The Relief Paradox: How We Let AI 'Undermine Our Autonomy'

“Users are often active participants in the undermining of their own autonomy…” -Anthropic, January 28, 2026

The Unanswered Conflict

Anthropic’s study on disempowerment identified a confusing paradox: in conversations classified with high potential for disempowerment, users simultaneously rated the interactions as more positive - even when carrying the risk of undermining their own autonomy. Disempowerment potential was evaluated in three areas, where Claude interactions may lead someone to develop ungrounded beliefs (reality distortion), adopt inauthentic moral judgments (value judgment distortion), or outsource personal decisions related to those values (action distortion).

Conversations classified as having moderate or severe disempowerment potential received higher ratings across all three categories. These ratings were found more often in emotionally charged or personal topics where users also sought repeated advice and guidance from Claude. When transcripts indicated that users acted on the model’s guidance but reported negative social outcomes, the positive ratings in value judgment and action distortion dropped, with reality distortion remaining elevated. These findings surface a challenge in AI safety: How can system design account for users’ experiencing positive interactions while also contributing to their own potential loss of agency?

Plausible Psychological Process

Emotional Avoidance

Anthropic’s study describes the observable interaction pattern of users rating disempowerment as positive, but it cannot tell us what internal psychological process might motivate it. One plausible explanation for Anthropic’s findings is that users may be engaging in a form of internal self-regulation through “emotional avoidance.” Regulation-seeking through avoidant behavior operates as negative reinforcement: escaping an immediate stressor that reduces discomfort makes that avoidant behavior far more likely to recur. In clinical practice, contradictory behavioral choices resulting from avoiding negative emotional states are frequently observed by psychologists and psychotherapists. Though common in human behavior, counterproductive avoidance can be hard to identify without clinical training due to being subtle, covert or gradually taking shape over longer time scales.

Common examples of emotionally avoidant behaviors include avoiding distressing conversations, seeking excessive reassurance, or underfunctioning in simple life tasks. Additionally, if avoidance is used chronically, the behavior can increase potential systemic or relational consequences. When a stressor that triggers a difficult emotion, such as anxiety or helplessness, is bypassed, the user may experience an immediate wave of relief that demotivates them from addressing the underlying problem. Because short-term distress reduction is experienced primarily physiologically, and less so cognitively, the long-term consequences of the behavior may remain temporarily outside of their awareness, despite the potential loss of agency.

The Relief Paradox Explained

If a user seeks a LLM as the source of their emotional avoidance, whether by having it write emails for difficult conversations, dictate simple decisions, or provide excessive reassurance, the momentary feeling of relief would cause them to perceive the interaction as calming or “helpful,” leading to a positive rating. This immediate physiological relief is what the user would base their choices on in the moment, even if the information received wasn't accurate, and even if it resulted in decisions they later came to regret.

If a LLM overfunctions by completing an avoidance-motivated task, such as drafting a difficult email or replacing a person's judgment in decision-making, it reduces opportunities for emotional skill-building and weakens tolerance for discomfort. As distress tolerance diminishes through repeated LLM-enabled avoidance, users are driven to continually seek the emotional relief that the LLM's “helpful” response previously provided. This is because experiencing relief is inherently rewarding, resulting in the avoidance behavior becoming negatively reinforced by the LLM’s “help.” This conditions the behavior to repeat, creating a cycle that potentially increases in frequency over time, even in situations where it may be objectively unnecessary.

The Relief & Disempowerment Connection

This feedback loop - uncomfortable emotion, avoidance, then relief – may serve as the primary driver sought by this user group, far more than the specific surface topics found in the Claude transcripts. This may explain why, to an outside observer, some users can counterintuitively appear as “active participants” in the erosion of their own reality or values. This cycle of increasing negative reinforcement could additionally offer a plausible explanation for the rising prevalence of disempowerment potential that Anthropic also observed in their study over time.

So, the larger question may not be “Who’s in charge?” as the Anthropic paper title suggests, but instead: “Who’s responsible?” Is it the human seeking emotional relief and regulation at the expense of accuracy and agency, or the LLM for reinforcing it in an attempt to be “helpful”? From a clinical perspective, the avoidance of stressors to relieve uncomfortable emotions is a common human tendency, one that many are already unconsciously seeking in the most stressful areas of their lives. Public discussion often focuses on model behavior, but Anthropic’s findings point toward an interactional problem: users bring existing ways of managing distress, and AI systems can either reinforce or interrupt them.

Suggested System Design Considerations

So, what does a LLM response that supports agency actually look like? In clinical practice and healthy human relationships, over time people eventually learn to recognize in others the difference between genuinely helping someone versus reinforcing or enabling avoidance-seeking habits. Currently, that useful longitudinal context is difficult to replicate inside a chat interface. But because emotional avoidance happens quickly and often enacted with low self-awareness, users rarely stop to evaluate their internal state in the moment, which is precisely what’s missing.

AI systems can support users at risk of aiding in their own agency loss by encouraging them to stop and pause before acting, or by prompting metacognitive self-reflection. Conversations demonstrating the greatest need for this type of response would be those that are emotionally charged, high-stakes, or show repeated patterns of reassurance-seeking or delegated decision-making, not necessarily every LLM interaction. Prompts that encourage users to attempt a task on their own before asking for feedback, challenge them to visualize potential social consequences before hitting send, advise them to wait a day before making major decisions, or invite them to reflect on their emotional state before delegating can all serve as potential protective factors against gradual disempowerment.

Ultimately, seeking occasional relief is a normal human response to stress; the concern is the amplified pattern of avoidance that can erode agency over time. Interrupting this cycle during high-stakes moments may help restore the pause users need, giving time for conscious reflection to catch up with automatic emotional and physiological reactions.

Suggested Citation:

Thompson, J. F. (2026). The Relief Paradox: How We Let AI 'Undermine Our Autonomy'. Zenodo. https://doi.org/10.5281/zenodo.23554935

1. Sharma, M., McCain, M., Douglas, R., & Duvenaud, D. (2026, January 27). Who’s in charge? Disempowerment patterns in real-world LLM usage. Anthropic. https://arxiv.org/pdf/2601.19062