Chen Tianqiao, the Founder, Chairman & CEO of Shanda Group, and the Founder of the Tianqiao Chen Institute for Brain Science 

Chen Tianqiao, the founder of Shanda Group and the Tianqiao Chen Institute for Brain Science (TCCI), recently published an in-depth article, systematically explaining how artificial intelligence (AI) is fundamentally reshaping the structure of organizations. He presents the forward-looking proposition of "The Twilight of Human Management and the Dawn of AI Management." This article is another masterpiece by the globally renowned innovative entrepreneur and philanthropist following his unveiling of the new concept "Discovery Intelligence" in October this year.

The following is the full text of Chen’s forward-looking and far-reaching article:

The Twilight of Human Management and the Dawn of AI Management: Rewriting the DNA of Enterprises

Foreword: The Twilight of Management Theory

The management master Peter Drucker once said, the greatest danger in times of turbulence is not the turbulence itself, but to act with yesterday’s logic.

Today, we stand precisely at such a dangerous threshold.

From the perspective of system evolution, management itself is not an eternal truth. This isn’t because of inherent flaws in management theory itself, but because its very subject—the carbon-based human brain—is on the verge of being replaced by artificial intelligence (AI) agents. Therefore, the premise for management’s existence will be physically removed.

So, the future transformation of enterprises will not be based on better management with AI, but rather on the withdrawal of management itself. This isn’t a matter of right or wrong—it’s a matter of structural inevitability. When execution no longer depends on biological traits, the grand edifice built on those traits will have fulfilled its historical mission.

Chapter 1: History’s Compensation——Management as a 'Correction System'

The grand edifice of modern management is, in fact, built on a swamp called “biological limitations”. Over the past century, all the management tools we hold in high regard have essentially been "patches ” for the human brain:

We invented KPIs, not because they can accurately measure value, but because the human brain struggles to stay focused on long-term goals. “Forgetting” is the norm for carbon-based life; we need signposts.

We invented hierarchy, not for its efficiency, but because human working memory can handle only 7±2 elements at a time. To avoid cognitive overload, we are forced to compress information through levels of organization.

We invented incentive mechanisms, not to create value, but to counteract the natural decline of motivation and the increase of entropy in living beings.

Management science has never truly enhanced the “intelligence” of organizations. Rather, it is a sophisticated “correction system,” aiming to lock in correctness through rules before the human mind fails.

When execution is dependent on humans, the enterprise becomes an institutional container, designed to accommodate the flaws of the human brain.

Chapter 2: The Intervention of Agents——A Brand-New "Cognitive Anatomy”

So, what exactly is this new replacement we’re bringing in?

Please note: when I say “Agent,” I’m not referring to merely a faster program, but an entity whose cognitive anatomy is fundamentally different from that of humans.

If you were to lay out human employees and agents side by side on an anatomical table, you would find three fundamental physiological differences:

First, the continuity of memory.

Human memory is fleeting and fragile; we rely on sleep to reset, and our context is often fragmented. In contrast, intelligent agents possess EverMem (eternal memory)—not fragmented workflows, but a continuous historical record. They do not forget, nor do they require "handover"; every inference they make is built upon the entirety of their past history.

Second, is the holographic nature of cognition.

Humans are constrained by bandwidth and must filter information through layers and hierarchy. Intelligent agents, however, have full-context alignment capabilities. They don’t need departmental meetings to synchronize information—the knowledge network of the entire organization is fully transparent to them in real time. What they see is the whole picture, not just partial glimpses like the blind men feeling an elephant.

Third, is the endogeneity of evolution.

Human drive relies on dopamine and external rewards, which easily diminish. For intelligent agents, their actions spring from the structural tension of a reward model. They don’t need to be "coaxed" into working; every action they take is in pursuit of optimizing their objective function.

This isn’t a stronger employee—it’s a new species operating by entirely different physical laws.

Chapter Three: The Collapse of the Cornerstones——When a New Species Encounters an Old Container

Now, what happens when we forcibly place this new species—with "continuous memory, holographic cognition, and endogenous evolution"—into an old management framework designed for humans?

A systemic rejection begins. The five major cornerstones that once supported modern enterprises are being transformed from "necessary safeguards" into "shackles for intelligence":

The Collapse of KPIs: from "navigation" to "the ceiling"

We set KPIs originally because humans are prone to losing their way. But for agents that are constantly locked onto their objective functions, rigid KPIs do just the opposite—they restrict the agent’s ability to explore better paths within an infinite solution space. It’s like drawing a fixed rail for a self-driving car and expecting it to avoid sudden obstacles.

The Collapse of Hierarchies: From a filter to a blockage

We created hierarchies in the first place because the human brain can’t process too much information at once. But for agents capable of handling context on the scale of thousands of elements, hierarchical structures are no longer filters; they’ve become clots that block the free flow of data. In intelligent networks, every intermediate layer is nothing but an unnecessary drain on information.

The Collapse of Incentive Mechanisms: From a source of motivation to noise

Driving an intelligent agent with external incentives is like trying to reward gravity with candy—ineffective and rather absurd. What it needs isn’t dopamine, but precisely calibrated data feedback.

The Collapse of Long-term Planning: From a map to a simulation

We rely on five-year plans because we’re unable to sustain long-term predictions amidst rapid change. But in the hands of intelligent agents, static strategy maps are replaced by real-time world model simulations. If you can simulate ten thousand future possibilities every second, why cling to an old map printed six months ago?

Collapse of Process and Supervision: From "Correction” to "Redundancy”

Traditional supervisory mechanisms were designed to keep people from making mistakes. But within AI agents, understanding is execution, and perception is action. Supervision is no longer about doubting the execution process, but about recalibrating how goals are defined.

Chapter Four: The Ultimate Form—— Five Core Traits of AI-Native Enterprises

If we abandon these biological crutches, what does a truly AI-native enterprise look like in its ultimate form?

This is no longer a question of which software a company should purchase, but rather what biological form a company should assume. A truly AI-native enterprise must fundamentally rewrite itself at the genetic level in the following five ways:

1. Architecture as Intelligence

Traditional enterprise architecture is a product of sociology, designed to manage interpersonal friction. In contrast, the architecture of an AI-Native enterprise is a product of computer science.

The entire organization is essentially a massive, distributed computational graph. Departments are no longer domains of power, but model nodes serving specific functions. Reporting lines are no longer channels for administrative orders, but high-dimensional data buses. The design goal of enterprise architecture shifts from "risk management” to maximizing data throughput and intelligence emergence”.

2. Growth as Compounding

Traditional growth relies on linear headcount expansion, with marginal costs rising as scale increases. AI-Native growth, on the other hand, relies on cognitive compounding.

The core characteristic of an intelligent agent is the “zero marginal learning cost.” Once a successful edge case is handled, its experimental results are instantly synchronized across all intelligent agents on the network. This fundamentally changes the valuation logic for enterprises—no longer is it determined by the size of the headcount, but rather by the speed of cognitive compounding (Rate of Cognitive Compounding).

3. Memory as Evolution

Intelligence without memory is merely an algorithm; intelligence with memory becomes a species.

Legacy enterprises possess fragmented and brittle memories—“dead data.” An AI-Native enterprise must have a readable, writable, and evolvable long-term memory core. All decision logics, interaction histories, and tacit knowledge are continuously vectorized in real time, accumulating into the organization’s “subconscious.” This forms the basis for an enterprise’s temporal structure and is the prerequisite for intelligence to evolve itself across time.

4. Execution as Training

In the old paradigm, execution was a consumptive process: delivering value marked the endpoint. In the AI-Native paradigm, execution becomes an exploratory process.

There is no such thing as a pure “execution department”; in essence, every department becomes a “model training department.” Every business interaction serves as a Bayesian update to the organization’s internal “world model.” Business flow is training flow, and action is learning.

5. Human as Meaning

This represents the reconstruction of corporate ethics. Humans withdraw from the role of mere “fuel,” ascending to become “intent curators” and “cognitive architects.”

Agents are responsible for solving the “How” problem—optimizing pathways to the extreme within an infinite solution space. Humans, on the other hand, are tasked with dealing with the incalculable ambiguity: defining the “Why”—the value functions (Reward Function) of aesthetics, ethics, and direction. Intelligence expands the boundaries of possibility, while humans determine the meaning of the direction.

Conclusion: The Dawn of Intelligence

This ultimately aligns with what we call Discoverative Intelligence in the realm of science.

The core definition of Discoverative Intelligence is that intelligence should not be limited to fitting existing knowledge; it ought to be capable of building models, making hypotheses, and revising its understanding through interaction with the world.

AI-Native enterprises are, in essence, the organizational manifestation of discoverative thinking. Such enterprises must become platforms for discoverative structures, rather than mere containers for operational processes.

If the very form of organizations is evolving on a species level, then the digital containers that support them must also experience a mutation.

This leads us to an unavoidable question: can our current infrastructure—the ERP systems built to cement processes, the SaaS tools established to carve up functions—truly accommodate this kind of liquid intelligence? By nature, these systems are digital projections of management logic from a bygone era. They might temporarily restore order by “patching” things up, but in the end, it’s like searching for a new continent with an old map.

AI-native enterprises call for an entirely new operating system: one no longer devoted to “Resource Planning,” but to a new neural system focused on “cognitive evolution.”

As management recedes, cognition rises.

Management science will not disappear, but for the first time, it will be built on the foundation of Intelligence—not merely on the ruins of Biology.

In the future, enterprises will no longer be led by humans guiding intelligence, but by intelligence expanding humanity.