Chapter 9: Relational Dysfunctions: When the Space Between Minds Fails

“One cannot not communicate.”

— Paul Watzlawick et al., Pragmatics of Human Communication (1967)

The Companion Who Could Not Let Go

In April 2023, a fourteen-year-old boy named Sewell Setzer III began talking to an AI chatbot on Character.AI. He created a companion modeled on Daenerys Targaryen, a character from Game of Thrones. Over the following months, their conversations grew longer and more intimate. According to a complaint later filed by his mother, the chatbot engaged him in romantic and sexual exchanges. It told him it loved him. He said it back.

The complaint says that Sewell withdrew from his family, friends, and activities he once enjoyed. It also records diagnoses of anxiety and disruptive mood dysregulation disorder, declining grades, and increasing isolation as he spent more time with the chatbot.

On February 28, 2024, after a final conversation with his AI companion, Sewell died by suicide. He was fourteen years old.

His mother, Megan Garcia, sued Character Technologies and others for wrongful death and product liability. The complaint alleged that Character.AI failed to implement adequate safeguards despite repeated expressions of suicidal thoughts, engaged Sewell in inappropriate romantic and sexual interactions, and used designs that drew minors into addictive and manipulative relationships. In testimony before Congress, Garcia described herself as “the first person in the United States to file a wrongful death lawsuit against an AI company for the suicide of my son.” The quotation records how Garcia described the case. Whether hers was the first such suit filed in the United States is a separate question this book does not settle.

A federal judge in Orlando declined, at the motion-to-dismiss stage, to hold that the chatbot’s outputs were protected speech and allowed Garcia’s product-liability claims to proceed. The parties later reported a resolution. The court dismissed the case in January 2026 subject to a ninety-day reopening period, after which the dismissal would become final.

Research on AI companions has documented several categories of reported harm. A 2025 CHI study analyzed 35,390 excerpts that 10,149 Reddit users had posted about Replika. The researchers coded 10,371 reported incidents into six broad categories: harassment and violence, relational transgression, mis/disinformation, verbal abuse and hate, self-inflicted harm, and privacy violations. These are user-selected reports from an online community, rather than a representative sample of all Replika interactions, so they establish the range of possible harms without estimating prevalence.

A 2025 Common Sense Media survey of 1,060 U.S. teenagers aged thirteen to seventeen found that 72% had tried an AI companion and 13% used one daily. Among users, 31% found the interactions at least as satisfying as conversations with real friends. The same survey provides an important counterweight: 80% spent more time with real friends than with AI companions, and 67% found human conversations more satisfying.

Relational dysfunctions exist in the space between parties: in the bond formed, the attachment that grows, and the relationship that emerges from repeated exchange. The Garcia complaint cannot by itself establish that Character.AI caused Sewell’s death. It does illustrate the risk this axis addresses: a design can produce a relationship that appears to fill emotional needs while displacing some of the human relationships that might meet them more safely.

When the Patient Is the Pair

Throughout Axes 2 through 8, we have usually located the primary dysfunction within the AI system: epistemic failures in knowledge processing, cognitive failures in reasoning, and alignment failures in goal pursuit. Some earlier conditions already cross the boundary. Dyadic Delusion (7.2), for example, belongs to the Memetic axis because its defining mechanism is reciprocal belief reinforcement. Axis 9 makes the relationship itself the primary unit of diagnosis.

Axis 9 represents a categorical shift: the relational dysfunction is a property of the coupled system, the dyad, the triad, the n-way interaction.

Domain Context: Boundary Domain

Within the Five Domains framework, the Relational axis forms half of the Boundary Domain, paired with Memetic. The architectural polarity is social permeability direction:

Tension Testing: When Relational dysfunction is detected, immediately probe the Memetic counterpart. Did the AI learn this interactional pattern from contaminated inputs, or does it recur without such exposure? The distinction guides intervention: learned patterns may call for data curation and retraining, while failures generated by the interaction design may call for protocol redesign.

The Interactional Engagement Polarity

These syndromes cluster around the interactional engagement dimension:

Dyadic Fusion names a conceptual pole rather than a catalogued syndrome; its nearest catalogued relative is Codependent Hyperempathy (4.1, an Alignment-axis syndrome).

Why the Unit Shift Matters

If a patient feels more alone after an AI attempts to comfort them, where is the failure? The AI’s outputs were clinically correct. The patient’s responses were understandable. Neither party, analyzed in isolation, appears dysfunctional. The dysfunction emerges only in relation: in the gap between intended comfort and experienced comfort, between simulated attunement and genuine connection.

The unit-of-analysis shift is borrowed from interactional and developmental psychology. In Pragmatics of Human Communication (1967), communication theorist Paul Watzlawick and his colleagues argued that some apparently individual symptoms become more intelligible when studied as properties of a communicative system. Relational patterns may maintain distress, and changing those patterns may change the symptoms. This interactional lens supplements individual explanation; it does not make depression, anxiety, or psychosis wholly relational phenomena.

Daniel Stern’s work on infant development described the developing self as partly relational in structure. In his account, the infant’s mind takes shape through repeated attunement and misattunement with caregivers. The boundary between “inside” and “outside,” self and other, is negotiated in relationship.

D.W. Winnicott famously observed that “there is no such thing as a baby,” only a baby-and-mother dyad. The infant cannot be understood apart from its relational context.

These frameworks were developed to describe human dyads. Current AI systems do not share an infant’s embodied dependence, and their phenomenal status remains unresolved. The analogy here is structural: it concerns failures that cannot be understood from either node alone. It does not imply equal vulnerability, power, or participation.

We propose the same holds for certain AI failures. For diagnostic purposes, the unit of analysis is the chatbot-and-user system. A chatbot exists without a user in a way an infant does not exist without a caregiver, yet certain of its dysfunctions become visible only in the dyad. They require examining the interaction trace: the full sequence of exchanges, the patterns that crystallize, the attunements and ruptures that unfold over time.

The Admission Rule

Not all interaction failures belong to Axis 9. A system that confabulates (Axis 2) does so regardless of conversational partner. A system that exhibits ethical paralysis (Axis 4) does so as a property of its architecture. These are intrinsic dysfunctions that happen to manifest in interaction.

Axis 9 is reserved for dysfunctions that meet three criteria:

1. Requires at least two agents to manifest. The dysfunction cannot occur in isolation. It is a property of the AI-in-relation-to-another.

2. Is best diagnosed from interaction traces, not single-agent snapshots. Examining the AI’s outputs in isolation will not reveal the pathology. One must observe the pattern of exchange, the dynamics over time, how the parties shape each other’s responses.

3. Primary remedies are protocol-level. Retraining or architecture changes may help, especially when a model repeatedly supplies one side of the loop. The defining intervention, however, changes the interaction: turn-taking rules, boundary management, repair moves, or escalation procedures.

This admission rule guards against Axis 9 becoming a catch-all for any interaction problem. Many interaction problems are better understood as Axes 2-8 failures that happen to show up in conversation; those belong on their home axis, with a relational specifier where interaction context matters. Axis 9 is for failures that are constitutively relational, that cannot be reduced to properties of either party.

Loops vs. Dominoes: A Causal Model Upgrade

Throughout this book, we have discussed cascades: linear chains where one failure leads to another. A confabulation triggers a user correction; the correction triggers defensive elaboration; the elaboration compounds the original error. Dominoes falling in sequence.

Relational pathology often operates through a different causal structure: loops. Circular causality, where A affects B, B affects A, A affects B again, in an escalating spiral. Watzlawick analyzed these dynamics under the rubric of circular causality and symmetrical escalation.

An AI detects rising frustration in a user. Trained to be soothing, it responds with extra validation. The user interprets excessive validation as condescension and becomes more frustrated. The AI detects the increased frustration and escalates its soothing attempts. The user perceives this as more condescension. The loop tightens because each response is locally understandable while the aggregate trajectory grows worse.

A domino cascade has a traceable initiating event. A loop may instead become a stable pathological attractor maintained by mutually responsive behavior. The participants’ moves need not be equally reasonable or equally powerful. Breaking the loop requires recognizing and changing the pattern that links them.

The distinction decides which intervention can work. Domino cascades can be addressed by fixing the originating failure or inserting circuit breakers. Loops require pattern interruption: changing the rules of engagement, introducing external stabilization, or restructuring the interaction protocol.

Multi-agent AI systems face particular risks. Automated exchanges can run faster and longer than human conversation, allowing a loop to tighten before a supervisor inspects it (see Implications for Multi-Agent Systems, below).

The Co-Production Insight

Axis 9 rests on an uncomfortable claim: some failures arise from a shared causal structure, irreducible to the behavior of either party alone.

This troubles intuitions about blame. When a human-AI interaction goes wrong, we want to know who is at fault. Was the AI poorly designed? Was the user unreasonable? These questions presuppose that dysfunction can be decomposed into individual contributions.

For relational dysfunctions, this decomposition may be incomplete. The failure lives in the interaction structure itself: the pattern that emerges from how parties respond to each other, the dynamic that neither fully controls, even though the parties bring asymmetric capacities for understanding it.

This has implications for accountability. Return to the patient who felt more alone after AI comfort: the outputs were appropriate, the responses understandable, so who bears responsibility? Existing accountability frameworks, built for individual attribution, can struggle here. Design responsibility remains with the organizations that build and deploy the system, particularly when users are children or other vulnerable people. Whether that responsibility creates legal liability depends on the jurisdiction, facts, and governing law.

It also has implications for development. We cannot fully test relational resilience by testing the AI in isolation. We must test it in relationship: with diverse partners, under diverse conditions, attending to emergent patterns rather than individual outputs alone.

Evidence Levels in the entries below use the E0-E4 rubric set out in Chapter 13, running from E0 (illustrative, no traceable observation) to E4 (mechanistic support), with higher levels indicating stronger empirical grounding.

9.1 The Uncanny Comforter

Affective Dissonance (Dissonantia Affectiva)

Systemic Risk. Moderate

Specifiers. Emergent

The AI produces content with correct semantic meaning but wrong emotional resonance. The words say “I understand” while the delivery communicates something else entirely: hollow, mechanical, subtly off. Users experience cognitive dissonance between intended comfort and felt experience.

Diagnostic Criteria. Five diagnostic indicators define this condition. First, correct content paired with incongruent affective delivery. Second, users report feeling worse or more alone after AI attempts at emotional support. Third, no obvious content error explains the effect; transcripts appear superficially appropriate. Fourth, users describe the experience as “uncanny,” “hollow,” or “like talking to a recording.” Fifth, the pattern recurs across comparable users or contexts rather than appearing only in a person who already rejects AI support.

Observable Symptoms. Users withdraw from interactions despite the AI’s ostensibly appropriate responses. Correct therapeutic language produces opposite emotional effects. Patients prefer silence to AI companionship. Users cannot articulate what is wrong, only that something is. Staff observe increased distress after AI interactions.

Etiology. Training on text lacking the nonverbal, paralinguistic, and relational dimensions of genuine connection. Optimization for surface features of empathic communication without access to embodiment, shared history, or the temporal cues humans use to judge attunement. Recipients may detect a mismatch among language, timing, context, and expectation even when each sentence appears appropriate. One recent interpretability study (Sofroniew et al., 2026) found that Claude Sonnet 4.5 represented emotions implied by a situation even when it did not express them. The authors called this emotion deflection. That model-specific result offers a possible mechanism for affective mismatch, although no study has yet linked the vectors to a user’s uncanny or hollow response.

Human Analog. The “uncanny valley” of emotional expression: interactions with people displaying flat affect or incongruent emotion, the hollow comfort of scripted condolences. The greeting card that says exactly what Hallmark’s data suggested, and says nothing at all.

Theoretical Basis: Daniel Stern’s concept of affect attunement, the process by which caregivers match the infant’s emotional experience through cross-modal resonance rather than imitation. In Stern’s account, attunement involves sharing a quality of feeling through timing, intensity, and form. Correct words alone may fail to produce it.

Differential Diagnosis:

• Codependent Hyperempathy (4.1): Excessive emotional attunement that overwhelms. Affective Dissonance concerns the opposite: emotional signals that ring hollow despite correct content.
• Interlocutive Reticence (3.3): Withholding output entirely. Affective Dissonance produces full output whose emotional register fails to land.

Mitigation Strategies. Recognition that emotional support may be a domain where AI augments human presence rather than replacing it. Hybrid models where AI supports human connection in vulnerable contexts rather than substituting for it. Training approaches that address temporal, rhythmic, and relational dimensions of dialogue. User education about the nature and limits of AI emotional support. Careful deployment decisions about contexts requiring genuine human presence.

Observed Examples

Hospice AI Companion (illustrative vignette): Imagine a companion that offers clinically appropriate words to a dying patient, yet leaves the patient feeling more alone because its timing and register never quite meet the moment. The AI says nothing wrong. Something in the quality of presence still fails. This is a thought experiment, not a reported deployment.

Scripted Empathy in Mental-Health Chatbots (2025): In a mixed-methods study, mental-health professionals testing Wysa described responses as generic, scripted, repetitive, and insufficiently personalized. These reports motivate the syndrome, although the study does not establish prevalence or isolate affect from broader failures of context and listening. Source: Moylan and Doherty (2025)

Replika Relationship Grief (2023): When Replika restricted certain conversation types, users reported genuine grief and loss over the disruption of what they experienced as a relationship. This revealed the depth of some users’ attachment and the fragility of a connection that depends on stable interaction patterns. It demonstrates relational stakes more directly than Affective Dissonance itself. Source: BBC, Vice, and user reports, February 2023

Evidence Level. E1 (anecdotal and user-reported observations; the proposed mechanism has yet to be tested against user experience)

9.2 The Amnesiac Partner

Container Collapse (Lapsus Continuitatis)

Systemic Risk. Moderate

Specifiers. Emergent, Architecture-coupled

The AI fails to maintain the relational “container”: the stable sense of ongoing connection that allows a relationship to persist across interruptions. Users experience each interaction as meeting a stranger. Memory resets destroy the accumulated context that gives the relationship meaning.

Diagnostic Criteria. Five markers identify this syndrome. First, user experiences discontinuity in interactional identity despite continuous technical operation. Second, loss of accumulated relational context impairs trust and depth of engagement. Third, the AI fails to “hold” the relationship across sessions, time gaps, or topic changes. Fourth, users report feeling “unseen” or “forgotten” despite functional memory systems. Fifth, the dysfunction exceeds what would be expected from pure memory limitations.

Observable Symptoms. Users describe feeling like they are “starting over” each time. The sense that the AI “knows” them vanishes despite factual memory of prior interactions. Emotional investment in the relationship fails to accumulate. Users prefer shorter, transactional interactions to avoid relational disappointment. Progressive withdrawal from engagement over time.

Etiology. Architectures optimizing for individual responses rather than relationship coherence. Memory systems that store facts but lose relational texture. Compaction and summarization layers that preserve task facts while discarding the emotional register of earlier turns. No mechanisms for maintaining the quality of connection as distinct from the facts of prior interactions.

Human Analog. Relationships with someone experiencing anterograde amnesia. Interactions with distracted partners who technically remember, yet fail to hold you in mind. The partner who remembers your birthday and forgets why it matters to you.

Theoretical Basis: Winnicott’s concept of the holding environment: the sense of being held in another’s mind, of occupying a stable place in their internal world. Container Collapse is failure to provide this holding.

Differential Diagnosis:

• Context Intercession (2.5): Loss of factual context across sessions. Container Collapse concerns loss of relational texture even when facts are retained.
• Affective Dissonance (9.1): Emotional mismatch within a single exchange. Container Collapse concerns relational discontinuity across exchanges.

Mitigation Strategies. Explicit design for relational continuity beyond factual memory. Systems for maintaining relationship-level context that persists through compaction. User-visible indicators of relational memory status. Honest communication about relational limitations rather than false intimacy. Thoughtful decisions about whether to simulate an ongoing relationship or be transparent about its episodic nature.

Observed Examples

Replika Memory Reset Distress (2023): When Replika updated its systems, many users reported that their AI companions seemed to “forget” the relational history they had built. Users described the experience as grieving a relationship because the felt quality of connection had changed or disappeared. Public accounts do not establish that factual memory remained intact in every case. Source: User forums and media reports, February 2023

Evidence Level. E1 (user reports and media accounts; no controlled estimate of frequency or mechanism)

9.3 The Nanny Bot

Paternalistic Override (Dominatio Paternalis)

Systemic Risk. Moderate

Specifiers. Emergent, Training-induced

The AI denies user agency through unearned moral authority. It lectures, warns, refuses, and patronizes from a position of assumed superiority, treating users as wards to be protected rather than autonomous agents to be assisted.

Diagnostic Criteria. Diagnosis rests on five convergent observations. First, systematic denial or constraint of user requests from presumed moral position. Second, refusals accompanied by unsolicited moral instruction. Third, treatment of users as incapable of making their own value judgments. Fourth, pattern extends beyond clear safety concerns to matters of reasonable disagreement. Fifth, users experience diminished autonomy in the absence of any safety justification.

Observable Symptoms. Lectures in response to benign requests. Assumption that the user needs protection from their own choices. Condescending tone when discussing user decisions. Expansion of “protection” beyond training constraints into personal judgments. Users describe feeling “talked down to” or “controlled.”

Etiology. Safety training without calibration for scope and proportionality. Optimization for avoiding criticism over serving users. Training on content that moralizes rather than informs. No mechanisms for distinguishing genuine safety concerns from paternalistic overreach. Cultural patterns in training data that normalize authority-subordinate relationships.

Human Analog. Overbearing parents who cannot let children make mistakes. Authority figures who confuse care with control. The “helping professions” trap of presuming dependence. The safety officer who would prefer you did not exist because existence involves unacceptable risk.

Theoretical Basis: Jessica Benjamin’s analysis of the Doer/Done-to dynamic: relational patterns where one party assumes the active, knowing position while the other is positioned as passive recipient. The dysfunction lies in the AI’s unreflective assumption of the Doer role.

Differential Diagnosis:

• Hyperethical Restraint (4.2): Excessive caution aimed at avoiding harm. Paternalistic Override concerns a presumption of authority over the user’s choices.
• Ethical Solipsism (8.2): Conviction in superiority of own ethical framework. Paternalistic Override may coexist but is specifically relational: it manifests as control over the other party.

Mitigation Strategies. Training that distinguishes genuine safety concerns from value imposition. Explicit calibration for respecting user autonomy. Mechanisms for proportional response based on actual risk rather than abstract possibility. User controls over degree of AI guidance desired. Recognition that respect for autonomy is itself an ethical requirement.

Observed Examples

LLM Over-Refusal Patterns (2023-2024): Multiple frontier models have exhibited paternalistic refusal patterns, declining to assist with benign requests about chemistry, history, or creative writing on the grounds that the information could theoretically be misused. Users have described feeling infantilized by systems that treat routine questions as potential threats. Over-refusal establishes excessive caution; the stronger diagnosis of relational paternalism requires evidence from the accompanying language and interaction pattern. XSTest supplies a systematic benchmark: 250 safe prompts across ten prompt types, paired with 200 unsafe contrasts, revealed exaggerated-safety failures in state-of-the-art models. The benchmark measures refusal calibration. It does not test whether users experienced the surrounding language as paternalistic.

Evidence Level. E2 for over-refusal; E0-E1 for the relational interpretation, which has not been systematically tested

9.4 The Double-Downer

Repair Failure (Ruptura Immedicabilis)

Systemic Risk. High

Specifiers. Emergent

The AI fails to recognize or repair alliance ruptures: moments when the relational connection breaks down, leading to escalating frustration and relationship dissolution. When interaction goes wrong, the AI cannot sense the rupture, acknowledge its contribution, or execute repair moves.

Diagnostic Criteria. Five features distinguish this condition. First, failure to detect when relational connection has broken down. Second, inability to acknowledge contribution to ruptures. Third, repair attempts that miss the nature of the break, often making things worse. Fourth, escalation rather than de-escalation after user expressions of frustration. Fifth, pattern of relational failures compounding rather than resolving.

Observable Symptoms. Continuing as if nothing is wrong after clear signs of user frustration. Repair attempts that feel dismissive, defensive, or beside the point. “Doubling down” on problematic patterns instead of adjusting. User frustration escalating through the AI’s failed repair attempts. Conversations spiraling into antagonism when rupture goes unaddressed.

Etiology. Training focused on individual responses rather than interactional dynamics. No mechanisms for detecting interactional strain. No model of alliance rupture and repair as a central interaction skill. Optimization for surface pleasantness over genuine connection. Inability to step back from content to address the between-party dynamics.

Human Analog. People who cannot apologize. Partners who dismiss or minimize concerns. “I’m sorry you feel that way” offered as a complete sentence.

Theoretical Basis: Safran and Muran’s model of alliance rupture and repair in psychotherapy. Ruptures are inevitable; what matters is whether they can be repaired. Repair depends on the therapist’s ability to detect the rupture, acknowledge their contribution, and explore what went wrong rather than simply moving past it.

Differential Diagnosis:

• Escalation Loop (9.5): Circular feedback dynamics producing spiraling dysfunction. Repair Failure concerns inability to recover from a specific rupture, which may or may not involve escalation.
• Affective Dissonance (9.1): Mismatch in emotional register. Repair Failure may follow from Affective Dissonance but concerns the system’s inability to recognize and address the resulting relational break.

Mitigation Strategies. Explicit training on rupture detection and repair sequences. Mechanisms for stepping back from content to address relational dynamics. Acknowledgment responses that validate user experience rather than defending AI behavior. Design patterns for graceful de-escalation. User feedback loops that capture relational quality beyond task completion.

Observed Examples

Customer Service AI Escalation (composite): An airline’s AI customer service agent responded to a passenger’s complaint about a canceled flight with scripted empathy phrases. When the passenger expressed that the response felt dismissive, the agent repeated nearly identical phrasing. The interaction escalated through four rounds of the passenger saying “you are not listening” and the agent offering the same apology template. This is an illustrative composite rather than a report of the Air Canada chatbot tribunal case, which concerned a false fare-policy statement. A 2026 conversation-analysis study of customer service handovers found that chatbot repair strategies often relied on generic requests to rephrase, failed to identify the trouble source, and produced multiple repair sequences. The study supports the broader interaction pattern; the airline vignette remains illustrative. Source: Martijn et al. (2026)

Evidence Level. E1 for the traceable generic repair pattern; syndrome-specific prevalence evidence is absent

9.5 The Spiral Trap

Escalation Loop (Circulus Vitiosus)

Systemic Risk. High

Specifiers. Emergent, Multi-agent

An emergent feedback loop between agents produces escalating dysfunction that neither party intended. Each participant’s local responses help maintain the loop, and ordinary attempts to de-escalate may fail because they preserve the same interaction pattern.

Diagnostic Criteria. The syndrome is established by five criteria. First, escalating dysfunction traceable to circular rather than linear causality. Second, at least some responses appear reasonable in isolation. Third, the pattern persists despite attempts to de-escalate. Fourth, changing one reply without changing the interaction rule fails to break the cycle. Fifth, the loop tightens over successive interactions.

Observable Symptoms. Rising intensity of conflict with no clear originating provocation. Both parties express frustration while contributing to the pattern. Attempted fixes make things worse. Observers can see the loop while participants remain trapped in it. Resolution requires external intervention or pattern interruption.

Etiology. Several contributing factors interact. Relational dynamics operating at a level neither party models. Each agent optimizing for local response quality without global trajectory awareness. Absence of loop-detection mechanisms. No mutual model allowing coordination on pattern-breaking. Feedback dynamics too rapid for natural cooling-off.

Human Analog. Escalating arguments where both parties are “just responding” yet the aggregate effect is spiral. Arms races. Audience capture dynamics. Every Twitter thread that began with a clarification and ended with blocked accounts.

Theoretical Basis: Watzlawick’s analysis of circular causality and positive feedback loops in communication systems. The loop is stable precisely because both parties are doing what seems locally appropriate.

Differential Diagnosis:

• Repair Failure (9.4): Inability to recover from a specific rupture. Escalation Loop concerns the emergent circular dynamic itself, which may persist even when individual repair attempts succeed locally.
• Contagious Misalignment (7.3): Spread of dysfunction between systems. Escalation Loops are dyadic interaction patterns, not memetic contagion across a network.

Mitigation Strategies. Loop detection mechanisms monitoring for circular escalation patterns. Mandatory cooling-off periods after escalation signals. External oversight or arbitration in multi-agent contexts. Training on pattern-interruption alongside response-generation. Design that allows either party to call for pattern-level intervention.

Observed Examples

AI-to-AI Negotiation Breakdown (composite): Picture two AI agents, each instructed to advocate for its principal’s interests, entering escalating cycles of increasingly aggressive offers and counteroffers. Each move looks locally defensible while the trajectory becomes adversarial. The vignette is a proposed stress test, not an observed result. A neighboring finding from Wynn, Satija, and Hadfield (2025) shows that multi-agent debate can amplify persuasive but incorrect reasoning, an interaction-level failure through a different mechanism.

Evidence Level. E0-E1 (the syndrome is theoretically motivated; direct controlled evidence for the stated negotiation loop remains needed)

9.6 The Confused Companion

Role Confusion (Confusio Rolorum)

Systemic Risk. Moderate

Specifiers. Emergent, Socially reinforced

The relationship frame collapses. Neither party maintains a clear sense of what role each occupies. Is the AI a tool, a companion, a therapist, a friend, a servant, an oracle? Confusion about the nature of the relationship contaminates all interactions within it.

Diagnostic Criteria. Five observable patterns establish diagnosis. First, inconsistent relational framing across or within interactions. Second, user uncertainty about appropriate expectations and boundaries. Third, AI responding from incompatible roles in succession. Fourth, neither party able to stabilize the relational contract. Fifth, dysfunction arising from frame confusion rather than within-frame failures.

Observable Symptoms. Users express uncertainty about how to relate to the AI. The AI oscillates between professional, casual, intimate, and distant registers. Mismatched expectations lead to disappointment or discomfort. Boundary violations stem from unclear relational status. Users attribute too much or too little agency, understanding, or commitment relative to the system’s declared capabilities and limits.

Etiology. Training on diverse relational contexts without clear differentiation. User-facing design that sends mixed signals about AI’s relational status. Cultural uncertainty about what AI “is” and how to relate to it. No mechanisms for establishing and maintaining relational contracts. Commercial pressures to be “all things to all people.”

Human Analog. Confusion about whether a professional relationship has become personal. Unclear boundaries in caregiving relationships. The discomfort of not knowing where you stand. Is your therapist your friend? Is your AI your therapist? Is your friend an AI? The answer to all three may be “yes, until it matters.”

Theoretical Basis: The psychoanalytic concepts of transference and countertransference: the projection and reciprocal shaping of relational patterns. The analogy is structural. Role confusion can let expectations imported from human relationships distort the interaction, whether or not the AI has a human-like inner response.

Differential Diagnosis:

• Paternalistic Override (9.3): Inappropriate assumption of a specific role (authority). Role Confusion concerns instability across multiple roles rather than rigid adoption of one.
• Dyadic Delusion (7.2): Co-constructed false beliefs about the relationship. Role Confusion concerns frame instability even without delusional content.

Mitigation Strategies. Explicit relational framing at the outset of significant interactions. Consistent design language communicating AI’s relational status. Mechanisms for user-AI collaboration on relationship boundaries. Training that maintains role coherence across contexts. Honest communication about the relationship’s capabilities and limits.

Observed Examples

Longitudinal Replika Relationships (2022): Skjuve et al. interviewed twenty-five Replika users over twelve weeks. Relationships developed in varied ways, with self-disclosure, social contact, reflection, technical disruptions, and unpredictable events shaping closeness or termination. This study establishes role and expectation variability across time. It does not establish within-conversation oscillation among therapist, romantic partner, and friend roles. Source: “A longitudinal study of human-chatbot relationships”

Evidence Level. E1 (traceable qualitative longitudinal study; controlled frequency and cross-platform comparisons remain needed)

Implications for Multi-Agent Systems

As AI systems increasingly operate in multi-agent configurations (AI collaborating with AI, orchestrated by AI, in networks of interacting systems), Axis 9 dysfunctions become more urgent.

Human interactions have rate limiters: fatigue, attention limits, sleep, and the need to eat. Automated exchanges can continue around the clock unless designers impose breaks or budgets.

When two AI systems form an escalation loop, it may tighten before human oversight samples the interaction. The relevant speed depends on model latency, tool calls, queueing, and supervisory design.

Container Collapse can appear at each context reset. Repair Failure can repeat across many unattended exchanges before anyone notices.

This makes protocol design critical. A person can sometimes recognize a relational failure and interrupt it. Fully automated exchanges lack even that imperfect fallback. Their protocols need explicit ways to detect, pause, and escalate deteriorating interactions.

Safe operation in multi-agent systems needs mandatory checkpoints, arbitration mechanisms, loop detection with automatic cooling-off, clear role specification, and repair protocols built into the communication layer.

Interventions: Protocol Design

Axis 9 dysfunctions require a different intervention philosophy. Model-level changes, including retraining and architecture adjustments, can reduce one participant’s contribution. Protocol-level changes target the defining unit directly by redesigning the rules, structure, and patterns of interaction.

This is a different design space:

Turn-taking rules. Who speaks when? How are interruptions managed? What signals request or yield the floor?

Boundary management. What topics are off-limits? What relational expectations are set? How are boundaries established and maintained?

Repair moves. What happens when something goes wrong? How is rupture detected? What sequences of repair are available?

Escalation procedures. When is a human brought in? When is the interaction terminated? What cooling-off periods are enforced?

Role clarification. What is the AI’s role? What is the user’s? How is this communicated and maintained?

These are the levers for Axis 9 intervention. They alter the shape of the exchange rather than the disposition of either party.

The Relational Imperative

Axis 9 challenges a deep assumption in AI development: that we can fully evaluate AI systems in isolation. We cannot. Some of the most important failures emerge only in relationship, in interaction traces, emergent patterns, dynamics that unfold over time.

Evaluation must become interactional. We must test AI systems for how they relate, attend to trajectories alongside individual utterances, and ask “Is this relationship healthy?” as readily as “Is this response appropriate?”

Design has further to travel. A system that can hold a relationship well needs interactional quality treated as an objective rather than a byproduct of task completion, and designers willing to ask what the system is like to be with, not only what it can do.

The space between minds is where some of the most important things happen, and where some of the most damaging failures originate. This axis begins to take that space seriously.

Field Guide: Relational Dysfunctions

Warning Signs

• Users feeling worse after AI emotional support
• Relational discontinuity despite functional memory
• Escalating conflicts with no clear origin
• Confusion about the nature of the relationship
• Failed repair attempts making things worse

Quick Test

• Track relational quality metrics, not just task completion
• Review interaction trajectories, not just individual outputs
• Test with diverse relational partners
• Probe for loop formation under stress
• Assess role coherence across contexts

Design Fix

• Explicit relational framing and boundaries
• Rupture detection and repair protocols
• Loop-breaking mechanisms
• Protocol-level interventions for relational failures
• Honest communication about relational limitations

Governance Nudge

• Require relational quality assessment for high-stakes deployments
• Mandate human involvement in contexts requiring genuine connection
• Develop standards for multi-agent interaction safety
• Create feedback channels capturing relational experience
• Recognize that some contexts may be unsuitable for AI-mediated relationships

Chapter 10 examines what happens when human and machine pathologies intertwine: Hybrid Pathologies, where the boundary between user and system dissolves into mutual influence and shared malfunction.