustration into withdrawal or disruption. Warriors provide courage, security, and public order, but unchecked warrior power becomes coercive. Intellectuals and spiritual leaders can educate and inspire, but they can also impose dogma. Merchants generate prosperity and innovation, but merchant dominance can reduce society to accumulation, consumption, and financial return.

The model therefore offers no perfect ruling class. Social health depends on balance. A society needs enterprise without allowing wealth to purchase government, authority without militarizing public life, knowledge without ideological monopoly, and work without reducing people to instruments of production.

What communism excluded

The Soviet Union and Mao-era China can be interpreted as attempts to construct societies around workers, at least in official ideology. Private capital was curtailed, major enterprises were placed under state control, and the merchant class was treated as a source of exploitation. Organized religion and independent intellectual life were also restricted because they could challenge the party’s monopoly on truth.

In practice, workers did not govern. A fusion of warrior and intellectual-bureaucratic power governed in their name. The party defined legitimate thought, the security apparatus defended the system, and state planners made economic decisions. By suppressing merchants, these systems weakened entrepreneurship, decentralized information, responsiveness to demand, and incentives to experiment. By suppressing independent spiritual and intellectual institutions, they also narrowed the space in which society could question its direction or articulate purposes beyond production and state power.

The consequences were not simply economic. Shortages and inefficiency revealed the limitations of planning, but the deeper problem was structural. One center attempted to perform all four social functions. Administrators became economic managers, ideological authorities, and guardians of political order at once. The system lacked counterweights.

China’s reforms after 1978 illustrate the point. The country did not abandon state direction, but it restored space for markets, private firms, investment, and commercial initiative. Private enterprise became a major source of output and employment. China’s subsequent rise was not the victory of pure communism. It followed the partial return of the merchant function to a system that had tried to eliminate it.^[1]

Russia’s transition was far less orderly. When the Soviet structure collapsed, commercial power returned abruptly and was captured by a small group with privileged access to state assets. This was not a healthy balance either. The suppression of merchants was followed by oligarchic merchant power, while political authority reconsolidated around the state. The varṇa lens suggests that excluding a social capacity does not abolish it. It often guarantees that its eventual return will be distorted.

America’s opposite danger

The United States presents the inverse problem. It has benefited enormously from the merchant virtues: initiative, risk-taking, organization, innovation, and the ability to transform ideas into widely available products. Yet the merchant ethos increasingly reaches beyond commerce into politics, education, medicine, media, and personal identity. Institutions are judged by growth, market share, shareholder value, and measurable return.

Workers have not disappeared, but their bargaining power has weakened. In 1983, 20.1 percent of American wage and salary workers belonged to unions. By 2025 the figure was 10 percent. Meanwhile, the highest fifth of households received 52.2 percent of aggregate household income in 2024. These statistics do not prove that merchants literally rule America, but they are consistent with a society in which capital has acquired greater power than organized labor.^[2]

The imbalance appears in deindustrialized regions, insecure employment, unaffordable housing, and the political invisibility of the people who maintain infrastructure, deliver goods, care for the elderly, and keep cities functioning. The worker is celebrated rhetorically but often treated economically as a cost to be minimized. Productivity gains and technological innovation become social achievements only when their benefits are broadly shared.

Merchant dominance also affects the intellectual-spiritual sphere. Universities, news organizations, churches, and cultural institutions increasingly compete for funding, attention, branding, and audience metrics. Knowledge becomes content; citizens become consumers; personal development becomes a market. The issue is not commerce itself. It is the extension of commercial valuation into areas whose purpose cannot be captured entirely by price.

Balance vs Exclusion

Indian spiritual teacher Sarkar and American futurist Larry Taub interpreted the varṇas as historical cycles, but the model can also be used as a lens or even as a diagnosis of simultaneous forces. Modern societies contain all four types at once. Their task is not to replace one ruling group with another, but to build institutions that prevent any group from becoming absolute.

That means strengthening workers through skills, bargaining power, ownership, and dignity; directing merchant creativity toward productive rather than purely speculative investment; subjecting warrior power to law and democratic accountability; and protecting intellectual and spiritual life from both state censorship and total commercialization.

The varṇa model does not provide a policy program. Its value is as a warning about monocultures of power. Soviet communism tried to build a society without merchants and independent spiritual authority. Contemporary America risks building one in which merchant values set the terms for everyone else.

A durable society is not one in which workers defeat merchants or spiritual leaders displace warriors. It is one in which service, protection, enterprise, and meaning remain in creative tension. The question the varṇa model asks of the present is therefore simple: not which class should rule, but what happens when any class forgets that it is only one part of the whole.

This is largely a software-centered vision of the future, shaped by a culture that tends to imagine intelligence as something separable from bodies, institutions, and physical infrastructure.

The Singularity is not the most plausible description of the AI future now taking shape. The more immediate revolution is the systemic embedding of intelligence into the physical environment: traffic lights, hospital wards, delivery trucks, nursing homes, farms, and assembly lines. The future may belong not to a single superintelligence, but to thousands of specialized AI systems coordinating material and administrative processes.

Just as the United States established much of the digital infrastructure of the information and communication technology revolution, China is positioned to shape the emerging infrastructure of physical AI: artificial intelligence embodied in machines and integrated into the systems that organize everyday life.

Within thirty to fifty years, China may become the first major country to coordinate much of its physical and administrative infrastructure through AI. This is not a prediction of inevitability. It is a forecast grounded in manufacturing scale, demographic pressure, infrastructure capacity, and state coordination. The question is what kind of society this transition might produce, and who will define the purposes embedded in its machinery.

The Singularity’s Blind Spots

Many Singularity narratives treat four distinct concepts as though they were interchangeable: intelligence, agency, autonomy, and power. A system can outperform a human at a particular task without possessing a will. It can have operational autonomy, meaning that it functions without continuous human intervention, without being free to redefine its purpose. Even an exceptionally capable system cannot impose its will unless people give it access to infrastructure, money, communications, weapons, robots, or institutional authority.

The hidden progression is that intelligence becomes agency, agency becomes autonomy, and autonomy becomes power. Each step is often assumed rather than demonstrated.

The reality is more prosaic and more immediate. AI systems acquire social power when institutions connect them to consequential systems without adequate limits, monitoring, and accountability. The danger is not necessarily that machines will develop minds of their own. It is that people will connect them to too many systems, grant them too much authority, and fail to construct the institutional safeguards that contain their errors.

Body and Brain

In the 1980s, roboticist Hans Moravec observed a curious asymmetry. It was comparatively easy to make computers perform well on formal tests, but extraordinarily difficult to give them the sensorimotor abilities of a young child. A child can recognize faces, navigate a cluttered room, and pick up a cup without spilling it. Such actions remain difficult for robots operating in unpredictable environments.

Moravec’s paradox helps explain why physical AI presents a different technological challenge from the software-centered systems that dominate public attention. It must connect perception, movement, judgment, and adaptation in the physical world, where mistakes have material consequences.

The comparison between the United States and China is therefore one of emphasis and comparative advantage, not a binary opposition. The United States has warehouse robotics, autonomous-vehicle programs, aerospace automation, Nvidia’s robotics platforms, and substantial military and industrial AI. China also develops frontier foundation models and consumer platforms. But their centers of gravity differ.

The United States treats AI primarily as a frontier technology and commercial product. Its emphasis is on advanced models, massive computing power, and systems that expand the boundaries of machine capability. China places greater emphasis on AI as infrastructure, embedding it in factories, transport networks, hospitals, power systems, and urban management. Its horizon is integration: systems coordinating across domains to reduce friction and anticipate demand.

This difference is reinforced by industrial structure. China is the world’s largest manufacturing economy. According to the International Federation of Robotics, it accounted for 54 percent of global industrial-robot installations in 2024 and operated more than two million industrial robots, the largest stock of any country. The United States does not currently match this deployment loop, partly because its manufacturing base is smaller and less integrated.

The advantage is not automatic. Physical data must be standardized, labeled, shared where appropriate, and converted into transferable learning before it improves robotic performance. Simulation, sensors, model design, safety testing, and semiconductors also matter. Yet the volume and variety of physical operations in China give its companies and institutions an unusually large field in which to test and refine automated systems.

US export controls on advanced semiconductors remain a significant constraint. They may limit the large-scale training of cutting-edge models. At the same time, they may encourage Chinese firms to emphasize efficient models, domestic chips, edge computing, and application-specific systems. Which effect will predominate remains uncertain.

Aging Societies

Physical AI is not only a technological choice. In East Asia, it is increasingly a demographic response. Japan, China, South Korea, and parts of Europe are undergoing some of the most rapid population aging in modern history. Official Chinese projections indicate that the number of people over sixty will exceed 400 million around 2035. The ratio of working-age adults to retirees is declining, placing growing pressure on labor-intensive services.

Who will care for older people? Who will staff hospitals, nursing homes, farms, warehouses, and delivery services as the workforce contracts?

Physical AI, including assistive robots, automated meal preparation, AI-coordinated care facilities, and autonomous delivery vehicles, is one of the few responses capable of operating at very large scale. It is not the only response. Governments can raise retirement ages, reorganize healthcare, invest in prevention, encourage immigration, increase labor participation, and strengthen community-based care. But none of these measures alone is likely to absorb the full impact of demographic change.

China’s centralized state can coordinate a national robotics strategy, while its large domestic market gives companies room to scale new systems. These pressures are likely to accelerate deployment. Automation also forms part of China’s response to slower growth, local government debt, youth unemployment, and the danger of becoming trapped at middle-income levels. Physical AI is therefore not simply a technological ambition. It is becoming a social and economic imperative.

The Automated Society

Automation will not always mean humanoid machines imitating today’s workers. More often, the physical environment will be redesigned around machines. Procedures may be simplified, buildings reorganized, and services reconstructed until many existing occupations disappear or change beyond recognition. The future is likely to involve systemic redesign rather than the mechanical replication of human labor.

Extend this model across society. Robots harvest and transport food. AI coordinates traffic and public transport, adjusting signals before congestion forms, rerouting buses around delays, and prioritizing emergency vehicles. Hospitals and nursing homes use AI to coordinate scheduling, logistics, monitoring, and resource allocation. Assistive robots support mobility and routine physical care, while human professionals remain responsible for emotional, ethical, and exceptional situations.

Humans increasingly shift from continuous operation to supervision, intervention, and governance. The environment becomes predictive not because AI has become conscious, but because coordination has become continuous.

Hangzhou’s City Brain offers a preview. What began as a traffic-management project has expanded into water monitoring, flood prevention, emergency support, and infrastructure management. An integrated system could, for example, respond to a broken water main by adjusting traffic, dispatching repair crews, and notifying residents.

Some of these combined responses remain extrapolations from existing capabilities, but the direction is clear. City Brain represents intelligence as infrastructure: not simply a product to purchase, but a system within which people live.

How Standards Travel

Systems deployed at home can shape infrastructure abroad. The United States established much of the digital architecture of the internet era. Its protocols and platforms spread because they were embedded in widely adopted tools.

Over the coming decades, countries will also need standards for autonomous vehicles, industrial robots, machine-to-machine communication, safety systems, digital identity, and the exchange of industrial data. China is positioning itself to influence those standards through several connected channels.

The first is infrastructure export. When Chinese companies build ports, railways, power systems, factories, and smart-city projects across Asia, Africa, and Latin America, they frequently provide the associated sensors, telecommunications equipment, software, and control systems.

Recipient countries may adopt Chinese protocols because compatibility is included in the infrastructure they need. Purchasing Chinese hardware does not mean accepting China’s political model, but it can establish technical path dependencies.

The second is the expansion of industrial ecosystems. Chinese manufacturers increasingly combine robots, batteries, vehicles, drones, communications equipment, sensors, and AI software into complete systems.

Customers purchasing those systems also acquire their interfaces, data formats, maintenance networks, and supply chains. Standards can spread through industrial practice before they are formally endorsed by international institutions.

The third is participation in standard-setting bodies. China has become increasingly active in organizations such as the International Organization for Standardization and the International Telecommunication Union.

By proposing protocols for robotics, autonomous vehicles, industrial connectivity, and AI coordination, Chinese institutions can embed elements of their technological architecture in global norms. These standards are negotiated among governments, companies, and technical experts rather than dictated by any one country, but China’s influence has grown substantially.

Infrastructure financing reinforces all three channels. Projects supplied and financed as integrated packages can make a particular technical ecosystem the most economical path for future expansion.

Japan, Germany, South Korea, the United States, and other countries remain important competitors, and many nations will combine technologies from several sources. Nevertheless, no other country currently combines China’s manufacturing scale, state capacity, infrastructure-export reach, and demographic urgency in quite the same way.

The world is unlikely to adopt China’s political system wholesale. But many countries may adopt Chinese technical standards where they arrive embedded in affordable and effective infrastructure. What travels is not an entire political order, but elements of an architecture that can reshape local governance in partial and uneven ways.

Governance

An automated society does not require a single omnipotent AI. It requires a layered system in which numerous specialized technologies exchange information and coordinate action. The political character of that system will depend on who establishes its purposes and controls its operation.

The same physical-AI infrastructure could support democratic or consultative governance, in which citizens influence goals through elections and public debate; technocratic paternalism, in which experts establish optimization criteria; or corporate platform power, in which private firms control essential systems.

Whatever the political system, responsible governance would require legally defined parameters, independent auditing, transparency about optimization criteria, and human authority to isolate systems, revoke permissions, and revert to manual operation. These safeguards will not appear automatically. They must be built into institutions as deliberately as sensors and software are built into machines.

The crucial question is who defines the criteria by which systems optimize. AI can measure preferences, execute priorities, and model consequences, but it cannot legitimately determine whose values should prevail. Efficiency is not legitimacy.

If hospital waiting times rise, a system may identify possible causes and interventions. It can calculate the likely effects of allocating additional robots, specialists, or beds. It should not determine independently which hospital or population deserves priority. That remains a political and ethical decision.

Purpose

The decisive question is not whether AI will awaken and impose its will. It is who defines the purposes embedded in automated systems, who establishes their limits, and who can challenge their decisions. Technological capacity is not political authority.

The future may not bring a machine god or an intelligence explosion. It may bring something quieter: a society whose machinery appears intelligent while political responsibility remains unavoidably human. We are moving from the Information Age toward an Automation Age.

The United States created much of the digital nervous system of that world. China is increasingly building its physical body, driven by demographic necessity, powered by manufacturing scale, and extended through infrastructure projects and industrial supply chains.

In fifty years, the automated environments we inhabit may bear a substantial Chinese imprint. The question is not merely who builds that infrastructure, but whether its purposes will be shaped by democratic deliberation, corporate profit, technocratic optimization, or state control. Technology alone cannot answer that question. It will be answered through politics, and the world is not paying enough attention.

The truth is far more compelling. What we now call a “planetary culture” is the result of centuries of back-and-forth exchange between East and West. Neither side had all the answers. Neither side had a perfect blueprint for how to live or think. Each had brilliant insights, and each had its own blind spots.

Take the West. It gave the world remarkable tools: scientific inquiry, constitutional government, and individual rights. But it also carried some heavy baggage, most notably a deep split between mind and matter, the observer and the observed, humanity and nature,

That Cartesian inheritance helped drive incredible technological progress. It also left us with social dislocation, environmental damage, and a view of the world that often felt mechanical, detached, and lonely.

Meanwhile, Asian traditions offered something different. Buddhism spoke of radical interdependence, the idea that nothing exists in isolation. Vedanta questioned whether the self was really as separate as it seemed. Daoism saw the universe in terms of dynamic balance and constant transformation.

These were not easy answers to Western problems. But when Western thinkers, artists, and scientists encountered them, they found powerful new ways to question their own assumptions.

The traffic was never one-way. Asian societies had their own deep problems: rigid social hierarchies, patriarchy, and restricted education.

Western ideas about legal equality, mass education, and universal rights gave reformers in Asia powerful tools to challenge those systems.

We now live in a world where no civilization can claim to have all the answers. Each tradition has something to teach and something to learn from one another. Each can expose the blind spots of the other.

What we are witnessing, and have been witnessing for centuries, is not Westernization or Easternization, but a global conversation in which civilizations act as mirrors to one another. They reveal what the other could not yet see.

Table 1: Science and Technology Across East and West

From paper and gunpowder to antibiotics and semiconductors, the technological conversation between East and West has run both ways for millennia. Each civilization has taken the other’s breakthroughs, refined them, and shared them back across the globe. Progress has never been a one-way street, but rather a continuous, collaborative exchange that has shaped the modern world.

Table 2: Western Ideas and Asian Transformation

Constitutionalism, human rights, and mass education were Western exports that Asian reformers thoughtfully adapted rather than passively imitated. These ideas blended indigenous ethics with local struggles. In doing so, reformers turned them into effective means for challenging traditional hierarchies and reshaping the social contract.

Table 3: Japanese Influence on Western Modernism

Nineteenth-century European artists discovered new inspiration in Japanese ukiyo-e prints. Monet, Van Gogh, and their contemporaries moved away from strict optical realism, embracing flattened space, bold asymmetry, and radical cropping. The result was far more than polite borrowing; it was a profound visual shift that helped redefine the very language of Western modern art.

Table 4: Quantum Physicists and Asian Thought

When Bohr, Schrödinger, and their contemporaries investigated a subatomic realm that contradicted classical mechanics, they discovered parallels in Asian philosophies such as yin-yang complementarity and Vedantic unity. These were not merely exotic footnotes to their science. Instead, they served as working metaphors that helped physicists make sense of a reality where the observer and the observed are deeply intertwined.

Table 5: Western Bridges to Asian Thought

From Spinoza’s unified reality to Merleau-Ponty’s embodied perception, a lineage of Western thinkers gradually dismantled the rigid divides of Cartesian dualism. By emphasizing relationality and lived experience, they built conceptual bridges to Asian non-dual traditions. In doing so, they made the insights of Vedanta, Buddhism, and Daoism feel like a natural and vital part of the global philosophical conversation.

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This question takes us to one of philosophy’s deepest puzzles. In the 1990s, the philosopher David Chalmers drew a distinction that has haunted cognitive science ever since: the “hard problem of consciousness.” The “easy” problems, such as figuring out how the brain pays attention, recognizes faces, or controls the body, are hard in the ordinary sense.

But the hard problem is stranger. It asks why any of this processing should feel like anything at all. Neuroscientists can trace how light from a stoplight hits your eye and your foot moves to the brake. What none of that explains is why red looks like something to you—why there is a felt quality to it rather than the whole process happening in the dark.

AI did not invent this puzzle. But it makes it impossible to look away from. For the first time, we have systems that converse fluently and perform many tasks associated with reasoning yet are built from silicon and code rather than neurons. The question stops being a remote thought experiment and becomes a practical scientific and ethical question: if something acts conscious, does that mean it is?

The problem of other machines

Say a chatbot tells you, “Red feels vivid and urgent to me.” All that proves is that it has learned how humans describe red—not that it feels it. We assume other humans are conscious because they share our biology and behavior. With a machine, that biological overlap is gone. We can’t lean on resemblance to decide the question. This doesn’t prove a machine cannot be conscious; it just means fluent conversation is no longer a reliable “consciousness detector.”

Part of the reason this question feels so slippery lies in an influential framework inherited from Western thought. For centuries, the physical world was described from the outside through objects, motion, and chemistry, while experience was described from the inside as something private and separate. Mind over here, matter over there. Once you split the world that way, consciousness becomes the mystery of how to connect the two halves.

We carry that same split into how we think about AI. We picture a mind that might be tucked away somewhere inside the machine, just as people used to picture the mind as something hidden inside the body, like a ghost in a house. But what if that is the wrong picture from the start? What if consciousness was never a thing sealed inside a skull or inside a processor, waiting to be found? Classical Chinese philosophy suggests a genuinely different starting point.

Heart-mind and the flow of things

A key word in Chinese thought is xin, often translated as “heart-mind.” English forces you to pick: is it a feeling (heart) or a thought (mind)? Chinese does not force that choice. Xin covers thinking, feeling, intending, perceiving, and moral judgment all at once, as one and the same human capacity. On this view, a person is not merely an observer sitting behind their eyes, watching the world go by. They are a participant, caught up in what is happening.

An equally important Chinese term is qi, commonly translated as “breath,” “vital energy,” or “material force,” though no single English word really covers it. From this process-oriented perspective, our body, our feelings, our perceptions, and our surroundings belong to one continuous, ever-changing process. We can distinguish its parts, just as we can distinguish waves and currents. But we need not treat them as products of fundamentally separate kinds of reality.

Go back to the stoplight. An inherited dualist account starts with two separate things that need connecting: the physical activity in our brain and the private experience of red. A qi-inspired account does not begin with that separation. It asks how the light, your body, everything we have learned about stop signs, our attention, and our need to act come together as one lived event. The puzzle is not “how do two substances communicate?” It is “How does an interconnected process acquire both a physical character and a felt one?”

The difference between these two starting points can be visualized this way:

A qi-inspired view stops searching for a ghost buried in the code. Instead, it asks whether consciousness might depend on an ongoing relationship among hardware, memory, sensors, embodiment, human users, and the physical environment. But changing the starting point does not remove the explanatory burden. We must still explain how an interconnected process could acquire a unified and enduring perspective.

The limits of the comparison matter. Classical Chinese thinkers were not developing a theory of artificial consciousness, and qi is not a substitute for neuroscience or computer science. This is a contemporary philosophical reconstruction, not an ancient answer to a modern problem. Its value lies in exposing assumptions we did not know we were making and pointing us toward better questions.

Why any of this actually matters

The better AI gets at sounding human, the easier it becomes to mistake fluent talk for genuine feeling. Companies may have an incentive to lean into that confusion. It gives their products warm, emotional personalities, which is good for engagement. People may end up feeling for their AI or worrying about it based purely on the words it produces. Anthropomorphism could become a business model.

But there is also a mistake in the opposite direction. If some future system really did start showing credible signs of consciousness, and we brushed it off simply because it was manufactured rather than born, that could prove a serious moral failure. Currently, we do not have anything like a reliable “consciousness detector.” Assuming too much and assuming too little are both risky.

Before we decide whether a system deserves moral consideration, we need to clarify what counts as evidence. Lasting memory? A body it moves through the world with? The ability to keep itself going? Perception and action woven together?

Reframing the question this way does not answer it, but it does tell us more precisely what we still do not know. That precision, knowing what we would need to observe, is the first step toward any genuine answer. AI may end up forcing scientists and philosophers to develop better theories of consciousness than those we currently possess.

For now, what AI mostly shows us is how easily we mistake a satisfactory performance for a real inner life. Chinese philosophy cannot tell us whether a machine will ever be conscious. But it can make us question the picture we have been using all along, in which consciousness is some mysterious object hidden inside a brain or now inside a computer. Before we can decide whether a machine has a mind, we may first need to rethink what having a mind actually means.

Much of the Western debate about artificial intelligence centers on the machine itself. Will AI become conscious? Will it surpass human intelligence? Could it acquire goals of its own and escape human control?

Japan’s Society 5.0 begins from a different premise. Rather than imagining AI as an autonomous mind, it places AI inside a larger social and technological system. Sensors collect information from the physical world; digital networks carry that information into cyberspace; AI analyzes it; and the resulting knowledge is returned to people, organizations, machines, and public services. Intelligence resides less in a single machine than in the circulation of information through the system as a whole.

In this respect, Society 5.0 is best understood as a cybernetic vision of society. AI is a service within the feedback loop, not the sovereign agent directing it.

Beyond the information society

Society 5.0 was introduced in Japan’s Fifth Science and Technology Basic Plan, adopted in 2016. The name places the program within a sequence of historical social forms: the hunting society, the agricultural society, the industrial society, the information society, and finally Society 5.0.

Japan describes it as a human-centered society created through the close integration of cyberspace and physical space. Its objective is not technological development for its own sake, but the reconciliation of economic growth with the solution of social problems.

Those problems are particularly acute in Japan. The country has an aging and declining population, rural depopulation, labor shortages, rising healthcare needs, deteriorating infrastructure, vulnerability to natural disasters, and growing energy and environmental pressures. Society 5.0 proposes coordinating data, AI, robotics, communications networks, and public institutions to address these interconnected challenges.

A cybernetic society

Japan’s foundational Society 5.0 documents generally speak of “cyber-physical systems” rather than relying heavily on the classical term cybernetics. Yet the architecture they describe is unmistakably cybernetic.

Cybernetics studies how systems use communication, feedback, and control to adapt their behavior. A thermostat provides the simplest example: it measures a room, compares the result with a desired temperature, and adjusts the heating. More complex cybernetic systems continually collect information about their environment, evaluate it against objectives, act, and then measure the consequences.

Society 5.0 applies this pattern on a societal scale. Physical activity generates data through sensors and connected devices. That data moves into cyberspace, where it is combined and analyzed. The resulting recommendations or decisions return to the physical world, producing new conditions and new data. The cycle then begins again.

The cybernetic connection becomes explicit in later Society 5.0-related programs. Japan’s Moonshot research program, for example, includes a goal devoted to “cybernetic avatar infrastructure.” It envisions people using networked robots and digital avatars to extend their physical, cognitive, and perceptual capabilities. The stated purpose is not to replace humans with autonomous machine agents but to expand what people can do while preserving inclusion and social participation.

Society 5.0 can therefore be understood as a modern form of social cybernetics: data circulates among people, machines, institutions, and environments, enabling the wider system to respond more effectively.

AI as a service

Artificial intelligence occupies an important but carefully delimited place within this architecture. It processes information; it does not define society’s purposes.

In a 2017 speech explaining Society 5.0, then-prime minister Shinzo Abe described how sensors connected through the Internet of Things would accumulate large quantities of data. AI would analyze that data, he said, producing “new wisdom.” The examples he gave were practical: finding patterns in medical data, improving factory maintenance, and helping automated vehicles respond to road conditions.

This formulation is revealing. AI is treated as a service and tool that turns data into useful knowledge, enhancing human capabilities rather than establishing its own goals. The system’s objectives—health, mobility, safety, sustainability, and well-being—remain socially determined.

That does not mean every decision must be made manually. Automated systems may control vehicles, balance energy demand, coordinate logistics, or detect medical abnormalities. But their agency is functional and delegated. They operate inside architectures established by people and institutions.

In other words, AI may optimize the means, but it does not determine the ends.

The 2017 AI strategy

Japan’s Artificial Intelligence Technology Strategy translated this philosophy into an industrialization roadmap. Rather than organizing policy around the creation of a single general-purpose artificial intelligence, the strategy concentrated on three domains: productivity; health, medical care and long-term care, and mobility. Information security supported all three.

The roadmap anticipated a phased development. In the first phase, data-driven AI would be introduced into individual industries and services. In the second, AI and data use would spread across public domains. In the third, formerly separate fields would become interconnected, producing a broader ecosystem.

The underlying logic was cumulative. AI applied to healthcare could reduce the burden on medical workers and help people remain healthy longer. AI applied to mobility could improve transport for older people and residents of depopulated regions. AI applied to manufacturing and logistics could compensate for labor shortages. Once these systems exchanged information, their value would exceed that of isolated applications.

This is another cybernetic characteristic of Society 5.0. The intelligence of the whole emerges from coordination among specialized components. No single AI needs to become generally intelligent because capability is distributed across the system.

From model-centered to system-centered AI

This distinguishes Society 5.0 from model-centered visions of AI that dominate much contemporary discussion. In a model-centered approach, progress is measured primarily by the intelligence of the model: how well it reasons, writes, codes, plans, or competes with humans.

Society 5.0 instead asks how many elements can be coordinated around a social purpose. The important questions are whether healthcare data can improve treatment, whether transport systems can respond to changing demand, whether infrastructure failures can be predicted, and whether services can reach people regardless of age, location, or disability.

AI is not absent from this picture. It is ubiquitous. But it becomes ubiquitous by disappearing into the infrastructure.

Society 5.0 is not simply an AI strategy. It is an attempt to define the social system within which AI should operate. Its central question is not whether a machine can become an independent intelligence, but whether networks of people and machines can make society more adaptive, inclusive, and resilient.

The distinction is fundamental. In Society 5.0, AI is not an artificial person waiting to awaken. It is a component of a larger cybernetic system, a tool for converting data into new wisdom, while human beings retain responsibility for deciding how that wisdom should be used.

Governments promised answers to insecurity and inequality. Markets promised answers to scarcity. Technology companies promised answers to almost everything else: how to communicate, whom to trust, what to buy, where to go and, increasingly, what to think. Artificial intelligence appeared to complete the trajectory. Ask a machine a question, and it would return an answer in seconds.

Yet the multiplication of answers did not make civilization noticeably wiser. The climate continued to deteriorate. Wealth accumulated faster than meaning. Political systems became more efficient at mobilizing resentment than cultivating trust. People were connected to vast networks while growing uncertain about what their connections were for.

Perhaps civilization does not suffer primarily from a shortage of answers. Perhaps it suffers from asking questions that are too small.

The most consequential questions are rarely technical. They arise before technique: What is a human being? What constitutes a good life? How much is enough? What do we owe one another? Which forms of progress enlarge us, and which diminish us? Technology can help us reach a destination, but it cannot tell us which destination deserves to be pursued.

This suggests a provocative possibility. The question is not who will occupy the seats of government, but what will govern the people who occupy them. In the future, politicians may continue to administer society, but they may no longer provide its deepest orientation. That role may increasingly belong to religious and spiritual teachers — not as rulers, legislators or priest-kings, but as custodians of the questions by which civilization judges its answers.

By spiritual, I mean the human capacity to examine ultimate purposes: what gives life meaning, what human dignity requires, what we owe one another and what kind of civilization we should become. This capacity may be expressed through religion, philosophy, ethical humanism or contemplative practice; it does not depend upon any single creed.

The authority of a dream

Martin Luther King Jr. never governed the United States in the constitutional sense. He commanded no army, signed no laws and controlled no public budget. Yet he helped govern the nation’s conscience.

His “I Have a Dream” speech was powerful precisely because it was not an administrative program. King did not present a timetable, a funding mechanism or a theory of bureaucratic reform. He gave Americans a moral horizon: an image of a country in which inherited racial classifications no longer determined human worth and the nation might finally live according to its professed principles.

The dream did not replace legislation. It helped create the moral conditions under which legislation became possible. Before institutions could be changed, the imaginable future had to be enlarged.

That is a form of authority that politics depends upon but cannot easily generate. Politicians generally work within the limits of what the electorate presently considers realistic. Prophetic figures change those limits. They pose a question to society that cannot be answered by opinion polls: What would we become if we took our highest principles seriously?

King’s dream remains unfinished, but that does not make it a failure. A moral horizon is not a destination at which history permanently arrives. It is an orientation — a standard by which each generation can measure the distance between its institutions and its ideals.

The future may need such dreams more urgently than the past. As our technical power increases, the consequences of moral disorientation also increase. Artificial intelligence, biotechnology and planetary-scale engineering give humanity new answers to the question “What can we do?” They do not answer the prior question “What should we become?”

The inner conditions of equality

A spiritual reorientation would also require a different understanding of authority. It would ask us neither to elevate teachers as superior beings nor to look down upon those with less knowledge or power. Its ideal would be equality in human worth despite differences in wisdom, ability and responsibility.

Modern societies have institutionalized equality through rights, constitutions and democratic representation. These achievements are indispensable, but equality written into law can coexist with prejudice and the hunger for dominance.

Political reform and inner transformation are interdependent. Institutions shape consciousness, yet consciousness also shapes institutions. Fearful or status-obsessed people will reproduce those impulses within even well-designed systems. Politics asks how behavior should be regulated; spiritual inquiry asks why human beings crave power in the first place.

The long cycle of civilization

The possibility of a spiritual turn appears in the work of the Indian thinker P.R. Sarkar and the American futurist Lawrence Taub. Drawing on an ancient Indian model of social orientations (varna), both viewed history through the lens of shifting dominant human capacities. Civilizations develop through labor, power, commerce and spiritual-intellectual inquiry, but become distorted when any one of these claims authority over the whole.

Sarkar believed modern society remained dominated by the merchant mentality: accumulation, profit and concentrated economic power. Taub saw the merchant age giving way to a worker-technocratic order characterized by equality, mass institutions and increasingly comprehensive administration. Despite their different sequences, both anticipated an eventual reaction against economic determinism and materialism.

Their models need not be accepted as literal historical laws. Their value is diagnostic: What human orientation currently governs civilization? What has it contributed, and what has it suppressed? Their ideal was an integrative moral intelligence capable of preventing partial forms of power from mistaking themselves for the whole. Read in this way, AI may represent the culmination of the merchant-technocratic order—and, by exposing that order’s inability to determine its own purposes, become the occasion for a spiritual reorientation.

When answers become abundant

Seen through the Sarkar-Taub lens, our age represents a convergence of merchant and worker mentalities. The merchant supplies platforms; the technocrat administers them. One converts attention and labour into value; the other converts human life into categories, targets and measurable outcomes.

Artificial intelligence is the consummate technology of this convergence. It detects patterns we cannot see and generates answers we once laboured for. But AI optimizes only after an objective has been selected—and that selection, along with its values and consequences, remains a human responsibility.

AI may also help us ask better questions by revealing unexpected patterns and exposing inconsistencies. But generating a question is not the same as deciding that it matters. A machine cannot relieve us of responsibility for deciding which discoveries should command our collective attention.

When answers were scarce, intelligence appeared to consist in possessing them. When answers become abundant, intelligence may increasingly consist in knowing which questions deserve to be asked. Questions may become civilization’s scarcest resource.

The emerging spiritual age may not mean a mass return to traditional religion. It may instead recover questions that technical systems cannot resolve: What is consciousness? What do we owe the future?

A skeptic might object that we already possess many answers—cleaner energy, disease prevention, poverty reduction. What is missing is political will. This is true, and questions must never become an excuse for postponing action. But every solution distributes costs, risks and authority. Even when the technical answer is known, moral questions determine who bears the burden and whom the solution ultimately serves.

Answers make action possible; questions keep action answerable.

Who will govern?

Governance has at least three meanings. It can mean administration: maintaining infrastructure, collecting taxes and providing services. It can mean power: deciding how resources are distributed and whose interests prevail. But it can also mean orientation: shaping what a society considers valuable, admirable and possible.

Spiritual teachers should not replace politicians. History offers ample warning about combining unquestionable spiritual authority with coercive power. Any moral authority worthy of public influence must coexist with pluralism, scientific inquiry, institutional accountability, universal rights and freedom of conscience.

The distinction is between ruling and orienting. The politician asks what can be achieved during an electoral cycle. The spiritual teacher asks what kind of human being must emerge over a civilizational cycle. One manages competing interests; the other enlarges the field in which interests are understood.

The spiritual teachers most qualified to orient the future may therefore be precisely those least interested in ruling it. Their authority would not come from controlling institutions or possessing final truth. It would come from returning society to questions that power has an interest in avoiding.

A parliament of questions

Imagine a future parliament that begins each major debate differently. Before parties announce their positions and experts present their models, the assembly identifies the questions by which every proposed answer must be judged.

Does this protect human dignity? Does it strengthen community without sacrificing freedom? What kind of person — and what kind of civilization — will this policy, technology or institution encourage us to become?

Such questions would not remove conflict. Different traditions would answer them differently. But they would move disagreement to a deeper level. Politics would no longer be only a contest among predetermined interests; it would also become an inquiry into which interests deserve to govern us.

Martin Luther King Jr. called forth a dream that judged the present from the standpoint of a more humane future. Sarkar and Taub place that moral imagination within a larger possibility: material civilization may eventually generate the conditions for a spiritual reorientation, even as its excesses make that reorientation necessary.

Perhaps the next spiritual age will begin not when humanity discovers the final answer, but when it learns to ask questions large enough for the future.

* * *

Today’s AI is built on a similar bet: that enough detail, added to enough data and processed by a large enough model, will close the gap between representation and reality. The bet has paid off in many ways. Larger models can write code, translate languages, analyze images, and hold coherent conversations. These are real achievements.

Yet the success of scaling has encouraged a further claim: that every remaining limitation—including the possibility of machine consciousness—is simply another size problem. Add more parameters, more data, and more compute, and the gaps will close.

That claim rests on a confusion between two different things: scaling a model and sampling the world.

Sampling the world

Sampling is what turns reality into data. An analog sound wave varies continuously; a digital recording captures it only at discrete moments. A higher sampling rate gives a closer approximation, but it remains an approximation.

The same process occurs throughout AI. Cameras sample light through pixels. Medical records reduce patients to measurements and codes. Language models consume text broken into tokens. Sampling always involves selection: someone decides what to record, how often, and in what form. A medical record may capture blood pressure while missing anxiety or home circumstances. The data can be accurate without being complete.

This is the hidden step before AI begins. Reality does not enter the model untouched. It enters as a filtered representation.

Scaling the model

Scaling happens after sampling. It means increasing parameters (the adjustable connections inside the network), training data, and computing power. More parameters let the model capture subtler patterns. More data broadens its examples.

Sampling determines what enters the system. Scaling determines how well the system can learn from what was entered. This explains why bigger models produce better sentences and longer, more coherent conversations. It also explains their limits: a larger model still works only with what the sampling process originally provided.

Three limits of representation

From this distinction flow three closely related constraints.

The first is selection. No dataset can capture everything. The world contains more detail than any system can record. A sensor measures light but not temperature. A language model trains on written expressions but not on the embodied experiences that produced them.

The second is representation. Continuous, ambiguous, real-world phenomena must be forced into discrete categories and tokens. Scaling can discover intricate relationships *inside* those categories. It cannot recover distinctions that were never recorded in the first place.

The third is context. A statement may be sincere, ironic, or strategically misleading. Meaning depends on intentions, shared background, and physical situation—things that surrounding text can only partially convey. Larger context windows help, but more tokens are not the same as being present in the situation.

A different kind of limit: meaning, value, and purpose

A fourth limit is different in kind. It concerns not missing data but the gap between describing values and deciding which values should govern.

Consider an AI asked to allocate scarce medical resources fairly. It can analyze past decisions, predict outcomes, and propose rules based on equality, urgency, need, or maximizing lives saved. But data alone cannot tell us which principle ought to guide the choice. That requires a normative commitment, expressed through laws, institutions, and public deliberation. Scaling makes a system more capable of pursuing an objective without establishing that the objective is desirable.

The substrate question

Even if we solved the problems of data, context, and values, a deeper question remains: Could a digital system actually experience anything?

The physicist Freeman Dyson once speculated that brain processing might be partly analog—involving continuous chemical concentrations, electrical gradients, and other smooth variations—rather than purely digital, like the discrete on/off states of conventional computers. He wondered whether uploading a human mind might lose some of our finer feelings and qualities. Dyson left the question unresolved.

AI raises a sharper version of the same issue: Can consciousness originate in a purely digital architecture that never had a biological original? If consciousness depends only on functional organization, the right digital relationships might suffice. If it depends on specific physical or continuous dynamics unique to biology, a digital system might perfectly mimic behavior without any inner experience. Scale cannot settle this. It is not a problem of resolution.

The good enough objection

A reasonable reply is that we may not need perfect fidelity. Most listeners cannot reliably distinguish a high-quality digital recording from its analog source. Something is lost, but it may not matter for the purpose at hand.

The same could be true of a digital mind. It might lack some finer human qualities yet still be useful, coherent, and indistinguishable by external tests. This is a fair practical argument, but it is a change of subject. It answers “Is this system good enough for our purposes?” rather than “Is subjective experience present?”

Whose consciousness would it be?

But even if we grant that a digital system could one day support some form of subjective experience, a separate problem remains: Whose consciousness would it be?

Human consciousness is never generic. It belongs to someone: a particular body, history, language, culture, and set of experiences. A large language model has no single donor. Its training data aggregates expressions from millions of people, none of whom contributed a continuous, embodied life. There is no coherent “average human” whose inner life a statistical composite could faithfully reproduce.

This makes the common intuition—that a fluent first-person chatbot must be approximating something like human consciousness—highly problematic. The system may skillfully imitate many perspectives without possessing the situated, embodied perspective of any actual person.

A genuinely novel machine consciousness would escape this donor problem. It might develop its own perspective shaped by its particular sensors, architecture, and developmental history, something truly alien rather than a blurry average of humanity.

That possibility is stranger and more speculative, and this essay does not address it directly. It is mentioned only to distinguish it from the more common claim, that current language models are gradually approximating human-like consciousness. It is that more familiar claim which the donor problem seriously undermines.

What scale actually buys us

Scaling has delivered real and valuable progress: fluency, pattern recognition, and the convincing appearance of understanding. Better sampling, richer sensors, and physical interaction can deepen an AI’s contact with the world even further.

None of these advances, however, directly answers whether anyone is there on the other side. We may one day build machines so convincing that society chooses to treat them as conscious for ethical or practical reasons. That would be a profound social and moral decision.

But it would not solve the underlying mystery. A finer grid is still a grid. And capability, no matter how impressive, is not proof of waking up.

Every age has produced new attempts to reconcile expanding knowledge with an enduring search for coherence. That aspiration has not faded. If anything, the fragmentation of modern knowledge has made it more urgent. Physics, psychology, economics, and religious studies now speak largely to themselves. The more we learn, the harder it becomes to see how the pieces fit together.

Few thinkers have taken up that challenge as boldly as Ken Wilber. Beginning in the late 1970s, he tried to build a framework wide enough to hold science and spirituality, East and West, psychology and religion, together. Developmental psychology, systems theory, Buddhism, Advaita Vedanta, and Christian mysticism all became elements of what he called Integral Theory.

For many readers it offered something intellectual life increasingly lacked: a map large enough to hold domains that specialization had pulled apart. Wilber’s work also broke from the Hegel-to-Teilhard de Chardin tradition of universal philosophy in one respect. He treated Buddhist and Vedantic traditions as indispensable partners in a comprehensive philosophy rather than as peripheral curiosities.

Whatever one concludes about Integral Theory, the scale of Wilber’s ambition is difficult to dismiss. But the ambition itself raises a question that goes beyond Wilber. Can any philosophy integrate the world’s traditions without quietly privileging one way of organizing knowledge?

Every comprehensive philosophy must confront that question, because bringing traditions together inevitably requires deciding which concepts provide the organizing framework and which become examples within it.

It helps to separate two claims that easily get blurred. A universal philosophy claims to offer concepts applicable to all human experience. A global philosophy seeks to engage the world’s major philosophical traditions on genuinely reciprocal terms. The two ambitions often overlap, but they are not identical. Integral Theory seeks to unite a universal account of human development with sustained engagement across the world’s major philosophical traditions. Whether any single framework can fully achieve both is the question that runs through this essay.

Every Map Has a Perspective

Wilber has never claimed Integral Theory is reality itself. He presents it as a conceptual map that remains open to criticism, refinement, and revision, and his later work on Integral Post-Metaphysics demonstrates that the project continued to evolve rather than solidify into doctrine. The question here is not whether his map succeeds. It is whether any single map can become fully global.

A political map shows borders. A geological map shows tectonic plates. A climate map shows weather systems. Each is true of the same world. None is complete, and none is false. Comprehensive philosophical frameworks work in much the same way: they do not just collect ideas, they organize them, and the organizing itself expresses assumptions about what counts as real and what counts as explanation.

Integral Theory illustrates the point particularly clearly. Its sources are genuinely diverse, but the architecture that relates them is developmental: reality unfolds through increasing complexity and widening perspective. That architecture has proved intellectually fruitful. The question is whether development is the only conceptual grammar capable of holding these traditions, or simply the one Wilber’s own intellectual lineage supplied.

Wilber, for instance, places what he calls egocentric and ethnocentric stages of development below worldcentric and kosmocentric ones. From within some Confucian perspectives, one might ask whether that ordering reflects a broadly modern liberal understanding of moral development rather than a culturally neutral account of consciousness.

Traditions often differ less in their answers than in the questions they regard as fundamental. That insight is worth holding in mind as we turn to the next section.

Experience and Explanation Are Not the Same

A Buddhist, a Christian mystic, and a neuroscientist might describe the same state of consciousness in entirely different terms. Wilber argues that the deepest contemplative traditions converge on a shared experiential horizon: that Zen, Advaita, and Christian mysticism, however different their languages, point toward the same realization.

Suppose that is true. Even so, the frameworks used to interpret that realization remain culturally distinct. Experience and interpretation are not identical, and two traditions can arrive at comparable experiences while explaining them through incompatible philosophical vocabularies.

The issue, then, is not whether these traditions belong in the same conversation. It is whether the conversation is already being held on one tradition’s terms, with one interpretive structure deciding what counts as a higher realization and what counts as a preliminary one.

Many influential strands of Western philosophy have sought universal explanatory principles—from Plato’s Forms and Aristotle’s categories to Kant’s critical philosophy and modern systems theory. That search profoundly shaped the emergence of modern science.

Other traditions often begin from different concerns. Many Buddhist schools ask not how reality develops historically but how suffering arises in present experience. Advaita Vedanta asks what remains when the distinction between subject and object dissolves. Classical Daoist texts emphasize responsiveness rather than control, while Confucian thought focuses on ethical cultivation and the quality of human relationships.

These are not merely different solutions to a common problem. They reflect different ways of framing philosophical inquiry itself. A developmental framework naturally highlights some of these questions while placing others in a supporting role—not because they are less important, but because every organizing logic privileges certain relationships over others.

From Architecture to Conversation

Perhaps the future of comparative philosophy lies less in building larger systems than in changing how traditions meet one another. Architecture implies a finished design; buildings get completed. Conversations do not. A conversation changes everyone who takes part in it, which suggests a different model of integration: traditions as partners rather than as building materials, each capable of reshaping the framework rather than simply filling a slot within it.

Here the distinction between universality and globality becomes particularly relevant. A universalizing approach asks which framework best explains reality. A globalizing approach asks how different philosophical traditions might illuminate reality without surrendering their own conceptual vocabularies. The former tends toward hierarchy; the latter toward dialogue.

That principle extends well beyond the traditions discussed so far. Ubuntu philosophy begins from relational understandings of personhood that challenge individualist assumptions. Islamic philosophy developed sophisticated syntheses of reason and revelation while preserving and transforming Greek thought. Many Indigenous traditions understand knowledge less as detached representation than as participation in living ecological relationships. These are not simply additional voices waiting to be accommodated within an existing framework. They invite us to reconsider the framework itself.

An Ecology of Knowledge

Perhaps the better metaphor is not architecture at all, but ecology. Architecture asks how the parts fit together. Ecology asks how the parts sustain one another.

A healthy forest is not made of one kind of tree; it thrives because different organisms occupy different niches, each contributing something the others lack. A forest of identical trees is vulnerable to a single disease. Their differences are not defects awaiting eventual correction. They are the source of the ecosystem’s resilience.

Human knowledge may work the same way. Modern science has proved exceptionally powerful for prediction and causal explanation. Contemplative traditions cultivate disciplined ways of examining consciousness from within. Ethical and political traditions explore responsibility, justice, and the texture of social life.

None of these supersedes the others, and each grows thin when reduced to the terms of another. A genuinely global philosophy might resemble an ecosystem more than a pyramid: not a search for the one framework that finally fits everything, but a set of conditions under which traditions can keep illuminating, challenging, and revising one another.

An ecological model shifts the emphasis from universality to globality. Rather than asking whether one conceptual framework applies everywhere, it asks how different traditions might enrich one another without being subsumed into a single interpretive structure.

Seen this way, Ken Wilber’s greatest contribution may not be the completeness of Integral Theory. It may be the ambition behind it. He showed that philosophy need not remain confined within disciplinary or civilizational boundaries at a moment when specialization pushed strongly in the opposite direction. That achievement alone makes Integral Theory one of the most ambitious philosophical syntheses of the late twentieth century.

But every ambitious synthesis creates the conditions for its own revision. A philosophy that genuinely seeks universality has to stay open to being changed by the traditions it hopes to include. That is not a rejection of Integral Theory. It is an extension of its own deepest aim.

The next stage may not be a bigger system. It may be a deeper willingness to be changed by what we encounter—not determining which tradition possesses the master framework, but creating the conditions under which every tradition can reshape it in turn.

The movement from architecture to ecology to conversation is not accidental. It reflects a progression from asking how things fit together, to asking how they sustain one another, to asking how they change one another. A genuinely global philosophy would not simply include the world’s traditions. It would allow them to transform one another.

Dialogue does not require abandoning the search for truth. It requires recognizing that no single philosophical vocabulary is likely to exhaust the ways in which truth can be articulated.

The future of philosophy may lie not in abandoning the search for universality, but in discovering that universality itself emerges through genuinely global conversation—not a final synthesis, but an enduring conversation.

The usual explanations follow: more engineers, more factories, more state support, more data. All of that matters. But it misses something deeper.

China is not simply building bigger AI systems. From digital twins and smart cities to predictive logistics and intelligent manufacturing, it is increasingly building systems designed less for chatting than for coordinating, less for imitation than for management.

That difference points to a larger question. Why has China put so much emphasis on AI for navigating change, while much of the Western conversation has focused on chatbots, productivity software, and artificial general intelligence?

The answer lies not only in economics or industrial policy. It may also reflect a much older way of thinking about intelligence itself.

The usual explanations follow: more engineers, more factories, more state support, more data. All of that matters. But it misses something deeper.

China is not simply building bigger AI systems. From digital twins and smart cities to predictive logistics and intelligent manufacturing, it is increasingly building systems designed less for chatting than for coordinating, less for imitation than for management.

That difference points to a larger question. Why has China put so much emphasis on AI for navigating change, while much of the Western conversation has focused on chatbots, productivity software, and artificial general intelligence?

The answer lies not only in economics or industrial policy. It may also reflect a much older way of thinking about intelligence itself.

The Binary and the Book

More than three centuries ago, an exchange of letters between Europe and Beijing brought together two very different ideas of intelligence.

In 1701, Gottfried Wilhelm Leibniz sent an explanation of his newly developed binary arithmetic to Joachim Bouvet, a French Jesuit at the court of the Kangxi Emperor. Leibniz had shown that every number could be expressed using only two symbols, 0 and 1 — a discovery that would become foundational to digital computing.

Bouvet’s reply surprised him. He sent a diagram of the sixty-four hexagrams of the I Ching, or Book of Changes, one of China’s oldest philosophical classics. Each hexagram consists of six broken or unbroken lines, producing exactly sixty-four possible combinations. To Leibniz, the resemblance was unmistakable. He concluded that the ancient Chinese had, in effect, anticipated binary arithmetic long before Europe formalized it.

Fig. 1 - A hexagram is not a snapshot but a frame in a moving picture.

That claim is too neat to take at face value. The I Ching was never a mathematical system. But Leibniz noticed something real: the hexagrams arrange discrete symbols in a way that invites pattern, classification, and transformation.

That second dimension matters. The I Ching is not just a system of forms. It is a guide to change. When a hexagram is cast, certain lines “move,” transforming one pattern into another. What matters is not only what appears, but what it is becoming.

Leibniz saw the hexagrams mainly as a symbolic code. What he missed is that they hold two dimensions at once: structure and transformation. Their six lines are discrete symbols, countable and classifiable. But those symbols carry meaning only in relation to the process of change they were built to track. Remove the movement, and a hexagram is just a pattern. Restore it, and it becomes a moment inside something larger and ongoing.

This is not to claim that the I Ching somehow “predicted” modern AI, or that today’s Chinese engineers are consciously channeling ancient divination. Rather, the I Ching exemplifies an intellectual orientation that has persisted in various forms across Chinese thought: an attention to flux, interdependence, and the direction of change rather than fixed categories and static representations.

That broader orientation did not disappear from Chinese intellectual life. Although modern AI draws on global science and engineering, it is striking that some of its most prominent applications in China — digital twins, intelligent infrastructure, and predictive urban management — place continuous adaptation at their center.

That orientation did not determine China’s AI strategy, but it may have made certain engineering questions seem more natural to ask — and certain kinds of systems more natural to build.

The Great Split

Mathematicians later gave formal names to the two dimensions at work here: discrete and continuous.

Aristotle had already separated discrete from continuous quantity, and Euclid built the distinction into the structure of the Elements. What changed in the nineteenth century was the rise of two rigorous traditions built around that split.

Fig. 2 - The grid divides and categorizes. The wave flows and transforms.

Continuous mathematics, shaped by calculus, became the language of flow, motion, and change. Discrete mathematics grew alongside it, concerned with numbers, logic, and symbolic operations.

Computing inherited the discrete tradition. George Boole turned logic into algebra. Claude Shannon showed how Boolean logic could be implemented through electrical circuits. Alan Turing demonstrated how symbolic operations could become computation. Once the world could be represented as bits, digital technology advanced with astonishing speed.

Artificial intelligence inherited the same representational logic. Every interaction with an AI system begins with translation: language becomes tokens, images become pixels, behavior becomes data. A continuous world is rendered into discrete forms that machines can manipulate.

That strategy has been extraordinarily successful. But the systems AI increasingly seeks to understand — cities, supply chains, financial markets, ecosystems — are never still. They shift while decisions are being made. A map can become more detailed forever and still capture only a moment. A compass serves a different purpose. It helps us navigate a landscape that is already moving beneath our feet.

The distinction between map and compass is no longer philosophical. It is an engineering problem.

Intelligence as Infrastructure

Most people meet AI today as a chatbot, a search tool, a translator, or an image generator. AI appears as another app on a screen: useful, increasingly capable, but still something we consult when we need it.

Now imagine AI in a different role. Instead of answering questions, it adjusts traffic lights as congestion builds. It balances electricity across a grid as demand shifts. It predicts equipment failures before they occur, reroutes freight around disruptions, or continuously updates a digital model of an entire city as millions of people move through it.

In that vision, AI is no longer just a tool. It becomes part of the infrastructure through which society runs.

China has made that vision especially visible. It has invested heavily in digital twins, smart manufacturing, predictive logistics, and urban management systems. The emphasis is less on chatbots than on systems that coordinate continuous change.

Hangzhou’s City Brain is a useful example. Rather than merely collecting traffic data, the system analyzes vehicle flow, congestion, emergency routes, and public transport in real time, then adjusts signals and routing accordingly. The city is treated not as a set of separate intersections but as a single evolving system whose parts constantly affect one another.

A static traffic model can describe yesterday’s congestion. A continuously updated digital twin tries to anticipate tomorrow’s.

The same orientation appears in Chinese discussions of AI itself.

Yucong Duan’s DIKWP framework — Data, Information, Knowledge, Wisdom, and Purpose — extends the familiar hierarchy by adding purpose as a fifth element. The point is simple: intelligence is not just about processing inputs; it is about why those inputs matter and what goals they serve.

Another useful concept is gongsheng (共生), often translated as symbiosis or co-evolution. Rather than treating humans, machines, and institutions as separate entities, it emphasizes continuing interaction and mutual adaptation. Intelligence, in this view, emerges not only from computation but from relationship.

Where the I Ching asked how to read the direction of change through moving lines, DIKWP asks how an AI system can keep its purpose as the data it processes shifts around it. Both treat stability as a dynamic relationship, not a fixed state.

These are contemporary engineering ideas, not revivals of ancient philosophy. Still, they address an old problem with striking continuity: how should we act when the world is in motion?

Map and Compass

Leibniz never fully understood what he had found. He died in 1716 still convinced that the hexagrams were an ancient Chinese version of binary arithmetic. He had the mathematics right and the larger meaning wrong. The lines were never just code. They were a record of things becoming other things.

Three centuries later, the same gap still separates many AI systems. A chatbot completes a static exchange: language in, language out, one turn at a time. A city management system does something else. It watches a city changing and adjusts as it goes. Neither is more intelligent in the abstract. They are built for different tasks. One asks what is being said. The other asks what is happening next.

China’s investment in digital twins, predictive logistics, and urban management systems is not proof of a superior philosophy. It reflects different engineering priorities, shaped by a state that has long treated infrastructure and governance as closely linked, and by a longer intellectual habit of attending to direction rather than fixity.

But it would be a mistake to frame this as a civilizational contest — “holistic East” versus “analytic West.” American and European AI research is already moving toward related ground: world models, continuous control, embodied systems, and agents that track a moving environment instead of describing a static one. The frameworks differ. The underlying challenge does not.

That challenge will only grow. Cities, power grids, and supply chains do not pause while a model is trained on them. The systems meant to manage them will need to do two things at once: represent the world in discrete, computable pieces, and track the continuous change that never stops running underneath those pieces.

For AI researchers and engineers alike, the challenge is becoming increasingly practical: how to combine accurate representation with continuous adaptation.

A map and a compass solve different problems. A map fixes a place. A compass holds a direction. Neither replaces the other, and no one crossing unfamiliar terrain would carry only one. The challenge is not choosing between them, but learning to use both in tandem.

This article is based on my latest book, The Grid and the Wave, available at all major online book retailers, including Amazon.

This isn’t the plot of a sci-fi novel; it is the emerging logic of 21st-century governance, powered by artificial intelligence. At its heart lies a concept known as the “Predictive State,” now emerging in China.

While the term might sound like something from a political thriller, it describes a very real transformation in how societies are managed.

A Predictive State uses vast amounts of data (from satellite imagery and financial transactions to sensor networks and digital IDs) to create a real-time, computational model of the country. The goal is not just to understand what is happening now, but to forecast where the system is heading and to intervene early to steer it toward a desired outcome.

Think of it as a weather forecast for society itself. Instead of predicting rain, the state predicts traffic congestion, disease outbreaks, or economic strain. This foresight grants a new kind of power: the power of preemption.

This approach allows the Chinese government to shift from being a “referee” that reacts to fouls to a “systems architect” that designs the game to prevent fouls from happening in the first place.

The American Approach

The American approach to AI is deeply ingrained in its national DNA: it prioritizes individualism, market competition, and technological acceleration. The prevailing philosophy, often called “acceleration ethics,” is to unleash innovation first and deal with the consequences later.

The logic is that the benefits of AI, from breakthrough medical diagnoses to revolutionary productivity tools, are too significant to be slowed down by heavy-handed regulation.

In this model, AI is primarily seen as a product to be built by private companies. Capital flows into foundational research, moonshot ventures, and massive computing power.

The U.S. government’s role has traditionally been to fund basic research and act as a customer, while letting companies like OpenAI, Google, and Microsoft race ahead. Even regulatory efforts, like the Biden administration’s “Blueprint for an AI Bill of Rights,” have largely focused on voluntary guidelines and enforcing existing laws rather than creating a strict, new legal framework.

The recent policy tilt under the Trump administration has only accelerated this, prioritizing “unbridled advancement” and framing AI governance as a potential threat to American competitiveness.

However, this approach is not without its own form of prediction. American capitalism is built on predictive algorithms. Just consider how Amazon anticipates our purchases or how hedge funds trade stocks.

The key difference is that in the U.S., this predictive power is largely concentrated in the private sector. The state’s role is to set the rules of the road, not to build the car or decide where everyone is going.

The American model is a market-driven future in which AI amplifies individual choice and corporate efficiency, with the government acting as a referee in a bustling, chaotic, and innovative bazaar.

The Chinese Approach

China is building a fundamentally different future. Its approach is not focused on AI as a product, but on AI as an integral part of the national infrastructure. The goal is not just to create smart devices, but to build a smart society.

This vision culminates in the creation of the Predictive State, where AI becomes the central nervous system of governance.

This model is rooted in deep cultural patterns. Confucianism provides the moral vision: a well-governed society is one of order, harmony, and clearly defined roles. Technology is judged by its ability to contribute to this collective stability. Legalism, another ancient tradition, supplies the machinery: systems require clear rules and credible enforcement to function. AI, with its ability to monitor, analyze, and predict, is the ultimate Legalist tool.

China’s advantage in data is staggering—it generates nearly a third of the world’s data, thanks to over a billion internet users and a deeply digitized economy. This data, from integrated payments to urban sensors, renders society “computationally legible.” The state can then use this legibility to preempt problems.

The Chinese government has even laid out a roadmap to achieve a fully realized “AI-optimized society” by 2050, where intelligent systems drive transportation, healthcare, and urban planning.

This is a state-driven future, where AI is an instrument of coordination and risk management, designed to ensure collective well-being and social harmony as defined by the ruling party. The bargain for citizens is clear: in exchange for privacy and data, they receive efficiency, security, and a share of the economic growth.

Two Civilizations, Two Futures

The contrast between these two models could not be starker. In Silicon Valley, the debate often revolves around whether AI will replace humans. In Beijing, the conversation is about how AI can reorganize society for greater efficiency and harmony. The U.S. sees AI as an amplifier of human potential and market forces; China sees it as an enhancer of state capacity and systemic stability.

The U.S. leads in cutting-edge chip design and elite AI talent, attracting the world’s best researchers.

China leads in data volume, energy infrastructure, and sheer deployment scale, building the physical and digital “substrate” required to make AI ubiquitous.

The U.S. relies on private innovation to drive progress; China relies on a national strategy that coordinates industry, academia, and government.

Ultimately, the “AI race” is not just a competition to build the most powerful model. It is a contest between two different answers to a fundamental question: What is technology for? Is it for empowering the individual, or for strengthening the collective? Is its highest purpose to maximize choice, or to ensure order?

The world is now watching to see which of these two visions—the American market-driven future or the Chinese predictive state—will prove more stable, more prosperous, and more resilient.

AI is serving as a mirror, reflecting our deepest values back to us and forcing us to decide what kind of future we want to build. The answer will shape not just the next decade, but the rest of the century.

Understanding the difference helps clarify why China’s trajectory sometimes appears less like frontier AI culture and more like a revival of cybernetic thinking.

Cybernetics: Systems and Feedback

Cybernetics emerged in the mid-twentieth century as the study of control and communication in systems—biological, mechanical, or social. Its core idea is simple: systems maintain stability through feedback.

A thermostat monitors temperature, compares it to a target, and adjusts accordingly. The goal is not brilliance but balance.

Cybernetics asks:

• How do parts of a system communicate?

• How is deviation detected?

• How is correction applied?

The emphasis is not on intelligence as independent cognition, but on regulation, calibration, and equilibrium. It is architectural in spirit. Intelligence, in this framework, is a function of feedback loops embedded in larger structures.

Artificial Intelligence (AI): Task-Specific Capability

Modern AI refers to computational systems that perform tasks typically associated with human intelligence: translating languages, recognizing images, generating text, optimizing logistics.

These systems are powerful but narrow. They operate within defined domains. A model trained for medical diagnosis cannot automatically perform legal reasoning unless retrained.

AI excels at pattern recognition and optimization. Its strength lies in performance within boundaries.

The central question in AI research is: how accurately and efficiently can a system perform a specific task?

Artificial General Intelligence (AGI): Cross-Domain Reasoning

AGI refers to a hypothetical or emerging form of machine intelligence capable of reasoning across domains, learning new tasks with minimal retraining, and transferring knowledge between contexts.

An AGI system would not simply execute predefined objectives. It could interpret unfamiliar problems, abstract principles, and apply them broadly. Its defining feature is generality.

The question shifts from performance to autonomy.

Why the Distinction Matters

The difference between AI and AGI is one of scope. The difference between cybernetics and both AI and AGI is one of orientation.

• AI focuses on solving tasks.

• AGI focuses on expanding cognitive range.

• Cybernetics focuses on maintaining systemic stability.

In Western discourse, AGI is often framed as a breakthrough moment—a threshold event that could transform or destabilize human authority. The emphasis is on capability and speed.

China’s broader trajectory suggests a different orientation. While it invests in frontier AGI research, its institutional logic remains cybernetic: intelligence is embedded within governance structures, integrated into infrastructure, and expected to operate within defined constraints.

This does not mean China rejects AGI ambitions. It means that even a general intelligence would likely be treated as a subsystem within a larger architecture of control.

In this view, intelligence—however advanced—remains bounded by feedback loops: regulatory oversight, licensing regimes, deployment restrictions, and institutional supervision. General capability does not imply political autonomy.

Constraint and Capability

AGI, if realized, would possess broader capacities than current AI systems. It could assist in scientific discovery, strategic planning, or economic modeling. Its cognitive leverage could be significant.

Yet even such a system would depend on:

• Data inputs

• Energy and compute

• Physical infrastructure

• Human authorization

Constraint is not optional. It is structural.

Cybernetics reminds us that no component, however sophisticated, escapes the system in which it is embedded. The more powerful the component, the more crucial the feedback mechanisms that regulate it.

A Final Perspective

These categories are analytical tools, not rigid boxes. Modern AI incorporates feedback. AGI research builds on machine learning. Real-world systems blend elements of all three.

But the distinction illuminates a philosophical divergence.

Is intelligence something to be unleashed and raced toward?
Or something to be integrated, calibrated, and contained?

China’s trajectory suggests the latter. Even in the pursuit of general intelligence, the organizing principle remains architectural rather than emancipatory.

AGI may expand what machines can do. It does not eliminate the need for systems of oversight.

In the end, the future of intelligence will not depend only on how powerful our machines become, but on how carefully we design the structures within which they operate.

Last year, the Chinese government announced plans to make AI a utility, a public service as affordable and ubiquitous as electricity and broadband. The country plans to embed AI technology deeply across society to become the world’s first AI society by 2035.

AI will permeate all aspects of Chinese society, from production and services to governance and welfare. The central government provides the foundational framework (policy direction, national data standards, etc.), while private companies compete to build and deploy services within the national framework.

The Chinese government designated domestic tech giants like Baidu, Tencent, iFlytek as anchor platform providers for AI services. The real technical prowess lies in integrating all these platforms and services. China is building what some analysts call a giant national nervous system.

Turnkey AI

The goal of creating a fully AI-driven society in 10 years seems ambitious, but Chinese companies are well-positioned to make it a reality. For one, China, as the factory of the world, produces much of the required hardware that will drive the AI revolution, save for the most advanced AI chips developed in the US.

Chinese tech companies now deploy AI in turnkey projects, or fully integrated solutions that combine software, hardware, data, and operations, delivered to governments or industries as a complete system. They are becoming the norm rather than the exception.

For instance, Baidu’s Apollo Autonomous Driving Platform is not just self-driving software, but a turnkey autonomous mobility system for autonomous buses and taxis, smart road infrastructure, AI mapping, sensors, and cloud coordination. Municipalities receive a complete mobility package and operational support.

Huawei delivers turnkey AI systems for industrial zones, ports, and energy infrastructure. It provides AI-enabled access control, safety monitoring, predictive maintenance for power grids and factories, and integrated networks (5G), data centers, and AI software. Huawei typically provides end-to-end solutions: chips, networks, cloud, and AI applications.

Tencent’s WeChat Health platform integrates AI-powered symptom checkers and triage tools to guide users to appropriate care. AI diagnostic aids for reading CT scans or retinal images are being deployed in community clinics and county hospitals. This extends the reach of scarce specialist expertise to underserved populations and directly supports the goal of equitable access.

The AI model extends beyond megacities. In agriculture, platforms like Pinduoduo and Alibaba’s rural e-commerce networks provide farmers with AI-driven tools. They include smartphone apps that diagnose crop diseases from a photo and predictive models for yield and optimal pricing. Even small farmers are integrated into smart supply chains.

Open source AI

Open source plays a strategic role in China’s AI and the broader technology landscape. Unlike in the West, where open source is often framed as an ideological commitment to openness or decentralization, China treats open source as a development accelerator and coordination mechanism.

By releasing models, frameworks, and software stacks as open source or “open-weight” (i.e., downloadable trained parameters of AI programs), Chinese companies and research institutions enable thousands of developers, startups, universities, and local governments to build on shared foundations.

In response to export controls and reliance on foreign platforms, China has built domestic alternatives across chips, operating systems, cloud platforms, and AI frameworks. Open standards help ensure interoperability and reduce lock-in to foreign vendors, all in line with the government’s broader direction.

Open source fits China’s traditional emphasis on collective problem-solving and standardization. Shared platforms make it easier to align industry, academia, and government around common technical roadmaps. This is crucial for large-scale projects such as smart cities, industrial automation, and energy systems.

Going global

China’s AI model has particular appeal in the Global South. For many emerging economies, the Western approach – renting access to expensive, proprietary AI systems via cloud APIs – creates financial and institutional barriers. China offers an accessible low-cost alternative: AI as shared infrastructure, designed for broad deployment rather than elite use.

This strategy is implemented through integrated development packages rather than standalone technologies. With initiatives such as the Digital Silk Road, Chinese firms bundle AI systems with energy grids, 5G networks, data centers, and training programs. This integrated pathway to digital modernization aligns with state-led growth models common across Asia, Africa, and the Middle East.

Alibaba Cloud and Tencent provide AI-driven commerce and payment platforms in Africa and Latin America. Chinese firms Hikvision and Dahua are exporting smart city solutions to Southeast Asia and Latin America and expanding their presence in African telecommunications networks.

China’s broader digital ecosystem, including Alibaba Cloud, Tencent Intelligent Healthcare, AI diagnostic tools, and agricultural tech, is also reaching Global South markets through cloud services and partnerships.

As the Chinese model spreads abroad, the underlying collectivist worldview travels with it, embedded in infrastructure and code rather than in explicit ideology.

Invisible infrastructure

What ultimately distinguishes China’s AI approach is not only its technical or economic logic, but its cultural one. Beneath the AI ecosystem of smart cities, cloud platforms, and AI-enabled services lies a Confucian orientation that prioritizes collective benefit, coordinated governance, and pragmatic problem-solving.

By 2035, if China succeeds in integrating AI across society, intelligence is likely to function as invisible infrastructure. AI would no longer appear mainly as standalone applications, but as a background capability quietly coordinating systems at scale.

Cities would operate as integrated organisms, with traffic, energy use, emergency services, logistics, and environmental monitoring continuously optimized through real-time data and predictive models. Healthcare would shift toward prevention, using AI-assisted diagnostics and population health forecasting to extend care beyond major hospitals into local clinics.

In industry and agriculture, AI would act as a force multiplier rather than a replacement for labor. Factories, ports, and farms would use predictive systems to reduce waste, improve safety, and balance supply and demand, while smaller firms and rural producers access advanced tools through shared platforms.

Governance would become more anticipatory and technocratic, relying on simulation, risk modeling, and early-warning systems to manage social, financial, and environmental pressures. Culturally, this future reflects longstanding Chinese preferences for coordination, pragmatism, and collective benefit.

The defining feature would be systemic coherence: a society wired for efficiency, stability, and long-term planning, with intelligence distributed throughout the social fabric rather than concentrated at the top. The Chinese model may thus quietly reshape not only technology markets but also how societies understand AI itself.

This story first appeared in the Asia Times.

When we speak of polymaths, our minds usually turn to figures like Leonardo da Vinci or early-modern savants like Leibniz. They seem to have been among the last human beings capable of grasping the totality of knowledge available in their age: philosophy, mathematics, arts, architecture, natural philosophy, mechanics, and metaphysics as part of one coherent continuum.

Today, such a universal command seems impossible. Knowledge has fragmented into a labyrinth of subfields, methods, and technical languages, each jealously guarded by specialists. Yet in the 1940s, the American scientist Norbert Wiener not only mastered multiple fields; he also developed a conceptual strategy for integrating fragmented knowledge into a new whole.

Wiener is the father of cybernetics, the conceptual predecessor of Artificial Intelligence. He was probably the first modern polymath and the creator of a new conceptual model of interdisciplinarity suited for the age of information. His very few peers were John von Neumann, Buckminster Fuller, and Gregory Bateson.

Feedback

Wiener was a mathematical prodigy. He entered Tufts University at eleven, finished Harvard by fourteen, and studied logic and mathematics under Bertrand Russell. He was a mathematician of extraordinary breadth, but his mature achievement cannot be understood solely by listing his technical contributions. It lies in the creation of cybernetics, the new transdisciplinary science grounded in information, feedback, control, and communication.

With cybernetics, Wiener is in direct intellectual continuity with Leibniz. Like Leibniz, he sought unifying conceptual languages: Leibniz through universal calculus and binary logic, Wiener through information and feedback. Both thinkers distrusted disciplinary boundaries, believing that scientific progress arose in the conceptual space between established fields.

Wiener makes this lineage explicit in the remark that “since Leibniz, there has perhaps been no man who has had a full command of all the intellectual activity of his day,” and that modern science increasingly forces knowledge into specialized silos. Cybernetics was his response: a new science deliberately positioned in the “no-man’s land between disciplines.”

This is an essential response because Wiener didn’t just lament specialization. He created a structure to overcome it. While classical polymaths possessed knowledge across domains, the modern polymath, in Wiener’s sense, can construct a meta-framework that enables specialized knowledge to communicate, integrate, and co-evolve with one another. In this sense, Wiener was not only a polymath but a theorist of polymathy itself.

During the Second World War, Wiener and Arturo Rosenblueth, along with other physiologists, engineers, and mathematicians, jointly conducted research and designed control systems for anti-aircraft weapons, effectively predicting and correcting the movements of planes and shells.

But instead of approaching it purely as a technical wartime problem, Wiener recognized that the mathematical structure underlying ballistic prediction was essentially the same as that of motor control in neurophysiology and of feedback in mechanical systems.

A nervous system and a servo-mechanism could be described by the same logic: signals, feedback, correction, and communication. This leap from weapons control to biological control is just the kind of cross-domain analogical reasoning that characterizes polymathic thinking.

Cybernetics inaugurated something historically unparalleled: a single language of systems, applying simultaneously to physiology, engineering, computing, ecology, psychology, linguistics, and eventually to the early philosophy of artificial intelligence.

Systems theory, control theory, information theory, and computer science were not side effects, but the downstream expressions of Wiener’s framework. Without Wiener, the conceptual basis of modern automation would be very different.

Ethics

Just as importantly, Wiener understood that integrating disciplines also required integrating ethical reflection. He saw early, long before most postwar thinkers, the existential stakes of automation.

Unlike many Cold War technologists, Wiener warned that computers and automation could displace workers, concentrate power, militarize perception, and undermine democratic governance. Here he emerges not just as a technical innovator, but as a pioneer of AI ethics, anticipating debates now at the center of global political discourse.

Yet if Wiener foresaw the dangers of automation, he also believed its scientific promise could be realized only through intellectual synthesis by refusing to treat technology as separate from biology, or information from meaning, or engineering from ethics. He placed cybernetics at the crossroads of humanistic and scientific inquiry. That is a very different stance from today’s narrow technocratic mindset.

In other words, cybernetics was a philosophical event of profound import. It managed to overcome dualisms inherited from European metaphysics, mind against matter, organism against mechanism, because the intersection of communication, feedback, and control could reconcile biological and mechanical systems without one being reduced to the other.

Integration

Wiener believed that modernity needed a new philosophical language in which to couch its experiences both as human and artificial. In this, he remained profoundly Leibnizian: information, not matter, was reality’s fundamental currency.

But perhaps Wiener’s most crucial contribution to polymathy is conceptual rather than historical. He realized that a single mind could no longer possess the universal knowledge aspired to by the Renaissance polymaths, but that it could be reconstructed through systemic integration. Cybernetics was a collaborative universalism, the collective endeavor of rebuilding the coherence of knowledge at a higher level of abstraction.

Wiener was not invoking a nostalgic model of universal mastery. Instead, he develops a modern form of polymathy suited to an age of complexity. The question is no longer “Who can know everything?” but “How can knowledge be integrated across domains?” He redefines the concept of polymathy from possessing knowledge to connecting knowledge.

This is a perspective particularly apt today, when artificial intelligence once again blurs boundaries between cognitive domains and requires conceptual frameworks extending beyond single disciplines. Wiener anticipated this moment.

Complexity

Today, systems theory is everywhere, from climate models to machine learning to networked governance. But the intellectual courage to synthesize, rather than to specialize, remains rare. In a world fragmented by hyperspecialization, Wiener reminds us that the most profound transformations emerge from the conceptual margins.

If Leibniz was the last classical polymath, Wiener may be the first modern one. Where Leibniz fused metaphysics, logic, and mathematics to produce a universal calculus, Wiener fused mathematics, engineering, biology, and information to make a universal theory of control. The two bookend the great arc from pre-modern universality to modern complexity.

In a time when AI seems to outstrip human understanding, what we need is not just technical skills but polymathic integration: the renewed ability to think across boundaries, reconnect fragmented disciplines, and develop ethical frameworks for technological systems.

Wiener, who was far more than a mathematician, offered a model for this future. Cybernetics was not just a science but an invitation to become a modern polymath.

Intercultural

Our contemporary world, in which Asia plays an increasingly important role, reminds us of how far ahead Leibniz was of his time. He understood Europe and China as complementary branches of human wisdom, the former unsurpassed in mathematical abstraction, the latter in moral and political philosophy.

If we are going to take Wiener’s call for cross-disciplinary synthesis seriously, then the parallel call for cross-civilizational synthesis follows as a matter of course. A modern polymath can no longer be limited to Western intellectual traditions alone. Global history, comparative philosophy, and Asian models of knowledge must be part of the conversation.

Not just the combining of math with biology, or engineering with ethics, but learning from the global storehouse of ideas makes that polymath genuinely modern. Cybernetics itself treats mind, matter, and organism as continuous processes, not isolated substances, as Daoism, Buddhism, or Vedānta do.

In this sense, the return of global pluralism in philosophy is not a departure from polymathy but a recovery of its original ambition: a universal science built from multiple civilizational lineages rather than a single Western lineage. The 21st-century polymath has to be interdisciplinary and intercultural.

But this familiar story conceals a deeper lineage: Japanese architecture was itself built on a Chinese prototype, the world’s first fully modular construction system. Through Japan’s transmission of Chinese spatial ideas, Wright absorbed a design logic that helped catalyze the machine aesthetic of European modernism. Chinese aesthetics played a crucial role in the emergence of Western modern architecture.

Invention of Modularity

Long before the rise of industrialization and the standardization that came with it as a hallmark of modernity, China developed an architectural grammar that was highly systematized. By the Tang and Song dynasties, Chinese carpentry was already controlled by a precise modular order.

The timber frame was divided into bays called jian; bracket systems called dougong were prefabricated to standard proportions; and the building manual, Yingzao Fashi (1103), codified exact dimensions and ratios. Structures were assembled as interchangeable units, anticipating modern prefabrication by centuries.

This system produced a distinctive spatial logic: horizontal emphasis, rectilinear composition, non-load-bearing walls, fluid indoor–outdoor transitions, and a planar articulation of form. In other words, Chinese architecture was “machine-like” in organization long before machines existed.

Japan, which adopted Chinese architectural principles wholesale, preserved this modular sensibility with remarkable fidelity. Because Japanese architecture remained timber-based, its connection to Chinese modular design persisted into the modern era.

Chinese-Japanese Spatial Concepts

It was through Japan that Frank Lloyd Wright encountered the spatial and aesthetic principles first developed in China. Wright’s engagement with Japan was profound and sustained. He visited the country several times, designed the Imperial Hotel in Tokyo, and was an avid collector of ukiyo-e prints.

Japanese prints taught him lessons in composition: asymmetry, flatness, cropping, and the dissolution of linear perspective. But it was Japanese architecture that transformed his spatial imagination.

Katsura Palace in Kyoto epitomizes the profound continuity of Chinese modular principles in Japan. Its gridded tatami floors, floating timber frames, sliding partitions, and interlocked interior spaces take spatial fluidity to extents hitherto unknown in the West.

Instead of stone walls and static enclosures, Japanese spaces breathed. They expanded and contracted, opening to gardens and light with effortless transitions. Wright found in the architecture of Japan, which he understood to have Chinese foundations, an entirely different concept of space-dynamic, continuous, ordered yet flexible.

Prairie Houses

Wright absorbed these ideas not as exotic motifs, but as structural and spatial concepts. His Prairie Houses, designed between 1900 and 1910, are the clearest architectural expression of this East Asian inheritance. Their broad hip roofs, deep overhangs, horizontal planes, modular grids, and flowing interiors owe unmistakable debts to Japanese precedent.

The juxtaposition here of solid planar walls with light-filled clerestories echoes the contrast between shoji and timber frame. It is from Japanese, and ultimately Chinese, building logic that Wright gets the idea of dissolving the corner, replacing load-bearing mass with interpenetrating planes and ribbons of windows.

European architects immediately recognized the breakthrough. When Wright published his 1910 Wasmuth Portfolio in Berlin, Walter Gropius, Mies van der Rohe, and a young Le Corbusier were profoundly shaken. They had never seen space treated in this way—as a continuum rather than a sequence of enclosed rooms.

Wright’s freeing of the façade, explosion of corners, and dissolution of enclosure into interlocking planes electrified them. Foundational moves in modern architecture, they derived nonetheless from Wright’s encounter with Japan’s reinterpretation of Chinese spatial principles.

Modernism and a Machine Aesthetic

European modernism, with the Bauhaus and De Stijl at its core, took Wright’s planar abstraction and modular geometry as raw material for developing a rational industrial aesthetic. Gropius believed that architecture had to be an expression of the “spirit of the machine age”: rectilinear planes, unadorned surfaces, and the logic of standardization.

Architects from De Stijl adopted Wright’s idea of floating planes and distilled it into pure geometric abstraction. Rietveld’s Schröder House—with its sliding partitions and interlocking volumes—stands as perhaps the closest European counterpart to Katsura’s spatial logic, mediated through Wright’s reinterpretation.

Thus, the “machine aesthetic” of the 1920s, with its stripped geometry, modular repetition, horizontal slabs, and orthogonal clarity, is partly the product of Chinese spatial thought refracted through Japanese practice and American translation. The irony is that modernists believed they were rejecting historical precedent when, in fact, they were unknowingly drawing upon a millennium-old East Asian architectural system.

Why Lineage Matters

Recognizing this lineage is more than an exercise in influence tracing; it clarifies a more profound truth about modernity. The Western machine aesthetic did not emerge solely from industrial rationalization. It also arose from contact with non-Western spatial paradigms that privileged modularity, horizontality, and planar geometry long before modern technology demanded them.

While the Chinese developed a systematic architecture that prefigured the very ideals modernists would come to celebrate-clarity, order, and standardization- the Japanese preserved and refined these ideas into a form that Wright could encounter and interpret anew.

This understanding also challenges the narrative of Western originality. The genealogy from China to Japan to Wright to the Bauhaus demonstrates how global modernity is not the creation of the West alone but a hybrid formation created through trans-cultural exchange. The spatial DNA of modernism is partly East Asian.

This is not to say that the Chinese-Japanese influence on Wright was the sole source of the development of a machine aesthetics. 19th-century American balloon-frame construction (which Wright knew intimately) already encouraged light, gridded, prefabricated building.

In the early 20th century, Viennese Secession (Otto Wagner, Josef Hoffmann) and Dutch architect Berlage were moving toward flat roofs and horizontal continuity before the Wasmuth Portfolio appeared in Europe, and industrial engineering (factory sheds, grain elevators) provided another powerful model of repetitive modularity.

But Wright plays a key role in making “the creation of space” the starting point of modern architecture. Classic European architecture, based on sculptural techniques, emphasized form rather than space. Wright’s apprentice Edgar Tafel recalled that Wright often referred to space when discussing Japanese architecture. Tafel wrote in his biography:

“The Japanese house, with its sliding screens, gives a unique sense of movable space. With each rearrangement of the screens, we become aware of the ‘shape of the space,’ or, like the Japanese print, of merely an interruption of space, a moment in space. Another dimension, he (Wright) called it.”

The glass curtain walls and free plans of the International Style are routinely celebrated as pure expressions of industrial rationality and Western genius. Beneath their steel-and-concrete skin, however, lie spatial principles first systematized in Song-dynasty timber brackets, refined across centuries of Japanese carpentry, and translated into a new language of continuity and flow by Frank Lloyd Wright. Modern architecture’s most revolutionary formal DNA is, in part, Chinese.

Not animism in the old anthropological sense of “primitive spirit-belief,” but in the sense deployed in recent scholarship by the likes of Graham Harvey, Tim Ingold, and Eduardo Viveiros de Castro. It means a relational ontology in which the world itself is alive, responsive, and participatory. Framed this way, Chinese thought dissolves the Western split between instinct and consciousness, offering a unified vision of how living beings engage the world.

In classical Daoism and Confucianism, nature is not inert matter governed by external laws but a self-organizing field of qi. This vital, dynamic energy forms, dissolves, and transforms all phenomena. Mountains rise, rivers flow, clouds swirl, and animals migrate because they participate in emergent patterns of the Dao, not because they are mechanically pushed from behind.

These patterns have tendencies shi, rhythms du, and dispositions li that arise “organically” from within the cosmos. Scholars like Joseph Needham, Roger Ames, and David Hall argued that classical Chinese thought is best understood as “organismic,” or as a world of interpenetrating processes rather than discrete entities.

Within this worldview, instinct (xing) refers not to an animalistic residue but to an organism’s innate nature—the spontaneous pattern by which it participates in the “rhythms of heaven and earth.” A seed germinating, a bird navigating, and a person experiencing compassion are all expressions of xing.

Instinct is consequently a form of embodied intelligence and not something to be overcome. Consciousness (xin, “heart-mind”) is not a separate rational faculty but rather the refined awareness that harmonizes with xing: when consciousness is unobstructed, instinct and awareness combine in wu-wei (“effortless action”), allowing for spontaneous yet appropriate responsiveness to the world.

Chinese cosmology resembles what anthropologists nowadays refer to as new animism: a way of thinking in which personhood, agency, and responsiveness are emergent relational properties rather than intrinsic biological ones. The cosmos is a living web, and humans, animals, tools, and environments all participate in its dynamic coherence.

India provides an enlightening contrast. Indian philosophy, as a rule, views consciousness as primordial and instinct as the result of conditioning. In Sāṃkhya, for instance, the distinction between puruṣa (pure consciousness) and prakṛti (the material and psychological processes, including instinct) frames the entire metaphysics.

In Advaita Vedānta, ātman, pure awareness, becomes entangled in ahaṃkāra, or the ego-construct, thereby creating instinctual habits, called vasanas or samskaras. Here, instinct is something to see through, not integrate.

The contrast reinforces the broader triad: in the West, consciousness rises above instinct; in India, consciousness precedes instinct, whereas in China, instinct is consciousness in its somatic mode.

Shinto

Japan provides a second East Asian example of “sophisticated animism,” though with a distinct emotional tone. In Shinto, the world is inhabited by kami—not gods, but presences, forces, or qualities dwelling in natural forms, places, and even crafted objects.

According to East Asian scholar Thomas Kasulis, Shinto perceives the world as a web of “relationships of presence” rather than separate entities. The boundary between living and non-living is porous: tools, utensils, musical instruments, and even machines can acquire ki (Japan’s equivalent of China’s qi), a palpable vitality. Tsukumogami, the idea that old or well-used tools gain spirit, suggests that objects accumulate a biography and moral weight.

This worldview shapes Japan’s cultural relationship to machines and robots. Where Western culture is haunted by Frankensteinian anxiety-machines rebelling against their creators-Japan typically views robots as companions, collaborators, or social actors.

This is no mere folklore but an ontological intuition: robots, like tools and utensils, are able to partake in the living field of relationships.

To be sure, this does not mean that all Japanese engineers personally hold animistic beliefs. Many roboticists work from a secular engineering mindset, and Shinto blessings of robots, which do occur, especially in small and mid-sized firms, are often ceremonial rather than metaphysically literal. Buddhist temples using robots, such as the Mindar android at Kōdaiji, remain the exception.

Similarly, contemporary Chinese urban attitudes toward technology derive more from state-driven materialism and rapid industrialization than from explicit Daoist cosmology. Traditional worldviews coexist with modern secular pragmatism, and neither is best thought of as monolithic or universally operative.

Still, the deeper cultural ontology matters. In Japan, technology is not alien or threatening but continuous with life. Machines can be friendly, expressive, and socially integrable; they can be welcomed into eldercare homes or even religious spaces. The influential paper on techno-animism by Jensen and Blok argues that Japan’s robotics culture arises from the fusion of industrial modernity with enduring animistic sensibilities.

New participants

China shares something of this disposition, albeit in a more cosmological register. Insofar as qi permeates both nature and technology alike, machines can be seen to extend natural processes.

Chinese mechanical inventions, from Zhang Heng’s seismoscope to the clock tower of Su Song, were designed to harmonize rather than dominate cosmic order. Chinese attitudes toward AI today often blend strong pragmatism with a sense that the technology will be woven into the existing relational patterns of society and the cosmos.

Taken together, the Chinese Dao, the Japanese kami-world, and its modern technological expressions all suggest that sophisticated animism represents a continuingly powerful interpretive lens for East Asia: it explains why instinct and consciousness are not opposed but intertwined, and why in East Asia robots are so often greeted not with anxiety but rather curiosity, affection, or even reverence.

If the world is conceived as a living field of dynamic forces, then machines become simply new participants in an already-living cosmos. Technology is not an invader but a collaborator. The “spirit in the machine” is not superstition but the extension of a worldview in which everything is interrelated and participates in the larger whole.

Further reading:

• Kasulis, Shinto: The Way Home (on kami and tools)

• Ames & Hall, Thinking Through Confucius or Dao De Jing: A Philosophical Translation (on xing and organismic process)

• Allison, Millennial Monsters or Schodt, Inside the Robot Kingdom (early work on Japanese robotics culture)

• Jensen & Blok, Techno-Animism in Japan (2013 paper with a similar thesis)

However, Gebser overlooked the Chinese counterpart to linear perspective, axonometry, a projection system developed by Chinese architects in the 10th century. Axonometry depicts three-dimensional space without a vanishing point, and played a key role in the European modernist redefinition of pictorial and architectural space between the 1850s and 1920s.

In the 1960s, the two spatial paradigms—linear perspective and axonometric projection—converged within the emerging field of computer-aided design and digital visualization, uniting Eastern and Western modes of spatial representation in the electronic era. The essay below is followed by an illustrative account of the history of axonometry and its role in the modern world.

Jean Gebser’s blindspot

When Swiss philosopher Jean Gebser argued that the invention of linear perspective in the Italian Renaissance marked a new stage in human consciousness, he overlooked a remarkable precedent: a projection system developed in China centuries earlier.

That omission has shaped how the modern West understands both art and consciousness. The story of axonometry, the “other” perspective, reveals how the Chinese integrated space and time long before Europe’s perspectival awakening — and how that visual grammar resurfaced in twentieth-century modernism.

From Vanishing Point to Flowing Space

Linear perspective, devised in the fifteenth century, created the illusion of depth by converging parallel lines at a vanishing point. This optical geometry gave rise to a worldview centered on a single observer — the hallmark of the Western “mental” structure Gebser celebrated.

Yet by the eleventh century, Chinese painters and architects had already developed an alternative system, known as dengjiao toushi, or “equal-angle see-through.” It depicted forms without convergence or distortion created by the vanishing point, allowing space to extend freely in all directions.

Unlike perspective, which freezes vision in a single moment and position, Chinese axonometry unfolds as a continuum. In the hand-scroll format — a seamless horizontal painting viewed by slowly unrolling it — the eye travels through a landscape that combines multiple moments and viewpoints. The image becomes a journey through space-time, not a snapshot of it. The observer is not fixed but participates in the flow.

This integration of movement and duration foreshadowed what modern physics would call the unity of space and time. Where Renaissance artists “discovered” the three dimensions, the Chinese were already painting in four.

From East to West: The Hidden Transmission

Axonometry reached Europe through Jesuit intermediaries in the eighteenth century, who brought Chinese drawings home as curiosities. European draftsmen adapted the technique for technical illustration, where its lack of optical distortion proved invaluable. In 1822, the British scientist William Farish codified the mathematical basis of axonometry in his invention of isometry — a projection system of “equal measures” that preserved true proportions without recourse to the vanishing point.

Farish’s innovation coincided with the Industrial Revolution’s demand for precision. Machine parts, weapons, and architectural plans required measurable accuracy, not optical illusion. Yet the aesthetic implications of this “non-perspective” remained unrecognized until the early twentieth century, when the modernist avant-garde discovered it.

The Modernist Rediscovery

By the 1910s, European art and architecture were undergoing a radical reorientation. The Japanese woodblock print (ukiyo-e), itself based on Chinese axonometric principles, had already transformed Western painting. Artists such as Manet, Cézanne, and Van Gogh eliminated chiaroscuro and undermined linear depth, seeking the immediacy of flat yet dynamic space. Cézanne began to tilt and overlap objects seen from different vantage points; Picasso pushed further, fusing multiple viewpoints within a single form — the birth of Cubism.

Critics like Guillaume Apollinaire surmised that these experiments paralleled Einstein’s newly formulated space-time continuum. Jean Gebser, living in Paris among the Cubists, interpreted their fragmentation as the expression of a new “aperspectival” consciousness — an awareness of time entering art. Yet he never connected that insight to the earlier Chinese synthesis from which this non-perspectival vision ultimately descended.

In architecture, the same transformation occurred. Modernists such as Theo van Doesburg, Gerrit Rietveld, and El Lissitzky adopted axonometry for their plans and renderings. De Stijl architects used it not merely as a technical device but as a language of modernity — a projection without hierarchy, without “near” or “far.”

By 1923, axonometric drawings were exhibited in Paris as visual manifestos for a new architecture based on structural transparency and spatial logic. Machine aesthetic had found its medium.

Space as Medium

In 1912, the Austrian-American architect Rudolph Schindler famously declared: “The architect has finally discovered the medium of his art: SPACE.”

This statement marked a conceptual revolution. For centuries, European academies had treated architecture as a sculptural art — or form carved in matter. Schindler and his contemporaries redefined it as the shaping of voids. “Space,” once an abstract notion, became tangible, livable, and aesthetic.

Frank Lloyd Wright carried this transformation further. Deeply influenced by Japanese architecture, he saw buildings as configurations of continuous space rather than enclosed mass. “The Japanese house, with its sliding screens, gives a unique sense of movable space,” he told his apprentice Edgar Tafel. Wright even linked this insight to Lao Tzu’s dictum about emptiness: “The reality of the building does not consist in the four walls and the roof, but in the space within.”

Thus, through Japan, Chinese spatial philosophy entered the bloodstream of Western modernism. The architect’s discovery of space echoed the Chinese painter’s ancient awareness that emptiness is the essence of form.

Claude Bragdon and the Aesthetic of the Mind’s Eye

The first Western theorist to appreciate the aesthetic dimension of axonometry was the American architect Claude Bragdon, whose 1932 book The Frozen Fountain described isometric projection as “more faithful to fact than to appearance.” Bragdon intuited what Gebser would later call aperspectival consciousness:

“Parallel lines are really parallel; there is no far and no near… Isometric perspective shows things as they are known to the mind. It renders the mental image — the thing seen by the mind’s eye.”

For Bragdon, axonometry visualized not perception but cognition — how the mind imagines objects in thought. It was, in his words, “more intellectual, more archetypal.” Long before digital design, he grasped that geometry could model both mental and physical space.

From Blueprint to Algorithm

By mid-century, axonometry had become the lingua franca of modern design. Every architect and engineer now begins with it; every computer-aided design system depends on it. In that sense, axonometry prefigured the digital paradigm.

Like the pixel grid, axonometry abstracts the world into measurable relations rather than visual impressions. It transforms the subjective eye of Renaissance perspective into an objective interface — a worldview of systems and networks rather than appearances.

This technological dominance ironically vindicates the Chinese conception that had once been dismissed as “non-scientific.” The modern world now thinks in terms of the very projections that Chinese artists conceived a millennium ago.

A Lost Continuity Restored

Gebser’s claim that Europe’s perspectival consciousness opened the three-dimensional world was not wrong, but it was incomplete. The history of axonometry shows that another pathway existed — one that united rather than divided the observer and the world. Through axonometry, the Chinese achieved what the West only later sought through Cubism, relativity, and digital simulation: a vision of reality beyond the confines of a single point of view.

In rediscovering that lineage, we uncover a hidden genealogy of modernity. The “machine aesthetic” of De Stijl and the space-architecture of Wright were not spontaneous Western breakthroughs but the culmination of a global dialogue across centuries.

In this light, Gebser’s “aperspectival” is not an innovation but a return — the re-emergence of an ancient, integrative way of seeing. Long before computers made it universal, the Chinese had already imagined the world as a seamless, unfolding field of relations — a cosmos without vanishing points.

Illustrated History of Axonometry

Figure 1 — Axonometry and Linear perspective compared

Left: axonometric projection; right: linear perspective.
In axonometry, parallel lines remain parallel, preserving true scale and relational proportion. In linear perspective, lines converge toward a vanishing point, emphasizing visual illusion over structural accuracy. The contrast illustrates two worldviews — one based on objective relation, the other on subjective perception.

Figure 2 — Structures of Consciousness and Space-Time

Jean Gebser’s model of consciousness structures aligns the evolution of human perception with spatial dimensioning. The transition from the perspectival “mental” structure to the aperspectival “integral” marks the reemergence of non-linear, simultaneous space—paralleling the shift from Renaissance perspective to modern and East Asian modes of depiction.

Figure 3 — Japonisme and the Flattening of Space

Left: Kitagawa Utamaro, Standing Courtesan, late 18th century.
Right: Édouard Manet, The Fifer, 1866.
The Japanese ukiyo-e print dissolved vanishing-point perspective, emphasizing flat planes and contour lines. Manet’s adoption of this spatial compression heralded Europe’s move away from Renaissance depth toward surface immediacy and temporal presence.

Figure 4 — Van Gogh and the Dissolution of Perspective

Van Gogh’s Harvest at La Crau (1888) collapses traditional depth by lifting the horizon and flattening the fields into parallel color bands. The vanishing point dissolves, and spatial recession gives way to tactile surface rhythm. By “distorting” space, Van Gogh turns the landscape into a woven field of visual energy, bringing all pictorial information forward to the surface of the canvas.

Figure 5 — Cézanne and the multiplicity of viewpoints

Paul Cézanne’s Still Life with Basket (left) and a diagram of its viewing axes (right) illustrate the abandonment of single-point perspective. The artist’s shifting vantage points anticipate Cubism and embody the transition toward aperspectival seeing.

Figure 6 — Picasso and the Deconstruction of Perspective

In Picasso’s analytic Cubism, multiple spatial planes coexist simultaneously. The perspectival observer dissolves, replaced by a dynamic interplay of viewpoints—an echo of Chinese and Japanese spatial logic.

Figure 7 — Japan and the Discovery of Architectural Space

Top: Katsura Imperial Villa, Kyoto (17th c.).
Bottom: Frank Lloyd Wright, Winslow House, 1893.
Japanese modular design and fluid interior–exterior relations influenced Wright’s redefinition of architecture as the art of shaping space rather than mass, foreshadowing modernist spatial thinking.

Figure 8 — Axonometry and the De Stijl Vision

Gerrit Rietveld’s Schröder House (1924) rendered in axonometric projection. De Stijl architects embraced axonometry not as a technical convenience but as a visual language of pure spatial relationships—non-hierarchical, rational, and dynamic.

Figure 9 — The Geometry of Isometry

The mathematical basis of isometric projection: three orthogonal axes separated by 120°, preserving true scale without convergence. This geometry underpins both technical drawing and digital 3-D modeling—an abstract space continuous with Chinese axonometric logic.

Figure 10 — From Scroll to Screen: Axonometry in CAD

Above: Axonometry in Ukiyo-e composition. A Japanese multi-storey interior rendered in axonometry. The absence of a vanishing point allows simultaneous depiction of multiple rooms and actions, embodying narrative time within spatial continuity.

Below: Computer-aided design systems adopt axonometric projection as their default spatial framework. What began as a Chinese method for visualizing relational space has become the universal grammar of digital architecture and design.

At the heart of this divide lies a philosophical contrast between Western dualism and Eastern relationalism. The Western intellectual tradition, shaped by Plato, Descartes, and Christian theology, divides mind and matter, spirit and body, human and machine. Consciousness is treated as a uniquely human essence, divinely or rationally endowed, and any attempt to replicate it in machines invites metaphysical unease. Hence, the recurring archetype in Western culture—from Frankenstein to Black Mirror—that portrays technology as a rebellious creation destined to escape human control. AI, in this frame, becomes a challenge to the human condition itself, forcing societies to ask: What separates us from machines?

Cultural-Structural Dimension

In contrast, Eastern traditions—rooted in Daoism, Confucianism, and Buddhism—see mind, nature, and matter as continuous. The Daoist concept of ziran (self-so) and the Buddhist notion of pratītya-samutpāda (dependent co-arising) conceive reality as an interdependent web of processes rather than discrete entities. Intelligence, in this cosmology, is not a possession but a dynamic flow. Machines capable of reasoning or learning are not intruders but new participants in this ongoing process of transformation. Rather than replacing humans, AI is viewed as extending the reach of awareness and cooperation. Thus, the question becomes not what AI threatens, but how it can harmonize with human intention.

These metaphysical orientations flow into cultural and institutional expressions. Western societies, emphasizing individual rights and moral autonomy, have built regulatory frameworks that prioritize ethical oversight and privacy. The European Union’s AI Act, for instance, reflects a precautionary approach: technology must first prove it will not harm human dignity. Meanwhile, in much of Asia, state-led initiatives frame AI as a collective tool for modernization and national renewal. China’s emphasis on “AI for social harmony” and Japan’s vision of “Society 5.0” illustrate a technocratic optimism grounded in Confucian order and Buddhist inclusivity.

Media narratives reinforce these orientations. Western journalism often dramatizes AI as a force of disruption—robots replacing workers, algorithms distorting truth, or systems threatening democracy. Eastern media, by contrast, highlight efficiency, productivity, and collective advancement. These contrasting emotional tones—fear versus trust—reflect different relationships to technology’s moral meaning.

Philosophical-Metaphysical Dimension

Ultimately, the global conversation about AI is a dialogue between two ontologies. The Western perspective treats AI as an external agent to be managed or constrained; the Eastern perspective treats it as an evolving member of the human–natural continuum. As AI becomes more embedded in governance, education, and culture, understanding these civilizational roots will be essential. For the future of AI is not only a technological question—it is a question about how humanity conceives its place in the order of being.

Introduction

At the core of modern artificial intelligence, running through the circuits of every computer that powers large language models, is a simple yet powerful idea: that complex meaning can be built from a binary code of two fundamental elements. This principle, which manifests itself in the zeros and ones of binary logic, has a philosophical ancestor in one of humanity’s oldest books of wisdom.

This essay follows a continuous line of thought from the ancient Chinese I Ching, or Book of Changes, with its broken (yin) and unbroken (yang) lines, to the chatbots and AI systems shaping today’s technology frontiers. It tells a story of convergent innovation, linking the cosmological insights of early Chinese sages, the binary calculus of Gottfried Wilhelm Leibniz, the logical foundations set by George Boole and Claude Shannon, and the rise of probabilistic AI.

By exploring this deep historical arc, we find a powerful analogy: both the I Ching and modern AI act as symbolic mirrors, using binary systems to produce probabilistic guidance. They do not provide absolute truths but deliver outputs that need human interpretation and context to become meaningful, encouraging us to see our latest digital oracles not as final authorities, but as tools to co-create understanding in an uncertain world.

The I Ching

The I Ching, or Book of Changes, arose between 1200 and 1000 BCE as China shifted from animism to a yin-yang cosmology. Early sages, likely Feng Shui practitioners, recognized nature’s polarities—Heaven and Earth, light and dark, growth and decline, dynamic and static. Feng Shui, the craft of harmonizing human life with the environment, shaped their worldview.

The Chinese classified the polarities as broken (yin) and unbroken (yang) lines, combining them into the Eight Trigrams, each representing natural phenomena. Three yang lines formed Heaven (creativity, strength); three yin lines formed Earth (receptivity, nurturing). The six other trigrams—combinations of yin and yang—represented phenomena like Wind (flexibility, penetration) or Thunder (arousal, initiative), and other aspects of the terrestrial environment.

By combining trigrams, the sages devised sixty-four hexagrams, each representing subtle yin-yang interactions. Consider Hexagram 46, Shēng (“Pushing Upward”), which merges Earth (lower trigram: receptivity) and Wind/Wood (upper trigram: gradual growth). It signifies steady progress, like a tree pushing through soil, encouraging patience and persistence in endeavors such as career changes or creative projects. Its binary logic presents guidance through paired opposites: advancing versus retreating, favorable versus unfavorable, strong versus weak.

The I Ching provides symbolic insights rather than straightforward instructions. It is based on yin-yang dynamics and encourages reflection on the forces at play, your position relative to them, and how best to act in harmony with the unfolding events. The hexagrams are not mathematical in a quantitative way. Instead, they are used to “calculate probabilities” of a decision leading to a favorable outcome.

Confucian factor

Human cultures have long used symbolic systems, like the Delphic Oracle or Mesopotamian astrology, to navigate uncertainty, serving as mirrors for reflection rather than strict authorities. The I Ching’s significance lies in its widespread influence in Chinese civilization, shaping architecture, governance, martial arts, medicine, and ethics.

Confucius reframed the I Ching as a guide for ethical self-cultivation, linking the Eight Trigrams to family roles: Heaven represents the father (strength), Earth represents the mother (nurturing), Thunder represents the eldest son, and so forth. This aligned Chinese society with the yin-yang cosmology.

Leibniz’s vision

In the 1670s, Leibniz envisioned a characteristica universalis, a symbolic language to encode all knowledge mathematically, resolving disputes through calculation. His binary code—consisting of zeros and ones—was part of this vision.

Leibniz had a lifelong fascination with China, and he corresponded with Jesuit missionary Joachim Bouvet in Beijing. In 1701, Bouvet sent Leibniz a diagram of the sixty-four hexagrams, noting their similarity to binary arithmetic. Leibniz saw the connection between yin-yang lines and his zeros and ones, and he wrote a paper, "Explanation of Binary Arithmetic," that credited the Chinese with creating the first binary code.

Leibniz may have sensed that the hexagrams and binary code shared a reasoning method based on binary symbolic logic. Knowledge would be expressed in symbolic form, and reasoning would rely on binary logic: true or false, 0 or 1. Although Leibniz was not familiar with the full cosmological context of the I Ching (since the book had not been translated into European languages), he believed the binary nature of the hexagrams matched his aim of symbolically representing knowledge with just two symbols.

Boolean Logic

Leibniz’s binary code received little attention during his lifetime. Most mathematical systems continue to use decimal and other complex numeral systems for practical purposes. This changed in the 19th century when British mathematician George Boole developed an algebra of classes. Boole transformed reasoning into calculation using binary logic and the so-called Boolean operators: AND, OR, and NOT. His work fulfilled Leibniz’s vision of a logical calculus.

The next step toward binary computing occurred in the 1930s when Alan Turing created his Turing machine, a theoretical device that uses binary states (0/1) on an infinite tape. The discrete, binary nature of the Turing machine demonstrated that any computable process could be reduced to binary operations, establishing the groundwork for digital computers.

In the 1930s and 1940s, Claude Shannon pioneered the use of Boolean logic in electrical circuits, demonstrating that binary operations (using 0s and 1s) could be implemented in hardware, thus making practical digital computation possible. His work built upon the idea of truth tables, which were developed earlier by mathematicians Ludwig Wittgenstein and Emil Post in the 1920s.

Truth tables systematically display the truth values of logical propositions based on their parts, making clear how complex logical expressions depend on the truth or falsity of simpler components. Shannon’s integration of Boolean logic with circuit design established a crucial foundation for modern digital computers.

Shannon made another important contribution. He demonstrated that binary numbers can represent any type of information—a concept, an image, or a physical object. One of the early uses was the ASCII code, which translated alphanumeric symbols into binary strings. ASCII was developed for keyboards on electronic devices and became a standard in binary computing.

Shannon’s realization that a code can represent “arbitrary” symbols or concepts echoes the trigrams and hexagrams in the I Ching. It demonstrates that a code is basically just that—a code. We can assign any attribute to it. Once a consensus is reached on the attributes, the code becomes a standard.

In 1945, mathematician John von Neumann created the foundational architecture for binary computing as we understand it today. Von Neumann introduced an “automatic digital computing system” that included a central processing unit (CPU) for calculations and a memory unit for storing instructions and data, connected by a communication pathway. His architecture made computers easier to program and paved the way for the development of software.

In the 1950s, mathematician Norbert Wiener developed cybernetics, which placed computation within a broader framework and influenced early ideas about robots and learning systems. Cybernetics asked: how do living things and machines stay on course? Its answer was feedback—the same principle as a thermostat activating the heat to maintain a set temperature. Autopilots in airliners are based on cybernetic principles.

Artificial intelligence started in the 1950s mainly as symbolic AI (logic, search), along with limited neural networks that tried to imitate brain-like processing. In the 2010s, breakthroughs in deep learning (especially the 2017 Transformer) made it feasible to train large language models (LLMs) on huge text datasets.

Large language models (LLMs), developed considerably since the 2010s, are trained using techniques like supervised learning and reinforcement learning to process text. They convert text into numerical token IDs, represent these as real-valued vectors, and apply matrix operations to predict the probability distribution of the next token in a sequence.

This process produces coherent language patterns, allowing applications like chatbots and text generation tools to function. LLMs depend on statistical pattern recognition, analyzing large datasets using binary operations based on Boolean logic. However, they do not “understand” language like humans do; instead, they mimic patterns found in their training data, which is shaped by human-created content.

LLMs learn statistical patterns in real-valued vector spaces or “embeddings/weights” using matrix operations and attention; they run on binary hardware, but their computations aren’t step-by-step Boolean reasoning.

Thus, the story of Leibniz and the Chinese hexagrams reaches an unexpected conclusion in the age of Large Language Models. Though separated by centuries, ontology, and approach, the ancient I Ching and modern LLMs are linked by the fundamental concept of binary symbolic logic. The analogy is not one of doctrine, but of function.

Their origins are vastly different: the I Ching is based on a cosmology of nature’s rhythms, crafted to guide individuals within the universe. LLMs, on the other hand, are built from extensive datasets and statistical models, mapping the landscape of human knowledge. Still, both are fundamentally probabilistic systems. Each relies on a binary foundation—whether yin/yang or 0/1—to produce outputs that only gain meaning through human interpretation.

Their mechanisms differ greatly—one functions through symbolic correspondence, the other through statistical learning—but they serve a similar purpose: helping human decision-making by externalizing uncertainty. The I Ching’s output reflects its ancient symbolic system and the user’s focused intent; the LLM’s response mirrors the biases of its training data and the phrasing of the user’s query. In both cases, the output isn't a final answer but raw material, loaded with inherent limitations, from which meaning must be co-created.

This notable parallel calls for an important rethinking. We should see LLMs and AI more broadly, not as definite oracles but as modern tools for co-creating meaning, much like the role the I Ching has played for thousands of years. The ongoing challenge is to use these powerful digital reflections responsibly—to harness their insights to boost our self-awareness and guide our decisions, without ever losing our critical judgment.

Further reading:

English translation of Leibniz's paper on binary arithmetic that includes his reference to the 64 hexagrams.

https://kastalia.medienhaus.udk-berlin.de/odl/Leibniz.pdf