Warning from a 30-Year Banker: The AI Job Crisis is 10x Crueler Than the 90s Layoffs. How to Build Your “Human Algorithm” Moat.

Preface: A Sense of Déjà Vu Spanning Thirty Years

There is an image in my memory that I can never erase. It was the winter of 1996. I had only been a loan officer for a few years. That afternoon, to complete a post-loan inspection for a non-performing loan, I took a bumpy bus to a state-owned textile factory on the outskirts of the city. That factory had once been the pride of our city; for my mother’s generation, securing a job there as a “textile girl” was a great honor.

When I stood before the factory’s massive, rust-stained gates, I was met not by the roar of machinery, but by a dead silence. Only the biting afternoon wind of a northern winter howled as it poured through the shattered windows, a sound like a dirge for a bygone era. The gates were chained shut. The glass of the guardhouse next to it was also broken, leaving nothing but a hollow frame. Through the gaps in the iron gate, I could see the overgrown weeds in the courtyard and a huge, red-painted slogan on the wall. The paint was peeling and faded, but I could still make out the words: “Time is money, efficiency is life.”

Just as I was preparing to leave, I saw several middle-aged men and women, about my parents’ age, standing blankly on the curb across the street, smoking one cigarette after another. Their eyes were fixed on the place where they had dedicated half their lives. I can never forget their expressions. There was no anger, no outcry, but rather a profound sense of bewilderment and helplessness. It was as if the ground had suddenly split open beneath their feet, and they had no idea which way to turn.

In that moment, for the first time, I viscerally and tangibly understood the term from my textbooks: “structural unemployment.” Later, we all came to call that wave, which swept over an entire generation, “the great layoff tide” (xiàgǎng cháo). Like a blunt knife, it slowly but deeply altered the destiny of countless families. That was my first and most brutal lesson, as a financial professional, in the power of “contractionary force.”

The lens pulls back to today, January 2026, thirty years later. I am sitting in a bright, clean office, the coffee in my hand still steaming. On my screen, news from the just-concluded Consumer Electronics Show in Las Vegas and the World Economic Forum in Davos flashes by. The headlines are almost unanimously dominated by a single keyword: Artificial Intelligence. The CEOs of NVIDIA, Google, and Microsoft, like oracles delivering prophecies, showcase the latest AI agents and humanoid robots capable of writing, coding, analyzing financial reports, and managing production lines—capable of almost anything. Meanwhile, in the snowy mountains of Switzerland, the world’s top politicians and economists are gravely discussing the “disruptive impact of AI on the global job market.”

In that moment, an intense sense of déjà vu, spanning three decades, struck me without warning. It was as if I was seeing that winter afternoon in 1996 all over again, seeing those bewildered, helpless eyes across from the factory.

The difference is, the machine that crushed their livelihoods back then was a tangible “efficiency bulldozer” that replaced human hands. Today, the machine laying siege to us is an intangible “intelligence algorithm” that aims to replace the human brain. The shockwave back then was largely confined to the secondary industry, with clear sectoral and geographical boundaries. Today’s tsunami is indiscriminate, cross-industry, and global.

I realized that we are entering a new era of “great unemployment.” And this one will likely be ten times crueler than the layoff tide of thirty years ago. Its cruelty lies not in the absolute number of the unemployed, but in the fundamental change in the nature of unemployment. It shakes the very foundation of our value as Homo sapiens, as the sole “intelligent beings” on this planet.

And so, the ultimate question struck my mind like a tolling bell: When machines can replace not only your hands but also your brain, where should we, the common people, build our “moat”?

Part 1: The Mirror of History—The Essential Differences Between Two “Great Unemployment” Eras

To understand the unique nature of today’s storm, we must return to the coordinates of history. Like a meticulous risk assessment officer, we must lay out two reports side-by-side for a line-by-line comparison. One is on the layoff tide of the 1990s; the other, on the unemployment wave being triggered by AI in 2026. Only by clearly seeing their essential differences can we truly comprehend what “ten times crueler” actually means.

I. The Difference in Speed and Scope: From “Bulldozer” to “Dimensional Strike”

First, the impact patterns of the two waves are worlds apart.

The layoff tide of the 1990s was more like a powerful “bulldozer.” Driven by market-oriented reforms and the global division of labor, this bulldozer had a clear objective: to level the inefficient and over-capacitated fortresses of traditional industry. While its force was immense, its advance was linear, and its operational range was bounded.

By linear, I mean its development was predictable. From the initial whispers of central policy and the tightening of bank credit to the point where companies could no longer pay wages and finally declared bankruptcy and restructuring, the process, though painful, often allowed for a buffer period of several months or even a year or two. Those affected, though anxious, still had some time and space to “dodge.”

By bounded, I mean its impact was primarily concentrated in specific industries and regions. For instance, heavy industry in the Northeast, coal in Shanxi, and textiles in Shanghai. A worker in a textile factory could clearly see his industry declining, but he could also see that in another part of the city, service industries like real estate, catering, and retail were booming. This meant his life had not hit a dead end; there were still “escape routes.” He could move from a sinking sector to a rising one. This was a bulldozer-style transformation; it destroyed the old but also left behind plenty of “ruins” and “empty lots” where a new ecosystem could grow.

The unemployment wave triggered by AI today, however, is of a completely different nature. If the former was a bulldozer, the latter is what the science fiction novel The Three-Body Problem describes as a “dimensional strike.”

What is a dimensional strike? It is an attack launched by a higher-dimensional civilization against a lower-dimensional one, an attack the latter can neither comprehend nor defend against. The impact of AI on the human job market is precisely this.

Its speed is exponential, not linear. AI’s development follows a kind of “intelligence Moore’s Law”; its capabilities don’t just increase by 10% annually, they can double every few months. Last year, AI-generated art was just an amusing toy producing flawed images; this year, the commercial posters it generates are already putting many human designers out of work. Last year, AI still needed significant human programmer intervention to write code; this year, it can independently complete entire software modules. This acceleration makes all career planning based on past experience seem fragile. The skills you spent four years in university acquiring may already be offered cheaply, or even for free, by AI the moment you graduate. It gives you no time to “dodge.”

Its scope is boundless, not localized. A bulldozer has a clear operational area, but a dimensional strike covers the entire plane. This time, AI is impacting not just the physical labor of blue-collar workers, but the “cognitive abilities” of the white-collar class—the very core asset that the entire education system and the middle class have prided themselves on for decades.

Let’s look at the list of affected professions: paralegals, junior accountants, software testers, graphic designers, translators, customer service representatives, data analysts, even radiologists… In the past, these were synonymous with stable, respectable, high-income jobs. They all required long-term educational investment and professional training. But today, their core workflows—information processing, pattern recognition, content generation—are precisely what AI excels at. AI is not competing with you for a specific job; it is directly dismantling the very “cognitive processes” upon which these jobs are built. It is not leveling a single factory; it is causing the very foundations of the entire industrial park to subside.

This boundlessness means a drastic reduction in “escape routes.” You jump from one industry to another that seems safe, only to find with horror that the dark cloud of AI looms there as well. This is an unprecedented systemic risk, creating a sense of inescapable pressure for everyone caught within it.

II. The Difference in Re-employment Paths: From “Switching Tracks” to “The Disappearance of Tracks”

If the difference in impact patterns determines the force of the blow we endure, then the difference in re-employment paths determines the difficulty of our “post-disaster reconstruction.”

Although the layoff tide of the 1990s caused immense pain, we must admit that China at that time was in a grand upward historical trajectory. The waves of urbanization and industrialization provided a “re-employment buffer zone” for tens of millions of laid-off workers—a path that was arduous but real. Their re-employment journey can be summarized as “switching tracks.”

This new track could be manual labor. A laid-off steelworker’s strong body was still a scarce resource. He could go to a construction site, work in home renovation, or find a place in the logistics industry. His working conditions worsened and his income became unstable, but he could still trade his “strength” for his family’s sustenance.

This new track could also be in the service industry. A laid-off female textile worker could open a small convenience store, become a waitress in a restaurant, or work in domestic services. As urban life became richer, the demand for these “human-centric” services exploded.

This new track could also be in the private economy. Many people “plunged into the sea” of business for the first time then, driving taxis, setting up food stalls, or starting clothing businesses. The doors to the market economy had just opened, and there were blank spaces everywhere. As long as you were willing to work hard and take risks, you could always find a way to make a living.

Thus, we see that while that generation of laid-off workers went through an extremely difficult period, most of them eventually reintegrated into the economic system by “switching tracks.” They moved from one “large collective” into countless “small ecosystems.” The core logic was “skill transfer”—they lost their specific factory skills, but their fundamental value as “labor” was not negated. Society still needed a vast amount of manual and service-oriented labor, which served as a crucial “safety net.”

However, the challenge posed by AI today is no longer about “switching tracks,” but about “the disappearance of tracks.”

AI is systematically eroding the tracks on the lower and middle rungs of the “cognitive ladder.” Let’s conduct a thought experiment. A junior accountant in 2026 has his daily work of processing invoices, reconciling accounts, and generating basic reports. Now, a company develops an AI financial software that can perform these tasks 24/7 with far greater accuracy than any human. This accountant is laid off.

According to the old logic, he should get “retrained” and switch to a new track. For example, seeing that programmers earn high salaries, he enrolls in a coding bootcamp. After a year of hard study, he prepares to transition into a junior programmer role. But upon graduation, he discovers to his despair that AI’s code-generation capabilities have shrunk the demand for “junior programmers” by 80%. The very track he was running towards was turning to sand and disappearing in the process.

He might think, then I’ll become a designer. Or a translator. Or a copywriter. Unfortunately, these tracks are undergoing the same process of erosion by AI. What AI is doing is turning all “standardized, repetitive” mental labor into its hunting ground. In the past, we needed ten years of arduous study to climb the first step of the cognitive ladder; now, AI has built a high-speed elevator between the ground and the first step, instantly displacing countless people who were in the middle of their climb.

What’s more terrifying is that this “disappearance of tracks” is not just happening at the low end. As large language models evolve, it is beginning to creep upwards, eroding the “middle-class tracks” that require complex analysis and judgment. When AI can assist doctors in making more accurate diagnoses, help lawyers draft more rigorous legal documents, and aid fund managers in building more effective investment models, the demand for “human experts” in these fields will inevitably undergo a structural change.

Therefore, re-employment in the 1990s was a transfer between parallel markets. Re-employment in the AI era is a brutal, vertical competition of climbing upwards. You are not only competing with your fellow humans, but also with a “silicon-based species” that learns a million times faster than you and whose operating costs approach zero. This increases the difficulty of “re-employment” exponentially.

III. The Difference in Psychological Deprivation: From “Losing One’s Livelihood” to “Losing One’s Meaning”

This is the most profound and cruelest difference between the two waves, touching upon the human spirit.

The layoffs of the 1990s inflicted immense psychological trauma on that generation. The core psychological deprivation was the economic insecurity from “losing one’s livelihood” and the disillusionment with the “work unit” (dānwèi), which had once been a source of identity and spiritual sustenance. The fall from being a respected state-owned enterprise employee to an unemployed person worrying about next month’s utility bills was enough to crush a person’s dignity.

However, we must see that this psychological trauma was primarily confined to the “survival” and “social” levels. It did not fundamentally negate the value of “labor” itself. The prevailing social value was still that “labor is glorious.” A laid-off worker who started pulling a rickshaw or doing manual labor, though his life was hard and his social status low, could still feel a sense of groundedness and dignity in “earning a living with his own two hands.” He could tell his children, “Dad may not be successful anymore, but I haven’t stolen or robbed. Every penny I earn is clean.”

This simple value system, rooted in a “work ethic,” was a vital psychological pillar that helped that generation navigate their spiritual crisis. They lost their “iron rice bowl,” but they did not lose their fundamental belief that “hard work can change one’s destiny.”

The psychological deprivation brought by the AI-era unemployment wave, however, is twofold and far more devastating. It not only makes you “lose your livelihood,” but it also makes you “lose your meaning.”

When a master’s graduate who spent seven years in law school discovers that the core skills he painstakingly acquired—case research, contract review, document drafting—can be performed by an AI application in seconds and with superior quality, what he feels is far more than just the panic of unemployment. It is a deeper collapse, a collapse of “self-worth.”

He will begin to question: What was the point of my seven years of effort? What is the value of my proud “professional knowledge” compared to a machine? Is the whole purpose of our education system, which invests enormous costs to cultivate so-called “knowledge workers,” only to have them be easily crushed by algorithms upon graduation?

This fundamental negation of personal value is something the laid-off workers of the 1990s did not experience. Their pain was “I am no longer useful”—my position in that specific factory is gone. The pain of the new generation of knowledge workers is “We are no longer useful”—as humans, our vaunted cognitive abilities of logic, analysis, and synthesis appear so clumsy and inefficient before a higher intelligence.

This is a deprivation of “meaning.” When the very capabilities upon which a person relies to define himself, gain social respect, and realize his life’s value are undermined by a non-human intelligence, he faces not just a simple economic crisis, but a profound “existential crisis.” He must answer a question that has never been so urgent: “Who am I? What, truly, is my unique value?”

The ancient classic The Great Learning states: “Things have their roots and branches; affairs have their end and their beginning. To know what is first and what is last will lead near to the Way.” If the layoff tide of the 1990s impacted the “end of affairs,” the specific jobs and livelihoods, the “branch” of the problem, then the unemployment wave triggered by AI impacts the “root of things,” the fundamental value of humans as cognitive subjects, the “root” of the problem. When the very foundation of our existence begins to tremble, the shock and cruelty it brings are naturally far greater than anything in the past.

This is the real reason I define it as “ten times crueler.” It is not just an economic transformation, but a stress test on human civilization. Every one of us is being pushed to the cliff’s edge of redefining our own value.

Part 2: The Four Forces Compass—Deconstructing the Mechanics of Survival in the AI Era

The grand transformations of any era are never caused by a single factor, but are the result of the interplay and entanglement of several fundamental forces rooted in human nature and the underlying logic of society. In the AI era, these four forces—the Evolutionary Force, the Expansionary Force, the Contractionary Force, and the Balancing Force—are churning the fate of every one of us in an unprecedented way.

I. The Evolutionary Force (The Dominant Force): The Unstoppable “Super-Efficiency Machine”

First, we must recognize that the fundamental force driving all of this is the “Evolutionary Force.”

Here, the Evolutionary Force specifically refers to the eternal human pursuit of increased efficiency and cognitive breakthroughs, represented by Artificial Intelligence. It is a structural, cycle-transcending force of progress. But today, this force has exhibited a terrifying characteristic: it has acquired the ability to evolve itself.

In past human history, whether it was the steam engine or the computer, the tools themselves did not self-iterate. Improving the efficiency of a steam engine required a genius like James Watt; increasing the computational power of a computer required countless engineers toiling in laboratories. The evolution of tools was always constrained by the speed of human intellectual evolution.

But AI is different. Especially since the advent of deep learning and large language models, AI has, for the first time, demonstrated the ability to “self-learn” and “self-optimize.” It is like a “super-apprentice” that never tires, never makes mistakes, and has zero learning costs. It can summarize patterns and iterate its own algorithms from massive amounts of data.

This means that AI, as an “Evolutionary Force,” has broken free from linear human control and entered an exponential track of self-acceleration. It is no longer a passive tool, but more like an active, ever-expanding “silicon-based life form.” Its sole mission is to pursue ultimate efficiency, optimizing every optimizable process to its physical limit. Its replication cost approaches zero; its marginal cost also approaches zero.

This “super-efficiency machine” that cannot be stopped is the greatest “variable” and “engine” of our time. It carries no emotional color, considers no ethical principles; it merely executes its underlying directive of “efficiency maximization” with cold, unwavering determination. Understanding this, we understand the source of all subsequent forces. This Evolutionary Force is the first beam of light projected onto the prism of our era—it is powerful, dazzling, and unstoppable.

II. The Expansionary Force (The Carnival of Capital): Hot Money Flooding into “Human-Free Zones”

The Evolutionary Force itself is neutral, but when it combines with the most primal human impulse—the “Expansionary Force”—it unleashes tremendous energy.

The Expansionary Force stems from human hope, ambition, and the profit-seeking instinct. In the economic sphere, its most direct manifestation is the movement of capital. The sole purpose of capital is to multiply. Wherever efficiency is highest, costs are lowest, and profit margins are greatest, capital will surge like a tide.

In the AI era, capital, that sharp-nosed beast, is pouncing with unprecedented fervor on every field empowered by AI. It has discovered that AI, this “super-efficiency machine,” is the perfect tool for achieving excess profits. In the past, the expansion of capital was always constrained by its greatest “cost item” and “uncertainty factor”: people. People need salaries, need rest, make mistakes, complain, and form unions. AI perfectly solves all these “troubles.”

Thus, we see global hot money madly pouring into “human-free zones” that can maximize “de-humanization.” A quantitative hedge fund using AI algorithms can manage tens of billions of dollars with a team of three. A digital media company using AI to generate content can operate three hundred social media accounts with a team of five, producing tens of thousands of articles and videos daily. A “dark factory” armed with humanoid robots and the industrial internet can operate 24/7 without any human intervention.

In these areas, the Expansionary Force of capital is maximized. Balance sheets become extremely “clean,” labor costs are compressed to a minimum, and profit margins are astonishingly high. For investors, this is undoubtedly a carnival. New wealth myths are being created at a speed even faster than during the internet era.

But the B-side of this carnival is a cold reality. For every “human-free zone” that capital floods into, it means that in the traditional “human-occupied zones,” countless people are losing their value. Aided by AI, capital is undertaking an unprecedented “migration,” moving en masse from the crowded, expensive, and inefficient “human labor market” to the vast, cheap, and efficient “computing power market.” For the economy as a whole, this may be a “Pareto improvement,” but for those left behind, it is an unmitigated catastrophe.

III. The Contractionary Force (The Individual’s Catastrophe): The Accelerated Depreciation of Human Capital

As the Evolutionary Force drives the Expansionary Force on its wild run, what most ordinary individuals feel is the bone-chilling “Contractionary Force.”

The Contractionary Force stems from human fear, conservatism, and risk aversion. In a downward economic cycle, it manifests as deleveraging and shrinking demand. But in this structural revolution triggered by AI, its core manifestation is that “human capital” is undergoing an unprecedented “accelerated depreciation.”

In my thirty-year career in banking risk management, I have reviewed tens of thousands of corporate financial statements and assessed countless “collaterals” for individuals and businesses. The word “collateral,” as cold as it sounds, most accurately reflects the value of an asset. Today, I want to use this lens to examine the “human capital” of each and every one of us.

In the past, a person’s knowledge, skills, and academic qualifications were their most important “collateral.” With this collateral, they could “take out a loan” from society in exchange for a stable job, a decent income, and a predictable future. A diploma from a prestigious university was like a prime piece of real estate in the city center—an absolute “hard currency” whose value was stable and would appreciate slowly over time. A professional license like a CPA or a law degree was like a high-value commercial property, capable of generating a steady stream of “rental” income.

But today, things have changed. Before the super-appraisal machine of AI, the “knowledge collateral” of all of us is being subjected to a rigorous “revaluation.” And for most people, the result is “accelerated depreciation.”

The programming language you spent four years learning may see its market value depreciate by 50% within two years due to the emergence of AI coding assistants. The foreign language translation skills you honed for years are rendered almost worthless in the face of real-time AI simultaneous interpretation. Your so-called “work experience,” if that experience is essentially just “data” that can be summarized and learned, can be mastered by AI in a single day, eclipsing your decade of accumulation.

This “accelerated depreciation” is the most brutal asset value shrinkage I have ever seen in my career. It is more devastating than any asset price crash in a financial crisis. Because if stocks fall, they might rise again; if your house price falls, it still has use value as long as you live in it. But once your core professional skills are replaced by AI, they may be permanently and irreversibly “written off,” like pagers and film rolls in their day.

I still remember reviewing the loan application for an AI startup last year and seeing their financial statements for the past three years. Those few thin sheets of paper sent a chill down my spine. I noticed a trend: in their cost structure, the line item for “R&D personnel salaries” grew at about 15% annually, a linear increase. But another line item, “Cloud Computing and AI Service Subscription Fees,” grew at 300% annually, an exponential increase. Even more astonishingly, their Revenue Per Employee had quintupled in three years.

From the perspective of a loan approval officer, this was a flawless, beautiful financial model. It meant lower marginal costs, higher per-capita efficiency, and stronger profitability. I ultimately approved the loan. But as I signed my name, I knew in my heart that behind every soaring number on that report was the cold fact of a “human job” being replaced by an algorithm. The company’s founder, and the capital chasing them, were using real financial choices to systematically and mercilessly vote for the elimination of humans. This is the micro-manifestation of the Contractionary Force. It is happening in every company pursuing efficiency, in every industry embracing AI.

IV. The Balancing Force (Society’s Struggle): The Failing “Shock Absorbers”

Faced with such powerful Evolutionary and Expansionary Forces, and the intense contraction they bring, society instinctively activates the “Balancing Force” to intervene and regulate.

The Balancing Force stems from society’s demand for stability and order, and it is usually executed by public sector entities like governments, educational institutions, and social organizations. Its goal is to act as a “shock absorber,” providing a buffer and a safety net for those thrown off the high-speed train of social change.

Today, we can already see various attempts at exerting this Balancing Force. Governments worldwide are promoting “universal digital skills re-education” with unprecedented vigor. At elite forums like Davos, “Universal Basic Income (UBI)” and the four-day work week have moved from utopian fringe ideas to serious policy agendas. The education system is also undergoing a difficult reform, trying to shift from “knowledge infusion” to “competency cultivation.”

These efforts are undoubtedly necessary and respectable. But we must soberly recognize that in the face of the “super-catfish” that is AI, our existing Balancing Force tools may be systematically failing.

The biggest problem is that these “shock absorbers” were designed for a world of “linear change.” They were prepared for the cyclical unemployment of the industrial age, the kind that happened “once a decade.” What we face today is a discontinuous shock of “exponential change.”

The pace of educational reform is measured in “years.” Establishing a new university major and updating its curriculum often takes a two to three-year cycle. But AI’s technological iteration is measured in “months” or even “weeks.” It’s like trying to catch a starship by building a wooden boat. The speed of educational reform simply cannot keep up with the speed of technological obsolescence.

The reform of social security systems is even slower and more complex, requiring broad social consensus and lengthy political negotiation. By the time a proposal for Universal Basic Income is debated and settled in parliament, millions from the middle class may have already fallen into abject poverty.

We are caught in an awkward “time lag”: the speed at which the problem is exploding is exponential, while the speed at which solutions are formed is linear. This causes society’s “shock absorbers” to seem inadequate in the face of immense impact, and they might even “fail” at critical moments.

This is the extremely severe mechanical environment we find ourselves in today. A triumphant “Evolutionary Force” is infinitely amplifying capital’s “Expansionary Force” and the individual’s “Contractionary Force,” while the “Balancing Force” responsible for maintaining stability is struggling to catch up, teetering on the brink of collapse. In this massively imbalanced structure, every ordinary person is like a small boat adrift in a stormy sea, facing the danger of being swallowed up at any moment.

So, where is the way out? When all external certainties are crumbling, the only thing we can do is to explore inward, to find the one “ballast stone” within us that cannot be copied by AI, priced by capital, or rendered obsolete by the times. And this is the core of what we will explore in the third part: rebuilding our moat.

Part 3: Rebuilding the Moat—The Ultimate Leap from “Skills” to the “Human Algorithm”

When all external certainties are shaking, and the “tracks” we rely on for survival disappear one by one, the instinctive human reaction is panic, a desperate attempt to grasp onto something. Thus, “learning new skills” has become the most popular “placebo” of our time. Today, we learn Python; tomorrow, Midjourney; the day after, we sign up for a short-video operations class. We seem to be caught in an arms race, madly stockpiling various “skill munitions,” hoping to arm ourselves with more certificates and newer tools to fend off the AI invasion.

But today, I must say something that may make you uncomfortable: In the age of AI, relying solely on “skills” to build a moat is akin to building a sandcastle in the face of a tsunami.

I. Shattering the “Skill” Myth: Why Your “Specialized Skill” Is No Longer a Moat

In the industrial and early information ages, a “specialized skill” could serve as a moat because of its “scarcity.” A skilled welder, a programmer who knew C++, possessed skills that were in short supply on the market. This scarcity was their bargaining power, their value.

But the advent of AI has fundamentally destroyed the scarcity of “standardized skills.”

Any skill that can be clearly defined, broken down into a standard process, and measured by data is inevitably the first territory AI will conquer. AI’s learning cost is near zero, its replication cost is zero, and its error rate is zero. It can take a once-scarce skill and “inflate” its availability to the point of worthlessness, almost for free. It’s like the alchemists of old, painstakingly seeking to turn stone into gold, only for AI to invent a machine that can infinitely replicate gold, making gold itself as cheap as sand.

The prompt engineering you spend six months learning today may be rendered meaningless by the next iteration of AI’s natural language understanding capabilities, because everyone will be able to converse with AI effortlessly. Your vaunted data analysis ability might be reduced to merely asking the right questions in the face of an AI agent that can independently clean, model, analyze, and derive insights from data.

Therefore, we must shatter this “skill myth.” To continue racing down the path of “learning more and newer skills” is essentially to pit our human “linear” learning speed against AI’s “exponential” iteration speed. It is a race we are destined to lose from the very beginning.

So, what is the real moat?

II. Defining the New Moat: “The Human Algorithm”

After long observation and reflection, I believe that in the AI era, the only eternal moat for the common person is not any external “skill,” but our internal, unique mental model that cannot be quantified, replicated, or encoded.

I call this “The Human Algorithm.”

Please note, the word “algorithm” here is not meant literally, as in writing code. It is a metaphor. It refers to that unique, stable operating mechanism deep within you for processing information, building connections, making decisions, and creating value. This mechanism is composed of the entirety of your life experience, your values, your emotional patterns, your aesthetic preferences, and your bodily intuition. It is not a learned “skill” but a cultivated “character.”

If AI’s algorithm, built of silicon and electricity, pursues ultimate “efficiency” in a world of 0s and 1s, then our “Human Algorithm,” built of flesh and blood, seeks profound “meaning” in a world of love and fear.

This “Human Algorithm” is composed of three core elements. These three elements are precisely the domains where AI finds it most difficult, perhaps even impossible, to ever truly reach.

A. The Ability for Deep Empathy and Building Trust

AI can simulate conversation, recognize emotions, and even offer words of comfort that are more considerate and “correct” than any human. But what it can never do is build genuine “trust” based on emotion, sacrifice, and long-term commitment.

Trust is the most efficient cooperation mechanism in human society. It cannot be encoded, cannot be quantified. It can only be slowly cultivated through repeated, concrete, real, and sometimes even “un-cost-effective” interactions.

In my banking risk management days, when approving a loan, beyond looking at the cold financial data, the final step was always to sit down and talk with the founder of the business face-to-face. Sometimes, a company’s data wasn’t perfect, even had flaws. But in conversation, I could see in his eyes an almost obsessive love for his enterprise; I could sense his craftsmanship in his meticulous attention to product details; I could see his sense of responsibility in his care for his employees. This was “human data” that could not be reflected in a spreadsheet. In the end, I might make the decision to “approve” based on the “trust” generated by this empathy. And experience often proved that such entrepreneurs had the lowest default rates.

The value of a top-tier family doctor lies not in how much medical knowledge he can recite—AI can do that better. His core value is that he knows your entire family’s medical history, your child’s allergies, and understands your fear when facing illness. He can explain complex medical conditions in a language you understand and give you comfort with a steady gaze. This relationship of trust, built on long-term companionship and deep empathy, is something AI cannot provide. This is his moat.

Therefore, the core value of professions that require the building and maintenance of “deep trust”—such as excellent managers, psychotherapists, educators, community organizers, and business professionals engaged in complex negotiations—will not be diminished by AI, but will instead be significantly highlighted.

B. The Ability for Cross-Disciplinary Integration and Complex Decision-Making

AI is an expert in “depth.” It can achieve perfection in a vertical, well-defined domain. But humans, especially exceptional ones, are masters of “breadth.”

Our world is full of “gray areas” where information is incomplete, rules are unclear, and objectives are contradictory. Making decisions in these areas requires not computation, but “judgment.” This judgment is an ability to creatively “integrate” knowledge from different fields, information from different dimensions, and even intuition and experience.

What does a successful entrepreneur need to integrate? He needs to integrate insights into market demand, understanding of cutting-edge technology, a grasp of human nature, the maneuvering of capital, and anticipation of macro policies. There are no standard answers for any of these. He must make one “non-consensus” decision after another amidst enormous uncertainty, relying on his own cognitive framework and value preferences. This is his moat.

An excellent art curator needs to seamlessly integrate knowledge of art history, the artist’s personal style, the spatial structure of the gallery, the psychological expectations of the audience, and the demands of commercial sponsorship to create a unique “experience.” This, too, is an integrative creation that AI cannot accomplish.

Therefore, the value of roles that require “cross-disciplinary integration” and “complex decision-making”—such as entrepreneurs, strategy consultants, product managers, directors, and outstanding investors—will become increasingly scarce.

C. The Ability for Aesthetic Taste and the Creation of Meaning

This is humanity’s final and most solid fortress.

AI can generate content. It can write poems, compose music, and create paintings. But it cannot define “beauty,” much less create “meaning.”

All works generated by AI are, in essence, the learning, imitation, and recombination of existing human data. It knows what color combinations and melodic progressions are most popular “statistically.” But it does not know “why” a particular work touches the human heart. It has no independent aesthetic standards, and certainly no unique “soul” born from life experience.

But humans can. A great brand creator can infuse his understanding of life and his commitment to certain values into a seemingly ordinary product, endowing it with a unique “brand personality” and “cultural meaning.” This makes consumers feel they are not just buying a function, but an identity and a spiritual belonging. From Apple’s minimalism to Patagonia’s environmentalism, behind them lies a powerful “meaning-creation ability.” This is their moat.

An artist is great not because his painting technique is superior, but because his work proposes a new way of seeing the world, expressing a profound human emotion never before expressed. Van Gogh’s The Starry Night moves us across a century not because its composition and brushwork are technically flawless, but because we can feel in it a burning, painful, yet incredibly brilliant life force.

This ability to bestow “aesthetics” and “meaning” upon things is a unique and ultimate creative power of humanity, rooted in our finite lives and rich emotional experiences. It is AI’s eternal “Achilles’ heel.”

III. Illumination from the “Human Prism”: How to Find Your “Human Algorithm”

At this point, you might ask: these abilities sound very advanced, seemingly distant from ordinary people like me. How can I build my own “Human Algorithm”?

The answer lies precisely within each of us. That unique “Human Algorithm” is not some standardized formula; it is hidden within your one-of-a-kind “Human Prism.”

The so-called “Human Prism” is your deepest, most stable core value system and life passion. It is the fundamental basis for your judgments of right and wrong, for the choices you make. It is the final resting place where “your heart finds peace.” Like a unique optical prism, it determines how you will refract the storms of the external world and ultimately carve out your own path in life.

Therefore, finding your “Human Algorithm” is not a process of chasing popular skills externally, but a journey of inward exploration and self-discovery.

Late at night, when all is quiet, turn off your phone, take out a piece of paper, and seriously ask yourself a few questions:

• Setting aside all the external noise about what you “should do” and “what makes more money,” what do you truly “love”? What activities make you lose track of time and feel genuine joy and immersion? Is it engaging in deep conversation with people, or solving a complex logical puzzle? Is it creating a small, beautiful object, or strategizing within a grand system?
• Looking back on your life, what was your proudest “moment of achievement”? In that moment, which ability did you primarily use? Was it your empathy and communication skills, your analytical and integrative abilities, or your imagination and creativity?
• Who is the person you admire most and wish to become? What is the quality in them that moves you the most?

The intersection of where your passion lies, where your talent lies, and where your role model lies—that is the very core of your “Human Algorithm.”

Once you find this core, all your subsequent efforts should be like an investor deploying capital around their core circle of competence—you must deliberately and systematically strengthen it.

If you discover your core is “deep empathy and building trust,” then stop trying to compete with AI on data analysis. Instead, invest more time in the study and practice of psychology, the art of communication, negotiation skills, and organizational behavior.

If you discover your core is “cross-disciplinary integration and complex decision-making,” then you should consciously break down disciplinary silos. Read widely in history, philosophy, art, business, and technology to train yourself to see problems from different perspectives, and proactively take on responsibilities that require you to “make the final call.”

If you discover your core is “aesthetic taste and the creation of meaning,” then you should nourish your sensibility. Go to the best exhibitions, read the best literature, listen to the best music, and bravely and continuously create and share the inspirations and thoughts from your heart through some medium (words, images, products, services).

Stop blindly patching your “skill library.” Starting today, please focus on polishing your unique “Human Prism,” on building and strengthening your inimitable “Human Algorithm.” This is the only reliable “ballast stone” for you in these stormy seas.

Conclusion: Heaven’s Mandate Is Our Nature; Following This Nature Is the Way

As our story concludes, it feels as if we have returned to that winter day thirty years ago. I can almost see those bewildered eyes again, staring at the textile factory.

Thirty years ago, they suffered from the loss of their “work unit” and “position”; thirty years later, we are anxious about the potential loss of our “skills” and “value.” History, it seems, always poses the same ultimate question to us in different ways: When the external things we rely on are no longer reliable, what does it mean to be human?

The arrival of AI is less of an “enemy” and more of a giant, clear “mirror.” It mercilessly reflects our past fragility and vanity—our dependence on standard answers, our numbness to repetitive labor, our avoidance of deep thought. In an almost cruel fashion, it strips away the outer shell of all “skills” that can be calculated and outsourced, ultimately presenting the core, naked “humanity” within us.

It forces us to turn away from a never-ending, outward-facing arms race of skills, and to begin an inward journey of profound exploration into our self-worth.

The opening line of The Doctrine of the Mean says: “What Heaven has conferred is called The Nature; an accordance with this nature is called The Path of duty; the regulation of this path is called Instruction.”

What this means is that which is bestowed upon us by a higher power is our “nature,” our most authentic self. To live and act in accordance with this nature is the “Way,” the path we ought to follow. And the process of constantly correcting and perfecting this path is “Instruction,” which is true education.

Perhaps, this is the ultimate “moat” and “way out” for every one of us in the age of AI. To find your unique “Heaven-mandated nature”—your passion, your talent, that unique “Human Algorithm” of yours. Then, to bravely and sincerely live and work in accordance with it, to live it out as your one-of-a-kind “Way” in this world.

This will be a more difficult, but also a more glorious, path. Our moat lies not in resisting the flood of change, but in harnessing it to complete our own evolution.

If you, too, wish to find your own path of “renewal” amidst this great transformation, welcome to the intellectual community of FinSages at Finsages.org. Together, we will face the future.

Note:

This is a foundational concept from the Confucian classic The Doctrine of the Mean (Zhong Yong). The original text is: “天命之谓性，率性之谓道，修道之谓教” (Tiān mìng zhī wèi xìng, shuài xìng zhī wèi dào, xiū dào zhī wèi jiào). The phrase encapsulates a philosophy of finding one’s path by understanding and cultivating one’s innate, authentic nature.