Could Mental Health AI Push Therapy Back to Its Roots?

A few weeks ago, I dipped into the r/therapists forum on Reddit to see how everyone was feeling about AI for mental health. The anxiety was palpable.  “I can't see any scenario where this ends well for our profession, and it feels so bleak,” one graduate student wrote. One therapist admitted to “crushing existential dread,” another speculating that insurers would happily swap out trained clinicians for cheaper digital stand-ins.

The fear is understandable. Large language models (LLMs) are already pretty good at the parts of therapy that have been reshaped to fit research protocols and insurance requirements. Active “listening,” teaching coping skills, delivering structured interventions—these are exactly the kinds of tasks AI can manage because they’re easy to measure and repeat. And measurability and repeatability are what payors prize. So in a sense, AI isn’t good at therapy. It’s just good at the version of therapy the system has forced us to provide.

When I really think about it, though, the most worrying part of all this isn’t that AI can pass for therapists. It’s that the profession has drifted so close to the mechanical that the resemblance feels believable. 

AI is forcing our profession to a crossroads: we can keep delivering therapy in the stripped-down way the system rewards, hoping AI doesn’t replace us faster than we can retire. Or we can double down on humanity as the core of the work. After all, that’s what Carl Rogers, founder of humanistic psychology, argued for decades ago—that the real power of therapy is in empathy, authenticity, and relationship.

How therapy split

Therapy didn’t start out as something that fit neatly into protocols or billing codes.  Sigmund Freud might be the grandfather of the field, but he’s not exactly the model most of us want to emulate. His version of therapy casts the analyst as a mirror: “opaque to his patients, and, like a mirror, should show them nothing but what is shown to him.” The therapist’s job was to offer a blank surface for projection, or basically a stage where unconscious conflicts could play out. His model has mostly been discarded.

So if we want to find the roots of the version of therapy that most therapists signed up to do (which is relational and human), then we need to look at Carl Rogers. Rogers argued that what made therapy work wasn’t interpretation or even technique but empathy and unconditional positive regard. Other “fathers,” like Irvin Yalom, existential psychiatrist, and the relational-cultural theorists, carried that thread forward, insisting that people grow in connection, not isolation.

Of course, behavior therapy approaches (and later CBT) took a different path. These models broke therapy into structured steps that could be written down, tested, and replicated. When the DSM-III reframed diagnoses in checklists in 1980, structured approaches fit neatly into that system. Relational ones did not.

That’s because in relational models, the therapist isn’t applying a miracle treatment protocol to a client, they are the treatment. The work happens in the relationship itself, because two people are sitting across from each other and trying to make sense of something hard. You can’t really write that into a manual or score it for fidelity, which is part of why a high medicalized system moved away from it. It’s easier for “measureable return-on-investment” systems to prioritize therapy as a process to be standardized instead of a relationship to be experienced. Unfortunately, that emphasis also makes it easier to replace the human parts of therapy with scripts and structures that machines can follow.

But research shows both sides help people get better. Humanistic therapies consistently perform just as well as structured ones in outcome studies: “Meta-analyses consistently demonstrate that all bona fide psychotherapies produce roughly equivalent outcomes,” Bruce Wampold writes in The Great Psychotherapy Debate. “This conclusion, often referred to as the Dodo bird verdict, has withstood decades of scrutiny.” But while they might all be effective, they’re not all easy to package. When insurers and researchers demanded evidence that could be manualized, measured, and reimbursed, structured therapies had the advantage.

What do people need, and what can AI for mental health really do?

Of course, just because therapies work about equally well doesn’t mean they work equally well for everyone. The Dodo bird verdict is about averages. In real life, some people do better with structure, while others do better with flexibility. Some need the accountability of a relationship, others can make progress from a workbook or app. Even within the same treatment model, some therapists consistently get better results than others (and some therapists get better results, no matter what model they’re using!).

One reason AI for mental health has moved so quickly into the structured corner of the field is that CBT can be written into manuals, turned into digital tools, and even self-applied. Research on bibliotherapy shows that for some people with mild depression or anxiety, working through a CBT book can be just as effective as sitting across from a therapist.

But there are always going to be people who don’t do well with CBT. In clinical trials, only about half of clients respond to CBT, and half reach remission. That doesn’t make CBT bad, because response rates for other “evidence-based” interventions are around the same, which shows that no single format works for everyone. And that’s kind of the point: therapy has never really been about giving a treatment that magically solves a problem. From the beginning, it has been about meeting people where they are and offering what they need in order to change.

The other half of therapy

If half of the clients respond to CBT, that means half don’t. And those people aren’t going to be helped by more of the same. They’ll need therapist-driven approaches, with models like Interpersonal Therapy, which works through patterns in relationships, or Emotion-Focused Therapy, which requires the therapist’s moment-to-moment attunement to emotion. These aren’t skills you can automate.

In grad school, those kinds of skills usually get grouped under “clinical skills” or “therapist factors,” and more specifically:

• Therapeutic alliance
• Attunement
• Empathy
• Rupture and repair
• Co-regulation

Ironically, these human-only skills are some of the strongest predictors of whether therapy will work. Meta-analyses consistently find that the quality of the therapeutic alliance explains more of the variance in outcomes than which model is being used. Empathy is so central that clients of more empathic therapists improve significantly more, even when controlling for things like diagnosis or homework completion.

Back to the beginning

We need to acknowledge that AI for mental health interventions will work for plenty of people. Workbooks, apps, and even chatbots can help someone with mild depression or anxiety get unstuck. But that’s not a loss for therapy, it’s a win. If lower-intensity tools meet people’s needs, let them. It will mean therapists aren’t stuck delivering the most mechanical version of our work.

That’s not to say there aren’t questions that need answers. 

How do we make sure people in crisis aren’t left staring at a screen? How do we safeguard privacy when sensitive information is being shared with a machine? How do we keep equity in the picture so that lower-income clients don’t get funneled into AI while higher-income clients get humans? These are real concerns, and they deserve attention.

But those concerns can be solved, and AI for mental health can take over the part of therapy that is most easily standardized. And if it does, it will still leave behind the parts of therapy that actually attracted most clinicians to this work in the first place: the presence of another human being who can offer empathy, authenticity, and connection. As Carl Rogers put it, “In my early professional years, I was asking the question: How can I treat, or cure, or change this person? Now I would phrase the question in this way: How can I provide a relationship which this person may use for their own personal growth?”

And in that sense, mental health AI may end up doing something surprising. It may push therapy back to its roots.