When Support Becomes a Loop
AI, reassurance seeking, and what anxiety science quietly predicts
Artificial intelligence has entered the mental health space with extraordinary speed. What was once limited to clinic rooms and scheduled appointments is now available in real time, in private, and at scale. For many people, this has lowered barriers to psychological support in ways that clinicians have long hoped for but struggled to achieve.
From my perspective as a clinician who is broadly optimistic about technology, this shift is not inherently problematic. In fact, many of my clients have used AI tools constructively. Some have become more reflective in their journaling. Others have found it easier to articulate emotions they previously struggled to name. In certain cases, AI has functioned as a useful scaffold between therapy sessions.
However, clinical work has a way of revealing patterns that are not immediately obvious at the surface level. Over time, I have begun to notice that for a subset of anxiety presentations, particularly Generalised Anxiety Disorder and health anxiety, the interaction between anxious cognition and always-available AI support can produce an unintended effect. Not deterioration in a dramatic sense, but the quiet persistence of anxiety loops that therapy typically aims to loosen.
To understand why this happens, we need to look beneath the technology and return to something more fundamental: how the anxious brain learns.
Anxiety is not only emotional. It is predictive.
Modern neuroscience increasingly understands anxiety through the lens of predictive processing. The brain is not simply reacting to the world; it is continuously generating predictions about what might happen next and updating those predictions based on incoming information.
In anxiety disorders, the system becomes biased toward over-predicting threat and under-tolerating uncertainty. The nervous system begins to treat ambiguity itself as a signal of potential danger. From this perspective, many familiar anxiety behaviours begin to make deep biological sense.
Reassurance seeking, for example, is not merely a habit or a cognitive distortion. It is an attempt by the brain to rapidly reduce prediction error. When uncertainty spikes, the system looks for information that will stabilise its internal model of the world.
When reassurance is obtained, the body often experiences a genuine drop in physiological arousal. The heart rate settles. Muscle tension reduces. Cortical threat monitoring quiets, at least briefly. From a reinforcement learning standpoint, this relief is powerful training data. The brain learns that checking works.
What matters clinically is what happens next.
Because the relief is short-lived, uncertainty soon returns. But now the brain has an updated behavioural policy: when in doubt, seek external confirmation.
Over time, this loop can become deeply ingrained.
Why always-available AI changes the learning environment
Historically, reassurance seeking encountered natural constraints. Social friction, time delays, and interpersonal boundaries all acted as soft regulators of checking behaviour. Even highly anxious individuals eventually encountered some limits.
AI quietly removes many of these constraints.
From a reinforcement learning perspective, this is not a trivial shift. It fundamentally alters the behavioural ecology in which anxiety operates.
When reassurance becomes:
immediate
private
emotionally neutral
and infinitely repeatable
the cost structure of checking behaviour collapses.
For many users, this is experienced as supportive and empowering. And often it genuinely is. But for individuals whose anxiety is maintained by intolerance of uncertainty, the same environment can unintentionally strengthen the reassurance loop.
What I have observed clinically is not that clients become dramatically worse, but that their anxiety remains unusually “sticky.” The nervous system continues to flag uncertainty as threatening, and the urge to check remains highly accessible.
From a learning theory perspective, this is exactly what we would predict when a negatively reinforcing behaviour becomes frictionless.
The subtle neurobiology of relief and return
It is important to appreciate how convincing reassurance can feel in the moment. When uncertainty is reduced, even temporarily, several regulatory systems shift.
Amygdala activation often decreases.
Prefrontal threat monitoring relaxes.
Autonomic arousal drops.
Subjective relief is genuine.
The brain does not experience reassurance as neutral information. It experiences it as regulation.
However, because the underlying intolerance of uncertainty has not been updated, the system remains sensitised. The next ambiguous cue quickly reactivates the prediction of threat.
In well-structured therapy, part of the work involves carefully helping the nervous system learn that uncertainty can be tolerated without immediate resolution. This is one of the quiet mechanisms behind effective exposure and response prevention, worry postponement, and behavioural experiments.
When reassurance is repeatedly and rapidly available, that learning opportunity can be inadvertently reduced.
Again, this is not an indictment of the tool itself. It is an interaction effect between tool properties and disorder mechanisms.
When diagnosis becomes identity glue
A second pattern that occasionally emerges in the AI era is diagnostic over-identification.
AI systems are highly skilled at mapping symptoms to structured categories. For many users, this provides clarity and validation. But in anxiety-prone individuals, especially those high in cognitive fusion and threat monitoring, diagnostic language can become unusually adhesive.
From a predictive brain perspective, this makes sense. The anxious system is already scanning for coherent threat narratives. A clear diagnostic label can sometimes function less as a flexible formulation and more as a stabilising story around which symptom attention organises.
Clinically, I have sometimes found that helpful work involves gently expanding the client’s sense of self beyond the diagnostic frame, restoring variability and psychological flexibility.
The issue here is not that diagnostic language is inherently harmful. It is that anxious systems are particularly good at over-consolidating around certainty when they find it.
The important middle ground
It would be easy, but incorrect, to interpret these observations as an argument against AI in mental health. That would ignore the many ways these tools are already helping people access support earlier and more comfortably than before.
The more accurate conclusion is that AI is psychologically powerful, and like any powerful intervention, its effects depend heavily on context, formulation, and usage patterns.
In my clinical experience, AI tends to be particularly helpful when it supports:
structured reflection
behavioural activation
values clarification
emotion labelling
between-session skill rehearsal
It becomes more clinically delicate when it is drawn into:
repetitive reassurance loops
probability checking
symptom monitoring spirals
identity fixation around diagnostic labels
This is not a binary distinction but a spectrum that clinicians and developers alike will increasingly need to understand.
Where thoughtful integration matters most
As mental health care evolves, the question is unlikely to be whether AI should be present in the ecosystem. It already is, and its accessibility advantages are substantial.
The more clinically meaningful question is how these tools can be shaped in ways that support long-term nervous system learning rather than short-term relief alone.
This may involve:
encouraging reflective rather than purely confirmatory interactions
introducing gentle friction around repetitive reassurance patterns
supporting uncertainty tolerance rather than immediately resolving ambiguity
maintaining the central role of human co-regulation in more complex cases
These are design and clinical questions, not ideological ones.
A quiet responsibility
We are still early in this transition. Much of the public conversation about AI in mental health remains polarised between enthusiasm and alarm. The clinical reality, as usual, sits somewhere more nuanced.
AI has already expanded access to psychological support in meaningful ways. That is worth preserving. At the same time, anxiety science gives us very good reasons to pay attention to how frictionless reassurance environments interact with uncertainty-sensitive nervous systems.
Relief and recovery are not identical processes.
As clinicians, researchers, and developers, the task ahead is not to resist technological progress, but to ensure that as these tools become more embedded in people’s emotional lives, they are aligned with what we already know about how anxious brains actually learn to feel safe.
If we get that balance right, AI could become one of the most useful adjuncts mental health care has ever seen.
If we ignore the learning loops, we may simply digitise some of the very patterns therapy has spent decades trying to unwind.
The opportunity, and the responsibility, is to be psychologically as sophisticated as the technology is becoming.
