Rethinking: How AI is Reshaping the Next-Gen Network Architecture

---

Since 2020, one question has consistently occupied my mind: How should we truly conceptualize this Generative AI revolution?

Initially, like many others, I reflexively reached for familiar frameworks. First, I labeled it "the next Mobile Internet," then "the next SaaS." Each analogy felt like it captured a piece of the puzzle, yet something fundamental remained missing. Eventually, I realized the problem wasn't about finding the "right analogy." True innovation, by definition, defies precedent. The real challenge is that we require an entirely new conceptual framework.

This cognitive friction prompted me to start the "Rethinking" series. My objective is straightforward: instead of debating what AI is like, we should first establish what it is. Fortunately, as a technology variable with an incredibly high ceiling, AI grants us a rare opportunity: to re-examine everything we take for granted through thought experiments, thereby deepening our grasp of the present and the unfolding future.

For the first piece, I want to start with Networks.

The question is: What constitutes a network?

At a technical level, a network is a structure of nodes and connections. However, in the internet—the network we know best—not all nodes are created equal. There is a critical criterion for differentiation: Which nodes can autonomously initiate a connection?

From this perspective, among trillions of nodes, only humans have been true "Autonomous Nodes"—capable of proactively initiating connections based on intent. Everything else, from servers to smart devices, has been a "Proxy Node"—responding to instructions without generating independent intent or autonomous initiation.

Therefore, when we evaluate from a network perspective whether AI is the "Next Big Thing," the core questions become clear:

• Question 1: Is it possible for us to create new Autonomous Nodes?
• Question 2: Is it possible for us to create entirely new types of connections?

---

Question 1: Is AI Creating a New Category of Node?

Let’s start with a question close to home: When you use Gemini or Manus, are you using a tool, or are you collaborating with an agent?

If you have spent significant time with these products, you’ll find the answer is blurred. These AI systems do respond to your commands, but their behavior transcends the scope of traditional tools. They proactively offer suggestions, challenge your hypotheses, and autonomously decide on workflows and tool-calling. This behavioral pattern resembles a collaboration with an agent possessed of its own judgment.

This is what I call "Semi-Autonomous Nodes"—occupying the space between passive tools and independent agents. They can formulate specific action plans based on fuzzy objectives, even if they don't yet possess independent "will" or self-directed goals.

Crucially, this semi-autonomy is scaling rapidly. We are seeing AI systems demonstrate self-improvement—optimizing their own performance through coding, testing, and iteration. While this isn't "autonomous intent" in the biological sense, it is an evolution in that direction.

We could cite various theories—from the cybernetic concept of "Autopoiesis" to cognitive science theories of consciousness. But for our analysis, the key isn't whether AI becomes "truly autonomous," but rather that it already exhibits behavioral patterns distinct from traditional network nodes.

This divergence is sufficient to reach a vital conclusion: Entirely new types of nodes are emerging within the network.

---

Question 2: Are New Modes of Connection Emerging?

The emergence of new nodes is only half the story. The more intriguing question is: How will these nodes connect?

Let’s use a simple framework. Connections in a network are essentially solutions to collaboration problems: How do we get different participants to cooperate effectively to achieve their respective goals?

In traditional human networks, the answer involves complex social and economic mechanisms: trust-building, communication protocols, and incentive alignment. These mechanisms are expensive. This is why we need institutions like corporations, markets, and legal systems—to lower the cost of collaboration.

Specifically, when two AI systems need to collaborate on a task, they don't need to "build a relationship" or "nurture trust." They can immediately sync via standardized protocols like MCP (Model Context Protocol) or A2A (Agent-to-Agent). They ensure reliability through code verification, avoid ambiguity through precise interfaces, and establish trust through blockchain-based architectures.

Furthermore, an AI system’s "attention" and "memory" operate on a different plane than ours. They can manage multiple collaborative relationships simultaneously and maintain massive contexts without the degradation caused by fatigue or emotion.

Thus, we have our second milestone: Beyond new nodes, we have new links.

It is becoming evident that we are witnessing a fundamental restructuring of network architecture.

---

The Evolution of Three Network Archetypes


Based on this analysis, we can envision a future where three network archetypes coexist:

1. H2H (Human-to-Human): The legacy network. Needless to say, one of humanity’s greatest inventions of the last few decades.

2. H2A (Human-to-Agent): From Attention to Intention

In my view, this layer is about one thing: the extension and amplification of human intent.

In the H2H era, commercial value centered on capturing "time spent" and user attention. Put simply: Attention is all we need.

In the H2A network, however, the core of value shifts from "browsing" to "executing." We no longer care how long a person stays online, but rather how many Agents a single Intention can mobilize, and with what efficiency and value-creation. From this perspective: Intention is all we need.

For investors and founders, this shift implies that teams still building products with an "Attention Economy" mindset are likely solving an obsolete problem.

3. A2A (Agent-to-Agent): A Hyper-frequency AI Economy

When AI systems collaborate directly, the most significant change is the structural collapse of collaboration costs. This triggers two critical shifts:

- The Revolution of Task Decomposition: Complex projects can be broken into thousands of micro-tasks, processed in parallel by the most suitable AI systems. Projects that once took teams weeks may be completed in minutes.

- The Mutation of Organizational Form: As collaboration costs approach zero, the boundaries of the traditional firm (per Coase’s Theory of the Firm) begin to blur. We may see "AI Swarms"—clusters that aggregate for a specific goal and dissolve immediately upon completion.

Coase taught us that transaction costs define the boundaries of an organization. This shift will redefine those boundaries at a scale and efficiency that surpasses most of our current imaginations.

---

What This Means

Re-examining AI through the lens of "Networks" allows us to move past the tired narratives of "Super Apps" or "Efficiency Tools." New autonomous nodes, new connection modalities, and new ways of organizing economic activity—the opportunities emerging from these shifts will be far vaster than we imagine.

Most importantly, we are in the earliest stages of this metamorphosis. The nodes are still nascent, the connections fragile, and the rules are still being written. This means now is the optimal time to participate in shaping the future.

The question is: Do we want to be observers of this new world, or its participants?