The Twilight of Management and the Dawn of Intelligence: Rewriting the Biological Genes of Enterprises

The Twilight of Management and the Dawn of Intelligence: Rewriting the Biological Genes of Enterprises

Preface: The Dusk of Management
 

Peter Drucker once said that in times of turbulence, the greatest danger is not the turbulence itself, but to act with yesterday’s logic.
Today, we are standing exactly on such a dangerous threshold.
 

From the perspective of system evolution, management has never been an eternal truth. Not because there is something inherently wrong with management theory, but because the very object it serves — the carbon-based human brain — is about to be physically replaced by intelligent agents. Once that happens, the foundational premise for management simply disappears.
 

Therefore, the future transformation of enterprises will not be “better management with AI,” but the withdrawal of management itself. This is not a question of right or wrong; it is a matter of structural inevitability. Once execution no longer depends on biological traits, the towering edifice of institutions built upon 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 limitation. For the past hundred years, all the management tools we worship have essentially been “patches” applied to the human brain:
 

We invented KPIs, not because they measure value with precision, but because the human brain struggles to lock onto long-term goals. “Forgetting” is the default state of carbon-based life; we need signposts.
 

We invented hierarchy, not because it is efficient, but because human working memory can only handle 7±2 chunks at a time. To avoid cognitive overload, we were forced to compress information through layers.
 

We invented incentive mechanisms, not to create value, but to combat the natural decline of motivation and the entropy increase inherent to biological organisms.
 

Management has never truly increased the intelligence of organizations. It is a sophisticated correction system, trying to lock in correctness with institutions before human cognition fails.
 

As long as execution depends on humans, the enterprise is essentially an institutional container built to compensate for the brain’s defects.
 

Chapter 2: The Intervention of Intelligent Agents — A New “Cognitive Anatomy”
 

So, what exactly is this replacement we are bringing in?
 

When I say “agent”, I do not mean a faster piece of software. I mean a being whose cognitive anatomy is fundamentally different from that of humans.
 

If we were to place a human employee and an intelligent agent side by side on an anatomical table, you would see three essential physiological differences:
 

First: The continuity of memory.
Human memory is momentary and fragile. We rely on sleep to reset ourselves, and our context frequently breaks. An agent, by contrast, has EverMem (everlasting memory). What it possesses is not fragmented workflows, but a continuous history. It does not forget, does not need “handoffs,” and every act of reasoning is built on the full foundation of its historical experience.
 

Second: The holographic nature of cognition.
Humans are bound by bandwidth and must rely on hierarchy to filter information. Agents, however, have Context Alignment — full-context alignment. They do not need weekly departmental meetings to “sync up.” The organization’s entire knowledge graph is transparent to them in real time. What they perceive is the whole, not the fragmented partial views of blind men touching an elephant.
 

Third: The endogenous nature of evolution.
Human motivation depends on dopamine and external rewards, and it decays easily. An agent’s behavior, in contrast, is driven by the structural tension of its reward model. It does not need to be “coaxed” into working; every action it takes is an attempt to help the objective function converge.
 

This is not a stronger employee.
This is a new species operating under entirely different physical laws.
 

Chapter 3: The Collapse of the Foundations — When a New Species Meets an Old Container
 

What happens when we force this new species — endowed with continuous memory, holographic cognition, and endogenous evolution — into an old management container designed for humans?
 

A systemic rejection reaction begins. The five foundational pillars that once supported modern enterprises start to mutate from “necessary safeguards” into “constraints on intelligence”:
 

The collapse of KPIs: from “navigation” to “ceiling.”
We needed KPIs because humans lose their way easily. But for agents that can continuously lock onto an objective function, rigid KPI targets actually cap their ability to search for better solutions in an infinite solution space. It is like laying down a fixed track for a self-driving car and still expecting it to avoid unexpected obstacles.
 

The collapse of hierarchy: from “filter” to “blockage.”
We needed hierarchy because the human brain cannot process too much information. For agents that can handle context at the scale of thousands of tokens, hierarchy is no longer a filter but a blood clot that blocks the free flow of data. In an intelligent network, every intermediate layer is an unnecessary loss of information.
 

The collapse of incentive mechanisms: from “source of drive” to “noise.”
Trying to motivate an agent with external rewards is like trying to reward gravity with candy — ineffective and absurd. It does not need dopamine; it needs accurate feedback data.
 

The collapse of long-term planning: from “map” to “simulation.”
We needed five-year plans because we could not sustain long-horizon projections in a rapidly changing environment. But in the hands of agents, static strategic maps are replaced by real-time world model simulations. If you can simulate ten thousand possible futures every second, why cling to a strategy map printed six months ago?
 

The collapse of process and supervision: from “correction” to “redundancy.”
Traditional supervision mechanisms were created to ensure that people do not make mistakes. But inside an agent, understanding is execution; perception is action. Supervision no longer rests on suspicion of the execution process, but on continuous recalibration of the definition of the objective itself.
 

Chapter 4: The Ultimate Form — Five Fundamental Traits of an AI-Native Enterprise
 

If we discard these biological crutches, what does the ultimate form of a truly AI-Native enterprise look like?
 

This is no longer about which software a company should buy, but about what biological form a company should exist in. A truly AI-Native enterprise must rewrite itself at the genetic level along five dimensions:
 

1. Architecture as Intelligence
 

Traditional organizational architecture is a product of sociology, built to manage interpersonal friction.
AI-Native architecture 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 for specific functions. Reporting lines are no longer channels of administrative orders, but high-dimensional data bases.
 

The design goal of enterprise architecture shifts from “risk control” to “maximizing data throughput and enabling intelligence emergence.”
 

2. Growth as Compounding
 

Traditional growth is driven by linear headcount expansion, with marginal costs rising alongside scale.
AI-Native growth is driven by cognitive compounding.
 

The defining feature of agents is zero marginal learning cost. The results of a single edge-case experiment can be instantly propagated to all agents across the network.
 

The logic of valuation will be completely transformed — no longer determined by the size of the headcount, but by the rate of cognitive compounding within the firm.
 

3. Memory as Evolution
 

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

The memory of traditional enterprises is discrete, fragile, and “dead data.” An AI-Native enterprise must possess a writable, evolvable long-term memory center. All decision logic, interaction histories, and tacit knowledge are continuously vectorized and deposited as the organization’s “subconscious.”
 

This is the foundation for the enterprise to build its temporal structure, and the precondition for intelligence to evolve itself across time.
 

4. Execution as Training
 

In the old paradigm, execution is a process of consumption; value delivery is the endpoint.
In the AI-Native paradigm, execution is a process of exploration.
 

There is no such thing as a “pure execution department.” Every department is, in essence, a model training department. Each business interaction is a Bayesian update to the organization’s internal world model.
 

Business flow is training flow. Action is learning.
 

5. Humans as Meaning
 

Here, enterprise ethics are rewritten. Humans step out of the role of “fuel” and ascend into the role of intent curator and cognitive architect.
 

Agents are responsible for solving the question of “how” within an infinite solution space, pushing path optimization to the extreme. Humans are responsible for handling what cannot be computed — defining “why” we act, defining the value functions of aesthetics, ethics, and direction.
 

Intelligence expands the boundary of what is possible.
Humans decide which direction is meaningful.
 

Epilogue: The Dawn of Intelligence
 

This converges with what we have proposed in the scientific domain as Discoverative AI.
 

The core doctrine of Discoverative AI is: intelligence should not be limited to fitting existing knowledge; it must be capable of building models, forming hypotheses, and revising its cognition through interaction with the world.
 

An AI-Native enterprise is the organizational projection of Discoverative thinking. It demands that the enterprise itself become a platform for discovering structures, not just a container for operating processes.
 

If the very form of organization is undergoing a species-level evolution, then the digital containers that host it must also mutate accordingly.
 

This forces us to confront an unavoidable question:
Can the infrastructure under our feet — the ERPs built to solidify processes, the SaaS tools created to slice up functions — still hold this fluid intelligence? These systems are, in essence, digital projections of an old management logic. They may bring temporary peace by patching and extending themselves, but that is still using an old map to search for a new continent.
 

AI-Native enterprises call for an entirely new operating system —
A new nervous system not devoted to Resource Planning, but to Cognitive Evolution.
 

When management withdraws, cognition rises.
 

Management theory will not disappear, but for the first time, it will be built on the bedrock of Intelligence, not on the ruins of Biology.
 

In the future, enterprises will no longer be about humans leading intelligence —
They will be about intelligence extending humanity.