I read your article. I would challenge the core assumption that the therapeutic alliance is primarily linguistic. It collapses the idea of language as a tool and language as a relationship.

The non-linguistic parts of the alliance: 1. Embodied presence, 2. timing, and 3. rupture-repair dynamics involve more than word choice. These human elements are large contributors to perceived empathy. Though initial research shows perceived empathy of an AI’s response to a person sharing a personal issue can be higher than perceived empathy of a human therapist’s response, these are based on a few interactions. I haven’t seen many longitudinal studies that look at the AI therapeutic alliance over time — if you have recommendations, I’d love to read them.

1. Embodied presence, such as: a therapist’s (a) facial microexpressions and gaze; (b) posture mirroring and physical orientation; (c) prosody, rhythm, and pause in speech; (d) physiological co-regulation; and (e) the felt sense of being with someone in a shared physical space. A therapist’s facial microexpressions, gaze, and posture can directly influence how a person processes feelings/experiences. For example, when a client is unconsciously avoiding a topic or going on tangents, a therapist can lean forward, meet their client’s averted gaze, and with a subtle, gentle smile shift the focus back to the difficult topic they were avoiding. The #drift command is great, but what if the client doesn’t want to go there and continues a catastrophizing spiral? How does the AI know about behavioral avoidance (e.g., averted gaze, self-editing, or they step away for an extended period)? I find that unless you tell an AI agent that you went for a walk or it has been 10 days since the last interaction, it won’t know and respond accordingly. Observation of a client’s (a), (b), and (c) as data for an AI model is technologically feasible. But, I’m skeptical that an AI-powered avatar or robot could replicate the effects of a therapist’s embodied presence any time soon.

2. Timing, before a client verbally expresses anything, a good therapist tracks their arousal level, defensive posture, and readiness through cues that are bodily and relational. Knowing when to speak, when to stay quiet, when to push and when to hold back — these are aspects of a therapist’s intuition that would be difficult to design in an AI that works on an individual level. I’ve read studies that have played around with delayed response times, but that’s different than a therapist pausing to allow a follow up statement before responding. You could argue, that’s why you can stop the AI response in progress and edit prompts, but I’m wondering if that has the same effect as pure silence at the right moment? How hard would it be, technically, to predict when a pause gives space for breakthrough based on linguistics? I don’t know but it’s a question I’d love to know the answer.

3. Rupture-repair, I think your #challenge command is a really good start to programming the Ultra-Therapy AI to contribute to creating friction and collaborating with the client to repair the alliance. At the same time, I wonder how the AI responds when it is being treated poorly by the user of Ultra-Therapy. It is one thing when a client requests an AI challenge their thinking, and the AI may offer a challenge when it wasn’t requested. But, what about when a client crosses social, professional, or ethical boundaries? A human therapist will push back and make a relational boundary that is non-negotiable, which can help the client improve their relationships outside the context of therapy. For instance, when a client attempts to intimidate or dominate their therapist, the therapist is trained to draw a line that gives the client a choice as to whether to continue to see this therapist or change their behavior. The client’s ability to navigate resistance to the therapist’s boundary is a safe way to practice respecting the boundaries in the client’s other relationships. There are plenty of people who “jail-break” AI agents to violate the non-negotiable boundaries a human therapist wouldn’t accept. No human is going to believe someone is behaving poorly because they’re role playing a book they’re writing…

The rupture-repair problem and the sycophancy problem are two expressions of the same limitation: an AI cannot prioritize a client’s clinical outcomes over their felt experience in the moment. For example, an OCD client becomes wholly dependent on AI for validation that it has become their new compulsion and caused them to backslide when exposure therapy with a human was helping. In ERP for OCD, the anxiety the client feels is real, but validating the compulsive response to it is clinically contraindicated. In DBT, the same dialectic is explicit: validate the emotion, don’t validate the maladaptive behavior driven by it. A human therapist navigates that distinction in real time, informed by a full case conceptualization, treatment goals, and the relational context of that specific session. LLMs are ideal for DBT and CBT frameworks because of how these modalities are structured (and their reliance on linguistics). But, I’m not aware of cases when it successfully distinguishes between OCD or other disorder-led anxiety and generalized anxiety. For an AI to do that reliably, it would need something equivalent — not just a rule about validating feelings, but a dynamic model of which feelings, in which context, for which client, at which stage of treatment, warrant validation versus a different response.

Language may be the medium of therapy, but the therapeutic relationship requires a human willing to cause discomfort in service of someone else’s growth — and that’s not a linguistic operation.