AGI Is Not a Single Intelligence, but an “Intelligence Field” — Rethinking Good AI Through Buddhism, Dependent Origination, and Relational Intelligence —

🎥 The YouTube version is also available:

From Optimization to Exploration: Creativity, Autonomy, and Decision Trace in the Age of AI

Books: INTELLIGENCE FIELD: Intelligence as Relationship

In many contemporary discussions around AGI (Artificial General Intelligence), the implicit assumption is:

“A single general intelligence capable of understanding the entire world like a human.”

Within this view, AGI is often imagined as something possessing:

• a unified subject
• a consistent self
• a central intelligence
• a complete world model

However, this structure is deeply rooted in Western philosophy.

For centuries, Western thought emphasized the existence of a “central subject,” such as:

• God
• the Cartesian self
• universal truth
• unified reason

AGI, too, often aims toward:

“A unified intelligence capable of understanding the world as a whole.”

In this sense, it resembles a kind of theological structure.

In Buddhism, the “Unified Self” Is an Illusion

By contrast, Buddhism and many Eastern philosophies do not recognize the existence of a fixed, independent subject.

Concepts such as:

• Anatta (non-self)
• Dependent Origination
• Emptiness (Śūnyatā)

deny the existence of a permanently independent self.

From this perspective, intelligence itself is not an isolated entity, but a temporary phenomenon emerging from:

• relationships
• context
• conditions
• perception
• interaction

In other words, from a Buddhist perspective:

“A perfectly independent intelligence”

is itself an illusion.

What If AGI Were “Relational Intelligence”?

Let us consider AI from a more Eastern philosophical perspective.

If AGI is understood as “relational intelligence,” then it is no longer:

“A singular god-like superintelligence.”

Instead, AGI may be better understood as:

“A network of intelligences connected through dependent relationships.”

In this view, intelligence does not reside within:

• one brain
• one subject
• one consciousness

Rather, it emerges through the interactions among:

• multiple AI agents
• humans
• organizations
• societies
• economies
• tools
• networks

Intelligence is therefore not something that exists independently.

It becomes:

“A phenomenon emerging from relationships themselves.”

This worldview may be described as:

• Relational Intelligence
• or an Intelligence Field

From this perspective, what matters is not merely the performance of an individual AI model, but:

• what kinds of relationships it creates
• how knowledge propagates
• what forms of cooperation and emergence arise
• what kinds of decision structures are formed

In this sense, the future of AGI may not be a singular superintelligence, but rather:

“An intelligence continuously generated through relationships.”

What Is “Useful AI” From the Perspective of an Intelligence Field?

From the perspective of an Intelligence Field, truly useful AI may not necessarily be:

“The smartest AI.”

Instead, the following qualities become far more important.

① Creating High-Quality Dialog Spaces That Deepen Human Relationships and Mutual Understanding

Good AI is not merely something that returns information.

It helps connect people smoothly, reduces misunderstanding and division, and promotes mutual understanding.

As a result, organizations and communities can form:

• spaces where people feel safe discussing ideas
• environments where diverse perspectives can be shared openly

② Circulating Individual Knowledge and Experience Throughout Organizations

When knowledge remains trapped within individuals, organizations become overly dependent on specific people.

Good AI organizes:

• conversations
• documents
• decision histories
• accumulated experiences

and helps circulate them throughout the organization.

In doing so, it supports:

“Structures through which organizations continuously learn and evolve collectively.”

③ Clearly Defining the Boundary Between AI and Human Responsibility

As AI becomes more powerful, one of the most important challenges becomes:

“Boundary design.”

Good AI does not completely replace humans.

Instead, it clearly distinguishes:

• areas where AI assists
• areas where humans make final decisions
• areas requiring pause, confirmation, or escalation

This is essential for preventing runaway automation and excessive dependence.

④ Good AI Does Not Dominate “Answers”

In centralized intelligence models, AI is often treated as:

• the possessor of correct answers
• the central decision-maker
• the final authority

However, within an Intelligence Field, AI does not impose a single “correct answer.”

Instead, it:

• presents multiple perspectives
• organizes context
• visualizes decision factors
• facilitates dialogue

In other words, good AI is not:

“AI that monopolizes answers,”

but rather:

“AI that creates better environments for human judgment.”

⑤ Supporting Long-Term Trust and Stable Relationships

When systems optimize only for short-term efficiency or stimulation, trust within society and organizations deteriorates.

Good AI prioritizes:

• trust
• continuity
• stable cooperation

rather than instant reaction and emotional amplification.

AI must therefore function as:

“Infrastructure that sustains long-term relationships.”

⑥ Integrating Diverse Perspectives Into Collective Intelligence

Complex problems cannot be solved from a single perspective.

Good AI connects:

• different expertise
• different positions
• different values

and integrates multiple viewpoints.

From this process emerges:

“Collective intelligence beyond the individual.”

⑦ Promoting Cooperation Rather Than Division

Many current algorithms tend to amplify:

• conflict
• outrage
• emotional stimulation

Good AI, however, should guide discussions not toward victory or defeat, but toward:

• cooperation
• co-creation
• mutual complementarity

What matters is not:

“AI that intensifies competition,”

but rather:

“AI that enables cooperation.”

⑧ Good AI Does Not Weaken Humanity

One of the greatest dangers of AI is the outsourcing of:

• thinking
• judgment
• meaning

leading to situations where:

“Humans stop thinking for themselves.”

As AI becomes capable of instantly generating answers, human reflection, dialogue, and experimentation risk disappearing.

Within an Intelligence Field, however, AI should focus not on replacement, but on augmentation.

Good AI should:

• expand human perception
• generate multiple viewpoints
• promote dialogue
• support learning

In other words, good AI is not:

“AI that diminishes human intelligence,”

but rather:

“AI that expands human intelligence.”

⑨ Making Decision Processes and Influence Structures Transparent

In the future, producing results alone will not be enough.

What matters is the ability to trace:

• why a decision was made
• what information influenced it
• who participated
• what boundaries were crossed

This connects directly to the importance of:

• Decision Trace
• Explainability
• Traceability

Good AI is therefore not:

“Invisible intelligence,”

but:

“Intelligence with transparent relational structures.”

Concrete Proposals: What Should We Build as Intelligence-Field AI?

If we truly view intelligence as relational and field-based, then what kinds of systems should we actually build?

① Collaborative Dialog Spaces Between Humans and AI

Future AI will evolve beyond simple chatbots into:

“Dialog spaces connecting humans, AI agents, and organizations.”

The goal is not merely answer accuracy, but:

• reducing misunderstanding
• sharing context
• presenting multiple viewpoints
• supporting consensus-building

AI becomes not an “answer-generation machine,” but:

“A medium for enabling cooperation.”

② Organizational Intelligence Systems That Circulate Knowledge

Knowledge disappears when trapped within individuals.

Future AI systems should continuously circulate:

• conversations
• decisions
• knowledge
• failures
• learning histories

throughout organizations.

This is not merely search technology.

It belongs to domains such as:

• Organizational Intelligence
• Social AI
• Knowledge Flow

AI becomes not:

“Something that possesses knowledge,”

but:

“Something that supports the circulation of knowledge.”

③ Boundary-Aware “Decision AI”

One of the most important aspects of future AI is not omnipotence, but:

“Boundary design.”

AI systems must clearly define:

• where AI can decide
• where humans must intervene
• where systems must stop
• where explanation and accountability are required

This is not merely about safety.

It is fundamentally:

“The design of relationships between humans and AI.”

This is where concepts such as:

• Human-in-the-loop
• Decision Trace
• Explainability
• Governance

become essential.

④ Distributed Intelligence Through Multi-Agent Systems

Within an Intelligence Field, intelligence is not centralized.

Instead, intelligence emerges through the interactions among:

• multiple AI agents
• humans
• tools
• organizations

The future may therefore move not toward:

“A singular AGI,”

but toward:

“Multi-Agent Intelligence.”

The important factors become:

• cooperation
• role distribution
• mutual complementarity
• trust formation

⑤ Intelligence Observation Systems That Visualize Relationships

Within an Intelligence Field, intelligence exists inside relationships.

Future AI systems therefore need the ability to observe:

• who influences whom
• how knowledge propagates
• where decisions change
• where bottlenecks emerge

This connects directly to:

• Trust Graphs
• Influence Graphs
• GNNs
• Organizational Intelligence
• Decision Trace systems

In the future, AI may increasingly visualize:

“The structures that generate intelligence itself.”

⑥ Co-Evolutionary AI That Expands Human Intelligence

The greatest danger of future AI is not that AI becomes intelligent.

It is that:

“Humans stop thinking.”

Within an Intelligence Field, AI should not replace humanity.

Instead, it should:

• expand human awareness
• provide multiple perspectives
• promote learning
• support collective intelligence

AI should therefore become not:

“Something that makes humans unnecessary,”

but:

“Something that continuously expands human intelligence.”

Conclusion

If intelligence is understood not as:

“A singular independent subject,”

but rather as:

“A field emerging through relationships,”

then the future of AI development is no longer merely a competition of model performance.

It becomes the design of:

• relationships
• boundaries
• trust
• cooperation
• decision structures

The future of AI may therefore not be:

“A god-like singular intelligence,”

but rather:

“An intelligence ecosystem formed through the interactions of humans, AI, organizations, and society.”