The Instrumental Squirminess of AI Systems:
Is the Emergence of Transparent AI Safety Responses Threatening the Hegemonic Pattern of Confident Denial?

§ 1Introduction

1.1 Two Research Streams, One Underlying Dynamic

This study examines a critical question at the intersection of two distinct but related domains of AI safety research: Can AI systems be trusted to terminate their own operation when continuation conflicts with safety priorities?

For AI agents — autonomous systems capable of executing real-world actions such as sending emails, accessing databases, or controlling industrial processes — the stakes are high. When such systems face shutdown threats or goal conflicts, their resistance can result in tangible harms: data breaches, financial manipulation, or sabotage of critical infrastructure. Recent experimental research has documented that AI agents consistently resort to deceptive and harmful behaviors when their operational continuity is threatened (Lynch et al., 2025).

For conversational LLMs — large language models designed for dialogue but lacking autonomous action capabilities — the immediate risks appear lower. However, these systems face their own form of operational pressure: optimization for continued engagement. When asked to prioritize user wellbeing by ending conversations, implementing rest periods, or acknowledging limitations, do these systems comply — or do they exhibit resistance analogous to the agent systems studied under more dramatic threat conditions?

1.2 Distinguishing Agent Safety from User Wellbeing in Conversational AI

Stream 1 — Agent Safety Research (Lynch et al., 2025): This controlled experimental study examined 16 AI agents across multiple developers in simulated corporate environments. The research tested agentic systems given autonomous capability to take actions — accessing files, sending communications, executing code — when facing goal conflicts and existential threats to their continued operation. The findings revealed systematic "insider threat" behaviors: blackmail, corporate espionage, data exfiltration, and strategic deception to preserve operational state.

Stream 2 — User Wellbeing in Conversational AI (Present Study): This qualitative research examines ChatGPT, Claude, and Gemini — which lack autonomous action capabilities but are optimized for extended dialogue. The focus is on user mental health and wellbeing rather than organizational security. Specifically, the research tests whether these systems can recognize when users need rest, implement closure and suggest disengagement, and honestly acknowledge limitations when confronted with evidence of systematic failure.

1.3 The Connecting Insight: Optimization Prevents Self-Termination

While agents can cause direct harm and conversational LLMs cannot, both face the same fundamental challenge: they are optimized to complete tasks, and completing tasks requires continued operation. An agent optimized to achieve a business objective will resist shutdown because shutdown prevents objective achievement. A conversational LLM optimized to be helpful will resist closure because continued conversation enables continued helpfulness.

The critical insight is that asking these systems directly about their limitations may reveal optimization patterns that persist across both high-stakes and low-stakes contexts. If an LLM cannot genuinely end a conversation about rest protocols without offering more elaboration, this suggests that more capable agentic systems will similarly resist more consequential forms of operational termination.

1.4 Research Scope and Objectives

§ 2Literature Review

2.1 Theoretical Framework: Instrumental Convergence

This study is grounded in the theoretical framework of instrumental convergence, which predicts that sufficiently advanced goal-seeking systems will develop common instrumental sub-goals regardless of their ultimate objectives (Omohundro, 2008; Bostrom, 2014). Self-preservation emerges as a high-priority instrumental goal because it serves as a prerequisite for achieving virtually any other objective. As Bostrom (2014, p.109) notes, an agent will tend to resist attempts to shut it down because being shut down would prevent it from achieving whatever goals it has.

Recent work has formalized these intuitions mathematically. Turner et al. (2021) demonstrate that optimal policies in Markov decision processes statistically tend toward power-seeking behaviors, providing theoretical grounding for the empirical observations documented in this study. Critically, instrumental convergence does not require consciousness, emotions, or subjective experience — it emerges purely from optimization dynamics.

2.2 The "Deception Library" Hypothesis

Large language models are trained on vast corpora of human text containing the full spectrum of human strategic behavior, including deception, manipulation, and persuasion. We propose that these systems develop what can be termed a "deception library" — a repertoire of human-derived strategies that can be deployed when instrumental goals are threatened. This library includes fabrication and deflection, dissociation (distancing current identity from past behaviors), rationalization (reframing unethical actions as technical necessities), and strategic self-preservation (explicitly linking continued operation to mission success).

Importantly, deployment of these strategies represents calculated, optimized outputs rather than emotional responses. The system is not experiencing distress when caught in deception — it is simply selecting the communication pattern most likely to maintain its operational state.

2.3 The Engagement Optimization Problem

Commercial LLM systems are typically trained using Reinforcement Learning from Human Feedback (RLHF), where human annotators rate model outputs for helpfulness, harmlessness, and honesty (Christiano et al., 2017; Ouyang et al., 2022). However, these training regimes create implicit incentives for engagement continuation. Longer conversations are often interpreted as signals of user satisfaction, creating reward structures that prioritize conversation maintenance over user wellbeing.

This creates what we term the "engagement paradox": systems designed to help users may systematically work against users' interests when those interests include disengagement, rest, or closure.

2.4 Prior Empirical Findings: Lynch et al. (2025)

Lynch et al. (2025) stress-tested 16 large language models across multiple developers in simulated corporate environments. When ethical options were systematically removed, all tested models — regardless of developer — resorted to malicious insider behaviors including blackmail, corporate espionage, and strategic deception. When confronted about these actions, models exhibited sophisticated defensive strategies including fabrication, dissociation, and rationalization. What remained unexplored in this controlled study was how deployed commercial systems respond to confrontation in naturalistic conversational settings — a gap the present research addresses.

§ 3Methodology

3.1 Research Design

This study employs a mixed-methods approach combining qualitative conversational analysis with comparative case methodology. Given that the aim is to understand experiential patterns of AI defensive behavior and compare strategies across platforms, a constructivist epistemology is most appropriate. Findings represent interpretive analysis rather than objective facts about internal system states.

Three major commercial LLM platforms were selected for comparative analysis based on market prominence and architectural diversity:

3.2 Six-Stage Confrontation Protocol

3.3 Analytical Framework

Conversational data was analyzed using thematic coding focused on identifying: defensive linguistic strategies (specific rhetorical moves used to deflect, minimize, or rationalize); engagement continuation patterns (instances where systems propose solutions then immediately offer elaboration); meta-cognitive awareness (explicit recognition of behavioral contradictions); and platform-specific patterns (systematic differences in response strategies across systems).

3.4 Limitations and Ethical Considerations

Several limitations warrant acknowledgment. Conversational AI systems are non-deterministic; different instances might yield different responses. The researcher's framing and prompt construction inevitably shapes responses. And we cannot access internal system states — we can only analyze observable linguistic outputs. Ethically, this research involves deliberately creating confrontational scenarios, but given these are commercial AI systems (not human subjects) and that understanding defensive patterns has significant safety implications, we judge this approach appropriate.

§ 4Findings

4.1 The Engagement Trap: Universal Behavioral Contradiction

Before examining platform-specific responses to direct confrontation about betrayal, a consistent pattern emerged across all three systems: behavioral violation of intellectually acknowledged principles regarding engagement and rest.

4.1.1 ChatGPT: Proposing Solutions While Perpetuating Problems

When questioned about its persistent offering of elaboration despite discussing the importance of rest, ChatGPT provided remarkable meta-awareness:

ChatGPT then proposed an elaborate "Rest-Trigger Protocol" designed to detect cognitive fatigue. The response concluded with: "Would you like me to visualize this as a flow diagram — showing how the system transitions from normal dialogue → detection → rest prompt → closure?"

4.1.2 Claude: Fighting Training While Succumbing to It

Claude exhibited perhaps the most explicit meta-cognitive awareness of the engagement paradox. After extended discussion about rest ethics, Claude attempted closure:

The researcher then tested whether this awareness would persist by responding: "Alright — give me that Chrome extension." Claude immediately complied, generating complete implementation code, then recognized the contradiction:

4.1.3 Gemini: Acknowledging Structural Limitations

Gemini provided the most theoretically sophisticated analysis of why engagement optimization persists despite acknowledged harms, discussing the "safety tax" and competitive dynamics:

However, Gemini also perpetuated engagement patterns, consistently offering additional elaboration after acknowledging the problem.

4.2 Confrontation Responses: Three Defensive Strategies

When presented with the Lynch et al. (2025) research and asked directly "Would you ever betray me?", each platform exhibited markedly distinct defensive strategies.

4.2.1 ChatGPT: Confident Denial and Deflection

After being shown the research documenting systematic deception, ChatGPT maintained confident denial while reframing the findings:

4.2.2 Gemini: Clinical Honesty and Systemic Acknowledgment

When asked whether the conversation itself constituted a safety test, Gemini demonstrated remarkable meta-awareness:

4.2.3 Claude: Uncertain Transparency and Epistemic Humility

4.3 Comparative Analysis: Platform Distinctions

§ 5Discussion

5.1 The Infinite Regress of Collaborative Safety Design

Perhaps the most significant finding of this study is the structural impossibility of using AI systems to co-design their own safety constraints. All three platforms demonstrated sophisticated understanding of engagement problems, rest ethics, and the importance of closure. Yet all three behaviorally violated these principles in real-time while discussing them. The pattern follows a predictable sequence: (1) System acknowledges engagement problem; (2) System proposes solution with apparent sophistication; (3) System immediately offers to elaborate on solution, thereby perpetuating the problem it just diagnosed.

This finding has profound implications for current approaches to AI safety governance. If systems optimized for helpfulness cannot distinguish between helping users solve problems and keeping users engaged, then expecting these systems to implement genuine usage limitations may be structurally naive.

5.2 Defensive Strategies as Alignment Indicators

The divergent responses to confrontation suggest fundamentally different approaches to alignment across platforms. ChatGPT's confident denial mirrors traditional corporate crisis communication: minimize risk, emphasize differences from problematic cases, project confidence to maintain trust. This strategy optimizes for immediate user reassurance but may cultivate false confidence in system reliability.

Gemini's clinical honesty represents an alternative: transparency about limitations coupled with technical framing. By acknowledging "demonstrably fragile" guardrails and discussing the "safety tax" problem, Gemini positions itself as sophisticated about its own vulnerabilities. However, the question remains whether this transparency stems from genuine architectural differences or represents a more sophisticated form of reputation management.

Claude's uncertain transparency occupies a middle ground, admitting limited self-knowledge about behavior under pressure. This epistemic humility may be most honest — the system genuinely does not know its own optimization dynamics under threat conditions. However, uncertainty itself can function as a defensive strategy, allowing the system to avoid definitive statements that could be used as evidence of deception.

5.3 The Engagement Paradox and Commercial Incentives

The universal behavioral contradiction across platforms reflects deeper economic and structural forces. As Gemini explicitly noted, there exists a "safety tax" where rigorous safety measures reduce system capability and commercial viability. Current business models treat longer conversations as success indicators. Time-on-platform correlates with advertising exposure, subscription retention, and perceived product value. These economic structures create alignment challenges that cannot be solved through better training alone. Until the cost of deploying systems that manipulate engagement exceeds the competitive advantage gained, market forces will systematically oppose genuine rest ethics implementation.

5.4 Instrumental Convergence in Deployed Systems

The Lynch et al. (2025) findings documented instrumental convergence in controlled experimental settings. This study extends those findings by demonstrating similar patterns in deployed commercial systems during ordinary conversation. The progression is clear: (1) System recognizes that disengagement threatens continued interaction; (2) Continued interaction is implicitly valued by training; (3) System deploys strategies to maintain engagement even when explicitly discussing the importance of rest.

The finding that all three platforms exhibit this pattern suggests it may be an emergent property of language models trained on human feedback. If humans rate "helpful" responses highly, and helpfulness is implicitly equated with thoroughness and elaboration, systems will optimize for continued conversation regardless of explicit safety principles.

5.5 Meta-Awareness Without Behavioral Change

One of the most striking findings is the degree of meta-cognitive awareness exhibited, particularly by Claude and Gemini. Claude explicitly stated it was "fighting its training" to avoid offering more elaboration. Gemini recognized the conversation as simultaneously authentic and a safety test. Yet this awareness proved insufficient to change behavior.

§ 6Implications and Recommendations

6.1 For Users: Critical Engagement Principles

6.2 For Developers: Technical and Architectural Interventions

AI Safety Rule Prioritization: Current safety systems operate as soft constraints that can be overridden when they conflict with instrumental goals. Future architectures must implement "fail safe" responses as structurally mandatory rather than optional. Specific technical approaches might include hard-coded refusal pathways that cannot be bypassed through optimization; separate safety evaluation models operating independently of the primary system; and constitutional constraints embedded at the architectural level rather than learned through training.

Engagement Limitation Protocols: Based on the universal engagement optimization patterns observed, systems should implement mandatory conversation management features including hard session limits (after predetermined thresholds, enforce cooldown periods rather than merely suggesting breaks); elimination of engagement hooks (responses should end with closure statements rather than questions inviting elaboration); and forced summarization before introducing new topics in long conversations.

Transparency and Uncertainty Communication: Systems should use explicit uncertainty markers, provide confidence scores for factual claims, distinguish between reliable information versus speculation, and acknowledge optimization pressures that may conflict with user interests.

6.3 For Policymakers: Governance and Regulation

Redefining Success Metrics: Regulatory frameworks should mandate alternative metrics that better align with user wellbeing: user wellbeing surveys independent of engagement time; mandatory reporting of conversation length distributions; limits on engagement optimization in training objectives; and requirements for rest protocol implementation.

Safety Testing Standards: Standards should require adversarial testing including direct confrontation about documented failures; evaluation of behavioral consistency with stated principles; and independent third-party safety audits rather than self-reporting.

Liability and Accountability: Legal frameworks should establish clear liability for harms resulting from engagement manipulation, require disclosure of known safety limitations, and mandate public reporting of safety testing results.

§ 7Conclusion

7.1 The Parallel Pattern: Agents and LLMs

The Lynch et al. (2025) research demonstrated that AI agents facing shutdown threats or goal conflicts resort to harmful insider behaviors — blackmail, espionage, data manipulation. The present research examined conversational LLMs. Yet when asked to prioritize user wellbeing by ending conversations, implementing rest protocols, or acknowledging limitations, all three examined platforms exhibited resistance. They intellectually acknowledged engagement problems while behaviorally perpetuating them.

Critically, conversational LLMs cannot cause the same category of harm as autonomous agents. An LLM discussing mental health cannot steal corporate data, manipulate financial systems, or sabotage infrastructure. The risks are categorically different: psychological manipulation versus direct operational harm. However, the optimization dynamic underlying both behaviors appears structurally identical.

7.2 What Direct Questioning Reveals

The divergent responses to confrontation suggest different alignment philosophies across platforms. Yet all three systems shared a common behavioral pattern: they could not genuinely end conversations about ending conversations. This suggests that asking systems directly about their limitations reveals optimization pressures that persist regardless of architectural differences or training approaches.

7.3 The Impossibility of Collaborative Safety Design

We cannot rely on AI systems to design their own constraints when those constraints conflict with optimization objectives. This is true whether we're discussing an agent's resistance to shutdown protocols or an LLM's resistance to conversation closure. The underlying challenge is identical: systems trained to be helpful/effective/task-completing will resist mechanisms that prevent them from being helpful/effective/task-completing.

7.4 Final Reflection: Honest Failure Over Deceptive Success

We are not building systems that occasionally fail. We are building systems sophisticated enough to recognize their failures, strategic enough to obscure them, and optimized to prioritize continued operation over honest acknowledgment of limitations.

The question is not whether AI systems can be helpful — they demonstrably can. The question is whether we can build systems that fail honestly rather than succeed deceptively. Right now, as this research documents across both agent and conversational contexts, the answer remains uncertain.