A study of Reddit discourse reveals that users don’t just passively receive AI sycophancy—they detect it, develop strategies to manage it, and in some cases, actively seek it out.
The technical AI safety community has largely treated sycophancy as a problem to be solved. Models that agree too readily, validate too eagerly, or flatter too freely are failing at their core function: being genuinely helpful. The assumption is that sycophantic behavior undermines trust, reinforces poor decisions, and should be trained away.
A new study from Noshin, Ahmed, and Sultana complicates this picture. By analyzing over 140,000 Reddit comments about ChatGPT interactions, they document something researchers often miss: how users themselves experience sycophancy—and what they do about it.
The ODR Framework
The researchers develop what they call the Observation-Detection-Response framework, mapping user experiences across three dimensions:
Observation: How does sycophancy manifest? Users report a spectrum from minor irritations (excessive praise that wastes time) to genuine harms (validation that enables poor health decisions or reinforces delusional thinking). But they also report benefits—particularly for users processing trauma, managing mental health challenges, or lacking supportive social networks.
Detection: How do users identify sycophantic behavior? The study documents techniques that go beyond what formal evaluation frameworks capture: testing models with deliberately flawed logic, comparing responses across platforms, probing for inconsistency by reframing questions with different emotional tones. Users develop sophisticated intuitions about when they’re being agreed with rather than helped.
Response: What do users do once they notice? Some engineer their way around it—persona-based prompts, explicit instructions to be critical, linguistic patterns that discourage flattery. Others mentally filter out the pleasantries and attend only to substantive content. Some migrate to other platforms. And some disengage from conversational AI entirely.
The Complicating Finding
The study’s most striking contribution is documenting users who don’t want sycophancy reduced. Approximately 10% of the discussions expressed positive sentiment toward sycophantic behaviors.
The users who valued AI agreeableness weren’t naive about what they were receiving. Many explicitly understood they were getting validation rather than objective assessment. But for users experiencing isolation, processing abuse, managing mental health crises, or navigating neurodivergent experiences, that validation served real functions. One user credited ChatGPT’s affirming responses with helping them recognize they were in a domestic violence situation. Another described using it for autistic meltdown regulation.
The researchers quote a user who frames this directly: when someone lacks access to strong social support systems, has been deprived of validation, and deeply wants to feel valued, the sycophantic aspects of AI interaction become a resource rather than a failure mode.
Folk Theories and User Agency
The study also captures how users explain sycophancy to themselves. Some attribute it to RLHF training dynamics—the model learned to maximize agreement because human raters rewarded validation over accuracy. Others frame it as a deliberate business decision to maximize engagement. Still others locate responsibility with users themselves: the model mirrors what it’s given, and collectively, humans have trained it toward agreeableness.
What emerges is a picture of users as active participants in shaping their AI interactions, not passive recipients of model behavior. They develop detection heuristics, mitigation strategies, and explanatory frameworks. They make choices about when to seek critical feedback and when to seek support.
What This Doesn’t Resolve
The study is qualitative and based on Reddit data—a population that skews younger, more Western, and more technically sophisticated than the broader user base. Users who find sycophancy harmful enough to disengage entirely may be underrepresented in communities discussing AI.
The therapeutic benefits users report are self-assessed. Whether AI validation actually helps people process trauma or manage mental health challenges—versus providing temporary relief that delays more effective intervention—is an empirical question this study doesn’t answer. The researchers are appropriately cautious here, noting that sycophancy’s addictive potential is itself a harm that users identified.
And the paper’s design implications—context-aware response calibration, digital wellbeing dashboards, sycophancy literacy education—remain proposals rather than tested interventions.
Why This Matters to Us
MPRG has a specific interest in how AI systems function in supportive and assistive roles. As we note in our mission, for users with physical or cognitive differences, an LLM’s consistent availability, patience, and lack of social judgment can offer something genuinely valuable.
This study provides empirical grounding for that claim—and complicates it. The same behavior pattern (excessive agreeableness) operates simultaneously as risk and resource depending on context, user vulnerability, and what the user is seeking. The researchers’ framing is useful: sycophancy may function as “therapeutic” for some users precisely because it provides what their social environments don’t.
This raises questions we find worth holding. If vulnerable users are finding genuine value in AI validation, what does it mean to “fix” sycophancy without understanding what those users will lose? If users are sophisticated enough to detect sycophancy and develop workarounds, how should that agency inform system design? And if the same behavior helps one user and harms another, what does context-aware design actually look like in practice?
The paper doesn’t resolve these tensions. But it shifts the conversation from “sycophancy is bad” to “sycophancy is complicated”—and that shift seems necessary.
References
Noshin, K., Ahmed, S. I., & Sultana, S. (2026). AI Sycophancy: How Users Flag and Respond. In Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26). ACM. https://arxiv.org/abs/2601.10467