The Big Tech Syndrome in China and the U.S.: Top Talent Leaves, Ordinary Employees Burn Out, and AI Is Exposing the Cracks in Organizational Design

For the past decade, the smartest and most ambitious people often saw joining a major tech company as a career milestone: high pay, equity, scale, resources, technical prestige, and the opportunity to help change the world.

But today, a growing pattern is hard to ignore. Top talent is leaving Big Tech to build new ventures. Meanwhile, those who remain often describe their work as deeply painful—yet still feel unable to leave.

AI Shifts the Talent Equation

AI is changing the balance of power among talent, organizations, and capital.

In the past, a strong engineer, researcher, or product leader needed a large company to build something truly consequential. Only Big Tech had the data centers, vast datasets, engineering depth, distribution channels, brand credibility, and long-term cash flow required to do so.

AI is changing that equation.

Cloud computing, open-source models, AI coding tools, mature foundation-model services, and more available venture capital now allow a small team with exceptional talent density to build, within months, what once required hundreds of people and coordination across multiple departments.

For truly exceptional people, the question is no longer: Can I build something meaningful? It is increasingly: Why should I build it inside someone else's system?

Startups vs. Large Companies

Staying at a large company offers more stable compensation, lower personal risk, and stronger institutional resources. But it also means that a project's direction can be repeatedly reshaped by business units, budgeting cycles, compliance demands, executive preferences, and quarterly earnings pressure.

Starting a company carries much higher risk. But it also offers ownership over the technical direction, control over product velocity, and a greater share of the value created. In a field like AI—where progress often depends on non-consensus judgment—the strongest people are especially reluctant to spend years persuading ten layers of management.

That is why organizational tension in the AI era may become sharper than it was during the mobile-internet era.

Large companies are naturally good at scaling: building compute infrastructure, integrating data, serving massive user bases, constructing sales systems, managing safety and regulation, and turning a new capability into a stable service.

But frontier AI breakthroughs rarely emerge from the most stable or most standardized environments. They are more likely to come from small groups that can experiment quickly, overturn their own assumptions, and tolerate a high rate of failure.

Put simply: large companies are good at scaling what is known. Startups are good at discovering what is not yet known.

Big Tech as AI Infrastructure

Big Tech will not disappear. The likely end state is a two-layer structure.

One layer will consist of major companies providing AI infrastructure: chips, cloud platforms, compute, model services, data, security, compliance, and global distribution. The other will consist of many small but highly capable teams experimenting across vertical industries, product experiences, model applications, and new interfaces.

Large companies may increasingly resemble the power grids, ports, and highways of the AI era. Startups will be the new cities built on top of that infrastructure.

But Big Tech faces a real risk: it may accumulate the most resources while losing the highest density of creativity. It may have GPUs, users, and cash flow—yet lack the people most willing to make bold bets, decide quickly, and overturn legacy systems.

At that point, Big Tech may shift from being the center of innovation to becoming an AI utility: stable, important, profitable, but no longer defining the future.

The Pain of Ordinary Employees

For ordinary employees who remain inside these companies, the pain often comes from a growing sense of lost control.

The problem is not simply that people work too much. The deeper issue is that many operate in a state of high effort, low autonomy, and low meaning.

Strategy shifts constantly. Today, the company is "all in on AI." Tomorrow, it demands commercialization. The day after, the focus becomes cost-cutting and efficiency.

Creative work is fragmented by meetings, slide decks, cross-functional coordination, approval chains, and postmortems. Frontline employees are held accountable for outcomes without having sufficient authority or resources. Performance systems turn colleagues into competitors. People spend more time proving what they have done than solving what customers actually need.

AI Amplifies Workplace Anxiety

On one hand, companies expect employees to use AI to work faster, with fewer people and lower costs. On the other hand, employees worry: if I genuinely become more productive, will I gain more autonomy—or simply receive more work, face a higher KPI, or become easier to replace?

This is the real dilemma for many Big Tech employees. They are not unaware that the organization is broken. They can see it. But they often cannot say it openly—and cannot easily leave.

Why not?

Because leaving a major company is not merely leaving a job. It means leaving an entire system of certainty: stable income, a recognizable résumé, professional networks, social status, family security, and a buffer before finding the next opportunity.

Especially when growth is slowing, hiring is tightening, and startup funding is more cautious, many people choose to stay even when they are unhappy.

So complaining while not leaving is not a contradiction. It is a rational calculation under asymmetric risk.

China vs. U.S. Pressure Differs

Employees in Chinese and American tech companies face similar problems, but the sources of their pressure differ.

In the United States, tech employees once placed greater faith in technological idealism, equity compensation, and career mobility. Today, they face layoffs, reorganizations, AI-driven job anxiety, and the reality that many companies have shifted from "changing the world" to maximizing efficiency and profitability.

In China, the pressure is often more layered. In addition to AI-driven demands for efficiency, employees face slower growth, narrower promotion paths, competition among business units, more hierarchical management, expectations of constant responsiveness, and a stronger dependence on stability.

American employees often ask: Am I still creating the future, or am I simply maintaining an old system?

Chinese employees more often ask: Will the effort I am putting in today still buy me a predictable future?

Yet beneath these differences lies the same underlying problem: as organizations grow, their systems of control begin to outweigh their systems of creation.

The Structural Root Cause

The "Big Tech syndrome" is not merely about overtime, bureaucracy, or a few bad managers. Its deeper cause is structural.

As companies become more complex, they add more processes, metrics, approvals, budgets, and reporting mechanisms. At first, these systems are necessary. But when they stop serving creation and begin serving management itself, organizations enter an absurd state: employees become busier, but the company does not necessarily become more creative; presentations become more polished, but real problems become harder to surface; everyone works for KPIs, while fewer people are accountable for genuine customer value.

Who is responsible?

It is too easy to blame employees, or middle managers alone.

Boards and CEOs are responsible for unclear strategy, misallocated resources, and unfair value distribution. Senior executives are responsible for silos, short-termism, and pushing anxiety downward through the organization. Middle managers are responsible for distorted information flows, unnecessary meetings, and the spread of performative "managing up." HR leaders and incentive-system designers are responsible for deciding what behavior the organization rewards.

If a company rewards political alignment, presentation skills, short-term metrics, and low-risk execution, that is exactly what it will produce—not innovation.

What AI Cannot Fix Alone

AI will not automatically cure the Big Tech syndrome.

It can reduce repetitive work, eliminate low-value coordination, and give frontline employees more capability. But it can also become a more precise tool for surveillance, harsher KPI management, and faster layoffs.

What determines a company's future is not whether it has deployed AI. It is whether it is willing to redistribute the productivity gains created by AI back to the people who create value: allowing employees to spend less time on meaningless reporting and more time making real judgments; allowing frontier teams to wait less for approvals and take more responsibility; enabling the organization to move from controlling people to amplifying them.

In the AI era, the scarcest resource is not just compute, data, or models. It is people who can make sound judgments amid uncertainty, take responsibility for consequences, and create new value.

Whether a large company can retain such people will ultimately depend on whether it understands one simple truth:

The strongest people do not want only high compensation. They want a stage worthy of betting their lives on.