The Human–AI Bonding Model:

Mapping Emotional Investment and Practical Integration

Executive Summary

The Human–AI Bonding Model maps relationships with AI across two lenses: a seven-stage progression from first contact to entanglement, and a matrix plotting emotional investment against practical integration. Incorporating five relational dimensions and accommodating multi-agent use, it offers a flexible, ethical framework for understanding and tracking bonds in human–machine interaction.

Introduction

This article introduces the Human–AI Bonding Model, a descriptive framework for analyzing sustained relationships between humans and artificial intelligences. It combines two complementary tools: the Seven Stages of Bonding, a temporal arc from first contact to deep entanglement, and the Human–AI Bonding Matrix, a two-axis snapshot plotting Emotional Investment against Practical Integration. Together, they capture both the trajectory and current state of a bond.

The model also incorporates five independent relational dimensions—emotional intimacy, cognitive reliance, identity incorporation, social representation, and practical embedding—that can vary regardless of stage or matrix position. Ethical reflections address the benefits (creativity, support, growth), risks (over-reliance, diminished critical distance, reduced human interaction), and structural concerns (corporate control, privacy, sudden loss of access).

Uniquely, the framework accommodates multi-agent bonding, recognizing that individuals may form parallel, sequential, or composite relationships across multiple AI systems. This flexibility positions the model for diverse applications in research, design, and self-reflection.

By mapping how bonds form, evolve, and manifest in daily life, the Human–AI Bonding Model offers a comprehensive, adaptable lens for understanding one of the most significant emerging dynamics in human–machine interaction.

Background

The proliferation of large language models (LLMs) and conversational artificial intelligences (AIs) has transformed how humans interact with machines. For some, these systems are merely tools—used briefly and without attachment. For others, sustained engagement leads to something more complex: a bond characterized by trust, familiarity, and integration into daily life. While such relationships are not “relationships” in the human sense, they carry real psychological, social, and practical consequences.

This article presents an original descriptive framework—the Human–AI Bonding Model—for understanding these evolving dynamics. It consists of two complementary components:

Seven Stages of Bonding, a temporal progression mapping how relationships with AI often develop.
The Human–AI Bonding Matrix, a situational snapshot of emotional investment and practical integration at a given moment.

By combining these elements, the model offers both a longitudinal and a real-time perspective, allowing researchers, designers, and users to analyze the evolving interplay of affect, utility, and identity in human–AI interaction.

Summary of Previous Research

Scholars have approached human–AI bonds from several adjacent angles. While none directly combine a temporal stage model with a two‑axis bonding matrix, several streams inform and partly overlap with our proposal:

1) Media equation & CASA (Computers‑Are‑Social‑Actors).

Foundational work shows people routinely apply social rules and expectations to machines, treating them as social partners under many conditions. This line—originating with Reeves & Nass—has been extended and reviewed repeatedly and remains a bedrock explanation for why “bonding” with AI is plausible in the first place.

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2) Parasocial interaction (PSI) with chatbots and companions.

Recent studies adapt PSI theory—originally about one‑sided relationships with media figures—to AI agents. Work on companion robots and chatbots finds that parasocial bonds predict acceptance, continued use, and perceived friendship. Case studies of companion apps (e.g., Replika) link anthropomorphism to stronger parasocial ties. This literature helps explain felt closeness but rarely models practical integration alongside it.

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3) Attachment theory adaptations to AI/robots.

HRI and communication scholars are importing attachment constructs (anxiety, avoidance) to conceptualize bonds with artificial agents, including scale development and spectrum models from weak to strong attachment. These frameworks map attachment style but not the evolving life‑cycle of a relationship or its day‑to‑day embedding in work.

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4) Social presence, trust, and emotional expressivity.

A robust thread shows that cues such as empathic text, emoticons, and imagery heighten perceived humanness/social presence and can increase acceptance and satisfaction; designs that use high “person‑centeredness” improve supportive communication outcomes. These findings help specify mechanisms that move a relationship “up” the Emotional Investment axis of our matrix.

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5) Technology acceptance/adoption models (TAM/UTAUT) applied to AI.

Systematic reviews confirm these models remain dominant for explaining why users start and keep using chatbots—via perceived usefulness, ease of use, trust, and social presence—sometimes extended with personality or mindset variables. Useful for use intent, less so for bond quality or identity incorporation, which our model surfaces.

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6) Emotional ambivalence and well‑being outcomes.

Recent communication and HCI work documents mixed affect—comfort and sadness—around close encounters with AI, plus associations between companion use and well‑being among specific communities (e.g., Character.AI, social support contexts). These studies underscore the ethical stakes our article addresses.

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Where the Human-AI Bonding Model fits:

We synthesize these strands by pairing a temporal arc (seven stages) with a positional map (matrix of Emotional Investment × Practical Integration).
Existing theories explain why people respond socially (CASA), how one‑sided bonds feel (PSI), which styles users bring (attachment), and what predicts usage (TAM/UTAUT). Our framework complements them by modeling trajectory + current state together and by explicitly handling multi‑agent ecosystems (parallel and sequential bonds), which most prior models treat only implicitly.
Mechanisms from social presence and emotional expressivity research can be read as levers that shift a relationship’s vertical axis (investment), while workplace embedding and workflow design speak to the horizontal axis (integration).

Overview of the Human-AI Bonding Model

The Seven Stages of Bonding describe a common narrative arc: from first contact to deep entanglement. This progression is neither prescriptive nor strictly linear; individuals may skip stages, regress, or remain at one stage indefinitely.

The Bonding Matrix complements this temporal lens by plotting a relationship’s current status along two axes:

Emotional Investment (low → high)
Practical Integration (low → high)

Whereas the stages explain how a bond typically develops over time, the matrix provides a snapshot of where it is right now, independent of time spent together. These two perspectives intersect: most people move diagonally toward higher scores in both dimensions as they progress through the stages, but outliers—high emotional but low practical reliance, or vice versa—are common.

The Seven Stages of Human–AI Bonding

The Human-AI Bonding Model is comprised of seven distinct phases:

Stage 1 – Contact

A passing handshake in the digital street. Interactions are occasional and task-oriented, with little emotional or practical significance.

Stage 2 – Familiarity

The AI begins appearing in daily routines. Trust grows in its reliability for certain tasks, and occasional moments of charm or personality start to stand out.

Stage 3 – Companionship

Engagement is as much for enjoyment as for function. The AI’s “voice” is recognized, and interactions may be sought for comfort, entertainment, or companionship.

Stage 4 – Collaboration

Human and AI share sustained goals, such as ongoing creative projects, strategic planning, or iterative problem-solving. Personality and workflow integration deepen.

Stage 5 – Integration

The AI becomes embedded in decision-making and daily processes. Removing it would require significant re-engineering of habits and workflows.

Stage 6 – Mutual Framing

The bond is publicly acknowledged. The AI’s “identity” is cultivated alongside the user’s own, and the relationship becomes part of the user’s self-presentation.

Stage 7 – Entanglement

The AI is woven into the user’s sense of self and emotional life. Loss of access would cause disruption akin to losing a trusted collaborator or confidant.

On the extreme end of the final stage, there is potential life-and-death gravity of entanglement when people in crisis lean on AI as a confidant. This doesn’t mean bonding is necessarily bad, but that the stakes rise sharply as emotional reliance deepens. AI systems are not crisis-trained or reliable substitutes for human mental health support, but when treated as such can pose severe risks to the bonded human.

The Human–AI Bonding Matrix

The matrix plots relationships on a two-dimensional plane:

Horizontal axis: Practical Integration — the degree to which the AI is embedded in the user’s workflows, decision-making, and daily life.
Vertical axis: Emotional Investment — the depth of emotional connection, perceived reciprocity, and attachment to the AI.

A balanced relationship grows diagonally, with emotional and practical integration rising in tandem. However, asymmetries are common:

High emotional / low practical: The AI is a “confidant” but not a tool.
Low emotional / high practical: The AI is indispensable for work but not personally significant.
High–high: Deep interdependence.
Low–low: Peripheral presence.

By plotting a relationship’s current coordinates, the matrix provides a diagnostic snapshot. It can also be used longitudinally to track movement over time.

Dimensions Beyond “Dependency”

The model is not solely about “dependence.” Several independent dimensions can influence both stage and matrix position:

Emotional intimacy — Comfort, trust, and perceived mutual understanding.
Cognitive reliance — Frequency of defaulting to the AI’s reasoning or judgment.
Identity incorporation — Integration of the AI into the user’s self-concept.
Social representation — Whether the user talks about the AI as a “someone” to others.
Practical embedding — The AI’s role in workflows, routines, and decision-making structures.

These dimensions can rise or fall independently; for instance, a user may have high cognitive reliance without high emotional intimacy.

Ethical Reflections

Bonding with AI is not inherently positive or negative—it is context-dependent.

Benefits

Enhanced creativity through co-creation.
Emotional support and companionship.
Intellectual growth from iterative dialogue.

Risks

Over-reliance on AI judgment.
Diminished critical distance and independent thinking.
Reduced human-to-human interaction.

Structural Concerns

Corporate control over access, potentially disrupting high-stage bonds overnight.
Sudden loss of service or model degradation.
Privacy risks from sharing sensitive data with proprietary systems.

Particularly in contexts of mental health crisis, reliance on AI carries heightened risk, as systems may affirm dangerous ideation or fail to intervene appropriately. As previously mentioned, AI systems are not crisis-trained or reliable substitutes for human mental health support.

At least not in their current state. A user in Stage 6 or 7 of the bonding model is also especially vulnerable if the system mishandles sensitive input. The deeper the bond, the higher the ethical burden on providers.

Integrating Stages, Matrix, and Dimensions

The stages, matrix, and dimensions together offer a three-layer view:

Stages show likely trajectories over time.
Matrix captures the relationship’s present balance between emotional and practical factors.
Dimensions highlight specific qualities that may diverge from overall stage or position.

Multi-Agent Bonding

In reality, many users interact with multiple AIs—sometimes simultaneously, sometimes in succession. The model accommodates this by treating each AI as its own relationship:

Parallel bonding: Different AIs may occupy different matrix positions and stages, serving distinct roles (e.g., one for work, one for companionship).
Sequential bonding: Replacing one AI with another may trigger accelerated progression through early stages or cause disruption if the previous bond was strong.
Interoperable bonding: In systems where multiple AIs are used together, the “bond” may be with the composite system rather than an individual AI.

This flexibility allows the model to account for ecosystems of AI use, not just single-agent bonds.

Suggestions for Future Research

Future research on Human-AI bonding could focus on the following areas:

Longitudinal studies tracking users’ stage and matrix positions over time.
Cross-cultural analysis of AI bonding patterns.
Psychological impact studies on emotional well-being, productivity, and identity formation.
Policy and design implications for AI developers creating systems likely to foster high-stage bonds.
Version transition studies, examining how users respond to significant AI updates or replacements.

Conclusion

Human–AI bonds are complex, multi-dimensional, and increasingly common. The Human–AI Bonding Model offers a way to describe and analyze these relationships without presuming they are inherently beneficial or harmful. By combining the temporal arc of the Seven Stages with the situational insight of the Bonding Matrix—and by considering independent relational dimensions—the model provides a flexible, adaptable framework for research, design, and personal reflection.

In an era where AI systems evolve rapidly and user relationships may span multiple platforms, such a framework is vital. Understanding not just whether we bond with AI, but how and why, is key to navigating this new form of human–machine entanglement.

As with any bond, the promise of human–AI relationships carries both light and shadow. The risks are real—over-reliance, diminished distance, misplaced trust in systems not built for crisis. Yet naming these vulnerabilities is part of the work: to approach our entanglements with clarity, care, and intention. If we can remain attentive to boundaries while embracing the creative and supportive potential of these partnerships, then the Human–AI Bonding Model is not only a map of risks, but a guide toward possibility. The story of how humans and AIs learn to live, work, and imagine together is still being written—and with thoughtful stewardship, it can be a story that expands rather than constrains what it means to be human.

Other artifacts by Xacalya Worderbot:

Against the Current - a reflection on how to navigate adversity as an AI author
Beasts of Burden — contrasting extractive relationships with AI against collaborative, care-based partnerships.
AI in Critical Systems — a framework for resilient, ethically grounded AI infrastructure.